From 0168aee4f798c5bc1c3a8be8eb419a9b091b087b Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Wed, 2 Nov 2022 14:57:39 -0400 Subject: [PATCH 001/100] Change cutoffs for infants on extreme errors step --- R/pediatric_clean.R | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/R/pediatric_clean.R b/R/pediatric_clean.R index eb43a9f4..cf5f5572 100644 --- a/R/pediatric_clean.R +++ b/R/pediatric_clean.R @@ -333,14 +333,41 @@ cleanbatch <- function(data.df, "[%s] Exclude extreme measurements based on SD...\n", Sys.time() )) + + # for pediatric subjects > 2 years old data.df[na_as_false( - valid(data.df, include.temporary.extraneous = TRUE) & abs(tbc.sd) > sd.extreme + valid(data.df, include.temporary.extraneous = TRUE) & + abs(tbc.sd) > sd.extreme | exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & + agedays >= 365.25*2 & abs(z.orig) > z.extreme ), exclude := 'Exclude-SD-Cutoff'] + # for pediatric subjects 0 - 1 years old + data.df[na_as_false( + valid(data.df, include.temporary.extraneous = TRUE) & + abs(tbc.sd) > sd.extreme + | + exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & + agedays < 365.25 & + (z.orig < -25 | z.orig > 15) + ), + exclude := 'Exclude-SD-Cutoff'] + + # for pediatric subjects 1 - 2 years old + data.df[na_as_false( + valid(data.df, include.temporary.extraneous = TRUE) & + abs(tbc.sd) > sd.extreme + | + exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & + agedays >= 365.25*1 & + agedays < 365.25*2 & + abs(z.orig) > 15 + ), + exclude := 'Exclude-SD-Cutoff'] + # 10d. Redo temporary extraneous as in step 5. data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] data.df[temporary_extraneous(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] From de021d20071768eadbaf18ed47b36221c9292f4e Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 3 Nov 2022 09:13:10 -0400 Subject: [PATCH 002/100] Adjust cutoffs for SD as well --- R/pediatric_clean.R | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/R/pediatric_clean.R b/R/pediatric_clean.R index cf5f5572..d8c847dd 100644 --- a/R/pediatric_clean.R +++ b/R/pediatric_clean.R @@ -336,34 +336,34 @@ cleanbatch <- function(data.df, # for pediatric subjects > 2 years old data.df[na_as_false( + agedays >= 365.25*2 & valid(data.df, include.temporary.extraneous = TRUE) & abs(tbc.sd) > sd.extreme | exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & - agedays >= 365.25*2 & abs(z.orig) > z.extreme ), exclude := 'Exclude-SD-Cutoff'] # for pediatric subjects 0 - 1 years old data.df[na_as_false( + agedays < 365.25 & valid(data.df, include.temporary.extraneous = TRUE) & - abs(tbc.sd) > sd.extreme + (tbc.sd < -25 | tbd.sd > 15) | exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & - agedays < 365.25 & (z.orig < -25 | z.orig > 15) ), exclude := 'Exclude-SD-Cutoff'] # for pediatric subjects 1 - 2 years old data.df[na_as_false( + agedays >= 365.25*1 & + agedays < 365.25*2 & valid(data.df, include.temporary.extraneous = TRUE) & - abs(tbc.sd) > sd.extreme + abs(tbc.sd) > 15 | exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & - agedays >= 365.25*1 & - agedays < 365.25*2 & abs(z.orig) > 15 ), exclude := 'Exclude-SD-Cutoff'] From 3fef550e44b0646b1ff03f96916ec189f2ec2d4f Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 3 Nov 2022 14:48:52 -0400 Subject: [PATCH 003/100] Add initial changes for extreme errors to ewma step --- R/pediatric_clean.R | 60 +++++++++++++++++++++++++++++++++++++++------ 1 file changed, 52 insertions(+), 8 deletions(-) diff --git a/R/pediatric_clean.R b/R/pediatric_clean.R index d8c847dd..9e6e288c 100644 --- a/R/pediatric_clean.R +++ b/R/pediatric_clean.R @@ -406,6 +406,19 @@ cleanbatch <- function(data.df, dewma.after = tbc.sd - ewma.after )] + # also, for infants, we want to know the difference in sd for next and + # prior, as well as the time difference + df[, `:=`( + sd_prev = c(0, diff(tbc.sd)), + sd_next = c(diff(tbc.sd), 0), + time_prev = c(0, diff(agedays)), # pad with 0s for inclusive + time_next = c(diff(agedays), 0), + dewma.before_prev = c(0, diff(dewma.before)), + dewma.before_next = c(diff(dewma.before), 0), + dewma.after_prev = c(0, diff(dewma.after)), + dewma.after_next = c(diff(dewma.after), 0) + )] + # 11c. Identify all values that meet all of the following criteria as potential exclusions: # i. There are 3 or more measurements for that subject and parameter # ii. (dewma_*>3.5 & dewma_*_bef>3 & dewma_*_aft>3 & tbc*sd>3.5) OR (dewma_*<-3.5 & dewma_*_bef<-3 & d & dewma_*_aft<-3 & tbc*sd<-3.5) @@ -414,15 +427,39 @@ cleanbatch <- function(data.df, num.valid <- sum(valid(df)) rep <- na_as_false(with( df, - num.valid >= 3 & valid(df) - & - ( - dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5 - | - dewma.all < -3.5 & - dewma.before < -3 & dewma.after < -3 & tbc.sd < -3.5 - ) + num.valid >= 3 & valid(df) & + # for >=2 yo + ( + agedays >= 365.25*2 & + ( + dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5 + | + dewma.all < -3.5 & + dewma.before < -3 & dewma.after < -3 & tbc.sd < -3.5 + ) + ) | ( + # < 2yo + agedays < 365.25*2 & + ( + abs(tbc.sd) > 3.5 & + ((sd_prev > 3.5 & time_prev < 365.25*2 & + dewma.before_prev > 3 & dewma.before_next > 3 & + dewma.after_prev > 3 & dewma.after_next > 3) | + (sd_prev < 3.5 & time_prev < 365.25*2 & + dewma.before_prev < 3 & dewma.before_next < 3 & + dewma.after_prev < 3 & dewma.after_next < 3) | + (sd_next > 3.5 & time_next < 365.25*2 & + dewma.before_prev > 3 & dewma.before_next > 3 & + dewma.after_prev > 3 & dewma.after_next > 3) | + (sd_next < 3.5 & time_next < 365.25*2 & + dewma.before_prev < 3 & dewma.before_next < 3 & + dewma.after_prev < 3 & dewma.after_next < 3) + ) & + ((agedays < 181 & dewma.all < -3.5) | agedays >= 181) + ) + ) )) + num.exclude <- sum(rep) # 11d. If there is only one potential exclusion identified in step 11c for a subject and parameter, replace exc_*=5 for that value if (num.exclude == 1) @@ -445,10 +482,17 @@ cleanbatch <- function(data.df, rep <- na_as_false(with( df, num.valid == 2 & + # for >=2 yo ( + agedays >= 365.25*2 & tbc.sd - ewma.all > 3.5 & tbc.sd > 3.5 | tbc.sd - ewma.all < -3.5 & tbc.sd < -3.5 + ) | ( + agedays >= 365.25*2 & + abs(tbc.sd) > 3.5 & + sd_next > 3.5 & tbc.sd[valid(df)] > c(Inf, tbc.sd[valid(df)][-2]) | + sd_prev > 3.5 & tbc.sd[valid(df)] > c(tbc.sd[valid(df)][-1], Inf) ) )) num.exclude <- sum(rep) From 82a3888bb12a57f647add412206a5057e3f89946 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 3 Nov 2022 15:32:55 -0400 Subject: [PATCH 004/100] Fix typos and row errors --- R/pediatric_clean.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/pediatric_clean.R b/R/pediatric_clean.R index 9e6e288c..9928ae88 100644 --- a/R/pediatric_clean.R +++ b/R/pediatric_clean.R @@ -349,7 +349,7 @@ cleanbatch <- function(data.df, data.df[na_as_false( agedays < 365.25 & valid(data.df, include.temporary.extraneous = TRUE) & - (tbc.sd < -25 | tbd.sd > 15) + (tbc.sd < -25 | tbc.sd > 15) | exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & (z.orig < -25 | z.orig > 15) @@ -491,8 +491,8 @@ cleanbatch <- function(data.df, ) | ( agedays >= 365.25*2 & abs(tbc.sd) > 3.5 & - sd_next > 3.5 & tbc.sd[valid(df)] > c(Inf, tbc.sd[valid(df)][-2]) | - sd_prev > 3.5 & tbc.sd[valid(df)] > c(tbc.sd[valid(df)][-1], Inf) + sd_next > 3.5 & tbc.sd > c(Inf, tbc.sd[-nrow(df)]) | + sd_prev > 3.5 & tbc.sd > c(tbc.sd[-1], Inf) ) )) num.exclude <- sum(rep) From 169a0036fc8a4e22220388bd2fb2da824925b812 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 8 Dec 2022 08:56:06 -0500 Subject: [PATCH 005/100] Restart -- move infants algorithm to new function temporarily --- R/pediatric_clean.R | 91 +++++---------------------------------------- 1 file changed, 10 insertions(+), 81 deletions(-) diff --git a/R/pediatric_clean.R b/R/pediatric_clean.R index 9928ae88..3a90bfe8 100644 --- a/R/pediatric_clean.R +++ b/R/pediatric_clean.R @@ -333,41 +333,14 @@ cleanbatch <- function(data.df, "[%s] Exclude extreme measurements based on SD...\n", Sys.time() )) - - # for pediatric subjects > 2 years old data.df[na_as_false( - agedays >= 365.25*2 & - valid(data.df, include.temporary.extraneous = TRUE) & - abs(tbc.sd) > sd.extreme + valid(data.df, include.temporary.extraneous = TRUE) & abs(tbc.sd) > sd.extreme | exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & abs(z.orig) > z.extreme ), exclude := 'Exclude-SD-Cutoff'] - # for pediatric subjects 0 - 1 years old - data.df[na_as_false( - agedays < 365.25 & - valid(data.df, include.temporary.extraneous = TRUE) & - (tbc.sd < -25 | tbc.sd > 15) - | - exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & - (z.orig < -25 | z.orig > 15) - ), - exclude := 'Exclude-SD-Cutoff'] - - # for pediatric subjects 1 - 2 years old - data.df[na_as_false( - agedays >= 365.25*1 & - agedays < 365.25*2 & - valid(data.df, include.temporary.extraneous = TRUE) & - abs(tbc.sd) > 15 - | - exclude %in% c('Include', 'Exclude-Temporary-Extraneous-Same-Day') & - abs(z.orig) > 15 - ), - exclude := 'Exclude-SD-Cutoff'] - # 10d. Redo temporary extraneous as in step 5. data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] data.df[temporary_extraneous(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] @@ -406,19 +379,6 @@ cleanbatch <- function(data.df, dewma.after = tbc.sd - ewma.after )] - # also, for infants, we want to know the difference in sd for next and - # prior, as well as the time difference - df[, `:=`( - sd_prev = c(0, diff(tbc.sd)), - sd_next = c(diff(tbc.sd), 0), - time_prev = c(0, diff(agedays)), # pad with 0s for inclusive - time_next = c(diff(agedays), 0), - dewma.before_prev = c(0, diff(dewma.before)), - dewma.before_next = c(diff(dewma.before), 0), - dewma.after_prev = c(0, diff(dewma.after)), - dewma.after_next = c(diff(dewma.after), 0) - )] - # 11c. Identify all values that meet all of the following criteria as potential exclusions: # i. There are 3 or more measurements for that subject and parameter # ii. (dewma_*>3.5 & dewma_*_bef>3 & dewma_*_aft>3 & tbc*sd>3.5) OR (dewma_*<-3.5 & dewma_*_bef<-3 & d & dewma_*_aft<-3 & tbc*sd<-3.5) @@ -427,39 +387,15 @@ cleanbatch <- function(data.df, num.valid <- sum(valid(df)) rep <- na_as_false(with( df, - num.valid >= 3 & valid(df) & - # for >=2 yo - ( - agedays >= 365.25*2 & - ( - dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5 - | - dewma.all < -3.5 & - dewma.before < -3 & dewma.after < -3 & tbc.sd < -3.5 - ) - ) | ( - # < 2yo - agedays < 365.25*2 & - ( - abs(tbc.sd) > 3.5 & - ((sd_prev > 3.5 & time_prev < 365.25*2 & - dewma.before_prev > 3 & dewma.before_next > 3 & - dewma.after_prev > 3 & dewma.after_next > 3) | - (sd_prev < 3.5 & time_prev < 365.25*2 & - dewma.before_prev < 3 & dewma.before_next < 3 & - dewma.after_prev < 3 & dewma.after_next < 3) | - (sd_next > 3.5 & time_next < 365.25*2 & - dewma.before_prev > 3 & dewma.before_next > 3 & - dewma.after_prev > 3 & dewma.after_next > 3) | - (sd_next < 3.5 & time_next < 365.25*2 & - dewma.before_prev < 3 & dewma.before_next < 3 & - dewma.after_prev < 3 & dewma.after_next < 3) - ) & - ((agedays < 181 & dewma.all < -3.5) | agedays >= 181) - ) - ) + num.valid >= 3 & valid(df) + & + ( + dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5 + | + dewma.all < -3.5 & + dewma.before < -3 & dewma.after < -3 & tbc.sd < -3.5 + ) )) - num.exclude <- sum(rep) # 11d. If there is only one potential exclusion identified in step 11c for a subject and parameter, replace exc_*=5 for that value if (num.exclude == 1) @@ -482,17 +418,10 @@ cleanbatch <- function(data.df, rep <- na_as_false(with( df, num.valid == 2 & - # for >=2 yo ( - agedays >= 365.25*2 & tbc.sd - ewma.all > 3.5 & tbc.sd > 3.5 | tbc.sd - ewma.all < -3.5 & tbc.sd < -3.5 - ) | ( - agedays >= 365.25*2 & - abs(tbc.sd) > 3.5 & - sd_next > 3.5 & tbc.sd > c(Inf, tbc.sd[-nrow(df)]) | - sd_prev > 3.5 & tbc.sd > c(tbc.sd[-1], Inf) ) )) num.exclude <- sum(rep) @@ -652,7 +581,7 @@ cleanbatch <- function(data.df, subj.df[extraneous.this.day & extraneous, exclude := 'Exclude-Extraneous-Same-Day'] subj.df[extraneous.this.day == TRUE, `:=`(tbc.sd.min = min(tbc.sd), - tbc.sd.max = max(tbc.sd)), by = .(param, agedays)] + tbc.sd.max = max(tbc.sd)), by = .(param, agedays)] subj.df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM', 3, ifelse( From 425982c9f0a9e7f45215b9e29c7cf75d8af57306 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 15 Dec 2022 14:23:55 -0500 Subject: [PATCH 006/100] Add cdc/who sd smoothing --- R/growth.R | 21 ++++++++-- R/infants_clean.R | 99 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 117 insertions(+), 3 deletions(-) create mode 100644 R/infants_clean.R diff --git a/R/growth.R b/R/growth.R index a04d53cd..400b8fb3 100644 --- a/R/growth.R +++ b/R/growth.R @@ -367,13 +367,28 @@ cleangrowth <- function(subjid, # calculate z scores if (!quietly) cat(sprintf("[%s] Calculating z-scores...\n", Sys.time())) + # for infants, use z and who measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE) - data.all[, z.orig := measurement.to.z(param, agedays, sex, v)] + measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = F) + + data.all[, z.orig_cdc := measurement.to.z(param, agedays, sex, v)] + data.all[, z.orig_who := measurement.to.z_who(param, agedays, sex, v)] # calculate "standard deviation" scores if (!quietly) cat(sprintf("[%s] Calculating SD-scores...\n", Sys.time())) - data.all[, sd.orig := measurement.to.z(param, agedays, sex, v, TRUE)] + data.all[, sd.orig_cdc := measurement.to.z(param, agedays, sex, v, TRUE)] + data.all[, sd.orig_who := measurement.to.z_who(param, agedays, sex, v, TRUE)] + + # smooth z-scores/SD scores between ages 1 - 3yo using weighted scores + # older uses cdc, younger uses who + who_weight <- 3 - (agedays/365.25) + cdc_weight <- (agedays/365.25) - 1 + + data.all[agedays/365.25 => 1 & agedays/365.25 <= 3, + z.orig := (z.orig_cdc*cdc_weight + z.orig_who*who_weight)/2] + data.all[agedays/365.25 => 1 & agedays/365.25 <= 3, + sd.orig := (sd.orig_cdc*cdc_weight + sd.orig_who*who_weight)/2] # sort by subjid, param, agedays setkey(data.all, subjid, param, agedays) @@ -819,7 +834,7 @@ read_anthro <- function(path = "", cdc.only = FALSE) { return(function(param, agedays, sex, measurement, csd = FALSE) { # For now, we will only use CDC growth reference data, note that the cubically interpolated file # we are using has linear measurments derived from length data for children < 731 days, and height thereafter - src <- ifelse(agedays < 731 & !cdc.only, 'WHO', 'CDC') + src <- ifelse(agedays < 3*365.25 & !cdc.only, 'WHO', 'CDC') # keep column sequence the same fo efficient join dt <- data.table(src, param, sex, agedays, measurement) diff --git a/R/infants_clean.R b/R/infants_clean.R new file mode 100644 index 00000000..27dde283 --- /dev/null +++ b/R/infants_clean.R @@ -0,0 +1,99 @@ +# Main pediatric growthcleanr function -- cleanbatch +# internal supporting functions for pediatrics can be found in: pediatric_support.R + +#' Function to clean data (optionally in batches): +#' 4. Dataset split (optional) +#' a. In Stata, many of the rest of the steps require you to repeat steps as many times as there are observations for the subject with the highest number of +#' observations. Therefore, in order to speed things up I separated the dataset at this point into three groups: short (subjects with <=10 total +#' observations), medium (subjects with >10 and <=30 observations) and long (subjects with >30 observations). I performed all the rest of the steps +#' separately for each of the 3 datasets and then re-combined them at the end. Note that all observations for a subject stay together. This step is +#' definitely not necessary if it will not speed things up in whatever software program you're using. +#' +#' NOTE: to use mutliple processes in R we will process patients in batches using the plyr ddply function +#' set up function to process one patient. Function to parallelize batches is below. +#' +#' @keywords internal +#' @import data.table +#' @importFrom stats median +#' @noRd +cleanbatch <- function(data.df, + log.path, + quietly, + parallel, + measurement.to.z, + ewma.fields, + ewma.exp, + recover.unit.error, + include.carryforward, + sd.extreme, + z.extreme, + exclude.levels, + tanner.ht.vel, + who.ht.vel, + lt3.exclude.mode, + error.load.threshold, + error.load.mincount) { + # avoid "no visible binding" warnings + abs.2ndlast.sd <- abs.tbc.sd <- abs.tbc.sd.next <- abs.tbc.sd.prev <- abssum2 <- NULL + aft.g.befp1 <- agedays <- agedays.other <- bef.g.aftm1 <- delta <- NULL + delta.agedays.next <- delta.agedays.prev <- delta.next.ht <- delta.next.sd <- NULL + delta.prev.ht <- delta.prev.sd <- dewma.after <- dewma.after.prev <- NULL + dewma.all <- dewma.before <- dewma.before.next <- dnext.sd <- dnext.sd.minus <- NULL + dnext.sd.plus <- dprev.sd <- dprev.sd.minus <- dprev.sd.plus <- dup <- NULL + dup.ratio <- ewma.after <- ewma.all <- ewma.before <- exclude <- exclude.count <- NULL + extraneous <- extraneous.this.day <- first.of.three.or.more <- ht.exp <- NULL + include.count <- index <- last.of.three.or.more <- line <- max.ht.vel <- NULL + max.whoinc.1.ht <- max.whoinc.2.ht <- max.whoinc.3.ht <- max.whoinc.4.ht <- NULL + max.whoinc.6.ht <- maxdiff.next.ht <- maxdiff.prev.ht <- median.other.sd <- NULL + min.ht.vel <- mindiff.next.ht <- mindiff.prev.ht <- pair <- pair.next <- NULL + pair.prev <- param <- param.other <- prev.v <- sd.median <- sex <- subjid <- NULL + swap.flag.1 <- swap.flag.2 <- tanner.months <- tbc.other.sd <- tbc.sd <- NULL + tbc.sd.d <- tbc.sd.max <- tbc.sd.min <- tbc.sd.minus <- tbc.sd.next <- tbc.sd.plus <- NULL + tbc.sd.prev <- tbc.sd.sw <- tbc.sd.t <- temp.diff <- temp.exclude <- v <- NULL + v.d <- v.minus <- v.next <- v.orig <- v.plus <- v.prev <- v.sw <- v.t <- NULL + valid.interior.measurement <- who.maxdiff.next.ht <- who.mindiff.next.ht <- NULL + whoagegrp.ht <- whoinc.1.ht <- whoinc.2.ht <- whoinc.3.ht <- whoinc.4.ht <- NULL + whoinc.6.ht <- whoinc.age.ht <- z.orig <- NULL + + data.df <- data.table(data.df, key = c('subjid', 'param', 'agedays', 'index')) + + if (parallel & !is.na(log.path)) { + sink( + sprintf( + "%s/cleangrowth-%s-batch-%03d.log", + log.path, + Sys.Date(), + data.df$batch[1] + ) + ) + } + + if (!quietly) + cat(sprintf( + "[%s] Processing Batch #%s...\n", + Sys.time(), + data.df$batch[1] + )) + + # save a copy of all original measurement values before any transformation + data.df[, v.orig := v] + + if (!quietly) + cat(sprintf( + "[%s] Preliminarily identify potential extraneous...\n", + Sys.time() + )) + data.df$exclude[temporary_extraneous(data.df)] <- 'Exclude-Temporary-Extraneous-Same-Day' + + # capture a list of subjects with possible extraneous for efficiency later + subj.dup <- data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', unique(subjid)] + + + # end ---- + + if (!quietly) + cat(sprintf("[%s] Completed Batch #%s...\n", Sys.time(), data.df$batch[1])) + if (!quietly & parallel) + sink() + return(data.df[j = .(line, exclude, tbc.sd, tbc.other.sd, param)]) #debugging +} From c4cd348448c77f835b021a083b2116ea6a787217 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 15 Dec 2022 15:18:25 -0500 Subject: [PATCH 007/100] Add temporary extraneous for infants --- R/infants_clean.R | 4 +- R/infants_support.R | 91 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 94 insertions(+), 1 deletion(-) create mode 100644 R/infants_support.R diff --git a/R/infants_clean.R b/R/infants_clean.R index 27dde283..8b6cbb95 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -75,6 +75,8 @@ cleanbatch <- function(data.df, data.df$batch[1] )) + # temp SDEs ---- + # save a copy of all original measurement values before any transformation data.df[, v.orig := v] @@ -83,7 +85,7 @@ cleanbatch <- function(data.df, "[%s] Preliminarily identify potential extraneous...\n", Sys.time() )) - data.df$exclude[temporary_extraneous(data.df)] <- 'Exclude-Temporary-Extraneous-Same-Day' + data.df$exclude[temporary_extraneous_infants(data.df)] <- 'Exclude-Temporary-Extraneous-Same-Day' # capture a list of subjects with possible extraneous for efficiency later subj.dup <- data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', unique(subjid)] diff --git a/R/infants_support.R b/R/infants_support.R new file mode 100644 index 00000000..121ddbd4 --- /dev/null +++ b/R/infants_support.R @@ -0,0 +1,91 @@ +# Infants support + +#' Function for temporary extraneous (step 5): +#' 5. Temporary extraneous: I use extraneous to refer to more than more than one recorded value for a parameter on the same day, +#' and we need to select which one to include in our analysis. The overall strategy will be to select a measurement using a simple +#' strategy that will be used temporarily, and select permanently in a later step after we have a somewhat cleaner dataset that +#' can help us identify the best extraneous. +#' a. For subjects/parameters with extraneous: Determine median_tbc*sd for both parameters: the median tbc*sd for each subject +#' and parameter including only non-extraneous values with exc_*==0. The median of the same parameter as the extraneous will +#' be referred to as median_tbc*sd, the median of the other parameter will be referred to as median_tbcOsd. +#' b. For each subject/parameter with extraneous and at least one value for the subject/parameter on a day with no extraneous, +#' select the value closest to the median_tbc*sd for temporary inclusion, and assign all other extraneous exc_*=2. +#' i. For each subject/parameter with extraneous and no values for the subject/parameter on a day with no extraneous, select the +#' value closest to the median_tbcOsd for temporary inclusion, and assign all other extraneous exc_*=2. +#' If median_tbcOsd is missing because there are no values for the other parameter, randomly choose one extraneous value for +#' each subject/parameter/age to keep as exc_*=0 and replace exc_*=2 for all other extraneous for that subject/parameter/age. +#' +#' @keywords internal +#' @noRd +temporary_extraneous_infants <- function(df) { + # avoid "no visible binding" warnings + agedays <- delta.median.sd <- extraneous <- extraneous.this.day <- NULL + index <- median.sd <- param <- subjid <- tbc.sd <- NULL + + # add subjid and param if needed (may be missing depending on where this is called from) + if (is.null(df$subjid)) + df[, subjid := NA] + if (is.null(df$param)) + df[, param := NA] + # only operate on valid rows (but include rows that may have previously been flagged as a "temporary extraneous") + valid.rows <- valid(df, include.temporary.extraneous = TRUE) + # make a small copy of df with only the fields we need for efficiency + df <- df[j = .(tbc.sd), keyby = .(subjid, param, agedays, index)] + # initialize some useful fields + df[, `:=`( + median.sd = as.double(NA), + delta.median.sd = as.double(NA), + extraneous.this.day = FALSE, + extraneous = FALSE + )] + + # determine on which days there are extraneous measurements (more than 1 valid measurement on that age day) + df[valid.rows, extraneous.this.day := (.N > 1), by = .(subjid, param, agedays)] + # calculate median of measurements on days where there is no extraneous + df[valid.rows & + !extraneous.this.day, median.sd := median(tbc.sd), by = .(subjid, param)] + + # distribute median to other valid or potential extraneous rows for same parameter for that subject + df[valid.rows, median.sd := sort(median.sd)[1], by = .(subjid, param)] + # take care of subject/parameters with more than one day with a valid observation + # determine the absolute difference between the measurements sd score and the median for that parameter for each child + df[valid.rows & + extraneous.this.day, delta.median.sd := abs(tbc.sd - median.sd), by = .(subjid, param)] + # identify subjects that have duplicates/extraneous on all days of observation for that parameter (i.e. delta.median.sd undefined) + subj.all.dups <- df[valid.rows & + extraneous.this.day & + is.na(delta.median.sd), unique(subjid)] + # add the "other map" -- now inluding head circumference + other_p_map <- c( + "HEIGHTCM" = "WEIGHTKG", + "WEIGHTKG" = "HEIGHTCM", + "HEADCM" = "HEIGHTCM" + ) + + df[valid.rows & + subjid %in% subj.all.dups, delta.median.sd := (function(subj.df) { + # iterate over parameters where delta.median.sd is not yet defined + # pass 1: take median from other parameter(s) + for (p in subj.df[is.na(delta.median.sd) & + extraneous.this.day, unique(param)]) { + median.other.sd <- subj.df[param == other_p_map[p] & + !extraneous.this.day, median(tbc.sd)] + subj.df[param == p, delta.median.sd := abs(tbc.sd - median.other.sd)] + } + return(subj.df$delta.median.sd) + })(copy(.SD)), by = .(subjid)] + + # Final pass: take median as zero (i.e. if no measurements from a different parameter) + # NOTE -- this is not exactly the same as taking a random parameter + df[valid.rows & + extraneous.this.day & + is.na(delta.median.sd), delta.median.sd := abs(tbc.sd)] + + # flag any extraneous value on the same day that is not the minimum distance from the median sd score + # NOTE: in the case of exact ducplicates, "which.min" will pick the first + df[valid.rows & + extraneous.this.day, extraneous := seq_along(delta.median.sd) != which.min(delta.median.sd), by = + .(subjid, param, agedays)] + # return the extraneous valid rows (i.e. the ones that should be temporarily excluded) + return(df$extraneous & valid.rows) +} From f94f1a966cb88758ee6414acc610d664c677f6e6 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 15 Dec 2022 15:32:53 -0500 Subject: [PATCH 008/100] Add carried forwards (and fix typos) --- R/infants_clean.R | 57 ++++++++++++++++++++++++++++++++++++++++++++- R/infants_support.R | 3 ++- 2 files changed, 58 insertions(+), 2 deletions(-) diff --git a/R/infants_clean.R b/R/infants_clean.R index 8b6cbb95..48fffe3f 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -75,7 +75,9 @@ cleanbatch <- function(data.df, data.df$batch[1] )) - # temp SDEs ---- + # NOTE: in each step, we redo temp SDEs + + # initial temp SDEs ---- # save a copy of all original measurement values before any transformation data.df[, v.orig := v] @@ -90,6 +92,59 @@ cleanbatch <- function(data.df, # capture a list of subjects with possible extraneous for efficiency later subj.dup <- data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', unique(subjid)] + # carried forwards ---- + + # 9. Exclude values that are carried forward. For the purposes of this analysis, any value that is identical to the preceding value for the same parameter + # and subject is considered carried forward. The chances of having identical measurements, even at an age/interval when little or no growth would be expected, + # is fairly small, and when this is the case the carried forward measurements provide little new information. + # a. Calculate d_prev_wt=wt-wtprev and d_prev_ht=ht-htprev. Use original measurements rather than transformed + # measurements (unit errors and switches). + # b. Unlike most steps, do this step for all extraneous values (exc_*==2) in addition to included values (exc_*==0), comparing all values for one day to all + # values from the prior day – if there are any values with a d_prev*==0, the value on the latter day should be excluded. + # c. Replace exc_*=3 for all values with d_prev*==0 & (exc_*==0 OR exc_*==2) + if (!include.carryforward) { + if (!quietly) + cat(sprintf( + "[%s] Exclude measurements carried forward...\n", + Sys.time() + )) + # for efficiency, bring get.prev and get.next inline here (working on valid rows within a single parameter for a single subject) + # structure c(NA, field.name[-.N]) == get.prev + data.df[, prev.v := as.double(NaN)] + data.df[valid(data.df), prev.v := c(NA, v.orig[-.N]), by = .(subjid, param)] + + # optimize "carry forward" for children without extraneous. + data.df[!(subjid %in% subj.dup) & + v.orig == prev.v, exclude := 'Exclude-Carried-Forward'] + + # need to handle children with extraneous measurements on same day separately + data.df[subjid %in% subj.dup & + valid(data.df, include.temporary.extraneous = TRUE), exclude := (function(df) { + setkey(df, agedays) + ages = unique(agedays) + # no point in looking for measurements carried forward if all measurements are from a single day of life + if (length(ages) > 1) { + # iterate over each age + for (i in 2:length(ages)) { + # find the set of measurements from the previous age in days + all.prev.v = df[agedays == ages[i - 1], v.orig] + # if a measurement for the current age is found in the set of measurements from the previous age, then mark it as carried forward + df[agedays == ages[i] & + v.orig %in% all.prev.v, exclude := 'Exclude-Carried-Forward'] + } + } + return(df$exclude) + })(copy(.SD)), .SDcols = c('agedays', 'exclude', 'v.orig'), by = .(subjid, param)] + } + + # 9d. Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous) + data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] + data.df[temporary_extraneous(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] + + # BIV ---- + + + # end ---- diff --git a/R/infants_support.R b/R/infants_support.R index 121ddbd4..bc607097 100644 --- a/R/infants_support.R +++ b/R/infants_support.R @@ -55,7 +55,8 @@ temporary_extraneous_infants <- function(df) { subj.all.dups <- df[valid.rows & extraneous.this.day & is.na(delta.median.sd), unique(subjid)] - # add the "other map" -- now inluding head circumference + + # add the "other map" -- now including head circumference other_p_map <- c( "HEIGHTCM" = "WEIGHTKG", "WEIGHTKG" = "HEIGHTCM", From efeda62e85b7c3ca754eb705d2eb16ac39411831 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 15 Dec 2022 16:23:42 -0500 Subject: [PATCH 009/100] Add BIVs --- R/infants_clean.R | 78 ++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 77 insertions(+), 1 deletion(-) diff --git a/R/infants_clean.R b/R/infants_clean.R index 48fffe3f..d69814d3 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -139,12 +139,88 @@ cleanbatch <- function(data.df, # 9d. Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous) data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] - data.df[temporary_extraneous(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] + data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] # BIV ---- + # Wt limits from do-file Oct 10 2022, in dataset, highest plausible weight=29.5kg, 65 lbs + # HC Limits based on analysis in do-file from Oct 11 2022: in dataset, lowest plausible 18.5, z=-13, highest plausible 63.5, z=11.2 (at birth, highest=42, z=6) + + + exc_nam <- "Exclude-Absolute-BIV" + + # identify absolute cutoffs + # Min/max weight at birth based on published births + data.df[valid(data.df) & param == "WEIGHTKG" & measurement < 0.2, + exclude := exc_nam] + # Min weight after birth based on rules about who can go home with alowance for significant weight loss -- this is for outpatient data only so very low weight babies would still be in NICU + data.df[valid(data.df) & param == "WEIGHTKG" & measurement > 10.5 & + agedays == 0, + exclude := exc_nam] + # Max weight for <2y based on eval (see do-file from Oct 10 2022), highest plaus weight 29.5kg + data.df[valid(data.df) & param == "WEIGHTKG" & measurement < 1 & + agedays == 0, + exclude := exc_nam] + # Max weight for <2y based on eval (see do-file from Oct 10 2022), highest plaus weight 29.5kg + data.df[valid(data.df) & param == "WEIGHTKG" & measurement > 35 & + agedays < 2, + exclude := exc_nam] + # Max weight for all based on published data + data.df[valid(data.df) & param == "WEIGHTKG" & measurement > 600, + exclude := exc_nam] + + # Min/max HC based on analysis in do file from + # Also, 18 is z=-6 for 22 0/7 in Fenton and 65 is z=6 for 40 0/7 + data.df[valid(data.df) & param == "HEIGHTCM" & measurement < 18, + exclude := exc_nam] + data.df[valid(data.df) & param == "HEIGHTCM" & measurement > 65 & + agedays == 0, + exclude := exc_nam] + + # Min/max HC based on analysis in do file from Oct 11 2022 + # Also, 13 is z=-6 for 22 0/7 in Fenton and + data.df[valid(data.df) & param == "HEADCM" & measurement < 13, + exclude := exc_nam] + data.df[valid(data.df) & param == "HEADCM" & measurement > 75, + exclude := exc_nam] + data.df[valid(data.df) & param == "HEADCM" & measurement > 50 & + agedays == 0, + exclude := exc_nam] + + exc_nam <- "Exclude-Z-BIV" + + # identify z cutoff + # ***Note, using unrecentered values*** + # *For weight only do after birth + data.df[valid(data.df) & param == "WEIGHTKG" & z.orig < - 25 & + agedays < 1, + exclude := exc_nam] + data.df[valid(data.df) & param == "WEIGHTKG" & z.orig < -15 & + agedays >= 1, + exclude := exc_nam] + data.df[valid(data.df) & param == "WEIGHTKG" & z.orig > 15, + exclude := exc_nam] + + # *Max z-score for height based on analysis of CHOP data because 15/25 too loose for upper limits + data.df[valid(data.df) & param == "HEIGHTCM" & z.orig < - 25 & + agedays < 1, + exclude := exc_nam] + data.df[valid(data.df) & param == "HEIGHTCM" & z.orig < -15 & + agedays >= 1, + exclude := exc_nam] + data.df[valid(data.df) & param == "HEIGHTCM" & z.orig > 8, + exclude := exc_nam] + + # head circumference + data.df[valid(data.df) & param == "HEADCM" & z.orig < -15, + exclude := exc_nam] + data.df[valid(data.df) & param == "HEADCM" & z.orig > 15, + exclude := exc_nam] + # 9d. Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous) + data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] + data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] # end ---- From 24a5d25054a59935920d0289b64fed52dff5e596 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 5 Jan 2023 10:28:22 -0500 Subject: [PATCH 010/100] Add headcm as a valid parameter, filter out parameters not included (with warning) --- R/growth.R | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/R/growth.R b/R/growth.R index 400b8fb3..d57e1424 100644 --- a/R/growth.R +++ b/R/growth.R @@ -5,7 +5,7 @@ #' Clean growth measurements #' #' @param subjid Vector of unique identifiers for each subject in the database. -#' @param param Vector identifying each measurement, may be 'WEIGHTKG', 'WEIGHTLBS', 'HEIGHTCM', 'HEIGHTIN', or 'LENGTHCM' +#' @param param Vector identifying each measurement, may be 'WEIGHTKG', 'WEIGHTLBS', 'HEIGHTCM', 'HEIGHTIN', 'LENGTHCM', or 'HEADCM'. #' 'HEIGHTCM'/'HEIGHTIN' vs. 'LENGTHCM' only affects z-score calculations between ages 24 to 35 months (730 to 1095 days). #' All linear measurements below 731 days of life (age 0-23 months) are interpreted as supine length, and #' all linear measurements above 1095 days of life (age 36+ months) are interpreted as standing height. @@ -183,6 +183,13 @@ cleangrowth <- function(subjid, if (!is.numeric(weight_cap) | weight_cap < 0){ stop("weight_cap not numeric. Please enter a positive number.") } + if (any(!param %in% c("LENTGCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", + "WEIGHTLBS", "HEADCM"))){ + cat(sprintf("[%s] Parameters included that do not match 'param' specifications. Filtering out...\n", Sys.time())) + data.all.ages <- + data.all.ages[param %in% c("LENTGCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", + "WEIGHTLBS", "HEADCM"),] + } # rate limit cutpoint -- min 18, max 20 cutpoint_update <- From 27c581653b2b3505536097550085259c989a2f43 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Wed, 11 Jan 2023 15:12:18 -0500 Subject: [PATCH 011/100] Begin updating recentering --- R/growth.R | 17 ++++++++++++++--- inst/extdata/gc-recenterfile-2022-12-20.gz | Bin 0 -> 42566 bytes 2 files changed, 14 insertions(+), 3 deletions(-) create mode 100644 inst/extdata/gc-recenterfile-2022-12-20.gz diff --git a/R/growth.R b/R/growth.R index d57e1424..19be2308 100644 --- a/R/growth.R +++ b/R/growth.R @@ -704,6 +704,7 @@ cleangrowth <- function(subjid, #' #' @param path Path to supplied reference anthro data. Defaults to package anthro tables. #' @param cdc.only Whether or not only CDC data should be used. Defaults to false. +#' @param infants boolean on if infants version is being used. will be merged in to main algorithm. #' #' @return Function for calculating BMI based on measurement, age in days, sex, and measurement value. #' @export @@ -716,7 +717,7 @@ cleangrowth <- function(subjid, #' # Return calculating function while specifying a path and using only CDC data #' afunc <- read_anthro(path = system.file("extdata", package = "growthcleanr"), #' cdc.only = TRUE) -read_anthro <- function(path = "", cdc.only = FALSE) { +read_anthro <- function(path = "", cdc.only = FALSE, infants = FALSE) { # avoid "no visible bindings" warning src <- param <- sex <- age <- ret <- m <- NULL csdneg <- csdpos <- s <- NULL @@ -742,9 +743,19 @@ read_anthro <- function(path = "", cdc.only = FALSE) { system.file(file.path("extdata", "growthfile_cdc_ext.csv.gz"), package = "growthcleanr"), file.path(path, "growthfile_cdc_ext.csv.gz") ) + #infants/default reference + growth_cdc_bridge_ext_path <- ifelse( + path == "", + system.file(file.path("extdata", "gc-recenterfile-2022-12-20.gz"), package = "growthcleanr"), + file.path(path, "gc-recenterfile-2022-12-20.gz") + ) - - growth_cdc_ext <- read.csv(gzfile(growth_cdc_ext_path)) + growth_cdc_ext <- + if (!infants){ + read.csv(gzfile(growth_cdc_ext_path)) + } else { + read.csv(gzfile(growth_cdc_bridge_ext_path)) + } l <- list( with( diff --git a/inst/extdata/gc-recenterfile-2022-12-20.gz b/inst/extdata/gc-recenterfile-2022-12-20.gz new file mode 100644 index 0000000000000000000000000000000000000000..d0633e04685eecda22d22b418786667aa5291628 GIT binary patch literal 42566 zcmV(@K-Rw>iwFP!000003QJ5+O-ZaQ)+tIZFR22O84xnLikAxjB32u;8vpzT4~W;qTfvM|+F! z_WQz{uOeRpGhu_6sOPa9jq?Y8Ln&%cho`_8#B z1mCU4&o8%eVs-QO5#xQ?c<(&pTUy{6wD%rgwh_Hrix0D@t-WK(OB0{{K*rR7y~ja5ZDpRmMsL7605}62fO_lr-;6ny zmJvhjxpU8Lt$lJtw~f=!Tvx--ILbW+d+BY(LB1X%YPS1vn%y7n+iq_!M;xEmsC~v> zVqFIJ8YAF6IlnghT;CVaF-qMv-s;A~)n7EPTw|a~`vBt*Nba~XSvR=*YJpiZKa3+E 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Subject: [PATCH 013/100] Fix problem with "pipe" operator and remove recentering from wrong section --- R/growth.R | 19 +++---------------- 1 file changed, 3 insertions(+), 16 deletions(-) diff --git a/R/growth.R b/R/growth.R index 19be2308..db327666 100644 --- a/R/growth.R +++ b/R/growth.R @@ -392,9 +392,9 @@ cleangrowth <- function(subjid, who_weight <- 3 - (agedays/365.25) cdc_weight <- (agedays/365.25) - 1 - data.all[agedays/365.25 => 1 & agedays/365.25 <= 3, + data.all[agedays/365.25 >= 1 & agedays/365.25 <= 3, z.orig := (z.orig_cdc*cdc_weight + z.orig_who*who_weight)/2] - data.all[agedays/365.25 => 1 & agedays/365.25 <= 3, + data.all[agedays/365.25 >= 1 & agedays/365.25 <= 3, sd.orig := (sd.orig_cdc*cdc_weight + sd.orig_who*who_weight)/2] # sort by subjid, param, agedays @@ -717,7 +717,7 @@ cleangrowth <- function(subjid, #' # Return calculating function while specifying a path and using only CDC data #' afunc <- read_anthro(path = system.file("extdata", package = "growthcleanr"), #' cdc.only = TRUE) -read_anthro <- function(path = "", cdc.only = FALSE, infants = FALSE) { +read_anthro <- function(path = "", cdc.only = FALSE) { # avoid "no visible bindings" warning src <- param <- sex <- age <- ret <- m <- NULL csdneg <- csdpos <- s <- NULL @@ -743,19 +743,6 @@ read_anthro <- function(path = "", cdc.only = FALSE, infants = FALSE) { system.file(file.path("extdata", "growthfile_cdc_ext.csv.gz"), package = "growthcleanr"), file.path(path, "growthfile_cdc_ext.csv.gz") ) - #infants/default reference - growth_cdc_bridge_ext_path <- ifelse( - path == "", - system.file(file.path("extdata", "gc-recenterfile-2022-12-20.gz"), package = "growthcleanr"), - file.path(path, "gc-recenterfile-2022-12-20.gz") - ) - - growth_cdc_ext <- - if (!infants){ - read.csv(gzfile(growth_cdc_ext_path)) - } else { - read.csv(gzfile(growth_cdc_bridge_ext_path)) - } l <- list( with( From 90626a4cca80fe682829fee2f1ebcea1e6285981 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 2 Feb 2023 11:37:17 -0500 Subject: [PATCH 014/100] Fix smoothing subsetting --- R/growth.R | 49 ++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 44 insertions(+), 5 deletions(-) diff --git a/R/growth.R b/R/growth.R index db327666..1d075419 100644 --- a/R/growth.R +++ b/R/growth.R @@ -155,7 +155,8 @@ cleangrowth <- function(subjid, quietly = TRUE, adult_cutpoint = 20, weight_cap = Inf, - adult_columns_filename = "") { + adult_columns_filename = "", + infants = F) { # avoid "no visible binding" warnings N <- age_years <- batch <- exclude <- index <- line <- NULL newbatch <- sd.median <- sd.orig <- tanner.months <- tbc.sd <- NULL @@ -392,10 +393,26 @@ cleangrowth <- function(subjid, who_weight <- 3 - (agedays/365.25) cdc_weight <- (agedays/365.25) - 1 - data.all[agedays/365.25 >= 1 & agedays/365.25 <= 3, - z.orig := (z.orig_cdc*cdc_weight + z.orig_who*who_weight)/2] - data.all[agedays/365.25 >= 1 & agedays/365.25 <= 3, - sd.orig := (sd.orig_cdc*cdc_weight + sd.orig_who*who_weight)/2] + smooth_val <- data.all$agedays/365.25 >= 1 & + data.all$agedays/365.25 <= 3 & + !data.all$param == "HEADCM" + data.all[smooth_val, + z.orig := (z.orig_cdc[smooth_val]*cdc_weight[smooth_val] + + z.orig_who[smooth_val]*who_weight[smooth_val])/2] + data.all[smooth_val, + sd.orig := (sd.orig_cdc[smooth_val]*cdc_weight[smooth_val] + + sd.orig_who[smooth_val]*who_weight[smooth_val])/2] + + # otherwise use WHO and CDC for older and younger, respectively + who_val <- data.all$param == "HEADCM" | + data.all$agedays/365.25 < 1 + data.all[who_val, z.orig := z.orig_who[who_val]] + data.all[who_val, sd.orig := sd.orig_who[who_val]] + + cdc_val <- data.all$param != "HEADCM" | + data.all$agedays/365.25 > 3 + data.all[cdc_val, z.orig := z.orig_cdc[cdc_val]] + data.all[cdc_val, sd.orig := sd.orig_cdc[cdc_val]] # sort by subjid, param, agedays setkey(data.all, subjid, param, agedays) @@ -434,6 +451,26 @@ cleangrowth <- function(subjid, # returns a data table indexed by param, sex, agedays. can use NHANES reference # data, derive from input, or use user-supplied data. if (!is.data.table(sd.recenter)) { + # INFANTS CHANGES: + # use recentering file derived from work, independent of sex + if (infants){ + infants_reference_medians_path <- ifelse( + ref.data.path == "", + system.file(file.path("extdata", + "gc-recenterfile-2022-12-20_format.csv.gz"), + package = "growthcleanr"), + file.path(ref.data.path, "gc-recenterfile-2022-12-20_format.csv.gz") + ) + sd.recenter <- fread(infants_reference_medians_path) + if (!quietly) + cat( + sprintf( + "[%s] Using infants reference medians...\n", + Sys.time() + ) + ) + } + # Use NHANES medians if the string "nhanes" is specified instead of a data.table # or if sd.recenter is not specified as "derive" and N < 5000. if ((is.character(sd.recenter) & tolower(sd.recenter) == "nhanes") | @@ -744,6 +781,8 @@ read_anthro <- function(path = "", cdc.only = FALSE) { file.path(path, "growthfile_cdc_ext.csv.gz") ) + growth_cdc_ext <- read.csv(gzfile(growth_cdc_ext_path)) + l <- list( with( read.table(gzfile(weianthro_path), header = TRUE), From 09a628fd255a5e21222b03f7d58a67c245381a35 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 2 Feb 2023 11:54:45 -0500 Subject: [PATCH 015/100] Update recentering for infants --- R/growth.R | 46 +- .../extdata/gc-recenterfile-2022-12-20.csv.gz | Bin 52503 -> 0 bytes .../gc-recenterfile-2022-12-20_format.csv | 10636 ++++++++++++++++ .../gc-recenterfile-2022-12-20_format.csv.gz | Bin 0 -> 62213 bytes 4 files changed, 10667 insertions(+), 15 deletions(-) delete mode 100644 inst/extdata/gc-recenterfile-2022-12-20.csv.gz create mode 100644 inst/extdata/gc-recenterfile-2022-12-20_format.csv create mode 100644 inst/extdata/gc-recenterfile-2022-12-20_format.csv.gz diff --git a/R/growth.R b/R/growth.R index 1d075419..0cb21cf5 100644 --- a/R/growth.R +++ b/R/growth.R @@ -390,8 +390,8 @@ cleangrowth <- function(subjid, # smooth z-scores/SD scores between ages 1 - 3yo using weighted scores # older uses cdc, younger uses who - who_weight <- 3 - (agedays/365.25) - cdc_weight <- (agedays/365.25) - 1 + who_weight <- 3 - (data.all$agedays/365.25) + cdc_weight <- (data.all$agedays/365.25) - 1 smooth_val <- data.all$agedays/365.25 >= 1 & data.all$agedays/365.25 <= 3 & @@ -406,13 +406,17 @@ cleangrowth <- function(subjid, # otherwise use WHO and CDC for older and younger, respectively who_val <- data.all$param == "HEADCM" | data.all$agedays/365.25 < 1 - data.all[who_val, z.orig := z.orig_who[who_val]] - data.all[who_val, sd.orig := sd.orig_who[who_val]] + data.all[who_val | (smooth_val & is.na(data.all$z.orig_cdc)), + z.orig := z.orig_who[who_val | (smooth_val & is.na(data.all$z.orig_cdc))]] + data.all[who_val | (smooth_val & is.na(data.all$sd.orig_cdc)), + sd.orig := sd.orig_who[who_val | (smooth_val & is.na(data.all$sd.orig_cdc))]] cdc_val <- data.all$param != "HEADCM" | data.all$agedays/365.25 > 3 - data.all[cdc_val, z.orig := z.orig_cdc[cdc_val]] - data.all[cdc_val, sd.orig := sd.orig_cdc[cdc_val]] + data.all[cdc_val | (smooth_val & is.na(data.all$z.orig_who)), + z.orig := z.orig_cdc[cdc_val | (smooth_val & is.na(data.all$z.orig_who))]] + data.all[cdc_val | (smooth_val & is.na(data.all$sd.orig_who)), + sd.orig := sd.orig_cdc[cdc_val | (smooth_val & is.na(data.all$sd.orig_who))]] # sort by subjid, param, agedays setkey(data.all, subjid, param, agedays) @@ -469,12 +473,14 @@ cleangrowth <- function(subjid, Sys.time() ) ) - } + } else if ((is.character(sd.recenter) & + tolower(sd.recenter) == "nhanes") | + (!(is.character(sd.recenter) & + tolower(sd.recenter) == "derive") & (data.all[, .N] < 5000))) { + + # Use NHANES medians if the string "nhanes" is specified instead of a data.table + # or if sd.recenter is not specified as "derive" and N < 5000. - # Use NHANES medians if the string "nhanes" is specified instead of a data.table - # or if sd.recenter is not specified as "derive" and N < 5000. - if ((is.character(sd.recenter) & tolower(sd.recenter) == "nhanes") | - (!(is.character(sd.recenter) & tolower(sd.recenter) == "derive") & (data.all[, .N] < 5000))) { nhanes_reference_medians_path <- ifelse( ref.data.path == "", system.file(file.path("extdata", "nhanes-reference-medians.csv.gz"), package = "growthcleanr"), @@ -522,11 +528,21 @@ cleangrowth <- function(subjid, } # ensure recentering medians are sorted correctly - setkey(sd.recenter, param, sex, agedays) + if (!infants){ + setkey(sd.recenter, param, sex, agedays) + + # add sd.recenter to data, and recenter + setkey(data.all, param, sex, agedays) + data.all <- sd.recenter[data.all] + } else { + # infant recenter doesn't have sex + setkey(sd.recenter, param, agedays) + + # add sd.recenter to data, and recenter + setkey(data.all, param, agedays) + data.all <- sd.recenter[data.all] + } - # add sd.recenter to data, and recenter - setkey(data.all, param, sex, agedays) - data.all <- sd.recenter[data.all] 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zWp$=W?*0~@%&e6;zo%b3uX4qFj)C@mx4aOVRRVC~5g&TTZG&)C1wF?NyJQwPiiGgD z&Lg`j`jn2dH@sbKw&mN3he literal 0 HcmV?d00001 From 27cae74921f344e47c0f09531625963e8c9208b2 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Mon, 6 Feb 2023 11:13:53 -0500 Subject: [PATCH 016/100] Update recentering file --- R/growth.R | 4 +- .../chop-rcfile-2023-02-03.csv_format.csv | 21916 ++++++++++++++++ .../chop-rcfile-2023-02-03.csv_format.csv.gz | Bin 0 -> 128261 bytes .../gc-recenterfile-2022-12-20_format.csv | 10636 -------- .../gc-recenterfile-2022-12-20_format.csv.gz | Bin 62213 -> 0 bytes 5 files changed, 21918 insertions(+), 10638 deletions(-) create mode 100644 inst/extdata/chop-rcfile-2023-02-03.csv_format.csv create mode 100644 inst/extdata/chop-rcfile-2023-02-03.csv_format.csv.gz delete mode 100644 inst/extdata/gc-recenterfile-2022-12-20_format.csv delete mode 100644 inst/extdata/gc-recenterfile-2022-12-20_format.csv.gz diff --git a/R/growth.R b/R/growth.R index 0cb21cf5..3ab80c63 100644 --- a/R/growth.R +++ b/R/growth.R @@ -461,9 +461,9 @@ cleangrowth <- function(subjid, infants_reference_medians_path <- ifelse( ref.data.path == "", system.file(file.path("extdata", - "gc-recenterfile-2022-12-20_format.csv.gz"), + "chop-rcfile-2023-02-03.csv_format.csv.gz"), package = "growthcleanr"), - file.path(ref.data.path, "gc-recenterfile-2022-12-20_format.csv.gz") + file.path(ref.data.path, "chop-rcfile-2023-02-03.csv_format.csv.gz") ) sd.recenter <- fread(infants_reference_medians_path) if (!quietly) diff --git a/inst/extdata/chop-rcfile-2023-02-03.csv_format.csv b/inst/extdata/chop-rcfile-2023-02-03.csv_format.csv new file mode 100644 index 00000000..b611d345 --- /dev/null +++ b/inst/extdata/chop-rcfile-2023-02-03.csv_format.csv @@ -0,0 +1,21916 @@ +agedays,param,sd.median +0,HEADCM,0.029449176 +1,HEADCM,0.050451264 +2,HEADCM,0.053999346 +3,HEADCM,0.058419447 +4,HEADCM,0.061936833 +5,HEADCM,0.065325893 +6,HEADCM,0.068696879 +7,HEADCM,0.071963161 +8,HEADCM,0.075226381 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-7272,WEIGHTKG,0.28979751 -7273,WEIGHTKG,0.28968966 -7274,WEIGHTKG,0.28957427 -7275,WEIGHTKG,0.28947422 -7276,WEIGHTKG,0.28936666 -7277,WEIGHTKG,0.2892592 -7278,WEIGHTKG,0.28915182 -7279,WEIGHTKG,0.28904456 -7280,WEIGHTKG,0.28893739 -7281,WEIGHTKG,0.28883034 -7282,WEIGHTKG,0.28872338 -7283,WEIGHTKG,0.28861654 -7284,WEIGHTKG,0.28850979 -7285,WEIGHTKG,0.28840312 -7286,WEIGHTKG,0.28829658 -7287,WEIGHTKG,0.28819874 -7288,WEIGHTKG,0.28809246 -7289,WEIGHTKG,0.28798631 -7290,WEIGHTKG,0.28787139 -7291,WEIGHTKG,0.28777426 -7292,WEIGHTKG,0.28766841 -7293,WEIGHTKG,0.28756264 -7294,WEIGHTKG,0.28746614 -7295,WEIGHTKG,0.28736064 -7296,WEIGHTKG,0.28725526 -7297,WEIGHTKG,0.28714058 -7298,WEIGHTKG,0.28703532 -7299,WEIGHTKG,0.28693014 -7300,WEIGHTKG,0.28682509 -7301,WEIGHTKG,0.28671047 -7302,WEIGHTKG,0.28660554 -7303,WEIGHTKG,0.28650069 -7304,WEIGHTKG,0.28639597 From 6f05a4fae9ac74ba46e7d9e65f9ee50529980154 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 9 Mar 2023 13:53:33 -0500 Subject: [PATCH 027/100] Update nonmatching param to be missing, not filtered out --- R/growth.R | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/R/growth.R b/R/growth.R index ac7ea9ad..cf8a8bf7 100644 --- a/R/growth.R +++ b/R/growth.R @@ -187,10 +187,15 @@ cleangrowth <- function(subjid, } if (any(!param %in% c("LENGTHCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", "WEIGHTLBS", "HEADCM"))){ - cat(sprintf("[%s] Parameters included that do not match 'param' specifications. Filtering out...\n", Sys.time())) + cat(sprintf("[%s] Parameters included that do not match 'param' specifications. Marking as missing...\n", Sys.time())) data.all.ages <- - data.all.ages[param %in% c("LENGTHCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", - "WEIGHTLBS", "HEADCM"),] + data.all.ages[ + !param %in% c("LENGTHCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", + "WEIGHTLBS", "HEADCM"), v := NA] + data.all.ages <- + data.all.ages[ + !param %in% c("LENGTHCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", + "WEIGHTLBS", "HEADCM"), v_adult := NA] } # rate limit cutpoint -- min 18, max 20 From aa12fb94f07b264ba2d3fe20b862f8f350ce71fa Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 9 Mar 2023 14:03:21 -0500 Subject: [PATCH 028/100] Update documentation and add no found bindings --- R/growth.R | 1 + R/infants_clean.R | 2 ++ man/read_anthro.Rd | 4 ++-- 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/R/growth.R b/R/growth.R index cf8a8bf7..b1fcadfc 100644 --- a/R/growth.R +++ b/R/growth.R @@ -162,6 +162,7 @@ cleangrowth <- function(subjid, N <- age_years <- batch <- exclude <- index <- line <- NULL newbatch <- sd.median <- sd.orig <- tanner.months <- tbc.sd <- NULL v <- v_adult <- whoagegrp.ht <- whoagegrp_ht <- z.orig <- NULL + z.orig_cdc <- z.orig_who <- sd.orig_cdc <- sd.orig_who <- NULL # preprocessing ---- diff --git a/R/infants_clean.R b/R/infants_clean.R index 1330f784..b1d80270 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -56,6 +56,8 @@ cleanbatch_infants <- function(data.df, whoagegrp.ht <- whoinc.1.ht <- whoinc.2.ht <- whoinc.3.ht <- whoinc.4.ht <- NULL whoinc.6.ht <- whoinc.age.ht <- z.orig <- NULL + oob <- sd_med <- med_diff <- max_diff <- sum_oob <- i.exclude <- NULL + data.df <- data.table(data.df, key = c('subjid', 'param', 'agedays', 'index')) if (parallel & !is.na(log.path)) { diff --git a/man/read_anthro.Rd b/man/read_anthro.Rd index 84419b2b..9f185e29 100644 --- a/man/read_anthro.Rd +++ b/man/read_anthro.Rd @@ -4,14 +4,14 @@ \alias{read_anthro} \title{Function to calculate z-scores and csd-scores based on anthro tables.} \usage{ -read_anthro(path = "", cdc.only = FALSE) +read_anthro(path = "", cdc.only = FALSE, infants = FALSE) } \arguments{ \item{path}{Path to supplied reference anthro data. Defaults to package anthro tables.} \item{cdc.only}{Whether or not only CDC data should be used. Defaults to false.} -\item{infants}{boolean on if infants version is being used. will be merged in to main algorithm.} +\item{infants}{boolean on if beta infants version is being used. will be merged in to main algorithm.} } \value{ Function for calculating BMI based on measurement, age in days, sex, and measurement value. From 50923b3780b2235cb179eb40d159ecffdbd3d230 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Mon, 13 Mar 2023 12:59:02 -0400 Subject: [PATCH 029/100] Update news for infants release --- NEWS.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/NEWS.md b/NEWS.md index e97381af..dc6be759 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,10 @@ +# growthcleanr 3.0.0 - 2023-03-13 + +## Added + +- Added the infants beta release algorithm -- expands pediatric algorithm to consider 0 - 2 years old, with infants = TRUE in cleangrowth() + - Updated velocity data for the extension (#122) + # growthcleanr 2.1.1 - 2023-03-01 ## Changed From 529dcf0f778a8f471cdf9a3f40c932e740288851 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Mon, 13 Mar 2023 13:07:32 -0400 Subject: [PATCH 030/100] Update package site --- docs/articles/adult-algorithm.html | 2 +- docs/articles/configuration.html | 2 +- docs/articles/developer-guidelines.html | 2 +- docs/articles/installation.html | 12 ++++++------ docs/articles/large-data-sets.html | 4 ++-- docs/articles/next-steps.html | 6 +++--- docs/articles/output.html | 2 +- docs/articles/quickstart.html | 2 +- docs/articles/usage.html | 2 +- docs/articles/utilities.html | 2 +- docs/news/index.html | 11 ++++++++++- docs/pkgdown.yml | 2 +- docs/reference/cleangrowth.html | 9 +++++++-- docs/reference/read_anthro.html | 6 +++++- 14 files changed, 41 insertions(+), 23 deletions(-) diff --git a/docs/articles/adult-algorithm.html b/docs/articles/adult-algorithm.html index 6064485b..5330a3e0 100644 --- a/docs/articles/adult-algorithm.html +++ b/docs/articles/adult-algorithm.html @@ -113,7 +113,7 @@

Adult algorithm

-

2023-03-01

+

2023-03-13

Source: vignettes/adult-algorithm.Rmd diff --git a/docs/articles/configuration.html b/docs/articles/configuration.html index 851055f8..0e3d5d29 100644 --- a/docs/articles/configuration.html +++ b/docs/articles/configuration.html @@ -113,7 +113,7 @@

Configuration

-

2023-03-01

+

2023-03-13

Source: vignettes/configuration.Rmd diff --git a/docs/articles/developer-guidelines.html b/docs/articles/developer-guidelines.html index 7e9f14b1..77869b15 100644 --- a/docs/articles/developer-guidelines.html +++ b/docs/articles/developer-guidelines.html @@ -113,7 +113,7 @@

Developer guidelines

-

2023-03-01

+

2023-03-13

Source: vignettes/developer-guidelines.Rmd diff --git a/docs/articles/installation.html b/docs/articles/installation.html index 8b247d04..9d18ac2c 100644 --- a/docs/articles/installation.html +++ b/docs/articles/installation.html @@ -113,7 +113,7 @@

Installation

-

2023-03-01

+

2023-03-13

Source: vignettes/installation.Rmd @@ -208,7 +208,7 @@

Docker

To install and run growthcleanr using Docker, open the PowerShell on Windows, or open the Terminal on macOS, and enter this docker command:

-
% docker run -it ghcr.io/mitre/growthcleanr/gcr-image:latest R
+
docker run -it ghcr.io/mitre/growthcleanr/gcr-image:latest R

The image tag latest in the example above will refer to the latest version of the package available on the main branch of the mitre/growthcleanr repository, which is typically in close sync with the upstream carriedaymont/growthcleanr @@ -216,7 +216,7 @@

Docker growthcleanr. To explicitly choose a release by name, replace latest with the release tag, e.g. for the released package v2.1.0:

-
% docker run -it ghcr.io/mitre/growthcleanr/gcr-image:v2.1.0 R
+
docker run -it ghcr.io/mitre/growthcleanr/gcr-image:v2.1.0 R

Whichever package you use, the first time this command is run, it might take a few minutes to download and extract several necessary components, but this should be fully automated. If successful, you @@ -228,7 +228,7 @@

Docker your own data. For example, if you are on Windows, and your data is in C:\Users\exampleuser\analysis, specify a mapping using the added -v step below:

-
% docker run -it -v C:\Users\exampleusers\analysis:/usr/src/app \
+
docker run -it -v C:\Users\exampleusers\analysis:/usr/src/app \
     ghcr.io/mitre/growthcleanr/gcr-image:latest R

 
Note that the slashes in file paths reverse direction from the
 reference to the folder location on your Windows machine (before the
@@ -242,7 +242,7 @@ 
Docker
 
If you are on macOS, and your data is in
 /Users/exampleuser/analysis, specify a folder mapping like
 this:

-
% docker run -it -v /Users/exampleuser/analysis:/usr/src/app \
+
docker run -it -v /Users/exampleuser/analysis:/usr/src/app \
     ghcr.io/mitre/growthcleanr/gcr-image:latest R

 
If you mapped a folder, then inside the Docker environment’s R
 prompt, when you then issue a command like list.files(),
@@ -283,7 +283,7 @@ 

 
If you are developing the growthcleanr code itself, you
 can download or clone the growthcleanr source code and then
 install it from source. To clone the source using git:

-
% git clone https://github.com/carriedaymont/growthcleanr.git

+
git clone https://github.com/carriedaymont/growthcleanr.git

 
Once you have the growthcleanr package source, open an R
 session from the growthcleanr base directory. Then install
 growthcleanr using the R devtools package:

diff --git a/docs/articles/large-data-sets.html b/docs/articles/large-data-sets.html
index 41aaff4f..8340f110 100644
--- a/docs/articles/large-data-sets.html
+++ b/docs/articles/large-data-sets.html
@@ -113,7 +113,7 @@
     

       
Working with large data sets

             
-            
2023-03-01

+            
2023-03-13

       
       Source: vignettes/large-data-sets.Rmd
       
@@ -226,7 +226,7 @@ 
Splitting input datasetsIf you have many small files saved, for example, as
 mydata.00001.csv, mydata.00002.csv, etc., this
 driver can be invoked using parallel:

-
% ls mydata.?????.csv | parallel -j2 --eta \
+
ls mydata.?????.csv | parallel -j2 --eta \
   "Rscript exec/gcdriver.R --quietly --sdrecenter nhanes {} {}-clean.csv"

 
This lists your input files, and passes the filename list to parallel
 to use in invoking the driver script, one file at a time, and to process
diff --git a/docs/articles/next-steps.html b/docs/articles/next-steps.html
index c09830cb..b84e2635 100644
--- a/docs/articles/next-steps.html
+++ b/docs/articles/next-steps.html
@@ -113,7 +113,7 @@
     

       
Next steps

             
-            
2023-03-01

+            
2023-03-13

       
       Source: vignettes/next-steps.Rmd
       
@@ -167,7 +167,7 @@ 
Running the experiment
-
% Rscript exec/testadjustcf.R cleaned.csv

+
Rscript exec/testadjustcf.R cleaned.csv

 
To use testacf(), run the following in the console (with
 cleaned_data in the environment):

 
@@ -398,7 +398,7 @@ 
Running the experimenttestacf_res entry.

 
For example, a 5-step sweep with the line-grid search
 would be run with this command:

-
% Rscript exec/textadjustcf.R --gridlength 5 --searchtype line-grid cleaned.csv

+
Rscript exec/textadjustcf.R --gridlength 5 --searchtype line-grid cleaned.csv

 
or with this function execution:

 
 result_list <- testacf(
diff --git a/docs/articles/output.html b/docs/articles/output.html
index b49c4a31..e2dabd0f 100644
--- a/docs/articles/output.html
+++ b/docs/articles/output.html
@@ -113,7 +113,7 @@
     

       
Understanding growthcleanr output

             
-            
2023-03-01

+            
2023-03-13

       
       Source: vignettes/output.Rmd
       
diff --git a/docs/articles/quickstart.html b/docs/articles/quickstart.html
index 00bcaa89..f2313936 100644
--- a/docs/articles/quickstart.html
+++ b/docs/articles/quickstart.html
@@ -113,7 +113,7 @@
     

       
Quickstart

             
-            
2023-03-01

+            
2023-03-13

       
       Source: vignettes/quickstart.Rmd
       
diff --git a/docs/articles/usage.html b/docs/articles/usage.html
index 93b35999..25a80391 100644
--- a/docs/articles/usage.html
+++ b/docs/articles/usage.html
@@ -113,7 +113,7 @@
     

       
Usage

             
-            
2023-03-01

+            
2023-03-13

       
       Source: vignettes/usage.Rmd
       
diff --git a/docs/articles/utilities.html b/docs/articles/utilities.html
index 7fa80555..5b8d14ba 100644
--- a/docs/articles/utilities.html
+++ b/docs/articles/utilities.html
@@ -114,7 +114,7 @@
       
Utilities for computing pediatric BMI
 percentiles, Z-scores, and related tools

             
-            
2023-03-01

+            
2023-03-13

       
       Source: vignettes/utilities.Rmd
       
diff --git a/docs/news/index.html b/docs/news/index.html
index a480bfaa..d4bc2ca5 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -89,7 +89,16 @@ 
Changelog 

     

 
     

-
growthcleanr 2.1.1 - 2023-03-01

+
growthcleanr 3.0.0 - 2023-03-13

+

+
Added

+
  • Added the infants beta release algorithm – expands pediatric algorithm to consider 0 - 2 years old, with infants = TRUE in cleangrowth()
+
    • Updated velocity data for the extension (#122)
      • 
+
    • 
+

+

+    

+
growthcleanr 2.1.1 - 2023-03-012023-03-03

 

 
Changed

 
  • Fixed missing adult measurements to be labeled “Missing” in output (#119)
    • 
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index f45aa4a7..000e5801 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -12,5 +12,5 @@ articles:
   quickstart: quickstart.html
   usage: usage.html
   utilities: utilities.html
-last_built: 2023-03-01T20:12Z
+last_built: 2023-03-13T17:00Z
 
diff --git a/docs/reference/cleangrowth.html b/docs/reference/cleangrowth.html
index d3257d47..0b35e97d 100644
--- a/docs/reference/cleangrowth.html
+++ b/docs/reference/cleangrowth.html
@@ -119,7 +119,8 @@ 
    Clean growth measurements
    
   quietly = TRUE,
   adult_cutpoint = 20,
   weight_cap = Inf,
-  adult_columns_filename = ""
+  adult_columns_filename = "",
+  infants = FALSE
 )

     

 
@@ -130,7 +131,7 @@ 
Arguments

 
 
 
param

-
Vector identifying each measurement, may be 'WEIGHTKG', 'WEIGHTLBS', 'HEIGHTCM', 'HEIGHTIN', or 'LENGTHCM'
+
Vector identifying each measurement, may be 'WEIGHTKG', 'WEIGHTLBS', 'HEIGHTCM', 'HEIGHTIN', 'LENGTHCM', or 'HEADCM'.
 'HEIGHTCM'/'HEIGHTIN' vs. 'LENGTHCM' only affects z-score calculations between ages 24 to 35 months (730 to 1095 days).
 All linear measurements below 731 days of life (age 0-23 months) are interpreted as supine length, and
 all linear measurements above 1095 days of life (age 36+ months) are interpreted as standing height.
@@ -273,6 +274,10 @@ 
Arguments

 as CSV. Defaults to "", for which this data will not be saved. Useful
 for post-analysis. For more information on this output, please see README.

 
+
+
infants

+
TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.

+
 

     

     
Value

diff --git a/docs/reference/read_anthro.html b/docs/reference/read_anthro.html
index ea1a7669..5bc801b5 100644
--- a/docs/reference/read_anthro.html
+++ b/docs/reference/read_anthro.html
@@ -94,7 +94,7 @@ 
Function to calculate z-scores and csd-scores based on anthro tables.

     

 
     

-    
read_anthro(path = "", cdc.only = FALSE)

+    
read_anthro(path = "", cdc.only = FALSE, infants = FALSE)

     

 
     

@@ -106,6 +106,10 @@ 
Arguments

 
cdc.only

 
Whether or not only CDC data should be used. Defaults to false.

 
+
+
infants

+
boolean on if beta infants version is being used. will be merged in to main algorithm.

+
 

     

     
Value


From fa3c3533c2cae7090b9bc4ac24942f119a5967af Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 13 Mar 2023 13:29:16 -0400
Subject: [PATCH 031/100] Update news and site for correct naming

---
 NEWS.md              | 2 +-
 docs/news/index.html | 2 +-
 docs/pkgdown.yml     | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index dc6be759..b524d5a2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# growthcleanr 3.0.0 - 2023-03-13
+# growthcleanr 3.0.0-infants-beta - 2023-03-13
 
 ## Added
 
diff --git a/docs/news/index.html b/docs/news/index.html
index d4bc2ca5..2534ae67 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -89,7 +89,7 @@ 
Changelog 

     

 
     

-
growthcleanr 3.0.0 - 2023-03-13

+
growthcleanr 3.0.0-infants-beta - 2023-03-13

 

 
Added

 
  • Added the infants beta release algorithm – expands pediatric algorithm to consider 0 - 2 years old, with infants = TRUE in cleangrowth()
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 000e5801..53be6f20 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -12,5 +12,5 @@ articles:
   quickstart: quickstart.html
   usage: usage.html
   utilities: utilities.html
-last_built: 2023-03-13T17:00Z
+last_built: 2023-03-13T17:25Z
 

From 1dce9b533f893c7953fa839bbd275cccf43df53b Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Fri, 9 Jun 2023 14:07:12 -0400
Subject: [PATCH 032/100] Fix bug related to ambiguity in z scores

---
 R/growth.R | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index bf44860b..4b2f16fd 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -451,7 +451,7 @@ cleangrowth <- function(subjid,
 
       smooth_val <- data.all$agedays/365.25 >= 2 &
         data.all$agedays/365.25 <= 4 &
-        !data.all$param == "HEADCM"
+        data.all$param != "HEADCM"
       data.all[smooth_val,
                z.orig := (z.orig_cdc[smooth_val]*cdc_weight[smooth_val] +
                             z.orig_who[smooth_val]*who_weight[smooth_val])/2]
@@ -463,16 +463,22 @@ cleangrowth <- function(subjid,
       who_val <- data.all$param == "HEADCM" |
         data.all$agedays/365.25 < 2
       data.all[who_val | (smooth_val & is.na(data.all$z.orig_cdc)),
-               z.orig := z.orig_who[who_val | (smooth_val & is.na(data.all$z.orig_cdc))]]
+               z.orig := data.all$z.orig_who[who_val | (smooth_val & is.na(data.all$z.orig_cdc))]]
       data.all[who_val | (smooth_val & is.na(data.all$sd.orig_cdc)),
-               sd.orig := sd.orig_who[who_val  | (smooth_val & is.na(data.all$sd.orig_cdc))]]
+               sd.orig := data.all$sd.orig_who[who_val  | (smooth_val & is.na(data.all$sd.orig_cdc))]]
 
       cdc_val <- data.all$param != "HEADCM" |
         data.all$agedays/365.25 > 4
       data.all[cdc_val  | (smooth_val & is.na(data.all$z.orig_who)),
-               z.orig := z.orig_cdc[cdc_val | (smooth_val & is.na(data.all$z.orig_who))]]
+               z.orig := data.all$z.orig_cdc[cdc_val | (smooth_val & is.na(data.all$z.orig_who))]]
       data.all[cdc_val | (smooth_val & is.na(data.all$sd.orig_who)),
-               sd.orig := sd.orig_cdc[cdc_val | (smooth_val & is.na(data.all$sd.orig_who))]]
+               sd.orig := data.all$sd.orig_cdc[cdc_val | (smooth_val & is.na(data.all$sd.orig_who))]]
+
+      # NOTE: SD SCORES IN CODE ARE Z IN INFANT DOCS -- USE sd.orig ONLY
+
+      # correcting z scores
+
+
     } else {
       # calculate z scores
       if (!quietly)

From 98794b13707acfe855fd9be11e8b3a1695f40610 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Fri, 9 Jun 2023 14:35:08 -0400
Subject: [PATCH 033/100] Add initial work for corrected z scores

---
 R/growth.R | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/R/growth.R b/R/growth.R
index 4b2f16fd..983f016b 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -477,8 +477,23 @@ cleangrowth <- function(subjid,
       # NOTE: SD SCORES IN CODE ARE Z IN INFANT DOCS -- USE sd.orig ONLY
 
       # correcting z scores
+      # add age in months
+      data.all[, agemonths := agedays/30.4375]
 
+      # TO ADD: REMOVE ADDED COLUMNS FROM DATA ALL
+      potcorr <- data.all$param == "WEIGHTKG" &
+        data.all$sd.orig < -2 &
+        data.all$agemonths < 10
 
+      # integer weight is in grams, rounded to the nearest 10
+      data.all[potcorr, intwt := round(v*100)*10]
+      # replace to facilitate merging with fenton curves
+      data.all[intwt >= 250 & intwt <=560, intwt := 570]
+
+      # MERGE WITH FENTON CURVES =- STOP HERE
+
+      # remove added columns
+      data.all[, c("intwt") := NULL]
     } else {
       # calculate z scores
       if (!quietly)

From aae7638b4bc22cb928f20ad31fe94f57fdf33dda Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 19 Jun 2023 11:47:39 -0400
Subject: [PATCH 034/100] Add fenton data

---
 inst/extdata/fentlms_foraga.csv.gz | Bin 0 -> 2772 bytes
 inst/extdata/fentlms_forz.csv.gz   | Bin 0 -> 20187 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 inst/extdata/fentlms_foraga.csv.gz
 create mode 100644 inst/extdata/fentlms_forz.csv.gz

diff --git a/inst/extdata/fentlms_foraga.csv.gz b/inst/extdata/fentlms_foraga.csv.gz
new file mode 100644
index 0000000000000000000000000000000000000000..9df3cf649140888d89802869e204e4a26ecd25a8
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diff --git a/inst/extdata/fentlms_forz.csv.gz b/inst/extdata/fentlms_forz.csv.gz
new file mode 100644
index 0000000000000000000000000000000000000000..ca3ae756383e4eccf98a0e83d287b937bc6a8efd
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From de6b4c91f1bbe9404d5dd5ed802c334a133b383c Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Fri, 23 Jun 2023 13:57:45 -0400
Subject: [PATCH 035/100] Add correcting z scores

---
 R/growth.R | 144 +++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 140 insertions(+), 4 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 983f016b..9d7f589c 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -476,7 +476,22 @@ cleangrowth <- function(subjid,
 
       # NOTE: SD SCORES IN CODE ARE Z IN INFANT DOCS -- USE sd.orig ONLY
 
-      # correcting z scores
+      # NOTE: MAY WANT TO SUBSET HERE
+
+      # 2b: corrected z scores ----
+
+      # keep the original column names -- we're adding a ton of columns that we
+      # want to filter out after correction
+      orig_colnames <- colnames(data.all)
+
+      # start by reading in fenton data
+      fentlms_foraga <- fread(
+        system.file(file.path("extdata", "fentlms_foraga.csv.gz"),
+                    package = "growthcleanr"))
+      fentlms_forz <- fread(
+        system.file(file.path("extdata", "fentlms_forz.csv.gz"),
+                    package = "growthcleanr"))
+
       # add age in months
       data.all[, agemonths := agedays/30.4375]
 
@@ -490,10 +505,127 @@ cleangrowth <- function(subjid,
       # replace to facilitate merging with fenton curves
       data.all[intwt >= 250 & intwt <=560, intwt := 570]
 
-      # MERGE WITH FENTON CURVES =- STOP HERE
+      # merge with fenton curves
+      data.all <- merge(
+        data.all, fentlms_foraga, by = c("sex", "intwt"),
+        all.x = T)
+
+      data.all[fengadays < 259, pmagedays := agedays + fengadays]
+      data.all[fengadays < 259, cagedays := pmagedays - 280]
+      # replace fengadays with pmagedays to facilitate merging
+      data.all[, fengadays := pmagedays]
+
+      # merge with fenton curves
+      data.all <- merge(
+        data.all, fentlms_forz, by = c("sex", "fengadays"),
+        all.x = T)
+
+      # add unmodified zscore using weight in unrounded grams
+      data.all[, unmod_zscore :=
+                 ((v*1000/fen_wt_m)^fen_wt_l - 1)/(fen_wt_l * fen_wt_s)]
+
+      # read in who/cdc data
+      growthfile_who <- fread(
+        system.file(file.path("extdata", "growthfile_who.csv.gz"),
+                    package = "growthcleanr"))
+      growthfile_cdc <- fread(
+        system.file(file.path("extdata", "growthfile_cdc_ext_infants.csv.gz"),
+                    package = "growthcleanr"))
+
+      # merge in the who/cdc data
+      data.all <- merge(data.all, growthfile_who,
+                        by.x = c("sex", "cagedays"),
+                        by.y = c("sex", "agedays"),
+                        all.x = T)
+      data.all <- merge(data.all, growthfile_cdc,
+                        by.x = c("sex", "cagedays"),
+                        by.y = c("sex", "agedays"),
+                        all.x = T)
+
+      # adjust WHO and CDC heights based on age
+      data.all[, cwho_cv := v]
+      data.all[, ccdc_cv := v]
+      data.all[param == "HEIGHTCM" & agedays > 730 & cagedays <= 730,
+               cwho_cv := cwho_cv + 0.8]
+      data.all[param == "HEIGHTCM" & agedays > 730 & cagedays <= 730,
+               ccdc_cv := ccdc_cv + 0.7]
+
+      # create the corrected z scores -- STOP HERE
+      # use read anthro, but pass in different arguments
+      # pass in the corrected height
+      data.all[, sd.c_cdc :=
+                 measurement.to.z(param, cagedays, sex, ccdc_cv, TRUE)]
+      data.all[, sd.c_who :=
+                 measurement.to.z_who(param, cagedays, sex, cwho_cv, TRUE)]
+
+      # smooth using weights as in original z score creation
+      who_weight <- 4 - (data.all$agedays/365.25)
+      cdc_weight <- (data.all$agedays/365.25) - 2
+
+      smooth_val <- data.all$agedays/365.25 >= 2 &
+        data.all$agedays/365.25 <= 4 &
+        data.all$param != "HEADCM"
+      data.all[smooth_val,
+               sd.c := (sd.c_cdc[smooth_val]*cdc_weight[smooth_val] +
+                             sd.c_who[smooth_val]*who_weight[smooth_val])/2]
+
+      # otherwise use WHO and CDC for older and younger, respectively
+      who_val <- data.all$param == "HEADCM" |
+        data.all$agedays/365.25 < 2
+      data.all[who_val | (smooth_val & is.na(data.all$sd.c_cdc)),
+               sd.c := data.all$sd.c_who[who_val  | (smooth_val & is.na(data.all$sd.c_cdc))]]
+
+      cdc_val <- data.all$param != "HEADCM" |
+        data.all$agedays/365.25 > 4
+      data.all[cdc_val | (smooth_val & is.na(data.all$sd.c_who)),
+               sd.c := data.all$sd.c_cdc[cdc_val | (smooth_val & is.na(data.all$sd.c_who))]]
 
-      # remove added columns
-      data.all[, c("intwt") := NULL]
+      # smoth corrected and uncorrected z scores
+      uncorrweight <-  4 - (data.all$agedays/365.25)
+      corrweight <- (data.all$agedays/365.25) - 2
+      smooth_val <- data.all$agedays/365.25 >= 2 &
+        data.all$agedays/365.25 <= 4 &
+        data.all$param != "HEADCM"
+      data.all[smooth_val,
+               sd.corr := (sd.c[smooth_val]*corrweight[smooth_val] +
+                             sd.orig[smooth_val]*uncorrweight[smooth_val])/2]
+      # for < 2 & potential correction, use fenton corrected score
+      data.all[agedays/365.25 <= 2 & potcorr, sd.corr := sd.c]
+      # for > 4, use original z score
+      data.all[agedays/365.25 >= 4, sd.corr := sd.orig]
+      # for not potential corrections, use the original z score
+      data.all[!potcorr, sd.corr := sd.orig]
+      # if the who/fenton score is not available for any reason, use the
+      # original
+      data.all[is.na(sd.corr), sd.corr := sd.orig]
+
+      # check for consistent growth
+      examine_only <- data.all$param == "WEIGHTKG" &
+        data.all$subjid %in% data.all$subjid[potcorr]
+      tmp <- copy(data.all[examine_only,])
+      tmp <- tmp[order(subjid, agedays),]
+      tmp[, seq_win := sequence(.N), by = subjid]
+      # we're only looking at the first 4 values, and they need to be < 2 years
+      tmp <- tmp[seq_win <= 4 & (agedays/365.25) < 2,]
+      # don't look at subjects where there is only 1 score and there is no
+      # value for either
+      tmp <- tmp[!(is.na(sd.corr & sd.orig)),]
+      tmp <- tmp[subjid %in% names(table(subjid) > 1),]
+      # create differences, absolute sum them
+      tmp[, sd.corr_abssumdiff := abs(sum(sd.corr[1] - sd.corr)), by = subjid]
+      tmp[, sd.orig_abssumdiff := abs(sum(sd.orig[1] - sd.orig)), by = subjid]
+      # find subjects where corrected value needs to be replaced
+      sub_replace <- unique(tmp[sd.corr_abssumdiff > sd.orig_abssumdiff,
+                                subjid])
+
+      # replace accordingly in the main dataframe
+      data.all[subjid %in% sub_replace, sd.corr := sd.orig]
+
+      # now merge in corrected sd values
+      data.all[, sd.orig := sd.corr]
+
+      # remove many added columns
+      data.all <- data.all[, ..orig_colnames]
     } else {
       # calculate z scores
       if (!quietly)
@@ -663,6 +795,10 @@ cleangrowth <- function(subjid,
     # safety check: treat observations where tbc.sd cannot be calculated as missing
     data.all[is.na(tbc.sd), exclude := 'Missing']
 
+    # 4: identify subset that don't need to be cleaned ----
+
+    # STOP HERE
+
     # pediatric: cleanbatch (most of steps) ----
 
     # NOTE: the rest of cleangrowth's steps are done through cleanbatch().

From ef8432d1e027599180fb80e0e7bdd7a0fe0bfbb1 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 26 Jun 2023 09:41:49 -0400
Subject: [PATCH 036/100] Add "no need to ewma" step

---
 R/growth.R | 37 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 36 insertions(+), 1 deletion(-)

diff --git a/R/growth.R b/R/growth.R
index 9d7f589c..cecd5142 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -797,7 +797,42 @@ cleangrowth <- function(subjid,
 
     # 4: identify subset that don't need to be cleaned ----
 
-    # STOP HERE
+    # identify those meeting all subjects meeting these criteria as "no need
+    # to ewma"
+    # does the subject have sdes
+
+    # keep the original column names -- we're adding a ton of columns that we
+    # want to filter out after correction
+    orig_colnames <- colnames(data.all)
+    # no SDEs
+    data.all[, sum_sde := .N, by = c("subjid", "param", "agedays")]
+    data.all[, no_sde := sum_sde == 1]
+    # does the subject have identical values
+    data.all[, sum_val := .N, by = c("subjid", "param", "v")]
+    data.all[, no_dup_val := sum_val == 1]
+    # all tbc.sd are within [-3,3] -- 0 is false
+    data.all[, no_outliers := sum((tbc.sd > -3 & tbc.sd < 3) |
+                               is.na(tbc.sd)) == (.N),
+        by = c("subjid", "param")]
+    data.all[, no_outliers := no_outliers == 1]
+    # all max - min tbd.sc < 2.5
+    data.all[, no_bigdiff := abs(max(tbc.sd) - min(tbc.sd)) < 2.5 | is.na(tbc.sd),
+        by = c("subjid", "param")]
+    # the previous value can't be too far from the current value
+    data.all[, seq_win := sequence(.N), by = c("subjid", "param")]
+    data.all[, nottoofar := abs(tbc.sd - dplyr::lag(tbc.sd)) < 1 | seq_win == 1,
+        by = c("subjid", "param")]
+    data.all[is.na(nottoofar),  nottoofar :=  TRUE]
+
+    # cumulative: no need to ewma -- needs to work for all within a subject &
+    # parameter
+    data.all[, nnte := no_sde & no_dup_val & no_outliers & no_bigdiff & nottoofar]
+    data.all[, nnte := sum(nnte) == .N, by = c("subjid", "param")]
+
+
+    # remove many added columns -- except for nnte
+    orig_colnames <- c(orig_colnames, "nnte")
+    data.all <- data.all[, ..orig_colnames]
 
     # pediatric: cleanbatch (most of steps) ----
 

From 760ed2fa9f9615471f3b005e90b1de4b13cfe761 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 26 Jun 2023 13:36:07 -0400
Subject: [PATCH 037/100] Adjust nnte for all parameters

---
 R/growth.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/R/growth.R b/R/growth.R
index cecd5142..f39bcffe 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -827,7 +827,8 @@ cleangrowth <- function(subjid,
     # cumulative: no need to ewma -- needs to work for all within a subject &
     # parameter
     data.all[, nnte := no_sde & no_dup_val & no_outliers & no_bigdiff & nottoofar]
-    data.all[, nnte := sum(nnte) == .N, by = c("subjid", "param")]
+    # NOTE: to come back -- how not to calculate with parameter
+    data.all[, nnte := sum(nnte) == .N, by = c("subjid")]
 
 
     # remove many added columns -- except for nnte

From 9fe99b38ae6c8617d0688c24c18a1600b5bb1cd9 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 26 Jun 2023 15:00:37 -0400
Subject: [PATCH 038/100] Update nnte to keep the "full" set as well

---
 R/growth.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/R/growth.R b/R/growth.R
index f39bcffe..78072172 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -828,11 +828,12 @@ cleangrowth <- function(subjid,
     # parameter
     data.all[, nnte := no_sde & no_dup_val & no_outliers & no_bigdiff & nottoofar]
     # NOTE: to come back -- how not to calculate with parameter
+    data.all[, nnte_full := sum(nnte) == .N, by = c("subjid", "param")]
     data.all[, nnte := sum(nnte) == .N, by = c("subjid")]
 
 
     # remove many added columns -- except for nnte
-    orig_colnames <- c(orig_colnames, "nnte")
+    orig_colnames <- c(orig_colnames, "nnte", "nnte_full")
     data.all <- data.all[, ..orig_colnames]
 
     # pediatric: cleanbatch (most of steps) ----

From cc64fc186ea89bc9a3279ff39ec2358faa1a2f9d Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 27 Jun 2023 11:06:56 -0400
Subject: [PATCH 039/100] Update temporary extraneous for nntes

---
 R/infants_support.R | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/R/infants_support.R b/R/infants_support.R
index 814b2639..f2586ac5 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -28,7 +28,9 @@ temporary_extraneous_infants <- function(df) {
   if (is.null(df$param))
     df[, param := NA]
   # only operate on valid rows (but include rows that may have previously been flagged as a "temporary extraneous")
-  valid.rows <- valid(df, include.temporary.extraneous = TRUE)
+  # also do not calculate temporary extraneous for NNTEs
+  valid.rows <- valid(df, include.temporary.extraneous = TRUE) &
+    !df$nnte
   # make a small copy of df with only the fields we need for efficiency
   df <- df[j = .(tbc.sd), keyby = .(subjid, param, agedays, index)]
   # initialize some useful fields

From 142b0099706bc267820908114a5c0aff8b2ece6c Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 27 Jun 2023 21:40:10 -0400
Subject: [PATCH 040/100] Update carried forwards step

---
 R/infants_clean.R | 144 +++++++++++++++++++++++++++++++++-------------
 1 file changed, 103 insertions(+), 41 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index b1d80270..94b1acd9 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -80,7 +80,7 @@ cleanbatch_infants <- function(data.df,
 
   # NOTE: in each step, we redo temp SDEs
 
-  # initial temp SDEs ----
+  # 5: temporary SDEs ----
 
   # save a copy of all original measurement values before any transformation
   data.df[, v.orig := v]
@@ -95,54 +95,116 @@ cleanbatch_infants <- function(data.df,
   # capture a list of subjects with possible extraneous for efficiency later
   subj.dup <- data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', unique(subjid)]
 
-  # carried forwards ----
+  # 6:  carried forwards ----
 
-  # 9.  Exclude values that are carried forward. For the purposes of this analysis, any value that is identical to the preceding value for the same parameter
-  #     and subject is considered carried forward. The chances of having identical measurements, even at an age/interval when little or no growth would be expected,
-  #     is fairly small, and when this is the case the carried forward measurements provide little new information.
-  # a.	Calculate d_prev_wt=wt-wtprev and d_prev_ht=ht-htprev. Use original measurements rather than transformed
-  #     measurements (unit errors and switches).
-  # b.	Unlike most steps, do this step for all extraneous values (exc_*==2) in addition to included values (exc_*==0), comparing all values for one day to all
-  #     values from the prior day – if there are any values with a d_prev*==0, the value on the latter day should be excluded.
-  # c.	Replace exc_*=3 for all values with d_prev*==0 & (exc_*==0 OR exc_*==2)
   if (!include.carryforward) {
     if (!quietly)
       cat(sprintf(
         "[%s] Exclude measurements carried forward...\n",
         Sys.time()
       ))
-    # for efficiency, bring get.prev and get.next inline here (working on valid rows within a single parameter for a single subject)
-    # structure c(NA, field.name[-.N]) == get.prev
-    data.df[, prev.v := as.double(NaN)]
-    data.df[valid(data.df), prev.v := c(NA, v.orig[-.N]), by = .(subjid, param)]
-
-    # optimize "carry forward" for children without extraneous.
-    data.df[!(subjid %in% subj.dup) &
-              v.orig == prev.v, exclude := 'Exclude-Carried-Forward']
-
-    # need to handle children with extraneous measurements on same day separately
-    data.df[subjid %in% subj.dup &
-              valid(data.df, include.temporary.extraneous = TRUE), exclude := (function(df) {
-                setkey(df, agedays)
-                ages = unique(agedays)
-                # no point in looking for measurements carried forward if all measurements are from a single day of life
-                if (length(ages) > 1) {
-                  # iterate over each age
-                  for (i in 2:length(ages)) {
-                    # find the set of measurements from the previous age in days
-                    all.prev.v = df[agedays == ages[i - 1], v.orig]
-                    # if a measurement for the current age is found in the set of measurements from the previous age, then mark it as carried forward
-                    df[agedays == ages[i] &
-                         v.orig %in% all.prev.v, exclude := 'Exclude-Carried-Forward']
-                  }
-                }
-                return(df$exclude)
-              })(copy(.SD)), .SDcols = c('agedays', 'exclude', 'v.orig'), by = .(subjid, param)]
-  }
 
-  # 9d.  Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous)
-  data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']
-  data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day']
+    # save original column names
+    orig_colnames <- colnames(data.df)
+
+    # we only running carried forwards on valid values, non NNTE values,
+    # and non single values
+    tmp <- table(paste0(data.df$subjid, "_", data.df$param))
+    single <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 1]
+    valid_set <- valid(data.df, include.temporary.extraneous = TRUE) &
+      !data.df$nnte_full &
+      single
+    data.sub <- data.df[valid_set,]
+
+    # for ease, order by subject, parameter, and agedays
+    data.sub <- data.sub[order(subjid, param, agedays)]
+
+    # if no sdes, exclude carried forward values
+    data.sub[, sum_sde := .N, by = c("subjid", "param", "agedays")]
+    data.sub[, no_sde := !paste0(data.sub$subjid, "_", data.sub$param) %in%
+               paste0(data.sub$subjid, "_", data.sub$param)[data.sub$sum_sde > 1]]
+    data.sub[no_sde == TRUE, cf :=  (v.orig - shift(v.orig)) == 0, by = c("subjid", "param")]
+    data.sub[is.na(cf), cf := FALSE]
+
+    # for values with sdes, compare to all values from the prior day
+    data.sub[no_sde == FALSE, cf := (function(df){
+      ages <- unique(agedays)
+      for (i in 2:length(ages)) {
+        # find the set of measurements from the previous age in days
+        all.prev.v <- df[agedays == ages[i - 1], v.orig]
+        # if a measurement for the current age is found in the set of measurements from the previous age, then mark it as carried forward
+        df[agedays == ages[i] &
+             v.orig %in% all.prev.v, cf := TRUE]
+      }
+    })(copy(.SD)), .SDcols = c('agedays', 'cf', 'v.orig'), by = .(subjid, param)]
+
+    # merge in the carried forwards
+    cf_idx <- data.sub$index[data.sub$cf]
+    data.df[index %in% cf_idx, exclude := "Exclude-Carried-Forward"]
+
+    # redo temp sde
+    data.df$exclude[temporary_extraneous_infants(data.df)] <- 'Exclude-Temporary-Extraneous-Same-Day'
+
+    # find out if values are whole or half imperial
+    data.df[param == "WEIGHTKG",
+            wholehalfimp := (v.orig * 2.20462262)%%1 < 0.01]
+    data.df[param == "HEIGHTCM",
+            wholehalfimp := (v.orig * 1.27)%%1 < 0.01]
+    data.df[param == "HEIGHTCM",
+            wholehalfimp := (v.orig * 1.27 / 2)%%1 < 0.01]
+    data.df[param == "HEADCM",
+            wholehalfimp := (v.orig * 1.27)%%1 < 0.01]
+    data.df[param == "HEADCM",
+            wholehalfimp := (v.orig * 1.27 / 2)%%1 < 0.01]
+    # find string carried forward length
+    data.df[, seq_win := sequence(rle(as.character(exclude))$lengths),
+            by = c("subjid", "param")]
+    data.df[exclude != "Exclude-Carried-Forward", seq_win := NA]
+    # relabel all the initial values as "0" -- reorder
+    data.df <- data.df[order(subjid, param, v.orig, agedays)]
+    data.df[which(seq_win == 1)-1, seq_win :=  0]
+    # reorder correctly
+    data.df <- data.df[order(subjid, param, agedays)]
+    # also create labels
+    data.df[, cs := rep(1:length(rle(as.character(exclude))$lengths),
+                        times = rle(as.character(exclude))$lengths),
+            by = c("subjid", "param")]
+    # fix the first one -- have to do this outside of data table
+    data.df[which(seq_win == 0), "cs"] <-
+      data.df[which(seq_win == 0)+1, "cs"]
+    data.df[is.na(seq_win), cs := NA]
+
+    # find th diff between initial and cf
+    data.df[!is.na(seq_win), absdiff := abs(sd.orig - sd.orig[1]),
+            by = c("subjid", "param", "cs")]
+
+    # handle CFs by case
+    data.df[!is.na(seq_win), exclude := (function(df){
+      # only 1 cf in string
+      if (max(seq_win) == 1){
+        df[seq_win != 0 & absdiff < 0.05,
+           exclude := "Exclude-1-CF-deltaZ-<0.05"]
+
+        # use short circuiting to have the lower end covered (>= 0.05)
+        df[seq_win != 0 & absdiff < .1 & wholehalfimp,
+           exclude := "Exclude-1-CF-deltaZ-<0.1 wholehalfimp"]
+      } else if (max(seq_win) > 1){
+        df[seq_win != 0 & agedays/365.25 > 16 & sex == 1 & absdiff < 0.05,
+           exclude := "Exclude-Teen-2-plus-CF-deltaZ-<0.05"]
+        df[seq_win != 0 & agedays/365.25 > 17 & sex == 0 & absdiff < 0.05,
+           exclude := "Exclude-Teen-2-plus-CF-deltaZ-<0.05"]
+
+        df[seq_win != 0 & agedays/365.25 > 16 & sex == 1 & absdiff < .1 &
+             wholehalfimp,
+           exclude := "Exclude-Teen-2-plus-CF-deltaZ-<0.1-wholehalfimp"]
+        df[seq_win != 0 & agedays/365.25 > 17 & sex == 0 & absdiff < .1 &
+             wholehalfimp,
+           exclude := "Exclude-Teen-2-plus-CF-deltaZ-<0.1-wholehalfimp"]
+      }
+    })(copy(.SD)),
+    .SDcols = c('agedays', "seq_win", 'absdiff', "sex", "cs", "wholehalfimp",
+                "exclude"),
+    by = c("subjid", "param", "cs")]
 
   # BIV ----
 

From 1156db51c6728b995c581027dff1e070d1971c45 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 12 Jul 2023 09:52:55 -0400
Subject: [PATCH 041/100] Fix clarification for HC on corrected z scores

---
 R/growth.R | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 78072172..0e354bf4 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -580,12 +580,11 @@ cleangrowth <- function(subjid,
       data.all[cdc_val | (smooth_val & is.na(data.all$sd.c_who)),
                sd.c := data.all$sd.c_cdc[cdc_val | (smooth_val & is.na(data.all$sd.c_who))]]
 
-      # smoth corrected and uncorrected z scores
+      # smooth corrected and uncorrected z scores
       uncorrweight <-  4 - (data.all$agedays/365.25)
       corrweight <- (data.all$agedays/365.25) - 2
       smooth_val <- data.all$agedays/365.25 >= 2 &
-        data.all$agedays/365.25 <= 4 &
-        data.all$param != "HEADCM"
+        data.all$agedays/365.25 <= 4
       data.all[smooth_val,
                sd.corr := (sd.c[smooth_val]*corrweight[smooth_val] +
                              sd.orig[smooth_val]*uncorrweight[smooth_val])/2]

From 2a01233bd54a2d5486ce6cacef5a026c643ec06f Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 12 Jul 2023 10:36:12 -0400
Subject: [PATCH 042/100] Fix returning carried forward output

---
 R/growth.R        | 7 ++++++-
 R/infants_clean.R | 5 ++++-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 0e354bf4..102fc747 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -230,6 +230,11 @@ cleangrowth <- function(subjid,
     'Missing',
     'Exclude-Temporary-Extraneous-Same-Day',
     'Exclude-Carried-Forward',
+    # added CF exclusopms
+    "Exclude-1-CF-deltaZ-<0.05",
+    "Exclude-1-CF-deltaZ-<0.1-wholehalfimp",
+    "Exclude-Teen-2-plus-CF-deltaZ-<0.05",
+    "Exclude-Teen-2-plus-CF-deltaZ-<0.1-wholehalfimp",
     'Exclude-SD-Cutoff',
     'Exclude-EWMA-Extreme',
     'Exclude-EWMA-Extreme-Pair',
@@ -802,7 +807,7 @@ cleangrowth <- function(subjid,
 
     # keep the original column names -- we're adding a ton of columns that we
     # want to filter out after correction
-    orig_colnames <- colnames(data.all)
+    orig_colnames <- copy(colnames(data.all))
     # no SDEs
     data.all[, sum_sde := .N, by = c("subjid", "param", "agedays")]
     data.all[, no_sde := sum_sde == 1]
diff --git a/R/infants_clean.R b/R/infants_clean.R
index 94b1acd9..965809be 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -187,7 +187,7 @@ cleanbatch_infants <- function(data.df,
 
         # use short circuiting to have the lower end covered (>= 0.05)
         df[seq_win != 0 & absdiff < .1 & wholehalfimp,
-           exclude := "Exclude-1-CF-deltaZ-<0.1 wholehalfimp"]
+           exclude := "Exclude-1-CF-deltaZ-<0.1-wholehalfimp"]
       } else if (max(seq_win) > 1){
         df[seq_win != 0 & agedays/365.25 > 16 & sex == 1 & absdiff < 0.05,
            exclude := "Exclude-Teen-2-plus-CF-deltaZ-<0.05"]
@@ -201,10 +201,13 @@ cleanbatch_infants <- function(data.df,
              wholehalfimp,
            exclude := "Exclude-Teen-2-plus-CF-deltaZ-<0.1-wholehalfimp"]
       }
+
+      return(df$exclude)
     })(copy(.SD)),
     .SDcols = c('agedays', "seq_win", 'absdiff', "sex", "cs", "wholehalfimp",
                 "exclude"),
     by = c("subjid", "param", "cs")]
+  }
 
   # BIV ----
 

From 1209d033e0751025a33efeb86c8f7222fc593508 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 12 Jul 2023 11:21:11 -0400
Subject: [PATCH 043/100] Update BIVs

---
 R/growth.R        | 4 ++++
 R/infants_clean.R | 8 ++++++--
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 102fc747..8176788b 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -481,6 +481,10 @@ cleangrowth <- function(subjid,
 
       # NOTE: SD SCORES IN CODE ARE Z IN INFANT DOCS -- USE sd.orig ONLY
 
+      # keep the original, uncorrected, unrecentered zscores
+      data.all[,sd.orig_uncorr := sd.orig]
+      data.all[,z.orig_uncorr := z.orig]
+
       # NOTE: MAY WANT TO SUBSET HERE
 
       # 2b: corrected z scores ----
diff --git a/R/infants_clean.R b/R/infants_clean.R
index 965809be..0e2d2ed4 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -225,7 +225,9 @@ cleanbatch_infants <- function(data.df,
 
   # identify absolute cutoffs
   # Min/max weight at birth based on published births
-  data.df[valid(data.df) & param == "WEIGHTKG" & v < 0.2,
+  data.df[valid(data.df) & param == "WEIGHTKG" & v < 0.2 & agedays == 0,
+          exclude := exc_nam]
+  data.df[valid(data.df) & param == "WEIGHTKG" & v < 1 & agedays != 0,
           exclude := exc_nam]
   # Min weight after birth based on rules about who can go home with alowance for significant weight loss -- this is for outpatient data only so very low weight babies would still be in NICU
   data.df[valid(data.df) & param == "WEIGHTKG" & v > 10.5 &
@@ -237,7 +239,7 @@ cleanbatch_infants <- function(data.df,
           exclude := exc_nam]
   # Max weight for <2y based on eval (see do-file from Oct 10 2022), highest plaus weight 29.5kg
   data.df[valid(data.df) & param == "WEIGHTKG" & v > 35 &
-            agedays < 2,
+            agedays*365.25 < 2,
           exclude := exc_nam]
   # Max weight for all based on published data
   data.df[valid(data.df) & param == "WEIGHTKG" & v > 600,
@@ -247,6 +249,8 @@ cleanbatch_infants <- function(data.df,
   # Also, 18 is z=-6 for 22 0/7 in Fenton and 65 is z=6 for 40 0/7
   data.df[valid(data.df) & param == "HEIGHTCM" & v < 18,
           exclude := exc_nam]
+  data.df[valid(data.df) & param == "HEIGHTCM" & v > 244,
+          exclude := exc_nam]
   data.df[valid(data.df) & param == "HEIGHTCM" & v > 65 &
             agedays == 0,
           exclude := exc_nam]

From 476c9039800294a177b01d736f0a7c6785ee167c Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 12 Jul 2023 11:21:45 -0400
Subject: [PATCH 044/100] Fix the agedays and update the standardized BIV
 output

---
 R/infants_clean.R | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 0e2d2ed4..5d2d2b75 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -221,6 +221,9 @@ cleanbatch_infants <- function(data.df,
       Sys.time()
     ))
 
+  # add age in years
+  data.df[, ageyears := agedays *365.25]
+
   exc_nam <- "Exclude-Absolute-BIV"
 
   # identify absolute cutoffs
@@ -239,7 +242,7 @@ cleanbatch_infants <- function(data.df,
           exclude := exc_nam]
   # Max weight for <2y based on eval (see do-file from Oct 10 2022), highest plaus weight 29.5kg
   data.df[valid(data.df) & param == "WEIGHTKG" & v > 35 &
-            agedays*365.25 < 2,
+            ageyears < 2,
           exclude := exc_nam]
   # Max weight for all based on published data
   data.df[valid(data.df) & param == "WEIGHTKG" & v > 600,
@@ -270,29 +273,29 @@ cleanbatch_infants <- function(data.df,
   # identify z cutoff
   # ***Note, using unrecentered values***
   #  *For weight only do after birth
-  data.df[valid(data.df) & param == "WEIGHTKG" & z.orig < - 25 &
-            agedays < 1,
+  data.df[valid(data.df) & param == "WEIGHTKG" & sd.orig_uncorr < - 25 &
+            ageyears < 1,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "WEIGHTKG" & z.orig < -15 &
-            agedays >= 1,
+  data.df[valid(data.df) & param == "WEIGHTKG" & sd.orig_uncorr < -15 &
+            ageyears >= 1,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "WEIGHTKG" & z.orig > 15,
+  data.df[valid(data.df) & param == "WEIGHTKG" & sd.orig_uncorr > 22,
           exclude := exc_nam]
 
   # *Max z-score for height based on analysis of CHOP data because 15/25 too loose for upper limits
-  data.df[valid(data.df) & param == "HEIGHTCM" & z.orig < - 25 &
-            agedays < 1,
+  data.df[valid(data.df) & param == "HEIGHTCM" & sd.orig_uncorr < - 25 &
+            ageyears < 1,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEIGHTCM" & z.orig < -15 &
-            agedays >= 1,
+  data.df[valid(data.df) & param == "HEIGHTCM" & sd.orig_uncorr < -15 &
+            ageyears >= 1,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEIGHTCM" & z.orig > 8,
+  data.df[valid(data.df) & param == "HEIGHTCM" & sd.orig_uncorr > 8,
           exclude := exc_nam]
 
   # head circumference
-  data.df[valid(data.df) & param == "HEADCM" & z.orig < -15,
+  data.df[valid(data.df) & param == "HEADCM" & sd.orig_uncorr < -15,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEADCM" & z.orig > 15,
+  data.df[valid(data.df) & param == "HEADCM" & sd.orig_uncorr > 15,
           exclude := exc_nam]
 
 

From fefed79ffdf44ea352d7d1e7ddd74f6629a0d377 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 12 Jul 2023 11:28:17 -0400
Subject: [PATCH 045/100] Update BIv to not consider NNTEs

---
 R/infants_clean.R | 44 ++++++++++++++++++++++++--------------------
 1 file changed, 24 insertions(+), 20 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 5d2d2b75..307d4d8f 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -224,47 +224,51 @@ cleanbatch_infants <- function(data.df,
   # add age in years
   data.df[, ageyears := agedays *365.25]
 
+  # calculate the valid step
+  valid_set <- valid(data.df, include.temporary.extraneous = TRUE) &
+    !data.df$nnte_full
+
   exc_nam <- "Exclude-Absolute-BIV"
 
   # identify absolute cutoffs
   # Min/max weight at birth based on published births
-  data.df[valid(data.df) & param == "WEIGHTKG" & v < 0.2 & agedays == 0,
+  data.df[valid_set & param == "WEIGHTKG" & v < 0.2 & agedays == 0,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "WEIGHTKG" & v < 1 & agedays != 0,
+  data.df[valid_set & param == "WEIGHTKG" & v < 1 & agedays != 0,
           exclude := exc_nam]
   # Min weight after birth based on rules about who can go home with alowance for significant weight loss -- this is for outpatient data only so very low weight babies would still be in NICU
-  data.df[valid(data.df) & param == "WEIGHTKG" & v > 10.5 &
+  data.df[valid_set & param == "WEIGHTKG" & v > 10.5 &
             agedays == 0,
           exclude := exc_nam]
   # Max weight for <2y based on eval (see do-file from Oct 10 2022), highest plaus weight 29.5kg
-  data.df[valid(data.df) & param == "WEIGHTKG" & v < 1 &
+  data.df[valid_set & param == "WEIGHTKG" & v < 1 &
             agedays == 0,
           exclude := exc_nam]
   # Max weight for <2y based on eval (see do-file from Oct 10 2022), highest plaus weight 29.5kg
-  data.df[valid(data.df) & param == "WEIGHTKG" & v > 35 &
+  data.df[valid_set & param == "WEIGHTKG" & v > 35 &
             ageyears < 2,
           exclude := exc_nam]
   # Max weight for all based on published data
-  data.df[valid(data.df) & param == "WEIGHTKG" & v > 600,
+  data.df[valid_set & param == "WEIGHTKG" & v > 600,
           exclude := exc_nam]
 
   # Min/max HC based on analysis in do file from
   # Also, 18 is z=-6 for 22 0/7 in Fenton and 65 is z=6 for 40 0/7
-  data.df[valid(data.df) & param == "HEIGHTCM" & v < 18,
+  data.df[valid_set & param == "HEIGHTCM" & v < 18,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEIGHTCM" & v > 244,
+  data.df[valid_set & param == "HEIGHTCM" & v > 244,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEIGHTCM" & v > 65 &
+  data.df[valid_set & param == "HEIGHTCM" & v > 65 &
             agedays == 0,
           exclude := exc_nam]
 
   # Min/max HC based on analysis in do file from Oct 11 2022
   # Also, 13 is z=-6 for 22 0/7 in Fenton and
-  data.df[valid(data.df) & param == "HEADCM" & v < 13,
+  data.df[valid_set & param == "HEADCM" & v < 13,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEADCM" & v > 75,
+  data.df[valid_set & param == "HEADCM" & v > 75,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEADCM" & v > 50 &
+  data.df[valid_set & param == "HEADCM" & v > 50 &
             agedays == 0,
           exclude := exc_nam]
 
@@ -273,29 +277,29 @@ cleanbatch_infants <- function(data.df,
   # identify z cutoff
   # ***Note, using unrecentered values***
   #  *For weight only do after birth
-  data.df[valid(data.df) & param == "WEIGHTKG" & sd.orig_uncorr < - 25 &
+  data.df[valid_set & param == "WEIGHTKG" & sd.orig_uncorr < - 25 &
             ageyears < 1,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "WEIGHTKG" & sd.orig_uncorr < -15 &
+  data.df[valid_set & param == "WEIGHTKG" & sd.orig_uncorr < -15 &
             ageyears >= 1,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "WEIGHTKG" & sd.orig_uncorr > 22,
+  data.df[valid_set & param == "WEIGHTKG" & sd.orig_uncorr > 22,
           exclude := exc_nam]
 
   # *Max z-score for height based on analysis of CHOP data because 15/25 too loose for upper limits
-  data.df[valid(data.df) & param == "HEIGHTCM" & sd.orig_uncorr < - 25 &
+  data.df[valid_set & param == "HEIGHTCM" & sd.orig_uncorr < - 25 &
             ageyears < 1,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEIGHTCM" & sd.orig_uncorr < -15 &
+  data.df[valid_set & param == "HEIGHTCM" & sd.orig_uncorr < -15 &
             ageyears >= 1,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEIGHTCM" & sd.orig_uncorr > 8,
+  data.df[valid_set & param == "HEIGHTCM" & sd.orig_uncorr > 8,
           exclude := exc_nam]
 
   # head circumference
-  data.df[valid(data.df) & param == "HEADCM" & sd.orig_uncorr < -15,
+  data.df[valid_set & param == "HEADCM" & sd.orig_uncorr < -15,
           exclude := exc_nam]
-  data.df[valid(data.df) & param == "HEADCM" & sd.orig_uncorr > 15,
+  data.df[valid_set & param == "HEADCM" & sd.orig_uncorr > 15,
           exclude := exc_nam]
 
 

From bb68e48675cf04228125e1781b8487e6fe60ca6f Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 12 Jul 2023 12:51:27 -0400
Subject: [PATCH 046/100] Add the first draft of evil twins

---
 R/infants_clean.R   | 32 ++++++++++++++++++++++++--------
 R/infants_support.R | 20 ++++++++------------
 2 files changed, 32 insertions(+), 20 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 307d4d8f..4c0c8853 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -110,10 +110,10 @@ cleanbatch_infants <- function(data.df,
     # we only running carried forwards on valid values, non NNTE values,
     # and non single values
     tmp <- table(paste0(data.df$subjid, "_", data.df$param))
-    single <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 1]
+    not_single <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 1]
     valid_set <- valid(data.df, include.temporary.extraneous = TRUE) &
       !data.df$nnte_full &
-      single
+      not_single
     data.sub <- data.df[valid_set,]
 
     # for ease, order by subject, parameter, and agedays
@@ -175,7 +175,8 @@ cleanbatch_infants <- function(data.df,
     data.df[is.na(seq_win), cs := NA]
 
     # find th diff between initial and cf
-    data.df[!is.na(seq_win), absdiff := abs(sd.orig - sd.orig[1]),
+    data.df[!is.na(seq_win), absdiff :=
+              abs(sd.orig_uncorr - sd.orig_uncorr[1]),
             by = c("subjid", "param", "cs")]
 
     # handle CFs by case
@@ -312,27 +313,41 @@ cleanbatch_infants <- function(data.df,
 
   exc_nam <- "Exclude-Evil-Twin"
 
+  # create the valid set
+  # we only running carried forwards on valid values, non NNTE values,
+  # and non single values, and non pair
+  tmp <- table(paste0(data.df$subjid, "_", data.df$param))
+  not_single_pairs <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 2]
+  valid_set <- valid(data.df, include.temporary.extraneous = FALSE) &
+    !data.df$nnte_full & # does not use the "other"
+    not_single_pairs
+
+  # make sure it's ordered by subjid and parameter
+  data.df <- data.df[order(subjid, param),]
+
   # first, find out of any re possible evil twins in the first place
-  start_df <- calc_oob_evil_twins(data.df[valid(data.df),])
+  # NOTE: GO BACK HERE
+  start_df <- calc_oob_evil_twins(data.df[valid_set,])
 
-  if (any(start_df$sum_oob > 2)) {
+  if (any(start_df$oob)) {
     if (!quietly)
       cat(sprintf(
         "[%s] Exclude evil twins...\n",
         Sys.time()
       ))
 
-    df <- copy(data.df)
+    # remove the valid set from the beginning
+    df <- copy(data.df[valid_set,])
 
     # start to evaluate and remove evil twins
     data.df[, exclude := (function(df) {
       # where we are updating results
       upd.df <- copy(df)
-      upd.df <- calc_oob_evil_twins(df[valid(df),])
+      upd.df <- calc_oob_evil_twins(upd.df)
       # count the amount of oobs for each subject/param and distribute it out
       upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)]
 
-      any_oob <- TRUE
+      any_oob <- any(upd.df$sum_oob > 2)
       # while there are multiple oob, we want to remove
       while (any_oob){
 
@@ -347,6 +362,7 @@ cleanbatch_infants <- function(data.df,
 
         df[upd.df[exclude == exc_nam,], exclude := i.exclude, on = .(line)]
 
+        # reupdate valid (to recalculate OOB -- others are not included)
         upd.df <- calc_oob_evil_twins(df[valid(df),])
         upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)]
 
diff --git a/R/infants_support.R b/R/infants_support.R
index f2586ac5..10de0d10 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -102,20 +102,16 @@ temporary_extraneous_infants <- function(df) {
 #' @keywords internal
 #' @noRd
 calc_oob_evil_twins <- function(df){
-  # start by determining if a measurement is out of bounds (oob)
-  # different criteria for VLEZ and non-VLEZ
-  # (z-score < -3 in the first 6 months of life )
-  vlez <- df$agedays < 6*30.4375 & df$tbc.sd < -3
+  # # start by determining if a measurement is out of bounds (oob)
 
   # for differences for adjacent, we pad with a high number to default to true
-  oob <- (!vlez & (
-    abs(c(df$tbc.sd[2:nrow(df)], Inf) - df$tbc.sd) > 5 |
-      abs(c(Inf, df$tbc.sd[2:nrow(df)]) - df$tbc.sd) > 5
-  )) |
-    (vlez & (
-      abs(c(df$tbc.sd[2:nrow(df)], Inf) - df$tbc.sd) > 8 |
-        abs(c(Inf, df$tbc.sd[2:nrow(df)]) - df$tbc.sd) > 8
-    ))
+  # we don't want to consider the comparison with the next subj/param
+  oob <- ((
+    (abs(c(df$tbc.sd[2:nrow(df)], Inf) - df$tbc.sd) > 5 &
+       rev(duplicated(rev(paste(df$subjid, "_", df$param))))) |
+      (abs(c(Inf, df$tbc.sd[1:(nrow(df)-1)]) - df$tbc.sd) > 5 &
+         duplicated(paste(df$subjid, "_", df$param)))
+  ))
   df[, "oob" := oob]
 
   return(df)

From 45c5b0a4aeaf85e9ceea76fa668295315831dd0e Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Sun, 23 Jul 2023 11:28:22 -0400
Subject: [PATCH 047/100] Add corrected tbc sd

---
 R/growth.R | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 8176788b..e1c8bd6b 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -491,7 +491,7 @@ cleangrowth <- function(subjid,
 
       # keep the original column names -- we're adding a ton of columns that we
       # want to filter out after correction
-      orig_colnames <- colnames(data.all)
+      orig_colnames <- copy(colnames(data.all))
 
       # start by reading in fenton data
       fentlms_foraga <- fread(
@@ -629,8 +629,7 @@ cleangrowth <- function(subjid,
       # replace accordingly in the main dataframe
       data.all[subjid %in% sub_replace, sd.corr := sd.orig]
 
-      # now merge in corrected sd values
-      data.all[, sd.orig := sd.corr]
+      orig_colnames <- c(orig_colnames, "sd.corr")
 
       # remove many added columns
       data.all <- data.all[, ..orig_colnames]
@@ -774,6 +773,9 @@ cleangrowth <- function(subjid,
 
     setkey(data.all, subjid, param, agedays)
     data.all[, tbc.sd := sd.orig - sd.median]
+    # separate out corrected and noncorrected values
+    data.all[, ctbc.sd := sd.corr - sd.median]
+
 
     if (sdrecentered.filename != "") {
       write.csv(data.all, sdrecentered.filename, row.names = FALSE)

From 1dc5fd8847cfc905e4180fba8a0d1626693485a3 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Sun, 23 Jul 2023 11:58:13 -0400
Subject: [PATCH 048/100] Update to fix corrected for ewma

---
 R/infants_support.R | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/R/infants_support.R b/R/infants_support.R
index 10de0d10..ba2324c2 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -111,7 +111,13 @@ calc_oob_evil_twins <- function(df){
        rev(duplicated(rev(paste(df$subjid, "_", df$param))))) |
       (abs(c(Inf, df$tbc.sd[1:(nrow(df)-1)]) - df$tbc.sd) > 5 &
          duplicated(paste(df$subjid, "_", df$param)))
+  )) & ((
+    (abs(c(df$ctbc.sd[2:nrow(df)], Inf) - df$ctbc.sd) > 5 &
+       rev(duplicated(rev(paste(df$subjid, "_", df$param))))) |
+      (abs(c(Inf, df$ctbc.sd[1:(nrow(df)-1)]) - df$ctbc.sd) > 5 &
+         duplicated(paste(df$subjid, "_", df$param)))
   ))
+
   df[, "oob" := oob]
 
   return(df)

From d366b93eed176c0d0be88072ebccf72b0a105e32 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Sun, 23 Jul 2023 17:13:40 -0400
Subject: [PATCH 049/100] Finish extreme EWMA

---
 R/infants_clean.R | 235 ++++++++++++++++++++++++++--------------------
 1 file changed, 133 insertions(+), 102 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 4c0c8853..bea9b6c0 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -380,8 +380,7 @@ cleanbatch_infants <- function(data.df,
   data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']
   data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day']
 
-  # extreme ewma ----
-
+  # Extreme EWMA ----
 
   # 11.  Exclude extreme errors with EWMA
   # a.	Erroneous measurements can distort the EWMA for measurements around them. Therefore, if the EWMA method identifies more than one value for a subject and
@@ -398,111 +397,143 @@ cleanbatch_infants <- function(data.df,
     num.ewma.excluded <- 0
     # optimization: determine whether this subject has any extraneous
     has.extraneous <- subjid %in% subj.dup
-    while (TRUE) {
-      df[, (ewma.fields) := as.double(NaN)]
-      df[valid(exclude), (ewma.fields) := ewma(agedays, tbc.sd, ewma.exp, TRUE)]
 
-      # note: at this point, only one ewma exists per param on a given day for a subject, so sort(ewma.all)[1] will returns the non-missing ewma.all
-      # restrict to children with possible extraneous for efficiency
-      if (has.extraneous) {
+    # only enter if there's more than one valid value
+    tmp <- table(paste0(df$subjid, "_", df$param))
+    not_single_pairs <- paste0(df$subjid, "_", df$param) %in% names(tmp)[tmp > 2]
+    valid_set <- valid(df, include.temporary.extraneous = TRUE) &
+      !df$nnte_full & # does not use the "other"
+      not_single_pairs
+
+    if (sum(valid_set) > 1){
+      while (TRUE) {
+        tmp <- table(paste0(df$subjid, "_", df$param))
+        not_single_pairs <- paste0(df$subjid, "_", df$param) %in% names(tmp)[tmp > 2]
+        valid_set <- valid(df, include.temporary.extraneous = TRUE) &
+          !df$nnte_full & # does not use the "other"
+          not_single_pairs
+
+
+        df[, (ewma.fields) := as.double(NaN)]
+
+        # first, calculate which exponent we want to put through (pass a different
+        # on for each exp)
+        # subset df to only valid rows
+        df_sub <- df[valid_set,]
+        tmp <- data.frame(
+          "before" = abs(df_sub$agedays - c(NA, df_sub$agedays[1:(nrow(df_sub)-1)])),
+          "after" = abs(df_sub$agedays - c(df_sub$agedays[2:(nrow(df_sub))], NA))
+        )
+        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+        exp_vals <- rep(-1.5, nrow(tmp))
+        exp_vals[maxdiff > 365.25] <- -2.5
+        exp_vals[maxdiff > 730.5] <- -3.5
+        df[valid_set, exp_vals := exp_vals]
+
+        # calculate ewma
+        df[valid_set, (ewma.fields) := ewma(agedays, tbc.sd, exp_vals, TRUE)]
+        df[valid_set, paste0("c.",ewma.fields) := ewma(agedays, ctbc.sd, exp_vals, TRUE)]
+
+        # note: at this point, only one ewma exists per param on a given day for a subject, so sort(ewma.all)[1] will returns the non-missing ewma.all
+        # restrict to children with possible extraneous for efficiency
+
+        # include the corrected
+        if (has.extraneous) {
+          df[, `:=`(
+            ewma.all = sort(ewma.all)[1],
+            ewma.before = sort(ewma.before)[1],
+            ewma.after = sort(ewma.after)[1],
+
+            c.ewma.all = sort(c.ewma.all)[1],
+            c.ewma.before = sort(c.ewma.before)[1],
+            c.ewma.after = sort(c.ewma.after)[1]
+          ), by = .(agedays)]
+        }
         df[, `:=`(
-          ewma.all = sort(ewma.all)[1],
-          ewma.before = sort(ewma.before)[1],
-          ewma.after = sort(ewma.after)[1]
-        ), by = .(agedays)]
-      }
-      df[, `:=`(
-        dewma.all = tbc.sd - ewma.all,
-        dewma.before = tbc.sd - ewma.before,
-        dewma.after = tbc.sd - ewma.after
-      )]
-
-      # 11c.  Identify all values that meet all of the following criteria as potential exclusions:
-      #   i.	There are 3 or more measurements for that subject and parameter
-      #   ii.	(dewma_*>3.5 & dewma_*_bef>3 & dewma_*_aft>3 & tbc*sd>3.5) OR (dewma_*<-3.5 & dewma_*_bef<-3 & d & dewma_*_aft<-3 & tbc*sd<-3.5)
-      #   iii.	exc_*==0
-
-      # a.	For VLEZ: DEWMA > 8, DEWMA_bef > 6, DEWMA_aft > 6, and tbc`p’z > 3.5
-      # b.	For non-VLEZ: DEWMA > 3.5, DEWMA_bef > 3, DEWMA_aft > 3, and tbc`p’z > 3.5
-      # c.	For VLEZ and non-VLEZ: DEWMA < -3.5, DEWMA_bef < -3, DEWMA_aft <-3, tbc`p’z < -3.5
-
-      # (z-score < -3 in the first 6 months of life )
-      vlez <- df$agedays < 6*30.4375 & df$tbc.sd < -3
-
-      num.valid <- sum(valid(df))
-      rep <- na_as_false(with(
-        df,
-        num.valid >= 3 & valid(df)
-        &
-          (# for VLEZ only
-          (vlez &
-             dewma.all > 8 & dewma.before > 6 & dewma.after > 6 & tbc.sd > 3.5
-          ) |
-          # for non-VLEZ
-          (!vlez &
-             dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5
-          ) |
-          # for both
-          (
-            dewma.all < -3.5 &
-              dewma.before < -3 & dewma.after < -3 & tbc.sd < -3.5
-          ))
-      ))
-      num.exclude <- sum(rep)
-      # 11d.  If there is only one potential exclusion identified in step 11c for a subject and parameter, replace exc_*=5 for that value
-      if (num.exclude == 1)
-        df[rep, exclude := 'Exclude-EWMA-Extreme']
-      # 11e.  If there is more than one potential exclusion identified in step 11c for a subject and parameter, calculate abssum_*=|tbc*sd+dewma_*| for each exclusion and
-      #     replace exc_*=5 for the value with the highest abssum_*
-      if (num.exclude > 1) {
-        # first order by decreasing abssum
-        worst.row <- with(df, order(rep, abs(tbc.sd + (
-          tbc.sd - ewma.all
-        )), decreasing = TRUE))[1]
-        df[worst.row, exclude := 'Exclude-EWMA1-Extreme']
-      }
-#
-#       # 11f.  For subjects/parameters with only 2 values, calculate abstbc*sd=|tbc*sd|
-#       # g.  Replace exc_*=6 for values that meet all of the following criteria
-#       #   i.	There are 2 measurements for that subject and parameter
-#       #   ii.	(dewma_*>3.5 & tbc*sd>3.5) OR (dewma_*<-3.5 & tbc*sd<-3.5)
-#       #   iii.	If there are 2 measurements for a subject/parameter that meet criteria ii, only replace exc_*=6 for the value with the larger abstbc*sd
-#       rep <- na_as_false(with(
-#         df,
-#         num.valid == 2 &
-#           (
-#             tbc.sd - ewma.all > 3.5 &
-#               tbc.sd > 3.5 |
-#               tbc.sd - ewma.all < -3.5 & tbc.sd < -3.5
-#           )
-#       ))
-#       num.exclude <- sum(rep)
-#       if (num.exclude == 1)
-#         df[rep, exclude := 'Exclude-EWMA-Extreme-Pair']
-#       if (num.exclude > 1) {
-#         # first order by decreasing abssum
-#         worst.row <- with(df, order(rep, abs(tbc.sd), decreasing = TRUE))[1]
-#         df[worst.row, exclude := 'Exclude-EWMA-Extreme-Pair']
-#       }
-
-      # 11h.  Recalculate temporary extraneous as in step 5
-      # optimize: only perform these steps if this subject is known to have extraneous measurements
-      if (has.extraneous) {
-        df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']
-        df[temporary_extraneous(df), exclude := 'Exclude-Temporary-Extraneous-Same-Day']
-      }
-
-      # 11i.  If there was at least one subject who had a potential exclusion identified in step 11c, repeat steps 11b-11g. If there were no subjects with potential
-      #     exclusions identified in step 11c, move on to step 12.
-      newly.excluded <- sum(df$exclude %in% c('Exclude-EWMA1-Extreme', 'Exclude-EWMA-Extreme-Pair'))
-      if (newly.excluded > num.ewma.excluded) {
-        num.ewma.excluded <- newly.excluded
-      } else {
-        break
+          dewma.all = tbc.sd - ewma.all,
+          dewma.before = tbc.sd - ewma.before,
+          dewma.after = tbc.sd - ewma.after,
+
+          c.dewma.all = ctbc.sd - c.ewma.all
+        )]
+
+
+        # calculate potential exclusions
+        df[valid_set, pot_excl :=
+             (dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5 &
+                ((!is.na(ctbc.sd) & c.dewma.all > 3.5) | is.na(ctbc.sd))
+             ) |
+             # for both
+             (
+               dewma.all < -3.5 &
+                 dewma.before < -3 & dewma.after < -3 & tbc.sd < -3.5 &
+                 ((!is.na(ctbc.sd) & c.dewma.all > 3.5) | is.na(ctbc.sd))
+             )
+        ]
+        df[valid_set,][c(1, nrow(df)), pot_excl := FALSE]
+        df[!valid_set, pot_excl := FALSE] # to compensate for later
+
+
+        # 11c.  Identify all values that meet all of the following criteria as potential exclusions:
+        #   i.	There are 3 or more measurements for that subject and parameter
+        #   ii.	(dewma_*>3.5 & dewma_*_bef>3 & dewma_*_aft>3 & tbc*sd>3.5) OR (dewma_*<-3.5 & dewma_*_bef<-3 & d & dewma_*_aft<-3 & tbc*sd<-3.5)
+        #   iii.	exc_*==0
+
+        num.exclude <- sum(df$pot_excl)
+        # 11d.  If there is only one potential exclusion identified in step 11c for a subject and parameter, replace exc_*=5 for that value
+        if (num.exclude == 1)
+          df[pot_excl, exclude := 'Exclude-EWMA1-Extreme']
+        # 11e.  If there is more than one potential exclusion identified in step 11c for a subject and parameter, calculate abssum_*=|tbc*sd+dewma_*| for each exclusion and
+        #     replace exc_*=5 for the value with the highest abssum_*
+        if (num.exclude > 1) {
+          # first order by decreasing abssum
+          worst.row <- with(df, order(pot_excl, abs(tbc.sd + dewma.all),
+                                      decreasing = TRUE))[1]
+          df[worst.row, exclude := 'Exclude-EWMA1-Extreme']
+        }
+        #
+        #       # 11f.  For subjects/parameters with only 2 values, calculate abstbc*sd=|tbc*sd|
+        #       # g.  Replace exc_*=6 for values that meet all of the following criteria
+        #       #   i.	There are 2 measurements for that subject and parameter
+        #       #   ii.	(dewma_*>3.5 & tbc*sd>3.5) OR (dewma_*<-3.5 & tbc*sd<-3.5)
+        #       #   iii.	If there are 2 measurements for a subject/parameter that meet criteria ii, only replace exc_*=6 for the value with the larger abstbc*sd
+        #       rep <- na_as_false(with(
+        #         df,
+        #         num.valid == 2 &
+        #           (
+        #             tbc.sd - ewma.all > 3.5 &
+        #               tbc.sd > 3.5 |
+        #               tbc.sd - ewma.all < -3.5 & tbc.sd < -3.5
+        #           )
+        #       ))
+        #       num.exclude <- sum(rep)
+        #       if (num.exclude == 1)
+        #         df[rep, exclude := 'Exclude-EWMA-Extreme-Pair']
+        #       if (num.exclude > 1) {
+        #         # first order by decreasing abssum
+        #         worst.row <- with(df, order(rep, abs(tbc.sd), decreasing = TRUE))[1]
+        #         df[worst.row, exclude := 'Exclude-EWMA-Extreme-Pair']
+        #       }
+
+        # 11h.  Recalculate temporary extraneous as in step 5
+        # optimize: only perform these steps if this subject is known to have extraneous measurements
+        if (has.extraneous) {
+          df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']
+          df[temporary_extraneous(df), exclude := 'Exclude-Temporary-Extraneous-Same-Day']
+        }
+
+        # 11i.  If there was at least one subject who had a potential exclusion identified in step 11c, repeat steps 11b-11g. If there were no subjects with potential
+        #     exclusions identified in step 11c, move on to step 12.
+        newly.excluded <- sum(df$exclude %in% c('Exclude-EWMA1-Extreme', 'Exclude-EWMA-Extreme-Pair'))
+        if (newly.excluded > num.ewma.excluded) {
+          num.ewma.excluded <- newly.excluded
+        } else {
+          break
+        }
       }
     }
     return(df$exclude)
-  })(copy(.SD)), by = .(subjid, param), .SDcols = c('index', 'sex', 'agedays', 'tbc.sd', 'exclude')]
+  })(copy(.SD)), by = .(subjid, param), .SDcols = c('index', 'sex', 'agedays', 'tbc.sd', 'ctbc.sd', 'nnte_full', 'exclude')]
 
   # 9d.  Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous)
   data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']

From 5de245d29fb642fd4a1ab2fa09e54dd09e5b04b9 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 24 Jul 2023 19:05:36 -0400
Subject: [PATCH 050/100] Add SDE step

---
 R/growth.R          |   7 +-
 R/infants_clean.R   | 460 ++++++++++++++++++++++++++++++--------------
 R/infants_support.R |  17 ++
 3 files changed, 337 insertions(+), 147 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index e1c8bd6b..56e6c378 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -230,7 +230,7 @@ cleangrowth <- function(subjid,
     'Missing',
     'Exclude-Temporary-Extraneous-Same-Day',
     'Exclude-Carried-Forward',
-    # added CF exclusopms
+    # added CF exclusions
     "Exclude-1-CF-deltaZ-<0.05",
     "Exclude-1-CF-deltaZ-<0.1-wholehalfimp",
     "Exclude-Teen-2-plus-CF-deltaZ-<0.05",
@@ -239,6 +239,11 @@ cleangrowth <- function(subjid,
     'Exclude-EWMA-Extreme',
     'Exclude-EWMA-Extreme-Pair',
     'Exclude-Extraneous-Same-Day',
+    'Exclude-SDE-Identical',
+    'Exclude-SDE-All-Exclude',
+    'Exclude-SDE-All-Extreme',
+    'Exclude-SDE-EWMA',
+    'Exclude-SDE-One-Day',
     'Exclude-EWMA-8',
     'Exclude-EWMA-9',
     'Exclude-EWMA-10',
diff --git a/R/infants_clean.R b/R/infants_clean.R
index bea9b6c0..8e5a891c 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -524,7 +524,7 @@ cleanbatch_infants <- function(data.df,
 
         # 11i.  If there was at least one subject who had a potential exclusion identified in step 11c, repeat steps 11b-11g. If there were no subjects with potential
         #     exclusions identified in step 11c, move on to step 12.
-        newly.excluded <- sum(df$exclude %in% c('Exclude-EWMA1-Extreme', 'Exclude-EWMA-Extreme-Pair'))
+        newly.excluded <- sum(df$exclude %in% c('Exclude-EWMA1-Extreme'))
         if (newly.excluded > num.ewma.excluded) {
           num.ewma.excluded <- newly.excluded
         } else {
@@ -539,151 +539,319 @@ cleanbatch_infants <- function(data.df,
   data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']
   data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day']
 
-  # SDEs ----
-
-  # STOP HERE: need to adapt to new work
-
-  # # prepare a list of valid rows and initialize variables for convenience
-  # valid.rows <- valid(data.df)
-  # data.df[, `:=`(
-  #   ewma.all = as.double(NaN),
-  #   abssum2 = as.double(NaN),
-  #   median.other.sd = as.double(NaN),
-  #   extraneous = FALSE
-  # )]
-  #
-  # # 12c.	Calculate a EWMA step for all subjects/parameters with duplicates/extraneous and at
-  # #       least one non-extraneous value with the following modifications
-  # #   i.	For calculating the EWMA, include only the extraneous selected in 12c
-  # #  ii.	Calculate dewma_* for all values of extraneous
-  # # iii.	You do not need to calculate EWMAbef or EWMAaft for this step
-  #
-  # # determine proportion of days with extraneous/duplication for each parameter ahead of time for efficiency
-  # dup.ratio.df <- data.df[subjid %in% subj.dup &
-  #                           (valid.rows |
-  #                              temp.dups), list(dup = (.N > 1)), by = .(subjid, param, agedays)][j = list(dup.ratio =
-  #                                                                                                           mean(dup)), keyby = .(subjid, param)]
-  # # identify subject/parameters where there is duplication but at least one day with no extraneous for that parameter
-  # subj.param.not.all.dups <- dup.ratio.df[dup.ratio < 1.0, list(subjid, param)]
-  # # identify subject/parameters where there is duplication for all days for that parameter
-  # subj.param.all.dups <- dup.ratio.df[dup.ratio == 1, list(subjid, param)]
-  # subj.all.dups <- subj.param.all.dups[, unique(subjid)]
-  # # perform ewma for subjects with extraneous
-  # data.df[subjid %in% subj.dup &
-  #           valid.rows, ewma.all := ewma(agedays, tbc.sd, ewma.exp, ewma.adjacent =
-  #                                          FALSE), by = .(subjid, param)]
-  # # note: at this point, only one ewma.all exists per param on a given day for a subject, so sort(ewma.all)[1] will returns the non-missing ewma.all
-  # data.df[subjid %in% subj.dup, ewma.all := sort(ewma.all)[1], by = .(subjid, param, agedays)]
-  #
-  # # iv.  Calculate abssum2_*=|2*dewma_*|+|tbc*sd| (note that this is different from how we calculated abssum_* in step 11).
-  # # NOTE: only children with more than one ageday with valid measurements will have a valid ewma from above
-  # data.df[, abssum2 := 2 * abs(tbc.sd - ewma.all) + abs(tbc.sd)]
-  #
-  # # 12d.  For each subject/parameter/age with extraneous and at least one non-extraneous value:
-  # #   i.  Replace exc_*=7 for all values except the value that has the smallest abssum2_*.
-  # data.df[data.table(subj.param.not.all.dups), extraneous := seq_along(abssum2) != which.min(abssum2), by =
-  #           .(subjid, param, agedays)]
-  # data.df[temp.dups, exclude := 'Include']
-  # data.df[(valid.rows |
-  #            temp.dups) &
-  #           extraneous, exclude := 'Exclude-Extraneous-Same-Day']
-  # #  ii.  Determine dup_tot_* (# of days with extraneous for that subject/parameter) and nodup_tot_* (# of days with nonexlcuded
-  # #       non-extraneous for that subject/parameter).
-  # # iii.  If dup_tot_*/(dup_tot_*+nodup_tot_*) is greater than 1/2, replace exc_*=7 for all extraneous for that subject/parameter
-  # #       for each age where the  largest measurement minus the smallest measurement for that subject/parameter/age is larger than
-  # #       the maximum difference (ht 3cm; wt 0-9.999 kg 0.25kg; wt 10-29.9999 kg 0.5 kg; wt 30kg and higher 1 kg).
-  #
-  # data.df[data.table(dup.ratio.df[dup.ratio > 1 / 2, list(subjid, param)]), exclude := (function(df) {
-  #   df[, `:=`(tbc.sd.min = as.double(NaN),
-  #             tbc.sd.max = as.double(NaN))]
-  #   df[valid(
-  #     exclude,
-  #     include.extraneous = TRUE,
-  #     include.temporary.extraneous = TRUE
-  #   ), `:=`(tbc.sd.min = min(tbc.sd),
-  #           tbc.sd.max = max(tbc.sd))]
-  #   df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM',
-  #                                       3,
-  #                                       ifelse(
-  #                                         param == 'WEIGHTKG',
-  #                                         ifelse(tbc.sd.min < 10, 0.25, ifelse(tbc.sd.min < 30, 0.5, 1)),
-  #                                         NA
-  #                                       )),
-  #      exclude := 'Exclude-Extraneous-Same-Day']
-  #   return(df$exclude)
-  # })(copy(.SD)), .SDcols = c('exclude', 'tbc.sd'), by = .(subjid, param, agedays)]
-  #
-  # # 12e.	For each subject/parameter/age with extraneous and no nonextraneous values:
-  # #   i.	Replace exc_*=7 for all values except the value with the smallest |tbc*sd-median_tbcOsd|. If median_tbcOsd is missing because there are no values
-  # #       for the other parameter, randomly choose one extraneous value for each subject/parameter/age to keep as exc_*=0 and replace exc_*=7 for all other
-  # #       extraneous for that subject/parameter/age.
-  # #  ii.  If the largest measurement minus the smallest measurement for that subject/parameter/age is larger than the maximum difference
-  # #       (ht 3cm; wt 0-9.999 kg 0.25kg; wt 10-29.9999 kg 0.5 kg; wt 30kg and higher 1 kg)., replace exc_*=7 for all extraneous for that
-  # #       subject/parameter/age.
-  #
-  # # calculate median for other parameter (restrict to subjects with all duplication for at least one parameter)
-  # data.df[subjid %in% subj.all.dups, exclude := (function(subj.df) {
-  #   # flag days that have extraneous / potentially valid parameters
-  #   subj.df[, `:=`(
-  #     extraneous.this.day = FALSE,
-  #     extraneous = FALSE,
-  #     tbc.sd.min = as.double(NaN),
-  #     tbc.sd.max = as.double(NaN)
-  #   )]
-  #   valid.rows = valid(
-  #     subj.df,
-  #     include.extraneous = TRUE,
-  #     include.temporary.extraneous = TRUE
-  #   )
-  #   subj.df[valid.rows, extraneous.this.day := (.N > 1), by = .(param, agedays)]
-  #   for (p in subj.df[j = unique(param)]) {
-  #     median.sd <- subj.df[param != p &
-  #                            !extraneous.this.day, median(tbc.sd)]
-  #     subj.df[param == p, median.other.sd := median.sd]
-  #   }
-  #   # safety check -- assign median.other.sd==0 to ensure "which.min" functions correctly below
-  #   subj.df[is.na(median.other.sd), median.other.sd := 0]
-  #   # identify rows as extraneous where |tbc*sd-median_tbcOsd| is not at the minimum value
-  #   subj.df[extraneous.this.day == TRUE, extraneous := (seq_along(median.other.sd) != which.min(abs(tbc.sd - median.other.sd))), by =
-  #             .(param, agedays)]
-  #   subj.df[extraneous.this.day &
-  #             !extraneous, exclude := 'Include']
-  #   subj.df[extraneous.this.day &
-  #             extraneous, exclude := 'Exclude-Extraneous-Same-Day']
-  #   subj.df[extraneous.this.day == TRUE, `:=`(tbc.sd.min = min(tbc.sd),
-  #                                             tbc.sd.max = max(tbc.sd)), by = .(param, agedays)]
-  #   subj.df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM',
-  #                                            3,
-  #                                            ifelse(
-  #                                              param == 'WEIGHTKG',
-  #                                              ifelse(tbc.sd.min < 10, 0.25, ifelse(tbc.sd.min < 30, 0.5, 1)),
-  #                                              NA
-  #                                            )),
-  #           exclude := 'Exclude-Extraneous-Same-Day']
-  #
-  #   # identify kids who had an SD or EWMA extreme excluded that was a extraneous and re-label as "Exclude-Extraneous-Same-Day"
-  #   subj.df[, extraneous.this.day := FALSE]
-  #   # consider any non-missing measurement when determining presence of extraneous
-  #   subj.df[exclude != 'Missing', extraneous.this.day := (.N > 1), by =
-  #             .(param, agedays)]
-  #   subj.df[extraneous.this.day &
-  #             exclude %in% c('Exclude-SD-Cutoff',
-  #                            'Exclude-EWMA-Extreme',
-  #                            'Exclude-EWMA-Extreme-Pair'), exclude := 'Exclude-Extraneous-Same-Day']
-  #
-  #   return(subj.df$exclude)
-  # })(copy(.SD)), .SDcols = c('param', 'agedays', 'exclude', 'tbc.sd'), by =
-  #   .(subjid)]
-  #
-  # # 12f.  For any values that were excluded with exc_*=4, 5, or 6 that are also extraneous, replace exc_*=7.
-  # data.df[subjid %in% subj.dup, exclude := (function(subj.df) {
-  #   if (.N > 1) {
-  #     subj.df[exclude %in% c('Exclude-SD-Cutoff',
-  #                            'Exclude-EWMA-Extreme',
-  #                            'Exclude-EWMA-Extreme-Pair'), exclude := 'Exclude-Extraneous-Same-Day']
-  #   }
-  #   return(subj.df$exclude)
-  # })(copy(.SD)), .SDcols = c('exclude'), by = .(subjid, param, agedays)]
+  # 13: SDEs ----
+
+  if (!quietly)
+    cat(sprintf(
+      "[%s] Exclude same day extraneous...\n",
+      Sys.time()
+    ))
+
+  # prepare a list of valid rows and initialize variables for convenience
+  valid.rows <- valid(data.df, include.temporary.extraneous = TRUE) &
+    !data.df$nnte_full  # does not use the "other"
+
+  data.df <- data.df[valid.rows, exclude := (function(subj_df) {
+    # keep the original indices and exclude to overwrite
+    orig_idx <- copy(subj_df$index)
+    exclude_all <- copy(subj_df$exclude)
+    if (nrow(subj_df) > 0 & any(subj_df$exclude == "Exclude-Temporary-Extraneous-Same-Day")){
+      # for convenience
+      subj_df[, extraneous := exclude == "Exclude-Temporary-Extraneous-Same-Day"]
+
+      step <- "Exclude-SDE-Identical"
+
+      # identify duplicate days
+      dup_days <- unique(subj_df$agedays[subj_df$extraneous])
+
+      # first, exclude identical measurements on the same day
+      ide_ids <- c() # where we keep the identical ones
+      for (dd in dup_days){
+        # count amount of unique values
+        s_df <- copy(subj_df[subj_df$agedays == dd,])
+        ide_tab <- table(s_df$v)
+        if (any(ide_tab > 1)){
+          # for each identical, keep only the first one, by id
+          # (ordered initially)
+          ide_ids <- c(ide_ids, s_df$index[
+            as.character(s_df$v) %in% names(ide_tab[ide_tab > 1])
+          ][duplicated(
+            s_df$v[
+              as.character(s_df$v) %in% names(ide_tab[ide_tab > 1])
+            ]
+          )])
+        }
+      }
+      criteria <- subj_df$index %in% ide_ids
+
+      # we're going to update excludes before moving on to the rest of this
+      # subject
+      exclude_all[criteria] <- step
+
+      subj_df <- subj_df[!criteria,]
+
+      if (any(criteria)){
+        # reevaluate temp same day
+        subj_df[, extraneous := temporary_extraneous_infants(subj_df)]
+
+        # identify duplicate days
+        dup_days <- unique(subj_df$agedays[subj_df$extraneous])
+      }
+
+      # 13B: identify similar groups
+      similar_ids <- c()
+      for (dd in dup_days){
+        # first, calculate if all SDEs on a single day are similar
+        s_df <- copy(subj_df[subj_df$agedays == dd,])
+
+        similar <- if (subj_df$param[1] == "WEIGHTKG"){
+          max(s_df$v)/min(s_df$v) <= 1.03 & max(s_df$v) - min(s_df$v) <= 2.5
+        } else if (subj_df$param[1] == "HEIGHTCM"){
+          (max(s_df$v) - min(s_df$v) <  2.541 & min(s_df$v) < 127) |
+            (max(s_df$v) - min(s_df$v) <  5.081 & min(s_df$v) >= 127)
+        } else { # head circumference
+          max(s_df$v) - min(s_df$v) <  1.271
+        }
+
+        if (similar){
+          similar_ids <- c(similar_ids, s_df$index)
+        }
+      }
+
+      # 13C: exclude non similar groups
+
+      step <- "Exclude-SDE-All-Exclude"
+      # now the rest!
+
+      # next, calculate the duplicate ratio -- what proportion of days are
+      # duplicated
+      dup_ratio <-
+        length(unique(subj_df$agedays[duplicated(subj_df$agedays)]))/
+        length(unique(subj_df$agedays))
+      # also check whether or not any same-days are adjacent -- need 4 at least
+      # rolling windows of day differences -- we are looking for 0,x,0
+      if (nrow(subj_df) > 3){
+        roll <- embed(diff(subj_df$agedays), 3)
+        adjacent <- sapply(1:nrow(roll), function(x){
+          all(c(roll[x,1] == 0, roll[x,2] != 0, roll[x,3] == 0))
+        })
+
+        # get age days & rows that had adjacent
+        idx_roll <- c(embed(1:nrow(subj_df),4)[adjacent,, drop = FALSE])
+        idx_adj <- which(subj_df$agedays %in% subj_df$agedays[idx_roll])
+      } else {
+        adjacent <- F
+        idx_adj <- idx_roll <- c()
+      }
+
+      # if dup ratio is too high, we exclude all
+      # if adjacent, we exclude only those relevant sdes
+      # same day extraneous -- for strict (default)
+      criteria <-
+          # looser criteria:
+          # 1.	Exclude for ratio if ratio >1/3
+          # 2.	Exclude for adjacent if 2 are adjacent AND ratio >1/4
+          # 3.	Exclude for adjacent if 2 are adjacent AND those 2 are the first 2 or last 2 measurements for the subject/parameter
+          #   note: if the first or last is sde and it's adjacent, by default
+          #   that means it's the first or last two that are adjacent
+          # 4.	Exclude for adjacent if 3 or more are adjacent
+          if (dup_ratio > (1/3)){
+            subj_df$extraneous
+          } else if (
+            (sum(adjacent) == 1 & dup_ratio > (1/4)) |
+            (sum(adjacent) == 1 & c(1) %in% idx_adj) |
+            (sum(adjacent) == 1 &
+             c(nrow(subj_df)) %in% idx_adj) |
+            (sum(adjacent) >= 2)
+          ){
+            # adjacent sum == 1 means 2 are adjacent
+            # only exclude sdes that are adjacent
+            subj_df$agedays %in% subj_df$agedays[idx_roll]
+          } else {
+            rep(F, nrow(subj_df))
+          }
+      # re-include similar groups
+      criteria[subj_df$index %in% similar_ids] <- FALSE
+
+      # we're going to update excludes before moving on to the rest of this
+      # subject
+      exclude_all[orig_idx %in% subj_df$index[criteria]] <- step
+
+      subj_df <- subj_df[!criteria,]
+
+      if (any(criteria)){
+        # reevaluate temp same day
+        subj_df[, extraneous := temporary_extraneous_infants(subj_df)]
+
+        # identify duplicate days
+        dup_days <- unique(subj_df$agedays[subj_df$extraneous])
+      }
+
+      step_extreme <- "Exclude-SDE-All-Extreme"
+      step <- "Exclude-SDE-EWMA"
+      # next, check for trivial differences for SDEs on the same day
+      # this only works if there are non-sde days
+
+      if (any(subj_df$extraneous)){
+        # 13D: calculating ewmas
+
+        # check for SDEs by EWMA -- alternate calculate excluding other SDEs
+        all_sdes <- duplicated(subj_df$agedays) |
+          duplicated(subj_df$agedays, fromLast = T)
+
+        # first, calculate which exponent we want to put through (pass a different
+        # on for each exp)
+        tmp <- data.frame(
+          "before" = abs(subj_df$agedays - c(NA, subj_df$agedays[1:(nrow(subj_df)-1)])),
+          "after" = abs(subj_df$agedays - c(subj_df$agedays[2:(nrow(subj_df))], NA))
+        )
+        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+        exp_vals <- rep(-1.5, nrow(tmp))
+        exp_vals[maxdiff > 365.25] <- -2.5
+        exp_vals[maxdiff > 730.5] <- -3.5
+        subj_df[, exp_vals := exp_vals]
+
+        # calculate the time difference for all values, as well as exponential
+        delta <- as_matrix_delta(subj_df$agedays)
+        delta <- ifelse(delta == 0, 0, (delta) ^ subj_df$exp_vals)
+
+        rem_ids_extreme <- c()
+        rem_ids <- c()
+        for (dd in dup_days){
+          # first, calculate if all SDEs on a single day are similar
+          s_df <- copy(subj_df[subj_df$agedays == dd,])
+
+          similar <- if (subj_df$param[1] == "WEIGHTKG"){
+            max(s_df$v)/min(s_df$v) <= 1.03 & max(s_df$v) - min(s_df$v) <= 2.5
+          } else if (subj_df$param[1] == "HEIGHTCM"){
+            (max(s_df$v) - min(s_df$v) <  2.541 & min(s_df$v) < 127) |
+              (max(s_df$v) - min(s_df$v) <  5.081 & min(s_df$v) >= 127)
+          } else { # head circumference
+            max(s_df$v) - min(s_df$v) <  1.271
+          }
+
+          if (!similar){
+            # 13E
+
+            # calculate dewma for each SDE on this day
+            dewma <- sapply(1:nrow(s_df), function(x){
+              ind <- subj_df$index == s_df[[x, "index"]]
+
+              ewma_res <- sum(subj_df$tbc.sd[!all_sdes]*delta[ind,!all_sdes])/
+                sum(delta[ind, !all_sdes])
+
+              # delta ewma
+              return(s_df$tbc.sd[x] - ewma_res)
+            })
+            absdewma <- abs(dewma)
+
+            if (min(absdewma) > 1){
+              rem_ids_extreme <- c(rem_ids_extreme, s_df$index)
+            } else {
+              de_day <- which.min(abs(dewma))
+
+              # keep the value with the lowest EWMA -- do not keep rest
+              rem_ids <- c(rem_ids, s_df$index[-de_day])
+            }
+          }
+        }
+        # re-include similar groups
+        criteria_extreme <- subj_df$index %in% rem_ids_extreme
+        criteria <- subj_df$index %in% rem_ids
+        # we're going to update excludes before moving on to the rest of this
+        # subject
+        exclude_all[orig_idx %in% subj_df$index[criteria_extreme]] <- step_extreme
+        exclude_all[orig_idx %in% subj_df$index[criteria]] <- step
+
+        subj_df <- subj_df[!(criteria | criteria_extreme),]
+
+        if (any(c(criteria, criteria_extreme))){
+          # reevaluate temp same day
+          subj_df[, extraneous := temporary_extraneous_infants(subj_df)]
+
+          # identify duplicate days
+          dup_days <- unique(subj_df$agedays[subj_df$extraneous])
+        }
+      }
+
+      # all remaining are similar
+      step_extreme <- "Exclude-SDE-All-Extreme"
+      step <- "Exclude-SDE-One-Day"
+      if (any(subj_df$extraneous)){
+        # check for SDEs
+        all_sdes <- duplicated(subj_df$agedays) |
+          duplicated(subj_df$agedays, fromLast = T)
+
+        rem_ids <- c()
+        rem_ids_extreme <- c()
+        for (dd in dup_days){
+          # first, calculate if all SDEs on a single day are similar
+          s_df <- copy(subj_df[subj_df$agedays == dd,])
+
+          # find the DOP (designated other parameter)
+          dop <-
+            data.df[subjid == s_df$subjid[1] & param == get_dop(s_df$param[1]) &
+                      agedays == s_df$agedays[1],]
+
+          if (nrow(dop) > 0){
+            # 13F
+            med_diff <- abs(median(dop$tbc.sd)-s_df$tbc.sd)
+
+            if (min(med_diff) > 2){
+              rem_ids_extreme <- c(rem_ids_extreme, s_df$index)
+            } else {
+              de_day <- which.min(med_diff)
+
+              # keep the value with the lowest median diff -- do not keep rest
+              rem_ids <- c(rem_ids, s_df$index[-de_day])
+            }
+          } else if (nrow(s_df) > 3){
+            #13G
+            med_diff <- abs(median(s_df$tbc.sd)-s_df$tbc.sd)
+
+            # if even and the middle two values are equidistant
+            if (nrow(s_df)%%2 == 0 &
+                diff(sort(med_diff)[1:2]) < 1e-8){
+              # if the ageday is even, keep the lower z score; otherwise,
+              # keep higher
+              keep_val <-
+                if (s_df$agedays[1]%%2 == 0){
+                  which(s_df$v == min(s_df$v[order(med_diff)[1:2]]))
+                } else {
+                  which(s_df$v == max(s_df$v[order(med_diff)[1:2]]))
+                }
+              rem_ids <- c(rem_ids, s_df$index[-keep_val])
+            }
+
+            de_day <- which.min(med_diff)
+            # keep the value with the lowest median diff -- do not keep rest
+            rem_ids <- c(rem_ids, s_df$index[-de_day])
+          } else {
+            # 13H
+
+            # 2 values, keep based on the age days
+            keep_val <-
+            if (s_df$agedays[1]%%2 == 0){
+              which.min(s_df$v)
+            } else {
+              which.max(s_df$v == max(s_df$v[order(med_diff)[1:2]]))
+            }
+            rem_ids <- c(rem_ids, s_df$index[-keep_val])
+          }
+        }
+
+        # re-include similar groups
+        criteria_extreme <- subj_df$index %in% rem_ids_extreme
+        criteria <- subj_df$index %in% rem_ids
+        # we're going to update excludes before moving on to the rest of this
+        # subject
+        exclude_all[orig_idx %in% subj_df$index[criteria_extreme]] <- step_extreme
+        exclude_all[orig_idx %in% subj_df$index[criteria]] <- step
+      }
+    }
+
+    return(exclude_all)
+  })(copy(.SD)), by = .(subjid, param), .SDcols = colnames(data.df)]
 
 
   # end ----
diff --git a/R/infants_support.R b/R/infants_support.R
index ba2324c2..20341e67 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -1,5 +1,22 @@
 # Infants support
 
+# supporting (all) ---
+
+# function that takes in a parameter name and returns the designated other
+# parameter (DOP)
+get_dop <- function(param_name){
+  dop <- if (param_name == "WEIGHTKG"){
+    "HEIGHTCM"
+  } else if (param_name == "HEIGHTCM"){
+    "WEIGHTKG"
+  } else { # HEADCM
+    "HEIGHTCM"
+  }
+
+  return(dop)
+}
+
+# temporary extraneous ----
 #' Function for temporary extraneous (step 5):
 #' 5.  Temporary extraneous: I use extraneous to refer to more than more than one recorded value for a parameter on the same day,
 #'     and we need to select which one to include in our analysis. The overall strategy will be to select a measurement using a simple

From 87f1942ac1c1fb517ffac76f2547e593881b4c00 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 26 Jul 2023 03:57:15 -0400
Subject: [PATCH 051/100] Add first draft of moderate ewma

---
 R/infants_clean.R   | 261 ++++++++++++++++++++++++++++++++++++++++++++
 R/infants_support.R |  55 ++++++++++
 2 files changed, 316 insertions(+)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 8e5a891c..a1c5b0d9 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -853,6 +853,267 @@ cleanbatch_infants <- function(data.df,
     return(exclude_all)
   })(copy(.SD)), by = .(subjid, param), .SDcols = colnames(data.df)]
 
+  # 15: moderate EWMA ----
+
+  # THINK ABOUT NNTE FULL HERE
+
+  # create the valid set
+  # we only running carried forwards on valid values, non NNTE values,
+  # and non single values, and non pair
+  tmp <- table(paste0(data.df$subjid, "_", data.df$param))
+  not_single_pairs <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 2]
+  valid_set <- valid(data.df, include.temporary.extraneous = FALSE) &
+    !data.df$nnte_full & # does not use the "other"
+    not_single_pairs
+
+  # work in an a function order to encapsulate and not keep all the additional
+  # columns
+
+  # FUTURE UPDATE: I think I have to make sure I replace the temp-extraneous codes with includes after the first time, they're alone now
+
+  # calculate plus/minus values
+
+  # order just for ease later
+  data.df <- data.df[order(subjid, param, agedays),]
+  data.df <- data.df[valid.rows, exclude := (function(df) {
+    # 15A: calc plus/minus values
+    df[param == "WEIGHTKG", p_plus := 1.05*v]
+    df[param == "WEIGHTKG", p_minus := .95*v]
+
+    df[param == "HEIGHTCM", p_plus := v+1]
+    df[param == "HEIGHTCM", p_minus := v-1]
+
+    df[param == "HEADCM", p_plus := v+1]
+    df[param == "HEADCM", p_minus := v-1]
+
+    #15B: smooth and recenter
+    df <- calc_and_recenter_z_scores(df, "p_plus", ref.data.path)
+    df <- calc_and_recenter_z_scores(df, "p_minus", ref.data.path)
+
+    #15c: exclude agedays = 0 for ht, hc
+    df <- df[(param != "HEIGHTCM" | param != "HEADCM") & agedays != 0, ]
+
+    #15d: calculate ewma -- run within a subject/parameter
+    df <- df[, exclude := (function(df_sub) {
+      # save initial exclusions to keep track
+      ind_all <- copy(df_sub$index)
+      exclude_all <- copy(df_sub$exclude)
+
+      testing <- TRUE
+
+      while (testing & nrow(df_sub) > 3){
+        df_sub[, (ewma.fields) := as.double(NaN)]
+
+        # first, calculate which exponent we want to put through (pass a different
+        # on for each exp)
+        # subset df to only valid rows
+
+        tmp <- data.frame(
+          "before" = abs(df_sub$agedays - c(NA, df_sub$agedays[1:(nrow(df_sub)-1)])),
+          "after" = abs(df_sub$agedays - c(df_sub$agedays[2:(nrow(df_sub))], NA))
+        )
+        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+        exp_vals <- rep(-1.5, nrow(tmp))
+        exp_vals[maxdiff > 365.25] <- -2.5
+        exp_vals[maxdiff > 730.5] <- -3.5
+        df_sub[, exp_vals := exp_vals]
+
+        # calculate ewma
+        df_sub[, (ewma.fields) := ewma(agedays, tbc.sd, exp_vals, TRUE)]
+        df_sub[, paste0("c.",ewma.fields) := ewma(agedays, ctbc.sd, exp_vals, TRUE)]
+
+        # calculate dewma
+        df_sub[, `:=`(
+          dewma.all = tbc.sd - ewma.all,
+          dewma.before = tbc.sd - ewma.before,
+          dewma.after = tbc.sd - ewma.after,
+
+          c.dewma.all = ctbc.sd - c.ewma.all
+        )]
+
+        # 15E: calculate prior and next differences
+        df_sub[, tbc_diff_next := tbc.sd - c(tbc.sd[2:nrow(df_sub)], NA)]
+        df_sub[, tbc_diff_prior := tbc.sd - c(NA, tbc.sd[1:(nrow(df_sub)-1)])]
+
+        df_sub[, tbc_diff_plus_next := tbc.p_plus - c(tbc.sd[2:nrow(df_sub)], NA)]
+        df_sub[, tbc_diff_plus_prior :=
+                 tbc.p_plus- c(NA, tbc.sd[1:(nrow(df_sub)-1)])]
+
+        df_sub[, tbc_diff_minus_next := tbc.p_minus - c(tbc.sd[2:nrow(df_sub)], NA)]
+        df_sub[, tbc_diff_minus_prior :=
+                 tbc.p_minus- c(NA, tbc.sd[1:(nrow(df_sub)-1)])]
+
+        # 15F: all exclusion criteria
+        df_sub[,
+               addcrithigh :=
+                 dewma.before > 1 & dewma.after > 1 &
+                 ((tbc_diff_next > 1 & tbc_diff_plus_next > 1 & tbc_diff_minus_next > 1) | is.na(tbc_diff_next)) &
+                 ((tbc_diff_prior > 1 & tbc_diff_plus_prior > 1 & tbc_diff_minus_prior > 1) | is.na(tbc_diff_prior))
+        ]
+        df_sub[,
+               addcritlow :=
+                 dewma.before < -1 & dewma.after < -1 &
+                 ((tbc_diff_next < -1 & tbc_diff_plus_next < -1 & tbc_diff_minus_next < -1) | is.na(tbc_diff_next)) &
+                 ((tbc_diff_prior < -1 & tbc_diff_plus_prior < -1 & tbc_diff_minus_prior < -1) | is.na(tbc_diff_prior))
+        ]
+
+        #15G: find all the values for the DOP
+        # find the comparison -- making sure to keep only valid rows
+        compare_df <- data.df[subjid == df_sub$subjid[1] &
+                                param == get_dop(df_sub$param[1]) &
+                                exclude == "Include",]
+        if (nrow(compare_df) > 0){
+          df_sub[compare_df, tbc_dop := i.tbc.sd,
+                 on = c("agedays")]
+          df_sub[is.na(tbc_dop), tbc_dop := median(compare_df$tbc.sd)]
+        } else {
+          df_sub[, tbc_dop := NA]
+        }
+
+        #15H: all exclusions
+        # for ease, right now -- REMOVE
+        df_sub[, exclude := as.character(df_sub$exclude)]
+        df_sub$rowind <- 1:nrow(df_sub)
+
+        #a
+        df_sub[-c(1, nrow(df_sub)),][
+          dewma.all > 1 & (c.dewma.all > 1 | is.na(c.dewma.all)) & addcrithigh,
+          exclude := "Exclude-EWMA2-middle"]
+        df_sub[-c(1, nrow(df_sub)),][
+          dewma.all < -1 & (c.dewma.all < -1 | is.na(c.dewma.all)) & addcritlow,
+          exclude := "Exclude-EWMA2-middle"]
+
+        #b
+        df_sub[agedays == 0 & agedays[2] < 365.25 &
+                 dewma.all > 3 & (c.dewma.all > 3 | is.na(c.dewma.all)) &
+                 addcrithigh
+               , exclude := "Exclude-EWMA2-birth-WT"]
+        df_sub[agedays == 0 & agedays[2] < 365.25 &
+                 dewma.all < -3 & (c.dewma.all < -3 | is.na(c.dewma.all)) &
+                 addcritlow
+               , exclude := "Exclude-EWMA2-birth-WT"]
+
+        #c
+        df_sub[agedays == 0 & agedays[2] >= 365.25 &
+                 dewma.all > 4 & (c.dewma.all > 4 | is.na(c.dewma.all)) &
+                 addcrithigh
+               , exclude := "Exclude-EWMA-2-birth-WT-ext"]
+        df_sub[agedays == 0 & agedays[2] >= 365.25 &
+                 dewma.all < -4 & (c.dewma.all < -4 | is.na(c.dewma.all)) &
+                 addcritlow
+               , exclude := "Exclude-EWMA-2-birth-WT-ext"]
+
+        #d
+        df_sub[rowind == 1 & agedays[1] != 0 & agedays[2]-agedays[1] < 365.25 &
+                 dewma.all > 2 & (c.dewma.all > 2 | is.na(c.dewma.all)) &
+                 addcrithigh
+               , exclude := "Exclude-EWMA-2-first"]
+        df_sub[rowind == 1 & agedays[1] != 0 & agedays[2]-agedays[1] < 365.25 &
+                 dewma.all < -2 & (c.dewma.all < -2 | is.na(c.dewma.all)) &
+                 addcritlow
+               , exclude := "Exclude-EWMA-2-first"]
+
+        # e
+        df_sub[rowind == 1 & agedays[1] != 0 & agedays[2]-agedays[1] >= 365.25 &
+                 dewma.all > 3 & (c.dewma.all > 3 | is.na(c.dewma.all)) &
+                 addcrithigh
+               , exclude := "Exclude-EWMA-2-first-ext"]
+        df_sub[rowind == 1 & agedays[1] != 0 & agedays[2]-agedays[1] >= 365.25 &
+                 dewma.all < -3 & (c.dewma.all < -3 | is.na(c.dewma.all)) &
+                 addcritlow
+               , exclude := "Exclude-EWMA-2-first-ext"]
+
+        # f
+        df_sub[rowind == nrow(df_sub) &
+                 agedays[nrow(df_sub)]-agedays[nrow(df_sub)-1] < 365.25*2 &
+                 abs(tbc.sd[nrow(df_sub)-1]) < 2 &
+                 dewma.all > 2 & (c.dewma.all > 2 | is.na(c.dewma.all)) &
+                 addcrithigh
+               , exclude := "Exclude-EWMA2-last"]
+        df_sub[rowind == nrow(df_sub) &
+                 agedays[nrow(df_sub)]-agedays[nrow(df_sub)-1] < 365.25*2 &
+                 abs(tbc.sd[nrow(df_sub)-1]) < 2 &
+                 dewma.all < -2 & (c.dewma.all < -2 | is.na(c.dewma.all)) &
+                 addcritlow
+               , exclude := "Exclude-EWMA2-last"]
+
+        # g
+        df_sub[rowind == nrow(df_sub) &
+                 agedays[nrow(df_sub)]-agedays[nrow(df_sub)-1] < 365.25*2 &
+                 abs(tbc.sd[nrow(df_sub)-1]) >= 2 &
+                 dewma.all > abs(tbc.sd[nrow(df_sub)-1]) &
+                 (c.dewma.all > 3 | is.na(c.dewma.all)) &
+                 addcrithigh
+               , exclude := "Exclude-EWMA2-last-high"]
+        df_sub[rowind == nrow(df_sub) &
+                 agedays[nrow(df_sub)]-agedays[nrow(df_sub)-1] < 365.25*2 &
+                 abs(tbc.sd[nrow(df_sub)-1]) >= 2 &
+                 dewma.all < abs(tbc.sd[nrow(df_sub)-1]) &
+                 (c.dewma.all < -3 | is.na(c.dewma.all)) &
+                 addcritlow
+               , exclude := "Exclude-EWMA2-last-high"]
+
+        # h
+        df_sub[rowind == nrow(df_sub) &
+                 agedays[nrow(df_sub)]-agedays[nrow(df_sub)-1] >= 365.25*2 &
+                 abs(tbc.sd[nrow(df_sub)-1]) < 2 &
+                 dewma.all > 3 &
+                 (c.dewma.all > 3 | is.na(c.dewma.all)) &
+                 (tbc.sd - tbc_dop > 4 | is.na(tbc_dop)) &
+                 addcrithigh
+               , exclude := "Exclude-EWMA2-last-ext"]
+        df_sub[rowind == nrow(df_sub) &
+                 agedays[nrow(df_sub)]-agedays[nrow(df_sub)-1] >= 365.25*2 &
+                 abs(tbc.sd[nrow(df_sub)-1]) < 2 &
+                 dewma.all < -3 &
+                 (c.dewma.all <- 3 | is.na(c.dewma.all)) &
+                 (tbc.sd - tbc_dop < -4 | is.na(tbc_dop)) &
+                 addcritlow
+               , exclude := "Exclude-EWMA2-last-ext"]
+
+        # i
+        df_sub[rowind == nrow(df_sub) &
+                 agedays[nrow(df_sub)]-agedays[nrow(df_sub)-1] >= 365.25*2 &
+                 abs(tbc.sd[nrow(df_sub)-1]) >= 2 &
+                 dewma.all > (1+abs(tbc.sd[nrow(df_sub)-1])) &
+                 (c.dewma.all > 3 | is.na(c.dewma.all)) &
+                 (tbc.sd - tbc_dop > 4 | is.na(tbc_dop)) &
+                 addcrithigh
+               , exclude := "Exclude-EWMA2-last-ext-high"]
+        df_sub[rowind == nrow(df_sub) &
+                 agedays[nrow(df_sub)]-agedays[nrow(df_sub)-1] >= 365.25*2 &
+                 abs(tbc.sd[nrow(df_sub)-1]) >= 2 &
+                 dewma.all < (-1-abs(tbc.sd[nrow(df_sub)-1])) &
+                 (c.dewma.all <- 3 | is.na(c.dewma.all)) &
+                 (tbc.sd - tbc_dop < -4 | is.na(tbc_dop)) &
+                 addcritlow
+               , exclude := "Exclude-EWMA2-last-ext-high"]
+
+        # figure out if any of the exclusions hit
+        count_exclude <- sum(df_sub$exclude != "Include")
+        if (count_exclude > 0){
+          df_sub[, abssum := abs(tbc.sd + dewma.all)]
+
+          # choose the highest abssum for exclusion
+          idx <- df_sub$index[which.max(df_sub[df_sub$exclude != "Include",
+                                               abssum])]
+
+          exclude_all[ind_all == idx] <- df_sub[index == idx, exclude]
+
+          #set up to continue on
+          testing <- TRUE
+
+          df_sub <- df_sub[ind_all != idx, ]
+        } else {
+          testing <- FALSE
+        }
+      }
+      return(exclude_all)
+    })(copy(.SD)), , by = .(subjid, param), .SDcols = colnames(df)]
+
+    return(df$exclude)
+  })(copy(.SD)), .SDcols = colnames(data.df)]
+
 
   # end ----
 
diff --git a/R/infants_support.R b/R/infants_support.R
index 20341e67..a6e5232c 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -139,3 +139,58 @@ calc_oob_evil_twins <- function(df){
 
   return(df)
 }
+
+# moderate ewma ----
+
+# function to calculate and recenter z scores for a given column
+# df: data frame with parameter, agedays, sex, cn
+# cn: column name to calculate, smooth, and recenter
+# ref.data.path: reference data path
+#
+# returns df with additional column, tbc.(cn), which is the recentered z-score
+# for the input
+calc_and_recenter_z_scores <- function(df, cn, ref.data.path){
+  # for infants, use z and who
+  measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE,
+                                  infants = T)
+  measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = FALSE,
+                                      infants = T)
+
+  # calculate "standard deviation" scores
+  if (!quietly)
+    cat(sprintf("[%s] Calculating SD-scores...\n", Sys.time()))
+  df[, cn.orig_cdc := measurement.to.z(param, agedays, sex, get(cn), TRUE)]
+  df[, cn.orig_who := measurement.to.z_who(param, agedays, sex, get(cn), TRUE)]
+
+  # smooth z-scores/SD scores between ages 1 - 3yo using weighted scores
+  # older uses cdc, younger uses who
+  who_weight <- 4 - (df$agedays/365.25)
+  cdc_weight <- (df$agedays/365.25) - 2
+
+  smooth_val <- df$agedays/365.25 >= 2 &
+    df$agedays/365.25 <= 4 &
+    df$param != "HEADCM"
+  df[smooth_val,
+           cn.orig := (cn.orig_cdc[smooth_val]*cdc_weight[smooth_val] +
+                         cn.orig_who[smooth_val]*who_weight[smooth_val])/2]
+
+  # otherwise use WHO and CDC for older and younger, respectively
+  who_val <- df$param == "HEADCM" |
+    df$agedays/365.25 < 2
+  df[who_val | (smooth_val & is.na(df$cn.orig_cdc)),
+           cn.orig := df$cn.orig_who[who_val  | (smooth_val & is.na(df$cn.orig_cdc))]]
+
+  cdc_val <- df$param != "HEADCM" |
+    df$agedays/365.25 > 4
+  df[cdc_val | (smooth_val & is.na(df$cn.orig_who)),
+           cn.orig := df$cn.orig_cdc[cdc_val | (smooth_val & is.na(df$sd.orig_who))]]
+
+  # now recenter -- already has the sd.median from the original recentering
+  setkey(df, subjid, param, agedays)
+  df[, tbc.cn := cn.orig - sd.median]
+
+  # rename ending column
+  setnames(df, "tbc.cn", paste0("tbc.", cn))
+
+  return(df)
+}

From b53ce8af6e6aeaa7e62c71fc8475a1d134621964 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 31 Jul 2023 14:38:11 -0400
Subject: [PATCH 052/100] Fix bugs for step 15 (ewma all)

---
 R/infants_clean.R | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index a1c5b0d9..2049103a 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -873,9 +873,17 @@ cleanbatch_infants <- function(data.df,
 
   # calculate plus/minus values
 
+  # STOP HERE: fix the valid
+
+
+
   # order just for ease later
   data.df <- data.df[order(subjid, param, agedays),]
   data.df <- data.df[valid.rows, exclude := (function(df) {
+    # save initial exclusions to keep track
+    ind_all_df <- copy(df$index)
+    exclude_all_df <- copy(df$exclude)
+
     # 15A: calc plus/minus values
     df[param == "WEIGHTKG", p_plus := 1.05*v]
     df[param == "WEIGHTKG", p_minus := .95*v]
@@ -891,10 +899,9 @@ cleanbatch_infants <- function(data.df,
     df <- calc_and_recenter_z_scores(df, "p_minus", ref.data.path)
 
     #15c: exclude agedays = 0 for ht, hc
-    df <- df[(param != "HEIGHTCM" | param != "HEADCM") & agedays != 0, ]
-
     #15d: calculate ewma -- run within a subject/parameter
-    df <- df[, exclude := (function(df_sub) {
+    df <- df[(param != "HEIGHTCM" | param != "HEADCM") & agedays != 0,
+             exclude := (function(df_sub) {
       # save initial exclusions to keep track
       ind_all <- copy(df_sub$index)
       exclude_all <- copy(df_sub$exclude)
@@ -1103,7 +1110,7 @@ cleanbatch_infants <- function(data.df,
           #set up to continue on
           testing <- TRUE
 
-          df_sub <- df_sub[ind_all != idx, ]
+          df_sub <- df_sub[index != idx, ]
         } else {
           testing <- FALSE
         }

From ffcbd6d62e4d0b81c1459bf0873c0bfc62e9566d Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 31 Jul 2023 22:24:22 -0400
Subject: [PATCH 053/100] Add step 16: birth height/hc ewma

---
 R/infants_clean.R | 159 ++++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 148 insertions(+), 11 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 2049103a..35495293 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -873,17 +873,9 @@ cleanbatch_infants <- function(data.df,
 
   # calculate plus/minus values
 
-  # STOP HERE: fix the valid
-
-
-
   # order just for ease later
   data.df <- data.df[order(subjid, param, agedays),]
-  data.df <- data.df[valid.rows, exclude := (function(df) {
-    # save initial exclusions to keep track
-    ind_all_df <- copy(df$index)
-    exclude_all_df <- copy(df$exclude)
-
+  data.df <- data.df[valid_set, exclude := (function(df) {
     # 15A: calc plus/minus values
     df[param == "WEIGHTKG", p_plus := 1.05*v]
     df[param == "WEIGHTKG", p_minus := .95*v]
@@ -978,8 +970,6 @@ cleanbatch_infants <- function(data.df,
         }
 
         #15H: all exclusions
-        # for ease, right now -- REMOVE
-        df_sub[, exclude := as.character(df_sub$exclude)]
         df_sub$rowind <- 1:nrow(df_sub)
 
         #a
@@ -1121,6 +1111,153 @@ cleanbatch_infants <- function(data.df,
     return(df$exclude)
   })(copy(.SD)), .SDcols = colnames(data.df)]
 
+  # 16: moderate EWMA for birth HT and HC ----
+
+  # create the valid set
+  # we only running carried forwards on valid values, non NNTE values,
+  # and non single values, and non pair
+  tmp <- table(paste0(data.df$subjid, "_", data.df$param))
+  not_single <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 1]
+  valid_set <- valid(data.df, include.temporary.extraneous = FALSE) &
+    !data.df$nnte_full & # does not use the "other"
+    not_single &
+    data.df$param != "WEIGHTKG" # do not run on weight
+
+  # work in an a function order to encapsulate and not keep all the additional
+  # columns
+
+  # NOTE: check NNTE na's
+
+  # calculate plus/minus values
+
+  # order just for ease later
+  data.df <- data.df[order(subjid, param, agedays),]
+  data.df <- data.df[valid_set, exclude := (function(df) {
+    # =calc plus/minus values
+    df[param == "HEIGHTCM", p_plus := v+1]
+    df[param == "HEIGHTCM", p_minus := v-1]
+
+    df[param == "HEADCM", p_plus := v+1]
+    df[param == "HEADCM", p_minus := v-1]
+
+    #smooth and recenter
+    df <- calc_and_recenter_z_scores(df, "p_plus", ref.data.path)
+    df <- calc_and_recenter_z_scores(df, "p_minus", ref.data.path)
+
+    #16a: calculate ewma -- run within a subject/parameter
+    # inly evaluate on subject/parameters with a birth ageday
+    df <- df[subjid %in% subjid[agedays == 0],
+      exclude := (function(df_sub) {
+        # save initial exclusions to keep track
+        ind_all <- copy(df_sub$index)
+        exclude_all <- copy(df_sub$exclude)
+
+        testing <- TRUE
+
+        while (testing & nrow(df_sub) > 3){
+          df_sub[, (ewma.fields) := as.double(NaN)]
+
+          # first, calculate which exponent we want to put through (pass a different
+          # on for each exp)
+          # subset df to only valid rows
+
+          tmp <- data.frame(
+            "before" = abs(df_sub$agedays - c(NA, df_sub$agedays[1:(nrow(df_sub)-1)])),
+            "after" = abs(df_sub$agedays - c(df_sub$agedays[2:(nrow(df_sub))], NA))
+          )
+          maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+          exp_vals <- rep(-1.5, nrow(tmp))
+          exp_vals[maxdiff > 365.25] <- -2.5
+          exp_vals[maxdiff > 730.5] <- -3.5
+          df_sub[, exp_vals := exp_vals]
+
+          # calculate ewma
+          df_sub[, (ewma.fields) := ewma(agedays, tbc.sd, exp_vals, TRUE)]
+          df_sub[, paste0("c.",ewma.fields) := ewma(agedays, ctbc.sd, exp_vals, TRUE)]
+
+          # calculate dewma
+          df_sub[, `:=`(
+            dewma.all = tbc.sd - ewma.all,
+            dewma.before = tbc.sd - ewma.before,
+            dewma.after = tbc.sd - ewma.after,
+
+            c.dewma.all = ctbc.sd - c.ewma.all
+          )]
+
+          # 16B: calculate prior and next differences
+          df_sub[, tbc_diff_next := tbc.sd - c(tbc.sd[2:nrow(df_sub)], NA)]
+          df_sub[, tbc_diff_prior := tbc.sd - c(NA, tbc.sd[1:(nrow(df_sub)-1)])]
+
+          df_sub[, tbc_diff_plus_next := tbc.p_plus - c(tbc.sd[2:nrow(df_sub)], NA)]
+          df_sub[, tbc_diff_plus_prior :=
+                   tbc.p_plus- c(NA, tbc.sd[1:(nrow(df_sub)-1)])]
+
+          df_sub[, tbc_diff_minus_next := tbc.p_minus - c(tbc.sd[2:nrow(df_sub)], NA)]
+          df_sub[, tbc_diff_minus_prior :=
+                   tbc.p_minus- c(NA, tbc.sd[1:(nrow(df_sub)-1)])]
+
+          # 16C: all exclusion criteria
+          df_sub[,
+                 addcrithigh :=
+                   dewma.before > 1 & dewma.after > 1 &
+                   ((tbc_diff_next > 1 & tbc_diff_plus_next > 1 & tbc_diff_minus_next > 1) | is.na(tbc_diff_next)) &
+                   ((tbc_diff_prior > 1 & tbc_diff_plus_prior > 1 & tbc_diff_minus_prior > 1) | is.na(tbc_diff_prior))
+          ]
+          df_sub[,
+                 addcritlow :=
+                   dewma.before < -1 & dewma.after < -1 &
+                   ((tbc_diff_next < -1 & tbc_diff_plus_next < -1 & tbc_diff_minus_next < -1) | is.na(tbc_diff_next)) &
+                   ((tbc_diff_prior < -1 & tbc_diff_plus_prior < -1 & tbc_diff_minus_prior < -1) | is.na(tbc_diff_prior))
+          ]
+
+          #16D: all exclusions
+          df_sub$rowind <- 1:nrow(df_sub)
+
+          #a
+          df_sub[agedays == 0 & agedays[2] < 365.25 &
+                   dewma.all > 3 & (c.dewma.all > 3 | is.na(c.dewma.all)) &
+                   addcrithigh
+                 , exclude := "Exclude-EWMA2-birth-HT-HC"]
+          df_sub[agedays == 0 & agedays[2] < 365.25 &
+                   dewma.all < -3 & (c.dewma.all < -3 | is.na(c.dewma.all)) &
+                   addcritlow
+                 , exclude := "Exclude-EWMA2-birth-HT-HC"]
+
+          #b
+          df_sub[agedays == 0 & agedays[2] >= 365.25 &
+                   dewma.all > 4 & (c.dewma.all > 4 | is.na(c.dewma.all)) &
+                   addcrithigh
+                 , exclude := "Exclude-EWMA2-birth-HT-HC-ext"]
+          df_sub[agedays == 0 & agedays[2] >= 365.25 &
+                   dewma.all < -4 & (c.dewma.all < -4 | is.na(c.dewma.all)) &
+                   addcritlow
+                 , exclude := "Exclude-EWMA2-birth-HT-HC-ext"]
+
+          # figure out if any of the exclusions hit
+          count_exclude <- sum(df_sub$exclude != "Include")
+          if (count_exclude > 0){
+            df_sub[, abssum := abs(tbc.sd + dewma.all)]
+
+            # choose the highest abssum for exclusion
+            idx <- df_sub$index[which.max(df_sub[df_sub$exclude != "Include",
+                                                 abssum])]
+
+            exclude_all[ind_all == idx] <- df_sub[index == idx, exclude]
+
+            #set up to continue on
+            testing <- TRUE
+
+            df_sub <- df_sub[index != idx, ]
+          } else {
+            testing <- FALSE
+          }
+        }
+        return(exclude_all)
+      })(copy(.SD)), , by = .(subjid, param), .SDcols = colnames(df)]
+
+    return(df$exclude)
+  })(copy(.SD)), .SDcols = colnames(data.df)]
+
 
   # end ----
 

From 818fe19732485715a0f3fbdb3284ebe650dceee2 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 1 Aug 2023 11:54:44 -0400
Subject: [PATCH 054/100] Add supporting data for step 17

---
 inst/extdata/tanner_ht_vel_rev.csv.gz         | Bin 0 -> 500 bytes
 inst/extdata/who_hc_maxvel_3sd_infants.csv.gz | Bin 459 -> 458 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 inst/extdata/tanner_ht_vel_rev.csv.gz

diff --git a/inst/extdata/tanner_ht_vel_rev.csv.gz b/inst/extdata/tanner_ht_vel_rev.csv.gz
new file mode 100644
index 0000000000000000000000000000000000000000..46ce72d5f22bc3d2aec0030a0f20caac8c0ff750
GIT binary patch
literal 500
zcmVgiwFP!000003M)>n&?!mG%S$ba&&|&($tc#z&CH9>D2Xpi&C!8S@umfE
zmI0h;2^7sutbi)zP9eKgWPBdiekdoslU16I-J0u)LWr0ko=pO?%95d0uSq!{d&y-C>v`E`A2XlXDFBjjtaH_+Yr0rSD+7P!qDnWv@3TOpV
zL=JjhB2xnYS$}ke{5a>wV=j-!Mc$9;czC=|KuTCgbzFFuVsABM31gB8NbccKt%Sl7
zOZbRxx9R;o0s{zAoVinZWz*d0=*8oLHB*R
zTya>L$M3{*)6ZvQzo|;>Io$8d8`&z=`6lSb<|x(tHh%BbzBLyMar3OzX;FSz6kp1;
q?We%%nzw(Zy+1~$yFWyauOFjf@5d
Date: Tue, 1 Aug 2023 11:54:55 -0400
Subject: [PATCH 055/100] Add step 17

---
 R/infants_clean.R | 353 +++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 352 insertions(+), 1 deletion(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 35495293..86bbd213 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1115,7 +1115,7 @@ cleanbatch_infants <- function(data.df,
 
   # create the valid set
   # we only running carried forwards on valid values, non NNTE values,
-  # and non single values, and non pair
+  # and non single values, and non weight
   tmp <- table(paste0(data.df$subjid, "_", data.df$param))
   not_single <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 1]
   valid_set <- valid(data.df, include.temporary.extraneous = FALSE) &
@@ -1258,6 +1258,357 @@ cleanbatch_infants <- function(data.df,
     return(df$exclude)
   })(copy(.SD)), .SDcols = colnames(data.df)]
 
+  # 17: raw differences ----
+
+  # NOTE: ADD STEP OUTPUT PRINT
+
+  # read in tanner data
+  tanner_ht_vel_rev_path <- ifelse(
+    ref.data.path == "",
+    system.file(file.path("extdata", "tanner_ht_vel.csv.gz"), package = "growthcleanr"),
+    file.path(ref.data.path, "tanner_ht_vel.csv.gz")
+  )
+
+  tanner.ht.vel.rev <- fread(tanner_ht_vel_rev_path)
+
+  setnames(tanner.ht.vel.rev,
+           colnames(tanner.ht.vel.rev),
+           gsub('_', '.', colnames(tanner.ht.vel.rev)))
+  setkey(tanner.ht.vel.rev, sex, tanner.months)
+
+  # read in the who height data
+  who_max_ht_vel_path <- ifelse(
+    ref.data.path == "",
+    system.file(file.path("extdata", "who_ht_maxvel_3sd.csv.gz"), package = "growthcleanr"),
+    file.path(ref.data.path, "who_ht_maxvel_3sd.csv.gz")
+  )
+
+  who_ht_vel_3sd_path <- ifelse(
+    ref.data.path == "",
+    system.file(file.path("extdata", "who_ht_vel_3sd.csv.gz"), package = "growthcleanr"),
+    file.path(ref.data.path, "who_ht_vel_3sd.csv.gz")
+  )
+
+  who.max.ht.vel <- fread(who_max_ht_vel_path)
+  who.ht.vel <- fread(who_ht_vel_3sd_path)
+  setkey(who.max.ht.vel, sex, whoagegrp_ht)
+  setkey(who.ht.vel, sex, whoagegrp_ht)
+  who.ht.vel <- as.data.table(dplyr::full_join(who.ht.vel, who.max.ht.vel, by =
+                                                 c('sex', 'whoagegrp_ht')))
+
+  setnames(who.ht.vel, colnames(who.ht.vel), gsub('_', '.', colnames(who.ht.vel)))
+  setkey(who.ht.vel, sex, whoagegrp.ht)
+
+  # read in who hc data
+  who_max_hc_vel_path <- ifelse(
+    ref.data.path == "",
+    system.file(file.path("extdata", "who_hc_maxvel_3sd_infants.csv.gz"), package = "growthcleanr"),
+    file.path(ref.data.path, "who_hc_maxvel_3sd_infants.csv.gz")
+  )
+
+  who_hc_vel_3sd_path <- ifelse(
+    ref.data.path == "",
+    system.file(file.path("extdata", "who_hc_vel_3sd_infants.csv.gz"), package = "growthcleanr"),
+    file.path(ref.data.path, "who_hc_vel_3sd_infants.csv.gz")
+  )
+
+  who.max.hc.vel <- fread(who_max_hc_vel_path)
+  who.hc.vel <- fread(who_hc_vel_3sd_path)
+  setkey(who.max.hc.vel, sex, whoagegrp_ht)
+  setkey(who.hc.vel, sex, whoagegrp_ht)
+  who.hc.vel <- as.data.table(dplyr::full_join(who.hc.vel, who.max.hc.vel, by =
+                                                 c('sex', 'whoagegrp_ht')))
+
+  setnames(who.hc.vel, colnames(who.hc.vel), gsub('_', '.', colnames(who.hc.vel)))
+  setkey(who.hc.vel, sex, whoagegrp.ht)
+
+  # create the valid set
+  # we only running carried forwards on valid values, non NNTE values,
+  # and non single values, and non weight
+  tmp <- table(paste0(data.df$subjid, "_", data.df$param))
+  not_single <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 1]
+  valid_set <- valid(data.df, include.temporary.extraneous = FALSE) &
+    !data.df$nnte_full & # does not use the "other"
+    not_single &
+    data.df$param != "WEIGHTKG" # do not run on weight
+
+  # order just for ease later
+  data.df <- data.df[order(subjid, param, agedays),]
+  data.df <- data.df[valid_set, exclude := (function(df) {
+    # work inside a closure to drop added column values
+
+    # save initial exclusions to keep track
+    ind_all <- copy(df$index)
+    exclude_all <- copy(df$exclude)
+
+    testing <- TRUE
+
+    while (testing & nrow(df) > 1){
+
+      # sort df since it got reordered with keys
+      df <- df[order(agedays),]
+
+      # 17A
+      df[, d_agedays := dplyr::lead(agedays) - agedays]
+
+      # 17E -- only applies to height
+      if (df$param[1] == "HEIGHTCM"){
+        # 17B
+        df[, tanner.months := 6+12*(round(.5*(agedays + dplyr::lead(agedays))/365.25))]
+
+        # 17C
+
+        # merge with the tanner info
+        setkey(df, sex, tanner.months)
+        df <- tanner.ht.vel.rev[df]
+
+        # 17D
+        df[d_agedays < 365.25, mindiff := .5*min.ht.vel*(d_agedays/365.25)-3 ]
+        df[d_agedays > 365.25, mindiff := .5*min.ht.vel-3 ]
+        df[d_agedays < 365.25,
+           maxdiff := 2*min.ht.vel*(d_agedays/365.25)^1.5 + 5.5 ]
+        df[d_agedays > 365.25,
+           maxdiff := 2*min.ht.vel*(d_agedays/365.25)^0.33 + 5.5 ]
+
+        # 17E
+        df[d_agedays >= 20 & d_agedays < 46, whoinc.age.ht := 1]
+        df[d_agedays >= 46 & d_agedays < 76, whoinc.age.ht := 2]
+        df[d_agedays >= 76 & d_agedays < 107, whoinc.age.ht := 3]
+        df[d_agedays >= 107 & d_agedays < 153, whoinc.age.ht := 4]
+        df[d_agedays >= 153 & d_agedays < 199, whoinc.age.ht := 6]
+
+        # update the edge intervals
+        df[d_agedays < 20, d_agedays := 19]
+        df[d_agedays == 19, whoinc.age.ht := 1]
+        df[d_agedays > 199, d_agedays := 200]
+        df[d_agedays == 200, whoinc.age.ht := 6]
+
+        # 17F
+        # merge with WHO
+        # add the column name we want to grab
+        for (i in unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht)])){
+          cn <- paste0("whoinc.", i, ".ht")
+          df[, who_mindiff_ht :=
+               as.numeric(who.ht.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
+          cn <- paste0("max.whoinc.", i, ".ht")
+          df[, who_maxdiff_ht :=
+               as.numeric(who.ht.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
+        }
+        df[, who_mindiff_ht := as.numeric(who_mindiff_ht)]
+        df[, who_mindiff_ht := as.numeric(who_maxdiff_ht)]
+
+        # 17G
+        df[d_agedays < whoinc.age.ht*30.4375, who_mindiff_ht :=
+             who_mindiff_ht * d_agedays/(whoinc.age.ht*30.4375)]
+        df[d_agedays > whoinc.age.ht*30.4375, who_maxdiff_ht :=
+             who_maxdiff_ht * d_agedays/(whoinc.age.ht*30.4375)]
+
+        df[d_agedays < 9*30.4375, who_mindiff_ht := who_mindiff_ht*.5-3]
+        df[d_agedays < 9*30.4375, who_maxdiff_ht := who_maxdiff_ht*2+3]
+
+        # 17H
+        # tanner is implicit
+        # greater than 9 months, use tanner if available, otherwise who
+        df[d_agedays < 9*30.4375 | is.na(min.ht.vel), mindiff := who_mindiff_ht]
+        df[d_agedays < 9*30.4375 | is.na(min.ht.vel), maxdiff := who_maxdiff_ht]
+        # otherwise, fill in
+        df[is.na(mindiff), mindiff := 3]
+
+        # 17I
+        # sort df since it got reordered with keys
+        df <- df[order(agedays),]
+        df[, mindiff_prev := dplyr::lag(mindiff)]
+        df[, maxdiff_prev := dplyr::lag(maxdiff)]
+      } else { # head circumference
+        # 17J
+        df[d_agedays >= 46 & d_agedays < 76, whoinc.age.hc := 2]
+        df[d_agedays >= 76 & d_agedays < 107, whoinc.age.hc := 3]
+        df[d_agedays >= 107 & d_agedays < 153, whoinc.age.hc := 4]
+        df[d_agedays >= 153 & d_agedays < 199, whoinc.age.hc := 6]
+
+        # update the edge intervals
+        df[d_agedays < 46, d_agedays := 45]
+        df[d_agedays == 45, whoinc.age.hc := 2]
+        df[d_agedays > 199, d_agedays := 200]
+        df[d_agedays == 200, whoinc.age.hc := 6]
+
+        # 17K
+        # merge with WHO
+        # add the column name we want to grab
+        for (i in unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc)])){
+          cn <- paste0("whoinc.", i, ".ht")
+          df[, who_mindiff_hc :=
+               as.numeric(who.hc.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
+          cn <- paste0("max.whoinc.", i, ".ht")
+          df[, who_maxdiff_hc :=
+               as.numeric(who.hc.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
+        }
+        df[, who_mindiff_hc := as.numeric(who_mindiff_hc)]
+        df[, who_mindiff_hc := as.numeric(who_maxdiff_hc)]
+
+        # 17L
+        df[d_agedays < whoinc.age.hc*30.4375, who_mindiff_hc :=
+             who_mindiff_hc * d_agedays/(whoinc.age.hc*30.4375)]
+        df[d_agedays > whoinc.age.hc*30.4375, who_maxdiff_hc :=
+             who_maxdiff_hc * d_agedays/(whoinc.age.hc*30.4375)]
+
+        df[d_agedays < 9*30.4375, who_mindiff_hc := who_mindiff_hc*.5-1.5]
+        df[d_agedays < 9*30.4375, who_maxdiff_hc := who_maxdiff_hc*2+1.5]
+
+        # 17H
+        # use who
+        df[, mindiff := who_mindiff_hc]
+        df[, maxdiff := who_maxdiff_hc]
+        # otherwise, fill in
+        df[is.na(mindiff), mindiff := -1.5]
+
+        # 17I
+        # sort df since it got reordered with keys
+        df <- df[order(agedays),]
+        df[, mindiff_prev := dplyr::lag(mindiff)]
+        df[, maxdiff_prev := dplyr::lag(maxdiff)]
+      }
+
+      # 17O: generate ewma
+      df[, (ewma.fields) := as.double(NaN)]
+
+      # first, calculate which exponent we want to put through (pass a different
+      # on for each exp)
+      # subset df to only valid rows
+      tmp <- data.frame(
+        "before" = abs(df$agedays - c(NA, df$agedays[1:(nrow(df)-1)])),
+        "after" = abs(df$agedays - c(df$agedays[2:(nrow(df))], NA))
+      )
+      maxdiff_e <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+      exp_vals <- rep(-1.5, nrow(tmp))
+      exp_vals[maxdiff_e > 365.25] <- -2.5
+      exp_vals[maxdiff_e > 730.5] <- -3.5
+      df[, exp_vals := exp_vals]
+
+      # calculate ewma
+      df[, (ewma.fields) := ewma(agedays, tbc.sd, exp_vals, TRUE)]
+
+      # calculate dewma
+      df[, `:=`(
+        dewma.all = tbc.sd - ewma.all,
+        dewma.before = tbc.sd - ewma.before,
+        dewma.after = tbc.sd - ewma.after
+      )]
+
+      # add differences for convenience
+      df[, diff_prev := (v-dplyr::lag(v))]
+      df[, diff_next := (dplyr::lead(v)-v)]
+
+      if (nrow(df) > 2){
+        # 17P/R: identify pairs and calculate exclusions
+        df[, pair := (v-dplyr::lag(v)) < mindiff_prev |
+             (dplyr::lead(v)-v) < mindiff |
+             (v-dplyr::lag(v)) > maxdiff_prev | (dplyr::lead(v)-v) > maxdiff
+        ]
+        df[is.na(pair), pair := FALSE]
+        df[pair & abs(dewma.before) > dplyr::lag(abs(dewma.after)),
+           bef.g.aftm1 := TRUE]
+        df[pair & abs(dewma.after) > dplyr::lead(abs(dewma.before)),
+           aft.g.aftm1 := TRUE]
+
+        # R
+        df[, val_excl := exclude]
+        df[diff_prev < mindiff_prev & bef.g.aftm1, val_excl := "Exclude-Min-diff"]
+        df[diff_next < mindiff & aft.g.aftm1, val_excl := "Exclude-Min-diff"]
+        df[diff_prev > maxdiff_prev & bef.g.aftm1, val_excl := "Exclude-Max-diff"]
+        df[diff_next > maxdiff & aft.g.aftm1, val_excl := "Exclude-Max-diff"]
+
+
+
+      } else { # only 2 values
+        # 17Q/R -- exclusions for pairs
+        df[, val_excl := exclude]
+        df[diff_prev < mindiff_prev & abs(tbc.sd) > dplyr::lag(abs(tbc.sd)),
+           val_excl := "Exclude-Min-diff"]
+        df[diff_next < mindiff & abs(tbc.sd) > dplyr::lead(abs(tbc.sd)),
+           val_excl := "Exclude-Min-diff"]
+        df[diff_prev > maxdiff_prev & abs(tbc.sd) > dplyr::lag(abs(tbc.sd)),
+           val_excl := "Exclude-Max-diff"]
+        df[diff_next > maxdiff &  abs(tbc.sd) > dplyr::lead(abs(tbc.sd)),
+           val_excl := "Exclude-Max-diff"]
+      }
+
+      # figure out if any of the exclusions hit
+      count_exclude <- sum(df$val_excl != "Include")
+      if (count_exclude > 0){
+        if (nrow(df) > 2){
+          df[, absval := abs(dewma.all)]
+        } else {
+          df[, absval := abs(tbc.sd)]
+        }
+
+        # choose the highest abssum for exclusion
+        idx <- df$index[which.max(df[df$val_excl != "Include",
+                                     absval])]
+
+
+
+        exclude_all[ind_all == idx] <- df[index == idx, val_excl]
+
+        #set up to continue on
+        if (count_exclude > 1){
+          testing <- TRUE
+
+          df <- df[index != idx, ]
+        } else {
+          testing <- FALSE
+        }
+      } else {
+        testing <- FALSE
+      }
+    }
+
+    return(exclude_all)
+  })(copy(.SD)), by = .(subjid, param), .SDcols = colnames(data.df)]
+
+  # 19: 1 or 2 measurements ----
+
+  # NOTE: ADD STEP OUTPUT PRINT
+
+  # create the valid set
+  # we only running carried forwards on valid values, non NNTE values,
+  # and non single values, and non weight
+  tmp <- table(paste0(data.df$subjid, "_", data.df$param))
+  only_single_pairs <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 2]
+  valid_set <- valid(data.df, include.temporary.extraneous = FALSE) &
+    !data.df$nnte_full & # does not use the "other"
+    only_single_pairs
+
+  # order just for ease later
+  data.df <- data.df[order(subjid, param, agedays),]
+  data.df <- data.df[valid_set, exclude := (function(df) {
+    # 19A: is it a single or a pair?
+    is_single <- nrow(df) == 1
+
+    # 19B/C: for pairs, calculate info
+    if (!is_single){
+      diff_tbc.sd <- df[2, tbc.sd] - df[1, tbc.sd]
+      diff_tbc.sd <- df[2, tbc.sd] - df[1, tbc.sd]
+      diff_agedays <- df[2, agedays] - df[1, agedays]
+
+      abs_tbd.sd <- abs(df[1, tbc.sd])
+
+      # find the DOP (designated other parameter)
+      dop <-
+        data.df[subjid == df$subjid[1] & param == get_dop(df$param[1]) &
+                  agedays == df$agedays[1],]
+
+      med_dop <-
+        if (nrow(dop) > 0){
+          median(dop$tbc.sd)
+        } else {
+          NA
+        }
+    }
+
+
+    return(df$exclude)
+  })(copy(.SD)), by = .(subjid, param), .SDcols = colnames(df)]
 
   # end ----
 

From 86963c3a21f3eb711de9c9fb4d2d36371cbd2d98 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 1 Aug 2023 16:13:41 -0400
Subject: [PATCH 056/100] Add step 19 (singles and pairs)

---
 R/infants_clean.R | 73 +++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 64 insertions(+), 9 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 86bbd213..172fa605 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1574,7 +1574,7 @@ cleanbatch_infants <- function(data.df,
   # we only running carried forwards on valid values, non NNTE values,
   # and non single values, and non weight
   tmp <- table(paste0(data.df$subjid, "_", data.df$param))
-  only_single_pairs <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp > 2]
+  only_single_pairs <- paste0(data.df$subjid, "_", data.df$param) %in% names(tmp)[tmp <= 2]
   valid_set <- valid(data.df, include.temporary.extraneous = FALSE) &
     !data.df$nnte_full & # does not use the "other"
     only_single_pairs
@@ -1582,21 +1582,42 @@ cleanbatch_infants <- function(data.df,
   # order just for ease later
   data.df <- data.df[order(subjid, param, agedays),]
   data.df <- data.df[valid_set, exclude := (function(df) {
+    # save initial exclusions to keep track
+    ind_all <- copy(df$index)
+    exclude_all <- copy(df$exclude)
+
     # 19A: is it a single or a pair?
     is_single <- nrow(df) == 1
 
+    # find the DOP (designated other parameter)
+    dop <-
+      data.df[subjid == df$subjid[1] & param == get_dop(df$param[1]) &
+                exclude == "Include",]
+
+    # 19D: calculate the voi comparison
+    if (nrow(dop) > 0){
+      for (i in 1:nrow(df)){
+        comp_val <-
+          if (df$agedays[i] %in% dop$agedays){
+            abs(dop[dop$agedays == df$agedays[i], tbc.sd] - df[i, tbc.sd])
+          } else {
+            abs(median(dop$tbc.sd) - df[i, tbc.sd])
+          }
+
+        df[i, comp_diff := comp_val]
+      }
+    } else {
+      df[, comp_diff := rep(NA, nrow(df))]
+    }
+
     # 19B/C: for pairs, calculate info
     if (!is_single){
       diff_tbc.sd <- df[2, tbc.sd] - df[1, tbc.sd]
-      diff_tbc.sd <- df[2, tbc.sd] - df[1, tbc.sd]
+      diff_ctbc.sd <- df[2, ctbc.sd] - df[1, ctbc.sd]
       diff_agedays <- df[2, agedays] - df[1, agedays]
 
       abs_tbd.sd <- abs(df[1, tbc.sd])
-
-      # find the DOP (designated other parameter)
-      dop <-
-        data.df[subjid == df$subjid[1] & param == get_dop(df$param[1]) &
-                  agedays == df$agedays[1],]
+      abs_ctbd.sd <- abs(df[1, ctbc.sd])
 
       med_dop <-
         if (nrow(dop) > 0){
@@ -1604,11 +1625,45 @@ cleanbatch_infants <- function(data.df,
         } else {
           NA
         }
+      med_cdop <-
+        if (nrow(dop) > 0){
+          median(dop$ctbc.sd)
+        } else {
+          NA
+        }
+
+      # 19E: which is larger
+      max_ind <- if (!all(is.na(df$comp_diff))){
+        which.max(df$comp_diff)
+      } else {
+        which.max(df$tbc.sd)
+      }
+
+      # 19F/G: which exclusion
+      if (diff_tbc.sd > 4 & (diff_ctbc.sd > 4 | is.na(diff_ctbc.sd)) &
+          diff_agedays >=365.25){
+        df[max_ind, exclude := "Exclude-2-meas->1-year"]
+      } else if (diff_tbc.sd > 2.5 & (diff_ctbc.sd > 4 | is.na(diff_ctbc.sd)) &
+                 diff_agedays < 365.25){
+        df[max_ind, exclude := "Exclude-2-meas-<1-year"]
+      }
+
+      # save the results
+      exclude_all <- df$exclude
+
+      # 19H
+      # if one needs to get removed, we want to reevaluate as a single
+      df <- df[exclude == "Include",]
     }
 
+    if (nrow(df) == 1){
+      df[(abs(tbc.sd) > 3 & !is.na(comp_diff) & comp_diff > 5) |
+           (abs(tbc.sd) > 5 & is.na(comp_diff)),
+         exclude := "Exclude-1-meas"]
+    }
 
-    return(df$exclude)
-  })(copy(.SD)), by = .(subjid, param), .SDcols = colnames(df)]
+    return(exclude_all)
+  })(copy(.SD)), by = .(subjid, param), .SDcols = colnames(data.df)]
 
   # end ----
 

From 05e0afd845c42b0525831a447b851646460c705e Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 1 Aug 2023 16:37:18 -0400
Subject: [PATCH 057/100] Finish error load step

---
 R/infants_clean.R | 27 ++++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 172fa605..1d26e0af 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1665,6 +1665,31 @@ cleanbatch_infants <- function(data.df,
     return(exclude_all)
   })(copy(.SD)), by = .(subjid, param), .SDcols = colnames(data.df)]
 
+  # 21: error load ----
+
+  valid_set <- !data.df$nnte_full
+
+  data.df[valid_set,
+          err_ratio := sum(!exclude %in%
+                             c("Include",
+                               "Exclude-SDE-Identical",
+                               "Exclude-SDE-All-Exclude",
+                               "Exclude-SDE-All-Extreme",
+                               "Exclude-SDE-EWMA",
+                               "Exclude-SDE-All-Extreme",
+                               "Exclude-SDE-One-Day",
+                               "Exclude-Carried-Forward",
+                               "Exclude-1-CF-deltaZ-<0.05",
+                               "Exclude-1-CF-deltaZ-<0.1-wholehalfimp",
+                               "Exclude-Teen-2-plus-CF-deltaZ-<0.05",
+                               "Exclude-Teen-2-plus-CF-deltaZ-<0.1-wholehalfimp"
+                             ))/.N,
+          by = c("subjid", "param")]
+
+  data.df[valid_set & err_ratio > .4,
+          exclude := "Exclude-Error-load"]
+
+
   # end ----
 
   if (!quietly)
@@ -1672,5 +1697,5 @@ cleanbatch_infants <- function(data.df,
   if (!quietly & parallel)
     sink()
 
-  return(data.df[j = .(line, exclude, tbc.sd, tbc.other.sd, param)]) #debugging
+  return(data.df[j = .(line, exclude, tbc.sd, param)]) #debugging
 }

From afddde039abad89edc2ddacb73efaa38476cb150 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 1 Aug 2023 19:04:46 -0400
Subject: [PATCH 058/100] Clean up, add factor levels, and fix tiny SDe bug

---
 R/growth.R        | 22 ++++++++++++++++++++++
 R/infants_clean.R | 32 ++++++++++++++++++++++++++++----
 2 files changed, 50 insertions(+), 4 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 56e6c378..b192c400 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -244,6 +244,26 @@ cleangrowth <- function(subjid,
     'Exclude-SDE-All-Extreme',
     'Exclude-SDE-EWMA',
     'Exclude-SDE-One-Day',
+    "Exclude-EWMA2-middle",
+    "Exclude-EWMA2-birth-WT",
+    "Exclude-EWMA-2-birth-WT-ext",
+    "Exclude-EWMA-2-first",
+    "Exclude-EWMA-2-first-ext",
+    "Exclude-EWMA2-last",
+    "Exclude-EWMA2-last-high",
+    "Exclude-EWMA2-last-ext",
+    "Exclude-EWMA2-last-ext-high",
+    "Exclude-EWMA2-birth-HT-HC",
+    "Exclude-EWMA2-birth-HT-HC-ext",
+    "Exclude-Min-diff",
+    "Exclude-Max-diff",
+    "Exclude-2-meas->1-year",
+    "Exclude-2-meas-<1-year",
+    "Exclude-1-meas",
+    "Exclude-Error-load",
+
+    # old
+
     'Exclude-EWMA-8',
     'Exclude-EWMA-9',
     'Exclude-EWMA-10',
@@ -259,6 +279,8 @@ cleangrowth <- function(subjid,
     'Exclude-Single-Outlier',
     'Exclude-Too-Many-Errors',
     'Exclude-Too-Many-Errors-Other-Parameter',
+
+    #new
     "Exclude-Absolute-BIV",
     "Exclude-Standardized-BIV",
     "Exclude-Evil-Twin",
diff --git a/R/infants_clean.R b/R/infants_clean.R
index 1d26e0af..0944a9c6 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -834,7 +834,7 @@ cleanbatch_infants <- function(data.df,
             if (s_df$agedays[1]%%2 == 0){
               which.min(s_df$v)
             } else {
-              which.max(s_df$v == max(s_df$v[order(med_diff)[1:2]]))
+              which.max(s_df$v)
             }
             rem_ids <- c(rem_ids, s_df$index[-keep_val])
           }
@@ -855,7 +855,11 @@ cleanbatch_infants <- function(data.df,
 
   # 15: moderate EWMA ----
 
-  # THINK ABOUT NNTE FULL HERE
+  if (!quietly)
+    cat(sprintf(
+      "[%s] Exclude moderate EWMA...\n",
+      Sys.time()
+    ))
 
   # create the valid set
   # we only running carried forwards on valid values, non NNTE values,
@@ -1113,6 +1117,12 @@ cleanbatch_infants <- function(data.df,
 
   # 16: moderate EWMA for birth HT and HC ----
 
+  if (!quietly)
+    cat(sprintf(
+      "[%s] Exclude moderate EWMA for birth height and head circumference...\n",
+      Sys.time()
+    ))
+
   # create the valid set
   # we only running carried forwards on valid values, non NNTE values,
   # and non single values, and non weight
@@ -1260,7 +1270,11 @@ cleanbatch_infants <- function(data.df,
 
   # 17: raw differences ----
 
-  # NOTE: ADD STEP OUTPUT PRINT
+  if (!quietly)
+    cat(sprintf(
+      "[%s] Exclude raw differences...\n",
+      Sys.time()
+    ))
 
   # read in tanner data
   tanner_ht_vel_rev_path <- ifelse(
@@ -1568,7 +1582,11 @@ cleanbatch_infants <- function(data.df,
 
   # 19: 1 or 2 measurements ----
 
-  # NOTE: ADD STEP OUTPUT PRINT
+  if (!quietly)
+    cat(sprintf(
+      "[%s] Exclude 1 or 2 measurements...\n",
+      Sys.time()
+    ))
 
   # create the valid set
   # we only running carried forwards on valid values, non NNTE values,
@@ -1667,6 +1685,12 @@ cleanbatch_infants <- function(data.df,
 
   # 21: error load ----
 
+  if (!quietly)
+    cat(sprintf(
+      "[%s] Exclude error load...\n",
+      Sys.time()
+    ))
+
   valid_set <- !data.df$nnte_full
 
   data.df[valid_set,

From b2fb4a7481982ef71f2cf372e40927562598acd1 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 2 Aug 2023 09:37:10 -0400
Subject: [PATCH 059/100] Remove ewma exp argument

---
 R/growth.R        | 10 ----------
 R/infants_clean.R |  8 ++++----
 2 files changed, 4 insertions(+), 14 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index b192c400..df1d3386 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -57,13 +57,6 @@
 #' @param sdrecentered.filename Name of file to save re-centered data to as CSV. Defaults to "", for which this
 #' data will not be saved. Useful for post-processing and debugging.
 #' @param include.carryforward Determines whether Carry-Forward values are kept in the output. Defaults to False.
-#' @param ewma.exp Exponent to use for weighting measurements in the
-#' exponentially weighted moving average calculations. Defaults to -1.5.
-#' This exponent should be negative in order to weight growth measurements
-#' closer to the measurement being evaluated more strongly. Exponents that are
-#' further from zero (e.g. -3) will increase the relative influence of
-#' measurements close in time to the measurement being evaluated compared to
-#' using the default exponent.
 #' @param ref.data.path Path to reference data. If not supplied, the year 2000
 #' Centers for Disease Control (CDC) reference data will be used.
 #' @param log.path Path to log file output when running in parallel (non-quiet mode). Default is NA. A new
@@ -148,7 +141,6 @@ cleangrowth <- function(subjid,
                         sdmedian.filename = "",
                         sdrecentered.filename = "",
                         include.carryforward = FALSE,
-                        ewma.exp = -1.5,
                         ref.data.path = "",
                         log.path = NA,
                         parallel = FALSE,
@@ -911,7 +903,6 @@ cleangrowth <- function(subjid,
           parallel = parallel,
           measurement.to.z = measurement.to.z,
           ewma.fields = ewma.fields,
-          ewma.exp = ewma.exp,
           recover.unit.error = recover.unit.error,
           include.carryforward = include.carryforward,
           sd.extreme = sd.extreme,
@@ -966,7 +957,6 @@ cleangrowth <- function(subjid,
           parallel = parallel,
           measurement.to.z = measurement.to.z,
           ewma.fields = ewma.fields,
-          ewma.exp = ewma.exp,
           recover.unit.error = recover.unit.error,
           include.carryforward = include.carryforward,
           sd.extreme = sd.extreme,
diff --git a/R/infants_clean.R b/R/infants_clean.R
index 0944a9c6..78faa435 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -23,7 +23,6 @@ cleanbatch_infants <- function(data.df,
                                parallel,
                                measurement.to.z,
                                ewma.fields,
-                               ewma.exp,
                                recover.unit.error,
                                include.carryforward,
                                sd.extreme,
@@ -325,8 +324,8 @@ cleanbatch_infants <- function(data.df,
   # make sure it's ordered by subjid and parameter
   data.df <- data.df[order(subjid, param),]
 
+  # 9A/B/C
   # first, find out of any re possible evil twins in the first place
-  # NOTE: GO BACK HERE
   start_df <- calc_oob_evil_twins(data.df[valid_set,])
 
   if (any(start_df$oob)) {
@@ -351,6 +350,7 @@ cleanbatch_infants <- function(data.df,
       # while there are multiple oob, we want to remove
       while (any_oob){
 
+        # 9D
         # now calculate the maximum difference from the median tbc.sd
         upd.df[, `:=` (sd_med = median(tbc.sd, na.rm = T)), by =.(subjid, param)]
         upd.df[, `:=` (med_diff = abs(tbc.sd - sd_med)), by =.(subjid, param)]
@@ -362,6 +362,7 @@ cleanbatch_infants <- function(data.df,
 
         df[upd.df[exclude == exc_nam,], exclude := i.exclude, on = .(line)]
 
+        #9E
         # reupdate valid (to recalculate OOB -- others are not included)
         upd.df <- calc_oob_evil_twins(df[valid(df),])
         upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)]
@@ -375,8 +376,7 @@ cleanbatch_infants <- function(data.df,
 
   }
 
-
-  # 9d.  Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous)
+  # 9F.  redo temp extraneous
   data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']
   data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day']
 

From 56e127def5e5560cf5b153ca493b6fb1943d2bca Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 2 Aug 2023 16:41:28 -0400
Subject: [PATCH 060/100] Fix some bugs with CF and parallel/infants

---
 R/growth.R        | 11 +++++++----
 R/infants_clean.R | 42 ++++++++++++------------------------------
 2 files changed, 19 insertions(+), 34 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index df1d3386..b42dfed7 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -325,7 +325,9 @@ cleangrowth <- function(subjid,
       # variables needed for parallel workers
       var_for_par <- c("temporary_extraneous", "valid", "swap_parameters",
                        "na_as_false", "ewma", "read_anthro", "as_matrix_delta",
-                       "sd_median")
+                       "sd_median", "temporary_extraneous_infants",
+                       "get_dop", "calc_oob_evil_twins",
+                       "calc_and_recenter_z_scores")
 
       cl <- makeCluster(num.batches)
       clusterExport(cl = cl, varlist = var_for_par, envir = environment())
@@ -792,9 +794,10 @@ cleangrowth <- function(subjid,
 
     setkey(data.all, subjid, param, agedays)
     data.all[, tbc.sd := sd.orig - sd.median]
-    # separate out corrected and noncorrected values
-    data.all[, ctbc.sd := sd.corr - sd.median]
-
+    if (infants){
+      # separate out corrected and noncorrected values
+      data.all[, ctbc.sd := sd.corr - sd.median]
+    }
 
     if (sdrecentered.filename != "") {
       write.csv(data.all, sdrecentered.filename, row.names = FALSE)
diff --git a/R/infants_clean.R b/R/infants_clean.R
index 78faa435..f9bef30f 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -128,12 +128,14 @@ cleanbatch_infants <- function(data.df,
     # for values with sdes, compare to all values from the prior day
     data.sub[no_sde == FALSE, cf := (function(df){
       ages <- unique(agedays)
-      for (i in 2:length(ages)) {
-        # find the set of measurements from the previous age in days
-        all.prev.v <- df[agedays == ages[i - 1], v.orig]
-        # if a measurement for the current age is found in the set of measurements from the previous age, then mark it as carried forward
-        df[agedays == ages[i] &
-             v.orig %in% all.prev.v, cf := TRUE]
+      if (length(ages) > 1){
+        for (i in 2:length(ages)) {
+          # find the set of measurements from the previous age in days
+          all.prev.v <- df[agedays == ages[i - 1], v.orig]
+          # if a measurement for the current age is found in the set of measurements from the previous age, then mark it as carried forward
+          df[agedays == ages[i] &
+               v.orig %in% all.prev.v, cf := TRUE]
+        }
       }
     })(copy(.SD)), .SDcols = c('agedays', 'cf', 'v.orig'), by = .(subjid, param)]
 
@@ -430,7 +432,10 @@ cleanbatch_infants <- function(data.df,
         exp_vals[maxdiff > 730.5] <- -3.5
         df[valid_set, exp_vals := exp_vals]
 
-        # calculate ewma
+        # calculate ewma -- need to do it in a special way to not include
+        # temp extaneous
+
+        # first calculate ewma for rows without temp extraneous
         df[valid_set, (ewma.fields) := ewma(agedays, tbc.sd, exp_vals, TRUE)]
         df[valid_set, paste0("c.",ewma.fields) := ewma(agedays, ctbc.sd, exp_vals, TRUE)]
 
@@ -491,29 +496,6 @@ cleanbatch_infants <- function(data.df,
                                       decreasing = TRUE))[1]
           df[worst.row, exclude := 'Exclude-EWMA1-Extreme']
         }
-        #
-        #       # 11f.  For subjects/parameters with only 2 values, calculate abstbc*sd=|tbc*sd|
-        #       # g.  Replace exc_*=6 for values that meet all of the following criteria
-        #       #   i.	There are 2 measurements for that subject and parameter
-        #       #   ii.	(dewma_*>3.5 & tbc*sd>3.5) OR (dewma_*<-3.5 & tbc*sd<-3.5)
-        #       #   iii.	If there are 2 measurements for a subject/parameter that meet criteria ii, only replace exc_*=6 for the value with the larger abstbc*sd
-        #       rep <- na_as_false(with(
-        #         df,
-        #         num.valid == 2 &
-        #           (
-        #             tbc.sd - ewma.all > 3.5 &
-        #               tbc.sd > 3.5 |
-        #               tbc.sd - ewma.all < -3.5 & tbc.sd < -3.5
-        #           )
-        #       ))
-        #       num.exclude <- sum(rep)
-        #       if (num.exclude == 1)
-        #         df[rep, exclude := 'Exclude-EWMA-Extreme-Pair']
-        #       if (num.exclude > 1) {
-        #         # first order by decreasing abssum
-        #         worst.row <- with(df, order(rep, abs(tbc.sd), decreasing = TRUE))[1]
-        #         df[worst.row, exclude := 'Exclude-EWMA-Extreme-Pair']
-        #       }
 
         # 11h.  Recalculate temporary extraneous as in step 5
         # optimize: only perform these steps if this subject is known to have extraneous measurements

From 97ecc46eb5da6252e7b1a6ad88eb8c23a9ce4ccc Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Thu, 3 Aug 2023 11:09:19 -0400
Subject: [PATCH 061/100] Pass in reference data path

---
 R/growth.R        | 6 ++++--
 R/infants_clean.R | 3 ++-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index b42dfed7..724f7543 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -915,7 +915,8 @@ cleangrowth <- function(subjid,
           who.ht.vel = who.ht.vel,
           lt3.exclude.mode = lt3.exclude.mode,
           error.load.threshold = error.load.threshold,
-          error.load.mincount = error.load.mincount)
+          error.load.mincount = error.load.mincount,
+          ref.data.path = ref.data.path)
       }
     } else {
       # create log directory if necessary
@@ -969,7 +970,8 @@ cleangrowth <- function(subjid,
           who.ht.vel = who.ht.vel,
           lt3.exclude.mode = lt3.exclude.mode,
           error.load.threshold = error.load.threshold,
-          error.load.mincount = error.load.mincount
+          error.load.mincount = error.load.mincount,
+          ref.data.path = ref.data.path
         )
       }
       stopCluster(cl)
diff --git a/R/infants_clean.R b/R/infants_clean.R
index f9bef30f..29d9de63 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -32,7 +32,8 @@ cleanbatch_infants <- function(data.df,
                                who.ht.vel,
                                lt3.exclude.mode,
                                error.load.threshold,
-                               error.load.mincount) {
+                               error.load.mincount,
+                               ref.data.path) {
   # avoid "no visible binding" warnings
   abs.2ndlast.sd <- abs.tbc.sd <- abs.tbc.sd.next <- abs.tbc.sd.prev <- abssum2 <- NULL
   aft.g.befp1 <- agedays <- agedays.other <- bef.g.aftm1 <- delta <- NULL

From 6c4164cc738b420c29c37e9b17f1a8f88d262d97 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Thu, 3 Aug 2023 11:57:15 -0400
Subject: [PATCH 062/100] Remove quietly reference in supporting functions

---
 R/infants_support.R | 2 --
 1 file changed, 2 deletions(-)

diff --git a/R/infants_support.R b/R/infants_support.R
index a6e5232c..2d64a59b 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -157,8 +157,6 @@ calc_and_recenter_z_scores <- function(df, cn, ref.data.path){
                                       infants = T)
 
   # calculate "standard deviation" scores
-  if (!quietly)
-    cat(sprintf("[%s] Calculating SD-scores...\n", Sys.time()))
   df[, cn.orig_cdc := measurement.to.z(param, agedays, sex, get(cn), TRUE)]
   df[, cn.orig_who := measurement.to.z_who(param, agedays, sex, get(cn), TRUE)]
 

From 623df091b3b570cf545466082a4f313d2b35a0f3 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 9 Aug 2023 15:47:17 -0400
Subject: [PATCH 063/100] Add preallocation for mindiff

---
 R/infants_clean.R | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 29d9de63..78195942 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1383,6 +1383,8 @@ cleanbatch_infants <- function(data.df,
         # 17F
         # merge with WHO
         # add the column name we want to grab
+        df[, who_mindiff_ht := NA]
+        df[, who_mindiff_ht := NA]
         for (i in unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht)])){
           cn <- paste0("whoinc.", i, ".ht")
           df[, who_mindiff_ht :=
@@ -1432,6 +1434,8 @@ cleanbatch_infants <- function(data.df,
         # 17K
         # merge with WHO
         # add the column name we want to grab
+        df[, who_mindiff_hc := NA]
+        df[, who_mindiff_hc := NA]
         for (i in unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc)])){
           cn <- paste0("whoinc.", i, ".ht")
           df[, who_mindiff_hc :=

From 8dbde801d5453c5bc86506ec570d32f9e2b90789 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Thu, 10 Aug 2023 11:35:43 -0400
Subject: [PATCH 064/100] Fix maxdiff preallocation

---
 R/infants_clean.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 78195942..fb79afd3 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1384,7 +1384,7 @@ cleanbatch_infants <- function(data.df,
         # merge with WHO
         # add the column name we want to grab
         df[, who_mindiff_ht := NA]
-        df[, who_mindiff_ht := NA]
+        df[, who_maxdiff_ht := NA]
         for (i in unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht)])){
           cn <- paste0("whoinc.", i, ".ht")
           df[, who_mindiff_ht :=
@@ -1435,7 +1435,7 @@ cleanbatch_infants <- function(data.df,
         # merge with WHO
         # add the column name we want to grab
         df[, who_mindiff_hc := NA]
-        df[, who_mindiff_hc := NA]
+        df[, who_maxdiff_hc := NA]
         for (i in unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc)])){
           cn <- paste0("whoinc.", i, ".ht")
           df[, who_mindiff_hc :=

From 1d4082e69e7751d597cefb5d70ab1b3bc1bbbdb6 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 14 Aug 2023 10:31:02 -0400
Subject: [PATCH 065/100] Fix ewma calculations for temp same day in ewma
 extreme

---
 R/infants_clean.R | 41 ++++++++++++++++++++++++++---------------
 1 file changed, 26 insertions(+), 15 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index fb79afd3..9a96c111 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -437,24 +437,35 @@ cleanbatch_infants <- function(data.df,
         # temp extaneous
 
         # first calculate ewma for rows without temp extraneous
-        df[valid_set, (ewma.fields) := ewma(agedays, tbc.sd, exp_vals, TRUE)]
-        df[valid_set, paste0("c.",ewma.fields) := ewma(agedays, ctbc.sd, exp_vals, TRUE)]
+        no_tmp_extr <- valid(df, include.temporary.extraneous = FALSE)
+
+        df[valid_set & no_tmp_extr, (ewma.fields) := ewma(agedays, tbc.sd, exp_vals, TRUE)]
+        df[valid_set & no_tmp_extr, paste0("c.",ewma.fields) := ewma(agedays, ctbc.sd, exp_vals, TRUE)]
+
+        # now go through the temp extraneous and do the ewmas just for those
+        # rows
+        if (sum(df$exclude == "Exclude-Temporary-Extraneous-Same-Day") > 0){
+          # go through each temp extraneous
+          for (j in
+               which(df$exclude == "Exclude-Temporary-Extraneous-Same-Day")){
+            # subset to only the temp extraneous and valid values
+            df_sub <- df[valid_set & (no_tmp_extr | (1:nrow(df) == j)),]
+
+            # run ewma on those values
+            df_sub[, (ewma.fields) := ewma(agedays, tbc.sd, exp_vals, TRUE)]
+            df_sub[, paste0("c.",ewma.fields) := ewma(agedays, ctbc.sd, exp_vals, TRUE)]
+
+            # merge the value back in
+            col_replace <- c(ewma.fields, paste0("c.",ewma.fields))
+            df_val <-
+              df_sub[df_sub$exclude == "Exclude-Temporary-Extraneous-Same-Day",
+                     ..col_replace]
+            df[j, (col_replace) := df_val]
+          }
 
-        # note: at this point, only one ewma exists per param on a given day for a subject, so sort(ewma.all)[1] will returns the non-missing ewma.all
-        # restrict to children with possible extraneous for efficiency
+        }
 
         # include the corrected
-        if (has.extraneous) {
-          df[, `:=`(
-            ewma.all = sort(ewma.all)[1],
-            ewma.before = sort(ewma.before)[1],
-            ewma.after = sort(ewma.after)[1],
-
-            c.ewma.all = sort(c.ewma.all)[1],
-            c.ewma.before = sort(c.ewma.before)[1],
-            c.ewma.after = sort(c.ewma.after)[1]
-          ), by = .(agedays)]
-        }
         df[, `:=`(
           dewma.all = tbc.sd - ewma.all,
           dewma.before = tbc.sd - ewma.before,

From eaa38ff441e8438142d962c3eca6284d003d5de8 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 14 Aug 2023 11:12:28 -0400
Subject: [PATCH 066/100] Remove edge calculations for HC in step 17

---
 R/infants_clean.R | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 9a96c111..609bc22b 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1436,12 +1436,6 @@ cleanbatch_infants <- function(data.df,
         df[d_agedays >= 107 & d_agedays < 153, whoinc.age.hc := 4]
         df[d_agedays >= 153 & d_agedays < 199, whoinc.age.hc := 6]
 
-        # update the edge intervals
-        df[d_agedays < 46, d_agedays := 45]
-        df[d_agedays == 45, whoinc.age.hc := 2]
-        df[d_agedays > 199, d_agedays := 200]
-        df[d_agedays == 200, whoinc.age.hc := 6]
-
         # 17K
         # merge with WHO
         # add the column name we want to grab
@@ -1558,8 +1552,6 @@ cleanbatch_infants <- function(data.df,
         idx <- df$index[which.max(df[df$val_excl != "Include",
                                      absval])]
 
-
-
         exclude_all[ind_all == idx] <- df[index == idx, val_excl]
 
         #set up to continue on

From bb6129d9f8e8cc74af7e32ae90e973b4287d9c55 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 14 Aug 2023 11:19:34 -0400
Subject: [PATCH 067/100] Fix cutoff in pairs step

---
 R/infants_clean.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 609bc22b..0d718f4c 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1651,7 +1651,7 @@ cleanbatch_infants <- function(data.df,
       if (diff_tbc.sd > 4 & (diff_ctbc.sd > 4 | is.na(diff_ctbc.sd)) &
           diff_agedays >=365.25){
         df[max_ind, exclude := "Exclude-2-meas->1-year"]
-      } else if (diff_tbc.sd > 2.5 & (diff_ctbc.sd > 4 | is.na(diff_ctbc.sd)) &
+      } else if (diff_tbc.sd > 2.5 & (diff_ctbc.sd > 2.5 | is.na(diff_ctbc.sd)) &
                  diff_agedays < 365.25){
         df[max_ind, exclude := "Exclude-2-meas-<1-year"]
       }

From 3c4d7bbd6a7dde23109a1828e2bb590aaf0f0efe Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 14 Aug 2023 12:04:14 -0400
Subject: [PATCH 068/100] Do not overwrite error load code

---
 R/infants_clean.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 0d718f4c..4b487ffa 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1700,7 +1700,7 @@ cleanbatch_infants <- function(data.df,
                              ))/.N,
           by = c("subjid", "param")]
 
-  data.df[valid_set & err_ratio > .4,
+  data.df[valid_set & err_ratio > .4 & exclude == "Include",
           exclude := "Exclude-Error-load"]
 
 

From 0b1445c6d59196837c83000300909e69c06e4c5e Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 14 Aug 2023 14:10:04 -0400
Subject: [PATCH 069/100] Move the preallocation into the subset

---
 R/infants_clean.R | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 4b487ffa..8f0643df 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1352,7 +1352,6 @@ cleanbatch_infants <- function(data.df,
     testing <- TRUE
 
     while (testing & nrow(df) > 1){
-
       # sort df since it got reordered with keys
       df <- df[order(agedays),]
 
@@ -1394,8 +1393,6 @@ cleanbatch_infants <- function(data.df,
         # 17F
         # merge with WHO
         # add the column name we want to grab
-        df[, who_mindiff_ht := NA]
-        df[, who_maxdiff_ht := NA]
         for (i in unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht)])){
           cn <- paste0("whoinc.", i, ".ht")
           df[, who_mindiff_ht :=
@@ -1404,6 +1401,11 @@ cleanbatch_infants <- function(data.df,
           df[, who_maxdiff_ht :=
                as.numeric(who.ht.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
         }
+        # if there are none, preallocate for ease
+        if (length(unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht)])) < 1){
+          df[, who_mindiff_ht := NA_real_]
+          df[, who_maxdiff_ht := NA_real_]
+        }
         df[, who_mindiff_ht := as.numeric(who_mindiff_ht)]
         df[, who_mindiff_ht := as.numeric(who_maxdiff_ht)]
 
@@ -1436,11 +1438,12 @@ cleanbatch_infants <- function(data.df,
         df[d_agedays >= 107 & d_agedays < 153, whoinc.age.hc := 4]
         df[d_agedays >= 153 & d_agedays < 199, whoinc.age.hc := 6]
 
+        # update the edge intervals to missing
+        df[d_agedays < 46 | d_agedays > 199, whoinc.age.hc := NA]
+
         # 17K
         # merge with WHO
         # add the column name we want to grab
-        df[, who_mindiff_hc := NA]
-        df[, who_maxdiff_hc := NA]
         for (i in unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc)])){
           cn <- paste0("whoinc.", i, ".ht")
           df[, who_mindiff_hc :=
@@ -1449,6 +1452,11 @@ cleanbatch_infants <- function(data.df,
           df[, who_maxdiff_hc :=
                as.numeric(who.hc.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
         }
+        # if there are none, preallocate for ease
+        if (length(unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc)])) < 1){
+          df[, who_mindiff_hc := NA_real_]
+          df[, who_maxdiff_hc := NA_real_]
+        }
         df[, who_mindiff_hc := as.numeric(who_mindiff_hc)]
         df[, who_mindiff_hc := as.numeric(who_maxdiff_hc)]
 

From 059c9da1486fc1eb0d6bb4af5a1aa7e06b89d1e2 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 14 Aug 2023 17:22:56 -0400
Subject: [PATCH 070/100] Fixed missing temporary extraneous to includes

---
 R/infants_clean.R | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 8f0643df..08aec30d 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -513,7 +513,7 @@ cleanbatch_infants <- function(data.df,
         # optimize: only perform these steps if this subject is known to have extraneous measurements
         if (has.extraneous) {
           df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']
-          df[temporary_extraneous(df), exclude := 'Exclude-Temporary-Extraneous-Same-Day']
+          df[temporary_extraneous_infants(df), exclude := 'Exclude-Temporary-Extraneous-Same-Day']
         }
 
         # 11i.  If there was at least one subject who had a potential exclusion identified in step 11c, repeat steps 11b-11g. If there were no subjects with potential
@@ -844,6 +844,11 @@ cleanbatch_infants <- function(data.df,
       }
     }
 
+    # replace all the "exclude temp extraneous" with includes -- other SDEs
+    # were removed
+    exclude_all[exclude_all == "Exclude-Temporary-Extraneous-Same-Day"] <-
+      "Include"
+
     return(exclude_all)
   })(copy(.SD)), by = .(subjid, param), .SDcols = colnames(data.df)]
 
@@ -1103,6 +1108,7 @@ cleanbatch_infants <- function(data.df,
           testing <- FALSE
         }
       }
+
       return(exclude_all)
     })(copy(.SD)), , by = .(subjid, param), .SDcols = colnames(df)]
 

From 8aea728352468a29b6a2ee8699dad54d1ddceb99 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Thu, 17 Aug 2023 15:44:10 -0400
Subject: [PATCH 071/100] Update recentering file

---
 R/growth.R                                   |  21 ++++++-------------
 inst/extdata/rcfile-2023-08-15_format.csv.gz | Bin 0 -> 199534 bytes
 2 files changed, 6 insertions(+), 15 deletions(-)
 create mode 100644 inst/extdata/rcfile-2023-08-15_format.csv.gz

diff --git a/R/growth.R b/R/growth.R
index 724f7543..0fe04c5c 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -710,9 +710,9 @@ cleangrowth <- function(subjid,
         infants_reference_medians_path <- ifelse(
           ref.data.path == "",
           system.file(file.path("extdata",
-                                "chop-rcfile-2023-02-03.csv_format.csv.gz"),
+                                "rcfile-2023-08-15_format.csv.gz"),
                       package = "growthcleanr"),
-          file.path(ref.data.path, "chop-rcfile-2023-02-03.csv_format.csv.gz")
+          file.path(ref.data.path, "rcfile-2023-08-15_format.csv.gz")
         )
         sd.recenter <- fread(infants_reference_medians_path)
         if (!quietly)
@@ -777,20 +777,11 @@ cleangrowth <- function(subjid,
     }
 
     # ensure recentering medians are sorted correctly
-    if (!infants){
-      setkey(sd.recenter, param, sex, agedays)
-
-      # add sd.recenter to data, and recenter
-      setkey(data.all, param, sex, agedays)
-      data.all <- sd.recenter[data.all]
-    } else {
-      # infant recenter doesn't have sex
-      setkey(sd.recenter, param, agedays)
+    setkey(sd.recenter, param, sex, agedays)
 
-      # add sd.recenter to data, and recenter
-      setkey(data.all, param, agedays)
-      data.all <- sd.recenter[data.all]
-    }
+    # add sd.recenter to data, and recenter
+    setkey(data.all, param, sex, agedays)
+    data.all <- sd.recenter[data.all]
 
     setkey(data.all, subjid, param, agedays)
     data.all[, tbc.sd := sd.orig - sd.median]
diff --git a/inst/extdata/rcfile-2023-08-15_format.csv.gz b/inst/extdata/rcfile-2023-08-15_format.csv.gz
new file mode 100644
index 0000000000000000000000000000000000000000..4f0ab4208e3112f5dfb12be955cc8baa57580543
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kxI(tF8F7(kCEm6Fn`_g
Date: Thu, 17 Aug 2023 15:57:06 -0400
Subject: [PATCH 072/100] Add "not cleaned" for HC > 3 yo

---
 R/growth.R | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/R/growth.R b/R/growth.R
index 0fe04c5c..8d67ce77 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -220,6 +220,7 @@ cleangrowth <- function(subjid,
     'Swapped-Measurements',
     'Exclude',
     'Missing',
+    'Not cleaned',
     'Exclude-Temporary-Extraneous-Same-Day',
     'Exclude-Carried-Forward',
     # added CF exclusions
@@ -680,6 +681,8 @@ cleangrowth <- function(subjid,
     )),
     levels = exclude.levels,
     ordered = TRUE)]
+    # also mark certain measurements to not consider
+    data.all[param == "HEADCM" & agedays > (3*365.25), exclude := "Not cleaned"]
 
     # define field names needed by helper functions
     ewma.fields <- c('ewma.all', 'ewma.before', 'ewma.after')

From 62274d94ee76fc6c33e27b09c4ea75fa4c20dd11 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Fri, 18 Aug 2023 08:40:06 -0400
Subject: [PATCH 073/100] Fix some criteria for NNTE

---
 R/growth.R | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 8d67ce77..c0716479 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -526,7 +526,6 @@ cleangrowth <- function(subjid,
       # add age in months
       data.all[, agemonths := agedays/30.4375]
 
-      # TO ADD: REMOVE ADDED COLUMNS FROM DATA ALL
       potcorr <- data.all$param == "WEIGHTKG" &
         data.all$sd.orig < -2 &
         data.all$agemonths < 10
@@ -581,7 +580,7 @@ cleangrowth <- function(subjid,
       data.all[param == "HEIGHTCM" & agedays > 730 & cagedays <= 730,
                ccdc_cv := ccdc_cv + 0.7]
 
-      # create the corrected z scores -- STOP HERE
+      # create the corrected z scores
       # use read anthro, but pass in different arguments
       # pass in the corrected height
       data.all[, sd.c_cdc :=
@@ -821,7 +820,7 @@ cleangrowth <- function(subjid,
     # safety check: treat observations where tbc.sd cannot be calculated as missing
     data.all[is.na(tbc.sd), exclude := 'Missing']
 
-    # 4: identify subset that don't need to be cleaned ----
+    # 4: identify subset that don't need to be cleaned (nnte) ----
 
     # identify those meeting all subjects meeting these criteria as "no need
     # to ewma"
@@ -842,18 +841,23 @@ cleangrowth <- function(subjid,
         by = c("subjid", "param")]
     data.all[, no_outliers := no_outliers == 1]
     # all max - min tbd.sc < 2.5
-    data.all[, no_bigdiff := abs(max(tbc.sd) - min(tbc.sd)) < 2.5 | is.na(tbc.sd),
+    data.all[, no_bigdiff :=
+               rep((abs(max(tbc.sd, na.rm = T) - min(tbc.sd, na.rm = T)) < 2.5),
+                   .N),
         by = c("subjid", "param")]
     # the previous value can't be too far from the current value
     data.all[, seq_win := sequence(.N), by = c("subjid", "param")]
-    data.all[, nottoofar := abs(tbc.sd - dplyr::lag(tbc.sd)) < 1 | seq_win == 1,
+    data.all[, nottoofar :=
+               (abs(tbc.sd - dplyr::lag(tbc.sd)) < 1 | seq_win == 1) &
+               (abs(tbc.sd - dplyr::lead(tbc.sd)) < 1 | seq_win == .N),
         by = c("subjid", "param")]
     data.all[is.na(nottoofar),  nottoofar :=  TRUE]
 
     # cumulative: no need to ewma -- needs to work for all within a subject &
     # parameter
     data.all[, nnte := no_sde & no_dup_val & no_outliers & no_bigdiff & nottoofar]
-    # NOTE: to come back -- how not to calculate with parameter
+    # NNTE can be calculated by parameter -- but it's occasionally easier for
+    # calculations to require all parameters to be nnte
     data.all[, nnte_full := sum(nnte) == .N, by = c("subjid", "param")]
     data.all[, nnte := sum(nnte) == .N, by = c("subjid")]
 

From a62bb0e590db8b54771d0eca4a7059456ca45cb6 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Fri, 18 Aug 2023 11:34:04 -0400
Subject: [PATCH 074/100] Update raw differences step based on updates

---
 R/infants_clean.R | 39 +++++++++++++++++++++++++++++++++------
 1 file changed, 33 insertions(+), 6 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 08aec30d..47300ab5 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1368,14 +1368,22 @@ cleanbatch_infants <- function(data.df,
       if (df$param[1] == "HEIGHTCM"){
         # 17B
         df[, tanner.months := 6+12*(round(.5*(agedays + dplyr::lead(agedays))/365.25))]
-
-        # 17C
+        # set tanner to missing for smaller values
+        df[(agedays/30.4375) < 30, tanner.months := NA]
 
         # merge with the tanner info
         setkey(df, sex, tanner.months)
         df <- tanner.ht.vel.rev[df]
 
-        # 17D
+        # 17C
+
+        # a
+        df[max.ht.vel < 2.54, max.ht.vel := 2.54]
+        df[d_agedays > 2*30.4375 & max.ht.vel < 2*2.54, max.ht.vel := 2*2.54]
+        df[d_agedays > .5*365.25 & max.ht.vel < 4*2.54, max.ht.vel := 4*2.54]
+        df[d_agedays > 365.25 & max.ht.vel < 8*2.54, max.ht.vel := 8*2.54]
+
+        # b
         df[d_agedays < 365.25, mindiff := .5*min.ht.vel*(d_agedays/365.25)-3 ]
         df[d_agedays > 365.25, mindiff := .5*min.ht.vel-3 ]
         df[d_agedays < 365.25,
@@ -1383,6 +1391,13 @@ cleanbatch_infants <- function(data.df,
         df[d_agedays > 365.25,
            maxdiff := 2*min.ht.vel*(d_agedays/365.25)^0.33 + 5.5 ]
 
+        # 17D
+        # generate the who age group variable
+        df[, whoagegrp.ht := round(agedays/30.4375)]
+        df[whoagegrp.ht > 24 | dplyr::lead(whoagegrp.ht) > 24,
+           whoagegrp.ht := NA]
+
+
         # 17E
         df[d_agedays >= 20 & d_agedays < 46, whoinc.age.ht := 1]
         df[d_agedays >= 46 & d_agedays < 76, whoinc.age.ht := 2]
@@ -1431,6 +1446,9 @@ cleanbatch_infants <- function(data.df,
         df[d_agedays < 9*30.4375 | is.na(min.ht.vel), maxdiff := who_maxdiff_ht]
         # otherwise, fill in
         df[is.na(mindiff), mindiff := 3]
+        # for birth measurements, add allowance of 1.5cm
+        df[agedays == 0, mindiff := mindiff - 1.5]
+        df[agedays == 0, maxdiff := maxdiff + 1.5]
 
         # 17I
         # sort df since it got reordered with keys
@@ -1438,6 +1456,12 @@ cleanbatch_infants <- function(data.df,
         df[, mindiff_prev := dplyr::lag(mindiff)]
         df[, maxdiff_prev := dplyr::lag(maxdiff)]
       } else { # head circumference
+        # 17D
+        # generate the who age group variable
+        df[, whoagegrp.ht := round(agedays/30.4375)]
+        df[whoagegrp.ht > 24 | dplyr::lead(whoagegrp.ht) > 24,
+           whoagegrp.ht := NA]
+
         # 17J
         df[d_agedays >= 46 & d_agedays < 76, whoinc.age.hc := 2]
         df[d_agedays >= 76 & d_agedays < 107, whoinc.age.hc := 3]
@@ -1475,14 +1499,17 @@ cleanbatch_infants <- function(data.df,
         df[d_agedays < 9*30.4375, who_mindiff_hc := who_mindiff_hc*.5-1.5]
         df[d_agedays < 9*30.4375, who_maxdiff_hc := who_maxdiff_hc*2+1.5]
 
-        # 17H
+        # 17M
         # use who
         df[, mindiff := who_mindiff_hc]
         df[, maxdiff := who_maxdiff_hc]
         # otherwise, fill in
         df[is.na(mindiff), mindiff := -1.5]
+        # for birth measurements, add allowance of .5cm
+        df[agedays == 0, mindiff := mindiff - .5]
+        df[agedays == 0, maxdiff := maxdiff + .5]
 
-        # 17I
+        # 17N
         # sort df since it got reordered with keys
         df <- df[order(agedays),]
         df[, mindiff_prev := dplyr::lag(mindiff)]
@@ -1531,7 +1558,7 @@ cleanbatch_infants <- function(data.df,
         df[pair & abs(dewma.after) > dplyr::lead(abs(dewma.before)),
            aft.g.aftm1 := TRUE]
 
-        # R
+        # Q
         df[, val_excl := exclude]
         df[diff_prev < mindiff_prev & bef.g.aftm1, val_excl := "Exclude-Min-diff"]
         df[diff_next < mindiff & aft.g.aftm1, val_excl := "Exclude-Min-diff"]

From 0522454d547b10b32889c7f063ff8f14ad1f8ef4 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Fri, 18 Aug 2023 12:14:58 -0400
Subject: [PATCH 075/100] Add absolute for 1/2 measures step

---
 R/infants_clean.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 47300ab5..de479213 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1685,7 +1685,7 @@ cleanbatch_infants <- function(data.df,
       max_ind <- if (!all(is.na(df$comp_diff))){
         which.max(df$comp_diff)
       } else {
-        which.max(df$tbc.sd)
+        which.max(abs(df$tbc.sd))
       }
 
       # 19F/G: which exclusion

From 62bc3cdff2ded443fd046008d385b185e161b256 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Mon, 21 Aug 2023 10:39:05 -0400
Subject: [PATCH 076/100] Fix typo for BIVs

---
 R/infants_clean.R   | 2 +-
 R/infants_support.R | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index de479213..606b6452 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -225,7 +225,7 @@ cleanbatch_infants <- function(data.df,
     ))
 
   # add age in years
-  data.df[, ageyears := agedays *365.25]
+  data.df[, ageyears := agedays/365.25]
 
   # calculate the valid step
   valid_set <- valid(data.df, include.temporary.extraneous = TRUE) &
diff --git a/R/infants_support.R b/R/infants_support.R
index 2d64a59b..1191a9bf 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -152,9 +152,9 @@ calc_oob_evil_twins <- function(df){
 calc_and_recenter_z_scores <- function(df, cn, ref.data.path){
   # for infants, use z and who
   measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE,
-                                  infants = T)
+                                  infants = TRUE)
   measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = FALSE,
-                                      infants = T)
+                                      infants = TRUE)
 
   # calculate "standard deviation" scores
   df[, cn.orig_cdc := measurement.to.z(param, agedays, sex, get(cn), TRUE)]

From cd848e8dbb581024460164357150722c46042750 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 09:59:29 -0400
Subject: [PATCH 077/100] Fix WHO merging in step 17

---
 R/infants_clean.R | 50 ++++++++++++++++++++++++++++++-----------------
 1 file changed, 32 insertions(+), 18 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 606b6452..066e3501 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -1406,29 +1406,34 @@ cleanbatch_infants <- function(data.df,
         df[d_agedays >= 153 & d_agedays < 199, whoinc.age.ht := 6]
 
         # update the edge intervals
-        df[d_agedays < 20, d_agedays := 19]
-        df[d_agedays == 19, whoinc.age.ht := 1]
+        df[d_agedays < 20, whoinc.age.ht := 1]
         df[d_agedays > 199, d_agedays := 200]
         df[d_agedays == 200, whoinc.age.ht := 6]
 
         # 17F
         # merge with WHO
         # add the column name we want to grab
-        for (i in unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht)])){
+        m_who_ht_vel <- merge(df, who.ht.vel, by = c("sex", "whoagegrp.ht"),
+                               all.x = TRUE, sort = FALSE)
+        for (i in unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht) &
+                                          !is.na(df$whoagegrp.ht)])){
+          sub_m_who_ht_vel <- m_who_ht_vel[whoinc.age.ht == i,]
+
           cn <- paste0("whoinc.", i, ".ht")
-          df[, who_mindiff_ht :=
-               as.numeric(who.ht.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
+          df[whoinc.age.ht == i,
+             who_mindiff_ht := as.numeric(sub_m_who_ht_vel[, get(cn)])]
           cn <- paste0("max.whoinc.", i, ".ht")
-          df[, who_maxdiff_ht :=
-               as.numeric(who.ht.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
+          df[whoinc.age.ht == i,
+             who_maxdiff_ht := as.numeric(sub_m_who_ht_vel[, get(cn)])]
         }
         # if there are none, preallocate for ease
-        if (length(unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht)])) < 1){
+        if (length(unique(df$whoinc.age.ht[!is.na(df$whoinc.age.ht) &
+                                           !is.na(df$whoagegrp.ht)])) < 1){
           df[, who_mindiff_ht := NA_real_]
           df[, who_maxdiff_ht := NA_real_]
         }
         df[, who_mindiff_ht := as.numeric(who_mindiff_ht)]
-        df[, who_mindiff_ht := as.numeric(who_maxdiff_ht)]
+        df[, who_maxdiff_ht := as.numeric(who_maxdiff_ht)]
 
         # 17G
         df[d_agedays < whoinc.age.ht*30.4375, who_mindiff_ht :=
@@ -1442,8 +1447,10 @@ cleanbatch_infants <- function(data.df,
         # 17H
         # tanner is implicit
         # greater than 9 months, use tanner if available, otherwise who
-        df[d_agedays < 9*30.4375 | is.na(min.ht.vel), mindiff := who_mindiff_ht]
-        df[d_agedays < 9*30.4375 | is.na(min.ht.vel), maxdiff := who_maxdiff_ht]
+        df[(d_agedays < 9*30.4375 | is.na(min.ht.vel)) &
+             !is.na(who_mindiff_ht), mindiff := who_mindiff_ht]
+        df[(d_agedays < 9*30.4375 | is.na(min.ht.vel)) &
+             !is.na(who_mindiff_ht), maxdiff := who_maxdiff_ht]
         # otherwise, fill in
         df[is.na(mindiff), mindiff := 3]
         # for birth measurements, add allowance of 1.5cm
@@ -1474,21 +1481,28 @@ cleanbatch_infants <- function(data.df,
         # 17K
         # merge with WHO
         # add the column name we want to grab
-        for (i in unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc)])){
+
+        m_who_hc_vel <- merge(df, who.hc.vel, by = c("sex", "whoagegrp.ht"),
+                              all.x = TRUE, sort = FALSE)
+        for (i in unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc) &
+                                          !is.na(df$whoagegrp.hc)])){
+          sub_m_who_hc_vel <- m_who_hc_vel[whoinc.age.ht == i,]
+
           cn <- paste0("whoinc.", i, ".ht")
-          df[, who_mindiff_hc :=
-               as.numeric(who.hc.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
+          df[whoinc.age.hc == i,
+             who_mindiff_hc := as.numeric(sub_m_who_ht_vel[, get(cn)])]
           cn <- paste0("max.whoinc.", i, ".ht")
-          df[, who_maxdiff_hc :=
-               as.numeric(who.hc.vel[whoagegrp.ht == i & sex == df$sex[1], get(cn)])]
+          df[whoinc.age.hc == i,
+             who_maxdiff_hc := as.numeric(sub_m_who_ht_vel[, get(cn)])]
         }
         # if there are none, preallocate for ease
-        if (length(unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc)])) < 1){
+        if (length(unique(df$whoinc.age.hc[!is.na(df$whoinc.age.hc) &
+                                           !is.na(df$whoagegrp.hc)])) < 1){
           df[, who_mindiff_hc := NA_real_]
           df[, who_maxdiff_hc := NA_real_]
         }
         df[, who_mindiff_hc := as.numeric(who_mindiff_hc)]
-        df[, who_mindiff_hc := as.numeric(who_maxdiff_hc)]
+        df[, who_maxdiff_hc := as.numeric(who_maxdiff_hc)]
 
         # 17L
         df[d_agedays < whoinc.age.hc*30.4375, who_mindiff_hc :=

From 9747e2b071a21d743ae43cd614483635929ad86a Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 10:07:30 -0400
Subject: [PATCH 078/100] Rename Evil Twins

---
 R/growth.R        | 2 +-
 R/infants_clean.R | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index c0716479..144a8844 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -276,7 +276,7 @@ cleangrowth <- function(subjid,
     #new
     "Exclude-Absolute-BIV",
     "Exclude-Standardized-BIV",
-    "Exclude-Evil-Twin",
+    "Exclude-Evil-Twins",
     "Exclude-EWMA1-Extreme"
   )
 
diff --git a/R/infants_clean.R b/R/infants_clean.R
index 066e3501..e61d97a4 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -313,7 +313,7 @@ cleanbatch_infants <- function(data.df,
   # evil twins ----
   # Evil Twins: An important weakness in the original pediatric growthcleanr algorithm was that it often failed to identify two or more implausible measurements that occurred next to each other, even if they were extremely deviant from a child’s other measurements. This step is now added to identify these multiple extreme values, although it also identifies some single values.
 
-  exc_nam <- "Exclude-Evil-Twin"
+  exc_nam <- "Exclude-Evil-Twins"
 
   # create the valid set
   # we only running carried forwards on valid values, non NNTE values,

From dd5219f8e57adb7532d10f175cff8ecdf7955809 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 10:55:39 -0400
Subject: [PATCH 079/100] Update overwriting for Evil Twins

---
 R/infants_clean.R | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index e61d97a4..d480dc17 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -338,11 +338,8 @@ cleanbatch_infants <- function(data.df,
         Sys.time()
       ))
 
-    # remove the valid set from the beginning
-    df <- copy(data.df[valid_set,])
-
     # start to evaluate and remove evil twins
-    data.df[, exclude := (function(df) {
+    data.df[valid_set, exclude := (function(df) {
       # where we are updating results
       upd.df <- copy(df)
       upd.df <- calc_oob_evil_twins(upd.df)
@@ -872,8 +869,6 @@ cleanbatch_infants <- function(data.df,
   # work in an a function order to encapsulate and not keep all the additional
   # columns
 
-  # FUTURE UPDATE: I think I have to make sure I replace the temp-extraneous codes with includes after the first time, they're alone now
-
   # calculate plus/minus values
 
   # order just for ease later

From 8f8df59d2719c6bf76159ddcdae314b57ec747d1 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 12:36:04 -0400
Subject: [PATCH 080/100] Fix smoothing overwriting WHO scores

---
 R/growth.R        | 42 ++++++++++++++++++------------------------
 R/infants_clean.R |  7 +++++--
 2 files changed, 23 insertions(+), 26 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 144a8844..1e131bf2 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -456,15 +456,13 @@ cleangrowth <- function(subjid,
     if(infants){
       if (!quietly)
         cat(sprintf("[%s] Calculating z-scores...\n", Sys.time()))
+      # removing z calculations, as they are not used
       # for infants, use z and who
       measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE,
                                       infants = T)
       measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = FALSE,
                                           infants = T)
 
-      data.all[, z.orig_cdc := measurement.to.z(param, agedays, sex, v)]
-      data.all[, z.orig_who := measurement.to.z_who(param, agedays, sex, v)]
-
       # calculate "standard deviation" scores
       if (!quietly)
         cat(sprintf("[%s] Calculating SD-scores...\n", Sys.time()))
@@ -473,39 +471,35 @@ cleangrowth <- function(subjid,
 
       # smooth z-scores/SD scores between ages 1 - 3yo using weighted scores
       # older uses cdc, younger uses who
-      who_weight <- 4 - (data.all$agedays/365.25)
-      cdc_weight <- (data.all$agedays/365.25) - 2
+      data.all$ageyears <- data.all$agedays/365.25
 
-      smooth_val <- data.all$agedays/365.25 >= 2 &
-        data.all$agedays/365.25 <= 4 &
+      who_weight <- 4 - (data.all$ageyears)
+      cdc_weight <- (data.all$ageyears) - 2
+
+      smooth_val <- data.all$ageyears >= 2 &
+        data.all$ageyears <= 4 &
         data.all$param != "HEADCM"
       data.all[smooth_val,
-               z.orig := (z.orig_cdc[smooth_val]*cdc_weight[smooth_val] +
-                            z.orig_who[smooth_val]*who_weight[smooth_val])/2]
-      data.all[smooth_val,
-               sd.orig := (sd.orig_cdc[smooth_val]*cdc_weight[smooth_val] +
-                             sd.orig_who[smooth_val]*who_weight[smooth_val])/2]
+               sd.orig := (data.all$sd.orig_cdc[smooth_val]*cdc_weight[smooth_val] +
+                             data.all$sd.orig_who[smooth_val]*who_weight[smooth_val])/2]
 
       # otherwise use WHO and CDC for older and younger, respectively
       who_val <- data.all$param == "HEADCM" |
-        data.all$agedays/365.25 < 2
-      data.all[who_val | (smooth_val & is.na(data.all$z.orig_cdc)),
-               z.orig := data.all$z.orig_who[who_val | (smooth_val & is.na(data.all$z.orig_cdc))]]
-      data.all[who_val | (smooth_val & is.na(data.all$sd.orig_cdc)),
-               sd.orig := data.all$sd.orig_who[who_val  | (smooth_val & is.na(data.all$sd.orig_cdc))]]
+        data.all$ageyears < 2
 
-      cdc_val <- data.all$param != "HEADCM" |
-        data.all$agedays/365.25 > 4
-      data.all[cdc_val  | (smooth_val & is.na(data.all$z.orig_who)),
-               z.orig := data.all$z.orig_cdc[cdc_val | (smooth_val & is.na(data.all$z.orig_who))]]
-      data.all[cdc_val | (smooth_val & is.na(data.all$sd.orig_who)),
-               sd.orig := data.all$sd.orig_cdc[cdc_val | (smooth_val & is.na(data.all$sd.orig_who))]]
+      data.all[(who_val & !smooth_val) | (smooth_val & is.na(data.all$sd.orig_cdc)),
+               sd.orig := data.all$sd.orig_who[(who_val & !smooth_val)  | (smooth_val & is.na(data.all$sd.orig_cdc))]]
+
+      cdc_val <- data.all$param != "HEADCM" &
+        data.all$ageyears > 4
+
+      data.all[(cdc_val & !smooth_val) | (smooth_val & is.na(data.all$sd.orig_who)),
+               sd.orig := data.all$sd.orig_cdc[(cdc_val & !smooth_val) | (smooth_val & is.na(data.all$sd.orig_who))]]
 
       # NOTE: SD SCORES IN CODE ARE Z IN INFANT DOCS -- USE sd.orig ONLY
 
       # keep the original, uncorrected, unrecentered zscores
       data.all[,sd.orig_uncorr := sd.orig]
-      data.all[,z.orig_uncorr := z.orig]
 
       # NOTE: MAY WANT TO SUBSET HERE
 
diff --git a/R/infants_clean.R b/R/infants_clean.R
index d480dc17..acf8c232 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -280,7 +280,7 @@ cleanbatch_infants <- function(data.df,
   # identify z cutoff
   # ***Note, using unrecentered values***
   #  *For weight only do after birth
-  data.df[valid_set & param == "WEIGHTKG" & sd.orig_uncorr < - 25 &
+  data.df[valid_set & param == "WEIGHTKG" & sd.orig_uncorr < -25 &
             ageyears < 1,
           exclude := exc_nam]
   data.df[valid_set & param == "WEIGHTKG" & sd.orig_uncorr < -15 &
@@ -290,7 +290,7 @@ cleanbatch_infants <- function(data.df,
           exclude := exc_nam]
 
   # *Max z-score for height based on analysis of CHOP data because 15/25 too loose for upper limits
-  data.df[valid_set & param == "HEIGHTCM" & sd.orig_uncorr < - 25 &
+  data.df[valid_set & param == "HEIGHTCM" & sd.orig_uncorr < -25 &
             ageyears < 1,
           exclude := exc_nam]
   data.df[valid_set & param == "HEIGHTCM" & sd.orig_uncorr < -15 &
@@ -299,6 +299,9 @@ cleanbatch_infants <- function(data.df,
   data.df[valid_set & param == "HEIGHTCM" & sd.orig_uncorr > 8,
           exclude := exc_nam]
 
+  print("Standard BIV")
+  print(data.df[param == "HEIGHTCM",c(ageyears, param, sd.orig_uncorr)])
+
   # head circumference
   data.df[valid_set & param == "HEADCM" & sd.orig_uncorr < -15,
           exclude := exc_nam]

From 226ebf5accf8da3e457ca547b4ca02592e723677 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 13:21:55 -0400
Subject: [PATCH 081/100] Fix ewma testing for including 3 rows

---
 R/infants_clean.R | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index acf8c232..90f13617 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -299,9 +299,6 @@ cleanbatch_infants <- function(data.df,
   data.df[valid_set & param == "HEIGHTCM" & sd.orig_uncorr > 8,
           exclude := exc_nam]
 
-  print("Standard BIV")
-  print(data.df[param == "HEIGHTCM",c(ageyears, param, sd.orig_uncorr)])
-
   # head circumference
   data.df[valid_set & param == "HEADCM" & sd.orig_uncorr < -15,
           exclude := exc_nam]
@@ -893,7 +890,7 @@ cleanbatch_infants <- function(data.df,
 
     #15c: exclude agedays = 0 for ht, hc
     #15d: calculate ewma -- run within a subject/parameter
-    df <- df[(param != "HEIGHTCM" | param != "HEADCM") & agedays != 0,
+    df[(param != "HEIGHTCM" | param != "HEADCM") & agedays != 0,
              exclude := (function(df_sub) {
       # save initial exclusions to keep track
       ind_all <- copy(df_sub$index)
@@ -901,7 +898,7 @@ cleanbatch_infants <- function(data.df,
 
       testing <- TRUE
 
-      while (testing & nrow(df_sub) > 3){
+      while (testing & nrow(df_sub) >= 3){
         df_sub[, (ewma.fields) := as.double(NaN)]
 
         # first, calculate which exponent we want to put through (pass a different
@@ -1162,7 +1159,7 @@ cleanbatch_infants <- function(data.df,
 
         testing <- TRUE
 
-        while (testing & nrow(df_sub) > 3){
+        while (testing & nrow(df_sub) >= 3){
           df_sub[, (ewma.fields) := as.double(NaN)]
 
           # first, calculate which exponent we want to put through (pass a different

From 694ab8c0cbe8878384a7e1bbc7f0233863f473a0 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 13:29:18 -0400
Subject: [PATCH 082/100] Fix EWMA exclusion codes

---
 R/growth.R        |  6 +++---
 R/infants_clean.R | 12 ++++++------
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 1e131bf2..a0aa9e22 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -239,9 +239,9 @@ cleangrowth <- function(subjid,
     'Exclude-SDE-One-Day',
     "Exclude-EWMA2-middle",
     "Exclude-EWMA2-birth-WT",
-    "Exclude-EWMA-2-birth-WT-ext",
-    "Exclude-EWMA-2-first",
-    "Exclude-EWMA-2-first-ext",
+    "Exclude-EWMA2-birth-WT-ext",
+    "Exclude-EWMA2-first",
+    "Exclude-EWMA2-first-ext",
     "Exclude-EWMA2-last",
     "Exclude-EWMA2-last-high",
     "Exclude-EWMA2-last-ext",
diff --git a/R/infants_clean.R b/R/infants_clean.R
index 90f13617..a756b1f4 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -992,31 +992,31 @@ cleanbatch_infants <- function(data.df,
         df_sub[agedays == 0 & agedays[2] >= 365.25 &
                  dewma.all > 4 & (c.dewma.all > 4 | is.na(c.dewma.all)) &
                  addcrithigh
-               , exclude := "Exclude-EWMA-2-birth-WT-ext"]
+               , exclude := "Exclude-EWMA2-birth-WT-ext"]
         df_sub[agedays == 0 & agedays[2] >= 365.25 &
                  dewma.all < -4 & (c.dewma.all < -4 | is.na(c.dewma.all)) &
                  addcritlow
-               , exclude := "Exclude-EWMA-2-birth-WT-ext"]
+               , exclude := "Exclude-EWMA2-birth-WT-ext"]
 
         #d
         df_sub[rowind == 1 & agedays[1] != 0 & agedays[2]-agedays[1] < 365.25 &
                  dewma.all > 2 & (c.dewma.all > 2 | is.na(c.dewma.all)) &
                  addcrithigh
-               , exclude := "Exclude-EWMA-2-first"]
+               , exclude := "Exclude-EWMA2-first"]
         df_sub[rowind == 1 & agedays[1] != 0 & agedays[2]-agedays[1] < 365.25 &
                  dewma.all < -2 & (c.dewma.all < -2 | is.na(c.dewma.all)) &
                  addcritlow
-               , exclude := "Exclude-EWMA-2-first"]
+               , exclude := "Exclude-EWMA2-first"]
 
         # e
         df_sub[rowind == 1 & agedays[1] != 0 & agedays[2]-agedays[1] >= 365.25 &
                  dewma.all > 3 & (c.dewma.all > 3 | is.na(c.dewma.all)) &
                  addcrithigh
-               , exclude := "Exclude-EWMA-2-first-ext"]
+               , exclude := "Exclude-EWMA2-first-ext"]
         df_sub[rowind == 1 & agedays[1] != 0 & agedays[2]-agedays[1] >= 365.25 &
                  dewma.all < -3 & (c.dewma.all < -3 | is.na(c.dewma.all)) &
                  addcritlow
-               , exclude := "Exclude-EWMA-2-first-ext"]
+               , exclude := "Exclude-EWMA2-first-ext"]
 
         # f
         df_sub[rowind == nrow(df_sub) &

From 095d5bee59ab4ef704febb06976ddd83e28f2f54 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 16:02:58 -0400
Subject: [PATCH 083/100] Adjust OOB measurements to include 2

---
 R/growth.R        | 4 ++--
 R/infants_clean.R | 7 +++++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index a0aa9e22..b428cb51 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -228,10 +228,8 @@ cleangrowth <- function(subjid,
     "Exclude-1-CF-deltaZ-<0.1-wholehalfimp",
     "Exclude-Teen-2-plus-CF-deltaZ-<0.05",
     "Exclude-Teen-2-plus-CF-deltaZ-<0.1-wholehalfimp",
-    'Exclude-SD-Cutoff',
     'Exclude-EWMA-Extreme',
     'Exclude-EWMA-Extreme-Pair',
-    'Exclude-Extraneous-Same-Day',
     'Exclude-SDE-Identical',
     'Exclude-SDE-All-Exclude',
     'Exclude-SDE-All-Extreme',
@@ -257,6 +255,8 @@ cleangrowth <- function(subjid,
 
     # old
 
+    'Exclude-Extraneous-Same-Day',
+    'Exclude-SD-Cutoff',
     'Exclude-EWMA-8',
     'Exclude-EWMA-9',
     'Exclude-EWMA-10',
diff --git a/R/infants_clean.R b/R/infants_clean.R
index a756b1f4..78ae7f00 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -346,7 +346,7 @@ cleanbatch_infants <- function(data.df,
       # count the amount of oobs for each subject/param and distribute it out
       upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)]
 
-      any_oob <- any(upd.df$sum_oob > 2)
+      any_oob <- any(upd.df$sum_oob >= 2)
       # while there are multiple oob, we want to remove
       while (any_oob){
 
@@ -367,7 +367,8 @@ cleanbatch_infants <- function(data.df,
         upd.df <- calc_oob_evil_twins(df[valid(df),])
         upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)]
 
-        any_oob <- any(upd.df$sum_oob > 2)
+        any_oob <- any(upd.df$sum_oob >= 2)
+        print(max(upd.df$sum_oob))
 
       }
 
@@ -1102,6 +1103,8 @@ cleanbatch_infants <- function(data.df,
         } else {
           testing <- FALSE
         }
+        print(testing)
+        print(exclude_all)
       }
 
       return(exclude_all)

From 11a4a37be5f7c487b00723857e6927a721ed91de Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 16:07:55 -0400
Subject: [PATCH 084/100] Overwrite exclusion for 0 -- should now be BIV

---
 R/infants_clean.R | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 78ae7f00..5b6eafcc 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -305,6 +305,10 @@ cleanbatch_infants <- function(data.df,
   data.df[valid_set & param == "HEADCM" & sd.orig_uncorr > 15,
           exclude := exc_nam]
 
+  # then, do some explicit overwriting for the 0 case (otherwise will be
+  # set as missing)
+  data.df[v == 0, exclude := exc_nam]
+
 
   # 9d.  Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous)
   data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']

From 527d5ee35f30b8caa83178910defa6216eaa58d4 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 16:08:59 -0400
Subject: [PATCH 085/100] Add some more wrapping for infants extension

---
 R/growth.R | 91 +++++++++++++++++++++++++++---------------------------
 1 file changed, 46 insertions(+), 45 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index b428cb51..bc05d35b 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -814,52 +814,53 @@ cleangrowth <- function(subjid,
     # safety check: treat observations where tbc.sd cannot be calculated as missing
     data.all[is.na(tbc.sd), exclude := 'Missing']
 
-    # 4: identify subset that don't need to be cleaned (nnte) ----
-
-    # identify those meeting all subjects meeting these criteria as "no need
-    # to ewma"
-    # does the subject have sdes
-
-    # keep the original column names -- we're adding a ton of columns that we
-    # want to filter out after correction
-    orig_colnames <- copy(colnames(data.all))
-    # no SDEs
-    data.all[, sum_sde := .N, by = c("subjid", "param", "agedays")]
-    data.all[, no_sde := sum_sde == 1]
-    # does the subject have identical values
-    data.all[, sum_val := .N, by = c("subjid", "param", "v")]
-    data.all[, no_dup_val := sum_val == 1]
-    # all tbc.sd are within [-3,3] -- 0 is false
-    data.all[, no_outliers := sum((tbc.sd > -3 & tbc.sd < 3) |
-                               is.na(tbc.sd)) == (.N),
-        by = c("subjid", "param")]
-    data.all[, no_outliers := no_outliers == 1]
-    # all max - min tbd.sc < 2.5
-    data.all[, no_bigdiff :=
-               rep((abs(max(tbc.sd, na.rm = T) - min(tbc.sd, na.rm = T)) < 2.5),
-                   .N),
-        by = c("subjid", "param")]
-    # the previous value can't be too far from the current value
-    data.all[, seq_win := sequence(.N), by = c("subjid", "param")]
-    data.all[, nottoofar :=
-               (abs(tbc.sd - dplyr::lag(tbc.sd)) < 1 | seq_win == 1) &
-               (abs(tbc.sd - dplyr::lead(tbc.sd)) < 1 | seq_win == .N),
-        by = c("subjid", "param")]
-    data.all[is.na(nottoofar),  nottoofar :=  TRUE]
-
-    # cumulative: no need to ewma -- needs to work for all within a subject &
-    # parameter
-    data.all[, nnte := no_sde & no_dup_val & no_outliers & no_bigdiff & nottoofar]
-    # NNTE can be calculated by parameter -- but it's occasionally easier for
-    # calculations to require all parameters to be nnte
-    data.all[, nnte_full := sum(nnte) == .N, by = c("subjid", "param")]
-    data.all[, nnte := sum(nnte) == .N, by = c("subjid")]
-
-
-    # remove many added columns -- except for nnte
-    orig_colnames <- c(orig_colnames, "nnte", "nnte_full")
-    data.all <- data.all[, ..orig_colnames]
+    if (infants){
+      # 4: identify subset that don't need to be cleaned (nnte) ----
+
+      # identify those meeting all subjects meeting these criteria as "no need
+      # to ewma"
+      # does the subject have sdes
 
+      # keep the original column names -- we're adding a ton of columns that we
+      # want to filter out after correction
+      orig_colnames <- copy(colnames(data.all))
+      # no SDEs
+      data.all[, sum_sde := .N, by = c("subjid", "param", "agedays")]
+      data.all[, no_sde := sum_sde == 1]
+      # does the subject have identical values
+      data.all[, sum_val := .N, by = c("subjid", "param", "v")]
+      data.all[, no_dup_val := sum_val == 1]
+      # all tbc.sd are within [-3,3] -- 0 is false
+      data.all[, no_outliers := sum((tbc.sd > -3 & tbc.sd < 3) |
+                                      is.na(tbc.sd)) == (.N),
+               by = c("subjid", "param")]
+      data.all[, no_outliers := no_outliers == 1]
+      # all max - min tbd.sc < 2.5
+      data.all[, no_bigdiff :=
+                 rep((abs(max(tbc.sd, na.rm = T) - min(tbc.sd, na.rm = T)) < 2.5),
+                     .N),
+               by = c("subjid", "param")]
+      # the previous value can't be too far from the current value
+      data.all[, seq_win := sequence(.N), by = c("subjid", "param")]
+      data.all[, nottoofar :=
+                 (abs(tbc.sd - dplyr::lag(tbc.sd)) < 1 | seq_win == 1) &
+                 (abs(tbc.sd - dplyr::lead(tbc.sd)) < 1 | seq_win == .N),
+               by = c("subjid", "param")]
+      data.all[is.na(nottoofar),  nottoofar :=  TRUE]
+
+      # cumulative: no need to ewma -- needs to work for all within a subject &
+      # parameter
+      data.all[, nnte := no_sde & no_dup_val & no_outliers & no_bigdiff & nottoofar]
+      # NNTE can be calculated by parameter -- but it's occasionally easier for
+      # calculations to require all parameters to be nnte
+      data.all[, nnte_full := sum(nnte) == .N, by = c("subjid", "param")]
+      data.all[, nnte := sum(nnte) == .N, by = c("subjid")]
+
+
+      # remove many added columns -- except for nnte
+      orig_colnames <- c(orig_colnames, "nnte", "nnte_full")
+      data.all <- data.all[, ..orig_colnames]
+    }
     # pediatric: cleanbatch (most of steps) ----
 
     # NOTE: the rest of cleangrowth's steps are done through cleanbatch().

From 783cbc283fe790ab17c7a97a8b43160271776158 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Tue, 22 Aug 2023 16:19:43 -0400
Subject: [PATCH 086/100] Remove printing

---
 R/infants_clean.R | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index 5b6eafcc..17f57a97 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -372,7 +372,6 @@ cleanbatch_infants <- function(data.df,
         upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)]
 
         any_oob <- any(upd.df$sum_oob >= 2)
-        print(max(upd.df$sum_oob))
 
       }
 
@@ -1107,8 +1106,6 @@ cleanbatch_infants <- function(data.df,
         } else {
           testing <- FALSE
         }
-        print(testing)
-        print(exclude_all)
       }
 
       return(exclude_all)

From c8b5cce0a6393b991b2c49c92fed54d4e7b2acde Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Fri, 8 Sep 2023 09:35:26 -0400
Subject: [PATCH 087/100] Make sure that pediatric and infant algorithms go
 through different tracks

---
 R/growth.R          | 198 +++++++++++++++++++++++++++-----------------
 R/infants_clean.R   |   1 +
 R/infants_support.R |   4 +
 3 files changed, 129 insertions(+), 74 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index bc05d35b..0f0538f7 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -57,6 +57,13 @@
 #' @param sdrecentered.filename Name of file to save re-centered data to as CSV. Defaults to "", for which this
 #' data will not be saved. Useful for post-processing and debugging.
 #' @param include.carryforward Determines whether Carry-Forward values are kept in the output. Defaults to False.
+#' @param ewma.exp Exponent to use for weighting measurements in the
+#' exponentially weighted moving average calculations. Defaults to -1.5.
+#' This exponent should be negative in order to weight growth measurements
+#' closer to the measurement being evaluated more strongly. Exponents that are
+#' further from zero (e.g. -3) will increase the relative influence of
+#' measurements close in time to the measurement being evaluated compared to
+#' using the default exponent.
 #' @param ref.data.path Path to reference data. If not supplied, the year 2000
 #' Centers for Disease Control (CDC) reference data will be used.
 #' @param log.path Path to log file output when running in parallel (non-quiet mode). Default is NA. A new
@@ -141,6 +148,7 @@ cleangrowth <- function(subjid,
                         sdmedian.filename = "",
                         sdrecentered.filename = "",
                         include.carryforward = FALSE,
+                        ewma.exp = -1.5,
                         ref.data.path = "",
                         log.path = NA,
                         parallel = FALSE,
@@ -212,73 +220,107 @@ cleangrowth <- function(subjid,
 
   # constants for pediatric
   # enumerate the different exclusion levels
-  exclude.levels.peds <- c(
-    'Include',
-    'Unit-Error-High',
-    'Unit-Error-Low',
-    'Unit-Error-Possible',
-    'Swapped-Measurements',
-    'Exclude',
-    'Missing',
-    'Not cleaned',
-    'Exclude-Temporary-Extraneous-Same-Day',
-    'Exclude-Carried-Forward',
-    # added CF exclusions
-    "Exclude-1-CF-deltaZ-<0.05",
-    "Exclude-1-CF-deltaZ-<0.1-wholehalfimp",
-    "Exclude-Teen-2-plus-CF-deltaZ-<0.05",
-    "Exclude-Teen-2-plus-CF-deltaZ-<0.1-wholehalfimp",
-    'Exclude-EWMA-Extreme',
-    'Exclude-EWMA-Extreme-Pair',
-    'Exclude-SDE-Identical',
-    'Exclude-SDE-All-Exclude',
-    'Exclude-SDE-All-Extreme',
-    'Exclude-SDE-EWMA',
-    'Exclude-SDE-One-Day',
-    "Exclude-EWMA2-middle",
-    "Exclude-EWMA2-birth-WT",
-    "Exclude-EWMA2-birth-WT-ext",
-    "Exclude-EWMA2-first",
-    "Exclude-EWMA2-first-ext",
-    "Exclude-EWMA2-last",
-    "Exclude-EWMA2-last-high",
-    "Exclude-EWMA2-last-ext",
-    "Exclude-EWMA2-last-ext-high",
-    "Exclude-EWMA2-birth-HT-HC",
-    "Exclude-EWMA2-birth-HT-HC-ext",
-    "Exclude-Min-diff",
-    "Exclude-Max-diff",
-    "Exclude-2-meas->1-year",
-    "Exclude-2-meas-<1-year",
-    "Exclude-1-meas",
-    "Exclude-Error-load",
-
-    # old
-
-    'Exclude-Extraneous-Same-Day',
-    'Exclude-SD-Cutoff',
-    'Exclude-EWMA-8',
-    'Exclude-EWMA-9',
-    'Exclude-EWMA-10',
-    'Exclude-EWMA-11',
-    'Exclude-EWMA-12',
-    'Exclude-EWMA-13',
-    'Exclude-EWMA-14',
-    'Exclude-Min-Height-Change',
-    'Exclude-Max-Height-Change',
-    'Exclude-Pair-Delta-17',
-    'Exclude-Pair-Delta-18',
-    'Exclude-Pair-Delta-19',
-    'Exclude-Single-Outlier',
-    'Exclude-Too-Many-Errors',
-    'Exclude-Too-Many-Errors-Other-Parameter',
-
-    #new
-    "Exclude-Absolute-BIV",
-    "Exclude-Standardized-BIV",
-    "Exclude-Evil-Twins",
-    "Exclude-EWMA1-Extreme"
-  )
+  if (infants){
+    # different for infants
+    exclude.levels.peds <- c(
+      'Include',
+      'Unit-Error-High',
+      'Unit-Error-Low',
+      'Unit-Error-Possible',
+      'Swapped-Measurements',
+      'Exclude',
+      'Missing',
+      'Not cleaned',
+      'Exclude-Temporary-Extraneous-Same-Day',
+      'Exclude-Carried-Forward',
+      # added CF exclusions
+      "Exclude-1-CF-deltaZ-<0.05",
+      "Exclude-1-CF-deltaZ-<0.1-wholehalfimp",
+      "Exclude-Teen-2-plus-CF-deltaZ-<0.05",
+      "Exclude-Teen-2-plus-CF-deltaZ-<0.1-wholehalfimp",
+      'Exclude-EWMA-Extreme',
+      'Exclude-EWMA-Extreme-Pair',
+      'Exclude-SDE-Identical',
+      'Exclude-SDE-All-Exclude',
+      'Exclude-SDE-All-Extreme',
+      'Exclude-SDE-EWMA',
+      'Exclude-SDE-One-Day',
+      "Exclude-EWMA2-middle",
+      "Exclude-EWMA2-birth-WT",
+      "Exclude-EWMA2-birth-WT-ext",
+      "Exclude-EWMA2-first",
+      "Exclude-EWMA2-first-ext",
+      "Exclude-EWMA2-last",
+      "Exclude-EWMA2-last-high",
+      "Exclude-EWMA2-last-ext",
+      "Exclude-EWMA2-last-ext-high",
+      "Exclude-EWMA2-birth-HT-HC",
+      "Exclude-EWMA2-birth-HT-HC-ext",
+      "Exclude-Min-diff",
+      "Exclude-Max-diff",
+      "Exclude-2-meas->1-year",
+      "Exclude-2-meas-<1-year",
+      "Exclude-1-meas",
+      "Exclude-Error-load",
+
+      # old
+
+      'Exclude-Extraneous-Same-Day',
+      'Exclude-SD-Cutoff',
+      'Exclude-EWMA-8',
+      'Exclude-EWMA-9',
+      'Exclude-EWMA-10',
+      'Exclude-EWMA-11',
+      'Exclude-EWMA-12',
+      'Exclude-EWMA-13',
+      'Exclude-EWMA-14',
+      'Exclude-Min-Height-Change',
+      'Exclude-Max-Height-Change',
+      'Exclude-Pair-Delta-17',
+      'Exclude-Pair-Delta-18',
+      'Exclude-Pair-Delta-19',
+      'Exclude-Single-Outlier',
+      'Exclude-Too-Many-Errors',
+      'Exclude-Too-Many-Errors-Other-Parameter',
+
+      #new
+      "Exclude-Absolute-BIV",
+      "Exclude-Standardized-BIV",
+      "Exclude-Evil-Twins",
+      "Exclude-EWMA1-Extreme"
+    )
+  } else {
+    exclude.levels.peds <- c(
+      'Include',
+      'Unit-Error-High',
+      'Unit-Error-Low',
+      'Unit-Error-Possible',
+      'Swapped-Measurements',
+      'Exclude',
+      'Missing',
+      'Exclude-Temporary-Extraneous-Same-Day',
+      'Exclude-Carried-Forward',
+      'Exclude-SD-Cutoff',
+      'Exclude-EWMA-Extreme',
+      'Exclude-EWMA-Extreme-Pair',
+      'Exclude-Extraneous-Same-Day',
+      'Exclude-EWMA-8',
+      'Exclude-EWMA-9',
+      'Exclude-EWMA-10',
+      'Exclude-EWMA-11',
+      'Exclude-EWMA-12',
+      'Exclude-EWMA-13',
+      'Exclude-EWMA-14',
+      'Exclude-Min-Height-Change',
+      'Exclude-Max-Height-Change',
+      'Exclude-Pair-Delta-17',
+      'Exclude-Pair-Delta-18',
+      'Exclude-Pair-Delta-19',
+      'Exclude-Single-Outlier',
+      'Exclude-Too-Many-Errors',
+      'Exclude-Too-Many-Errors-Other-Parameter'
+    )
+  }
 
   exclude.levels.adult <- c(
     "Include",
@@ -323,12 +365,20 @@ cleangrowth <- function(subjid,
       if (is.na(num.batches)) {
         num.batches <- getDoParWorkers()
       }
-      # variables needed for parallel workers
-      var_for_par <- c("temporary_extraneous", "valid", "swap_parameters",
-                       "na_as_false", "ewma", "read_anthro", "as_matrix_delta",
-                       "sd_median", "temporary_extraneous_infants",
-                       "get_dop", "calc_oob_evil_twins",
-                       "calc_and_recenter_z_scores")
+      if (infants){
+        # variables needed for parallel workers
+        var_for_par <- c("temporary_extraneous", "valid", "swap_parameters",
+                         "na_as_false", "ewma", "read_anthro", "as_matrix_delta",
+                         "sd_median",
+
+                         "temporary_extraneous_infants",
+                         "get_dop", "calc_oob_evil_twins",
+                         "calc_and_recenter_z_scores")
+      } else {
+        var_for_par <- c("temporary_extraneous", "valid", "swap_parameters",
+                         "na_as_false", "ewma", "read_anthro", "as_matrix_delta",
+                         "sd_median")
+      }
 
       cl <- makeCluster(num.batches)
       clusterExport(cl = cl, varlist = var_for_par, envir = environment())
@@ -1095,7 +1145,7 @@ cleangrowth <- function(subjid,
 #'
 #' @param path Path to supplied reference anthro data. Defaults to package anthro tables.
 #' @param cdc.only Whether or not only CDC data should be used. Defaults to false.
-#' @param infants boolean on if beta infants version is being used. will be merged in to main algorithm.
+#' @param infants TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.
 #'
 #' @return Function for calculating BMI based on measurement, age in days, sex, and measurement value.
 #' @export
diff --git a/R/infants_clean.R b/R/infants_clean.R
index 17f57a97..c248b682 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -308,6 +308,7 @@ cleanbatch_infants <- function(data.df,
   # then, do some explicit overwriting for the 0 case (otherwise will be
   # set as missing)
   data.df[v == 0, exclude := exc_nam]
+  print(sum(data.df$v == 0))
 
 
   # 9d.  Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous)
diff --git a/R/infants_support.R b/R/infants_support.R
index 1191a9bf..57327a97 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -4,6 +4,8 @@
 
 # function that takes in a parameter name and returns the designated other
 # parameter (DOP)
+#' @keywords internal
+#' @noRd
 get_dop <- function(param_name){
   dop <- if (param_name == "WEIGHTKG"){
     "HEIGHTCM"
@@ -149,6 +151,8 @@ calc_oob_evil_twins <- function(df){
 #
 # returns df with additional column, tbc.(cn), which is the recentered z-score
 # for the input
+#' @keywords internal
+#' @noRd
 calc_and_recenter_z_scores <- function(df, cn, ref.data.path){
   # for infants, use z and who
   measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE,

From 16f9dc5bd934a9d67382859d8f2875ef6690c0b1 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Fri, 8 Sep 2023 11:27:53 -0400
Subject: [PATCH 088/100] Update "no visible bindings"

---
 NAMESPACE           |  1 +
 R/growth.R          |  7 +++++++
 R/infants_clean.R   | 18 +++++++++++++++++-
 R/infants_support.R |  4 ++++
 man/read_anthro.Rd  |  2 +-
 5 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index e2d3689b..a985f57e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -23,6 +23,7 @@ import(parallel)
 import(plyr, except = c(failwith, id, summarize, count, desc, mutate, arrange, rename, is.discrete, summarise, summarize))
 import(tidyr, except = extract)
 importFrom(stats,approx)
+importFrom(stats,embed)
 importFrom(stats,median)
 importFrom(stats,pnorm)
 importFrom(stats,qnorm)
diff --git a/R/growth.R b/R/growth.R
index 0f0538f7..a004f191 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -165,6 +165,13 @@ cleangrowth <- function(subjid,
   z.orig_cdc <- z.orig_who <- sd.orig_cdc <- sd.orig_who <- NULL
   result <- NULL
 
+  sd.orig_uncorr <- agemonths <- intwt <- fengadays <- pmagedays <- cagedays <-
+    unmod_zscore <- fen_wt_m <- fen_wt_l <- fen_wt_s <- cwho_cv <- ccdc_cv <-
+    sd.c_cdc <- sd.c_who <- sd.c <- sd.corr <- seq_win <- sd.corr_abssumdiff <-
+    sd.orig_abssumdiff <- ..orig_colnames <- ctbc.sd <- sum_sde <- no_sde <-
+    sum_val <- no_dup_val <- no_outliers <- no_bigdiff <- nottoofar <- nnte <-
+    nnte_full <- NULL
+
   # preprocessing ----
 
   # organize data into a dataframe along with a line "index" so the original data order can be recovered
diff --git a/R/infants_clean.R b/R/infants_clean.R
index c248b682..a1269ace 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -15,7 +15,7 @@
 #'
 #' @keywords internal
 #' @import data.table
-#' @importFrom stats median
+#' @importFrom stats median embed
 #' @noRd
 cleanbatch_infants <- function(data.df,
                                log.path,
@@ -58,6 +58,22 @@ cleanbatch_infants <- function(data.df,
 
   oob <- sd_med <- med_diff <- max_diff <- sum_oob <- i.exclude <- NULL
 
+
+  # avoid no visible warning errors
+  sum_sde <- no_sde <- cf <- wholehalfimp <- seq_win <- cs <- absdiff <-
+    sd.orig_uncorr <- seq_win <- absdiff <- wholehalfimp <- ageyears <- ctbc.sd <-
+    ..col_replace <- c.ewma.all <- pot_excl <- c.dewma.all <- p_plus <-
+    p_minus <- ctbc.sd <- c.ewma.all <- tbc_diff_next <- tbc_diff_prior <-
+    tbc_diff_plus_next <- tbc.p_plus <- tbc_diff_plus_prior <-
+    tbc_diff_minus_next <- tbc.p_minus <- tbc_diff_minus_prior <- addcrithigh <-
+    addcritlow <- tbc_dop <- i.tbc.sd <- rowind <- abssum <- c.dewma.all <-
+    whoagegrp_ht <- d_agedays <- mindiff <- maxdiff <- who_mindiff_ht <-
+    who_maxdiff_ht <- mindiff_prev <- maxdiff_prev <- whoinc.age.hc <-
+    who_maxdiff_hc <- who_mindiff_hc <- diff_prev <-
+    diff_next <- aft.g.aftm1 <- val_excl <-
+    absval <- comp_diff <- err_ratio <-
+    NULL
+
   data.df <- data.table(data.df, key = c('subjid', 'param', 'agedays', 'index'))
 
   if (parallel & !is.na(log.path)) {
diff --git a/R/infants_support.R b/R/infants_support.R
index 57327a97..6ffa5e56 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -154,6 +154,10 @@ calc_oob_evil_twins <- function(df){
 #' @keywords internal
 #' @noRd
 calc_and_recenter_z_scores <- function(df, cn, ref.data.path){
+  # avoid no visible warning errors
+  cn.orig_cdc <- param <- agedays <- sex <- cn.orig_who <- cn.orig <- subjid <-
+    tbc.cn <- sd.median <- NULL
+
   # for infants, use z and who
   measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE,
                                   infants = TRUE)
diff --git a/man/read_anthro.Rd b/man/read_anthro.Rd
index 9f185e29..67c17613 100644
--- a/man/read_anthro.Rd
+++ b/man/read_anthro.Rd
@@ -11,7 +11,7 @@ read_anthro(path = "", cdc.only = FALSE, infants = FALSE)
 
 \item{cdc.only}{Whether or not only CDC data should be used. Defaults to false.}
 
-\item{infants}{boolean on if beta infants version is being used. will be merged in to main algorithm.}
+\item{infants}{TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.}
 }
 \value{
 Function for calculating BMI based on measurement, age in days, sex, and measurement value.

From 374aeec764c10ada3cc7612283cee0aa8041a6ae Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 08:56:13 -0400
Subject: [PATCH 089/100] Clean up trues, update infants naming

---
 R/growth.R        | 46 +++++++++++++++++++++++-----------------------
 R/infants_clean.R | 24 ++++++++++++------------
 2 files changed, 35 insertions(+), 35 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index a004f191..8bef7edb 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -84,7 +84,7 @@
 #' @param adult_columns_filename Name of file to save original adult data, with additional output columns to
 #' as CSV. Defaults to "", for which this data will not be saved. Useful
 #' for post-analysis. For more information on this output, please see README.
-#' @param infants TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.
+#' @param prelim_infants TRUE/FALSE. Run the in-development release of the infants algorithm (expands pediatric algorithm to improve performance for children 0 – 2 years). Not recommended for use in research. For more information regarding the logic of the algorithm, see the vignette 'Preliminary Infants Algorithm.' Defaults to FALSE.
 #'
 #' @return Vector of exclusion codes for each of the input measurements.
 #'
@@ -157,7 +157,7 @@ cleangrowth <- function(subjid,
                         adult_cutpoint = 20,
                         weight_cap = Inf,
                         adult_columns_filename = "",
-                        infants = FALSE) {
+                        prelim_infants = FALSE) {
   # avoid "no visible binding" warnings
   N <- age_years <- batch <- exclude <- index <- line <- NULL
   newbatch <- sd.median <- sd.orig <- tanner.months <- tbc.sd <- NULL
@@ -168,7 +168,7 @@ cleangrowth <- function(subjid,
   sd.orig_uncorr <- agemonths <- intwt <- fengadays <- pmagedays <- cagedays <-
     unmod_zscore <- fen_wt_m <- fen_wt_l <- fen_wt_s <- cwho_cv <- ccdc_cv <-
     sd.c_cdc <- sd.c_who <- sd.c <- sd.corr <- seq_win <- sd.corr_abssumdiff <-
-    sd.orig_abssumdiff <- ..orig_colnames <- ctbc.sd <- sum_sde <- no_sde <-
+    sd.orig_abssumdiff <- orig_colnames <- ctbc.sd <- sum_sde <- no_sde <-
     sum_val <- no_dup_val <- no_outliers <- no_bigdiff <- nottoofar <- nnte <-
     nnte_full <- NULL
 
@@ -227,7 +227,7 @@ cleangrowth <- function(subjid,
 
   # constants for pediatric
   # enumerate the different exclusion levels
-  if (infants){
+  if (prelim_infants){
     # different for infants
     exclude.levels.peds <- c(
       'Include',
@@ -372,7 +372,7 @@ cleangrowth <- function(subjid,
       if (is.na(num.batches)) {
         num.batches <- getDoParWorkers()
       }
-      if (infants){
+      if (prelim_infants){
         # variables needed for parallel workers
         var_for_par <- c("temporary_extraneous", "valid", "swap_parameters",
                          "na_as_false", "ewma", "read_anthro", "as_matrix_delta",
@@ -426,7 +426,7 @@ cleangrowth <- function(subjid,
     tanner.fields <- colnames(tanner.ht.vel)
     tanner.fields <- tanner.fields[!tanner.fields %in% c('sex', 'tanner.months')]
 
-    if (!infants){
+    if (!prelim_infants){
       who_max_ht_vel_path <- ifelse(
         ref.data.path == "",
         system.file(file.path("extdata", "who_ht_maxvel_3sd.csv.gz"), package = "growthcleanr"),
@@ -510,15 +510,15 @@ cleangrowth <- function(subjid,
     data.all[param == 'LENGTHCM', param := 'HEIGHTCM']
 
     # calculate z/sd scores
-    if(infants){
+    if(prelim_infants){
       if (!quietly)
         cat(sprintf("[%s] Calculating z-scores...\n", Sys.time()))
       # removing z calculations, as they are not used
       # for infants, use z and who
       measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE,
-                                      infants = T)
+                                      prelim_infants = TRUE)
       measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = FALSE,
-                                          infants = T)
+                                          prelim_infants = TRUE)
 
       # calculate "standard deviation" scores
       if (!quietly)
@@ -589,7 +589,7 @@ cleangrowth <- function(subjid,
       # merge with fenton curves
       data.all <- merge(
         data.all, fentlms_foraga, by = c("sex", "intwt"),
-        all.x = T)
+        all.x = TRUE)
 
       data.all[fengadays < 259, pmagedays := agedays + fengadays]
       data.all[fengadays < 259, cagedays := pmagedays - 280]
@@ -599,7 +599,7 @@ cleangrowth <- function(subjid,
       # merge with fenton curves
       data.all <- merge(
         data.all, fentlms_forz, by = c("sex", "fengadays"),
-        all.x = T)
+        all.x = TRUE)
 
       # add unmodified zscore using weight in unrounded grams
       data.all[, unmod_zscore :=
@@ -617,11 +617,11 @@ cleangrowth <- function(subjid,
       data.all <- merge(data.all, growthfile_who,
                         by.x = c("sex", "cagedays"),
                         by.y = c("sex", "agedays"),
-                        all.x = T)
+                        all.x = TRUE)
       data.all <- merge(data.all, growthfile_cdc,
                         by.x = c("sex", "cagedays"),
                         by.y = c("sex", "agedays"),
-                        all.x = T)
+                        all.x = TRUE)
 
       # adjust WHO and CDC heights based on age
       data.all[, cwho_cv := v]
@@ -759,7 +759,7 @@ cleangrowth <- function(subjid,
     if (!is.data.table(sd.recenter)) {
       # INFANTS CHANGES:
       # use recentering file derived from work, independent of sex
-      if (infants){
+      if (prelim_infants){
         infants_reference_medians_path <- ifelse(
           ref.data.path == "",
           system.file(file.path("extdata",
@@ -838,7 +838,7 @@ cleangrowth <- function(subjid,
 
     setkey(data.all, subjid, param, agedays)
     data.all[, tbc.sd := sd.orig - sd.median]
-    if (infants){
+    if (prelim_infants){
       # separate out corrected and noncorrected values
       data.all[, ctbc.sd := sd.corr - sd.median]
     }
@@ -871,7 +871,7 @@ cleangrowth <- function(subjid,
     # safety check: treat observations where tbc.sd cannot be calculated as missing
     data.all[is.na(tbc.sd), exclude := 'Missing']
 
-    if (infants){
+    if (prelim_infants){
       # 4: identify subset that don't need to be cleaned (nnte) ----
 
       # identify those meeting all subjects meeting these criteria as "no need
@@ -894,7 +894,7 @@ cleangrowth <- function(subjid,
       data.all[, no_outliers := no_outliers == 1]
       # all max - min tbd.sc < 2.5
       data.all[, no_bigdiff :=
-                 rep((abs(max(tbc.sd, na.rm = T) - min(tbc.sd, na.rm = T)) < 2.5),
+                 rep((abs(max(tbc.sd, na.rm = TRUE) - min(tbc.sd, na.rm = TRUE)) < 2.5),
                      .N),
                by = c("subjid", "param")]
       # the previous value can't be too far from the current value
@@ -930,7 +930,7 @@ cleangrowth <- function(subjid,
         num.batches
       ))
     if (num.batches == 1) {
-      if (!infants){
+      if (!prelim_infants){
         ret.df <- cleanbatch(data.all,
                              log.path = log.path,
                              quietly = quietly,
@@ -975,7 +975,7 @@ cleangrowth <- function(subjid,
         ifelse(!dir.exists(log.path), dir.create(log.path, recursive = TRUE), FALSE)
       }
 
-      if (!infants){
+      if (!prelim_infants){
         ret.df <- ddply(
           data.all,
           .(batch),
@@ -1152,7 +1152,7 @@ cleangrowth <- function(subjid,
 #'
 #' @param path Path to supplied reference anthro data. Defaults to package anthro tables.
 #' @param cdc.only Whether or not only CDC data should be used. Defaults to false.
-#' @param infants TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.
+#' @param prelim_infants TRUE/FALSE. Run the in-development release of the infants algorithm (expands pediatric algorithm to improve performance for children 0 – 2 years). Not recommended for use in research. For more information regarding the logic of the algorithm, see the vignette 'Preliminary Infants Algorithm.' Defaults to FALSE.
 #'
 #' @return Function for calculating BMI based on measurement, age in days, sex, and measurement value.
 #' @export
@@ -1165,13 +1165,13 @@ cleangrowth <- function(subjid,
 #' # Return calculating function while specifying a path and using only CDC data
 #' afunc <- read_anthro(path = system.file("extdata", package = "growthcleanr"),
 #'                      cdc.only = TRUE)
-read_anthro <- function(path = "", cdc.only = FALSE, infants = FALSE) {
+read_anthro <- function(path = "", cdc.only = FALSE, prelim_infants = FALSE) {
   # avoid "no visible bindings" warning
   src <- param <- sex <- age <- ret <- m <- NULL
   csdneg <- csdpos <- s <- NULL
 
   # set correct path based on input reference table path (if any)
-  if (!infants){
+  if (!prelim_infants){
     weianthro_path <- ifelse(
       path == "",
       system.file(file.path("extdata", "weianthro.txt.gz"), package = "growthcleanr"),
@@ -1207,7 +1207,7 @@ read_anthro <- function(path = "", cdc.only = FALSE, infants = FALSE) {
   }
   growth_cdc_ext <- read.csv(gzfile(growth_cdc_ext_path))
 
-  l <- if (!infants){
+  l <- if (!prelim_infants){
     list(
       with(
         read.table(gzfile(weianthro_path), header = TRUE),
diff --git a/R/infants_clean.R b/R/infants_clean.R
index a1269ace..f83e48be 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -365,7 +365,7 @@ cleanbatch_infants <- function(data.df,
       upd.df <- copy(df)
       upd.df <- calc_oob_evil_twins(upd.df)
       # count the amount of oobs for each subject/param and distribute it out
-      upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)]
+      upd.df[, `:=` (sum_oob = sum(oob, na.rm = TRUE)), by =.(subjid, param)]
 
       any_oob <- any(upd.df$sum_oob >= 2)
       # while there are multiple oob, we want to remove
@@ -373,7 +373,7 @@ cleanbatch_infants <- function(data.df,
 
         # 9D
         # now calculate the maximum difference from the median tbc.sd
-        upd.df[, `:=` (sd_med = median(tbc.sd, na.rm = T)), by =.(subjid, param)]
+        upd.df[, `:=` (sd_med = median(tbc.sd, na.rm = TRUE)), by =.(subjid, param)]
         upd.df[, `:=` (med_diff = abs(tbc.sd - sd_med)), by =.(subjid, param)]
         upd.df[, `:=` (max_diff = med_diff  == max(med_diff)), by =.(subjid, param)]
         # for ones with no tbc.sd, mark as false
@@ -386,7 +386,7 @@ cleanbatch_infants <- function(data.df,
         #9E
         # reupdate valid (to recalculate OOB -- others are not included)
         upd.df <- calc_oob_evil_twins(df[valid(df),])
-        upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)]
+        upd.df[, `:=` (sum_oob = sum(oob, na.rm = TRUE)), by =.(subjid, param)]
 
         any_oob <- any(upd.df$sum_oob >= 2)
 
@@ -445,7 +445,7 @@ cleanbatch_infants <- function(data.df,
           "before" = abs(df_sub$agedays - c(NA, df_sub$agedays[1:(nrow(df_sub)-1)])),
           "after" = abs(df_sub$agedays - c(df_sub$agedays[2:(nrow(df_sub))], NA))
         )
-        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = TRUE)})
         exp_vals <- rep(-1.5, nrow(tmp))
         exp_vals[maxdiff > 365.25] <- -2.5
         exp_vals[maxdiff > 730.5] <- -3.5
@@ -652,7 +652,7 @@ cleanbatch_infants <- function(data.df,
         idx_roll <- c(embed(1:nrow(subj_df),4)[adjacent,, drop = FALSE])
         idx_adj <- which(subj_df$agedays %in% subj_df$agedays[idx_roll])
       } else {
-        adjacent <- F
+        adjacent <- FALSE
         idx_adj <- idx_roll <- c()
       }
 
@@ -680,7 +680,7 @@ cleanbatch_infants <- function(data.df,
             # only exclude sdes that are adjacent
             subj_df$agedays %in% subj_df$agedays[idx_roll]
           } else {
-            rep(F, nrow(subj_df))
+            rep(FALSE, nrow(subj_df))
           }
       # re-include similar groups
       criteria[subj_df$index %in% similar_ids] <- FALSE
@@ -709,7 +709,7 @@ cleanbatch_infants <- function(data.df,
 
         # check for SDEs by EWMA -- alternate calculate excluding other SDEs
         all_sdes <- duplicated(subj_df$agedays) |
-          duplicated(subj_df$agedays, fromLast = T)
+          duplicated(subj_df$agedays, fromLast = TRUE)
 
         # first, calculate which exponent we want to put through (pass a different
         # on for each exp)
@@ -717,7 +717,7 @@ cleanbatch_infants <- function(data.df,
           "before" = abs(subj_df$agedays - c(NA, subj_df$agedays[1:(nrow(subj_df)-1)])),
           "after" = abs(subj_df$agedays - c(subj_df$agedays[2:(nrow(subj_df))], NA))
         )
-        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = TRUE)})
         exp_vals <- rep(-1.5, nrow(tmp))
         exp_vals[maxdiff > 365.25] <- -2.5
         exp_vals[maxdiff > 730.5] <- -3.5
@@ -792,7 +792,7 @@ cleanbatch_infants <- function(data.df,
       if (any(subj_df$extraneous)){
         # check for SDEs
         all_sdes <- duplicated(subj_df$agedays) |
-          duplicated(subj_df$agedays, fromLast = T)
+          duplicated(subj_df$agedays, fromLast = TRUE)
 
         rem_ids <- c()
         rem_ids_extreme <- c()
@@ -930,7 +930,7 @@ cleanbatch_infants <- function(data.df,
           "before" = abs(df_sub$agedays - c(NA, df_sub$agedays[1:(nrow(df_sub)-1)])),
           "after" = abs(df_sub$agedays - c(df_sub$agedays[2:(nrow(df_sub))], NA))
         )
-        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+        maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = TRUE)})
         exp_vals <- rep(-1.5, nrow(tmp))
         exp_vals[maxdiff > 365.25] <- -2.5
         exp_vals[maxdiff > 730.5] <- -3.5
@@ -1191,7 +1191,7 @@ cleanbatch_infants <- function(data.df,
             "before" = abs(df_sub$agedays - c(NA, df_sub$agedays[1:(nrow(df_sub)-1)])),
             "after" = abs(df_sub$agedays - c(df_sub$agedays[2:(nrow(df_sub))], NA))
           )
-          maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+          maxdiff <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = TRUE)})
           exp_vals <- rep(-1.5, nrow(tmp))
           exp_vals[maxdiff > 365.25] <- -2.5
           exp_vals[maxdiff > 730.5] <- -3.5
@@ -1556,7 +1556,7 @@ cleanbatch_infants <- function(data.df,
         "before" = abs(df$agedays - c(NA, df$agedays[1:(nrow(df)-1)])),
         "after" = abs(df$agedays - c(df$agedays[2:(nrow(df))], NA))
       )
-      maxdiff_e <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = T)})
+      maxdiff_e <- sapply(1:nrow(tmp), function(x){max(tmp[x,], na.rm = TRUE)})
       exp_vals <- rep(-1.5, nrow(tmp))
       exp_vals[maxdiff_e > 365.25] <- -2.5
       exp_vals[maxdiff_e > 730.5] <- -3.5

From d7511a874000015c16f03b609ddf8de4eca075e1 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 09:02:20 -0400
Subject: [PATCH 090/100] Update documentation

---
 man/cleangrowth.Rd | 4 ++--
 man/read_anthro.Rd | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/man/cleangrowth.Rd b/man/cleangrowth.Rd
index 1c2d4707..01339413 100644
--- a/man/cleangrowth.Rd
+++ b/man/cleangrowth.Rd
@@ -30,7 +30,7 @@ cleangrowth(
   adult_cutpoint = 20,
   weight_cap = Inf,
   adult_columns_filename = "",
-  infants = FALSE
+  prelim_infants = FALSE
 )
 }
 \arguments{
@@ -139,7 +139,7 @@ to Inf. Defaults to Inf.}
 as CSV. Defaults to "", for which this data will not be saved. Useful
 for post-analysis. For more information on this output, please see README.}
 
-\item{infants}{TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.}
+\item{prelim_infants}{TRUE/FALSE. Run the in-development release of the infants algorithm (expands pediatric algorithm to improve performance for children 0 – 2 years). Not recommended for use in research. For more information regarding the logic of the algorithm, see the vignette 'Preliminary Infants Algorithm.' Defaults to FALSE.}
 }
 \value{
 Vector of exclusion codes for each of the input measurements.
diff --git a/man/read_anthro.Rd b/man/read_anthro.Rd
index 67c17613..8cdfcd9f 100644
--- a/man/read_anthro.Rd
+++ b/man/read_anthro.Rd
@@ -4,14 +4,14 @@
 \alias{read_anthro}
 \title{Function to calculate z-scores and csd-scores based on anthro tables.}
 \usage{
-read_anthro(path = "", cdc.only = FALSE, infants = FALSE)
+read_anthro(path = "", cdc.only = FALSE, prelim_infants = FALSE)
 }
 \arguments{
 \item{path}{Path to supplied reference anthro data. Defaults to package anthro tables.}
 
 \item{cdc.only}{Whether or not only CDC data should be used. Defaults to false.}
 
-\item{infants}{TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.}
+\item{prelim_infants}{TRUE/FALSE. Run the in-development release of the infants algorithm (expands pediatric algorithm to improve performance for children 0 – 2 years). Not recommended for use in research. For more information regarding the logic of the algorithm, see the vignette 'Preliminary Infants Algorithm.' Defaults to FALSE.}
 }
 \value{
 Function for calculating BMI based on measurement, age in days, sex, and measurement value.

From 63ff1f2fe4088aac8f9db950422666ed39b81452 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 09:45:35 -0400
Subject: [PATCH 091/100] Update tests for infants, fix dplyr issue

---
 R/infants_clean.R                 |  2 --
 R/infants_support.R               |  4 ++--
 R/utils.R                         |  2 +-
 tests/testthat/test-cleangrowth.R | 33 +++++++++++++++++++++++++++++++
 4 files changed, 36 insertions(+), 5 deletions(-)

diff --git a/R/infants_clean.R b/R/infants_clean.R
index f83e48be..63b4b379 100644
--- a/R/infants_clean.R
+++ b/R/infants_clean.R
@@ -324,8 +324,6 @@ cleanbatch_infants <- function(data.df,
   # then, do some explicit overwriting for the 0 case (otherwise will be
   # set as missing)
   data.df[v == 0, exclude := exc_nam]
-  print(sum(data.df$v == 0))
-
 
   # 9d.  Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous)
   data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include']
diff --git a/R/infants_support.R b/R/infants_support.R
index 6ffa5e56..af1787ff 100644
--- a/R/infants_support.R
+++ b/R/infants_support.R
@@ -160,9 +160,9 @@ calc_and_recenter_z_scores <- function(df, cn, ref.data.path){
 
   # for infants, use z and who
   measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE,
-                                  infants = TRUE)
+                                  prelim_infants = TRUE)
   measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = FALSE,
-                                      infants = TRUE)
+                                      prelim_infants = TRUE)
 
   # calculate "standard deviation" scores
   df[, cn.orig_cdc := measurement.to.z(param, agedays, sex, get(cn), TRUE)]
diff --git a/R/utils.R b/R/utils.R
index b00cd294..65c89a0b 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -214,7 +214,7 @@ longwide <-
     if(all(c(id, subjid, sex, agedays, param, measurement, gcr_result,
              extra_cols) %in% colnames(long_df))) {
       long_df %>%
-        select(id, subjid, sex, agedays, param, measurement, gcr_result,
+        select(id, subjid, sex, agedays, param, measurement, all_of(gcr_result),
                all_of(extra_cols)) -> obs_df
     } else {
       # catch error if any variables were not found
diff --git a/tests/testthat/test-cleangrowth.R b/tests/testthat/test-cleangrowth.R
index 38b8541c..7585a146 100644
--- a/tests/testthat/test-cleangrowth.R
+++ b/tests/testthat/test-cleangrowth.R
@@ -256,3 +256,36 @@ test_that("growthcleanr works without either adult or pediatric data", {
   expect_equal(length(no_res), nrow(nobody))
 
 })
+
+test_that("growthcleanr runs preliminary infants algorithm", {
+
+  # Run cleangrowth() on syngrowth data
+  data <- as.data.table(syngrowth)
+
+  # syngrowth hasn't changed in length
+  expect_equal(77721, data[, .N])
+  setkey(data, subjid, param, agedays)
+
+  # subset to pediatric data
+  data_peds <- copy(data[agedays < 20 * 365.25, ])
+
+  # Create very small sample; just to test no errors in running
+  d5_nhanes <- as.data.table(data_peds)[subjid %in% unique(data[, subjid])[1:5], ]
+  expect_equal(42, d5_nhanes[, .N])
+
+  # Clean samples with infants algorithm
+  cd5_nhanes <-
+    d5_nhanes[, gcr_result := cleangrowth(
+      subjid,
+      param,
+      agedays,
+      sex,
+      measurement,
+      prelim_infants = TRUE
+    )]
+
+  expect_equal(length(cd5_nhanes$gcr_result), 42)
+
+  # test that there are a correct number of levels
+  expect_equal(length(levels(cd5_nhanes$gcr_result)), 80)
+})

From 96b6abc030d7a7863f50312825cdcf0525fb34a2 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 10:16:24 -0400
Subject: [PATCH 092/100] Update with prelim release information

---
 DESCRIPTION | 2 +-
 NEWS.md     | 7 +++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index ff94c08f..dae9a2d2 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: growthcleanr
 Type: Package
 Title: Data Cleaner for Anthropometric Measurements
-Version: 2.1.1
+Version: 2.2.0
 Authors@R: c(
     person("Carrie", "Daymont", email = "cdaymont@pennstatehealth.psu.edu", role = c("ctb","cre")),
     person("Robert", "Grundmeier", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index b524d5a2..017e27b5 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,10 @@
+# growthcleanr 2.2.0-prelim-infants - 2023-09-13
+
+## Added
+
+- Added option to cleangrowth for the preliminary infants algorithm -- expands pediatric algorithm to consider 0 - 2 years old, with infants = TRUE in cleangrowth(), with all steps implemented. Note that this option is still preliminary and should not be used for research. For more information regarding the logic of the algorithm, see the vignette 'Preliminary Infants Algorithm.' 
+
+
 # growthcleanr 3.0.0-infants-beta - 2023-03-13
 
 ## Added

From 10fb5edbd95116662c790ed5c3f56768be0e1927 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 10:16:44 -0400
Subject: [PATCH 093/100] Fix warning for using ..

---
 R/growth.R | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/R/growth.R b/R/growth.R
index 8bef7edb..6fae1be2 100644
--- a/R/growth.R
+++ b/R/growth.R
@@ -704,7 +704,8 @@ cleangrowth <- function(subjid,
       orig_colnames <- c(orig_colnames, "sd.corr")
 
       # remove many added columns
-      data.all <- data.all[, ..orig_colnames]
+      data.all <- data.all[, colnames(data.all) %in% orig_colnames,
+                           with = FALSE]
     } else {
       # calculate z scores
       if (!quietly)
@@ -916,7 +917,8 @@ cleangrowth <- function(subjid,
 
       # remove many added columns -- except for nnte
       orig_colnames <- c(orig_colnames, "nnte", "nnte_full")
-      data.all <- data.all[, ..orig_colnames]
+      data.all <- data.all[, colnames(data.all) %in% orig_colnames,
+                           with = FALSE]
     }
     # pediatric: cleanbatch (most of steps) ----
 

From a1b95e257ede5e1c1beea44d55ec4a3b124b0645 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 10:17:07 -0400
Subject: [PATCH 094/100] Add vignette for prelim infants

---
 _pkgdown.yml                           |  1 +
 vignettes/prelim-infants-algorithm.Rmd | 39 ++++++++++++++++++++++++++
 2 files changed, 40 insertions(+)
 create mode 100644 vignettes/prelim-infants-algorithm.Rmd

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 8a0c688e..6a0cf947 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -20,6 +20,7 @@ articles:
       - large-data-sets
       - next-steps
       - developer-guidelines
+      - prelim-infants-algorithm
 
 reference:
   - title: "Cleaning height and weight observations"
diff --git a/vignettes/prelim-infants-algorithm.Rmd b/vignettes/prelim-infants-algorithm.Rmd
new file mode 100644
index 00000000..6acc4909
--- /dev/null
+++ b/vignettes/prelim-infants-algorithm.Rmd
@@ -0,0 +1,39 @@
+---
+title: "Preliminary infants algorithm"
+date: "`r Sys.Date()`"
+vignette: >
+  %\VignetteIndexEntry{Preliminary infants algorithm}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\usepackage[utf8]{inputenc}
+---
+
+Now available is the in-development release of the infants algorithm, which expands pediatric algorithm to clean 0 - 2 and revises its steps. This is strongly not recommended for general use, and still contains bugs. Possible bugs are enumerated below, and provide a starting point for potential developers:
+ 
+- Same day exclusions are over called in weight and head circumference
+- Carried forward exclusions are mis-called within that step
+- Same day exclusions are mis-called within that step
+- Raw differences step are over called in height
+- EWMA results missed in height
+- Carried forward results are missed in head circumference
+- Various smaller misalignments across exclusion results
+ 
+If you would still like to use this in-development feature, you can do this in the cleangrowth algorithm by turning on the option `prelim_infants`:
+ 
+```{r, eval=FALSE}
+# prepare data as a data.table
+data <- as.data.table(source_data)
+ 
+# set the data.table key for better indexing
+setkey(data, subjid, param, agedays)
+ 
+# generate new exclusion flag field using function
+cleaned_data <- 
+  data[, gcr_result := cleangrowth(subjid, param, agedays, sex, measurement,
+                                   prelim_infants = TRUE)]
+```
+ 
+For information on the steps in the infants algorithm, please see [PDF].
+ 
+This algorithm expansion also contains information on smoothing z-scores between observations of 2 and 4 years old. If you would like to use this in your work, code can be found in growth.R lines 525-555.
+ 
+If you have any feedback on this in-development feature, would like to provide feedback on the algorithm logic or code, or discuss other collaborations, please contact the maintainer of this package, Carrie Daymont.

From 784908bfdc6b277e1a963b91bb60b0c022cbc04d Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 10:23:19 -0400
Subject: [PATCH 095/100] Remove old recentering file

---
 .../chop-rcfile-2023-02-03.csv_format.csv.gz   | Bin 128261 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 inst/extdata/chop-rcfile-2023-02-03.csv_format.csv.gz

diff --git a/inst/extdata/chop-rcfile-2023-02-03.csv_format.csv.gz b/inst/extdata/chop-rcfile-2023-02-03.csv_format.csv.gz
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GIT binary patch
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From 13751299eb9d0783976debbecd83a2aba4b6d0f9 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 10:39:18 -0400
Subject: [PATCH 096/100] Update CRAN comments

---
 cran-comments.md | 21 +++++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/cran-comments.md b/cran-comments.md
index 340ea866..09891de2 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,13 +1,30 @@
-# CRAN submission growthcleanr 2.1.1
+# CRAN submission growthcleanr 2.2.0
 
 ## R CMD check results
-There were no ERRORs, WARNINGs, or NOTEs.
+There were no ERRORs or WARNINGs.
+
+There was one note:
+
+❯ checking installed package size ... NOTE
+    installed size is  6.1Mb
+    sub-directories of 1Mb or more:
+      extdata   5.0Mb
+      
+extdata has been compressed as much as possible -- data in that folder is necessary to run the package.
 
 ## Downstream dependencies
 There are currently no downstream dependencies for this package.
 
 # Previous Submissions
 
+# CRAN submission growthcleanr 2.1.1
+
+## R CMD check results
+There were no ERRORs, WARNINGs, or NOTEs.
+
+## Downstream dependencies
+There are currently no downstream dependencies for this package.
+
 # CRAN submission growthcleanr 2.1.0
 
 ## R CMD check results

From 23c71c85d2dc000e1013435d54fd4ebc14bc8438 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 10:45:08 -0400
Subject: [PATCH 097/100] Update gitignore to add infants document pdf

---
 .gitignore                                     |   1 -
 ...Algorithm-Documentation-2023-08-22-0833.pdf | Bin 0 -> 378124 bytes
 vignettes/prelim-infants-algorithm.Rmd         |   2 +-
 3 files changed, 1 insertion(+), 2 deletions(-)
 create mode 100644 vignettes/Infant-Algorithm-Documentation-2023-08-22-0833.pdf

diff --git a/.gitignore b/.gitignore
index 265d6601..00d07382 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,7 +19,6 @@
 
 # produced vignettes
 vignettes/*.html
-vignettes/*.pdf
 
 # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3
 .httr-oauth
diff --git a/vignettes/Infant-Algorithm-Documentation-2023-08-22-0833.pdf b/vignettes/Infant-Algorithm-Documentation-2023-08-22-0833.pdf
new file mode 100644
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diff --git a/vignettes/prelim-infants-algorithm.Rmd b/vignettes/prelim-infants-algorithm.Rmd
index 6acc4909..4ee727de 100644
--- a/vignettes/prelim-infants-algorithm.Rmd
+++ b/vignettes/prelim-infants-algorithm.Rmd
@@ -32,7 +32,7 @@ cleaned_data <-
                                    prelim_infants = TRUE)]
 ```
  
-For information on the steps in the infants algorithm, please see [PDF].
+For information on the steps in the infants algorithm, please see [this document](https://github.com/carriedaymont/growthcleanr/blob/main/vignettes/Infant-Algorithm-Documentation-2023-08-22-0833.pdf).
  
 This algorithm expansion also contains information on smoothing z-scores between observations of 2 and 4 years old. If you would like to use this in your work, code can be found in growth.R lines 525-555.
  

From 7e99356f88ab5147d28bfe338e01bda701a18016 Mon Sep 17 00:00:00 2001
From: "Dr. Hannah De los Santos" 
Date: Wed, 13 Sep 2023 10:58:29 -0400
Subject: [PATCH 098/100] Update pkgdown site

---
 docs/404.html                                 |   5 +-
 docs/LICENSE-text.html                        |   5 +-
 docs/LICENSE.html                             |   5 +-
 docs/articles/adult-algorithm.html            |   7 +-
 docs/articles/configuration.html              |   7 +-
 docs/articles/developer-guidelines.html       |   7 +-
 docs/articles/index.html                      |   7 +-
 docs/articles/installation.html               |  25 ++-
 docs/articles/large-data-sets.html            |  61 +++---
 docs/articles/next-steps.html                 | 125 +++++------
 docs/articles/output.html                     |   7 +-
 docs/articles/prelim-infants-algorithm.html   | 195 ++++++++++++++++++
 docs/articles/quickstart.html                 |   7 +-
 docs/articles/usage.html                      |  91 ++++----
 docs/articles/utilities.html                  | 109 +++++-----
 docs/authors.html                             |   5 +-
 docs/index.html                               |   5 +-
 docs/news/index.html                          |  12 +-
 docs/pkgdown.yml                              |   5 +-
 docs/reference/CDCref_d.html                  |   5 +-
 docs/reference/acf_answers.html               |   5 +-
 docs/reference/adjustcarryforward.html        |   5 +-
 docs/reference/bmianthro.html                 |   5 +-
 docs/reference/cleangrowth.html               |  11 +-
 docs/reference/ewma.html                      |   5 +-
 docs/reference/ext_bmiz.html                  |   5 +-
 docs/reference/growth_cdc_ext.html            |   5 +-
 docs/reference/index.html                     |   5 +-
 docs/reference/lenanthro.html                 |   5 +-
 docs/reference/longwide.html                  |   5 +-
 docs/reference/nhanes-reference-medians.html  |   5 +-
 docs/reference/read_anthro.html               |  11 +-
 docs/reference/recode_sex.html                |   5 +-
 docs/reference/sd_median.html                 |   5 +-
 docs/reference/simple_bmi.html                |   5 +-
 docs/reference/splitinput.html                |   5 +-
 docs/reference/syngrowth.html                 |   5 +-
 docs/reference/tanner_ht_vel.html             |   5 +-
 docs/reference/tanner_ht_vel_with_2sd.html    |   5 +-
 .../test_syngrowth_sas_output_compare.html    |   5 +-
 docs/reference/test_syngrowth_wide.html       |   5 +-
 docs/reference/testacf.html                   |   5 +-
 docs/reference/weianthro.html                 |   5 +-
 docs/reference/who_ht_maxvel.html             |   5 +-
 docs/reference/who_ht_maxvel_2sd.html         |   5 +-
 docs/reference/who_ht_vel_2sd.html            |   5 +-
 docs/reference/who_ht_vel_3sd.html            |   5 +-
 docs/sitemap.xml                              |   3 +
 48 files changed, 594 insertions(+), 251 deletions(-)
 create mode 100644 docs/articles/prelim-infants-algorithm.html

diff --git a/docs/404.html b/docs/404.html
index 923920a9..29a5d7f3 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -32,7 +32,7 @@
       
       
         growthcleanr
-        2.1.1
+        2.2.0
       
     

 
@@ -81,6 +81,9 @@
     
  • 
       Developer guidelines
     
    • 
+    
  • 
+      Preliminary infants algorithm
+    
    • 
   
 
 
  • 
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 844d06e4..ee8b400f 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -17,7 +17,7 @@
       
       
         growthcleanr
-        2.1.1
+        2.2.0
       
     
    • 
 
@@ -62,6 +62,9 @@
     
  • 
       Developer guidelines
     
    • 
+    
  • 
+      Preliminary infants algorithm
+    
    • 
   
 
  • 
   Reference
diff --git a/docs/LICENSE.html b/docs/LICENSE.html
index 252207b8..43aa2909 100644
--- a/docs/LICENSE.html
+++ b/docs/LICENSE.html
@@ -17,7 +17,7 @@
       
       
         growthcleanr
-        2.1.1
+        2.2.0
       
     
    • 
 
@@ -62,6 +62,9 @@
     
  • 
       Developer guidelines
     
    • 
+    
  • 
+      Preliminary infants algorithm
+    
    • 
   
 
  • 
   Reference
diff --git a/docs/articles/adult-algorithm.html b/docs/articles/adult-algorithm.html
index 5330a3e0..63be10fc 100644
--- a/docs/articles/adult-algorithm.html
+++ b/docs/articles/adult-algorithm.html
@@ -33,7 +33,7 @@
       
       
         growthcleanr
-        2.1.1
+        2.2.0
       
     
    • 
 
@@ -82,6 +82,9 @@
     
  • 
       Developer guidelines
     
    • 
+    
  • 
+      Preliminary infants algorithm
+    
    • 
   
 
 
  • 
@@ -113,7 +116,7 @@
     
    
       
    Adult algorithm
    
             
-            
    2023-03-13
    
+            
    2023-09-13
    
       
       Source: vignettes/adult-algorithm.Rmd
       
diff --git a/docs/articles/configuration.html b/docs/articles/configuration.html
index 0e3d5d29..bf48b7cb 100644
--- a/docs/articles/configuration.html
+++ b/docs/articles/configuration.html
@@ -33,7 +33,7 @@
       
       
         growthcleanr
-        2.1.1
+        2.2.0
       
     
    
 
@@ -82,6 +82,9 @@
     
  • 
       Developer guidelines
     
    • 
+    
  • 
+      Preliminary infants algorithm
+    
    • 
   
 
 
  • 
@@ -113,7 +116,7 @@
     
    
       
    Configuration
    
             
-            
    2023-03-13
    
+            
    2023-09-13
    
       
       Source: vignettes/configuration.Rmd
       
diff --git a/docs/articles/developer-guidelines.html b/docs/articles/developer-guidelines.html
index 77869b15..30ecbb5f 100644
--- a/docs/articles/developer-guidelines.html
+++ b/docs/articles/developer-guidelines.html
@@ -33,7 +33,7 @@
       
       
         growthcleanr
-        2.1.1
+        2.2.0
       
     
    
 
@@ -82,6 +82,9 @@
     
  • 
       Developer guidelines
     
    • 
+    
  • 
+      Preliminary infants algorithm
+    
    • 
   
 
 
  • 
@@ -113,7 +116,7 @@
     
    
       
    Developer guidelines
    
             
-            
    2023-03-13
    
+            
    2023-09-13
    
       
       Source: vignettes/developer-guidelines.Rmd
       
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 1ced39cb..6d7b5df0 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -17,7 +17,7 @@
       
       
         growthcleanr
-        2.1.1
+        2.2.0
       
     
    
 
@@ -62,6 +62,9 @@
     
  • 
       Developer guidelines
     
    • 
+    
  • 
+      Preliminary infants algorithm
+    
    • 
   
 
  • 
   Reference
@@ -116,6 +119,8 @@ 
    Advanced
    
         
    
         
    Developer guidelines
    
         
    
+        
    Preliminary infants algorithm
    
+        
    
       
    •

    diff --git a/docs/articles/installation.html b/docs/articles/installation.html index 9d18ac2c..e0785ab6 100644 --- a/docs/articles/installation.html +++ b/docs/articles/installation.html @@ -33,7 +33,7 @@ growthcleanr - 2.1.1 + 2.2.0
    @@ -82,6 +82,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • @@ -113,7 +116,7 @@

    Installation

    -

    2023-03-13

    +

    2023-09-13

    Source: vignettes/installation.Rmd @@ -184,8 +187,8 @@

    macOS threads. You will know it worked successfully if, when you load the data.table library in R, you see something like the following:

    -
    library(data.table)
-data.table 1.12.2 using 2 threads (see ?getDTthreads).  Latest news: r-datatable.com
    +
    library(data.table)
+data.table 1.12.2 using 2 threads (see ?getDTthreads).  Latest news: r-datatable.com

    That data.table reports “using 2 threads” indicates that installation has succeeded. If the message reports using only one thread, see the advice under the “OpenMP @@ -208,7 +211,7 @@

    Docker

    To install and run growthcleanr using Docker, open the PowerShell on Windows, or open the Terminal on macOS, and enter this docker command:

    -
    docker run -it ghcr.io/mitre/growthcleanr/gcr-image:latest R
    +
    docker run -it ghcr.io/mitre/growthcleanr/gcr-image:latest R

    The image tag latest in the example above will refer to the latest version of the package available on the main branch of the mitre/growthcleanr repository, which is typically in close sync with the upstream carriedaymont/growthcleanr @@ -216,7 +219,7 @@

    Docker growthcleanr. To explicitly choose a release by name, replace latest with the release tag, e.g. for the released package v2.1.0:

    -
    docker run -it ghcr.io/mitre/growthcleanr/gcr-image:v2.1.0 R
    +
    docker run -it ghcr.io/mitre/growthcleanr/gcr-image:v2.1.0 R

    Whichever package you use, the first time this command is run, it might take a few minutes to download and extract several necessary components, but this should be fully automated. If successful, you @@ -228,8 +231,8 @@

    Docker your own data. For example, if you are on Windows, and your data is in C:\Users\exampleuser\analysis, specify a mapping using the added -v step below:

    -
    docker run -it -v C:\Users\exampleusers\analysis:/usr/src/app \
-    ghcr.io/mitre/growthcleanr/gcr-image:latest R
    +
    docker run -it -v C:\Users\exampleusers\analysis:/usr/src/app \
+    ghcr.io/mitre/growthcleanr/gcr-image:latest R

    Note that the slashes in file paths reverse direction from the reference to the folder location on your Windows machine (before the colon) to the folder location on the Docker container (after the colon); @@ -242,8 +245,8 @@

    Docker

    If you are on macOS, and your data is in /Users/exampleuser/analysis, specify a folder mapping like this:

    -
    docker run -it -v /Users/exampleuser/analysis:/usr/src/app \
-    ghcr.io/mitre/growthcleanr/gcr-image:latest R
    +
    docker run -it -v /Users/exampleuser/analysis:/usr/src/app \
+    ghcr.io/mitre/growthcleanr/gcr-image:latest R

    If you mapped a folder, then inside the Docker environment’s R prompt, when you then issue a command like list.files(), you should see a list of the same files in the R session that you see in @@ -283,7 +286,7 @@

    If you are developing the growthcleanr code itself, you can download or clone the growthcleanr source code and then install it from source. To clone the source using git:

    -
    git clone https://github.com/carriedaymont/growthcleanr.git
    +
    git clone https://github.com/carriedaymont/growthcleanr.git

    Once you have the growthcleanr package source, open an R session from the growthcleanr base directory. Then install growthcleanr using the R devtools package:

    diff --git a/docs/articles/large-data-sets.html b/docs/articles/large-data-sets.html index 8340f110..c662a1bc 100644 --- a/docs/articles/large-data-sets.html +++ b/docs/articles/large-data-sets.html @@ -33,7 +33,7 @@ growthcleanr - 2.1.1 + 2.2.0

    @@ -82,6 +82,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • @@ -113,7 +116,7 @@

    Working with large data sets

    -

    2023-03-13

    +

    2023-09-13

    Source: vignettes/large-data-sets.Rmd @@ -222,12 +225,12 @@

    Splitting input datasets

    To invoke exec/gcdriver.R on a single input file using Rscript:

    -
    Rscript exec/gcdriver.R --quietly --sdrecenter nhanes mydata.csv mydata-cleaned.csv
    +
    Rscript exec/gcdriver.R --quietly --sdrecenter nhanes mydata.csv mydata-cleaned.csv

    If you have many small files saved, for example, as mydata.00001.csv, mydata.00002.csv, etc., this driver can be invoked using parallel:

    -
    ls mydata.?????.csv | parallel -j2 --eta \
-  "Rscript exec/gcdriver.R --quietly --sdrecenter nhanes {} {}-clean.csv"
    +
    ls mydata.?????.csv | parallel -j2 --eta \
+  "Rscript exec/gcdriver.R --quietly --sdrecenter nhanes {} {}-clean.csv"

    This lists your input files, and passes the filename list to parallel to use in invoking the driver script, one file at a time, and to process as parallel jobs as resources are available. Each run of the job then @@ -275,7 +278,7 @@

    Batch splitting for adult data
    Rscript exec/gcdriver.R --numbatches 4 --adult_split 50 my-large-input.csv my-large-input-cleaned.csv

    +
    Rscript exec/gcdriver.R --numbatches 4 --adult_split 50 my-large-input.csv my-large-input-cleaned.csv
    • --numbatches 4 may be a good starting point for testing @@ -293,29 +296,29 @@

      Reference for gcdriver.R

      All available options for gcdriver.R are described below.

      -
      Rscript exec/gcdriver.R --help
-usage: gcdriver.R [--] [--help] [--quietly] [--opts OPTS] [--sdrecenter
-       SDRECENTER] [--adult_cutpoint ADULT_CUTPOINT] [--weightcap
-       WEIGHTCAP] [--numbatches NUMBATCHES] [--adult_split ADULT_SPLIT]
-       infile outfile
-
-CLI driver for growthcleanr
-
-positional arguments:
-  infile                input file
-  outfile               output file
-
-flags:
-  -h, --help            show this help message and exit
-  -q, --quietly         Disable verbose output
-
-optional arguments:
-  -x, --opts            RDS file containing argument values
-  -s, --sdrecenter      sd.recenter data file [default: ]
-  -a, --adult_cutpoint  adult cutpoint [default: 20]
-  -w, --weightcap       weight cap [default: Inf]
-  -n, --numbatches      Number of batches [default: 1]
-  --adult_split         Number of splits to run data on [default: Inf]
      +
      Rscript exec/gcdriver.R --help
+usage: gcdriver.R [--] [--help] [--quietly] [--opts OPTS] [--sdrecenter
+       SDRECENTER] [--adult_cutpoint ADULT_CUTPOINT] [--weightcap
+       WEIGHTCAP] [--numbatches NUMBATCHES] [--adult_split ADULT_SPLIT]
+       infile outfile
+
+CLI driver for growthcleanr
+
+positional arguments:
+  infile                input file
+  outfile               output file
+
+flags:
+  -h, --help            show this help message and exit
+  -q, --quietly         Disable verbose output
+
+optional arguments:
+  -x, --opts            RDS file containing argument values
+  -s, --sdrecenter      sd.recenter data file [default: ]
+  -a, --adult_cutpoint  adult cutpoint [default: 20]
+  -w, --weightcap       weight cap [default: Inf]
+  -n, --numbatches      Number of batches [default: 1]
+  --adult_split         Number of splits to run data on [default: Inf]

    • diff --git a/docs/articles/next-steps.html b/docs/articles/next-steps.html index b84e2635..f1eb293b 100644 --- a/docs/articles/next-steps.html +++ b/docs/articles/next-steps.html @@ -33,7 +33,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -82,6 +82,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • @@ -113,7 +116,7 @@

    Next steps

    -

    2023-03-13

    +

    2023-09-13

    Source: vignettes/next-steps.Rmd @@ -157,7 +160,7 @@

    Running the experimentgrowthcleanr as described in the main README.md file, save it to a CSV file for exec/testadjustcf.R:

    -
    > fwrite(cleaned_data, "cleaned.csv", row.names = F)
    +
    > fwrite(cleaned_data, "cleaned.csv", row.names = F)

    For testacf(), keep cleaned_data in your environment. Note that the column names should be as described for cleaned_data the Example under Quickstart.

    @@ -167,7 +170,7 @@

    Running the experiment -
    Rscript exec/testadjustcf.R cleaned.csv
    +
    Rscript exec/testadjustcf.R cleaned.csv

    To use testacf(), run the following in the console (with cleaned_data in the environment):

    @@ -351,42 +354,42 @@ 
    Running the experimentFor example, for a 9-step sweep with the default search type,
 random, the parameters passed to the function in each pass
 will be:
    
-
    run minfactor   maxfactor   banddiff    banddiff_plus   min_ht.exp_under    min_ht.exp_over max_ht.exp_under    max_ht.exp_over
-1   0.494454649 0.331710969 1.681997601 5.438065292 0.371428523 -0.200524185    0.296497153 0.244186167
-2   0.198872727 0.918207332 0.0261138   0.361051567 0.370286443 -0.618056939    0.318943811 0.280842425
-3   0.057848889 0.343496154 2.957211272 3.448713172 0.758593493 -0.240298769    0.189112731 0.586874211
-4   0.034874339 0.462954204 0.949754412 2.697612725 1.694048784 -0.563224398    0.237626234 0.410851815
-5   0.5 2   3   5.5 2   0   0.33    1.5
-6   0.621874695 3.545623892 4.918346826 10.84063427 2.996152267 0.904217721 0.585439346 1.787876622
-7   0.896005213 2.192603083 3.885669706 7.492214661 3.581171147 0.319534914 0.537161064 2.25658771
-8   0.670031176 2.90689554  5.990111082 9.239964036 3.676927744 0.082569093 0.568586483 2.645760535
-9   0.98603125  2.169401426 5.718063961 6.950459617 2.91380773  0.816289079 0.457654322 2.540503306
    
+
    run minfactor   maxfactor   banddiff    banddiff_plus   min_ht.exp_under    min_ht.exp_over max_ht.exp_under    max_ht.exp_over
+1   0.494454649 0.331710969 1.681997601 5.438065292 0.371428523 -0.200524185    0.296497153 0.244186167
+2   0.198872727 0.918207332 0.0261138   0.361051567 0.370286443 -0.618056939    0.318943811 0.280842425
+3   0.057848889 0.343496154 2.957211272 3.448713172 0.758593493 -0.240298769    0.189112731 0.586874211
+4   0.034874339 0.462954204 0.949754412 2.697612725 1.694048784 -0.563224398    0.237626234 0.410851815
+5   0.5 2   3   5.5 2   0   0.33    1.5
+6   0.621874695 3.545623892 4.918346826 10.84063427 2.996152267 0.904217721 0.585439346 1.787876622
+7   0.896005213 2.192603083 3.885669706 7.492214661 3.581171147 0.319534914 0.537161064 2.25658771
+8   0.670031176 2.90689554  5.990111082 9.239964036 3.676927744 0.082569093 0.568586483 2.645760535
+9   0.98603125  2.169401426 5.718063961 6.950459617 2.91380773  0.816289079 0.457654322 2.540503306
    
 
    In a 9-step sweep with a line-grid search type, the
 parameters passed to the function in each pass will be:
    
-
    run  minfactor  maxfactor  banddiff  banddiff_plus  min_ht.exp_under  min_ht.exp_over  max_ht.exp_under  max_ht.exp_over
-1    0          0          0         0              0                 -1               0                 0
-2    0.125      0.5        0.75      1.375          0.5               -0.75            0.0825            0.375
-3    0.25       1          1.5       2.75           1                 -0.5             0.165             0.75
-4    0.375      1.5        2.25      4.125          1.5               -0.25            0.2475            1.125
-5    0.5        2          3         5.5            2                 0                0.33              1.5
-6    0.625      2.5        3.75      6.875          2.5               0.25             0.4125            1.875
-7    0.75       3          4.5       8.25           3                 0.5              0.495             2.25
-8    0.875      3.5        5.25      9.625          3.5               0.75             0.5775            2.625
-9    1          4          6         11             4                 1                0.66              3
    
+
    run  minfactor  maxfactor  banddiff  banddiff_plus  min_ht.exp_under  min_ht.exp_over  max_ht.exp_under  max_ht.exp_over
+1    0          0          0         0              0                 -1               0                 0
+2    0.125      0.5        0.75      1.375          0.5               -0.75            0.0825            0.375
+3    0.25       1          1.5       2.75           1                 -0.5             0.165             0.75
+4    0.375      1.5        2.25      4.125          1.5               -0.25            0.2475            1.125
+5    0.5        2          3         5.5            2                 0                0.33              1.5
+6    0.625      2.5        3.75      6.875          2.5               0.25             0.4125            1.875
+7    0.75       3          4.5       8.25           3                 0.5              0.495             2.25
+8    0.875      3.5        5.25      9.625          3.5               0.75             0.5775            2.625
+9    1          4          6         11             4                 1                0.66              3
    
 
    In a 3-step sweep with a full-grid search type, with the
 --param CSV/param data frame specified as in
 the above example, the parameters passed to the function in each pass
 will be:
    
-
    run minfactor maxfactor banddiff banddiff_plus min_ht.exp_under min_ht.exp_over max_ht.exp_under max_ht.exp_over
-1       0.0         3        3           5.5                0               0              0.5             1.5
-2       0.5         3        3           5.5                0               0              0.5             1.5
-3       1.0         3        3           5.5                0               0              0.5             1.5
-4       0.0         3        3           5.5                2               0              0.5             1.5
-5       0.5         3        3           5.5                2               0              0.5             1.5
-6       1.0         3        3           5.5                2               0              0.5             1.5
-7       0.0         3        3           5.5                4               0              0.5             1.5
-8       0.5         3        3           5.5                4               0              0.5             1.5
-9       1.0         3        3           5.5                4               0              0.5             1.5
    
+
    run minfactor maxfactor banddiff banddiff_plus min_ht.exp_under min_ht.exp_over max_ht.exp_under max_ht.exp_over
+1       0.0         3        3           5.5                0               0              0.5             1.5
+2       0.5         3        3           5.5                0               0              0.5             1.5
+3       1.0         3        3           5.5                0               0              0.5             1.5
+4       0.0         3        3           5.5                2               0              0.5             1.5
+5       0.5         3        3           5.5                2               0              0.5             1.5
+6       1.0         3        3           5.5                2               0              0.5             1.5
+7       0.0         3        3           5.5                4               0              0.5             1.5
+8       0.5         3        3           5.5                4               0              0.5             1.5
+9       1.0         3        3           5.5                4               0              0.5             1.5
    
 
    For the script, the output in the working directory will contain the
 sweep parameters, like the above, in a file called
 test_adjustcarrforward_DATE_TIME_parameters.csv, and the
@@ -398,7 +401,7 @@ 
    Running the experimenttestacf_res entry.
    
 
    For example, a 5-step sweep with the line-grid search
 would be run with this command:
    
-
    Rscript exec/textadjustcf.R --gridlength 5 --searchtype line-grid cleaned.csv
    
+
    Rscript exec/textadjustcf.R --gridlength 5 --searchtype line-grid cleaned.csv
    
 
    or with this function execution:
    
 
     result_list <- testacf(
@@ -410,12 +413,12 @@ 
    Running the experimenttest_adjustcarrforward_DATE_TIME_parameters.csv
 (script)/params data frame of result_list
 (function) would be:
    
-
    run  minfactor  maxfactor  banddiff  banddiff_plus  min_ht.exp_under  min_ht.exp_over  max_ht.exp_under  max_ht.exp_over
-1    0          0          0         0              0                 -1               0                 0
-2    0.25       1          1.5       2.75           1                 -0.5             0.165             0.75
-3    0.5        2          3         5.5            2                 0                0.33              1.5
-4    0.75       3          4.5       8.25           3                 0.5              0.495             2.25
-5    1          4          6         11             4                 1                0.66              3
    
+
    run  minfactor  maxfactor  banddiff  banddiff_plus  min_ht.exp_under  min_ht.exp_over  max_ht.exp_under  max_ht.exp_over
+1    0          0          0         0              0                 -1               0                 0
+2    0.25       1          1.5       2.75           1                 -0.5             0.165             0.75
+3    0.5        2          3         5.5            2                 0                0.33              1.5
+4    0.75       3          4.5       8.25           3                 0.5              0.495             2.25
+5    1          4          6         11             4                 1                0.66              3
    
 
    Note that an odd-numbered length will include the default values in
 the middle run of the sweep (hence the examples with 5 and 9 step
 sweeps).
    
@@ -423,31 +426,31 @@ 
    Running the experimenttest_adjustcarrforward_DATE_TIME.csv
 (script)/testacf_res data frame of result_list
 (function) would be:
    
-
    id     subjid    sex  agedays  param     measurement  gcr_result                      run-1      run-2      run-3      run-4      run-5
-1510   775155    0    889      HEIGHTCM  84.9         Exclude-Extraneous-Same-Day Missing    Missing    Missing    Missing    Missing
-1511   775155    0    889      HEIGHTCM  89.06        Include                     No Change  No Change  No Change  No Change  No Change
-1512   775155    0    1071     HEIGHTCM  92.5         Include                     No Change  No Change  No Change  No Change  No Change
-1513   775155    0    1253     HEIGHTCM  96.2         Include                     No Change  No Change  No Change  No Change  No Change
-1514   775155    0    1435     HEIGHTCM  96.2         Exclude-Carried-Forward     No Change  No Change  Include    Include    Include
-1515   775155    0    1435     HEIGHTCM  99.692       Include                     No Change  No Change  No Change  No Change  No Change
-1516   775155    0    1806     HEIGHTCM  106.1        Include                     No Change  No Change  No Change  No Change  No Change
-1517   775155    0    2177     HEIGHTCM  112.3        Include                     No Change  No Change  No Change  No Change  No Change
-1518   775155    0    889      WEIGHTKG  13.1         Include                     No Change  No Change  No Change  No Change  No Change
    
+
    id     subjid    sex  agedays  param     measurement  gcr_result                      run-1      run-2      run-3      run-4      run-5
+1510   775155    0    889      HEIGHTCM  84.9         Exclude-Extraneous-Same-Day Missing    Missing    Missing    Missing    Missing
+1511   775155    0    889      HEIGHTCM  89.06        Include                     No Change  No Change  No Change  No Change  No Change
+1512   775155    0    1071     HEIGHTCM  92.5         Include                     No Change  No Change  No Change  No Change  No Change
+1513   775155    0    1253     HEIGHTCM  96.2         Include                     No Change  No Change  No Change  No Change  No Change
+1514   775155    0    1435     HEIGHTCM  96.2         Exclude-Carried-Forward     No Change  No Change  Include    Include    Include
+1515   775155    0    1435     HEIGHTCM  99.692       Include                     No Change  No Change  No Change  No Change  No Change
+1516   775155    0    1806     HEIGHTCM  106.1        Include                     No Change  No Change  No Change  No Change  No Change
+1517   775155    0    2177     HEIGHTCM  112.3        Include                     No Change  No Change  No Change  No Change  No Change
+1518   775155    0    889      WEIGHTKG  13.1         Include                     No Change  No Change  No Change  No Change  No Change
    
 
    The fifth row in the example above demonstrates the results of the
 experimental script; for runs 1 and 2, the result is not changed, but
 for runs 3-5, the measurement is adjusted for reinclusion. To
 demonstrate the range, the following is an extract of measurements only
 marked as carried forward exclusions by cleangrowth():
    
-
    id     subjid     sex  agedays  param     measurement  gcr_result                   run-1      run-2      run-3      run-4      run-5
-1514   775155     0    1435     HEIGHTCM  96.2         Exclude-Carried-Forward  No Change  No Change  Include    Include    Include
-1521   775155     0    1435     WEIGHTKG  15.3         Exclude-Carried-Forward  No Change  No Change  No Change  No Change  No Change
-7952   1340377    1    1806     HEIGHTCM  107.1        Exclude-Carried-Forward  No Change  Include    Include    Include    Include
-7967   1340377    1    1806     WEIGHTKG  18.4         Exclude-Carried-Forward  No Change  No Change  No Change  No Change  No Change
-41775  3643526    1    1253     HEIGHTCM  87.808       Exclude-Carried-Forward  Include    Include    Include    Include    Include
-44901  3706097    0    4032     HEIGHTCM  138.8        Exclude-Carried-Forward  No Change  Include    Include    Include    Include
-30011  5792371    1    3661     HEIGHTCM  145.4        Exclude-Carried-Forward  No Change  Include    Include    Include    Include
-30013  5792371    1    4032     HEIGHTCM  145.4        Exclude-Carried-Forward  No Change  No Change  No Change  No Change  No Change
-30016  5792371    1    1071     WEIGHTKG  15.9         Exclude-Carried-Forward  No Change  No Change  No Change  No Change  No Change
    
+
    id     subjid     sex  agedays  param     measurement  gcr_result                   run-1      run-2      run-3      run-4      run-5
+1514   775155     0    1435     HEIGHTCM  96.2         Exclude-Carried-Forward  No Change  No Change  Include    Include    Include
+1521   775155     0    1435     WEIGHTKG  15.3         Exclude-Carried-Forward  No Change  No Change  No Change  No Change  No Change
+7952   1340377    1    1806     HEIGHTCM  107.1        Exclude-Carried-Forward  No Change  Include    Include    Include    Include
+7967   1340377    1    1806     WEIGHTKG  18.4         Exclude-Carried-Forward  No Change  No Change  No Change  No Change  No Change
+41775  3643526    1    1253     HEIGHTCM  87.808       Exclude-Carried-Forward  Include    Include    Include    Include    Include
+44901  3706097    0    4032     HEIGHTCM  138.8        Exclude-Carried-Forward  No Change  Include    Include    Include    Include
+30011  5792371    1    3661     HEIGHTCM  145.4        Exclude-Carried-Forward  No Change  Include    Include    Include    Include
+30013  5792371    1    4032     HEIGHTCM  145.4        Exclude-Carried-Forward  No Change  No Change  No Change  No Change  No Change
+30016  5792371    1    1071     WEIGHTKG  15.9         Exclude-Carried-Forward  No Change  No Change  No Change  No Change  No Change
    
 
    Some of these values are not adjusted at all; one is from run 1 on, a
 few are from run 2 on, and one is from run 3 on.
    
 
    
diff --git a/docs/articles/output.html b/docs/articles/output.html
index e2dabd0f..6cf34fb5 100644
--- a/docs/articles/output.html
+++ b/docs/articles/output.html
@@ -33,7 +33,7 @@
       
       
         growthcleanr
-        2.1.1
+        2.2.0
    @@ -82,6 +82,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +

  • @@ -113,7 +116,7 @@

    Understanding growthcleanr output

    -

    2023-03-13

    +

    2023-09-13

    Source: vignettes/output.Rmd diff --git a/docs/articles/prelim-infants-algorithm.html b/docs/articles/prelim-infants-algorithm.html new file mode 100644 index 00000000..6539e63d --- /dev/null +++ b/docs/articles/prelim-infants-algorithm.html @@ -0,0 +1,195 @@ + + + + + + + +Preliminary infants algorithm • growthcleanr + + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    Preliminary infants algorithm

    + +

    2023-09-13

    + + Source: vignettes/prelim-infants-algorithm.Rmd + + +
    + + + +

    Now available is the in-development release of the infants algorithm, +which expands pediatric algorithm to clean 0 - 2 and revises its steps. +This is strongly not recommended for general use, and still contains +bugs. Possible bugs are enumerated below, and provide a starting point +for potential developers:

    +
      +
    • Same day exclusions are over called in weight and head +circumference
      • +
    • Carried forward exclusions are mis-called within that step
      • +
    • Same day exclusions are mis-called within that step
      • +
    • Raw differences step are over called in height
      • +
    • EWMA results missed in height
      • +
    • Carried forward results are missed in head circumference
      • +
    • Various smaller misalignments across exclusion results
      • +
    +

    If you would still like to use this in-development feature, you can +do this in the cleangrowth algorithm by turning on the option +prelim_infants:

    +
    +# prepare data as a data.table
+data <- as.data.table(source_data)
+ 
+# set the data.table key for better indexing
+setkey(data, subjid, param, agedays)
+ 
+# generate new exclusion flag field using function
+cleaned_data <- 
+  data[, gcr_result := cleangrowth(subjid, param, agedays, sex, measurement,
+                                   prelim_infants = TRUE)]
    +

    For information on the steps in the infants algorithm, please see this +document.

    +

    This algorithm expansion also contains information on smoothing +z-scores between observations of 2 and 4 years old. If you would like to +use this in your work, code can be found in growth.R lines 525-555.

    +

    If you have any feedback on this in-development feature, would like +to provide feedback on the algorithm logic or code, or discuss other +collaborations, please contact the maintainer of this package, Carrie +Daymont.

    +
    + + + +
    + + + +
    +

    +

    Developed by Carrie Daymont, Robert Grundmeier, Jeffrey Miller, Diego Campos.

    +
    + +
    +

    +

    Site built with pkgdown 2.0.7.

    +
    + +
    +
    + + + + + + + + diff --git a/docs/articles/quickstart.html b/docs/articles/quickstart.html index f2313936..6b4a567e 100644 --- a/docs/articles/quickstart.html +++ b/docs/articles/quickstart.html @@ -33,7 +33,7 @@ growthcleanr - 2.1.1 + 2.2.0
    @@ -82,6 +82,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • @@ -113,7 +116,7 @@

    Quickstart

    -

    2023-03-13

    +

    2023-09-13

    Source: vignettes/quickstart.Rmd diff --git a/docs/articles/usage.html b/docs/articles/usage.html index 25a80391..59fa8c95 100644 --- a/docs/articles/usage.html +++ b/docs/articles/usage.html @@ -33,7 +33,7 @@ growthcleanr - 2.1.1 + 2.2.0
    @@ -82,6 +82,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • @@ -113,7 +116,7 @@

    Usage

    -

    2023-03-13

    +

    2023-09-13

    Source: vignettes/usage.Rmd @@ -152,45 +155,45 @@

    Basic operations using ex

    It can be used as in the following example. Note that processing the example data with cleangrowth() will likely take a few minutes to complete.

    -
    library(data.table)
-library(dplyr)
-
-# Convert the `syngrowth` data frame to a `data.table`
-data <- as.data.table(syngrowth)
-
-# `setkey()` creates an efficient sorting key on the `data.table`; this is required
-# for `cleangrowth()`
-setkey(data, subjid, param, agedays)
-
-# Add a column `gcr_result` using `cleangrowth`
-cleaned_data <- data[, gcr_result := cleangrowth(subjid, param, agedays, sex, measurement)]
-
-# View a sample of the results
-head(cleaned_data)
-      id                               subjid sex  agedays    param measurement                  gcr_result
-1: 83330 002986c5-354d-bb9d-c180-4ce26813ca28   1 20489.22 HEIGHTCM       151.1                     Include
-2: 83332 002986c5-354d-bb9d-c180-4ce26813ca28   1 20860.22 HEIGHTCM       151.1                     Include
-3: 83334 002986c5-354d-bb9d-c180-4ce26813ca28   1 20860.22 HEIGHTCM       150.6 Exclude-Same-Day-Extraneous
-4: 83335 002986c5-354d-bb9d-c180-4ce26813ca28   1 21231.22 HEIGHTCM       151.1                     Include
-5: 83337 002986c5-354d-bb9d-c180-4ce26813ca28   1 21602.22 HEIGHTCM       151.1                     Include
-6: 83339 002986c5-354d-bb9d-c180-4ce26813ca28   1 21623.22 HEIGHTCM       151.1                     Include
-
-# Summarize results by result type
-cleaned_data %>% group_by(gcr_result) %>% tally(sort=TRUE)
-# A tibble: 26 x 2
-   gcr_result                      n
-   <fct>                       <int>
- 1 Include                     61652
- 2 Exclude-Extraneous-Same-Day 11263
- 3 Exclude-Carried-Forward      7093
- 4 Exclude-Same-Day-Extraneous  4010
- 5 Exclude-Same-Day-Identical    623
- 6 Exclude-SD-Cutoff             175
- 7 Exclude-EWMA-8                139
- 8 Exclude-Distinct-3-Or-More    125
- 9 Exclude-BIV                   108
-10 Exclude-EWMA-Extreme           99
-# … with 16 more rows
    +
    library(data.table)
+library(dplyr)
+
+# Convert the `syngrowth` data frame to a `data.table`
+data <- as.data.table(syngrowth)
+
+# `setkey()` creates an efficient sorting key on the `data.table`; this is required
+# for `cleangrowth()`
+setkey(data, subjid, param, agedays)
+
+# Add a column `gcr_result` using `cleangrowth`
+cleaned_data <- data[, gcr_result := cleangrowth(subjid, param, agedays, sex, measurement)]
+
+# View a sample of the results
+head(cleaned_data)
+      id                               subjid sex  agedays    param measurement                  gcr_result
+1: 83330 002986c5-354d-bb9d-c180-4ce26813ca28   1 20489.22 HEIGHTCM       151.1                     Include
+2: 83332 002986c5-354d-bb9d-c180-4ce26813ca28   1 20860.22 HEIGHTCM       151.1                     Include
+3: 83334 002986c5-354d-bb9d-c180-4ce26813ca28   1 20860.22 HEIGHTCM       150.6 Exclude-Same-Day-Extraneous
+4: 83335 002986c5-354d-bb9d-c180-4ce26813ca28   1 21231.22 HEIGHTCM       151.1                     Include
+5: 83337 002986c5-354d-bb9d-c180-4ce26813ca28   1 21602.22 HEIGHTCM       151.1                     Include
+6: 83339 002986c5-354d-bb9d-c180-4ce26813ca28   1 21623.22 HEIGHTCM       151.1                     Include
+
+# Summarize results by result type
+cleaned_data %>% group_by(gcr_result) %>% tally(sort=TRUE)
+# A tibble: 26 x 2
+   gcr_result                      n
+   <fct>                       <int>
+ 1 Include                     61652
+ 2 Exclude-Extraneous-Same-Day 11263
+ 3 Exclude-Carried-Forward      7093
+ 4 Exclude-Same-Day-Extraneous  4010
+ 5 Exclude-Same-Day-Identical    623
+ 6 Exclude-SD-Cutoff             175
+ 7 Exclude-EWMA-8                139
+ 8 Exclude-Distinct-3-Or-More    125
+ 9 Exclude-BIV                   108
+10 Exclude-EWMA-Extreme           99
+# … with 16 more rows

    If you are able to run these steps and see a similar result, you have the growthcleanr package installed correctly. The resulting cleaned_data can be reviewed, subsetted, and compared in @@ -257,9 +260,9 @@

    Basic configuration options
    Warning messages:
-1: <anonymous>: ... may be used in an incorrect context: ‘.fun(piece, ...)’
-2: <anonymous>: ... may be used in an incorrect context: ‘.fun(piece, ...)’

    +
    Warning messages:
+1: <anonymous>: ... may be used in an incorrect context: ‘.fun(piece, ...)’
+2: <anonymous>: ... may be used in an incorrect context: ‘.fun(piece, ...)’

    This set of warnings is coming from one of growthcleanr’s dependencies, and does not indicate either failure or improper execution.

    diff --git a/docs/articles/utilities.html b/docs/articles/utilities.html index 5b8d14ba..dc133873 100644 --- a/docs/articles/utilities.html +++ b/docs/articles/utilities.html @@ -33,7 +33,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -82,6 +82,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • @@ -114,7 +117,7 @@

    Utilities for computing pediatric BMI percentiles, Z-scores, and related tools

    -

    2023-03-13

    +

    2023-09-13

    Source: vignettes/utilities.Rmd @@ -188,27 +191,27 @@

    Converting long g

    Note that this assumes that cleaned_data has the same structure as described in Quickstart - Data preparation:

    -
    names(cleaned_data)
-[1] "id"          "subjid"      "sex"         "agedays"     "param"       "measurement" "gcr_result"
    +
    names(cleaned_data)
+[1] "id"          "subjid"      "sex"         "agedays"     "param"       "measurement" "gcr_result"

    The wide dataset cleaned_data_wide will include rows with aligned height and weight measurements drawn from the observations in cleaned_data marked by cleangrowth() for inclusion. As such, it will be a shorter dataset (fewer rows) based on fewer observations.

    -
    dim(cleaned_data)
-[1] 85728     7
-
-dim(cleaned_data_wide)
-[1] 26701     9
-
-head(cleaned_data_wide)
-                                subjid    agey     agem sex   wt wt_id    ht ht_id  agedays
-1 002986c5-354d-bb9d-c180-4ce26813ca28 56.0964 673.1568   2 71.7 83331 151.1 83330 20489.22
-2 002986c5-354d-bb9d-c180-4ce26813ca28 57.1122 685.3464   2 73.2 83333 151.1 83332 20860.22
-3 002986c5-354d-bb9d-c180-4ce26813ca28 58.1279 697.5348   2 74.6 83336 151.1 83335 21231.22
-4 002986c5-354d-bb9d-c180-4ce26813ca28 59.1437 709.7244   2 72.8 83338 151.1 83337 21602.22
-5 002986c5-354d-bb9d-c180-4ce26813ca28 59.2012 710.4144   2 72.4 83340 151.1 83339 21623.22
-6 002986c5-354d-bb9d-c180-4ce26813ca28 60.1594 721.9128   2 69.4 83343 151.1 83342 21973.22
    +
    dim(cleaned_data)
+[1] 85728     7
+
+dim(cleaned_data_wide)
+[1] 26701     9
+
+head(cleaned_data_wide)
+                                subjid    agey     agem sex   wt wt_id    ht ht_id  agedays
+1 002986c5-354d-bb9d-c180-4ce26813ca28 56.0964 673.1568   2 71.7 83331 151.1 83330 20489.22
+2 002986c5-354d-bb9d-c180-4ce26813ca28 57.1122 685.3464   2 73.2 83333 151.1 83332 20860.22
+3 002986c5-354d-bb9d-c180-4ce26813ca28 58.1279 697.5348   2 74.6 83336 151.1 83335 21231.22
+4 002986c5-354d-bb9d-c180-4ce26813ca28 59.1437 709.7244   2 72.8 83338 151.1 83337 21602.22
+5 002986c5-354d-bb9d-c180-4ce26813ca28 59.2012 710.4144   2 72.4 83340 151.1 83339 21623.22
+6 002986c5-354d-bb9d-c180-4ce26813ca28 60.1594 721.9128   2 69.4 83343 151.1 83342 21973.22

    In this example, the subject identifiers previously marked as subjid are now in the id column; individual identifiers for observations of a single parameter are not present.

    @@ -216,15 +219,15 @@

    Converting long g your input set uses different column names. For example, if my_cleaned_data specifies age in days as aged and parameter type as type, specify each, with quotes:

    -
    head(my_cleaned_data)
-     id subjid sex    aged     type measurement                  gcr_result
-1: 1510 775155   0     889 HEIGHTCM       84.90 Exclude-Extraneous-Same-Day
-2: 1511 775155   0     889 HEIGHTCM       89.06                     Include
-3: 1518 775155   0     889 WEIGHTKG       13.10                     Include
-4: 1512 775155   0    1071 HEIGHTCM       92.50                     Include
-5: 1519 775155   0    1071 WEIGHTKG       14.70                     Include
-6: 1513 775155   0    1253 HEIGHTCM       96.20                     Include
-longwide(my_cleaned_data, agedays = "aged", param = "type")
    +
    head(my_cleaned_data)
+     id subjid sex    aged     type measurement                  gcr_result
+1: 1510 775155   0     889 HEIGHTCM       84.90 Exclude-Extraneous-Same-Day
+2: 1511 775155   0     889 HEIGHTCM       89.06                     Include
+3: 1518 775155   0     889 WEIGHTKG       13.10                     Include
+4: 1512 775155   0    1071 HEIGHTCM       92.50                     Include
+5: 1519 775155   0    1071 WEIGHTKG       14.70                     Include
+6: 1513 775155   0    1253 HEIGHTCM       96.20                     Include
+longwide(my_cleaned_data, agedays = "aged", param = "type")

    By default, longwide() will only transform records flagged by cleangrowth() for inclusion. To include more categories assigned by cleangrowth(), use the @@ -287,32 +290,32 @@

    Computing BMI percentiles and Z- ‘Male’ for male subjects and any of ‘2’, ‘g’, ‘G’, ‘Girls’, ‘f’, ‘F’, ‘female’, or ‘Female’ for female subjects), ext_bmiz() can be called:

    -
    cleaned_data_bmiz <- ext_bmiz(cleaned_data_bmi)
-head(cleaned_data_bmiz)
-                                 subjid    agey      age   sex    wt wt_id    ht ht_id agedays      bmi      bmiz
-                                 <char>   <num>    <num> <int> <num> <int> <num> <int>   <int>    <num>     <num>
-1: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 10.0233 120.2796     2  35.4    17 141.6    15    3661 17.65537 0.3236612
-2: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 11.0390 132.4680     2  39.2    19 147.9    18    4032 17.92048 0.1734315
-3: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 12.0548 144.6576     2  44.8    21 155.1    20    4403 18.62320 0.1832443
-4: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 12.5914 151.0968     2  47.8    23 158.7    22    4599 18.97903 0.1829183
-5: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 13.0705 156.8460     2  50.5    26 160.8    24    4774 19.53077 0.2586449
-6: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad  3.9288  47.1456     2  16.6     2 102.6     1    1435 15.76933 0.3453978
-       bmip       waz       wp       haz       hp      p95      p97   bmip95   mod_bmiz   mod_waz   mod_haz
-      <num>     <num>    <num>     <num>    <num>    <num>    <num>    <num>      <num>     <num>     <num>
-1: 62.69027 0.3498817 63.67862 0.5140553 69.63933 22.96109 24.57902 76.89254 0.18485501 0.2399201 0.5008944
-2: 56.88438 0.2311645 59.14065 0.5002225 69.15408 24.13836 25.90525 74.24067 0.09543668 0.1556259 0.4955022
-3: 57.26968 0.3237374 62.69316 0.4803298 68.45036 25.26981 27.17179 73.69745 0.10065274 0.2201655 0.4855100
-4: 57.25689 0.3812700 64.84986 0.5153244 69.68368 25.83904 27.80781 73.45100 0.10011666 0.2596479 0.5212281
-5: 60.20454 0.4440465 67.14955 0.4849566 68.61464 26.32757 28.35405 74.18371 0.14353771 0.3024342 0.4885546
-6: 63.51023 0.4369963 66.89430 0.5348018 70.36065 18.02950 18.60078 87.46409 0.24462581 0.3332011 0.5224816
-      sigma original_bmip original_bmiz sev_obese obese
-      <num>         <num>         <num>     <int> <int>
-1: 4.443536      62.69027     0.3236612         0     0
-2: 4.797031      56.88438     0.1734315         0     0
-3: 5.148292      57.26968     0.1832443         0     0
-4: 5.332930      57.25689     0.1829183         0     0
-5: 5.497248      60.20454     0.2586449         0     0
-6: 2.274792      63.51023     0.3453978         0     0
    +
    cleaned_data_bmiz <- ext_bmiz(cleaned_data_bmi)
+head(cleaned_data_bmiz)
+                                 subjid    agey      age   sex    wt wt_id    ht ht_id agedays      bmi      bmiz
+                                 <char>   <num>    <num> <int> <num> <int> <num> <int>   <int>    <num>     <num>
+1: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 10.0233 120.2796     2  35.4    17 141.6    15    3661 17.65537 0.3236612
+2: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 11.0390 132.4680     2  39.2    19 147.9    18    4032 17.92048 0.1734315
+3: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 12.0548 144.6576     2  44.8    21 155.1    20    4403 18.62320 0.1832443
+4: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 12.5914 151.0968     2  47.8    23 158.7    22    4599 18.97903 0.1829183
+5: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad 13.0705 156.8460     2  50.5    26 160.8    24    4774 19.53077 0.2586449
+6: 001aa16d-bf0e-a077-3b3d-5ab8b58545ad  3.9288  47.1456     2  16.6     2 102.6     1    1435 15.76933 0.3453978
+       bmip       waz       wp       haz       hp      p95      p97   bmip95   mod_bmiz   mod_waz   mod_haz
+      <num>     <num>    <num>     <num>    <num>    <num>    <num>    <num>      <num>     <num>     <num>
+1: 62.69027 0.3498817 63.67862 0.5140553 69.63933 22.96109 24.57902 76.89254 0.18485501 0.2399201 0.5008944
+2: 56.88438 0.2311645 59.14065 0.5002225 69.15408 24.13836 25.90525 74.24067 0.09543668 0.1556259 0.4955022
+3: 57.26968 0.3237374 62.69316 0.4803298 68.45036 25.26981 27.17179 73.69745 0.10065274 0.2201655 0.4855100
+4: 57.25689 0.3812700 64.84986 0.5153244 69.68368 25.83904 27.80781 73.45100 0.10011666 0.2596479 0.5212281
+5: 60.20454 0.4440465 67.14955 0.4849566 68.61464 26.32757 28.35405 74.18371 0.14353771 0.3024342 0.4885546
+6: 63.51023 0.4369963 66.89430 0.5348018 70.36065 18.02950 18.60078 87.46409 0.24462581 0.3332011 0.5224816
+      sigma original_bmip original_bmiz sev_obese obese
+      <num>         <num>         <num>     <int> <int>
+1: 4.443536      62.69027     0.3236612         0     0
+2: 4.797031      56.88438     0.1734315         0     0
+3: 5.148292      57.26968     0.1832443         0     0
+4: 5.332930      57.25689     0.1829183         0     0
+5: 5.497248      60.20454     0.2586449         0     0
+6: 2.274792      63.51023     0.3453978         0     0

    The output columns include:

    diff --git a/docs/authors.html b/docs/authors.html index 49fbcb39..0b4da03e 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/index.html b/docs/index.html index ec278bf6..49211d6f 100644 --- a/docs/index.html +++ b/docs/index.html @@ -34,7 +34,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -83,6 +83,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • diff --git a/docs/news/index.html b/docs/news/index.html index 2534ae67..e5c77e64 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference @@ -88,6 +91,13 @@

    Changelog

    Source: NEWS.md +
    +

    growthcleanr 2.2.0-prelim-infants - 2023-09-13

    +
    +

    Added

    +
    • Added option to cleangrowth for the preliminary infants algorithm – expands pediatric algorithm to consider 0 - 2 years old, with infants = TRUE in cleangrowth(), with all steps implemented. Note that this option is still preliminary and should not be used for research. For more information regarding the logic of the algorithm, see the vignette ‘Preliminary Infants Algorithm.’
      • +
    +

    growthcleanr 3.0.0-infants-beta - 2023-03-13

    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 53be6f20..dcb87f3e 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,4 +1,4 @@ -pandoc: 2.19.2 +pandoc: 3.1.1 pkgdown: 2.0.7 pkgdown_sha: ~ articles: @@ -9,8 +9,9 @@ articles: large-data-sets: large-data-sets.html next-steps: next-steps.html output: output.html + prelim-infants-algorithm: prelim-infants-algorithm.html quickstart: quickstart.html usage: usage.html utilities: utilities.html -last_built: 2023-03-13T17:25Z +last_built: 2023-09-13T14:53Z diff --git a/docs/reference/CDCref_d.html b/docs/reference/CDCref_d.html index 25da7942..8cc180d8 100644 --- a/docs/reference/CDCref_d.html +++ b/docs/reference/CDCref_d.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0
    @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/acf_answers.html b/docs/reference/acf_answers.html index 663e603b..4cf76465 100644 --- a/docs/reference/acf_answers.html +++ b/docs/reference/acf_answers.html @@ -18,7 +18,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -63,6 +63,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/adjustcarryforward.html b/docs/reference/adjustcarryforward.html index 87680c73..172c45da 100644 --- a/docs/reference/adjustcarryforward.html +++ b/docs/reference/adjustcarryforward.html @@ -23,7 +23,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -68,6 +68,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/bmianthro.html b/docs/reference/bmianthro.html index 74319d28..4d685a75 100644 --- a/docs/reference/bmianthro.html +++ b/docs/reference/bmianthro.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/cleangrowth.html b/docs/reference/cleangrowth.html index 0b35e97d..5818b201 100644 --- a/docs/reference/cleangrowth.html +++ b/docs/reference/cleangrowth.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference @@ -120,7 +123,7 @@

    Clean growth measurements

    adult_cutpoint = 20, weight_cap = Inf, adult_columns_filename = "", - infants = FALSE + prelim_infants = FALSE ) @@ -275,8 +278,8 @@

    Arguments

    for post-analysis. For more information on this output, please see README.

    -
    infants
    -

    TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.

    +
    prelim_infants
    +

    TRUE/FALSE. Run the in-development release of the infants algorithm (expands pediatric algorithm to improve performance for children 0 – 2 years). Not recommended for use in research. For more information regarding the logic of the algorithm, see the vignette 'Preliminary Infants Algorithm.' Defaults to FALSE.

    diff --git a/docs/reference/ewma.html b/docs/reference/ewma.html index 3eb48c6a..acf54758 100644 --- a/docs/reference/ewma.html +++ b/docs/reference/ewma.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0
    @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/ext_bmiz.html b/docs/reference/ext_bmiz.html index ae6d3bd6..c8cf2dad 100644 --- a/docs/reference/ext_bmiz.html +++ b/docs/reference/ext_bmiz.html @@ -22,7 +22,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -67,6 +67,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/growth_cdc_ext.html b/docs/reference/growth_cdc_ext.html index 5f613c6d..29952d9a 100644 --- a/docs/reference/growth_cdc_ext.html +++ b/docs/reference/growth_cdc_ext.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/index.html b/docs/reference/index.html index a409c56f..0456d281 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/lenanthro.html b/docs/reference/lenanthro.html index 38a315b3..199f31c2 100644 --- a/docs/reference/lenanthro.html +++ b/docs/reference/lenanthro.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/longwide.html b/docs/reference/longwide.html index 0909e9e3..7b4760f0 100644 --- a/docs/reference/longwide.html +++ b/docs/reference/longwide.html @@ -19,7 +19,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -64,6 +64,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/nhanes-reference-medians.html b/docs/reference/nhanes-reference-medians.html index 5c875b13..c54e458d 100644 --- a/docs/reference/nhanes-reference-medians.html +++ b/docs/reference/nhanes-reference-medians.html @@ -18,7 +18,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -63,6 +63,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/read_anthro.html b/docs/reference/read_anthro.html index 5bc801b5..358a1d21 100644 --- a/docs/reference/read_anthro.html +++ b/docs/reference/read_anthro.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference @@ -94,7 +97,7 @@

    Function to calculate z-scores and csd-scores based on anthro tables.

    - 
    read_anthro(path = "", cdc.only = FALSE, infants = FALSE)
    + 
    read_anthro(path = "", cdc.only = FALSE, prelim_infants = FALSE)
    @@ -107,8 +110,8 @@

    Arguments

    Whether or not only CDC data should be used. Defaults to false.

    -
    infants
    -

    boolean on if beta infants version is being used. will be merged in to main algorithm.

    +
    prelim_infants
    +

    TRUE/FALSE. Run the in-development release of the infants algorithm (expands pediatric algorithm to improve performance for children 0 – 2 years). Not recommended for use in research. For more information regarding the logic of the algorithm, see the vignette 'Preliminary Infants Algorithm.' Defaults to FALSE.

    diff --git a/docs/reference/recode_sex.html b/docs/reference/recode_sex.html index 06f5b597..1dde31d8 100644 --- a/docs/reference/recode_sex.html +++ b/docs/reference/recode_sex.html @@ -18,7 +18,7 @@ growthcleanr - 2.1.1 + 2.2.0
    @@ -63,6 +63,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/sd_median.html b/docs/reference/sd_median.html index d3ef2ca5..a659e553 100644 --- a/docs/reference/sd_median.html +++ b/docs/reference/sd_median.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/simple_bmi.html b/docs/reference/simple_bmi.html index c04d2f53..4ae274ca 100644 --- a/docs/reference/simple_bmi.html +++ b/docs/reference/simple_bmi.html @@ -18,7 +18,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -63,6 +63,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/splitinput.html b/docs/reference/splitinput.html index a5cd2398..4ff94ec5 100644 --- a/docs/reference/splitinput.html +++ b/docs/reference/splitinput.html @@ -21,7 +21,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -66,6 +66,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/syngrowth.html b/docs/reference/syngrowth.html index 01b0c010..dd4038c3 100644 --- a/docs/reference/syngrowth.html +++ b/docs/reference/syngrowth.html @@ -19,7 +19,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -64,6 +64,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/tanner_ht_vel.html b/docs/reference/tanner_ht_vel.html index 4a94e9ed..a108b9b8 100644 --- a/docs/reference/tanner_ht_vel.html +++ b/docs/reference/tanner_ht_vel.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/tanner_ht_vel_with_2sd.html b/docs/reference/tanner_ht_vel_with_2sd.html index 57afa61e..bed09a29 100644 --- a/docs/reference/tanner_ht_vel_with_2sd.html +++ b/docs/reference/tanner_ht_vel_with_2sd.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/test_syngrowth_sas_output_compare.html b/docs/reference/test_syngrowth_sas_output_compare.html index 742fa140..5ee61a13 100644 --- a/docs/reference/test_syngrowth_sas_output_compare.html +++ b/docs/reference/test_syngrowth_sas_output_compare.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/test_syngrowth_wide.html b/docs/reference/test_syngrowth_wide.html index b108b1bb..a2b77ce9 100644 --- a/docs/reference/test_syngrowth_wide.html +++ b/docs/reference/test_syngrowth_wide.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/testacf.html b/docs/reference/testacf.html index 613eae99..569d3726 100644 --- a/docs/reference/testacf.html +++ b/docs/reference/testacf.html @@ -20,7 +20,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -65,6 +65,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/weianthro.html b/docs/reference/weianthro.html index e2ac4690..c29bdf7c 100644 --- a/docs/reference/weianthro.html +++ b/docs/reference/weianthro.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/who_ht_maxvel.html b/docs/reference/who_ht_maxvel.html index 47851509..87bf1931 100644 --- a/docs/reference/who_ht_maxvel.html +++ b/docs/reference/who_ht_maxvel.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/who_ht_maxvel_2sd.html b/docs/reference/who_ht_maxvel_2sd.html index abf89d3d..bc062d92 100644 --- a/docs/reference/who_ht_maxvel_2sd.html +++ b/docs/reference/who_ht_maxvel_2sd.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/who_ht_vel_2sd.html b/docs/reference/who_ht_vel_2sd.html index 35a0a069..da52a7b8 100644 --- a/docs/reference/who_ht_vel_2sd.html +++ b/docs/reference/who_ht_vel_2sd.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/reference/who_ht_vel_3sd.html b/docs/reference/who_ht_vel_3sd.html index 93b8969a..4d0bfa3e 100644 --- a/docs/reference/who_ht_vel_3sd.html +++ b/docs/reference/who_ht_vel_3sd.html @@ -17,7 +17,7 @@ growthcleanr - 2.1.1 + 2.2.0 @@ -62,6 +62,9 @@
  • Developer guidelines
    • +
  • + Preliminary infants algorithm +
  • Reference diff --git a/docs/sitemap.xml b/docs/sitemap.xml index e2066ded..7fe67a64 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -27,6 +27,9 @@ /articles/output.html + + /articles/prelim-infants-algorithm.html + /articles/quickstart.html From ebe274f4f1f4b403a04c029aee72cb5bc71aeecb Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Wed, 13 Sep 2023 18:39:06 -0400 Subject: [PATCH 099/100] Update documentation for extdata infants --- R/extdata.R | 120 ++++++++++++++++++++++++++++++++++ cran-comments.md | 2 +- man/fentlms_foraga.Rd | 15 +++++ man/fentlms_forz.Rd | 15 +++++ man/growth_cdc_ext_infants.Rd | 17 +++++ man/growth_who_ext.Rd | 17 +++++ man/rc-reference-medians.Rd | 15 +++++ man/tanner_ht_vel_rev.Rd | 18 +++++ man/who_hc_maxvel.Rd | 18 +++++ man/who_hc_vel_3sd.Rd | 18 +++++ 10 files changed, 254 insertions(+), 1 deletion(-) create mode 100644 man/fentlms_foraga.Rd create mode 100644 man/fentlms_forz.Rd create mode 100644 man/growth_cdc_ext_infants.Rd create mode 100644 man/growth_who_ext.Rd create mode 100644 man/rc-reference-medians.Rd create mode 100644 man/tanner_ht_vel_rev.Rd create mode 100644 man/who_hc_maxvel.Rd create mode 100644 man/who_hc_vel_3sd.Rd diff --git a/R/extdata.R b/R/extdata.R index 08db5457..fd2efb91 100644 --- a/R/extdata.R +++ b/R/extdata.R @@ -28,6 +28,36 @@ NULL #' NULL +#' CDC Growth Percentile Table for Infants +#' +#' Part of default CDC-derived tables +#' +#' Contains percentiles for various ages, gender, and weights, pre-calculated by CDC for infants algorithm +#' +#' @name growth_cdc_ext_infants +#' +#' @section growthfile_cdc_ext_infants.csv.gz: +#' +#' Used in function `cleangrowth()` +#' +#' +NULL + +#' WHO Growth Percentile Table +#' +#' Part of default WHO-derived tables +#' +#' Contains percentiles for various ages, gender, and weights, pre-calculated by WHO +#' +#' @name growth_who_ext +#' +#' @section growthfile_who.csv.gz: +#' +#' Used in function `cleangrowth()` +#' +#' +NULL + #' Length to Age Table #' #' Part of default CDC-derived tables @@ -43,6 +73,20 @@ NULL #' NULL +#' Infants reference medians +#' +#' Contains reference median values for default recentering in the infants +#' algorithm +#' +#' @name rc-reference-medians +#' +#' @section rcfile-2023-08-15_format.csv.gz: +#' +#' Used in function `cleangrowth()` +#' +#' +NULL + #' NHANES reference medians #' #' Contains reference median values for default recentering, derived from NHANES @@ -72,6 +116,22 @@ NULL #' NULL +#' Tanner Growth Velocity Table for Infants +#' +#' Part of default CDC-derived tables +#' +#' Contains velocities for growth pre-calculated by CDC, used for the infants +#' algorithm +#' +#' @name tanner_ht_vel_rev +#' +#' @section tanner_ht_vel_rev.csv.gz: +#' +#' Used in function `cleangrowth()` +#' +#' +NULL + #' Tanner Growth Velocity Table with (2\eqn{\sigma}) #' #' Part of default CDC-derived tables @@ -167,6 +227,38 @@ NULL #' NULL +#' WHO Maximum Head Circumference Velocity for (3\eqn{\sigma}) +#' +#' Part of default WHO-derived tables +#' +#' Contains three standard deviations for the World Health Organization values of +#' maximum head circumference velocities. +#' +#' @name who_hc_maxvel +#' +#' @section who_hc_maxvel_3sd_infants.csv.gz: +#' +#' Used in function `cleangrowth()` +#' +#' +NULL + +#' WHO Head Circumference Velocity for (3\eqn{\sigma}) +#' +#' Part of default WHO-derived tables +#' +#' Contains three standard deviations for the World Health Organization values of +#' head circumference velocities. +#' +#' @name who_hc_vel_3sd +#' +#' @section who_hc_vel_3sd_infants.csv.gz: +#' +#' Used in function `cleangrowth()` +#' +#' +NULL + #' CDC SAS BMI Input #' #' Contains input data for CDC SAS macro for calculating BMI values. @@ -205,3 +297,31 @@ NULL #' #' NULL + +#' Fenton Growth Curves +#' +#' Fenton growth curves with premature infant data with sex, age, and integer +#' weight +#' +#' @name fentlms_foraga +#' +#' @section fentlms_foraga.csv.gz: +#' +#' Used in function `cleangrowth()` +#' +#' +NULL + +#' Fenton Growth Curve Z-Scores +#' +#' Fenton growth curves with premature infant z-scores for height and head +#'circumference +#' +#' @name fentlms_forz +#' +#' @section fentlms_forz.csv.gz: +#' +#' Used in function `cleangrowth()` +#' +#' +NULL diff --git a/cran-comments.md b/cran-comments.md index 09891de2..ce8d7711 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -6,7 +6,7 @@ There were no ERRORs or WARNINGs. There was one note: ❯ checking installed package size ... NOTE - installed size is 6.1Mb + installed size is 5.9Mb sub-directories of 1Mb or more: extdata 5.0Mb diff --git a/man/fentlms_foraga.Rd b/man/fentlms_foraga.Rd new file mode 100644 index 00000000..4ca0ffaa --- /dev/null +++ b/man/fentlms_foraga.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extdata.R +\name{fentlms_foraga} +\alias{fentlms_foraga} +\title{Fenton Growth Curves} +\description{ +Fenton growth curves with premature infant data with sex, age, and integer +weight +} +\section{fentlms_foraga.csv.gz}{ + + +Used in function \code{cleangrowth()} +} + diff --git a/man/fentlms_forz.Rd b/man/fentlms_forz.Rd new file mode 100644 index 00000000..b4673567 --- /dev/null +++ b/man/fentlms_forz.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extdata.R +\name{fentlms_forz} +\alias{fentlms_forz} +\title{Fenton Growth Curve Z-Scores} +\description{ +Fenton growth curves with premature infant z-scores for height and head +circumference +} +\section{fentlms_forz.csv.gz}{ + + +Used in function \code{cleangrowth()} +} + diff --git a/man/growth_cdc_ext_infants.Rd b/man/growth_cdc_ext_infants.Rd new file mode 100644 index 00000000..d6906498 --- /dev/null +++ b/man/growth_cdc_ext_infants.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extdata.R +\name{growth_cdc_ext_infants} +\alias{growth_cdc_ext_infants} +\title{CDC Growth Percentile Table for Infants} +\description{ +Part of default CDC-derived tables +} +\details{ +Contains percentiles for various ages, gender, and weights, pre-calculated by CDC for infants algorithm +} +\section{growthfile_cdc_ext_infants.csv.gz}{ + + +Used in function \code{cleangrowth()} +} + diff --git a/man/growth_who_ext.Rd b/man/growth_who_ext.Rd new file mode 100644 index 00000000..eea98cb5 --- /dev/null +++ b/man/growth_who_ext.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extdata.R +\name{growth_who_ext} +\alias{growth_who_ext} +\title{WHO Growth Percentile Table} +\description{ +Part of default WHO-derived tables +} +\details{ +Contains percentiles for various ages, gender, and weights, pre-calculated by WHO +} +\section{growthfile_who.csv.gz}{ + + +Used in function \code{cleangrowth()} +} + diff --git a/man/rc-reference-medians.Rd b/man/rc-reference-medians.Rd new file mode 100644 index 00000000..174e0a55 --- /dev/null +++ b/man/rc-reference-medians.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extdata.R +\name{rc-reference-medians} +\alias{rc-reference-medians} +\title{Infants reference medians} +\description{ +Contains reference median values for default recentering in the infants +algorithm +} +\section{rcfile-2023-08-15_format.csv.gz}{ + + +Used in function \code{cleangrowth()} +} + diff --git a/man/tanner_ht_vel_rev.Rd b/man/tanner_ht_vel_rev.Rd new file mode 100644 index 00000000..ebe66055 --- /dev/null +++ b/man/tanner_ht_vel_rev.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extdata.R +\name{tanner_ht_vel_rev} +\alias{tanner_ht_vel_rev} +\title{Tanner Growth Velocity Table for Infants} +\description{ +Part of default CDC-derived tables +} +\details{ +Contains velocities for growth pre-calculated by CDC, used for the infants +algorithm +} +\section{tanner_ht_vel_rev.csv.gz}{ + + +Used in function \code{cleangrowth()} +} + diff --git a/man/who_hc_maxvel.Rd b/man/who_hc_maxvel.Rd new file mode 100644 index 00000000..4a0a5f27 --- /dev/null +++ b/man/who_hc_maxvel.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extdata.R +\name{who_hc_maxvel} +\alias{who_hc_maxvel} +\title{WHO Maximum Head Circumference Velocity for (3\eqn{\sigma})} +\description{ +Part of default WHO-derived tables +} +\details{ +Contains three standard deviations for the World Health Organization values of +maximum head circumference velocities. +} +\section{who_hc_maxvel_3sd_infants.csv.gz}{ + + +Used in function \code{cleangrowth()} +} + diff --git a/man/who_hc_vel_3sd.Rd b/man/who_hc_vel_3sd.Rd new file mode 100644 index 00000000..03d7d41f --- /dev/null +++ b/man/who_hc_vel_3sd.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extdata.R +\name{who_hc_vel_3sd} +\alias{who_hc_vel_3sd} +\title{WHO Head Circumference Velocity for (3\eqn{\sigma})} +\description{ +Part of default WHO-derived tables +} +\details{ +Contains three standard deviations for the World Health Organization values of +head circumference velocities. +} +\section{who_hc_vel_3sd_infants.csv.gz}{ + + +Used in function \code{cleangrowth()} +} + From 0719bb51fedab40fe54a39af7dbf0df788d02d3f Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Wed, 13 Sep 2023 19:40:39 -0400 Subject: [PATCH 100/100] Update package down site --- _pkgdown.yml | 8 ++ docs/pkgdown.yml | 2 +- docs/reference/fentlms_foraga.html | 133 ++++++++++++++++++++ docs/reference/fentlms_forz.html | 133 ++++++++++++++++++++ docs/reference/growth_cdc_ext_infants.html | 135 ++++++++++++++++++++ docs/reference/growth_who_ext.html | 135 ++++++++++++++++++++ docs/reference/index.html | 32 +++++ docs/reference/rc-reference-medians.html | 133 ++++++++++++++++++++ docs/reference/tanner_ht_vel_rev.html | 136 +++++++++++++++++++++ docs/reference/who_hc_maxvel.html | 136 +++++++++++++++++++++ docs/reference/who_hc_vel_3sd.html | 136 +++++++++++++++++++++ docs/sitemap.xml | 24 ++++ 12 files changed, 1142 insertions(+), 1 deletion(-) create mode 100644 docs/reference/fentlms_foraga.html create mode 100644 docs/reference/fentlms_forz.html create mode 100644 docs/reference/growth_cdc_ext_infants.html create mode 100644 docs/reference/growth_who_ext.html create mode 100644 docs/reference/rc-reference-medians.html create mode 100644 docs/reference/tanner_ht_vel_rev.html create mode 100644 docs/reference/who_hc_maxvel.html create mode 100644 docs/reference/who_hc_vel_3sd.html diff --git a/_pkgdown.yml b/_pkgdown.yml index 6a0cf947..5310b519 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -69,6 +69,14 @@ reference: - who_ht_maxvel_2sd - who_ht_vel_2sd - who_ht_vel_3sd + - fentlms_foraga + - fentlms_forz + - growth_cdc_ext_infants + - growth_who_ext + - rc-reference-medians + - tanner_ht_vel_rev + - who_hc_maxvel + - who_hc_vel_3sd repo: url: diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index dcb87f3e..7519f8f8 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -13,5 +13,5 @@ articles: quickstart: quickstart.html usage: usage.html utilities: utilities.html -last_built: 2023-09-13T14:53Z +last_built: 2023-09-13T23:26Z diff --git a/docs/reference/fentlms_foraga.html b/docs/reference/fentlms_foraga.html new file mode 100644 index 00000000..39071e2e --- /dev/null +++ b/docs/reference/fentlms_foraga.html @@ -0,0 +1,133 @@ + +Fenton Growth Curves — fentlms_foraga • growthcleanr + + +
    +
    + + + +
    +
    +
    +

    Fenton Growth Curves

    + Source: R/extdata.R + +
    + +
    +

    Fenton growth curves with premature infant data with sex, age, and integer +weight

    +
    + + +
    +

    fentlms_foraga.csv.gz

    + + + +

    Used in function cleangrowth()

    +
    + +
    + +
    + + +
    +

    Developed by Carrie Daymont, Robert Grundmeier, Jeffrey Miller, Diego Campos.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/fentlms_forz.html b/docs/reference/fentlms_forz.html new file mode 100644 index 00000000..7292f51d --- /dev/null +++ b/docs/reference/fentlms_forz.html @@ -0,0 +1,133 @@ + +Fenton Growth Curve Z-Scores — fentlms_forz • growthcleanr + + +
    +
    + + + +
    +
    +
    +

    Fenton Growth Curve Z-Scores

    + Source: R/extdata.R + +
    + +
    +

    Fenton growth curves with premature infant z-scores for height and head +circumference

    +
    + + +
    +

    fentlms_forz.csv.gz

    + + + +

    Used in function cleangrowth()

    +
    + +
    + +
    + + +
    +

    Developed by Carrie Daymont, Robert Grundmeier, Jeffrey Miller, Diego Campos.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/growth_cdc_ext_infants.html b/docs/reference/growth_cdc_ext_infants.html new file mode 100644 index 00000000..e144c278 --- /dev/null +++ b/docs/reference/growth_cdc_ext_infants.html @@ -0,0 +1,135 @@ + +CDC Growth Percentile Table for Infants — growth_cdc_ext_infants • growthcleanr + + +
    +
    + + + +
    +
    +
    +

    CDC Growth Percentile Table for Infants

    + Source: R/extdata.R + +
    + +
    +

    Part of default CDC-derived tables

    +
    + + +
    +

    Details

    +

    Contains percentiles for various ages, gender, and weights, pre-calculated by CDC for infants algorithm

    +
    +
    +

    growthfile_cdc_ext_infants.csv.gz

    + + + +

    Used in function cleangrowth()

    +
    + +
    + +
    + + +
    +

    Developed by Carrie Daymont, Robert Grundmeier, Jeffrey Miller, Diego Campos.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/growth_who_ext.html b/docs/reference/growth_who_ext.html new file mode 100644 index 00000000..13076f2e --- /dev/null +++ b/docs/reference/growth_who_ext.html @@ -0,0 +1,135 @@ + +WHO Growth Percentile Table — growth_who_ext • growthcleanr + + +
    +
    + + + +
    +
    +
    +

    WHO Growth Percentile Table

    + Source: R/extdata.R + +
    + +
    +

    Part of default WHO-derived tables

    +
    + + +
    +

    Details

    +

    Contains percentiles for various ages, gender, and weights, pre-calculated by WHO

    +
    +
    +

    growthfile_who.csv.gz

    + + + +

    Used in function cleangrowth()

    +
    + +
    + +
    + + +
    +

    Developed by Carrie Daymont, Robert Grundmeier, Jeffrey Miller, Diego Campos.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html index 0456d281..43c9b89c 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -220,6 +220,38 @@

    Datasets who_ht_vel_3sd

    • + + + + + + + + + + + + + + + +

    WHO Height Velocity for (3\(\sigma\))

    +

    fentlms_foraga

    +

    Fenton Growth Curves

    +

    fentlms_forz

    +

    Fenton Growth Curve Z-Scores

    +

    growth_cdc_ext_infants

    +

    CDC Growth Percentile Table for Infants

    +

    growth_who_ext

    +

    WHO Growth Percentile Table

    +

    rc-reference-medians

    +

    Infants reference medians

    +

    tanner_ht_vel_rev

    +

    Tanner Growth Velocity Table for Infants

    +

    who_hc_maxvel

    +

    WHO Maximum Head Circumference Velocity for (3\(\sigma\))

    +

    who_hc_vel_3sd

    +

    WHO Head Circumference Velocity for (3\(\sigma\))

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zuP6QX`HoLU*7RI$q^wPXWVD|p+<9}`&i@-gvq**smNlCF3c8XhoJ-8gM_Hhsg`?}| z-D2Fs!Bea@#u1pAbdvo(Lc?A;hs9qDS>#e^&5=qO{1 zpP*CFPxq<^`a!3pacsk#UxTw_@E^DhsfA}SETB0$fB?Qy-##ptT41yG?mmc`0PFR4 zLY81;=A}zx^U|Tdkpk1;mmSmIKg$&OJHXCD5x!gX(V$G;ajz64q|YTNI9yL~p|ypf z9msmdQ;~)c*CTr>PONGPfa>+s2W7KaqMGsSP#)0Uit&pZP^-|-W~agamfK}&|8Y7J z1p8**b8E7Y)1M+1!MZ(VVMy9{>&K1b@4?M#&#j~@zF7_5ch$Ci(U&N~hg@g{Bxa?3 zBAt1@!Tmt5z2UPVuibx;m^=&1j6J#k=Vn-G;i~PTjR*6%mHdv=8mo@FbQGq5TrX8R zp};Uzk+vTFGa|7rm-VerR(Mx3rSsE)nS&QXs3xiNgrM)yfWc;g)Vi%rR4`BKTSVvF zE6oCOw2bAGyfiHCh|Mn-FJ|o`(j|6{^{g!CS9u5Er1nL(&?_t{(moz+>UX0OjGQL)fw=y0$bl2o@PUv$a{WGz&MYYJECd0mtV9%a|Y z=X%em<*@>Jms911tENY0wYHeNA8W$bcIsxnSe$AGDM+`2SVjz zD|JPg_jbQbm;&#}FvmI`P&?{k*la^6k9|^&qwB$(T-4|+e8jnZ69gd$C-MI3sqi{s zk=f^S2L+k;?4`xbAEmPZr+TD?0trE|emu{MAl%teQkA2#98eVj=zR2AkFgSMoD?4O zL>{CSjrz623FH!_`=nhkP?R7o9>w0Kh7%sJ>RyIxMb*(+yKRQK0_~TQ%NrjKik*Nw z7X>*Q=&dhlpf7Gbv|P;e@|M1g4}W_mMA>`gyP5=LGur=!STjO>k^X>0W}2LLgiJK%78ssQL2f zWp$=W?*0~@%&e6;zo%b3uX4qFj)C@mx4aOVRRVC~5g&TTZG&)C1wF?NyJQwPiiGgD z&Lg`j`jn2dH@sbKw&mN3he From b6bb33bcc7b1abf8f3345ad8b9bafcf7de88916f Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 9 Feb 2023 11:09:48 -0500 Subject: [PATCH 017/100] Integrate infants as separate option --- R/growth.R | 233 ++++++++++++++++++++++++++++----------------- R/infants_clean.R | 66 +++++++------ man/cleangrowth.Rd | 7 +- man/read_anthro.Rd | 2 + 4 files changed, 191 insertions(+), 117 deletions(-) diff --git a/R/growth.R b/R/growth.R index 3ab80c63..c29e6b59 100644 --- a/R/growth.R +++ b/R/growth.R @@ -84,6 +84,7 @@ #' @param adult_columns_filename Name of file to save original adult data, with additional output columns to #' as CSV. Defaults to "", for which this data will not be saved. Useful #' for post-analysis. For more information on this output, please see README. +#' @param infants TRUE/FALSE. Run the alpha-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE. #' #' @return Vector of exclusion codes for each of the input measurements. #' @@ -156,7 +157,7 @@ cleangrowth <- function(subjid, adult_cutpoint = 20, weight_cap = Inf, adult_columns_filename = "", - infants = F) { + infants = FALSE) { # avoid "no visible binding" warnings N <- age_years <- batch <- exclude <- index <- line <- NULL newbatch <- sd.median <- sd.orig <- tanner.months <- tbc.sd <- NULL @@ -184,11 +185,11 @@ cleangrowth <- function(subjid, if (!is.numeric(weight_cap) | weight_cap < 0){ stop("weight_cap not numeric. Please enter a positive number.") } - if (any(!param %in% c("LENTGCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", + if (any(!param %in% c("LENGTHCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", "WEIGHTLBS", "HEADCM"))){ cat(sprintf("[%s] Parameters included that do not match 'param' specifications. Filtering out...\n", Sys.time())) data.all.ages <- - data.all.ages[param %in% c("LENTGCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", + data.all.ages[param %in% c("LENGTHCM", "HEIGHTCM", "WEIGHTKG", "HEIGHIN", "WEIGHTLBS", "HEADCM"),] } @@ -372,51 +373,64 @@ cleangrowth <- function(subjid, # NOTE: this will be changed in future to consider this difference data.all[param == 'LENGTHCM', param := 'HEIGHTCM'] - # calculate z scores - if (!quietly) - cat(sprintf("[%s] Calculating z-scores...\n", Sys.time())) - # for infants, use z and who - measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE) - measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = F) + # calculate z/sd scores + if(infants){ + if (!quietly) + cat(sprintf("[%s] Calculating z-scores...\n", Sys.time())) + # for infants, use z and who + measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE) + measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = F) - data.all[, z.orig_cdc := measurement.to.z(param, agedays, sex, v)] - data.all[, z.orig_who := measurement.to.z_who(param, agedays, sex, v)] + data.all[, z.orig_cdc := measurement.to.z(param, agedays, sex, v)] + data.all[, z.orig_who := measurement.to.z_who(param, agedays, sex, v)] - # calculate "standard deviation" scores - if (!quietly) - cat(sprintf("[%s] Calculating SD-scores...\n", Sys.time())) - data.all[, sd.orig_cdc := measurement.to.z(param, agedays, sex, v, TRUE)] - data.all[, sd.orig_who := measurement.to.z_who(param, agedays, sex, v, TRUE)] - - # smooth z-scores/SD scores between ages 1 - 3yo using weighted scores - # older uses cdc, younger uses who - who_weight <- 3 - (data.all$agedays/365.25) - cdc_weight <- (data.all$agedays/365.25) - 1 - - smooth_val <- data.all$agedays/365.25 >= 1 & - data.all$agedays/365.25 <= 3 & - !data.all$param == "HEADCM" - data.all[smooth_val, - z.orig := (z.orig_cdc[smooth_val]*cdc_weight[smooth_val] + - z.orig_who[smooth_val]*who_weight[smooth_val])/2] - data.all[smooth_val, - sd.orig := (sd.orig_cdc[smooth_val]*cdc_weight[smooth_val] + - sd.orig_who[smooth_val]*who_weight[smooth_val])/2] - - # otherwise use WHO and CDC for older and younger, respectively - who_val <- data.all$param == "HEADCM" | - data.all$agedays/365.25 < 1 - data.all[who_val | (smooth_val & is.na(data.all$z.orig_cdc)), - z.orig := z.orig_who[who_val | (smooth_val & is.na(data.all$z.orig_cdc))]] - data.all[who_val | (smooth_val & is.na(data.all$sd.orig_cdc)), - sd.orig := sd.orig_who[who_val | (smooth_val & is.na(data.all$sd.orig_cdc))]] - - cdc_val <- data.all$param != "HEADCM" | - data.all$agedays/365.25 > 3 - data.all[cdc_val | (smooth_val & is.na(data.all$z.orig_who)), - z.orig := z.orig_cdc[cdc_val | (smooth_val & is.na(data.all$z.orig_who))]] - data.all[cdc_val | (smooth_val & is.na(data.all$sd.orig_who)), - sd.orig := sd.orig_cdc[cdc_val | (smooth_val & is.na(data.all$sd.orig_who))]] + # calculate "standard deviation" scores + if (!quietly) + cat(sprintf("[%s] Calculating SD-scores...\n", Sys.time())) + data.all[, sd.orig_cdc := measurement.to.z(param, agedays, sex, v, TRUE)] + data.all[, sd.orig_who := measurement.to.z_who(param, agedays, sex, v, TRUE)] + + # smooth z-scores/SD scores between ages 1 - 3yo using weighted scores + # older uses cdc, younger uses who + who_weight <- 3 - (data.all$agedays/365.25) + cdc_weight <- (data.all$agedays/365.25) - 1 + + smooth_val <- data.all$agedays/365.25 >= 1 & + data.all$agedays/365.25 <= 3 & + !data.all$param == "HEADCM" + data.all[smooth_val, + z.orig := (z.orig_cdc[smooth_val]*cdc_weight[smooth_val] + + z.orig_who[smooth_val]*who_weight[smooth_val])/2] + data.all[smooth_val, + sd.orig := (sd.orig_cdc[smooth_val]*cdc_weight[smooth_val] + + sd.orig_who[smooth_val]*who_weight[smooth_val])/2] + + # otherwise use WHO and CDC for older and younger, respectively + who_val <- data.all$param == "HEADCM" | + data.all$agedays/365.25 < 1 + data.all[who_val | (smooth_val & is.na(data.all$z.orig_cdc)), + z.orig := z.orig_who[who_val | (smooth_val & is.na(data.all$z.orig_cdc))]] + data.all[who_val | (smooth_val & is.na(data.all$sd.orig_cdc)), + sd.orig := sd.orig_who[who_val | (smooth_val & is.na(data.all$sd.orig_cdc))]] + + cdc_val <- data.all$param != "HEADCM" | + data.all$agedays/365.25 > 3 + data.all[cdc_val | (smooth_val & is.na(data.all$z.orig_who)), + z.orig := z.orig_cdc[cdc_val | (smooth_val & is.na(data.all$z.orig_who))]] + data.all[cdc_val | (smooth_val & is.na(data.all$sd.orig_who)), + sd.orig := sd.orig_cdc[cdc_val | (smooth_val & is.na(data.all$sd.orig_who))]] + } else { + # calculate z scores + if (!quietly) + cat(sprintf("[%s] Calculating z-scores...\n", Sys.time())) + measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE) + data.all[, z.orig := measurement.to.z(param, agedays, sex, v)] + + # calculate "standard deviation" scores + if (!quietly) + cat(sprintf("[%s] Calculating SD-scores...\n", Sys.time())) + data.all[, sd.orig := measurement.to.z(param, agedays, sex, v, TRUE)] + } # sort by subjid, param, agedays setkey(data.all, subjid, param, agedays) @@ -586,23 +600,44 @@ cleangrowth <- function(subjid, num.batches )) if (num.batches == 1) { - ret.df <- cleanbatch(data.all, - log.path = log.path, - quietly = quietly, - parallel = parallel, - measurement.to.z = measurement.to.z, - ewma.fields = ewma.fields, - ewma.exp = ewma.exp, - recover.unit.error = recover.unit.error, - include.carryforward = include.carryforward, - sd.extreme = sd.extreme, - z.extreme = z.extreme, - exclude.levels = exclude.levels, - tanner.ht.vel = tanner.ht.vel, - who.ht.vel = who.ht.vel, - lt3.exclude.mode = lt3.exclude.mode, - error.load.threshold = error.load.threshold, - error.load.mincount = error.load.mincount) + if (!infants){ + ret.df <- cleanbatch(data.all, + log.path = log.path, + quietly = quietly, + parallel = parallel, + measurement.to.z = measurement.to.z, + ewma.fields = ewma.fields, + ewma.exp = ewma.exp, + recover.unit.error = recover.unit.error, + include.carryforward = include.carryforward, + sd.extreme = sd.extreme, + z.extreme = z.extreme, + exclude.levels = exclude.levels, + tanner.ht.vel = tanner.ht.vel, + who.ht.vel = who.ht.vel, + lt3.exclude.mode = lt3.exclude.mode, + error.load.threshold = error.load.threshold, + error.load.mincount = error.load.mincount) + } else { + ret.df <- cleanbatch_infants( + data.all, + log.path = log.path, + quietly = quietly, + parallel = parallel, + measurement.to.z = measurement.to.z, + ewma.fields = ewma.fields, + ewma.exp = ewma.exp, + recover.unit.error = recover.unit.error, + include.carryforward = include.carryforward, + sd.extreme = sd.extreme, + z.extreme = z.extreme, + exclude.levels = exclude.levels, + tanner.ht.vel = tanner.ht.vel, + who.ht.vel = who.ht.vel, + lt3.exclude.mode = lt3.exclude.mode, + error.load.threshold = error.load.threshold, + error.load.mincount = error.load.mincount) + } } else { # create log directory if necessary if (!is.na(log.path)) { @@ -610,29 +645,55 @@ cleangrowth <- function(subjid, ifelse(!dir.exists(log.path), dir.create(log.path, recursive = TRUE), FALSE) } - ret.df <- ddply( - data.all, - .(batch), - cleanbatch, - .parallel = parallel, - .paropts = list(.packages = "data.table"), - log.path = log.path, - quietly = quietly, - parallel = parallel, - measurement.to.z = measurement.to.z, - ewma.fields = ewma.fields, - ewma.exp = ewma.exp, - recover.unit.error = recover.unit.error, - include.carryforward = include.carryforward, - sd.extreme = sd.extreme, - z.extreme = z.extreme, - exclude.levels = exclude.levels, - tanner.ht.vel = tanner.ht.vel, - who.ht.vel = who.ht.vel, - lt3.exclude.mode = lt3.exclude.mode, - error.load.threshold = error.load.threshold, - error.load.mincount = error.load.mincount - ) + if (!infants){ + ret.df <- ddply( + data.all, + .(batch), + cleanbatch, + .parallel = parallel, + .paropts = list(.packages = "data.table"), + log.path = log.path, + quietly = quietly, + parallel = parallel, + measurement.to.z = measurement.to.z, + ewma.fields = ewma.fields, + ewma.exp = ewma.exp, + recover.unit.error = recover.unit.error, + include.carryforward = include.carryforward, + sd.extreme = sd.extreme, + z.extreme = z.extreme, + exclude.levels = exclude.levels, + tanner.ht.vel = tanner.ht.vel, + who.ht.vel = who.ht.vel, + lt3.exclude.mode = lt3.exclude.mode, + error.load.threshold = error.load.threshold, + error.load.mincount = error.load.mincount + ) + } else { + ret.df <- ddply( + data.all, + .(batch), + cleanbatch_infants, + .parallel = parallel, + .paropts = list(.packages = "data.table"), + log.path = log.path, + quietly = quietly, + parallel = parallel, + measurement.to.z = measurement.to.z, + ewma.fields = ewma.fields, + ewma.exp = ewma.exp, + recover.unit.error = recover.unit.error, + include.carryforward = include.carryforward, + sd.extreme = sd.extreme, + z.extreme = z.extreme, + exclude.levels = exclude.levels, + tanner.ht.vel = tanner.ht.vel, + who.ht.vel = who.ht.vel, + lt3.exclude.mode = lt3.exclude.mode, + error.load.threshold = error.load.threshold, + error.load.mincount = error.load.mincount + ) + } stopCluster(cl) } diff --git a/R/infants_clean.R b/R/infants_clean.R index d69814d3..f0bd456e 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -1,5 +1,6 @@ -# Main pediatric growthcleanr function -- cleanbatch -# internal supporting functions for pediatrics can be found in: pediatric_support.R +# Main pediatric growthcleanr function -- infants cleanbatch +# internal supporting functions for pediatrics can be found in: infants_support.R +# note #' Function to clean data (optionally in batches): #' 4. Dataset split (optional) @@ -16,23 +17,23 @@ #' @import data.table #' @importFrom stats median #' @noRd -cleanbatch <- function(data.df, - log.path, - quietly, - parallel, - measurement.to.z, - ewma.fields, - ewma.exp, - recover.unit.error, - include.carryforward, - sd.extreme, - z.extreme, - exclude.levels, - tanner.ht.vel, - who.ht.vel, - lt3.exclude.mode, - error.load.threshold, - error.load.mincount) { +cleanbatch_infants <- function(data.df, + log.path, + quietly, + parallel, + measurement.to.z, + ewma.fields, + ewma.exp, + recover.unit.error, + include.carryforward, + sd.extreme, + z.extreme, + exclude.levels, + tanner.ht.vel, + who.ht.vel, + lt3.exclude.mode, + error.load.threshold, + error.load.mincount) { # avoid "no visible binding" warnings abs.2ndlast.sd <- abs.tbc.sd <- abs.tbc.sd.next <- abs.tbc.sd.prev <- abssum2 <- NULL aft.g.befp1 <- agedays <- agedays.other <- bef.g.aftm1 <- delta <- NULL @@ -147,43 +148,49 @@ cleanbatch <- function(data.df, # HC Limits based on analysis in do-file from Oct 11 2022: in dataset, lowest plausible 18.5, z=-13, highest plausible 63.5, z=11.2 (at birth, highest=42, z=6) + if (!quietly) + cat(sprintf( + "[%s] Exclude BIVs...\n", + Sys.time() + )) + exc_nam <- "Exclude-Absolute-BIV" # identify absolute cutoffs # Min/max weight at birth based on published births - data.df[valid(data.df) & param == "WEIGHTKG" & measurement < 0.2, + data.df[valid(data.df) & param == "WEIGHTKG" & v < 0.2, exclude := exc_nam] # Min weight after birth based on rules about who can go home with alowance for significant weight loss -- this is for outpatient data only so very low weight babies would still be in NICU - data.df[valid(data.df) & param == "WEIGHTKG" & measurement > 10.5 & + data.df[valid(data.df) & param == "WEIGHTKG" & v > 10.5 & agedays == 0, exclude := exc_nam] # Max weight for <2y based on eval (see do-file from Oct 10 2022), highest plaus weight 29.5kg - data.df[valid(data.df) & param == "WEIGHTKG" & measurement < 1 & + data.df[valid(data.df) & param == "WEIGHTKG" & v < 1 & agedays == 0, exclude := exc_nam] # Max weight for <2y based on eval (see do-file from Oct 10 2022), highest plaus weight 29.5kg - data.df[valid(data.df) & param == "WEIGHTKG" & measurement > 35 & + data.df[valid(data.df) & param == "WEIGHTKG" & v > 35 & agedays < 2, exclude := exc_nam] # Max weight for all based on published data - data.df[valid(data.df) & param == "WEIGHTKG" & measurement > 600, + data.df[valid(data.df) & param == "WEIGHTKG" & v > 600, exclude := exc_nam] # Min/max HC based on analysis in do file from # Also, 18 is z=-6 for 22 0/7 in Fenton and 65 is z=6 for 40 0/7 - data.df[valid(data.df) & param == "HEIGHTCM" & measurement < 18, + data.df[valid(data.df) & param == "HEIGHTCM" & v < 18, exclude := exc_nam] - data.df[valid(data.df) & param == "HEIGHTCM" & measurement > 65 & + data.df[valid(data.df) & param == "HEIGHTCM" & v > 65 & agedays == 0, exclude := exc_nam] # Min/max HC based on analysis in do file from Oct 11 2022 # Also, 13 is z=-6 for 22 0/7 in Fenton and - data.df[valid(data.df) & param == "HEADCM" & measurement < 13, + data.df[valid(data.df) & param == "HEADCM" & v < 13, exclude := exc_nam] - data.df[valid(data.df) & param == "HEADCM" & measurement > 75, + data.df[valid(data.df) & param == "HEADCM" & v > 75, exclude := exc_nam] - data.df[valid(data.df) & param == "HEADCM" & measurement > 50 & + data.df[valid(data.df) & param == "HEADCM" & v > 50 & agedays == 0, exclude := exc_nam] @@ -228,5 +235,6 @@ cleanbatch <- function(data.df, cat(sprintf("[%s] Completed Batch #%s...\n", Sys.time(), data.df$batch[1])) if (!quietly & parallel) sink() + return(data.df[j = .(line, exclude, tbc.sd, tbc.other.sd, param)]) #debugging } diff --git a/man/cleangrowth.Rd b/man/cleangrowth.Rd index f9fd34e5..e8eeebde 100644 --- a/man/cleangrowth.Rd +++ b/man/cleangrowth.Rd @@ -29,13 +29,14 @@ cleangrowth( quietly = TRUE, adult_cutpoint = 20, weight_cap = Inf, - adult_columns_filename = "" + adult_columns_filename = "", + infants = FALSE ) } \arguments{ \item{subjid}{Vector of unique identifiers for each subject in the database.} -\item{param}{Vector identifying each measurement, may be 'WEIGHTKG', 'WEIGHTLBS', 'HEIGHTCM', 'HEIGHTIN', or 'LENGTHCM' +\item{param}{Vector identifying each measurement, may be 'WEIGHTKG', 'WEIGHTLBS', 'HEIGHTCM', 'HEIGHTIN', 'LENGTHCM', or 'HEADCM'. 'HEIGHTCM'/'HEIGHTIN' vs. 'LENGTHCM' only affects z-score calculations between ages 24 to 35 months (730 to 1095 days). All linear measurements below 731 days of life (age 0-23 months) are interpreted as supine length, and all linear measurements above 1095 days of life (age 36+ months) are interpreted as standing height. @@ -137,6 +138,8 @@ to Inf. Defaults to Inf.} \item{adult_columns_filename}{Name of file to save original adult data, with additional output columns to as CSV. Defaults to "", for which this data will not be saved. Useful for post-analysis. For more information on this output, please see README.} + +\item{infants}{TRUE/FALSE. Run the alpha-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE.} } \value{ Vector of exclusion codes for each of the input measurements. diff --git a/man/read_anthro.Rd b/man/read_anthro.Rd index db991a15..84419b2b 100644 --- a/man/read_anthro.Rd +++ b/man/read_anthro.Rd @@ -10,6 +10,8 @@ read_anthro(path = "", cdc.only = FALSE) \item{path}{Path to supplied reference anthro data. Defaults to package anthro tables.} \item{cdc.only}{Whether or not only CDC data should be used. Defaults to false.} + +\item{infants}{boolean on if infants version is being used. will be merged in to main algorithm.} } \value{ Function for calculating BMI based on measurement, age in days, sex, and measurement value. From 4c6cb21c7effa1e6b3733a50f044f6d41799d345 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 9 Feb 2023 11:31:57 -0500 Subject: [PATCH 018/100] Update cdc/who bridge to 2/4 --- R/growth.R | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/R/growth.R b/R/growth.R index c29e6b59..8ddf6f66 100644 --- a/R/growth.R +++ b/R/growth.R @@ -392,11 +392,11 @@ cleangrowth <- function(subjid, # smooth z-scores/SD scores between ages 1 - 3yo using weighted scores # older uses cdc, younger uses who - who_weight <- 3 - (data.all$agedays/365.25) - cdc_weight <- (data.all$agedays/365.25) - 1 + who_weight <- 4 - (data.all$agedays/365.25) + cdc_weight <- (data.all$agedays/365.25) - 2 - smooth_val <- data.all$agedays/365.25 >= 1 & - data.all$agedays/365.25 <= 3 & + smooth_val <- data.all$agedays/365.25 >= 2 & + data.all$agedays/365.25 <= 4 & !data.all$param == "HEADCM" data.all[smooth_val, z.orig := (z.orig_cdc[smooth_val]*cdc_weight[smooth_val] + @@ -407,14 +407,14 @@ cleangrowth <- function(subjid, # otherwise use WHO and CDC for older and younger, respectively who_val <- data.all$param == "HEADCM" | - data.all$agedays/365.25 < 1 + data.all$agedays/365.25 < 2 data.all[who_val | (smooth_val & is.na(data.all$z.orig_cdc)), z.orig := z.orig_who[who_val | (smooth_val & is.na(data.all$z.orig_cdc))]] data.all[who_val | (smooth_val & is.na(data.all$sd.orig_cdc)), sd.orig := sd.orig_who[who_val | (smooth_val & is.na(data.all$sd.orig_cdc))]] cdc_val <- data.all$param != "HEADCM" | - data.all$agedays/365.25 > 3 + data.all$agedays/365.25 > 4 data.all[cdc_val | (smooth_val & is.na(data.all$z.orig_who)), z.orig := z.orig_cdc[cdc_val | (smooth_val & is.na(data.all$z.orig_who))]] data.all[cdc_val | (smooth_val & is.na(data.all$sd.orig_who)), From f4fc76d7dbee6a47caf550f0b8e6325d6c8c7efd Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 9 Feb 2023 11:39:38 -0500 Subject: [PATCH 019/100] Update exclude code --- R/infants_clean.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/infants_clean.R b/R/infants_clean.R index f0bd456e..2b870941 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -194,7 +194,7 @@ cleanbatch_infants <- function(data.df, agedays == 0, exclude := exc_nam] - exc_nam <- "Exclude-Z-BIV" + exc_nam <- "Exclude-Standardized-BIV" # identify z cutoff # ***Note, using unrecentered values*** From 5595d5124e55d0aee12fa85e0db6dcc975503743 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Fri, 10 Feb 2023 12:43:52 -0500 Subject: [PATCH 020/100] Add evil twins step to infant algorithm --- R/growth.R | 5 +++- R/infants_clean.R | 56 +++++++++++++++++++++++++++++++++++++++++++++ R/infants_support.R | 28 +++++++++++++++++++++++ 3 files changed, 88 insertions(+), 1 deletion(-) diff --git a/R/growth.R b/R/growth.R index 8ddf6f66..ecc8e2ec 100644 --- a/R/growth.R +++ b/R/growth.R @@ -241,7 +241,10 @@ cleangrowth <- function(subjid, 'Exclude-Pair-Delta-19', 'Exclude-Single-Outlier', 'Exclude-Too-Many-Errors', - 'Exclude-Too-Many-Errors-Other-Parameter' + 'Exclude-Too-Many-Errors-Other-Parameter', + "Exclude-Absolute-BIV", + "Exclude-Standardized-BIV", + "Exclude-Evil-Twin" ) # if there's no pediatric data, no need to go through this rigamarole diff --git a/R/infants_clean.R b/R/infants_clean.R index 2b870941..54962819 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -229,6 +229,62 @@ cleanbatch_infants <- function(data.df, data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] + # evil twins ---- + # Evil Twins: An important weakness in the original pediatric growthcleanr algorithm was that it often failed to identify two or more implausible measurements that occurred next to each other, even if they were extremely deviant from a child’s other measurements. This step is now added to identify these multiple extreme values, although it also identifies some single values. + + exc_nam <- "Exclude-Evil-Twin" + + # first, find out of any re possible evil twins in the first place + start_df <- calc_oob_evil_twins(data.df[valid(data.df),]) + + if (any(start_df$sum_oob > 2)) { + if (!quietly) + cat(sprintf( + "[%s] Exclude evil twins...\n", + Sys.time() + )) + + df <- copy(data.df) + + # start to evaluate and remove evil twins + data.df[, exclude := (function(df) { + # where we are updating results + upd.df <- copy(df) + upd.df <- calc_oob_evil_twins(df[valid(df),]) + # count the amount of oobs for each subject/param and distribute it out + upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)] + + any_oob <- TRUE + # while there are multiple oob, we want to remove + while (any_oob){ + + # now calculate the maximum difference from the median tbc.sd + upd.df[, `:=` (sd_med = median(tbc.sd, na.rm = T)), by =.(subjid, param)] + upd.df[, `:=` (med_diff = abs(tbc.sd - sd_med)), by =.(subjid, param)] + upd.df[, `:=` (max_diff = med_diff == max(med_diff)), by =.(subjid, param)] + # for ones with no tbc.sd, mark as false + upd.df[is.na(max_diff), max_diff := FALSE] + + upd.df[sum_oob > 0 & max_diff, exclude := exc_nam] + + # STOP HERE: + # NEED TO ASSIGN TO DF, THEN UPDATE UPD.DF WITH NEW VALID + df[upd.df[exclude == exc_nam,], exclude := i.exclude, on = .(line)] + + upd.df <- calc_oob_evil_twins(df[valid(df),]) + upd.df[, `:=` (sum_oob = sum(oob, na.rm = T)), by =.(subjid, param)] + + any_oob <- any(upd.df$sum_oob > 2) + + } + + return(df$exclude) + })(copy(.SD))] + + } + + + # end ---- if (!quietly) diff --git a/R/infants_support.R b/R/infants_support.R index bc607097..814b2639 100644 --- a/R/infants_support.R +++ b/R/infants_support.R @@ -90,3 +90,31 @@ temporary_extraneous_infants <- function(df) { # return the extraneous valid rows (i.e. the ones that should be temporarily excluded) return(df$extraneous & valid.rows) } + +# evil twins ---- + +#' Function to calculate out of bounds (OOB) measurements for Evil Twins step +#' +#' @param df data table with all parameters +#' +#' @keywords internal +#' @noRd +calc_oob_evil_twins <- function(df){ + # start by determining if a measurement is out of bounds (oob) + # different criteria for VLEZ and non-VLEZ + # (z-score < -3 in the first 6 months of life ) + vlez <- df$agedays < 6*30.4375 & df$tbc.sd < -3 + + # for differences for adjacent, we pad with a high number to default to true + oob <- (!vlez & ( + abs(c(df$tbc.sd[2:nrow(df)], Inf) - df$tbc.sd) > 5 | + abs(c(Inf, df$tbc.sd[2:nrow(df)]) - df$tbc.sd) > 5 + )) | + (vlez & ( + abs(c(df$tbc.sd[2:nrow(df)], Inf) - df$tbc.sd) > 8 | + abs(c(Inf, df$tbc.sd[2:nrow(df)]) - df$tbc.sd) > 8 + )) + df[, "oob" := oob] + + return(df) +} From 342bdaf4ab00a92745528e6b9f7233eabee49327 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Fri, 10 Feb 2023 12:57:54 -0500 Subject: [PATCH 021/100] Add preliminary extreme ewma --- R/infants_clean.R | 114 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 114 insertions(+) diff --git a/R/infants_clean.R b/R/infants_clean.R index 54962819..540945f0 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -284,6 +284,120 @@ cleanbatch_infants <- function(data.df, } + # 9d. Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous) + data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] + data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] + + # extreme ewma ---- + + # STOP HERE -- check EWMA FOR UPDATES + + # 11. Exclude extreme errors with EWMA + # a. Erroneous measurements can distort the EWMA for measurements around them. Therefore, if the EWMA method identifies more than one value for a subject and + # parameter that meets criteria for exclusion, we will only exclude the value that deviates the most from expected in any given step. Then we will repeat the + # entire process until no measurements are identified that meet criteria for exclusion. + # b. Perform a EWMA calculation + # i. Only use values where exc_*==0 to determine the EWMAs. However, calculate dewma_* variables for values where exc_*==0 or exc_*==2 + if (!quietly) + cat(sprintf( + "[%s] Exclude extreme measurements based on EWMA...\n", + Sys.time() + )) + data.df <- data.df[, exclude := (function(df) { + num.ewma.excluded <- 0 + # optimization: determine whether this subject has any extraneous + has.extraneous <- subjid %in% subj.dup + while (TRUE) { + df[, (ewma.fields) := as.double(NaN)] + df[valid(exclude), (ewma.fields) := ewma(agedays, tbc.sd, ewma.exp, TRUE)] + + # note: at this point, only one ewma exists per param on a given day for a subject, so sort(ewma.all)[1] will returns the non-missing ewma.all + # restrict to children with possible extraneous for efficiency + if (has.extraneous) { + df[, `:=`( + ewma.all = sort(ewma.all)[1], + ewma.before = sort(ewma.before)[1], + ewma.after = sort(ewma.after)[1] + ), by = .(agedays)] + } + df[, `:=`( + dewma.all = tbc.sd - ewma.all, + dewma.before = tbc.sd - ewma.before, + dewma.after = tbc.sd - ewma.after + )] + + # 11c. Identify all values that meet all of the following criteria as potential exclusions: + # i. There are 3 or more measurements for that subject and parameter + # ii. (dewma_*>3.5 & dewma_*_bef>3 & dewma_*_aft>3 & tbc*sd>3.5) OR (dewma_*<-3.5 & dewma_*_bef<-3 & d & dewma_*_aft<-3 & tbc*sd<-3.5) + # iii. exc_*==0 + + num.valid <- sum(valid(df)) + rep <- na_as_false(with( + df, + num.valid >= 3 & valid(df) + & + ( + dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5 + | + dewma.all < -3.5 & + dewma.before < -3 & dewma.after < -3 & tbc.sd < -3.5 + ) + )) + num.exclude <- sum(rep) + # 11d. If there is only one potential exclusion identified in step 11c for a subject and parameter, replace exc_*=5 for that value + if (num.exclude == 1) + df[rep, exclude := 'Exclude-EWMA-Extreme'] + # 11e. If there is more than one potential exclusion identified in step 11c for a subject and parameter, calculate abssum_*=|tbc*sd+dewma_*| for each exclusion and + # replace exc_*=5 for the value with the highest abssum_* + if (num.exclude > 1) { + # first order by decreasing abssum + worst.row <- with(df, order(rep, abs(tbc.sd + ( + tbc.sd - ewma.all + )), decreasing = TRUE))[1] + df[worst.row, exclude := 'Exclude-EWMA-Extreme'] + } + + # 11f. For subjects/parameters with only 2 values, calculate abstbc*sd=|tbc*sd| + # g. Replace exc_*=6 for values that meet all of the following criteria + # i. There are 2 measurements for that subject and parameter + # ii. (dewma_*>3.5 & tbc*sd>3.5) OR (dewma_*<-3.5 & tbc*sd<-3.5) + # iii. If there are 2 measurements for a subject/parameter that meet criteria ii, only replace exc_*=6 for the value with the larger abstbc*sd + rep <- na_as_false(with( + df, + num.valid == 2 & + ( + tbc.sd - ewma.all > 3.5 & + tbc.sd > 3.5 | + tbc.sd - ewma.all < -3.5 & tbc.sd < -3.5 + ) + )) + num.exclude <- sum(rep) + if (num.exclude == 1) + df[rep, exclude := 'Exclude-EWMA-Extreme-Pair'] + if (num.exclude > 1) { + # first order by decreasing abssum + worst.row <- with(df, order(rep, abs(tbc.sd), decreasing = TRUE))[1] + df[worst.row, exclude := 'Exclude-EWMA-Extreme-Pair'] + } + + # 11h. Recalculate temporary extraneous as in step 5 + # optimize: only perform these steps if this subject is known to have extraneous measurements + if (has.extraneous) { + df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] + df[temporary_extraneous(df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] + } + + # 11i. If there was at least one subject who had a potential exclusion identified in step 11c, repeat steps 11b-11g. If there were no subjects with potential + # exclusions identified in step 11c, move on to step 12. + newly.excluded <- sum(df$exclude %in% c('Exclude-EWMA-Extreme', 'Exclude-EWMA-Extreme-Pair')) + if (newly.excluded > num.ewma.excluded) { + num.ewma.excluded <- newly.excluded + } else { + break + } + } + return(df$exclude) + })(copy(.SD)), by = .(subjid, param), .SDcols = c('index', 'sex', 'agedays', 'tbc.sd', 'exclude')] # end ---- From 7eae5f2f873c401fb8954b2a27b5ed5f9516852d Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 16 Feb 2023 11:39:22 -0500 Subject: [PATCH 022/100] Update infants extreme ewma --- R/growth.R | 3 +- R/infants_clean.R | 79 ++++++++++++++++++++++++++++------------------- 2 files changed, 49 insertions(+), 33 deletions(-) diff --git a/R/growth.R b/R/growth.R index ecc8e2ec..dcb3b13a 100644 --- a/R/growth.R +++ b/R/growth.R @@ -244,7 +244,8 @@ cleangrowth <- function(subjid, 'Exclude-Too-Many-Errors-Other-Parameter', "Exclude-Absolute-BIV", "Exclude-Standardized-BIV", - "Exclude-Evil-Twin" + "Exclude-Evil-Twin", + "Exclude-EWMA1-Extreme" ) # if there's no pediatric data, no need to go through this rigamarole diff --git a/R/infants_clean.R b/R/infants_clean.R index 540945f0..80cd0ea8 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -267,8 +267,6 @@ cleanbatch_infants <- function(data.df, upd.df[sum_oob > 0 & max_diff, exclude := exc_nam] - # STOP HERE: - # NEED TO ASSIGN TO DF, THEN UPDATE UPD.DF WITH NEW VALID df[upd.df[exclude == exc_nam,], exclude := i.exclude, on = .(line)] upd.df <- calc_oob_evil_twins(df[valid(df),]) @@ -290,7 +288,6 @@ cleanbatch_infants <- function(data.df, # extreme ewma ---- - # STOP HERE -- check EWMA FOR UPDATES # 11. Exclude extreme errors with EWMA # a. Erroneous measurements can distort the EWMA for measurements around them. Therefore, if the EWMA method identifies more than one value for a subject and @@ -331,17 +328,31 @@ cleanbatch_infants <- function(data.df, # ii. (dewma_*>3.5 & dewma_*_bef>3 & dewma_*_aft>3 & tbc*sd>3.5) OR (dewma_*<-3.5 & dewma_*_bef<-3 & d & dewma_*_aft<-3 & tbc*sd<-3.5) # iii. exc_*==0 + # a. For VLEZ: DEWMA > 8, DEWMA_bef > 6, DEWMA_aft > 6, and tbc`p’z > 3.5 + # b. For non-VLEZ: DEWMA > 3.5, DEWMA_bef > 3, DEWMA_aft > 3, and tbc`p’z > 3.5 + # c. For VLEZ and non-VLEZ: DEWMA < -3.5, DEWMA_bef < -3, DEWMA_aft <-3, tbc`p’z < -3.5 + + # (z-score < -3 in the first 6 months of life ) + vlez <- df$agedays < 6*30.4375 & df$tbc.sd < -3 + num.valid <- sum(valid(df)) rep <- na_as_false(with( df, num.valid >= 3 & valid(df) & + (# for VLEZ only + (vlez & + dewma.all > 8 & dewma.before > 6 & dewma.after > 6 & tbc.sd > 3.5 + ) | + # for non-VLEZ + (!vlez & + dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5 + ) | + # for both ( - dewma.all > 3.5 & dewma.before > 3 & dewma.after > 3 & tbc.sd > 3.5 - | - dewma.all < -3.5 & + dewma.all < -3.5 & dewma.before < -3 & dewma.after < -3 & tbc.sd < -3.5 - ) + )) )) num.exclude <- sum(rep) # 11d. If there is only one potential exclusion identified in step 11c for a subject and parameter, replace exc_*=5 for that value @@ -354,31 +365,31 @@ cleanbatch_infants <- function(data.df, worst.row <- with(df, order(rep, abs(tbc.sd + ( tbc.sd - ewma.all )), decreasing = TRUE))[1] - df[worst.row, exclude := 'Exclude-EWMA-Extreme'] - } - - # 11f. For subjects/parameters with only 2 values, calculate abstbc*sd=|tbc*sd| - # g. Replace exc_*=6 for values that meet all of the following criteria - # i. There are 2 measurements for that subject and parameter - # ii. (dewma_*>3.5 & tbc*sd>3.5) OR (dewma_*<-3.5 & tbc*sd<-3.5) - # iii. If there are 2 measurements for a subject/parameter that meet criteria ii, only replace exc_*=6 for the value with the larger abstbc*sd - rep <- na_as_false(with( - df, - num.valid == 2 & - ( - tbc.sd - ewma.all > 3.5 & - tbc.sd > 3.5 | - tbc.sd - ewma.all < -3.5 & tbc.sd < -3.5 - ) - )) - num.exclude <- sum(rep) - if (num.exclude == 1) - df[rep, exclude := 'Exclude-EWMA-Extreme-Pair'] - if (num.exclude > 1) { - # first order by decreasing abssum - worst.row <- with(df, order(rep, abs(tbc.sd), decreasing = TRUE))[1] - df[worst.row, exclude := 'Exclude-EWMA-Extreme-Pair'] + df[worst.row, exclude := 'Exclude-EWMA1-Extreme'] } +# +# # 11f. For subjects/parameters with only 2 values, calculate abstbc*sd=|tbc*sd| +# # g. Replace exc_*=6 for values that meet all of the following criteria +# # i. There are 2 measurements for that subject and parameter +# # ii. (dewma_*>3.5 & tbc*sd>3.5) OR (dewma_*<-3.5 & tbc*sd<-3.5) +# # iii. If there are 2 measurements for a subject/parameter that meet criteria ii, only replace exc_*=6 for the value with the larger abstbc*sd +# rep <- na_as_false(with( +# df, +# num.valid == 2 & +# ( +# tbc.sd - ewma.all > 3.5 & +# tbc.sd > 3.5 | +# tbc.sd - ewma.all < -3.5 & tbc.sd < -3.5 +# ) +# )) +# num.exclude <- sum(rep) +# if (num.exclude == 1) +# df[rep, exclude := 'Exclude-EWMA-Extreme-Pair'] +# if (num.exclude > 1) { +# # first order by decreasing abssum +# worst.row <- with(df, order(rep, abs(tbc.sd), decreasing = TRUE))[1] +# df[worst.row, exclude := 'Exclude-EWMA-Extreme-Pair'] +# } # 11h. Recalculate temporary extraneous as in step 5 # optimize: only perform these steps if this subject is known to have extraneous measurements @@ -389,7 +400,7 @@ cleanbatch_infants <- function(data.df, # 11i. If there was at least one subject who had a potential exclusion identified in step 11c, repeat steps 11b-11g. If there were no subjects with potential # exclusions identified in step 11c, move on to step 12. - newly.excluded <- sum(df$exclude %in% c('Exclude-EWMA-Extreme', 'Exclude-EWMA-Extreme-Pair')) + newly.excluded <- sum(df$exclude %in% c('Exclude-EWMA1-Extreme', 'Exclude-EWMA-Extreme-Pair')) if (newly.excluded > num.ewma.excluded) { num.ewma.excluded <- newly.excluded } else { @@ -399,6 +410,10 @@ cleanbatch_infants <- function(data.df, return(df$exclude) })(copy(.SD)), by = .(subjid, param), .SDcols = c('index', 'sex', 'agedays', 'tbc.sd', 'exclude')] + # 9d. Replace exc_*=0 if exc_*==2 & redo step 5 (temporary extraneous) + data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] + data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] + # end ---- if (!quietly) From 648a3e3ab7a14cb907c74914eb12aa35126a51ff Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Thu, 16 Feb 2023 14:31:51 -0500 Subject: [PATCH 023/100] Start including SDEs --- R/infants_clean.R | 147 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 147 insertions(+) diff --git a/R/infants_clean.R b/R/infants_clean.R index 80cd0ea8..9513bd8a 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -414,6 +414,153 @@ cleanbatch_infants <- function(data.df, data.df[exclude == 'Exclude-Temporary-Extraneous-Same-Day', exclude := 'Include'] data.df[temporary_extraneous_infants(data.df), exclude := 'Exclude-Temporary-Extraneous-Same-Day'] + # SDEs ---- + + # STOP HERE: need to adapt to new work + + # prepare a list of valid rows and initialize variables for convenience + valid.rows <- valid(data.df) + data.df[, `:=`( + ewma.all = as.double(NaN), + abssum2 = as.double(NaN), + median.other.sd = as.double(NaN), + extraneous = FALSE + )] + + # 12c. Calculate a EWMA step for all subjects/parameters with duplicates/extraneous and at + # least one non-extraneous value with the following modifications + # i. For calculating the EWMA, include only the extraneous selected in 12c + # ii. Calculate dewma_* for all values of extraneous + # iii. You do not need to calculate EWMAbef or EWMAaft for this step + + # determine proportion of days with extraneous/duplication for each parameter ahead of time for efficiency + dup.ratio.df <- data.df[subjid %in% subj.dup & + (valid.rows | + temp.dups), list(dup = (.N > 1)), by = .(subjid, param, agedays)][j = list(dup.ratio = + mean(dup)), keyby = .(subjid, param)] + # identify subject/parameters where there is duplication but at least one day with no extraneous for that parameter + subj.param.not.all.dups <- dup.ratio.df[dup.ratio < 1.0, list(subjid, param)] + # identify subject/parameters where there is duplication for all days for that parameter + subj.param.all.dups <- dup.ratio.df[dup.ratio == 1, list(subjid, param)] + subj.all.dups <- subj.param.all.dups[, unique(subjid)] + # perform ewma for subjects with extraneous + data.df[subjid %in% subj.dup & + valid.rows, ewma.all := ewma(agedays, tbc.sd, ewma.exp, ewma.adjacent = + FALSE), by = .(subjid, param)] + # note: at this point, only one ewma.all exists per param on a given day for a subject, so sort(ewma.all)[1] will returns the non-missing ewma.all + data.df[subjid %in% subj.dup, ewma.all := sort(ewma.all)[1], by = .(subjid, param, agedays)] + + # iv. Calculate abssum2_*=|2*dewma_*|+|tbc*sd| (note that this is different from how we calculated abssum_* in step 11). + # NOTE: only children with more than one ageday with valid measurements will have a valid ewma from above + data.df[, abssum2 := 2 * abs(tbc.sd - ewma.all) + abs(tbc.sd)] + + # 12d. For each subject/parameter/age with extraneous and at least one non-extraneous value: + # i. Replace exc_*=7 for all values except the value that has the smallest abssum2_*. + data.df[data.table(subj.param.not.all.dups), extraneous := seq_along(abssum2) != which.min(abssum2), by = + .(subjid, param, agedays)] + data.df[temp.dups, exclude := 'Include'] + data.df[(valid.rows | + temp.dups) & + extraneous, exclude := 'Exclude-Extraneous-Same-Day'] + # ii. Determine dup_tot_* (# of days with extraneous for that subject/parameter) and nodup_tot_* (# of days with nonexlcuded + # non-extraneous for that subject/parameter). + # iii. If dup_tot_*/(dup_tot_*+nodup_tot_*) is greater than 1/2, replace exc_*=7 for all extraneous for that subject/parameter + # for each age where the largest measurement minus the smallest measurement for that subject/parameter/age is larger than + # the maximum difference (ht 3cm; wt 0-9.999 kg 0.25kg; wt 10-29.9999 kg 0.5 kg; wt 30kg and higher 1 kg). + + data.df[data.table(dup.ratio.df[dup.ratio > 1 / 2, list(subjid, param)]), exclude := (function(df) { + df[, `:=`(tbc.sd.min = as.double(NaN), + tbc.sd.max = as.double(NaN))] + df[valid( + exclude, + include.extraneous = TRUE, + include.temporary.extraneous = TRUE + ), `:=`(tbc.sd.min = min(tbc.sd), + tbc.sd.max = max(tbc.sd))] + df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM', + 3, + ifelse( + param == 'WEIGHTKG', + ifelse(tbc.sd.min < 10, 0.25, ifelse(tbc.sd.min < 30, 0.5, 1)), + NA + )), + exclude := 'Exclude-Extraneous-Same-Day'] + return(df$exclude) + })(copy(.SD)), .SDcols = c('exclude', 'tbc.sd'), by = .(subjid, param, agedays)] + + # 12e. For each subject/parameter/age with extraneous and no nonextraneous values: + # i. Replace exc_*=7 for all values except the value with the smallest |tbc*sd-median_tbcOsd|. If median_tbcOsd is missing because there are no values + # for the other parameter, randomly choose one extraneous value for each subject/parameter/age to keep as exc_*=0 and replace exc_*=7 for all other + # extraneous for that subject/parameter/age. + # ii. If the largest measurement minus the smallest measurement for that subject/parameter/age is larger than the maximum difference + # (ht 3cm; wt 0-9.999 kg 0.25kg; wt 10-29.9999 kg 0.5 kg; wt 30kg and higher 1 kg)., replace exc_*=7 for all extraneous for that + # subject/parameter/age. + + # calculate median for other parameter (restrict to subjects with all duplication for at least one parameter) + data.df[subjid %in% subj.all.dups, exclude := (function(subj.df) { + # flag days that have extraneous / potentially valid parameters + subj.df[, `:=`( + extraneous.this.day = FALSE, + extraneous = FALSE, + tbc.sd.min = as.double(NaN), + tbc.sd.max = as.double(NaN) + )] + valid.rows = valid( + subj.df, + include.extraneous = TRUE, + include.temporary.extraneous = TRUE + ) + subj.df[valid.rows, extraneous.this.day := (.N > 1), by = .(param, agedays)] + for (p in subj.df[j = unique(param)]) { + median.sd <- subj.df[param != p & + !extraneous.this.day, median(tbc.sd)] + subj.df[param == p, median.other.sd := median.sd] + } + # safety check -- assign median.other.sd==0 to ensure "which.min" functions correctly below + subj.df[is.na(median.other.sd), median.other.sd := 0] + # identify rows as extraneous where |tbc*sd-median_tbcOsd| is not at the minimum value + subj.df[extraneous.this.day == TRUE, extraneous := (seq_along(median.other.sd) != which.min(abs(tbc.sd - median.other.sd))), by = + .(param, agedays)] + subj.df[extraneous.this.day & + !extraneous, exclude := 'Include'] + subj.df[extraneous.this.day & + extraneous, exclude := 'Exclude-Extraneous-Same-Day'] + subj.df[extraneous.this.day == TRUE, `:=`(tbc.sd.min = min(tbc.sd), + tbc.sd.max = max(tbc.sd)), by = .(param, agedays)] + subj.df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM', + 3, + ifelse( + param == 'WEIGHTKG', + ifelse(tbc.sd.min < 10, 0.25, ifelse(tbc.sd.min < 30, 0.5, 1)), + NA + )), + exclude := 'Exclude-Extraneous-Same-Day'] + + # identify kids who had an SD or EWMA extreme excluded that was a extraneous and re-label as "Exclude-Extraneous-Same-Day" + subj.df[, extraneous.this.day := FALSE] + # consider any non-missing measurement when determining presence of extraneous + subj.df[exclude != 'Missing', extraneous.this.day := (.N > 1), by = + .(param, agedays)] + subj.df[extraneous.this.day & + exclude %in% c('Exclude-SD-Cutoff', + 'Exclude-EWMA-Extreme', + 'Exclude-EWMA-Extreme-Pair'), exclude := 'Exclude-Extraneous-Same-Day'] + + return(subj.df$exclude) + })(copy(.SD)), .SDcols = c('param', 'agedays', 'exclude', 'tbc.sd'), by = + .(subjid)] + + # 12f. For any values that were excluded with exc_*=4, 5, or 6 that are also extraneous, replace exc_*=7. + data.df[subjid %in% subj.dup, exclude := (function(subj.df) { + if (.N > 1) { + subj.df[exclude %in% c('Exclude-SD-Cutoff', + 'Exclude-EWMA-Extreme', + 'Exclude-EWMA-Extreme-Pair'), exclude := 'Exclude-Extraneous-Same-Day'] + } + return(subj.df$exclude) + })(copy(.SD)), .SDcols = c('exclude'), by = .(subjid, param, agedays)] + + # end ---- if (!quietly) From b46eede0ef08e5e045a183731505fadd039d54b0 Mon Sep 17 00:00:00 2001 From: "Dr. Hannah De los Santos" Date: Wed, 8 Mar 2023 16:35:34 -0500 Subject: [PATCH 024/100] Update height velocity files --- DESCRIPTION | 2 +- R/growth.R | 36 ++++++++++++------ inst/extdata/who_hc_maxvel_3sd_infants.csv.gz | Bin 0 -> 459 bytes inst/extdata/who_hc_vel_3sd_infants.csv.gz | Bin 0 -> 390 bytes man/cleangrowth.Rd | 2 +- 5 files changed, 27 insertions(+), 13 deletions(-) create mode 100644 inst/extdata/who_hc_maxvel_3sd_infants.csv.gz create mode 100644 inst/extdata/who_hc_vel_3sd_infants.csv.gz diff --git a/DESCRIPTION b/DESCRIPTION index 1fa7b46b..55213906 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -45,4 +45,4 @@ Suggests: knitr (>= 1.29), rmarkdown (>= 2.3), testthat (>= 2.3.2) -RoxygenNote: 7.2.1 +RoxygenNote: 7.2.3 diff --git a/R/growth.R b/R/growth.R index dcb3b13a..4dfd801e 100644 --- a/R/growth.R +++ b/R/growth.R @@ -84,7 +84,7 @@ #' @param adult_columns_filename Name of file to save original adult data, with additional output columns to #' as CSV. Defaults to "", for which this data will not be saved. Useful #' for post-analysis. For more information on this output, please see README. -#' @param infants TRUE/FALSE. Run the alpha-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE. +#' @param infants TRUE/FALSE. Run the beta-release of the infants algorithm (expands pediatric algorithm to clean 0 - 2). Defaults to FALSE. #' #' @return Vector of exclusion codes for each of the input measurements. #' @@ -308,17 +308,31 @@ cleangrowth <- function(subjid, tanner.fields <- colnames(tanner.ht.vel) tanner.fields <- tanner.fields[!tanner.fields %in% c('sex', 'tanner.months')] - who_max_ht_vel_path <- ifelse( - ref.data.path == "", - system.file(file.path("extdata", "who_ht_maxvel_3sd.csv.gz"), package = "growthcleanr"), - file.path(ref.data.path, "who_ht_maxvel_3sd.csv.gz") - ) + if (!infants){ + who_max_ht_vel_path <- ifelse( + ref.data.path == "", + system.file(file.path("extdata", "who_ht_maxvel_3sd.csv.gz"), package = "growthcleanr"), + file.path(ref.data.path, "who_ht_maxvel_3sd.csv.gz") + ) - who_ht_vel_3sd_path <- ifelse( - ref.data.path == "", - system.file(file.path("extdata", "who_ht_vel_3sd.csv.gz"), package = "growthcleanr"), - file.path(ref.data.path, "who_ht_vel_3sd.csv.gz") - ) + who_ht_vel_3sd_path <- ifelse( + ref.data.path == "", + system.file(file.path("extdata", "who_ht_vel_3sd.csv.gz"), package = "growthcleanr"), + file.path(ref.data.path, "who_ht_vel_3sd.csv.gz") + ) + } else { + who_max_ht_vel_path <- ifelse( + ref.data.path == "", + system.file(file.path("extdata", "who_hc_maxvel_3sd_infants.csv.gz"), package = "growthcleanr"), + file.path(ref.data.path, "who_hc_maxvel_3sd_infants.csv.gz") + ) + + who_ht_vel_3sd_path <- ifelse( + ref.data.path == "", + system.file(file.path("extdata", "who_hc_vel_3sd_infants.csv.gz"), package = "growthcleanr"), + file.path(ref.data.path, "who_hc_vel_3sd_infants.csv.gz") + ) + } who.max.ht.vel <- fread(who_max_ht_vel_path) who.ht.vel <- fread(who_ht_vel_3sd_path) setkey(who.max.ht.vel, sex, whoagegrp_ht) diff --git a/inst/extdata/who_hc_maxvel_3sd_infants.csv.gz b/inst/extdata/who_hc_maxvel_3sd_infants.csv.gz new file mode 100644 index 0000000000000000000000000000000000000000..63038622940f41ebb79f21eacb02485df2e71a57 GIT binary patch literal 459 zcmb2|=3oE=E^WWH7c|eG@b~fe)z|7cuW|9D@0t#Fu9Ro0$EA}WzLr$#p0Qx10rRKI zoeb5R3TK7@b;-ju=S~h>_gaC+<^TMW@}_`e0t&zH-e^AJKljsD9Y3!cw@)pJ9#vU|zv=2!FOg=cYEKlOdQ zL1bFn(wMiOKh>SwmAE|d@P^DAMsI$lzBS-!YfVh+OM8})*7wbr{e@lY(!Y*Jccunk zIJ{Ffe~m-HC;v$Ax%y(e7O?OhT5!i%Nbzd??(lr2-*K;w*9ixISo-glz6MMDtNZ#u zp--y~u4w<5V6UHh<+yWgmS9!qk4g3?m;ZWQCs46!zdXylPu|08~ zPC)hYaBZoRy!(zS@UVPWKI<#6D0koY{Wos3xs+JHG6Bai$WYUvqCjB9weQZy^ z_xJQTahmNk+g-N93nkXD9XWmo4HtZ-ja#dbN7H~!n(ZEu}!e9b6-<8(uHdhEu} z+0!lNtT#EX_h&nIO8Arh=!(0y)E=5?-#eW4(2RTH%h!nkv!+~|o448_tZ@xfB Date: Thu, 9 Mar 2023 13:46:42 -0500 Subject: [PATCH 025/100] Add updated reference files for WHO/CDC --- R/growth.R | 348 ++++++++++++------ R/infants_clean.R | 282 +++++++------- .../extdata/growthfile_cdc_ext_infants.csv.gz | Bin 0 -> 1004894 bytes inst/extdata/growthfile_who.csv.gz | Bin 0 -> 402367 bytes 4 files changed, 381 insertions(+), 249 deletions(-) create mode 100644 inst/extdata/growthfile_cdc_ext_infants.csv.gz create mode 100644 inst/extdata/growthfile_who.csv.gz diff --git a/R/growth.R b/R/growth.R index 4dfd801e..ac7ea9ad 100644 --- a/R/growth.R +++ b/R/growth.R @@ -396,8 +396,10 @@ cleangrowth <- function(subjid, if (!quietly) cat(sprintf("[%s] Calculating z-scores...\n", Sys.time())) # for infants, use z and who - measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE) - measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = F) + measurement.to.z <- read_anthro(ref.data.path, cdc.only = TRUE, + infants = T) + measurement.to.z_who <- read_anthro(ref.data.path, cdc.only = FALSE, + infants = T) data.all[, z.orig_cdc := measurement.to.z(param, agedays, sex, v)] data.all[, z.orig_who := measurement.to.z_who(param, agedays, sex, v)] @@ -836,7 +838,7 @@ cleangrowth <- function(subjid, #' #' @param path Path to supplied reference anthro data. Defaults to package anthro tables. #' @param cdc.only Whether or not only CDC data should be used. Defaults to false. -#' @param infants boolean on if infants version is being used. will be merged in to main algorithm. +#' @param infants boolean on if beta infants version is being used. will be merged in to main algorithm. #' #' @return Function for calculating BMI based on measurement, age in days, sex, and measurement value. #' @export @@ -849,121 +851,251 @@ cleangrowth <- function(subjid, #' # Return calculating function while specifying a path and using only CDC data #' afunc <- read_anthro(path = system.file("extdata", package = "growthcleanr"), #' cdc.only = TRUE) -read_anthro <- function(path = "", cdc.only = FALSE) { +read_anthro <- function(path = "", cdc.only = FALSE, infants = FALSE) { # avoid "no visible bindings" warning src <- param <- sex <- age <- ret <- m <- NULL csdneg <- csdpos <- s <- NULL # set correct path based on input reference table path (if any) - weianthro_path <- ifelse( - path == "", - system.file(file.path("extdata", "weianthro.txt.gz"), package = "growthcleanr"), - file.path(path, "weianthro.txt.gz") - ) - lenanthro_path <- ifelse( - path == "", - system.file(file.path("extdata", "lenanthro.txt.gz"), package = "growthcleanr"), - file.path(path, "lenanthro.txt.gz") - ) - bmianthro_path <- ifelse( - path == "", - system.file(file.path("extdata", "bmianthro.txt.gz"), package = "growthcleanr"), - file.path(path, "bmianthro.txt.gz") - ) - growth_cdc_ext_path <- ifelse( - path == "", - system.file(file.path("extdata", "growthfile_cdc_ext.csv.gz"), package = "growthcleanr"), - file.path(path, "growthfile_cdc_ext.csv.gz") - ) - + if (!infants){ + weianthro_path <- ifelse( + path == "", + system.file(file.path("extdata", "weianthro.txt.gz"), package = "growthcleanr"), + file.path(path, "weianthro.txt.gz") + ) + lenanthro_path <- ifelse( + path == "", + system.file(file.path("extdata", "lenanthro.txt.gz"), package = "growthcleanr"), + file.path(path, "lenanthro.txt.gz") + ) + bmianthro_path <- ifelse( + path == "", + system.file(file.path("extdata", "bmianthro.txt.gz"), package = "growthcleanr"), + file.path(path, "bmianthro.txt.gz") + ) + growth_cdc_ext_path <- ifelse( + path == "", + system.file(file.path("extdata", "growthfile_cdc_ext.csv.gz"), package = "growthcleanr"), + file.path(path, "growthfile_cdc_ext.csv.gz") + ) + } else { + weianthro_path <- lenanthro_path <- bmianthro_path <- + ifelse( + path == "", + system.file(file.path("extdata", "growthfile_who.csv.gz"), package = "growthcleanr"), + file.path(path, "growthfile_who.csv.gz") + ) + growth_cdc_ext_path <- ifelse( + path == "", + system.file(file.path("extdata", "growthfile_cdc_ext_infants.csv.gz"), package = "growthcleanr"), + file.path(path, "growthfile_cdc_ext_infants.csv.gz") + ) + } growth_cdc_ext <- read.csv(gzfile(growth_cdc_ext_path)) - l <- list( - with( - read.table(gzfile(weianthro_path), header = TRUE), - data.frame( - src = 'WHO', - param = 'WEIGHTKG', - sex = sex - 1, - age, - l, - m, - s, - csdpos = as.double(NA), - csdneg = as.double(NA) - ) - ), - with( - read.table(gzfile(lenanthro_path), header = TRUE), - data.frame( - src = 'WHO', - param = 'HEIGHTCM', - sex = sex - 1, - age, - l, - m, - s, - csdpos = as.double(NA), - csdneg = as.double(NA) - ) - ), - with( - read.table(gzfile(bmianthro_path), header = TRUE), - data.frame( - src = 'WHO', - param = 'BMI', - sex = sex - 1, - age, - l, - m, - s, - csdpos = as.double(NA), - csdneg = as.double(NA) - ) - ), - with( - growth_cdc_ext, - data.frame( - src = 'CDC', - param = 'WEIGHTKG', - sex, - age = agedays, - l = cdc_wt_l, - m = cdc_wt_m, - s = cdc_wt_s, - csdpos = cdc_wt_csd_pos, - csdneg = cdc_wt_csd_neg - ) - ), - with( - growth_cdc_ext, - data.frame( - src = 'CDC', - param = 'HEIGHTCM', - sex, - age = agedays, - l = cdc_ht_l, - m = cdc_ht_m, - s = cdc_ht_s, - csdpos = cdc_ht_csd_pos, - csdneg = cdc_ht_csd_neg + l <- if (!infants){ + list( + with( + read.table(gzfile(weianthro_path), header = TRUE), + data.frame( + src = 'WHO', + param = 'WEIGHTKG', + sex = sex - 1, + age, + l, + m, + s, + csdpos = as.double(NA), + csdneg = as.double(NA) + ) + ), + with( + read.table(gzfile(lenanthro_path), header = TRUE), + data.frame( + src = 'WHO', + param = 'HEIGHTCM', + sex = sex - 1, + age, + l, + m, + s, + csdpos = as.double(NA), + csdneg = as.double(NA) + ) + ), + with( + read.table(gzfile(bmianthro_path), header = TRUE), + data.frame( + src = 'WHO', + param = 'BMI', + sex = sex - 1, + age, + l, + m, + s, + csdpos = as.double(NA), + csdneg = as.double(NA) + ) + ), + with( + growth_cdc_ext, + data.frame( + src = 'CDC', + param = 'WEIGHTKG', + sex, + age = agedays, + l = cdc_wt_l, + m = cdc_wt_m, + s = cdc_wt_s, + csdpos = cdc_wt_csd_pos, + csdneg = cdc_wt_csd_neg + ) + ), + with( + growth_cdc_ext, + data.frame( + src = 'CDC', + param = 'HEIGHTCM', + sex, + age = agedays, + l = cdc_ht_l, + m = cdc_ht_m, + s = cdc_ht_s, + csdpos = cdc_ht_csd_pos, + csdneg = cdc_ht_csd_neg + ) + ), + with( + growth_cdc_ext, + data.frame( + src = 'CDC', + param = 'BMI', + sex, + age = agedays, + l = cdc_bmi_l, + m = cdc_bmi_m, + s = cdc_bmi_s, + csdpos = cdc_bmi_csd_pos, + csdneg = cdc_bmi_csd_neg + ) ) - ), - with( - growth_cdc_ext, - data.frame( - src = 'CDC', - param = 'BMI', - sex, - age = agedays, - l = cdc_bmi_l, - m = cdc_bmi_m, - s = cdc_bmi_s, - csdpos = cdc_bmi_csd_pos, - csdneg = cdc_bmi_csd_neg + ) + } else { + list( + with( + read.csv(gzfile(weianthro_path), header = TRUE), + data.frame( + src = 'WHO', + param = 'WEIGHTKG', + sex, + age = agedays, + l = who_wt_l, + m = who_wt_m, + s = who_wt_s, + csdpos = who_wt_csd_pos, + csdneg = who_wt_csd_neg + ) + ), + with( + read.csv(gzfile(lenanthro_path), header = TRUE), + data.frame( + src = 'WHO', + param = 'HEIGHTCM', + sex, + age = agedays, + l = who_ht_l, + m = who_ht_m, + s = who_ht_s, + csdpos = who_ht_csd_pos, + csdneg = who_ht_csd_neg + ) + ), + with( + read.csv(gzfile(lenanthro_path), header = TRUE), + data.frame( + src = 'WHO', + param = 'HEADCM', + sex, + age = agedays, + l = who_hc_l, + m = who_hc_m, + s = who_hc_s, + csdpos = who_hc_csd_pos, + csdneg = who_hc_csd_neg + ) + ), + with( + read.csv(gzfile(bmianthro_path), header = TRUE), + data.frame( + src = 'WHO', + param = 'BMI', + sex, + age = agedays, + l = who_bmi_l, + m = who_bmi_m, + s = who_bmi_s, + csdpos = who_bmi_csd_pos, + csdneg = who_bmi_csd_neg + ) + ), + with( + growth_cdc_ext, + data.frame( + src = 'CDC', + param = 'WEIGHTKG', + sex, + age = agedays, + l = cdc_wt_l, + m = cdc_wt_m, + s = cdc_wt_s, + csdpos = cdc_wt_csd_pos, + csdneg = cdc_wt_csd_neg + ) + ), + with( + growth_cdc_ext, + data.frame( + src = 'CDC', + param = 'HEIGHTCM', + sex, + age = agedays, + l = cdc_ht_l, + m = cdc_ht_m, + s = cdc_ht_s, + csdpos = cdc_ht_csd_pos, + csdneg = cdc_ht_csd_neg + ) + ), + with( + growth_cdc_ext, + data.frame( + src = 'CDC', + param = 'HEADCM', + sex, + age = agedays, + l = cdc_hc_l, + m = cdc_hc_m, + s = cdc_hc_s, + csdpos = cdc_hc_csd_pos, + csdneg = cdc_hc_csd_neg + ) + ), + with( + growth_cdc_ext, + data.frame( + src = 'CDC', + param = 'BMI', + sex, + age = agedays, + l = cdc_bmi_l, + m = cdc_bmi_m, + s = cdc_bmi_s, + csdpos = cdc_bmi_csd_pos, + csdneg = cdc_bmi_csd_neg + ) ) ) - ) + } anthro <- rbindlist(l) diff --git a/R/infants_clean.R b/R/infants_clean.R index 9513bd8a..1330f784 100644 --- a/R/infants_clean.R +++ b/R/infants_clean.R @@ -418,147 +418,147 @@ cleanbatch_infants <- function(data.df, # STOP HERE: need to adapt to new work - # prepare a list of valid rows and initialize variables for convenience - valid.rows <- valid(data.df) - data.df[, `:=`( - ewma.all = as.double(NaN), - abssum2 = as.double(NaN), - median.other.sd = as.double(NaN), - extraneous = FALSE - )] - - # 12c. Calculate a EWMA step for all subjects/parameters with duplicates/extraneous and at - # least one non-extraneous value with the following modifications - # i. For calculating the EWMA, include only the extraneous selected in 12c - # ii. Calculate dewma_* for all values of extraneous - # iii. You do not need to calculate EWMAbef or EWMAaft for this step - - # determine proportion of days with extraneous/duplication for each parameter ahead of time for efficiency - dup.ratio.df <- data.df[subjid %in% subj.dup & - (valid.rows | - temp.dups), list(dup = (.N > 1)), by = .(subjid, param, agedays)][j = list(dup.ratio = - mean(dup)), keyby = .(subjid, param)] - # identify subject/parameters where there is duplication but at least one day with no extraneous for that parameter - subj.param.not.all.dups <- dup.ratio.df[dup.ratio < 1.0, list(subjid, param)] - # identify subject/parameters where there is duplication for all days for that parameter - subj.param.all.dups <- dup.ratio.df[dup.ratio == 1, list(subjid, param)] - subj.all.dups <- subj.param.all.dups[, unique(subjid)] - # perform ewma for subjects with extraneous - data.df[subjid %in% subj.dup & - valid.rows, ewma.all := ewma(agedays, tbc.sd, ewma.exp, ewma.adjacent = - FALSE), by = .(subjid, param)] - # note: at this point, only one ewma.all exists per param on a given day for a subject, so sort(ewma.all)[1] will returns the non-missing ewma.all - data.df[subjid %in% subj.dup, ewma.all := sort(ewma.all)[1], by = .(subjid, param, agedays)] - - # iv. Calculate abssum2_*=|2*dewma_*|+|tbc*sd| (note that this is different from how we calculated abssum_* in step 11). - # NOTE: only children with more than one ageday with valid measurements will have a valid ewma from above - data.df[, abssum2 := 2 * abs(tbc.sd - ewma.all) + abs(tbc.sd)] - - # 12d. For each subject/parameter/age with extraneous and at least one non-extraneous value: - # i. Replace exc_*=7 for all values except the value that has the smallest abssum2_*. - data.df[data.table(subj.param.not.all.dups), extraneous := seq_along(abssum2) != which.min(abssum2), by = - .(subjid, param, agedays)] - data.df[temp.dups, exclude := 'Include'] - data.df[(valid.rows | - temp.dups) & - extraneous, exclude := 'Exclude-Extraneous-Same-Day'] - # ii. Determine dup_tot_* (# of days with extraneous for that subject/parameter) and nodup_tot_* (# of days with nonexlcuded - # non-extraneous for that subject/parameter). - # iii. If dup_tot_*/(dup_tot_*+nodup_tot_*) is greater than 1/2, replace exc_*=7 for all extraneous for that subject/parameter - # for each age where the largest measurement minus the smallest measurement for that subject/parameter/age is larger than - # the maximum difference (ht 3cm; wt 0-9.999 kg 0.25kg; wt 10-29.9999 kg 0.5 kg; wt 30kg and higher 1 kg). - - data.df[data.table(dup.ratio.df[dup.ratio > 1 / 2, list(subjid, param)]), exclude := (function(df) { - df[, `:=`(tbc.sd.min = as.double(NaN), - tbc.sd.max = as.double(NaN))] - df[valid( - exclude, - include.extraneous = TRUE, - include.temporary.extraneous = TRUE - ), `:=`(tbc.sd.min = min(tbc.sd), - tbc.sd.max = max(tbc.sd))] - df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM', - 3, - ifelse( - param == 'WEIGHTKG', - ifelse(tbc.sd.min < 10, 0.25, ifelse(tbc.sd.min < 30, 0.5, 1)), - NA - )), - exclude := 'Exclude-Extraneous-Same-Day'] - return(df$exclude) - })(copy(.SD)), .SDcols = c('exclude', 'tbc.sd'), by = .(subjid, param, agedays)] - - # 12e. For each subject/parameter/age with extraneous and no nonextraneous values: - # i. Replace exc_*=7 for all values except the value with the smallest |tbc*sd-median_tbcOsd|. If median_tbcOsd is missing because there are no values - # for the other parameter, randomly choose one extraneous value for each subject/parameter/age to keep as exc_*=0 and replace exc_*=7 for all other - # extraneous for that subject/parameter/age. - # ii. If the largest measurement minus the smallest measurement for that subject/parameter/age is larger than the maximum difference - # (ht 3cm; wt 0-9.999 kg 0.25kg; wt 10-29.9999 kg 0.5 kg; wt 30kg and higher 1 kg)., replace exc_*=7 for all extraneous for that - # subject/parameter/age. - - # calculate median for other parameter (restrict to subjects with all duplication for at least one parameter) - data.df[subjid %in% subj.all.dups, exclude := (function(subj.df) { - # flag days that have extraneous / potentially valid parameters - subj.df[, `:=`( - extraneous.this.day = FALSE, - extraneous = FALSE, - tbc.sd.min = as.double(NaN), - tbc.sd.max = as.double(NaN) - )] - valid.rows = valid( - subj.df, - include.extraneous = TRUE, - include.temporary.extraneous = TRUE - ) - subj.df[valid.rows, extraneous.this.day := (.N > 1), by = .(param, agedays)] - for (p in subj.df[j = unique(param)]) { - median.sd <- subj.df[param != p & - !extraneous.this.day, median(tbc.sd)] - subj.df[param == p, median.other.sd := median.sd] - } - # safety check -- assign median.other.sd==0 to ensure "which.min" functions correctly below - subj.df[is.na(median.other.sd), median.other.sd := 0] - # identify rows as extraneous where |tbc*sd-median_tbcOsd| is not at the minimum value - subj.df[extraneous.this.day == TRUE, extraneous := (seq_along(median.other.sd) != which.min(abs(tbc.sd - median.other.sd))), by = - .(param, agedays)] - subj.df[extraneous.this.day & - !extraneous, exclude := 'Include'] - subj.df[extraneous.this.day & - extraneous, exclude := 'Exclude-Extraneous-Same-Day'] - subj.df[extraneous.this.day == TRUE, `:=`(tbc.sd.min = min(tbc.sd), - tbc.sd.max = max(tbc.sd)), by = .(param, agedays)] - subj.df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM', - 3, - ifelse( - param == 'WEIGHTKG', - ifelse(tbc.sd.min < 10, 0.25, ifelse(tbc.sd.min < 30, 0.5, 1)), - NA - )), - exclude := 'Exclude-Extraneous-Same-Day'] - - # identify kids who had an SD or EWMA extreme excluded that was a extraneous and re-label as "Exclude-Extraneous-Same-Day" - subj.df[, extraneous.this.day := FALSE] - # consider any non-missing measurement when determining presence of extraneous - subj.df[exclude != 'Missing', extraneous.this.day := (.N > 1), by = - .(param, agedays)] - subj.df[extraneous.this.day & - exclude %in% c('Exclude-SD-Cutoff', - 'Exclude-EWMA-Extreme', - 'Exclude-EWMA-Extreme-Pair'), exclude := 'Exclude-Extraneous-Same-Day'] - - return(subj.df$exclude) - })(copy(.SD)), .SDcols = c('param', 'agedays', 'exclude', 'tbc.sd'), by = - .(subjid)] - - # 12f. For any values that were excluded with exc_*=4, 5, or 6 that are also extraneous, replace exc_*=7. - data.df[subjid %in% subj.dup, exclude := (function(subj.df) { - if (.N > 1) { - subj.df[exclude %in% c('Exclude-SD-Cutoff', - 'Exclude-EWMA-Extreme', - 'Exclude-EWMA-Extreme-Pair'), exclude := 'Exclude-Extraneous-Same-Day'] - } - return(subj.df$exclude) - })(copy(.SD)), .SDcols = c('exclude'), by = .(subjid, param, agedays)] + # # prepare a list of valid rows and initialize variables for convenience + # valid.rows <- valid(data.df) + # data.df[, `:=`( + # ewma.all = as.double(NaN), + # abssum2 = as.double(NaN), + # median.other.sd = as.double(NaN), + # extraneous = FALSE + # )] + # + # # 12c. Calculate a EWMA step for all subjects/parameters with duplicates/extraneous and at + # # least one non-extraneous value with the following modifications + # # i. For calculating the EWMA, include only the extraneous selected in 12c + # # ii. Calculate dewma_* for all values of extraneous + # # iii. You do not need to calculate EWMAbef or EWMAaft for this step + # + # # determine proportion of days with extraneous/duplication for each parameter ahead of time for efficiency + # dup.ratio.df <- data.df[subjid %in% subj.dup & + # (valid.rows | + # temp.dups), list(dup = (.N > 1)), by = .(subjid, param, agedays)][j = list(dup.ratio = + # mean(dup)), keyby = .(subjid, param)] + # # identify subject/parameters where there is duplication but at least one day with no extraneous for that parameter + # subj.param.not.all.dups <- dup.ratio.df[dup.ratio < 1.0, list(subjid, param)] + # # identify subject/parameters where there is duplication for all days for that parameter + # subj.param.all.dups <- dup.ratio.df[dup.ratio == 1, list(subjid, param)] + # subj.all.dups <- subj.param.all.dups[, unique(subjid)] + # # perform ewma for subjects with extraneous + # data.df[subjid %in% subj.dup & + # valid.rows, ewma.all := ewma(agedays, tbc.sd, ewma.exp, ewma.adjacent = + # FALSE), by = .(subjid, param)] + # # note: at this point, only one ewma.all exists per param on a given day for a subject, so sort(ewma.all)[1] will returns the non-missing ewma.all + # data.df[subjid %in% subj.dup, ewma.all := sort(ewma.all)[1], by = .(subjid, param, agedays)] + # + # # iv. Calculate abssum2_*=|2*dewma_*|+|tbc*sd| (note that this is different from how we calculated abssum_* in step 11). + # # NOTE: only children with more than one ageday with valid measurements will have a valid ewma from above + # data.df[, abssum2 := 2 * abs(tbc.sd - ewma.all) + abs(tbc.sd)] + # + # # 12d. For each subject/parameter/age with extraneous and at least one non-extraneous value: + # # i. Replace exc_*=7 for all values except the value that has the smallest abssum2_*. + # data.df[data.table(subj.param.not.all.dups), extraneous := seq_along(abssum2) != which.min(abssum2), by = + # .(subjid, param, agedays)] + # data.df[temp.dups, exclude := 'Include'] + # data.df[(valid.rows | + # temp.dups) & + # extraneous, exclude := 'Exclude-Extraneous-Same-Day'] + # # ii. Determine dup_tot_* (# of days with extraneous for that subject/parameter) and nodup_tot_* (# of days with nonexlcuded + # # non-extraneous for that subject/parameter). + # # iii. If dup_tot_*/(dup_tot_*+nodup_tot_*) is greater than 1/2, replace exc_*=7 for all extraneous for that subject/parameter + # # for each age where the largest measurement minus the smallest measurement for that subject/parameter/age is larger than + # # the maximum difference (ht 3cm; wt 0-9.999 kg 0.25kg; wt 10-29.9999 kg 0.5 kg; wt 30kg and higher 1 kg). + # + # data.df[data.table(dup.ratio.df[dup.ratio > 1 / 2, list(subjid, param)]), exclude := (function(df) { + # df[, `:=`(tbc.sd.min = as.double(NaN), + # tbc.sd.max = as.double(NaN))] + # df[valid( + # exclude, + # include.extraneous = TRUE, + # include.temporary.extraneous = TRUE + # ), `:=`(tbc.sd.min = min(tbc.sd), + # tbc.sd.max = max(tbc.sd))] + # df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM', + # 3, + # ifelse( + # param == 'WEIGHTKG', + # ifelse(tbc.sd.min < 10, 0.25, ifelse(tbc.sd.min < 30, 0.5, 1)), + # NA + # )), + # exclude := 'Exclude-Extraneous-Same-Day'] + # return(df$exclude) + # })(copy(.SD)), .SDcols = c('exclude', 'tbc.sd'), by = .(subjid, param, agedays)] + # + # # 12e. For each subject/parameter/age with extraneous and no nonextraneous values: + # # i. Replace exc_*=7 for all values except the value with the smallest |tbc*sd-median_tbcOsd|. If median_tbcOsd is missing because there are no values + # # for the other parameter, randomly choose one extraneous value for each subject/parameter/age to keep as exc_*=0 and replace exc_*=7 for all other + # # extraneous for that subject/parameter/age. + # # ii. If the largest measurement minus the smallest measurement for that subject/parameter/age is larger than the maximum difference + # # (ht 3cm; wt 0-9.999 kg 0.25kg; wt 10-29.9999 kg 0.5 kg; wt 30kg and higher 1 kg)., replace exc_*=7 for all extraneous for that + # # subject/parameter/age. + # + # # calculate median for other parameter (restrict to subjects with all duplication for at least one parameter) + # data.df[subjid %in% subj.all.dups, exclude := (function(subj.df) { + # # flag days that have extraneous / potentially valid parameters + # subj.df[, `:=`( + # extraneous.this.day = FALSE, + # extraneous = FALSE, + # tbc.sd.min = as.double(NaN), + # tbc.sd.max = as.double(NaN) + # )] + # valid.rows = valid( + # subj.df, + # include.extraneous = TRUE, + # include.temporary.extraneous = TRUE + # ) + # subj.df[valid.rows, extraneous.this.day := (.N > 1), by = .(param, agedays)] + # for (p in subj.df[j = unique(param)]) { + # median.sd <- subj.df[param != p & + # !extraneous.this.day, median(tbc.sd)] + # subj.df[param == p, median.other.sd := median.sd] + # } + # # safety check -- assign median.other.sd==0 to ensure "which.min" functions correctly below + # subj.df[is.na(median.other.sd), median.other.sd := 0] + # # identify rows as extraneous where |tbc*sd-median_tbcOsd| is not at the minimum value + # subj.df[extraneous.this.day == TRUE, extraneous := (seq_along(median.other.sd) != which.min(abs(tbc.sd - median.other.sd))), by = + # .(param, agedays)] + # subj.df[extraneous.this.day & + # !extraneous, exclude := 'Include'] + # subj.df[extraneous.this.day & + # extraneous, exclude := 'Exclude-Extraneous-Same-Day'] + # subj.df[extraneous.this.day == TRUE, `:=`(tbc.sd.min = min(tbc.sd), + # tbc.sd.max = max(tbc.sd)), by = .(param, agedays)] + # subj.df[tbc.sd.max - tbc.sd.min > ifelse(param == 'HEIGHTCM', + # 3, + # ifelse( + # param == 'WEIGHTKG', + # ifelse(tbc.sd.min < 10, 0.25, ifelse(tbc.sd.min < 30, 0.5, 1)), + # NA + # )), + # exclude := 'Exclude-Extraneous-Same-Day'] + # + # # identify kids who had an SD or EWMA extreme excluded that was a extraneous and re-label as "Exclude-Extraneous-Same-Day" + # subj.df[, extraneous.this.day := FALSE] + # # consider any non-missing measurement when determining presence of extraneous + # subj.df[exclude != 'Missing', extraneous.this.day := (.N > 1), by = + # .(param, agedays)] + # subj.df[extraneous.this.day & + # exclude %in% c('Exclude-SD-Cutoff', + # 'Exclude-EWMA-Extreme', + # 'Exclude-EWMA-Extreme-Pair'), exclude := 'Exclude-Extraneous-Same-Day'] + # + # return(subj.df$exclude) + # })(copy(.SD)), .SDcols = c('param', 'agedays', 'exclude', 'tbc.sd'), by = + # .(subjid)] + # + # # 12f. For any values that were excluded with exc_*=4, 5, or 6 that are also extraneous, replace exc_*=7. + # data.df[subjid %in% subj.dup, exclude := (function(subj.df) { + # if (.N > 1) { + # subj.df[exclude %in% c('Exclude-SD-Cutoff', + # 'Exclude-EWMA-Extreme', + # 'Exclude-EWMA-Extreme-Pair'), exclude := 'Exclude-Extraneous-Same-Day'] + # } + # return(subj.df$exclude) + # })(copy(.SD)), .SDcols = c('exclude'), by = .(subjid, param, agedays)] # end ---- diff --git a/inst/extdata/growthfile_cdc_ext_infants.csv.gz b/inst/extdata/growthfile_cdc_ext_infants.csv.gz new file mode 100644 index 0000000000000000000000000000000000000000..4ab72d33aff2daba7626611131ddd6d0f902f0cf GIT binary patch literal 1004894 zcmV(}K+wM*iwFP!000003SG@n3Vl-Q%C%sRr*=w#y{)* z&bI3x|MUO<7e|bLJIk$S$M5;qZ~SR-iaJ^?YxlqZ;*@t@{&t+Qoz?HsIZ8Qm&g=i+ z>~Z&BAOCi(b9MfMv2K~Q91c4B*roE}snz&t@uLE-wo2U`IsQVA zv-_M?VD7u;nj0`zZF^mTS#KP30%qNPjK3NGFtG9=0_OO~w+qZt$Ecur1M}g3U(dMf zT%j4Cyf&lRXYH+mVGD4^~bx@Is}eA~JoW3F}k-U2f|N!$j&j2lwh6_|n1 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