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Data_Analysis_Processing_Time.Rmd
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Data_Analysis_Processing_Time.Rmd
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---
title: "Microplastics Intercalibration Study Data Analysis: Processing Time"
author: "Southern California Coastal Water Research Project, University of Toronto & State Water Resources Control Board of California"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
toc_depth: 4
number_sections: true
toc_float: true
---
![](www/logobanner.png)
```{r Install Packages, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
#### Packages ####
library(tidyverse)#A ton of useful functions
library(knitr) #Needed to display table
library(kableExtra)#data tables
library(Steel.Dwass.test) #steeldwass test
```
```{r Data Import, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
Results <- read_csv("https://mpchecker.sccwrp.org/checker/intercal-data?table=tbl_results_master", guess_max = 100000) #Raw data file
Lab_Info <- read_csv("https://mpchecker.sccwrp.org/checker/intercal-data?table=tbl_labinformation", guess_max = 100000) #Import .csv file containing all lab information for clean water
Extraction <- read_csv("https://mpchecker.sccwrp.org/checker/intercal-data?table=tbl_sampleextraction", guess_max = 100000) #Raw data file for other matrices
Microscopy <- read_csv("https://mpchecker.sccwrp.org/checker/intercal-data?table=tbl_microscopysettings", guess_max = 100000) #Raw data file for other matrices
NileRed <- read_csv("https://mpchecker.sccwrp.org/checker/intercal-data?table=tbl_nileredsettings", guess_max = 100000) #Raw data file for other matrices
FTIR <- read_csv("https://mpchecker.sccwrp.org/checker/intercal-data?table=tbl_ftirsettings", guess_max = 100000) #Raw data file for other matrices
Raman <- read_csv("https://mpchecker.sccwrp.org/checker/intercal-data?table=tbl_ramansettings", guess_max = 100000) #Raw data file for other matrices
```
```{r}
```
```{r Data Tidying - Extraction, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
#Remove "Lab" from relevant columns in other matrices data frame
Lab_Info$labid <- gsub("Lab","",as.character(Lab_Info$labid))
Extraction$labid <- gsub("Lab","",as.character(Extraction$labid))
Extraction$sampleid <- gsub("Lab","",as.character(Extraction$sampleid))
#Select data relating to expertise for visual microscopy
Expertise <- Lab_Info %>%
select(labid, matrix, expertiseextraction, expertisevisualmicroscopy, expertisenilered, expertiseftir, expertiseraman, expertisepy_gcms)
#Join expertise data to results data frame
Extraction <- Extraction %>%
left_join(Expertise, by = c("labid" = "labid", "matrix" = "matrix"))
Time_Extraction_Sample <- Extraction %>%
mutate(sampletype = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD")) %>%
#Removed lab D - incomparable with other data - deviation from methods
filter(!(labid == "D"&sampletype == "CW")) %>%
#Removed pygcms data data
filter(!(labid == "T"&sampletype == "CW")) %>%
filter(!(labid == "F"&sampletype == "CW")) %>%
#Removed second set from Lab N - 2nd set of samples processed
filter(!(labid == "N"&sampletype == "CW"&(grepl("5|6|7|8", sampleid)))) %>%
#Removed labs that submitted clean water data late (want to match with original clean water data analysis)
filter(!(labid == "WW"&sampletype == "CW")) %>%
filter(!(labid == "NN"&sampletype == "CW")) %>%
#Exclude augmentation data
filter(!(labid %in% c("R","D", "L") & grepl("5|6|7|8", sampleid) & sampletype %in% c("Sediment"))) %>%
filter(!(labid %in% c("L") & grepl("5|6|7|8|9|10|11|12", sampleid) & sampletype %in% c("Fish Tissue"))) %>%
#drop blank samples
mutate(blank = ifelse(grepl("4|8", sampleid),"Y", "N")) %>%
filter(blank == "N") %>%
#drop where time isn't reported
filter(time != -88) %>%
group_by(sampleid) %>%
mutate(time_sample = sum(time)) %>%
ungroup()
Time_Extraction_Lab <- Time_Extraction_Sample %>%
ungroup() %>%
group_by(matrix, labid, time_sample, expertiseextraction) %>%
summarise() %>%
ungroup() %>%
group_by(matrix, labid) %>%
mutate(time_lab = mean(time_sample)) %>%
ungroup() %>%
mutate(matrix = case_when(
matrix == "CW" ~ "Clean Water",
matrix == "FT" ~ "Fish Tissue",
matrix == "SD" ~ "Sediment",
matrix == "DW" ~ "Dirty Water"))
```
```{r Data Tidying - Microscopy, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
#Remove "Lab" from relevant columns in other matrices data frame
Microscopy$labid <- gsub("Lab","",as.character(Microscopy$labid))
Microscopy$sampleid <- gsub("Lab","",as.character(Microscopy$sampleid))
#Assign matrices to sampleids
Microscopy <- Microscopy %>%
mutate(matrix = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD"))
#Join expertise data to results data frame
Microscopy <- Microscopy %>%
left_join(Expertise, by = c("labid" = "labid", "matrix" = "matrix"))
Time_Microscopy_Sample <- Microscopy %>%
mutate(sampletype = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD")) %>%
#Removed lab D - incomparable with other data - deviation from methods
filter(!(labid == "D"&sampletype == "CW")) %>%
#Removed pygcms data data
filter(!(labid == "T"&sampletype == "CW")) %>%
filter(!(labid == "F"&sampletype == "CW")) %>%
#Removed second set from Lab N - 2nd set of samples processed
filter(!(labid == "N"&sampletype == "CW"&(grepl("5|6|7|8", sampleid)))) %>%
#Removed labs that submitted clean water data late (want to match with original clean water data analysis)
filter(!(labid == "WW"&sampletype == "CW")) %>%
filter(!(labid == "NN"&sampletype == "CW")) %>%
#Exclude augmentation data
filter(!(labid %in% c("R","D", "L") & grepl("5|6|7|8", sampleid) & sampletype %in% c("Sediment"))) %>%
filter(!(labid %in% c("L") & grepl("5|6|7|8|9|10|11|12", sampleid) & sampletype %in% c("Fish Tissue"))) %>%
#drop blank samples
mutate(blank = ifelse(grepl("4|8", sampleid),"Y", "N")) %>%
filter(blank == "N") %>%
#drop where time isn't reported
filter(time != -88) %>%
group_by(sampleid) %>%
mutate(time_sample = sum(time))
Time_Microscopy_Lab <- Time_Microscopy_Sample %>%
ungroup() %>%
group_by(matrix, labid, sampleid, time_sample, expertisevisualmicroscopy) %>%
summarise() %>%
ungroup() %>%
group_by(matrix, labid) %>%
mutate(time_lab = mean(time_sample)) %>%
ungroup() %>%
mutate(matrix = case_when(
matrix == "CW" ~ "Clean Water",
matrix == "FT" ~ "Fish Tissue",
matrix == "SD" ~ "Sediment",
matrix == "DW" ~ "Dirty Water"))
```
```{r Data Tidying - NileRed, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
#Remove "Lab" from relevant columns in other matrices data frame
NileRed$labid <- gsub("Lab","",as.character(NileRed$labid))
NileRed$sampleid <- gsub("Lab","",as.character(NileRed$sampleid))
#Assign matrices to sampleids
NileRed <- NileRed %>%
mutate(matrix = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD"))
#Join expertise data to results data frame
NileRed <- NileRed %>%
left_join(Expertise, by = c("labid" = "labid", "matrix" = "matrix"))
Time_NileRed_Sample <- NileRed %>%
#remove fake lab
filter(labid != "ACME") %>%
mutate(sampletype = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD")) %>%
#Removed lab D - incomparable with other data - deviation from methods
filter(!(labid == "D"&sampletype == "CW")) %>%
#Removed pygcms data data
filter(!(labid == "T"&sampletype == "CW")) %>%
filter(!(labid == "F"&sampletype == "CW")) %>%
#Removed second set from Lab N - 2nd set of samples processed
filter(!(labid == "N"&sampletype == "CW"&(grepl("5|6|7|8", sampleid)))) %>%
#Removed labs that submitted clean water data late (want to match with original clean water data analysis)
filter(!(labid == "WW"&sampletype == "CW")) %>%
filter(!(labid == "NN"&sampletype == "CW")) %>%
#Exclude augmentation data
filter(!(labid %in% c("R","D", "L") & grepl("5|6|7|8", sampleid) & sampletype %in% c("Sediment"))) %>%
filter(!(labid %in% c("L") & grepl("5|6|7|8|9|10|11|12", sampleid) & sampletype %in% c("Fish Tissue"))) %>%
#drop blank samples
mutate(blank = ifelse(grepl("4|8", sampleid),"Y", "N")) %>%
filter(blank == "N") %>%
#drop where time isn't reported
filter(time != -88) %>%
group_by(sampleid) %>%
mutate(time_sample = sum(time))
Time_NileRed_Lab <- Time_NileRed_Sample %>%
ungroup() %>%
group_by(matrix, labid, time_sample, expertisenilered) %>%
summarise() %>%
ungroup() %>%
group_by(matrix, labid) %>%
mutate(time_lab = mean(time_sample)) %>%
ungroup() %>%
mutate(matrix = case_when(
matrix == "CW" ~ "Clean Water",
matrix == "FT" ~ "Fish Tissue",
matrix == "SD" ~ "Sediment",
matrix == "DW" ~ "Dirty Water"))
```
```{r Data Tidying - FTIR, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
#Remove "Lab" from relevant columns in other matrices data frame
FTIR$labid <- gsub("Lab","",as.character(FTIR$labid))
FTIR$sampleid <- gsub("Lab","",as.character(FTIR$sampleid))
#Assign matrices to sampleids
FTIR <- FTIR %>%
mutate(matrix = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD"))
#Join expertise data to results data frame
FTIR <- FTIR %>%
left_join(Expertise, by = c("labid" = "labid", "matrix" = "matrix"))
Time_FTIR_Sample <- FTIR %>%
#remove fake lab
filter(labid != "ACME") %>%
mutate(sampletype = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD")) %>%
#Removed lab D - incomparable with other data - deviation from methods
filter(!(labid == "D"&sampletype == "CW")) %>%
#Removed pygcms data data
filter(!(labid == "T"&sampletype == "CW")) %>%
filter(!(labid == "F"&sampletype == "CW")) %>%
#Removed second set from Lab N - 2nd set of samples processed
filter(!(labid == "N"&sampletype == "CW"&(grepl("5|6|7|8", sampleid)))) %>%
#Removed labs that submitted clean water data late (want to match with original clean water data analysis)
filter(!(labid == "WW"&sampletype == "CW")) %>%
filter(!(labid == "NN"&sampletype == "CW")) %>%
#Exclude augmentation data
filter(!(labid %in% c("R","D", "L") & grepl("5|6|7|8", sampleid) & sampletype %in% c("Sediment"))) %>%
filter(!(labid %in% c("L") & grepl("5|6|7|8|9|10|11|12", sampleid) & sampletype %in% c("Fish Tissue"))) %>%
#drop blank samples
mutate(blank = ifelse(grepl("4|8", sampleid),"Y", "N")) %>%
filter(blank == "N") %>%
# Where labs have entered 0 for time, this is assumed that time was not recorded
mutate(time = na_if(time, "0")) %>%
#drop where time isn't reported
filter(time != -88) %>%
drop_na(time) %>%
group_by(sampleid) %>%
mutate(time_sample = sum(time))
Time_FTIR_Lab <- Time_FTIR_Sample %>%
ungroup() %>%
group_by(matrix, labid, time_sample, expertiseftir) %>%
summarise() %>%
ungroup() %>%
group_by(matrix, labid) %>%
mutate(time_lab = mean(time_sample)) %>%
ungroup() %>%
mutate(matrix = case_when(
matrix == "CW" ~ "Clean Water",
matrix == "FT" ~ "Fish Tissue",
matrix == "SD" ~ "Sediment",
matrix == "DW" ~ "Dirty Water"))
```
```{r Data Tidying - Raman, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
#Remove "Lab" from relevant columns in other matrices data frame
Raman$labid <- gsub("Lab","",as.character(Raman$labid))
Raman$sampleid <- gsub("Lab","",as.character(Raman$sampleid))
#Assign matrices to sampleids
Raman <- Raman %>%
mutate(matrix = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD"))
#Join expertise data to results data frame
Raman <- Raman %>%
left_join(Expertise, by = c("labid" = "labid", "matrix" = "matrix"))
Time_Raman_Sample <- Raman %>%
#remove fake lab
filter(labid != "ACME") %>%
mutate(sampletype = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD")) %>%
#Removed lab D - incomparable with other data - deviation from methods
filter(!(labid == "D"&sampletype == "CW")) %>%
#Removed pygcms data data
filter(!(labid == "T"&sampletype == "CW")) %>%
filter(!(labid == "F"&sampletype == "CW")) %>%
#Removed second set from Lab N - 2nd set of samples processed
filter(!(labid == "N"&sampletype == "CW"&(grepl("5|6|7|8", sampleid)))) %>%
#Removed labs that submitted clean water data late (want to match with original clean water data analysis)
filter(!(labid == "WW"&sampletype == "CW")) %>%
filter(!(labid == "NN"&sampletype == "CW")) %>%
#Exclude augmentation data
filter(!(labid %in% c("R","D", "L") & grepl("5|6|7|8", sampleid) & sampletype %in% c("Sediment"))) %>%
filter(!(labid %in% c("L") & grepl("5|6|7|8|9|10|11|12", sampleid) & sampletype %in% c("Fish Tissue"))) %>%
#drop blank samples
mutate(blank = ifelse(grepl("4|8", sampleid),"Y", "N")) %>%
filter(blank == "N") %>%
#drop where time isn't reported
filter(time != -88) %>%
group_by(sampleid) %>%
mutate(time_sample = sum(time)) %>%
#Correction to time for Raman analysis for Lab BB - submitted incorrectly
mutate(time_sample = case_when(grepl("BB_FT_5", sampleid) ~ 26.683,
grepl("BB_FT_6", sampleid) ~ 23.133,
grepl("BB_FT_7", sampleid) ~ 16.65,
TRUE ~ as.numeric(time_sample)))
Time_Raman_Lab <- Time_Raman_Sample %>%
ungroup() %>%
group_by(matrix, labid, time_sample, expertiseraman) %>%
summarise() %>%
ungroup() %>%
group_by(matrix, labid) %>%
mutate(time_lab = mean(time_sample)) %>%
ungroup() %>%
mutate(matrix = case_when(
matrix == "CW" ~ "Clean Water",
matrix == "FT" ~ "Fish Tissue",
matrix == "SD" ~ "Sediment",
matrix == "DW" ~ "Dirty Water"))
```
```{r Data Tidying - Images and measurements, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
#Tidying of microscopy and nile red data to ensure proper merge
Results_M <- Results %>%
#remove fake lab
filter(labid != "ACME") %>%
mutate(sampletype = case_when(grepl("CW", sampleid) ~ "CW",
grepl("DW", sampleid) ~ "DW",
grepl("FT", sampleid) ~ "FT",
grepl("SD", sampleid) ~ "SD")) %>%
#Removed lab D - incomparable with other data - deviation from methods
filter(!(labid == "D"&sampletype == "CW")) %>%
#Removed pygcms data data
filter(!(labid == "T"&sampletype == "CW")) %>%
filter(!(labid == "F"&sampletype == "CW")) %>%
#Removed second set from Lab N - 2nd set of samples processed
filter(!(labid == "N"&sampletype == "CW"&(grepl("5|6|7|8", sampleid)))) %>%
#Removed labs that submitted clean water data late (want to match with original clean water data analysis)
filter(!(labid == "WW"&sampletype == "CW")) %>%
filter(!(labid == "NN"&sampletype == "CW")) %>%
#Exclude augmentation data
filter(!(labid %in% c("R","D", "L") & grepl("5|6|7|8", sampleid) & sampletype %in% c("Sediment"))) %>%
filter(!(labid %in% c("L") & grepl("5|6|7|8|9|10|11|12", sampleid) & sampletype %in% c("Fish Tissue")))
#Select expertise data for clean water
Expertise <- Lab_Info %>%
select(labid, expertiseextraction, expertisevisualmicroscopy, expertisenilered, expertiseftir, expertiseraman, expertisepy_gcms)
#Join expertise data to results data frame
Results_M <- Results_M %>%
left_join(Expertise, by = c("labid" = "labid"))
```
```{r Measurements Tidying, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
Measurement_Time <- Results_M %>%
filter(!grepl("4", sampleid)) %>% #Remove results for blanks
filter(!grepl("8", sampleid)) %>% #Remove results for blanks
mutate(timeimagesmeasurements = na_if(timeimagesmeasurements, "0")) %>% # Where labs have entered 0 for time, this is assumed that time was not recorded
filter(!grepl("-88", timeimagesmeasurements)) %>%
filter(!grepl("88", timeimagesmeasurements)) %>%
drop_na(timeimagesmeasurements)
Measurement_Time_Sample <- Measurement_Time %>%
#By Sample
group_by(sampletype, labid, sampleid, sizefraction) %>%
#Time per size fraction per sample
summarise(time_sizefraction = mean(timeimagesmeasurements)) %>% #Time per sizefraction
ungroup() %>%
#Time per sample
group_by(sampletype, labid, sampleid) %>%
mutate(time_sample = sum(time_sizefraction)) %>%
ungroup() %>%
#By Lab - Mean of time per sample
group_by(sampletype, labid) %>% #The only difference from above is that I don't group by sample
mutate(time_lab = mean(time_sample)) %>% # Mean time per lab for 1 sample
mutate(matrix = case_when(
sampletype == "CW" ~ "Clean Water",
sampletype == "FT" ~ "Fish Tissue",
sampletype == "SD" ~ "Sediment",
sampletype == "DW" ~ "Dirty Water")) %>%
subset(select = -c(sampletype))
Measurement_Time_Lab <- Measurement_Time_Sample %>%
ungroup()%>%
group_by(matrix, labid, time_lab) %>%
summarise()%>%
ungroup() %>%
group_by(matrix) %>%
mutate(median = median(time_lab)) %>%
mutate(mean = mean(time_lab)) %>% #Mean across all labs
mutate(sd = sd(time_lab)) #SD across all labs
```
```{r Count Particles Analyzed by Each Method, echo = FALSE, results = 'hide', error = FALSE, warning = FALSE, message = FALSE}
Results_Count <- Results %>%
#Removed lab D - incomparable with other data - deviation from methods
filter(!(labid == "D"&sampletype == "CW")) %>%
#Removed pygcms data data
filter(!(labid == "T"&sampletype == "CW")) %>%
filter(!(labid == "F"&sampletype == "CW")) %>%
#Removed second set from Lab N - 2nd set of samples processed
filter(!(labid == "N"&sampletype == "CW"&(grepl("5|6|7|8", sampleid)))) %>%
#Removed labs that submitted clean water data late (want to match with original clean water data analysis)
filter(!(labid == "WW"&sampletype == "CW")) %>%
filter(!(labid == "NN"&sampletype == "CW")) %>%
rename("microscopy" = stereoscope) %>%
rename("nilered" = fluorescencestaining)
#Particles Counted for Microscopy
Results_Count_Microscopy <- Results_Count %>%
#Create factor for sampletype
mutate(sampletype_f = as.factor(sampletype)) %>%
#Create factor for microscopy
mutate(microscopy_f = as.factor(microscopy)) %>%
#Select particles analyzed by microscopy
# filter(microscopy_f == "Yes") %>%
group_by(sampletype_f, sampleid) %>%
#Total particles per sample
mutate(total = n_distinct(sampleid, particleid)) %>%
ungroup() %>%
group_by(labid, sampleid, total) %>%
summarise()
#Join DF for number of particles to microscopy DF
Time_Microscopy_Particle <- Time_Microscopy_Sample %>%
group_by(labid, sampleid, matrix, time_sample) %>%
summarise() %>%
left_join(Results_Count_Microscopy, by = c("sampleid" = "sampleid", "labid" = "labid")) %>%
mutate(time_particle_sample_min = (time_sample/total)*60) %>%
group_by(labid, matrix) %>%
mutate(time_particle_lab_min = mean(time_particle_sample_min)) %>%
ungroup() %>%
group_by(matrix) %>%
mutate(mean = mean(time_particle_lab_min)) %>% #Mean across all labs
mutate(sd = sd(time_particle_lab_min)) #SD across all labs
#Particles Counted for FTIR
Results_Count_FTIR <- Results_Count %>%
#Create factor for sampletype
mutate(sampletype_f = as.factor(sampletype)) %>%
#Select particles analyzed by FTIR
filter(ftir == "Yes") %>%
group_by(sampletype_f, sampleid) %>%
#Total particles per sample
mutate(total = n_distinct(sampleid, particleid)) %>%
ungroup() %>%
group_by(labid, sampleid, total) %>%
summarise()
#Join DF for number of particles to FITR DF
Time_FTIR_Particle <- Time_FTIR_Sample %>%
group_by(labid, sampleid, matrix, time_sample) %>%
summarise() %>%
left_join(Results_Count_FTIR, by = c("sampleid" = "sampleid", "labid" = "labid")) %>%
mutate(time_particle_sample_min = (time_sample/total)*60) %>%
group_by(labid, matrix) %>%
mutate(time_particle_lab_min = mean(time_particle_sample_min)) %>%
ungroup() %>%
# group_by(matrix) %>%
mutate(mean = mean(time_particle_lab_min)) %>% #Mean across all labs
mutate(sd = sd(time_particle_lab_min)) %>% #SD across all labs
mutate(max = max(time_particle_lab_min)) %>%
mutate(min = min(time_particle_lab_min)) %>%
mutate(n_samples = n_distinct(sampleid)) %>%
mutate(n_labs = n_distinct(labid)) %>%
ungroup() %>%
group_by(mean, sd, min, max, n_samples, n_labs) %>%
summarise()
#Particles Counted for Raman
Results_Count_Raman <- Results_Count %>%
#Count all particles for lab BB as Raman (submission error)
mutate(raman = if_else(labid == "BB" & sampletype == "FT", "Yes", raman)) %>%
#Create factor for sampletype
mutate(sampletype_f = as.factor(sampletype)) %>%
#Select particles analyzed by FTIR
filter(raman == "Yes") %>%
group_by(sampletype_f, sampleid) %>%
#Total particles per sample
mutate(total = n_distinct(sampleid, particleid)) %>%
ungroup() %>%
group_by(labid, sampleid, total) %>%
summarise()
#Join DF for number of particles to Raman DF
Time_Raman_Particle <- Time_Raman_Sample %>%
group_by(labid, sampleid, matrix, time_sample) %>%
summarise() %>%
left_join(Results_Count_Raman, by = c("sampleid" = "sampleid", "labid" = "labid")) %>%
drop_na(total) %>%
mutate(time_particle_sample_min = (time_sample/total)*60) %>%
group_by(labid, matrix) %>%
mutate(time_particle_lab_min = mean(time_particle_sample_min)) %>%
ungroup() %>%
group_by(matrix) %>%
mutate(mean = mean(time_particle_lab_min)) %>% #Mean across all labs
mutate(sd = sd(time_particle_lab_min)) #SD across all labs
```
# Extraction Time
```{r Extraction Time, echo = FALSE, error = FALSE, warning = FALSE, message = FALSE, fig.height= 7, fig.width= 10}
a <- Time_Extraction_Sample %>%
ggplot(aes(x = labid, y = time_sample)) +
theme_test() +
geom_point(aes(color = as.factor(expertiseextraction), fill = as.factor(expertiseextraction)), alpha = 0.7, width = .05, size = 3) +
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. - 0.25, yend=..y..))+
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. + 0.25, yend=..y..))+
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "right",
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "darkcyan", "darkblue", "orchid"))+
scale_color_manual(values = c("royalblue", "darkcyan", "darkblue", "orchid"))+
expand_limits(y = 0) +
facet_wrap(~matrix) +
labs(x = "Labs", y = "Hours", fill = "Experience", color = "Experience", title = "Sample Extraction Time", caption = "Points represent individual samples spiked with microplastic particles.")
a
b <- Time_Extraction_Sample %>%
group_by(matrix, labid, sampleid, time_sample) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Sample ID" = sampleid, "Time (hours)" = time_sample)
kable(b, digits = 2, caption = "Extraction Time per Sample") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
c <- Time_Extraction_Lab %>%
ggplot(aes(x = matrix, y = time_lab)) +
theme_test() +
geom_boxplot(alpha = 0.5,aes(color = matrix, fill = matrix), outlier.shape = NA) +
geom_point(aes(color = matrix, fill = matrix, alpha = 0.7), width = .05, size = 3) +
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "none",
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
scale_color_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
expand_limits(y = 0) +
labs(x = "Matrix", y = "Hours", title = "Sample Extraction Time", caption = "Points represent the mean sample extraction time from individual labs.")
c
d <- Time_Extraction_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Time (hours)" = time_lab)
kable(d, digits = 2, caption = "Mean Extraction Time per Lab") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
e <- Time_Extraction_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
ungroup() %>%
group_by(matrix) %>%
mutate(median = median(time_lab)) %>%
mutate(mean = mean(time_lab)) %>%
mutate(sd = sd(time_lab)) %>%
mutate(n = n_distinct(labid)) %>%
ungroup() %>%
group_by(matrix, median, mean, sd, n) %>%
summarise() %>%
rename("Matrix" = matrix, "Median" = median, "Mean (hours)" = mean, "Standard Deviation (hours)" = sd, "Number of Labs" = n)
kable(e, digits = 2, caption = "Mean Extraction Time by Matrix") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
```
```{r Extraction Time - Statistical Tests}
Time_Extraction_Lab_Stats <- Time_Extraction_Lab %>%
group_by(matrix, time_lab) %>%
summarise() %>%
ungroup()
shapiro.test(Time_Extraction_Lab_Stats$time_lab) #Not normal
bartlett.test(time_lab ~ matrix, data = Time_Extraction_Lab_Stats) #Unequal variance
kruskal.test(time_lab ~ matrix, data = Time_Extraction_Lab_Stats) #p < 0.05
Steel.Dwass(Time_Extraction_Lab_Stats$time_lab, Time_Extraction_Lab_Stats$matrix)
```
# Microscopy Time
```{r Microscopy Time, echo = FALSE, error = FALSE, warning = FALSE, message = FALSE, fig.height= 7, fig.width= 10}
a <- Time_Microscopy_Sample %>%
ggplot(aes(x = labid, y = time_sample)) +
theme_test() +
geom_point(aes(color = as.factor(expertisevisualmicroscopy), fill = as.factor(expertisevisualmicroscopy)), alpha = 0.7, width = .05, size = 3) +
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. - 0.25, yend=..y..))+
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. + 0.25, yend=..y..))+
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "right",
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "darkcyan", "darkblue"))+
scale_color_manual(values = c("royalblue", "darkcyan", "darkblue"))+
expand_limits(y = 0) +
facet_wrap(~matrix) +
labs(x = "Labs", y = "Hours", fill = "Experience", color = "Experience", title = "Microscopy Time", caption = "Points represent individual samples spiked with microplastic particles.")
a
b <- Time_Microscopy_Sample %>%
group_by(matrix, labid, sampleid, time_sample) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Sample ID" = sampleid, "Time (hours)" = time_sample)
kable(b, digits = 2, caption = "Microscopy Time per Sample") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
c <- Time_Microscopy_Lab %>%
ggplot(aes(x = matrix, y = time_lab)) +
theme_test() +
geom_boxplot(alpha = 0.5,aes(color = matrix, fill = matrix), outlier.shape = NA) +
geom_point(aes(color = matrix, fill = matrix, alpha = 0.7), width = .05, size = 3) +
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "none",
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
scale_color_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
expand_limits(y = 0) +
labs(x = "Matrix", y = "Hours", title = "Microscopy Time", caption = "Points represent the mean sample extraction time from individual labs.")
c
d <- Time_Microscopy_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Time (hours)" = time_lab)
kable(d, digits = 2, caption = "Mean Microscopy Time per Lab") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
e <- Time_Microscopy_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
ungroup() %>%
group_by(matrix) %>%
mutate(median = median(time_lab)) %>%
mutate(mean = mean(time_lab)) %>%
mutate(sd = sd(time_lab)) %>%
mutate(n = n_distinct(labid)) %>%
ungroup() %>%
group_by(matrix, median, mean, sd, n) %>%
summarise() %>%
rename("Matrix" = matrix, "Median" = median, "Mean (hours)" = mean, "Standard Deviation (hours)" = sd, "Number of Labs" = n)
kable(e, digits = 2, caption = "Mean Microscopy by Matrix") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
f <- Time_Microscopy_Particle %>%
group_by(matrix, labid, time_particle_lab_min, mean, sd) %>%
summarise() %>%
ungroup() %>%
group_by(matrix) %>%
mutate(n = n_distinct(labid)) %>%
ungroup() %>%
group_by(matrix, mean, sd, n) %>%
summarise() %>%
rename("Matrix" = matrix, "Mean (min)" = mean, "Standard Deviation (min)" = sd, "Number of Labs" = n)
kable(f, digits = 2, caption = "Mean Microscopy by Matrix") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
```
```{r Microscopy Time - Statistical Tests}
#Sample
Time_Microscopy_Lab_Stats <- Time_Microscopy_Lab %>%
group_by(matrix, time_lab) %>%
summarise() %>%
ungroup()
shapiro.test(Time_Microscopy_Lab_Stats$time_lab) #Not normal
bartlett.test(time_lab ~ matrix, data = Time_Microscopy_Lab_Stats) #Unequal variance
kruskal.test(time_lab ~ matrix, data = Time_Microscopy_Lab_Stats) #p > 0.05
#Particle
Time_Microscopy_Lab_Stats_Particle <- Time_Microscopy_Particle %>%
group_by(matrix, time_particle_lab_min) %>%
summarise() %>%
ungroup()
shapiro.test(log10(Time_Microscopy_Lab_Stats_Particle$time_particle_lab_min)) #Normal after log transformation
bartlett.test(log10(time_particle_lab_min) ~ matrix, data = Time_Microscopy_Lab_Stats_Particle) #Equal variance after log transformation
x <- aov(log10(time_particle_lab_min) ~ matrix, data = Time_Microscopy_Lab_Stats_Particle) #p < 0.05
summary(x) #No difference p > 0.05
```
# Nile Red Time
```{r NileRed Time, echo = FALSE, error = FALSE, warning = FALSE, message = FALSE, fig.height= 7, fig.width= 10}
a <- Time_NileRed_Sample %>%
ggplot(aes(x = labid, y = time_sample)) +
theme_test() +
geom_point(aes(color = as.factor(expertisenilered), fill = as.factor(expertisenilered)), alpha = 0.7, width = .05, size = 3) +
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. - 0.25, yend=..y..))+
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. + 0.25, yend=..y..))+
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "right",
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "darkcyan", "darkblue"))+
scale_color_manual(values = c("royalblue", "darkcyan", "darkblue"))+
expand_limits(y = 0) +
facet_wrap(~matrix) +
labs(x = "Labs", y = "Hours", fill = "Experience", color = "Experience", title = "Nile Red Time", caption = "Points represent individual samples spiked with microplastic particles.")
a
b <- Time_NileRed_Sample %>%
group_by(matrix, labid, sampleid, time_sample) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Sample ID" = sampleid, "Time (hours)" = time_sample)
kable(b, digits = 2, caption = "Nile Red Time per Sample") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
c <- Time_NileRed_Lab %>%
ggplot(aes(x = matrix, y = time_lab)) +
theme_test() +
geom_boxplot(alpha = 0.5,aes(color = matrix, fill = matrix), outlier.shape = NA) +
geom_point(aes(color = matrix, fill = matrix, alpha = 0.7), width = .05, size = 3) +
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "none",
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
scale_color_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
expand_limits(y = 0) +
labs(x = "Matrix", y = "Hours", title = "Nile Red Time", caption = "Points represent the mean sample extraction time from individual labs.")
c
d <- Time_NileRed_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Time (hours)" = time_lab)
kable(d, digits = 2, caption = "Mean Nile Red Time per Lab") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
e <- Time_NileRed_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
ungroup() %>%
group_by(matrix) %>%
mutate(mean = mean(time_lab)) %>%
mutate(sd = sd(time_lab)) %>%
mutate(n = n_distinct(labid)) %>%
ungroup() %>%
group_by(matrix, mean, sd, n) %>%
summarise() %>%
rename("Matrix" = matrix, "Mean (hours)" = mean, "Standard Deviation (hours)" = sd, "Number of Labs" = n)
kable(e, digits = 2, caption = "Mean Nile Red by Matrix") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
```
# FTIR Time
```{r FTIR Time, echo = FALSE, error = FALSE, warning = FALSE, message = FALSE, fig.height= 7, fig.width= 10}
a <- Time_FTIR_Sample %>%
ggplot(aes(x = labid, y = time_sample)) +
theme_test() +
geom_point(aes(color = as.factor(expertiseftir), fill = as.factor(expertiseftir)), alpha = 0.7, width = .05, size = 3) +
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. - 0.25, yend=..y..))+
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. + 0.25, yend=..y..))+
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "right",
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "darkcyan", "darkblue"))+
scale_color_manual(values = c("royalblue", "darkcyan", "darkblue"))+
expand_limits(y = 0) +
facet_wrap(~matrix) +
labs(x = "Labs", y = "Hours", fill = "Experience", color = "Experience", title = "FTIR Time", caption = "Points represent individual samples spiked with microplastic particles.")
a
b <- Time_FTIR_Sample %>%
group_by(matrix, labid, sampleid, time_sample) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Sample ID" = sampleid, "Time (hours)" = time_sample)
kable(b, digits = 2, caption = "FTIR Time per Sample") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
c <- Time_FTIR_Lab %>%
ggplot(aes(x = matrix, y = time_lab)) +
theme_test() +
geom_boxplot(alpha = 0.5,aes(color = matrix, fill = matrix), outlier.shape = NA) +
geom_point(aes(color = matrix, fill = matrix, alpha = 0.7), width = .05, size = 3) +
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "none",
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
scale_color_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
expand_limits(y = 0) +
labs(x = "Matrix", y = "Hours", title = "FTIR Time", caption = "Points represent the mean sample extraction time from individual labs.")
c
d <- Time_FTIR_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Time (hours)" = time_lab)
kable(d, digits = 2, caption = "Mean FTIR Time per Lab") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
e <- Time_FTIR_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
ungroup() %>%
group_by(matrix) %>%
mutate(median = median(time_lab)) %>%
mutate(mean = mean(time_lab)) %>%
mutate(sd = sd(time_lab)) %>%
mutate(n = n_distinct(labid)) %>%
ungroup() %>%
group_by(matrix, median, mean, sd, n) %>%
summarise() %>%
rename("Matrix" = matrix, "Median" = median, "Mean (hours)" = mean, "Standard Deviation (hours)" = sd, "Number of Labs" = n)
kable(e, digits = 2, caption = "Mean FTIR by Matrix") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
```
```{r FTIR Time - Statistical Tests}
#Sample
Time_FTIR_Lab_Stats <- Time_FTIR_Lab %>%
group_by(matrix, time_lab) %>%
summarise() %>%
ungroup()
shapiro.test(log10(Time_FTIR_Lab_Stats$time_lab)) #Normal after log transformation
bartlett.test(log10(time_lab) ~ matrix, data = Time_FTIR_Lab_Stats) #Equal variance after log transformation
x <- aov(log10(time_lab) ~ matrix, data = Time_FTIR_Lab_Stats) #p > 0.05
summary(x)
TukeyHSD(x)
#Particle
Time_FTIR_Lab_Stats_Particle <- Time_FTIR_Particle %>%
group_by(matrix, time_particle_lab_min) %>%
summarise() %>%
ungroup()
shapiro.test(log10(Time_FTIR_Lab_Stats_Particle$time_particle_lab_min)) #Normal after log transformation
bartlett.test(log10(time_particle_lab_min) ~ matrix, data = Time_FTIR_Lab_Stats_Particle) #Equal variance after log transformation
x <- aov(log10(time_particle_lab_min) ~ matrix, data = Time_FTIR_Lab_Stats_Particle) #p < 0.05
summary(x) #No difference p > 0.05
TukeyHSD(x)
```
# Raman Time
```{r Raman Time, echo = FALSE, error = FALSE, warning = FALSE, message = FALSE, fig.height= 7, fig.width= 10}
a <- Time_Raman_Sample %>%
ggplot(aes(x = labid, y = time_sample)) +
theme_test() +
geom_point(aes(color = as.factor(expertiseraman), fill = as.factor(expertiseraman)), alpha = 0.7, width = .05, size = 3) +
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. - 0.25, yend=..y..))+
stat_summary(fun="mean", geom="segment", mapping=aes(xend=..x.. + 0.25, yend=..y..))+
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "right",
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "darkcyan", "darkblue"))+
scale_color_manual(values = c("royalblue", "darkcyan", "darkblue"))+
expand_limits(y = 0) +
facet_wrap(~matrix) +
labs(x = "Labs", y = "Hours", fill = "Experience", color = "Experience", title = "Raman Time", caption = "Points represent individual samples spiked with microplastic particles.")
a
b <- Time_Raman_Sample %>%
group_by(matrix, labid, sampleid, time_sample) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Sample ID" = sampleid, "Time (hours)" = time_sample)
kable(b, digits = 2, caption = "Raman Time per Sample") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
c <- Time_Raman_Lab %>%
ggplot(aes(x = matrix, y = time_lab)) +
theme_test() +
geom_boxplot(alpha = 0.5,aes(color = matrix, fill = matrix), outlier.shape = NA) +
geom_point(aes(color = matrix, fill = matrix, alpha = 0.7), width = .05, size = 3) +
theme(text = element_text(size = 12),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "none",
axis.ticks.x = element_blank())+
scale_fill_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
scale_color_manual(values = c("royalblue", "forestgreen", "darksalmon", "goldenrod4"))+
expand_limits(y = 0) +
labs(x = "Matrix", y = "Hours", title = "Raman Time", caption = "Points represent the mean sample extraction time from individual labs.")
c
d <- Time_Raman_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
rename("Matrix" = matrix, "Lab ID" = labid, "Time (hours)" = time_lab)
kable(d, digits = 2, caption = "Mean Raman Time per Lab") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(width = "700px", height = "200px")
e <- Time_Raman_Lab %>%
group_by(matrix, labid, time_lab) %>%
summarise() %>%
ungroup() %>%
group_by(matrix) %>%