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Looked after school leavers achieving SCQF4.R
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Looked after school leavers achieving SCQF4.R
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################################################################################
################################################################################
######### #########
##### Looked after school leavers with 1 or more #####
##### qualification at SCQF Level 4 #####
######### #########
################################################################################
################################################################################
## This script analyses Scottish Government data on the number of looked after
## school leavers achieving 1 qualification at SCQF level 4 or better
## The data at local authority level is not published routinely; this needs to be
## requested from the Children and Families Directorate (email:
## childrens.statistics@gov.scot).
################################################################################
##### install packages etc #####
################################################################################
## remove any existing objects from global environment
rm(list=ls())
## install packages
#install.packages("tidyverse")
library(tidyverse) # all kinds of stuff
library(stringr) # for strings
organisation <- "HS"
## set file pathways
# NHS HS PHO Team Large File repository file pathways
data_folder <- "X:/ScotPHO Profiles/Data/"
lookups <- "X:/ScotPHO Profiles/Data/Lookups/"
###############################################.
## Packages/Filepaths/Functions ----
###############################################.
## HOW TO USE THESE FUNCTIONS
# FUNCTION ONE: ANALYZE_FIRST
# filename - Name of the raw file the function reads without the "_raw.sav" at the end
# geography - what is the base geography of the raw file: council or datazone2011
# adp - To calculate the data for ADP level as well change it to TRUE, default is false.
# measure - crude rate (crude), standardized rate(stdrate), percentage (percent),
# time_agg - Aggregation period used expressed in year, e.g. 3
# pop - Name of the population file. Only used for those that need a denominator.
# yearstart - Start of the period you want to run an analysis for
# yearend - End of the period you want to run an analysis for
# epop_age - Type of european population to use: 16+, <16, 0to25, 11to25, 15to25.
# Only used for standardize rates.
# FUNCTION TWO: ANALYZE_SECOND
# filename - Name of the formatted file the function reads without the "_formatted.sav" at the end
# measure - crude rate (crude), standardized rate(stdrate), percentage (percent)
# percentage with finite population correction factor (perc_pcf)
# time_agg - Aggregation period used expressed in year, e.g. 3
# ind_id - indicator code/number
# year_type - calendar, financial, school or annual snapshot. This last one should
# be used like "Month snapshot" e.g. "August snapshot"
# crude rate - Only for crude rate cases. Population the rate refers to, e.g. 1000 = crude rate per 1000 people
# epop_total - the total european population for the ages needed. For all ages the Epop_total = 200000 (100000 per sex group)
# pop - Only for crude rate cases that need finite population correction factor. Reference population.
source("./1.indicator_analysis.R") #Normal indicator functions
source("./2.deprivation_analysis.R") # deprivation function..
################################################################################
##### read in prepared data #####
################################################################################
# read in csv
looked_after_SCQF4<- read.csv(paste0(data_folder, "Received Data/looked_after_SCQF4_raw.csv"))
saveRDS(looked_after_SCQF4, file=paste0(data_folder, "Prepared Data/looked_after_SCQF4_raw.rds"))
###############################################.
## Part 2 - Run analysis functions ----
###############################################.
analyze_first(filename = "looked_after_SCQF4", geography = "council",
measure = "percent", yearstart = 2009, yearend = 2017, time_agg = 1)
analyze_second(filename = "looked_after_SCQF4", measure = "percent", time_agg = 1,
ind_id = 13007, year_type = "school")
#for QA :)
looked_after_SCQF4_denom <- readRDS("X:/ScotPHO Profiles/Data/Temporary/looked_after_SCQF4_formatted.rds")
write.csv (looked_after_SCQF4_denom, "X:/ScotPHO Profiles/Data/Temporary/looked_after_SCQF4_formatted.csv")