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Stage2_missing_table1.R
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Stage2_missing_table1.R
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## =============================================================================
## Project: Post covid vaccinated project
##
##
## Purpose:
## Apply stage 2. Create missing data, range and descriptive statistics tables
## - Table 0 - Missing data and range
## - Table 1 - Descriptive statistics
##
## Authors: Genevieve Cezard, Rochelle Knight
## Combined by: Genevieve Cezard
## Reviewer: Yinghui Wei
##
## Content:
## 0. Load relevant libraries and read data/arguments
## 1. Output missing data and range check tables
## 2. Output table 1
##
## NOTE: This code outputs 3 .csv files and 1 R dataset
## Output files have a specific name to reflect either the Vaccinated
## or Electively unvaccinated cohort
##
## =============================================================================
###############################################
# 0. Load relevant libraries and read in data #
###############################################
library(readr)
library(dplyr)
library(data.table)
library(tidyverse)
library(lubridate)
# Specify command arguments ----------------------------------------------------
args <- commandArgs(trailingOnly=TRUE)
if(length(args)==0){
cohort_name <- "electively_unvaccinated" # interactive testing
} else {
cohort_name <- args[[1]]
}
fs::dir_create(here::here("output", "not-for-review"))
fs::dir_create(here::here("output", "review", "descriptives"))
# Determine which outcome groups are active
active_analyses <- read_rds("lib/active_analyses.rds")
active_analyses <- active_analyses %>% filter(active == TRUE)
outcome_groups <- unique(active_analyses$outcome_group)
# Define stage2 function -------------------------------------------------------
stage2 <- function(cohort_name, covid_history, group) {
# Load relevant data
input <- readr::read_rds(file.path("output", paste0("input_",cohort_name,"_stage1.rds")))
# Select data depending on covid history
if(covid_history == "without_covid_history"){
input <- filter(input, sub_bin_covid19_confirmed_history==F)
}
if(covid_history == "with_covid_history"){
input <- filter(input, sub_bin_covid19_confirmed_history==T)
}
################################
# 1. Output missing data table #
################################
N <- nrow(input)
#-----------------------------------------------------------------------#
# 1.a. Create a table with missing data information (N,%) for variables #
#-----------------------------------------------------------------------#
covariate_names <- tidyselect::vars_select(names(input), starts_with(c('sub_bin_','cov_','qa_','vax_cat','exp_cat'), ignore.case = TRUE))
check_missing <- data.frame(variable = as.vector(covariate_names), N_missing = NA)
for (i in covariate_names){
if (is.factor(input[,i])) {
check_missing[check_missing$variable==i,]$N_missing <- nrow(input[input[,i]=="Missing",])
} else {
check_missing[check_missing$variable==i,]$N_missing <- nrow(input[is.na(input[,i]),])
}
}
check_missing$Perc_missing <- 100*(check_missing$N_missing/N)
#---------------------------------------------------------------#
# 1.b. Create a table with min and max for numerical covariates #
#---------------------------------------------------------------#
check_range <- data.frame(variable = character(), Minimum_value = character(), Maximum_value = character())
numeric_var_names=colnames(select_if(input, is.numeric))
numeric_var_names = numeric_var_names [!numeric_var_names == "patient_id"]
for (i in numeric_var_names){
check_range[nrow(check_range)+1,1] <- i
check_range[nrow(check_range),2] <- min(na.omit(input %>% dplyr::select(i)))
check_range[nrow(check_range),3] <- max(na.omit(input %>% dplyr::select(i)))
}
#---------------------------------------------------------------------------#
# 1.a. c. Output table merging missing and range information for covariates #
#---------------------------------------------------------------------------#
check_both <- merge(x=check_missing, y=check_range, by = "variable",all.x=TRUE)
write.csv(check_both, file = file.path("output/not-for-review", paste0("Check_missing_range_",cohort_name, "_",covid_history, ".csv")) , row.names=F)
#---------------------------------------------------------#
# 1.d. Create a table with min and max for date variables #
#---------------------------------------------------------#
check_dates <- data.frame(variable = character(), Earliest_date = character(), Latest_date = character())
date_variables_names <- colnames(select_if(input, is.Date))
input_date <- input[,date_variables_names]
for (i in date_variables_names){
date_var <- input_date %>% pull(i)
check_dates[nrow(check_dates)+1,1] <- i
check_dates[nrow(check_dates),2] <- paste0("",min(na.omit(date_var)))
check_dates[nrow(check_dates),3] <- paste0("",max(na.omit(date_var)))
}
write.csv(check_dates, file = file.path("output/not-for-review", paste0("Check_dates_range_",cohort_name, "_",covid_history, ".csv")) , row.names=F)
#####################
# 2. Output table 1 #
#####################
#Define populations of interest
pop <- data.frame(rbind(c("Whole_population","!is.na(input$patient_id)"),
c("COVID_exposed","is.na(input$exp_date_covid19_confirmed)==F"),
c("COVID_hospitalised","input$sub_cat_covid19_hospital=='hospitalised'"),
c("COVID_non_hospitalised","input$sub_cat_covid19_hospital=='non_hospitalised'")),
stringsAsFactors = FALSE)
colnames(pop) <- c("name","condition")
# Define age groups
input$cov_cat_age_group <- ""
input$cov_cat_age_group <- ifelse(input$cov_num_age>=18 & input$cov_num_age<=29, "18-29", input$cov_cat_age_group)
input$cov_cat_age_group <- ifelse(input$cov_num_age>=30 & input$cov_num_age<=39, "30-39", input$cov_cat_age_group)
input$cov_cat_age_group <- ifelse(input$cov_num_age>=40 & input$cov_num_age<=49, "40-49", input$cov_cat_age_group)
input$cov_cat_age_group <- ifelse(input$cov_num_age>=50 & input$cov_num_age<=59, "50-59", input$cov_cat_age_group)
input$cov_cat_age_group <- ifelse(input$cov_num_age>=60 & input$cov_num_age<=69, "60-69", input$cov_cat_age_group)
input$cov_cat_age_group <- ifelse(input$cov_num_age>=70 & input$cov_num_age<=79, "70-79", input$cov_cat_age_group)
input$cov_cat_age_group <- ifelse(input$cov_num_age>=80 & input$cov_num_age<=89, "80-89", input$cov_cat_age_group)
input$cov_cat_age_group <- ifelse(input$cov_num_age>=90, "90+", input$cov_cat_age_group)
# Define consultation rate groups
input$cov_cat_consulation_rate_group <- ""
input$cov_cat_consulation_rate_group <- ifelse(input$cov_num_consulation_rate==0, "0", input$cov_cat_consulation_rate_group)
input$cov_cat_consulation_rate_group <- ifelse(input$cov_num_consulation_rate>=1 & input$cov_num_consulation_rate<=5, "1-6", input$cov_cat_consulation_rate_group)
input$cov_cat_consulation_rate_group <- ifelse(input$cov_num_consulation_rate>=6, "6+", input$cov_cat_consulation_rate_group)
# Populate table 1
covar_names <- active_analyses %>% filter(outcome_group==group)
covar_names<-str_split(active_analyses$covariates, ";")[[1]]
#categorical_cov <- colnames(input)[grep("cov_cat", colnames(input))]
categorical_cov <- covar_names[grep("cov_cat", covar_names)]
categorical_cov <- append(categorical_cov, c("cov_cat_age_group","cov_cat_consulation_rate_group"))
#numerical_cov <- colnames(input)[grep("cov_num", colnames(input))]
numerical_cov <- covar_names[grep("cov_num", covar_names)]
numerical_cov <- numerical_cov[!numerical_cov=="cov_num_age"]
#binary_cov <- colnames(input)[grep("cov_bin", colnames(input))]
binary_cov <- covar_names[grep("cov_bin", covar_names)]
binary_cov_table <- crossing(covar_names[grep("cov_bin", covar_names)], c("TRUE","FALSE"))
colnames(binary_cov_table) <- c("Covariate","Covariate_level")
# Base table
table1 <- input %>%
dplyr::select(c(categorical_cov,numerical_cov))
table1 <- table1 %>%
mutate_if(is.character,as.factor)
table1 <- table1 %>%
mutate_if(is.logical,as.factor)
table1 <- as.data.frame(summary(table1,maxsum=50))
table1 <- rename(table1, Covariate_level = Freq, Covariate = Var2)
#table1 <- table1 %>%
# filter(!str_detect(Covariate_level, "^FALSE"))
table1 <- table1 %>%
dplyr::select("Covariate","Covariate_level")
table1$Covariate <- gsub("\\s","",table1$Covariate) # Remove spaces
table1$Covariate_level <- sub('\\:.*', '', table1$Covariate_level) # Remove everything after :
table1 <- table1 %>%
filter(!(Covariate == "cov_num_consulation_rate" & !Covariate_level=="Mean "))
table1_count_all <- as.data.frame(matrix(nrow = 1, ncol = 2))
colnames(table1_count_all) <- c("Covariate","Covariate_level")
table1_count_all[1,] <- c("All","All")
table1 <- rbind(table1_count_all,table1)
table1 <- table1[!is.na(table1$Covariate_level),]
table1 <- rbind(table1, binary_cov_table)
for (j in 1:nrow(pop)) {
population <- pop[j,]$name
df <- subset(input, eval(parse(text = pop[j,]$condition)))
# Count population size
pop_summary <- data.frame(matrix(ncol=3))
colnames(pop_summary) <- c("Covariate","Covariate_level",population)
pop_summary[1,] <- c("All","All",nrow(df))
# Categorical covariates
cat_pop <- df %>% dplyr::select(categorical_cov)
cat_pop <- cat_pop %>% mutate_if(is.character,as.factor)
cat_summary <- as.data.frame(summary(cat_pop,maxsum=50))
cat_summary[,population] <- cat_summary$Freq
cat_summary <- rename(cat_summary, Covariate_level = Freq, Covariate = Var2)
cat_summary <- cat_summary %>%
dplyr::select("Covariate","Covariate_level",population)
# Numerical covariates
num_pop <- df %>% dplyr::select(numerical_cov)
num_summary <- as.data.frame(summary(num_pop))
num_summary[,population] <- num_summary$Freq
num_summary <- rename(num_summary, Covariate_level = Freq, Covariate = Var2)
num_summary <- num_summary %>% dplyr::select("Covariate","Covariate_level",population)
num_summary <- num_summary %>% filter(startsWith(num_summary$Covariate_level, "Mean")==T)
# Binary covariates
bin_pop <- df %>% dplyr::select(binary_cov)
bin_pop <- bin_pop %>% mutate_if(is.logical,as.factor)
bin_summary <- as.data.frame(summary(bin_pop))
bin_summary[,population] <- bin_summary$Freq
bin_summary <- rename(bin_summary, Covariate_level = Freq, Covariate = Var2)
#bin_summary <- bin_summary %>% filter(str_detect(Covariate_level, "^TRUE"))
bin_summary <- bin_summary%>% dplyr::select("Covariate","Covariate_level",population)
bin_summary$Covariate_level <- gsub("\\s","",bin_summary$Covariate_level) #Remove spaces
# Population summary
pop_summary <- rbind(pop_summary,cat_summary,num_summary,bin_summary)
# Tidy summary table
pop_summary$Covariate <- gsub("\\s","",pop_summary$Covariate) #Remove spaces
pop_summary$Covariate_level <- sub('\\:.*', '', pop_summary$Covariate_level) #Remove everything after :
pop_summary[,population] <- gsub(".*:", "",pop_summary[,population])#Remove everything before:
pop_summary <- pop_summary %>% drop_na(Covariate_level)#Remove rows with NA
unique(pop_summary$Covariate_level)
# Left join onto base table
table1 <- left_join(table1,pop_summary, by=c("Covariate","Covariate_level"))
}
# Tidy table 1
table1$Covariate <- gsub("cov_bin_", "History of ",table1$Covariate)
table1$Covariate <- gsub("cov_\\D\\D\\D_", "",table1$Covariate)
table1$Covariate <- gsub("_", " ",table1$Covariate)
# Add in suppression controls for counts <=5 and then alter totals accordingly
table1_suppressed <- table1[0,]
for(i in unique(table1$Covariate[which(!startsWith(table1$Covariate_level, "Mean"))])){
df<- table1 %>% filter(Covariate == i)
df <- df %>% mutate(across(!c("Covariate","Covariate_level"),as.numeric))
df$No_infection <- df$Whole_population - df$COVID_exposed
if(any(df$COVID_hospitalised <= 5 | df$COVID_non_hospitalised <= 5 | is.na(df$COVID_hospitalised)| is.na(df$COVID_non_hospitalised))){
df$COVID_hospitalised <- "[Redacted]"
df$COVID_non_hospitalised <- "[Redacted]"
}
if(any(df$COVID_exposed <= 5 | df$No_infection <=5 | is.na(df$COVID_exposed) | is.na(df$No_infection))){
df$COVID_exposed <- "[Redacted]"
}
if(any(df$Whole_population <= 5 | is.na(df$Whole_population))){
df$Whole_population <- "[Redacted]"
}
df <- df %>% mutate(across(!c("Covariate","Covariate_level"),as.character))
df$No_infection <- NULL
if(i == "consulation rate group"){
df <- rbind(df, table1 %>% filter(startsWith(Covariate_level, "Mean")))
}
table1_suppressed <- rbind(table1_suppressed,df)
}
#table1_suppressed[which(startsWith(table1_suppressed$Covariate_level, "Mean")),] <- table1[startsWith(table1$Covariate_level, "Mean"),]
table1_suppressed <- table1_suppressed %>% filter(!str_detect(Covariate_level, "^FALSE"))
# Save table 1
write.csv(table1_suppressed, file = file.path("output/review/descriptives", paste0("Table1_",cohort_name, "_",covid_history,"_",group, ".csv")) , row.names=F)
}
# Run function using specified commandArgs
for(group in outcome_groups){
if(cohort_name == "both"){
stage2("vaccinated", "with_covid_history", group)
stage2("vaccinated", "without_covid_history", group)
stage2("electively_unvaccinated", "with_covid_history", group)
stage2("electively_unvaccinated", "without_covid_history", group)
}else{
stage2(cohort_name, "with_covid_history", group)
stage2(cohort_name, "without_covid_history", group)
}
}