131 lines
4.3 KiB
R
Executable file
131 lines
4.3 KiB
R
Executable file
#!/usr/bin/Rscript
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getwd()
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setwd("~/git/mosaic_2020/")
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getwd()
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########################################################################
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# TASK: read data
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########################################################################
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# load libraries, packages and local imports
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source("Header_TT.R")
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########################################################################
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maindir = "~/git/mosaic_2020/"
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outdir = paste0(maindir, "output/")
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ifelse(!dir.exists(outdir), dir.create(outdir), FALSE)
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outdir_stats = paste0(maindir, "output/stats/")
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ifelse(!dir.exists(outdir_stats), dir.create(outdir_stats), FALSE)
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outdir_plots = paste0(maindir, "output/plots/")
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ifelse(!dir.exists(outdir_plots), dir.create(outdir_plots), FALSE)
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########################################################################
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# static file read: csv
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#==============
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# all patients
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#==============
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all_df <- read.csv("/home/backup/MOSAIC/MEDIATOR_Data/master_file/Mosaic_master_file_from_stata.csv"
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, fileEncoding = 'latin1')
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# meta data columns
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meta_data_cols = c("mosaic", "gender", "age"
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, "adult"
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, "flustat", "type"
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, "obesity"
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#, "obese2"
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#, "height", "height_unit"
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#, "weight", "weight_unit"
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#, "ia_height_ftin", "ia_height_m", "ia_weight"
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#, "visual_est_bmi", "bmi_rating"
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)
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# check if these columns to select are present in the data
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meta_data_cols%in%colnames(all_df)
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all(meta_data_cols%in%colnames(all_df))
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metadata_all = all_df[, meta_data_cols]
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#==============
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# adult patients
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#==============
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adult_df = all_df[all_df$age>=18,]
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if (table(adult_df$adult == 1)[[1]] == nrow(adult_df) ){
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cat ("\nPASS: adult df extracted successfully")
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} else{
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cat ("\nFAIL: adult df number mismatch!")
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}
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#=================================
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# FLU positive: adult patients
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#=================================
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# extract the flu positive population
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fp_adults = adult_df[adult_df$flustat == 1,]
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if (table(fp_adults$flustat == 1)[[1]] == nrow(fp_adults) ){
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cat ("\nPASS: adult df extracted successfully")
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} else{
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cat ("\nFAIL: adult df number mismatch!")
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}
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#-----------------------------------
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# asthma and copd status correction
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# for conflicting field!
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#------------------------------------
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# Reassign the copd and asthma status and do some checks
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table(fp_adults$ia_exac_copd); sum(is.na(fp_adults$ia_exac_copd))
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fp_adults$ia_exac_copd[fp_adults$ia_exac_copd< 1]<- 0
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fp_adults$ia_exac_copd[is.na(fp_adults$ia_exac_copd)] <- 0
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table(fp_adults$ia_exac_copd); sum(is.na(fp_adults$ia_exac_copd))
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# check copd and asthma status
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table(fp_adults$ia_exac_copd, fp_adults$asthma)
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check_copd_and_asthma_1<- subset(fp_adults, ia_exac_copd ==1 & asthma == 1) # check this is 3
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# reassign these 3 so these are treated as non-asthmatics as copd with asthma is NOT TRUE asthma
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fp_adults$asthma[fp_adults$ia_exac_copd == 1 & fp_adults$asthma == 1]= 0
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table(fp_adults$ia_exac_copd, fp_adults$asthma)
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foo<- subset(fp_adults, asthma==1 & ia_exac_copd ==1) # check that its 0
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rm(check_copd_and_asthma_1, foo)
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if ( table(fp_adults$ia_exac_copd, fp_adults$asthma) [[2,2]] == 0 ){
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cat("\nPASS: asthma and copd do not conflict")
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} else{
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cat ("\nFAIL: asthma and copd conflict not resolved!")
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quit()
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}
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#=============================================
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# FLU positive adult patients: without asthma
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#=============================================
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#cat("\nExtracting flu positive without asthma")
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#table(fp_adults$asthma)
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#cat("\nNo. of asthmatics:", table(fp_adults$asthma)[[2]]
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# , "\nNo. of non-asthmatics:", table(fp_adults$asthma)[[1]])
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#str(fp_adults$asthma)
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#table(fp_adults$obesity)
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#table(fp_adults$obesity, fp_adults$asthma)
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#fp_adults_na = fp_adults[fp_adults$asthma == 0,]
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#table(fp_adults_na$obesity)
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#table(fp_adults_na$obesity, fp_adults_na$asthma)
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#============
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# hc
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#============
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#hc_data<- read.csv("/home/backup/MOSAIC/MEDIATOR_Data/master_file/Mediators_for_HC.csv")
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#str(hc_data)
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#table(hc_data$Timepoint, hc_data$Sample)
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########################################################################
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# quick sanity checks
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table(adult_df$ia_exac_copd==1 & adult_df$asthma == 1) # check this is 4
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table(fp_adults$ia_exac_copd==1 & fp_adults$asthma == 1) # check this is 3
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# clear unnecessary variables
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rm(metadata_all)
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rm(all_df, adult_df)
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