368 lines
12 KiB
R
368 lines
12 KiB
R
#!/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: Extract clinical data columns and recode as required for analysis
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# corrects the asthma and copd status for patients
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# creates age_bins and other intervals for clinical params
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# merges steroid ics data and outcome var based on T1 resp score
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# The steroid_ics data file is read from outdir and has been manually sourced
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# TODO: for extra caution add and run checks on the steroid_ics file
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########################################################################
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#====================
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# Input: source data
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# and steroid ics file
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# This file contains steroid_ics data
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# and another outcome variable based on T1_resp score
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#====================
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source("read_data.R")
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source("colnames_clinical_meds.R")
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# read: steroid_ics file
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infile_ics = paste0(outdir, "data_ics.csv")
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infile_ics
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clinical_ics = read.csv(infile_ics)
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str(clinical_ics)
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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(all_df, adult_df, metadata_all)
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########################################################################
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# Clinical_data extraction
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########################################################################
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cat("\nExtracting:", length(clinical_cols), "cols from fp_adults")
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clinical_df = fp_adults[, clinical_cols]
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# sanity checks
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if ( sum(table(clinical_df$obesity)) & sum(table(clinical_df$age>=18)) & sum(table(clinical_df$death)) & sum(table(clinical_df$asthma)) == nrow(clinical_df) ){
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cat("\nPASS: binary data obs are complete, n =", nrow(clinical_df))
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}else{
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cat("\nFAIL: Incomplete data for binary outcomes. Please check and decide!")
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quit()
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}
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table(clinical_df$ia_exac_copd)
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########################################################################
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#==================================
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# asthma and copd status correction
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# for conflicting field!
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#=================================
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# Reassign the copd and asthma status and do some checks
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table(clinical_df$ia_exac_copd); sum(is.na(clinical_df$ia_exac_copd))
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clinical_df$ia_exac_copd[clinical_df$ia_exac_copd< 1]<- 0
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clinical_df$ia_exac_copd[is.na(clinical_df$ia_exac_copd)] <- 0
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table(clinical_df$ia_exac_copd); sum(is.na(clinical_df$ia_exac_copd))
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# check copd and asthma status
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table(clinical_df$ia_exac_copd, clinical_df$asthma)
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check_copd_and_asthma_1<- subset(clinical_df, ia_exac_copd ==1 & asthma == 1) # check this is 3
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# reassign these 3 so these are treated as non-asthmatics as copd with asthma is NOT TRUE asthma
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clinical_df$asthma[clinical_df$ia_exac_copd == 1 & clinical_df$asthma == 1]= 0
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table(clinical_df$ia_exac_copd, clinical_df$asthma)
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foo<- subset(clinical_df, 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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#=====================================================================
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#=================================
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# resp scores: In, max and t1 & t2
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#=================================
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# count the resp scores
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max_resp_score_table<- table(clinical_df$max_resp_score)
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max_resp_score_table
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T1_resp_score_table<- table(clinical_df$T1_resp_score)
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T1_resp_score_table
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T2_resp_score_table<- table(clinical_df$T2_resp_score)
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T2_resp_score_table
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Inresp_sev<- table(clinical_df$inresp_sev)
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Inresp_sev
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# Reassign the resp score so all 4 are replace by 3
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clinical_df$max_resp_score[clinical_df$max_resp_score ==4 ] <- 3
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revised_resp_score_table<- table(clinical_df$max_resp_score)
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revised_resp_score_table
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clinical_df$T1_resp_score[clinical_df$T1_resp_score ==4 ] <- 3
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revised_T1_resp_score_table<- table(clinical_df$T1_resp_score)
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revised_T1_resp_score_table
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clinical_df$T2_resp_score[clinical_df$T2_resp_score == 4]<- 3
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revised_T2_resp_score_table<- table(clinical_df$T2_resp_score)
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revised_T2_resp_score_table
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clinical_df$inresp_sev[clinical_df$inresp_sev == 4]<- 3
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revised_Inresp_sev<- table(clinical_df$inresp_sev)
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revised_Inresp_sev
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#=====================================================================
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# Remove these after checking
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rm(max_resp_score_table, T1_resp_score_table, T2_resp_score_table, Inresp_sev
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, revised_resp_score_table, revised_T1_resp_score_table, revised_T2_resp_score_table, revised_Inresp_sev)
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#=====================================================================
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# Binning
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# "(": not inclusive
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# "]": inclusive
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#========
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# age
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#========
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# Create categories of variables
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clinical_df$age = round(clinical_df$age, digits = 0)
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table(clinical_df$age)
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table(clinical_df$asthma, clinical_df$age)
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min(clinical_df$age); max(clinical_df$age)
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max_age_interval = round_any(max(clinical_df$age), 10, f = ceiling)
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max_age_interval
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min_age = min(clinical_df$age); min_age #19
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min_age_interval = min_age - 1; min_age_interval
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#age_bins = cut(clinical_df$age, c(0,18,30,40,50,60,70,80,90))
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age_bins = cut(clinical_df$age, c(min_age_interval, 30, 40, 50, 60, 70, max_age_interval))
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clinical_df$age_bins = age_bins
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dim(clinical_df) # 133 27
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# age_bins (to keep consistent with the results table)
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class(clinical_df$age_bins)
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levels(clinical_df$age_bins)
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#"(18,30]" "(30,40]" "(40,50]" "(50,60]" "(60,70]" "(70,80]"
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table(clinical_df$asthma, clinical_df$age_bins)
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# (18,30] (30,40] (40,50] (50,60] (60,70] (70,80]
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#0 25 17 25 14 11 1
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#1 11 8 12 5 3 2
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if (sum(table(clinical_df$asthma, clinical_df$age_bins)) == nrow(clinical_df) ){
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cat("\nPASS: age_bins assigned successfully")
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}else{
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cat("\nFAIL: no. mismatch when assigning age_bins")
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quit()
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}
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# reassign
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class(clinical_df$age_bins)
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levels(clinical_df$age_bins) <- c("(18,30]","(30,40]","(40,50]","(50,80]","(50,80]","(50,80]")
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table(clinical_df$asthma, clinical_df$age_bins)
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table(clinical_df$asthma, clinical_df$age_bins)
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# (18,30] (30,40] (40,50] (50,80]
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#0 25 17 25 26
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#1 11 8 12 9
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sum(table(clinical_df$asthma, clinical_df$age_bins)) == nrow(clinical_df)
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#===========================
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# O2 saturation binning
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#===========================
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clinical_df$o2_sat_admis = round(clinical_df$o2_sat_admis, digits = 0)
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table(clinical_df$o2_sat_admis)
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tot_o2 = sum(table(clinical_df$o2_sat_admis))- table(clinical_df$o2_sat_admis)[["-1"]]
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tot_o2
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o2_sat_bin = cut(clinical_df$o2_sat_admis, c(0,92,100))
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clinical_df$o2_sat_bin = o2_sat_bin
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table(clinical_df$o2_sat_bin)
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sum(table(clinical_df$o2_sat_bin)) == tot_o2
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#===========================
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# Onset to initial binning
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#===========================
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max_in = max(clinical_df$onset_2_initial); max_in #23
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min_in = min(clinical_df$onset_2_initial) - 1 ; min_in # -6
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tot_onset2ini = sum(table(clinical_df$onset_2_initial))
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tot_onset2ini
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onset_initial_bin = cut(clinical_df$onset_2_initial, c(min_in, 4, max_in))
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clinical_df$onset_initial_bin = onset_initial_bin
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sum(table(clinical_df$onset_initial_bin)) == tot_onset2ini
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#=======================
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# seasonal flu: sfluv
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#=======================
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# should be a factor
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if (! is.factor(clinical_df$sfluv)){
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clinical_df$sfluv = as.factor(clinical_df$sfluv)
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}
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class(clinical_df$sfluv) #[1] "factor"
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levels(clinical_df$sfluv)
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table(clinical_df$asthma, clinical_df$sfluv)
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# reassign
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levels(clinical_df$sfluv) <- c("0", "0", "1")
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table(clinical_df$asthma, clinical_df$sfluv)
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#=======================
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# h1n1v
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#=======================
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# should be a factor
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if (! is.factor(clinical_df$h1n1v)){
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clinical_df$h1n1v = as.factor(clinical_df$h1n1v)
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}
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class(clinical_df$h1n1v) #[1] "factor"
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levels(clinical_df$h1n1v)
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table(clinical_df$asthma, clinical_df$h1n1v)
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# reassign
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levels(clinical_df$h1n1v) <- c("0", "0", "1")
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table(clinical_df$asthma, clinical_df$h1n1v)
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#=======================
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# ethnicity
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#=======================
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class(clinical_df$ethnicity) # integer
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table(clinical_df$asthma, clinical_df$ethnicity)
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clinical_df$ethnicity[clinical_df$ethnicity == 4] <- 2
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table(clinical_df$asthma, clinical_df$ethnicity)
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#=======================
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# pneumonia
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#=======================
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class(clinical_df$ia_cxr) # integer
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# ia_cxr 2 ---> yes pneumonia (1)
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# 1 ---> no (0)
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# ! 1 or 2 -- > "unkown"
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# reassign the pneumonia codes
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#0: not performed
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#1: normal
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#2: findings consistent with pneumonia
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#3: abnormal
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#-1: not recorded
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#-2: n/a specified by the clinician # not in the data...
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#-3: unknown specified by clinician
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table(clinical_df$ia_cxr)
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#-3 -1 0 1 2 3
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#5 48 13 47 17 3
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# change these first else recoding 0 will be a problem as 0 already exists, mind you -2 categ doesn't exist
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clinical_df$ia_cxr[clinical_df$ia_cxr == -3 | clinical_df$ia_cxr == -1 | clinical_df$ia_cxr == 0 | clinical_df$ia_cxr == 3 ] <- NA
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table(clinical_df$ia_cxr)
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# 1 2
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#69 47 17
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clinical_df$ia_cxr[clinical_df$ia_cxr == 1] <- 0
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clinical_df$ia_cxr[clinical_df$ia_cxr == 2] <- 1
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table(clinical_df$ia_cxr)
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# 0 1
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#69 47 17
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#=======================
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# smoking [tricky one]
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#=======================
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class(clinical_df$smoking) # integer
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table(clinical_df$asthma, clinical_df$smoking)
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# orig
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# -3 -1 1 2 3 4
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#0 15 9 22 2 15 30
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#1 4 2 13 0 4 17
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# -3 -1 1 2 3 4
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#0 14 9 20 2 15 30
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#1 5 2 15 0 4 17
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# never smoking, 4 and 2 -- > no (0)
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#1 and 3 ---> yes (1)
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#!-3 and -1 ---- > NA
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################# smoking
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#1: current daily ===> categ smoker(1)
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#2: occasional =====> categ no smoker(0)
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#3: ex-smoker ===> categ smoker(1)
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#4: never =====> categ no smoker(0)
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#-1: not recorded =====> categ blank (NA)
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#-2: n/a specified by the clinician =====> categ blank (NA)
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#-3: unknown specified by clinician=====> categ blank (NA)
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table(clinical_df$smoking)
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#-3 -1 1 2 3 4
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#19 11 35 2 19 47
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# reassign the smoking codes
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clinical_df$smoking[clinical_df$smoking == 4 | clinical_df$smoking == 2 ] <- 0
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clinical_df$smoking[clinical_df$smoking == 1 | clinical_df$smoking == 3 ] <- 1
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clinical_df$smoking[clinical_df$smoking == -1 | clinical_df$smoking == -2 | clinical_df$smoking == -3 ] <- NA
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table(clinical_df$smoking)
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# 0 1
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#30 49 54
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table(clinical_df$asthma, clinical_df$smoking)
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# orig
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# 0 1
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#0 24 32 37
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#1 6 17 17
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# 0 1
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#0 23 32 35
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#1 7 17 19
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################################################################
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#=========================
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# Merge: clinical_df and infile ics
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#=========================
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merging_cols = intersect( names(clinical_df), names(clinical_ics) )
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clinical_df_ics = merge(clinical_df, clinical_ics, by = merging_cols, all = T); clinical_df_ics
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colnames(clinical_df_ics)
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# change colname of logistic_outcome
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c1 = which(colnames(clinical_df_ics) == "logistic_outcome")
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colnames(clinical_df_ics)[c1] <- "t1_resp_recoded"
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if (nrow(clinical_df_ics) == nrow(clinical_df) & nrow(clinical_ics)){
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cat("\nPASS: No. of rows match, nrow =", nrow(clinical_df_ics)
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, "\nChecking ncols...")
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if ( ncol(clinical_df_ics) == ncol(clinical_df) + ncol(clinical_ics) - length(merging_cols) ){
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cat("\nPASS: No. of cols match, ncol =", ncol(clinical_df_ics))
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} else {
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cat("\nFAIL: ncols mismatch"
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, "Expected ncols:", ncol(clinical_df) + ncol(clinical_ics) - length(merging_cols)
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, "\nGot:", ncol(clinical_df_ics))
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}
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} else {
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cat("\nFAIL: nrows mismatch"
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, "\nExpected nrows:", nrow(fp_adults))
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}
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#======================
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# writing output file
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#======================
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outfile_name_reg = "clinical_df_recoded.csv"
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outfile_reg = paste0(outdir, outfile_name_reg)
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cat("\nWriting clinical file for regression:", outfile_reg)
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write.csv(clinical_df_ics, file = outfile_reg)
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################################################################
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rm(age_bins, max_age_interval, max_in, min_in
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, o2_sat_bin, onset_initial_bin, tot_o2
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, tot_onset2ini, meta_data_cols
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, clinical_df)
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