reformatting code to select needed df for analysis
This commit is contained in:
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a6cbaab40a
commit
b72c4df796
7 changed files with 243 additions and 102 deletions
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@ -25,7 +25,7 @@ clinical_cols = c("mosaic"
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, "onsfindeath"
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, "onset_2_initial"
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, "o2_sat_admis"
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, "o2_sat_suppl"
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#, "o2_sat_suppl"
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, "ethnicity"
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, "smoking"
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, "ia_cxr"
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@ -13,18 +13,25 @@ getwd()
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#====================
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source("read_data.R")
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#============================
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# Data to use: Important step
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#============================
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# select df to use
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my_data = fp_adults
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# clear unnecessary variables
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rm(all_df)
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rm(all_df, adult_df, fp_adults_na)
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########################################################################
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#=========
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# sam
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#=========
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sam_regex = regex(".*_sam[1-3]{1}$", ignore_case = T)
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sam_cols_i = str_extract(colnames(adult_df), sam_regex) # not boolean
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#sam_cols_b = colnames(adult_df)%in%sam_cols_i # boolean
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sam_cols_i = str_extract(colnames(my_data), sam_regex) # not boolean
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#sam_cols_b = colnames(my_data)%in%sam_cols_i # boolean
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sam_cols = colnames(adult_df)[colnames(adult_df)%in%sam_cols_i]
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sam_cols = colnames(my_data)[colnames(my_data)%in%sam_cols_i]
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# this contains log columns + daysamp_samXX: omitting these
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sam_regex_log_days = regex("log|day.*_sam[1-3]{1}$", ignore_case = T, perl = T)
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@ -48,7 +55,7 @@ cat("Extracting SAM cols + metadata_cols")
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if ( length(sam_cols_to_extract) == length(meta_data_cols) + length(sam_cols_clean) ){
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cat("Extracing", length(sam_cols_to_extract), "columns for sam")
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sam_df = adult_df[, sam_cols_to_extract]
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sam_df = my_data[, sam_cols_to_extract]
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}else{
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cat("FAIL: length mismatch"
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, "Expeceted to extract:", length(meta_data_cols) + length(sam_cols_clean), "columns"
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@ -61,10 +68,10 @@ colnames_sam_df = colnames(sam_df); colnames_sam_df
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# serum
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#=========
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serum_regex = regex(".*_serum[1-3]{1}$", ignore_case = T)
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serum_cols_i = str_extract(colnames(adult_df), serum_regex) # not boolean
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#serum_cols_b = colnames(adult_df)%in%serum_cols_i # boolean
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serum_cols_i = str_extract(colnames(my_data), serum_regex) # not boolean
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#serum_cols_b = colnames(my_data)%in%serum_cols_i # boolean
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serum_cols = colnames(adult_df)[colnames(adult_df)%in%serum_cols_i]
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serum_cols = colnames(my_data)[colnames(my_data)%in%serum_cols_i]
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# this contains log columns + dayserump_serumXX: omitting these
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serum_regex_log_days = regex("log|day.*_serum[1-3]{1}$", ignore_case = T, perl = T)
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@ -88,7 +95,7 @@ cat("Extracting SERUM cols + metadata_cols")
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if ( length(serum_cols_to_extract) == length(meta_data_cols) + length(serum_cols_clean) ){
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cat("Extracing", length(serum_cols_to_extract), "columns for serum")
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serum_df = adult_df[, serum_cols_to_extract]
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serum_df = my_data[, serum_cols_to_extract]
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}else{
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cat("FAIL: length mismatch"
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, "Expeceted to extract:", length(meta_data_cols) + length(serum_cols_clean), "columns"
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@ -101,10 +108,10 @@ colnames_serum_df = colnames(serum_df); colnames_serum_df
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# npa
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#=========
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npa_regex = regex(".*_npa[1-3]{1}$", ignore_case = T)
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npa_cols_i = str_extract(colnames(adult_df), npa_regex) # not boolean
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#npa_cols_b = colnames(adult_df)%in%npa_cols_i # boolean
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npa_cols_i = str_extract(colnames(my_data), npa_regex) # not boolean
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#npa_cols_b = colnames(my_data)%in%npa_cols_i # boolean
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npa_cols = colnames(adult_df)[colnames(adult_df)%in%npa_cols_i]
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npa_cols = colnames(my_data)[colnames(my_data)%in%npa_cols_i]
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# this contains log columns + daynpap_npaXX: omitting these
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npa_regex_log_days = regex("log|day|vl_samptime|ct.*_npa[1-3]{1}$", ignore_case = T, perl = T)
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@ -128,7 +135,7 @@ cat("Extracting NPA cols + metadata_cols")
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if ( length(npa_cols_to_extract) == length(meta_data_cols) + length(npa_cols_clean) ){
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cat("Extracing", length(npa_cols_to_extract), "columns for npa")
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npa_df = adult_df[, npa_cols_to_extract]
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npa_df = my_data[, npa_cols_to_extract]
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}else{
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cat("FAIL: length mismatch"
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, "Expeceted to extract:", length(meta_data_cols) + length(npa_cols_clean), "columns"
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@ -166,21 +173,21 @@ for (i in extra_cols){
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}
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}
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tail(colnames_check_f)
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# write file?
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quick_check = as.data.frame(cbind(metadata_all$mosaic
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, metadata_all$adult
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, metadata_all$age
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, metadata_all$obesity
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, metadata_all$obese2
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))
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colnames(quick_check) = c("mosaic", "adult", "age", "obesity", "obese2")
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##########################################################################
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# LF data
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##########################################################################
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cols_to_omit = c("adult", "obese2"
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, "height", "height_unit", "weight"
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, "weight_unit", "visual_est_bmi", "bmi_rating")
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cols_to_omit = c("adult"
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#, "obese2"
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#, "height", "height_unit", "weight"
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#, "weight_unit", "visual_est_bmi", "bmi_rating"
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)
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pivot_cols = meta_data_cols
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# subselect pivot_cols
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pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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ncols_omitted = table(meta_data_cols%in%cols_to_omit)[[2]]
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ncols_omitted
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#==============
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# lf data: sam
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@ -198,11 +205,11 @@ pivot_cols = meta_data_cols
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# subselect pivot_cols
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pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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if (length(pivot_cols) == length(meta_data_cols) - length(cols_to_omit)){
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if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
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cat("PASS: pivot cols successfully extracted")
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}else{
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cat("FAIL: length mismatch! pivot cols could not be extracted"
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, "\nExpected length:", length(meta_data_cols) - length(cols_to_omit)
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, "\nExpected length:", length(meta_data_cols) - ncols_omitted
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, "\nGot:",length(pivot_cols) )
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quit()
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}
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@ -249,11 +256,11 @@ serum_wf = serum_df_adults[wf_cols]
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pivot_cols = meta_data_cols
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pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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if (length(pivot_cols) == length(meta_data_cols) - length(cols_to_omit)){
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if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
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cat("PASS: pivot cols successfully extracted")
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}else{
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cat("FAIL: length mismatch! pivot cols could not be extracted"
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, "\nExpected length:", length(meta_data_cols) - length(cols_to_omit)
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, "\nExpected length:", length(meta_data_cols) - ncols_omitted
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, "\nGot:",length(pivot_cols) )
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quit()
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}
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@ -296,11 +303,11 @@ npa_wf = npa_df_adults[wf_cols]
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pivot_cols = meta_data_cols
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pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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if (length(pivot_cols) == length(meta_data_cols) - length(cols_to_omit)){
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if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
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cat("PASS: pivot cols successfully extracted")
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}else{
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cat("FAIL: length mismatch! pivot cols could not be extracted"
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, "\nExpected length:", length(meta_data_cols) - length(cols_to_omit)
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, "\nExpected length:", length(meta_data_cols) - ncols_omitted
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, "\nGot:",length(pivot_cols) )
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quit()
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}
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@ -333,7 +340,7 @@ if (
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rm(sam_regex, sam_regex_log_days, sam_cols, sam_cols_clean, sam_cols_i, sam_cols_to_extract, sam_cols_to_omit)
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rm(serum_regex, serum_regex_log_days, serum_cols, serum_cols_clean, serum_cols_i, serum_cols_to_extract, serum_cols_to_omit)
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rm(npa_regex, npa_regex_log_days, npa_cols, npa_cols_clean, npa_cols_i, npa_cols_to_extract, npa_cols_to_omit)
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rm(adult_df)
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rm(my_data)
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rm(colnames_check)
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rm(i, j
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#, expected_cols
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@ -344,3 +351,4 @@ rm(sam_df_adults, serum_df_adults, npa_df_adults)
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# rm df
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rm(sam_df, serum_df, npa_df)
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rm(colnames_check_f, fp_adults)
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@ -53,31 +53,36 @@ if ( sum(table(clinical_df$obesity)) & sum(table(clinical_df$age>=18)) & sum(tab
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table(clinical_df$ia_exac_copd)
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str(clinical_df)
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#clinical_df$o2_sat_suppl
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########################################################################
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#==================================
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# asthma and copd status correction
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# for conflicting field!
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# Check asthma and copd conflict
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#=================================
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if ( table(fp_adults$ia_exac_copd, fp_adults$asthma) [[2,2]] == 0){
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cat("PASS: asthma and copd do not conflict")
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}else{
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cat("Conflict detected in asthm and copd filed, attempting to resolve...")
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# Reassign the copd and asthma status and do some checks
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table(clinical_df$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$ia_exac_copd))
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table(fp_adults$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$ia_exac_copd))
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# check copd and asthma status
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table(clinical_df$ia_exac_copd, clinical_df$asthma)
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check_copd_and_asthma_1<- subset(clinical_df, ia_exac_copd ==1 & asthma == 1) # check this is 3
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table(fp_adults$ia_exac_copd, fp_adults$asthma)
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check_copd_and_asthma_1<- subset(fp_adults, ia_exac_copd ==1 & asthma == 1) # check this is 3
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# reassign these 3 so these are treated as non-asthmatics as copd with asthma is NOT TRUE asthma
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clinical_df$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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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$asthma)
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foo<- subset(fp_adults, asthma==1 & ia_exac_copd ==1) # check that its 0
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rm(check_copd_and_asthma_1, foo)
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cat("Check status again...")
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}
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#=====================================================================
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#=================================
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# resp scores: In, max and t1 & t2
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@ -125,29 +130,30 @@ rm(max_resp_score_table, T1_resp_score_table, T2_resp_score_table, Inresp_sev
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# age
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#========
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# Create categories of variables
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clinical_df$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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clinical_df$age_int = round(clinical_df$age, digits = 0)
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table(clinical_df$age_int)
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table(clinical_df$asthma, clinical_df$age_int)
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min(clinical_df$age_int); max(clinical_df$age_int)
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max_age_interval = round_any(max(clinical_df$age), 10, f = ceiling)
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max_age_interval = round_any(max(clinical_df$age_int), 10, f = ceiling)
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max_age_interval
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min_age = min(clinical_df$age); min_age #19
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min_age = min(clinical_df$age_int); min_age #19
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min_age_interval = min_age - 1; min_age_interval
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#age_bins = cut(clinical_df$age, c(0,18,30,40,50,60,70,80,90))
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age_bins = cut(clinical_df$age, c(min_age_interval, 30, 40, 50, 60, 70, max_age_interval))
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#age_bins = cut(clinical_df$age_int, c(0,18,30,40,50,60,70,80,90))
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age_bins = cut(clinical_df$age_int, c(min_age_interval, 30, 40, 50, 60, 70, max_age_interval))
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clinical_df$age_bins = age_bins
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dim(clinical_df) # 133 27
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dim(clinical_df) # 133 28
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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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#1 11 8 12 5 2 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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@ -156,7 +162,7 @@ if (sum(table(clinical_df$asthma, clinical_df$age_bins)) == nrow(clinical_df) ){
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quit()
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}
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# reassign
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# reassign levels
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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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@ -170,11 +176,25 @@ 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$o2_sat_admis
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n1 = sum(is.na(clinical_df$o2_sat_admis))
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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$o2_sat_admis)[["-1"]]
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tot_o2
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n_text_code = table(clinical_df$o2_sat_admis)[["-1"]]
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clinical_df$o2_sat_admis[clinical_df$o2_sat_admis <0] <- NA
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n2 = sum(is.na(clinical_df$o2_sat_admis))
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if (n2 == n1 + n_text_code) {
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cat ("PASS: -1 code converted to NA")
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} else{
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cat("FAIL: something went wrong!")
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}
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o2_sat_bin = cut(clinical_df$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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@ -184,6 +204,8 @@ sum(table(clinical_df$o2_sat_bin)) == tot_o2
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#===========================
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# Onset to initial binning
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#===========================
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clinical_df$onset_2_initial
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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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@ -198,14 +220,15 @@ 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$sfluv) #[1] "factor"
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class(clinical_df$sfluv)
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levels(clinical_df$sfluv)
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table(clinical_df$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$sfluv)
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@ -213,14 +236,16 @@ table(clinical_df$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$h1n1v) #[1] "factor"
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class(clinical_df$h1n1v)
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levels(clinical_df$h1n1v)
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table(clinical_df$h1n1v)
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table(clinical_df$asthma, clinical_df$h1n1v)
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||||
|
||||
# reassign
|
||||
levels(clinical_df$h1n1v) <- c("0", "0", "1")
|
||||
table(clinical_df$asthma, clinical_df$h1n1v)
|
||||
|
|
@ -229,18 +254,21 @@ table(clinical_df$asthma, clinical_df$h1n1v)
|
|||
# ethnicity
|
||||
#=======================
|
||||
class(clinical_df$ethnicity) # integer
|
||||
table(clinical_df$ethnicity)
|
||||
table(clinical_df$asthma, clinical_df$ethnicity)
|
||||
|
||||
clinical_df$ethnicity[clinical_df$ethnicity == 4] <- 2
|
||||
table(clinical_df$ethnicity)
|
||||
table(clinical_df$asthma, clinical_df$ethnicity)
|
||||
|
||||
#=======================
|
||||
# pneumonia
|
||||
#=======================
|
||||
table(clinical_df$ia_cxr)
|
||||
class(clinical_df$ia_cxr) # integer
|
||||
# ia_cxr 2 ---> yes pneumonia (1)
|
||||
# 1 ---> no (0)
|
||||
# ! 1 or 2 -- > "unkown"
|
||||
# ! 1 or 2 -- > "unknown"
|
||||
|
||||
# reassign the pneumonia codes
|
||||
#0: not performed
|
||||
|
|
@ -251,7 +279,6 @@ class(clinical_df$ia_cxr) # integer
|
|||
#-2: n/a specified by the clinician # not in the data...
|
||||
#-3: unknown specified by clinician
|
||||
|
||||
|
||||
table(clinical_df$ia_cxr)
|
||||
#-3 -1 0 1 2 3
|
||||
#5 48 13 47 17 3
|
||||
|
|
@ -262,6 +289,8 @@ table(clinical_df$ia_cxr)
|
|||
# 1 2
|
||||
#69 47 17
|
||||
|
||||
sum(is.na(clinical_df$ia_cxr))
|
||||
|
||||
clinical_df$ia_cxr[clinical_df$ia_cxr == 1] <- 0
|
||||
clinical_df$ia_cxr[clinical_df$ia_cxr == 2] <- 1
|
||||
table(clinical_df$ia_cxr)
|
||||
|
|
@ -306,7 +335,7 @@ clinical_df$smoking[clinical_df$smoking == 4 | clinical_df$smoking == 2 ] <- 0
|
|||
clinical_df$smoking[clinical_df$smoking == 1 | clinical_df$smoking == 3 ] <- 1
|
||||
clinical_df$smoking[clinical_df$smoking == -1 | clinical_df$smoking == -2 | clinical_df$smoking == -3 ] <- NA
|
||||
|
||||
table(clinical_df$smoking)
|
||||
table(clinical_df$smoking); sum(is.na(clinical_df$smoking))
|
||||
# 0 1
|
||||
#30 49 54
|
||||
|
||||
|
|
@ -316,17 +345,13 @@ table(clinical_df$asthma, clinical_df$smoking)
|
|||
# 0 1
|
||||
#0 24 32 37
|
||||
#1 6 17 17
|
||||
|
||||
# 0 1
|
||||
#0 23 32 35
|
||||
#1 7 17 19
|
||||
|
||||
################################################################
|
||||
|
||||
#=========================
|
||||
# Merge: clinical_df and infile ics
|
||||
#=========================
|
||||
merging_cols = intersect( names(clinical_df), names(clinical_ics) )
|
||||
merging_cols
|
||||
|
||||
clinical_df_ics = merge(clinical_df, clinical_ics, by = merging_cols, all = T); clinical_df_ics
|
||||
|
||||
|
|
@ -351,6 +376,15 @@ if (nrow(clinical_df_ics) == nrow(clinical_df) & nrow(clinical_ics)){
|
|||
, "\nExpected nrows:", nrow(fp_adults))
|
||||
}
|
||||
|
||||
# change the factor vars to integers
|
||||
str(clinical_df_ics)
|
||||
factor_vars = lapply(clinical_df_ics, class) == "factor"
|
||||
table(factor_vars)
|
||||
clinical_df_ics[, factor_vars] <- lapply(clinical_df_ics[, factor_vars], as.integer)
|
||||
table(factor_vars)
|
||||
|
||||
str(clinical_df_ics)
|
||||
|
||||
#======================
|
||||
# writing output file
|
||||
#======================
|
||||
|
|
@ -359,9 +393,8 @@ outfile_reg = paste0(outdir, outfile_name_reg)
|
|||
|
||||
cat("\nWriting clinical file for regression:", outfile_reg)
|
||||
|
||||
write.csv(clinical_df_ics, file = outfile_reg)
|
||||
#write.csv(clinical_df_ics, file = outfile_reg)
|
||||
################################################################
|
||||
|
||||
rm(age_bins, max_age_interval, max_in, min_in
|
||||
, o2_sat_bin, onset_initial_bin, tot_o2
|
||||
, tot_onset2ini, meta_data_cols
|
||||
|
|
|
|||
|
|
@ -214,4 +214,36 @@ comb_stats_categ_df_f = comb_stats_categ_df[order(comb_stats_categ_df$p_signif
|
|||
# write output file
|
||||
#******************
|
||||
cat("Chisq and fishers test results in:", outfile_clin_categ)
|
||||
write.csv(comb_stats_categ_df_f, outfile_clin_categ, row.names = FALSE)
|
||||
#write.csv(comb_stats_categ_df_f, outfile_clin_categ, row.names = FALSE)
|
||||
|
||||
#==================
|
||||
#0 date not recorded
|
||||
#-1 not recorded
|
||||
#-2 n/a specified by clinician
|
||||
#-3 unknown specified by
|
||||
|
||||
|
||||
chisq_test(table(clinical_df_ics$obesity, clinical_df_ics$smoking))
|
||||
|
||||
chisq_test(table(clinical_df_ics$obesity, clinical_df_ics$max_resp_score))
|
||||
|
||||
chisq_test(table(clinical_df_ics$obesity, clinical_df_ics$T1_resp_score))
|
||||
chisq_test(table(clinical_df_ics$obesity, clinical_df_ics$t1_resp_recoded))
|
||||
|
||||
|
||||
obese_df = clinical_df_ics[clinical_df_ics$obesity == 1,]
|
||||
not_ob_df = clinical_df_ics[clinical_df_ics$obesity == 0,]
|
||||
|
||||
wilcox.test(obese_df$age, not_ob_df$age, paired = F)
|
||||
wilcox.test(obese_df$los, not_ob_df$los, paired = F)
|
||||
wilcox.test(obese_df$o2_sat_admis, not_ob_df$o2_sat_admis, paired = F)
|
||||
wilcox.test(obese_df$onset_2_initial, not_ob_df$onset_2_initial, paired = F)
|
||||
wilcox.test(obese_df$onset2final, not_ob_df$onset2final, paired = F)
|
||||
wilcox.test(obese_df$onsfindeath, not_ob_df$onsfindeath, paired = F)
|
||||
|
||||
clinical_df_ics$age
|
||||
clinical_df_ics$los
|
||||
clinical_df_ics$o2_sat_admis #***** (already bin)
|
||||
clinical_df_ics$onset_2_initial # ***** (already bin)
|
||||
clinical_df_ics$onset2final
|
||||
clinical_df_ics$onsfindeath
|
||||
|
|
|
|||
|
|
@ -25,27 +25,28 @@ outfile_clinical_unpaired
|
|||
# Unpaired stats for clinical data b/w groups: wilcoxon UNpaired analysis
|
||||
# No correction required
|
||||
########################################################################
|
||||
|
||||
str(clinical_df_ics)
|
||||
numerical_cols = c("age"
|
||||
#, "vl_pfu_ul_npa1"
|
||||
, "vl_pfu_ul_npa1"
|
||||
, "los"
|
||||
, "onset2final"
|
||||
, "onsfindeath"
|
||||
, "onset_2_initial"
|
||||
, "o2_sat_admis")
|
||||
#, "onset_2_initial" # already bin
|
||||
#, "o2_sat_admis"# already bin
|
||||
)
|
||||
|
||||
metadata_cols = c("mosaic", "obesity")
|
||||
|
||||
clinical_df_numerical = clinical_df[, c(metadata_cols, numerical_cols)]
|
||||
clinical_df_numerical = clinical_df_ics[, c(metadata_cols, numerical_cols)]
|
||||
|
||||
pivot_cols = metadata_cols
|
||||
#pivot_cols = metadata_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
|
||||
expected_rows_clinical_lf = nrow(clinical_df_numerical) * (length(clinical_df_numerical) - length(pivot_cols)); expected_rows_clinical_lf
|
||||
|
||||
|
||||
# lf data colnames
|
||||
keycol <- "clinical_params"
|
||||
valuecol <- "value"
|
||||
gathercols <- c("age", "los", "onset2final", "onsfindeath", "onset_2_initial", "o2_sat_admis")
|
||||
gathercols <- numerical_cols
|
||||
|
||||
clinical_lf = gather_(clinical_df_numerical, keycol, valuecol, gathercols)
|
||||
|
||||
|
|
@ -70,12 +71,15 @@ stats_un_clinical = compare_means(value~obesity
|
|||
#, data = clinical_lf_comp
|
||||
, paired = FALSE)
|
||||
|
||||
head(stats_un_clinical)
|
||||
|
||||
# rstatix
|
||||
stat_df <- clinical_lf %>%
|
||||
group_by(clinical_params) %>%
|
||||
wilcox_test(value ~ obesity, paired = F) %>%
|
||||
add_significance("p")
|
||||
stat_df$p_format = round(stat_df$p, digits = 3)
|
||||
stat_df
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each clinical param: Overall
|
||||
|
|
@ -101,31 +105,39 @@ n_all_gp = merge(n_all, n_gp
|
|||
#----------------------------------------
|
||||
# calculate n_obs for each clinical param: complete cases
|
||||
#----------------------------------------
|
||||
n_comp = data.frame(table(clinical_lf_comp$clinical_params))
|
||||
n_comp = data.frame(table(clinical_lf$clinical_params))
|
||||
colnames(n_comp) = c("clinical_params", "n_complete")
|
||||
n_comp$clinical_params = as.character(n_comp$clinical_params)
|
||||
n_comp
|
||||
|
||||
n_gp_comp_lf = data.frame(table(clinical_lf_comp$clinical_params, clinical_lf_comp$obesity)); n_gp_comp_lf
|
||||
n_gp_comp_lf = data.frame(table(clinical_lf$clinical_params
|
||||
, clinical_lf$obesity)); n_gp_comp_lf
|
||||
n_gp_comp = spread(n_gp_comp_lf, "Var2", "Freq"); n_gp_comp
|
||||
colnames(n_gp_comp)
|
||||
colnames(n_gp_comp) = c("clinical_params"
|
||||
, paste0("n_complete_gp", colnames(n_gp_comp)[2])
|
||||
, paste0("n_complete_gp", colnames(n_gp_comp)[3]))
|
||||
|
||||
|
||||
#---------
|
||||
# merge 1
|
||||
#---------
|
||||
n_comp_gp = merge(n_comp, n_gp_comp
|
||||
, by = intersect( names(n_comp), names(n_gp_comp))
|
||||
, all = T)
|
||||
n_comp_gp
|
||||
|
||||
#---------
|
||||
# merge 2
|
||||
#---------
|
||||
merge_cols = intersect(names(n_all_gp), names(n_comp_gp)); merge_cols
|
||||
|
||||
n_df = merge(n_all_gp, n_comp_gp, by = merge_cols, all = T); n_df
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#===================================
|
||||
#----------------------------------
|
||||
# Merge 3: merge stats + n_obs df
|
||||
#----------------------------------
|
||||
merging_cols = intersect(names(stats_un_clinical), names(n_df)); merging_cols
|
||||
|
||||
if (all(n_df$clinical_params%in%stats_un_clinical$clinical_params)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_clinical = merge(stats_un_clinical, n_df, by = merging_cols, all = T)
|
||||
|
|
@ -188,6 +200,7 @@ if( length(my_col_order2) == ncol(stats_clinical_df) && (all(my_col_order2%in%co
|
|||
quit()
|
||||
}
|
||||
# assign nice column names like replace "." with "_"
|
||||
# same ordering as my_col_order2, just minor formatting
|
||||
colnames(stats_clinical_df_f) = c("clinical_params"
|
||||
, "method"
|
||||
, "group1"
|
||||
|
|
@ -208,4 +221,4 @@ colnames(stats_clinical_df_f)
|
|||
# write output file
|
||||
#******************
|
||||
cat("UNpaired stats for clinical data for groups in:", outfile_clinical_unpaired)
|
||||
#write.csv(stats_clinical_df_f, outfile_clinical_unpaired, row.names = FALSE)
|
||||
write.csv(stats_clinical_df_f, outfile_clinical_unpaired, row.names = FALSE)
|
||||
|
|
|
|||
|
|
@ -365,6 +365,14 @@ colnames(combined_unpaired_stats_f) = c("mediator"
|
|||
, "p_bon_signif")
|
||||
|
||||
colnames(combined_unpaired_stats_f)
|
||||
|
||||
# count how many meds are significant
|
||||
n_sig = length(combined_unpaired_stats_f$mediator[combined_unpaired_stats_f$p_signif<0.05])
|
||||
cat("\nTotal no. of statistically significant mediators in", toupper(my_sample_type)
|
||||
, "are:", n_sig)
|
||||
|
||||
sig_meds = combined_unpaired_stats_f[combined_unpaired_stats_f$p_signif<0.05,]
|
||||
|
||||
########################################################################
|
||||
#******************
|
||||
# write output file
|
||||
|
|
|
|||
61
read_data.R
61
read_data.R
|
|
@ -30,11 +30,11 @@ meta_data_cols = c("mosaic", "gender", "age"
|
|||
, "adult"
|
||||
, "flustat", "type"
|
||||
, "obesity"
|
||||
, "obese2"
|
||||
, "height", "height_unit"
|
||||
, "weight", "weight_unit"
|
||||
, "ia_height_ftin", "ia_height_m", "ia_weight"
|
||||
, "visual_est_bmi", "bmi_rating"
|
||||
#, "obese2"
|
||||
#, "height", "height_unit"
|
||||
#, "weight", "weight_unit"
|
||||
#, "ia_height_ftin", "ia_height_m", "ia_weight"
|
||||
#, "visual_est_bmi", "bmi_rating"
|
||||
)
|
||||
|
||||
# check if these columns to select are present in the data
|
||||
|
|
@ -55,9 +55,9 @@ if (table(adult_df$adult == 1)[[1]] == nrow(adult_df) ){
|
|||
cat ("\nFAIL: adult df number mismatch!")
|
||||
}
|
||||
|
||||
#==============
|
||||
#=================================
|
||||
# FLU positive: adult patients
|
||||
#==============
|
||||
#=================================
|
||||
# extract the flu positive population
|
||||
fp_adults = adult_df[adult_df$flustat == 1,]
|
||||
|
||||
|
|
@ -67,6 +67,53 @@ if (table(fp_adults$flustat == 1)[[1]] == nrow(fp_adults) ){
|
|||
cat ("\nFAIL: adult df number mismatch!")
|
||||
}
|
||||
|
||||
#=============================================
|
||||
# FLU positive adult patients: without asthma
|
||||
#=============================================
|
||||
#-----------------------------------
|
||||
# asthma and copd status correction
|
||||
# for conflicting field!
|
||||
#------------------------------------
|
||||
# Reassign the copd and asthma status and do some checks
|
||||
table(fp_adults$ia_exac_copd); sum(is.na(fp_adults$ia_exac_copd))
|
||||
|
||||
fp_adults$ia_exac_copd[fp_adults$ia_exac_copd< 1]<- 0
|
||||
fp_adults$ia_exac_copd[is.na(fp_adults$ia_exac_copd)] <- 0
|
||||
|
||||
table(fp_adults$ia_exac_copd); sum(is.na(fp_adults$ia_exac_copd))
|
||||
|
||||
# check copd and asthma status
|
||||
table(fp_adults$ia_exac_copd, fp_adults$asthma)
|
||||
check_copd_and_asthma_1<- subset(fp_adults, ia_exac_copd ==1 & asthma == 1) # check this is 3
|
||||
|
||||
# reassign these 3 so these are treated as non-asthmatics as copd with asthma is NOT TRUE asthma
|
||||
fp_adults$asthma[fp_adults$ia_exac_copd == 1 & fp_adults$asthma == 1]= 0
|
||||
table(fp_adults$ia_exac_copd, fp_adults$asthma)
|
||||
|
||||
foo<- subset(fp_adults, asthma==1 & ia_exac_copd ==1) # check that its 0
|
||||
|
||||
rm(check_copd_and_asthma_1, foo)
|
||||
|
||||
if ( table(fp_adults$ia_exac_copd, fp_adults$asthma) [[2,2]] == 0 ){
|
||||
cat("\nPASS: asthma and copd do not conflict")
|
||||
} else{
|
||||
cat ("\nFAIL: asthma and copd conflict not resolved!")
|
||||
quit()
|
||||
}
|
||||
|
||||
cat("\nExtracting flu positive without asthma")
|
||||
table(fp_adults$asthma)
|
||||
cat("\nNo. of asthmatics:", table(fp_adults$asthma)[[2]]
|
||||
, "\nNo. of non-asthmatics:", table(fp_adults$asthma)[[1]])
|
||||
str(fp_adults$asthma)
|
||||
|
||||
table(fp_adults$obesity)
|
||||
table(fp_adults$obesity, fp_adults$asthma)
|
||||
|
||||
fp_adults_na = fp_adults[fp_adults$asthma == 0,]
|
||||
table(fp_adults_na$obesity)
|
||||
table(fp_adults_na$obesity, fp_adults_na$asthma)
|
||||
|
||||
#============
|
||||
# hc
|
||||
#============
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue