initialised dir with data extraction script
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0dab1d5097
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237
mosaic_bmi_data_extraction.R
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237
mosaic_bmi_data_extraction.R
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all_df <- read.csv("/home/pub/Work/MOSAIC/MOSAIC_from_work/MASTER/MOSAIC_2015_MASTER_Aki_stata_20150721/Mosaic_master_file_from_stata.csv"
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, fileEncoding='latin1') # as there is some weird encoding problems!
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meta_data_cols = c("mosaic", "gender", "age", "adult", "flustat", "type"
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, "obesity", "obese2", "height", "height_unit", "weight"
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, "weight_unit", "visual_est_bmi", "bmi_rating")
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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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# use regex
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library(stringr)
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#=========
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# sam
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#=========
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sam_regex = regex(".*_sam[1-3]{1}$", ignore_case = T)
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sam_cols_i = str_extract(colnames(all_df), sam_regex) # not boolean
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#sam_cols_b = colnames(all_df)%in%sam_cols_i # boolean
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sam_cols = colnames(all_df)[colnames(all_df)%in%sam_cols_i]
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# this contains log columns + daysamp_samXX: omitting these
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sam_regex_log_days = regex("log|day.*_sam[1-3]{1}$", ignore_case = T, perl = T)
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sam_cols_to_omit = sam_cols[grepl(sam_regex_log_days, sam_cols)]; sam_cols_to_omit
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sam_cols_clean = sam_cols[!sam_cols%in%sam_cols_to_omit]; sam_cols_clean
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length(sam_cols_clean)
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if( length(sam_cols_clean) == length(sam_cols) - length(sam_cols_to_omit) ){
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cat("PASS: clean cols extracted"
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, "\nNo. of clean SAM cols to extract:", length(sam_cols_clean))
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}else{
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cat("FAIL: length mismatch. Aborting further cols extraction"
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, "Expected length:", length(sam_cols) - length(sam_cols_to_omit)
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, "Got:", length(sam_cols_clean) )
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}
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sam_cols_to_extract = c(meta_data_cols, sam_cols_clean)
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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 = all_df[, 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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, "Got:", length(sam_cols_to_extract))
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}
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colnames_sam_df = colnames(sam_df); colnames_sam_df
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#=========
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# serum
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#=========
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serum_regex = regex(".*_serum[1-3]{1}$", ignore_case = T)
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serum_cols_i = str_extract(colnames(all_df), serum_regex) # not boolean
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#serum_cols_b = colnames(all_df)%in%serum_cols_i # boolean
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serum_cols = colnames(all_df)[colnames(all_df)%in%serum_cols_i]
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# this contains log columns + dayserump_serumXX: omitting these
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serum_regex_log_days = regex("log|day.*_serum[1-3]{1}$", ignore_case = T, perl = T)
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serum_cols_to_omit = serum_cols[grepl(serum_regex_log_days, serum_cols)]; serum_cols_to_omit
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serum_cols_clean = serum_cols[!serum_cols%in%serum_cols_to_omit]; serum_cols_clean
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length(serum_cols_clean)
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if( length(serum_cols_clean) == length(serum_cols) - length(serum_cols_to_omit) ){
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cat("PASS: clean cols extracted"
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, "\nNo. of clean serum cols to extract:", length(serum_cols_clean))
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}else{
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cat("FAIL: length mismatch. Aborting further cols extraction"
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, "Expected length:", length(serum_cols) - length(serum_cols_to_omit)
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, "Got:", length(serum_cols_clean) )
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}
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serum_cols_to_extract = c(meta_data_cols, serum_cols_clean)
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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 = all_df[, 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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, "Got:", length(serum_cols_to_extract))
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}
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colnames_serum_df = colnames(serum_df); colnames_serum_df
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#=========
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# npa
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#=========
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npa_regex = regex(".*_npa[1-3]{1}$", ignore_case = T)
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npa_cols_i = str_extract(colnames(all_df), npa_regex) # not boolean
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#npa_cols_b = colnames(all_df)%in%npa_cols_i # boolean
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npa_cols = colnames(all_df)[colnames(all_df)%in%npa_cols_i]
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# this contains log columns + daynpap_npaXX: omitting these
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npa_regex_log_days = regex("log|day|vl_samptime|ct.*_npa[1-3]{1}$", ignore_case = T, perl = T)
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npa_cols_to_omit = npa_cols[grepl(npa_regex_log_days, npa_cols)]; npa_cols_to_omit
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npa_cols_clean = npa_cols[!npa_cols%in%npa_cols_to_omit]; npa_cols_clean
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length(npa_cols_clean)
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if( length(npa_cols_clean) == length(npa_cols) - length(npa_cols_to_omit) ){
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cat("PASS: clean cols extracted"
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, "\nNo. of clean npa cols to extract:", length(npa_cols_clean))
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}else{
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cat("FAIL: length mismatch. Aborting further cols extraction"
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, "Expected length:", length(npa_cols) - length(npa_cols_to_omit)
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, "Got:", length(npa_cols_clean) )
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}
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npa_cols_to_extract = c(meta_data_cols, npa_cols_clean)
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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 = all_df[, 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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, "Got:", length(npa_cols_to_extract))
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}
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colnames_npa_df = colnames(npa_df); colnames_npa_df
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colnames_check = as.data.frame(cbind(colnames_sam_df, colnames_serum_df, colnames_npa_df))
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tail(colnames_check)
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# unmatched lengths
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#colnames_check[117,1] <- NA
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#colnames_check[117,2] <- NA
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if ( ncol(sam_df) == ncol(serum_df) ){
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start = ncol(sam_df)+1
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extra_cols = start:ncol(npa_df)
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}
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colnames_check_f = colnames_check
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tail(colnames_check_f)
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for (i in extra_cols){
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for (j in 1:2) {
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cat("\ni:", i
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,"\nj:", j)
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colnames_check_f[i,j] <- NA
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#colnames_check_f[i, j]< - NA
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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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##########################################################################
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# LF data
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##########################################################################
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#=========
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# lf data: sam
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#=========
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str(sam_df)
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table(sam_df$obesity); table(sam_df$obese2)
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sam_df_adults = sam_df[sam_df$adult == 1,]
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cols_to_omit = c("adult", "flustat", "type", "obesity"
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, "height", "height_unit", "weight", "weight_unit","visual_est_bmi", "bmi_rating")
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pivot_cols = meta_data_cols
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pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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if (length(pivot_cols) == length(meta_data_cols) - length(cols_to_omit)){
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cat("PASS: pivot cols successfully extracted")
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}else{
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cat("FAIL: length mismatch! pivot cols could not be extracted"
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, "\nExpected length:", length(meta_data_cols) - length(cols_to_omit)
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, "\nGot:",length(pivot_cols) )
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quit()
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}
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expected_rows_sam_lf = nrow(sam_df_adults) * (length(sam_df_adults) - length(pivot_cols)); expected_rows_sam_lf
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# using regex:
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sam_adults_lf = sam_df_adults %>%
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tidyr::pivot_longer(-all_of(pivot_cols), names_to = c("mediator", "sample_type", "timepoint"),
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names_pattern = "(.*)_(.*)([1-3]{1})",
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values_to = "value")
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if ((nrow(sam_lf) == expected_rows_sam_lf) & (sum(table(is.na(sam_adults_lf$mediator))) == expected_rows_sam_lf)) {
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cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
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, "\nNo. of rows: ", nrow(sam_lf)
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, "\nNo. of cols: ", ncol(sam_lf)))
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} else{
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cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
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, "\nExpected no. of rows: ", expected_rows_sam_lf
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, "\nGot: ", nrow(sam_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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# remove unnecessary variables
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rm(sam_regex, sam_regex_log_days, sam_cols, sam_cols_b, 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(all_df)
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rm(colnames_check)
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157
mosaic_bmi_test.R
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157
mosaic_bmi_test.R
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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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, sep = ",")
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install.packages("readr")
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library(readr)
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df2 = read_csv("/home/backup/MOSAIC/MEDIATOR_Data/master_file/Mosaic_master_file_from_stata.csv"
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, col_names = T)
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foo = as.data.frame(colnames(df2))
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head(foo)
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head(df2)
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# sam
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sam_df = read.csv("/home/backup/MOSAIC/MEDIATOR_Data/mediator_data_analysis/SAM-oct-2015/SAM_adults_publication/SAM_only.csv"
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, fileEncoding = "latin1")
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# extract the 36 HC
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# master file for HC: called "Mediators_for_HC.csv" in /home/tanu/MASTERS/Birkbeck/MSc_Project/MOSAIC/MEDIATOR_Data/master_file
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all_healthy<- read.csv(file.choose())
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serum_hc<- subset(all_healthy, Timepoint == "HC" & Sample == "Serum")
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length(unique(serum_hc$MOSAIC)) # check:36
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#=====================
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# File for patients(comprehensive): called "Mosaic_master_file_from_stata.csv"
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all_df <- read.csv("/home/pub/Work/MOSAIC/MOSAIC_from_work/MASTER/MOSAIC_2015_MASTER_Aki_stata_20150721/Mosaic_master_file_from_stata.csv"
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, fileEncoding='latin1') # as there is some weird encoding problems!
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meta_data_cols = c("mosaic", "gender", "age", "adult", "flustat", "type"
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, "obesity", "obese2", "height", "height_unit", "weight"
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, "weight_unit", "visual_est_bmi", "bmi_rating")
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# check if these columns to select are present in the data
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meta_data_cols%in%colnames(all_df)
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all(meta_data_cols%in%colnames(all_df))
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metadata_all = all_df[, meta_data_cols]
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sam = all_df[, grepl("_sam1", colnames(all_df))]
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# use regex
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library(stringr)
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serum_regex = regex(".*_serum[1-3]{1}$", ignore_case = T)
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npa_regex = regex(".*_npa[1-3]{1}$", ignore_case = T)
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#=========
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# sam
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#=========
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sam_regex = regex(".*_sam[1-3]{1}$", ignore_case = T)
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sam_cols_i = str_extract(colnames(all_df), sam_regex) # not boolean
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sam_cols_b = colnames(all_df)%in%sam_cols_i # boolean
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sam_cols = colnames(all_df)[colnames(all_df)%in%sam_cols_i]
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# this contains log columns as well as daysamp_samXX
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sam_regex_log_days = regex("log|day.*_sam[1-3]{1}$", ignore_case = T, perl =T)
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sam_cols_to_omit = sam_cols[grepl(sam_regex_log_days, sam_cols)]; sam_cols_to_omit
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sam_cols_clean = sam_cols[!sam_cols%in%sam_cols_to_omit]; sam_cols_clean
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length(sam_cols_clean)
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if( length(sam_cols_clean) == length(sam_cols) - length(sam_cols_to_omit) ){
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cat("PASS: clean cols extracted"
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, "\nNo. of clean cols to extract", length(sam_cols_clean))
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}
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sam_cols_to_extract = c(meta_data_cols, sam_cols_clean)
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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 = all_df[, 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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, "Got:", length(sam_cols_to_extract))
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}
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colnames_sam_df = colnames(sam_df); colnames_sam_df
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#=========
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# serum
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#=========
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serum_cols_i = str_extract(colnames(all_df), serum_regex)
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table(colnames(all_df)%in%serum_cols_i)
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serum_cols = colnames(all_df)[colnames(all_df)%in%serum_cols_i]
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serum_cols_to_extract = c(meta_data_cols, serum_cols)
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if ( length(serum_cols_to_extract) == length(meta_data_cols) + length(serum_cols) ){
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cat("Extracing", length(serum_cols_to_extract), "columns for serum")
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serum_df = all_df[, 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), "columns"
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, "Got:", length(serum_cols_to_extract))
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}
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#=========
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# npa
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#=========
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npa_cols_i= str_extract(colnames(all_df), npa_regex)
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table(colnames(all_df)%in%npa_cols_i)
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npa_cols = colnames(all_df)[colnames(all_df)%in%npa_cols_i]
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npa_cols_to_extract = c(meta_data_cols, npa_cols)
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if ( length(npa_cols_to_extract) == length(meta_data_cols) + length(npa_cols) ){
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cat("Extracing", length(npa_cols_to_extract), "columns for NPA")
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npa_df = all_df[, 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), "columns"
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, "Got:", length(npa_cols_to_extract))
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}
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#################################
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#=========
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# lf data: sam
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#=========
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sam_df_v2 = sam_df
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sam_df = sam_df[1:10, ]
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pivot_cols = meta_data_cols
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expected_rows_sam_lf = nrow(sam_df) * (length(sam_df) - length(pivot_cols)); expected_rows_sam_lf
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# using regex:
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sam_lf = sam_df %>%
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tidyr::pivot_longer(-all_of(pivot_cols), names_to = c("mediator", "sample_type", "timepoint"),
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names_pattern = "(.*)_(.*)([1-3]{1})",
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values_to = "value")
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if ((nrow(sam_lf) == expected_rows_sam_lf) & (sum(table(is.na(sam_lf$mediator))) == expected_rows_sam_lf)) {
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cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
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, "\nNo. of rows: ", nrow(sam_lf)
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, "\nNo. of cols: ", ncol(sam_lf)))
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} else{
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cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
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, "\nExpected no. of rows: ", expected_rows_sam_lf
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, "\nGot: ", nrow(sam_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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