added loess data in read_data.R and plotting scripts for loess
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5 changed files with 716 additions and 8 deletions
247
data_formatting_loess.R
Executable file
247
data_formatting_loess.R
Executable file
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#!/usr/bin/Rscript
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getwd()
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setwd('~/git/covid_analysis/')
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getwd()
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############################################################
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# TASK: data cleaning and extraction: for loess plots
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############################################################
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# source data
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source("read_data.R")
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# clear unwanted variables
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rm(lf_data, wf_data)
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#==================================
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# output: formatted and clean data: loess
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#==================================
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outfile_icu_wf = paste0(datadir,"/icu_covid_wf_v3_loess.csv")
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outfile_icu_lf = paste0(datadir,"/icu_covid_lf_v3_loess.csv")
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outfile_colnames = paste0(datadir, "/colnames_check_v3_loess.csv")
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#%%========================================================
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colnames_loess = as.data.frame(colnames(my_data))
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# some numerical cols are characters, change these
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#my_data <- as.data.frame(sapply(my_data, function(x) as.numeric(as.character((x)))))
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#str(my_data)
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# colnames
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all_cols_loess = as.data.frame(colnames(my_data))
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#write.csv(all_cols_loess, "colnames_loess.csv")
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#==================================
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# select the mediators to analyse
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#==================================
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cols_to_select = c("id",
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"sRAGEpgmLt1",
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"sICAM1ngmLt1",
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"PSELECTINngmLt1",
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"sVCAM1ngmLt1",
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"Angiopoietin2pgmLt1",
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"sESelectinngmLt1",
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"sESelectinngmLt2",
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"Angiopoietin2pgmLt2",
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"sRAGEpgmLt2",
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"sICAM1ngmLt2",
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"PSELECTINngmLt2",
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"sVCAM1ngmLt2",
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"sESelectinngmLt3",
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"Angiopoietin2pgmLt3",
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"sRAGEpgmLt3",
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"sICAM1ngmLt3",
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"PSELECTINngmLt3",
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"sVCAM1ngmLt3",
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"days_from_symptons_onset_t1",
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"days_from_hospitalization_t1",
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"outcomes0death1recovery2other",
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"PF_t1",
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"PF_t2" ,
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"PF_t3" ,
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"studygroup0coorteA1coorteB2coorteC")
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# check if these columns to select are present in the data
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cols_to_select%in%colnames(my_data)
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all(cols_to_select%in%colnames(my_data))
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table(my_data$studygroup0coorteA1coorteB2coorteC)
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# subset
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my_df = my_data[,cols_to_select]
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dim(my_df)
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# some numerical cols are characters, change these
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str(my_df)
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my_df <- as.data.frame(sapply(my_df, function(x) as.numeric(as.character((x)))))
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str(my_df)
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# add column name subject_id with "S" prefix to id or simply add it to id
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my_df$id = paste0("S", my_df$id)
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# assign nicer colnames
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original_colnames = colnames(my_df)
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orig_cols = as.data.frame(colnames(my_df))
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my_colnames = c("id",
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"sRAGE_pgmL_t1",
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"sICAM1_ngmL_t1",
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"PSelectin_ngmL_t1",
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"sVCAM1_ngmL_t1",
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"Angiopoietin2_pgmL_t1",
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"sESelectin_ngmL_t1",
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"sESelectin_ngmL_t2",
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"Angiopoietin2_pgmL_t2",
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"sRAGE_pgmL_t2",
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"sICAM1_ngmL_t2",
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"PSelectin_ngmL_t2",
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"sVCAM1_ngmL_t2",
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"sESelectin_ngmL_t3",
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"Angiopoietin2_pgmL_t3",
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"sRAGE_pgmL_t3",
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"sICAM1_ngmL_t3",
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"PSelectin_ngmL_t3",
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"sVCAM1_ngmL_t3",
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"days_from_symptons_onset_t1",
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"days_from_hospitalization_t1",
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"outcomes",
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"PF_units_t1",
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"PF_units_t2" ,
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"PF_units_t3" ,
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"studygroup")
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if (length(original_colnames) == length(my_colnames) && all(cols_to_select%in%colnames(my_data))){
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print("PASS: length of colnames match. Assigning clean colnames")
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colnames(my_df) = my_colnames
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revised_colnames = colnames(my_df)
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colnames_check = as.data.frame(cbind(original_colnames, revised_colnames))
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} else{
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cat(paste0("FAIL:length mismatch when assigning colnames"
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, "\nExpected length of colnames: ", length(original_colnames)
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, "\nGot: ", length(my_colnames)))
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quit()
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}
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print(colnames(my_df))
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n_patients = length(unique(my_df$id))
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print(paste0("Total no. of patients:", n_patients))
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table(my_df$studygroup)
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table(my_df$outcomes)
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table(my_df$studygroup, my_df$outcomes)
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#%% subset only icu patients
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my_df_icu = my_df[my_df$studygroup == 0,]
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n_icupatients = length(unique(my_df_icu$id))
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cat(paste0("Total no. of rows in original df:", nrow(my_df)
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, "\nTotal no. of unique patients:",n_patients
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, "\nTotal no. of ICU patients:", n_icupatients))
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table(my_df_icu$studygroup, my_df_icu$outcomes)
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tab1 = table(my_df_icu$studygroup, my_df_icu$outcomes)
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cat(paste0("no. of icu patients who"
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, "\ndied:", tab1[1], " ~ ", tab1[1]/n_icupatients*100, "%"
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, "\nrecovered:", tab1[2]," ~ ", tab1[2]/n_icupatients*100, "%"))
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#, "\nother:", tab1[3], " ~ ", tab1[3]/n_icupatients*100, "%"))
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table(my_df$studygroup)
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#=====================================================
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#=========
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# lf data
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#=========
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pivot_cols = c("id"
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, "studygroup"
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, "days_from_symptons_onset_t1"
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, "days_from_hospitalization_t1"
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, "outcomes")
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expected_rows_lf = nrow(my_df_icu) * (length(my_df_icu) - length(pivot_cols))
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# using regex:
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df_lf = my_df_icu %>%
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tidyr::pivot_longer(-all_of(pivot_cols), names_to = c("mediator", "units", "timepoint"),
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names_pattern = "(.*)_(.*)_(.*)",
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values_to = "value")
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if ((nrow(df_lf) == expected_rows_lf) & (sum(table(is.na(df_lf$mediator))) == expected_rows_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(df_lf)
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, "\nNo. of cols: ", ncol(df_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_lf
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, "\nGot: ", nrow(df_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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class(df_lf) # hmmm
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str(df_lf)
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class(as.data.frame(df_lf))
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str(as.data.frame(df_lf))
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# COMMENT: slight difference in class and structure b/w the outout from pivot and when you convert to df
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# I will use the df as I am familiar with it!
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lf_df = as.data.frame(df_lf)
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class(lf_df)
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str(lf_df)
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# sort by mediator and timepoint
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lf_df = lf_df[order(lf_df$mediator, lf_df$timepoint),]
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table(is.na(lf_df$mediator))
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#=========
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# wf data
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#=========
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# icu data is your wf data
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# sort icu data by columnames
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auto_col_order = order(names(my_df_icu))
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#my_col_order = c(1,25, 24, 20, 6, 9, 15, 21, 22, 23, 4, 12, 18, 7, 8, 14, 3, 11, 17, 2, 10, 16, 5, 13, 19)
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my_col_order = c(1, 26, 20, 21, 22
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, 23, 24, 25
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, 6, 9, 15
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, 2, 10, 16
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, 4, 12, 18
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, 5, 13, 19
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, 7, 8, 14
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, 3, 11, 17)
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if(length(auto_col_order) == length(my_col_order)){
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print("PASS: column order successfully generated. Reordering column in wf data")
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wf_df = my_df_icu[, my_col_order]
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} else{
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cat(paste0("FAIL:length mismatch of column orders"
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, "\nExpected column order for: ", length(auto_col_order)
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, "\nGot:", length(my_col_order)))
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quit()
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}
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#all.equal(my_df_icu, wf_df)
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#===========================================================
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#%% write icu files
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# lf_data
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write.csv(lf_df, outfile_icu_lf, row.names = F)
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cat(paste0("Finsihed wrting lf data:"
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, "\nNo. of rows: ", nrow(lf_df)
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, "\nNo. of cols: ", ncol(lf_df)))
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# column names to check
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write.csv(colnames_check, outfile_colnames, row.names = F)
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cat(paste0("Finsihed wrting colnames original and revised:"
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, "\nNo. of rows: ", nrow(colnames_check)
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, "\nNo. of cols: ", ncol(colnames_check)))
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# wf_data: only original
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write.csv(wf_df, outfile_icu_wf, row.names = F)
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cat(paste0("\nFinsihed wrting wf data:"
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, "\nNo. of rows: ", nrow(wf_df)
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, "\nNo. of cols: ", ncol(wf_df)))
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# COMMENT: wf_data for scaled values not written out!
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#=======================================================
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# end of script
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