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
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@ -27,12 +27,10 @@ rm(my_data)
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output_plots = paste0(outdir_plots, "output_plots.pdf")
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corr_and_hmap = paste0(outdir_plots, "corr_hmap.pdf")
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#%%========================================================
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# read file
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############################################################
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# data assignment for plots
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wf = wf_data
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lf = lf_data
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#=====================
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# data for plots: LF
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#=====================
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247
data_formatting_loess.R
Executable file
247
data_formatting_loess.R
Executable file
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@ -0,0 +1,247 @@
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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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226
loess_plots_days_into_hospitalisation.R
Executable file
226
loess_plots_days_into_hospitalisation.R
Executable file
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@ -0,0 +1,226 @@
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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: Loess plots for days into hospital till T1
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############################################################
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#==========================================================
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#=============
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# input:source data
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#=============
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source("read_data.R")
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# clear unwanted variables
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rm(my_data, wf_data, lf_data)
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#=============
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# Output
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#=============
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output_plots_loess_hosp = paste0(outdir_plots, "output_plots_v3_loess_hosp.pdf")
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#%%========================================================
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#=====================
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# data for plots
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#=====================
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# remove PF
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table(lf_data_loess$mediator)
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lf_loess = lf_data_loess[lf_data_loess$mediator!= "PF",]
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table(lf_loess$mediator)
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#%=======================================================
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hosp_days_min = min(lf_loess$days_from_hospitalization_t1); hosp_days_min
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hosp_days_max = max(lf_loess$days_from_hospitalization_t1);hosp_days_max
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my_xscale_hosp = seq(hosp_days_min , hosp_days_max, 5)
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my_xscale_hosp
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#####################################################################
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# 95% CI: t1_data
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# days_from_hospitalization_t1
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#####################################################################
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lf_loess_t1 = lf_loess[lf_loess$timepoint == "t1",]
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# Output plots as one pdf
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cat("Output plots will be in:", output_plots_loess_hosp)
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pdf(output_plots_loess_hosp, width = 15, height = 8)
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#-----------
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# linear
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#-----------
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p1_hosp = ggplot(lf_loess_t1, aes(x = days_from_hospitalization_t1
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, y = value
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, colour = factor(outcomes))) +
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#geom_point() +
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geom_smooth(method = "loess", size = 1.5, na.rm = T) +
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facet_wrap(~mediator, nrow = 2, scales = "free_y")+
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labs(title = "Hospital days into t1: Linear scale and 95% CI"
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, x = "Days from hospitalisation to t1"
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, y = "T1 Levels")+
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scale_x_continuous(breaks = my_xscale_hosp, limits = c(hosp_days_min , hosp_days_max))+
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scale_colour_discrete(name = "Patient outcome"
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, labels = c("Death", "Recovered"))+
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theme(axis.text.x = element_text(size = 13)
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, axis.text.y = element_text(size = 13
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 13)
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, axis.title.y = element_text(size = 13)
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, plot.title = element_text(size = 15, hjust = 0.5)
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, strip.text.x = element_text(size = 13, colour = "black")
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, legend.title = element_text(color = "black", size = 13)
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, legend.text = element_text(size = 13)
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, legend.position = "right"
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, legend.direction = "vertical")
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p1_hosp
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#-----------
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# log
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#-----------
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p2_hosp = ggplot(lf_loess_t1, aes(x = days_from_hospitalization_t1
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, y = value
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, colour = factor(outcomes))) +
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scale_y_log10()+
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#geom_point() +
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geom_smooth(method = "loess", size = 1.5, na.rm = T) +
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facet_wrap(~mediator, nrow = 2, scales = "free_y")+
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labs(title = "Hospital days into t1: Log scale and 95% CI"
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, x = "Days from hospitalisation to t1"
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, y = "T1 Levels (Log10)")+
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scale_x_continuous(breaks = my_xscale_hosp, limits = c(hosp_days_min , hosp_days_max))+
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scale_colour_discrete(name = "Patient outcome"
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, labels = c("Death", "Recovered"))+
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theme(axis.text.x = element_text(size = 13)
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, axis.text.y = element_text(size = 13
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 13)
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, axis.title.y = element_text(size = 13)
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, plot.title = element_text(size = 15, hjust = 0.5)
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, strip.text.x = element_text(size = 13, colour = "black")
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, legend.title = element_text(color = "black", size = 13)
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, legend.text = element_text(size = 13)
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, legend.position = "right"
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, legend.direction = "vertical")
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p2_hosp
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#####################################################################
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# 50% CI: t1_data
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# days_from_hospitalization_t1
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#####################################################################
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#-----------
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# linear
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#-----------
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p3_hosp = ggplot(lf_loess_t1, aes(x = days_from_hospitalization_t1
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, y = value
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, colour = factor(outcomes))) +
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#geom_point() +
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geom_smooth(method = "loess", size = 1.5, na.rm = T, level = 0.50) +
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facet_wrap(~mediator, nrow = 2, scales = "free_y")+
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labs(title = "Hospital days into t1: Linear scale and 50% CI"
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, x = "Days from hospitalisation to t1"
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, y = "T1 Levels") +
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scale_x_continuous(breaks = my_xscale_hosp, limits = c(hosp_days_min , hosp_days_max))+
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scale_colour_discrete(name = "Patient outcome"
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, labels = c("Death", "Recovered"))+
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theme(axis.text.x = element_text(size = 13)
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, axis.text.y = element_text(size = 13
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 13)
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, axis.title.y = element_text(size = 13)
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, plot.title = element_text(size = 15, hjust = 0.5)
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, strip.text.x = element_text(size = 13, colour = "black")
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, legend.title = element_text(color = "black", size = 13)
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, legend.text = element_text(size = 13)
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, legend.position = "right"
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, legend.direction = "vertical")
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p3_hosp
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#-----------
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# log
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#-----------
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p4_hosp = ggplot(lf_loess_t1, aes(x = days_from_hospitalization_t1
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, y = value
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, colour = factor(outcomes))) +
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scale_y_log10()+
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#geom_point() +
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geom_smooth(method = "loess", size = 1.5, na.rm = T, level = 0.50) +
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facet_wrap(~mediator, nrow = 2, scales = "free_y")+
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labs(title = "Hospital days into t1: Log scale and 50% CI"
|
||||
, x = "Days from hospitalisation to t1"
|
||||
, y = "T1 Levels (Log10)")+
|
||||
|
||||
scale_x_continuous(breaks = my_xscale_hosp, limits = c(hosp_days_min , hosp_days_max))+
|
||||
|
||||
scale_colour_discrete(name = "Patient outcome"
|
||||
, labels = c("Death", "Recovered"))+
|
||||
theme(axis.text.x = element_text(size = 13)
|
||||
, axis.text.y = element_text(size = 13
|
||||
, angle = 0
|
||||
, hjust = 1
|
||||
, vjust = 0)
|
||||
, axis.title.x = element_text(size = 13)
|
||||
, axis.title.y = element_text(size = 13)
|
||||
, plot.title = element_text(size = 15, hjust = 0.5)
|
||||
, strip.text.x = element_text(size = 13, colour = "black")
|
||||
, legend.title = element_text(color = "black", size = 13)
|
||||
, legend.text = element_text(size = 13)
|
||||
, legend.position = "right"
|
||||
, legend.direction = "vertical")
|
||||
p4_hosp
|
||||
dev.off()
|
||||
#####################################################################
|
||||
# 95% CI: Combined data *** only if required
|
||||
# days_from_hospitalization_t1
|
||||
#####################################################################
|
||||
|
||||
#-----------
|
||||
# linear
|
||||
#-----------
|
||||
|
||||
#p1_all_hosp = ggplot(lf_loess, aes(x = days_from_hospitalization_t1
|
||||
# , y = value
|
||||
# , colour = factor(outcomes))) +
|
||||
# #geom_point() +
|
||||
# geom_smooth(method = "loess", size = 1.5, na.rm = T, level = 0.90) +
|
||||
# facet_wrap(~mediator, nrow = 2, scales = "free_y")+
|
||||
# labs(title = "Hospital days into t1: linear scale and 95% CI"
|
||||
# , x = "Days from hospitalisation to t1"
|
||||
# , y = "Combined Levels")+
|
||||
|
||||
# scale_x_continuous(breaks = my_xscale_hosp, limits = c(hosp_days_min , hosp_days_max))+
|
||||
|
||||
# scale_colour_discrete(name = "Patient outcome"
|
||||
# , labels = c("Death", "Recovered"))
|
||||
|
||||
#p1_all_hosp
|
||||
|
||||
#-----------
|
||||
# log
|
||||
#-----------
|
||||
#p2_all_hosp = ggplot(lf_loess, aes(x = days_from_hospitalization_t1
|
||||
# , y = value
|
||||
# , colour = factor(outcomes))) +
|
||||
# scale_y_log10()+
|
||||
# #geom_point() +
|
||||
# geom_smooth(method = "loess", size = 1.5, na.rm = T) +
|
||||
# facet_wrap(~mediator, nrow = 2, scales = "free_y")+
|
||||
# labs(title = "Hospital days into t1: Log scale and 95% CI"
|
||||
# , x = "Days from hospitalisation to t1"
|
||||
# , y = "Combined Levels (Log10)")+
|
||||
# scale_x_continuous(breaks = my_xscale_hosp, limits = c(hosp_days_min , hosp_days_max))+
|
||||
|
||||
# scale_colour_discrete(name = "Patient outcome"
|
||||
# , labels = c("Death", "Recovered"))
|
||||
#p2_all_hosp
|
||||
227
loess_plots_symptom_onset.R
Executable file
227
loess_plots_symptom_onset.R
Executable file
|
|
@ -0,0 +1,227 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd('~/git/covid_analysis/')
|
||||
getwd()
|
||||
############################################################
|
||||
# TASK: Loess plots: symptom onset
|
||||
############################################################
|
||||
#=============
|
||||
# input:source data
|
||||
#=============
|
||||
source("read_data.R")
|
||||
|
||||
# clear unwanted variables
|
||||
rm(my_data, wf_data, lf_data)
|
||||
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
output_plots_loess_symp = paste0(outdir_plots, "output_plots_v3_loess_symp.pdf")
|
||||
|
||||
############################################################
|
||||
#=====================
|
||||
# data for plots
|
||||
#=====================
|
||||
# remove PF
|
||||
table(lf_data_loess$mediator)
|
||||
lf_loess = lf_data_loess[lf_data_loess$mediator!= "PF",]
|
||||
table(lf_loess$mediator)
|
||||
|
||||
#%%=================================================================
|
||||
symp_days_min = min(lf_loess$days_from_symptons_onset_t1); symp_days_min
|
||||
symp_days_max = max(lf_loess$days_from_symptons_onset_t1);symp_days_max
|
||||
|
||||
my_xscale = seq(symp_days_min , symp_days_max, 5)
|
||||
my_xscale
|
||||
|
||||
#####################################################################
|
||||
# 95% CI: t1_data
|
||||
# days_from_symptons_onset_t1
|
||||
#####################################################################
|
||||
|
||||
lf_loess_t1 = lf_loess[lf_loess$timepoint == "t1",]
|
||||
|
||||
# Output plots as one pdf
|
||||
cat("Output plots will be in:", output_plots_loess_symp)
|
||||
pdf(output_plots_loess_symp, width = 15, height = 8)
|
||||
|
||||
#-----------
|
||||
# linear
|
||||
#-----------
|
||||
p1 = ggplot(lf_loess_t1, aes(x = days_from_symptons_onset_t1
|
||||
, y = value
|
||||
, colour = factor(outcomes))) +
|
||||
#geom_point() +
|
||||
geom_smooth(method = "loess", size = 1.5, na.rm = T) +
|
||||
facet_wrap(~mediator, nrow = 2, scales = "free_y")+
|
||||
labs(title = "Days into t1: Linear scale and 95% CI"
|
||||
, x = "Days from symptom onset to t1"
|
||||
, y = "T1 Levels")+
|
||||
scale_x_continuous(breaks = my_xscale, limits = c(symp_days_min , symp_days_max))+
|
||||
scale_colour_discrete(name = "Patient outcome"
|
||||
, labels = c("Death", "Recovered")) +
|
||||
theme(axis.text.x = element_text(size = 13)
|
||||
, axis.text.y = element_text(size = 13
|
||||
, angle = 0
|
||||
, hjust = 1
|
||||
, vjust = 0)
|
||||
, axis.title.x = element_text(size = 13)
|
||||
, axis.title.y = element_text(size = 13)
|
||||
, plot.title = element_text(size = 15, hjust = 0.5)
|
||||
, strip.text.x = element_text(size = 13, colour = "black")
|
||||
, legend.title = element_text(color = "black", size = 13)
|
||||
, legend.text = element_text(size = 13)
|
||||
, legend.position = "right"
|
||||
, legend.direction = "vertical")
|
||||
|
||||
p1
|
||||
|
||||
#-----------
|
||||
# log
|
||||
#-----------
|
||||
p2 = ggplot(lf_loess_t1, aes(x = days_from_symptons_onset_t1
|
||||
, y = value
|
||||
, colour = factor(outcomes))) +
|
||||
scale_y_log10()+
|
||||
#geom_point() +
|
||||
geom_smooth(method = "loess", size = 1.5, na.rm = T) +
|
||||
facet_wrap(~mediator, nrow = 2, scales = "free_y")+
|
||||
labs(title = "Days into t1: Log scale and 95% CI"
|
||||
, x = "Days from symptom onset to t1"
|
||||
, y = "T1 Levels (Log10)")+
|
||||
|
||||
scale_x_continuous(breaks = my_xscale, limits = c(symp_days_min , symp_days_max))+
|
||||
|
||||
scale_colour_discrete(name = "Patient outcome"
|
||||
, labels = c("Death", "Recovered"))+
|
||||
theme(axis.text.x = element_text(size = 13)
|
||||
, axis.text.y = element_text(size = 13
|
||||
, angle = 0
|
||||
, hjust = 1
|
||||
, vjust = 0)
|
||||
, axis.title.x = element_text(size = 13)
|
||||
, axis.title.y = element_text(size = 13)
|
||||
, plot.title = element_text(size = 15, hjust = 0.5)
|
||||
, strip.text.x = element_text(size = 13, colour = "black")
|
||||
, legend.title = element_text(color = "black", size = 13)
|
||||
, legend.text = element_text(size = 13)
|
||||
, legend.position = "right"
|
||||
, legend.direction = "vertical")
|
||||
p2
|
||||
|
||||
|
||||
#####################################################################
|
||||
# 50% CI: t1_data
|
||||
# days_from_symptons_onset_t1
|
||||
#####################################################################
|
||||
|
||||
#-----------
|
||||
# linear
|
||||
#-----------
|
||||
p3 = ggplot(lf_loess_t1, aes(x = days_from_symptons_onset_t1
|
||||
, y = value
|
||||
, colour = factor(outcomes))) +
|
||||
#geom_point() +
|
||||
geom_smooth(method = "loess", size = 1.5, na.rm = T, level = 0.50) +
|
||||
facet_wrap(~mediator, nrow = 2, scales = "free_y")+
|
||||
labs(title = "Days into t1: Linear scale and 50% CI"
|
||||
, x = "Days from symptom onset to t1"
|
||||
, y = "T1 Levels")+
|
||||
scale_x_continuous(breaks = my_xscale, limits = c(symp_days_min , symp_days_max))+
|
||||
scale_colour_discrete(name = "Patient outcome"
|
||||
, labels = c("Death", "Recovered"))+
|
||||
theme(axis.text.x = element_text(size = 13)
|
||||
, axis.text.y = element_text(size = 13
|
||||
, angle = 0
|
||||
, hjust = 1
|
||||
, vjust = 0)
|
||||
, axis.title.x = element_text(size = 13)
|
||||
, axis.title.y = element_text(size = 13)
|
||||
, plot.title = element_text(size = 15, hjust = 0.5)
|
||||
, strip.text.x = element_text(size = 13, colour = "black")
|
||||
, legend.title = element_text(color = "black", size = 13)
|
||||
, legend.text = element_text(size = 13)
|
||||
, legend.position = "right"
|
||||
, legend.direction = "vertical")
|
||||
|
||||
p3
|
||||
|
||||
#-----------
|
||||
# log
|
||||
#-----------
|
||||
p4 = ggplot(lf_loess_t1, aes(x = days_from_symptons_onset_t1
|
||||
, y = value
|
||||
, colour = factor(outcomes))) +
|
||||
scale_y_log10()+
|
||||
#geom_point() +
|
||||
geom_smooth(method = "loess", size = 1.5, na.rm = T, level = 0.5) +
|
||||
facet_wrap(~mediator, nrow = 2, scales = "free_y")+
|
||||
labs(title = "Days into t1: Log scale and 50% CI"
|
||||
, x = "Days from symptom onset to t1"
|
||||
, y = "T1 Levels (Log10)")+
|
||||
|
||||
scale_x_continuous(breaks = my_xscale, limits = c(symp_days_min , symp_days_max))+
|
||||
|
||||
scale_colour_discrete(name = "Patient outcome"
|
||||
, labels = c("Death", "Recovered")) +
|
||||
theme(axis.text.x = element_text(size = 13)
|
||||
, axis.text.y = element_text(size = 13
|
||||
, angle = 0
|
||||
, hjust = 1
|
||||
, vjust = 0)
|
||||
, axis.title.x = element_text(size = 13)
|
||||
, axis.title.y = element_text(size = 13)
|
||||
, plot.title = element_text(size = 15, hjust = 0.5)
|
||||
, strip.text.x = element_text(size = 13, colour = "black")
|
||||
, legend.title = element_text(color = "black", size = 13)
|
||||
, legend.text = element_text(size = 13)
|
||||
, legend.position = "right"
|
||||
, legend.direction = "vertical")
|
||||
p4
|
||||
dev.off()
|
||||
#####################################################################
|
||||
# 95% CI: Combined data *** only if required
|
||||
# days_from_symptons_onset_t1
|
||||
#####################################################################
|
||||
|
||||
#-----------
|
||||
# linear
|
||||
#-----------
|
||||
|
||||
#p1_all = ggplot(lf_loess, aes(x = days_from_symptons_onset_t1
|
||||
# , y = value
|
||||
# , colour = factor(outcomes))) +
|
||||
# #geom_point() +
|
||||
# geom_smooth(method = "loess", size = 1.5, na.rm = T) +
|
||||
# facet_wrap(~mediator, nrow = 2, scales = "free_y")+
|
||||
# labs(title = "Days into t1: linear scale and 95% CI"
|
||||
# , x = "Days from symptom onset to t1"
|
||||
# , y = "Combined Levels")+
|
||||
# scale_x_continuous(breaks = my_xscale, limits = c(symp_days_min , symp_days_max))+
|
||||
|
||||
# scale_colour_discrete(name = "Patient outcome"
|
||||
# , labels = c("Death", "Recovered"))
|
||||
|
||||
#p1_all
|
||||
|
||||
#-----------
|
||||
# log
|
||||
#-----------
|
||||
#p2_all = ggplot(lf_loess, aes(x = days_from_symptons_onset_t1
|
||||
# , y = value
|
||||
# , colour = factor(outcomes))) +
|
||||
# scale_y_log10()+
|
||||
# #geom_point() +
|
||||
# geom_smooth(method = "loess", size = 1.5, na.rm = T) +
|
||||
# facet_wrap(~mediator, nrow = 2, scales = "free_y")+
|
||||
# labs(title = "Days into t1: Log scale and 95% CI"
|
||||
# , x = "Days from symptom onset to t1"
|
||||
# , y = "Combined Levels (Log10)")+
|
||||
# scale_x_continuous(breaks = my_xscale, limits = c(symp_days_min , symp_days_max))+
|
||||
|
||||
# scale_colour_discrete(name = "Patient outcome"
|
||||
# , labels = c("Death", "Recovered"))
|
||||
#p2_all
|
||||
|
||||
|
||||
|
||||
10
read_data.R
10
read_data.R
|
|
@ -21,6 +21,10 @@ infile_covid = paste0(datadir,"/covid19_v3.csv")
|
|||
|
||||
infile_icu_wf = paste0(datadir, "/icu_covid_wf_v3.csv")
|
||||
infile_icu_lf = paste0(datadir, "/icu_covid_lf_v3.csv")
|
||||
|
||||
infile_icu_wf_loess = paste0(datadir, "/icu_covid_wf_v3_loess.csv")
|
||||
infile_icu_lf_loess = paste0(datadir, "/icu_covid_lf_v3_loess.csv")
|
||||
|
||||
#%%========================================================
|
||||
# read files
|
||||
my_data = read.csv(infile_covid, stringsAsFactors = F)
|
||||
|
|
@ -30,4 +34,10 @@ dim(wf_data)
|
|||
|
||||
lf_data = read.csv(infile_icu_lf, stringsAsFactors = F)
|
||||
dim(lf_data)
|
||||
|
||||
wf_data_loess = read.csv(infile_icu_wf_loess, stringsAsFactors = F)
|
||||
dim(wf_data_loess)
|
||||
|
||||
lf_data_loess = read.csv(infile_icu_lf_loess, stringsAsFactors = F)
|
||||
dim(lf_data_loess)
|
||||
#%%========================================================
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue