separated fisher test only and renamed logistic_outcome col
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6e999898df
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2 changed files with 98 additions and 130 deletions
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@ -3,20 +3,22 @@ getwd()
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setwd("~/git/mosaic_2020/")
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getwd()
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############################################################
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# TASK: unpaired (time) analysis of clinical data
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# TASK: Contingency table analysis i.e chisq and fishers
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# data: clincial data of flu positive adult patients
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# group: obesity
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# Chisq test
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#https://www.google.com/search?q=chisq+test+on+long+format+data+in+R+using+group+by&source=lmns&bih=828&biw=1280&client=firefox-b-d&hl=en-GB&sa=X&ved=2ahUKEwjItpL7xI7tAhUGTBoKHXlSBa8Q_AUoAHoECAEQAA
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############################################################
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#my_sample_type = "npa"
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#=============
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# Input
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#=============
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source("data_extraction_formatting_clinical.R")
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str(clinical_df)
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str(clinical_df_ics)
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clinical_df$sfluv = as.integer(clinical_df$sfluv)
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clinical_df$h1n1v = as.integer(clinical_df$h1n1v)
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clinical_df_ics$sfluv = as.integer(clinical_df_ics$sfluv)
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clinical_df_ics$h1n1v = as.integer(clinical_df_ics$h1n1v)
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#=============
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# Output
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@ -44,58 +46,28 @@ categorical_cols = c( "death"
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, "com_noasthma"
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, "T2_resp_score"
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, "inresp_sev"
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, "steroid")
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, "steroid"
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, "age_bins"
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, "o2_sat_bin"
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, "onset_initial_bin"
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, "t1_resp_recoded"
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, "steroid_ics")
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metadata_cols = c("mosaic", "obesity")
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categ_df = clinical_df[, c(metadata_cols, categorical_cols)]
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categ_df = clinical_df_ics[, c(metadata_cols, categorical_cols)]
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# quick test
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tab = table(categ_df$obesity, categ_df$death)
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tab
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chisq.test(tab)
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pivot_cols = metadata_cols
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#pivot_cols = metadata_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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expected_rows_categ_lf = nrow(categ_df) * ( length(categ_df) - length(pivot_cols) ); expected_rows_categ_lf
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colnames(categ_df)
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keycol <- "clinical_categ_params"
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valuecol <- "value"
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gathercols <- c("death"
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#, "flustat"
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, "sfluv", "h1n1v"
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, "gender", "asthma"
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#, "o2_sat_suppl"
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, "ethnicity"
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, "smoking", "ia_cxr", "max_resp_score", "T1_resp_score"
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, "com_noasthma", "T2_resp_score", "inresp_sev", "steroid")
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categ_lf = gather_(categ_df, keycol, valuecol, gathercols)
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#categ_lf = gather(categ_df, clinical_categ_params, value, death:T2_resp_score, factor_key = F)
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if( nrow(categ_lf) == expected_rows_categ_lf){
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cat("PASS: long format data created successfully"
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, "\nnrow:", nrow(categ_lf)
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, "\nncol:", ncol(categ_lf))
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}
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#=============================================
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# Chisq test: clinical categorical vars
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#https://www.google.com/search?q=chisq+test+on+long+format+data+in+R+using+group+by&source=lmns&bih=828&biw=1280&client=firefox-b-d&hl=en-GB&sa=X&ved=2ahUKEwjItpL7xI7tAhUGTBoKHXlSBa8Q_AUoAHoECAEQAA
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#=============================================
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#=============================================
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# wf data
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#=============================================
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#========================================================
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# Data: for chisq and fisher
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#========================================================
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stats_df = subset(categ_df, select = -c(mosaic))
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drop_cols = c("mosaic", "obesity")
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my_categ_cols = colnames(categ_df)[!colnames(categ_df)%in%drop_cols]
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my_categ_cols = colnames(categ_df)[!colnames(categ_df)%in%metadata_cols]
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if ( length(my_categ_cols) == ncol(categ_df) - length(drop_cols) ){
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cat("PASS: variables for chisq test successfully extracted")
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@ -103,16 +75,19 @@ if ( length(my_categ_cols) == ncol(categ_df) - length(drop_cols) ){
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cat("FAIL: length mismatch when extracting variables for chisq")
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quit()
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}
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# quick test
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tab = table(stats_df$obesity, stats_df$smoking)
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tab
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test1 = chisq.test(tab)
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########################################################################
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#--------------------
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# chisq test
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#--------------------
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chisq_df = data.frame()
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for (i in my_categ_cols){
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#print(i)
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df = data.frame(clinical_categ_params = NA, n_obs = NA, method = NA, test_statistic = NA, p = NA)
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df = data.frame(clinical_categ_params = NA
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, n_obs = NA
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, method = NA
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#, test_statistic = NA
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, p = NA)
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my_param_val = (get(i, stats_df))
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tab = table(stats_df$obesity, my_param_val)
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print(tab)
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@ -123,20 +98,21 @@ for (i in my_categ_cols){
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my_param_name = i
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my_n_obs = sum(my_chi_test$observed)
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my_method = my_chi_test$method
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my_test_statistic = my_chi_test$statistic[[1]]
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#my_test_statistic = my_chi_test$statistic[[1]]
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my_pval = my_chi_test$p.value
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# assiging to loop df
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df$clinical_categ_params = my_param_name
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df$n_obs = my_n_obs
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df$method = my_method
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df$test_statistic = my_test_statistic
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#df$test_statistic = my_test_statistic
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df$p = my_pval
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print(df)
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chisq_df = rbind(chisq_df, df)
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}
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# formatting
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chisq_df$p_format = format.pval(chisq_df$p)
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chisq_df$p_signif = chisq_df$p
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@ -147,34 +123,29 @@ chisq_df = dplyr::mutate(chisq_df, p_signif = case_when(p_signif == 0.05 ~ "."
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, p_signif <0.05 ~ '*'
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, p_signif == 0.1 ~ '.'
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, TRUE ~ 'ns'))
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#=============================================
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# Fishers test
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#=============================================
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# quick test
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tab2 = table(stats_df$obesity, stats_df$smoking)
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tab2
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#test2 = fisher_test(tab2); test2
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test3 = fisher.test(stats_df$obesity, stats_df$com_noasthma); test3
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tab = table(stats_df$obesity, stats_df$smoking); tab
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test1 = chisq.test(tab); test1
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test2 = chisq_test(tab); test2 #rstatix
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sum(tab)
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stats_df_copy = stats_df
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int_vars <- lapply(stats_df_copy, class)%in%c("integer", "numeric")
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int_vars
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stats_df_copy[, int_vars] <- lapply(stats_df_copy[, int_vars], as.factor)
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str(stats_df_copy)
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#================================================================================
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#--------------------
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# Fishers test: without any OR
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# see standalone script for this!
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#--------------------
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two_lev = lapply(stats_df_copy, nlevels) == 2
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fisher_cols_df1 = names(two_lev)[two_lev == TRUE]
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fisher_cols_df1
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fisher_df = data.frame()
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drop_cols = c("mosaic", "obesity")
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fisher_cols_df1 = fisher_cols_df1[!fisher_cols_df1%in%drop_cols]
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fisher_cols_df1
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fisher_df1 = data.frame()
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for (i in fisher_cols_df1){
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for (i in my_categ_cols){
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cat(i, "\n===============\n")
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df = data.frame(clinical_categ_params = NA, n_obs = NA, method = NA, test_statistic = NA, p = NA, ci_low = NA, ci_high = NA)
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df = data.frame(clinical_categ_params = NA
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, n_obs = NA
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, method = NA
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#, test_statistic = NA
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, p = NA)
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my_param_val = (get(i, stats_df))
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tab = table(stats_df$obesity, my_param_val)
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print(tab)
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@ -185,74 +156,62 @@ for (i in fisher_cols_df1){
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my_param_name = i
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my_n_obs = sum(tab)
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my_method = my_fisher_test$method
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my_test_statistic = my_fisher_test$statistic[[1]] # FIXME|
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#my_test_statistic = my_fisher_test$estimate[[1]]
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my_pval = my_fisher_test$p.value
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my_ci_low = my_fisher_test$conf.int[[1]]
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my_ci_hi = my_fisher_test$conf.int[[2]]
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# assiging to loop df
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df$clinical_categ_params = my_param_name
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df$n_obs = my_n_obs
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df$method = my_method
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df$test_statistic = my_test_statistic
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#df$test_statistic = my_test_statistic
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df$p = my_pval
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df$ci_low = my_ci_low
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df$ci_high = my_ci_hi
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print(df)
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fisher_df1 = rbind(fisher_df1, df)
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fisher_df = rbind(fisher_df, df)
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}
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#=============
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# formatting
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fisher_df$p_format = format.pval(fisher_df$p)
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fisher_df$p_signif = fisher_df$p
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three_lev = lapply(stats_df_copy, nlevels) == 3
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fisher_cols_df2 = names(three_lev)[three_lev == TRUE]
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fisher_cols_df2
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fisher_df = dplyr::mutate(fisher_df, p_signif = case_when(p_signif == 0.05 ~ "."
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, p_signif <=0.0001 ~ '****'
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, p_signif <=0.001 ~ '***'
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, p_signif <=0.01 ~ '**'
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, p_signif <0.05 ~ '*'
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, p_signif == 0.1 ~ '.'
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, TRUE ~ 'ns'))
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#drop_cols = c("mosaic", "obesity")
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#fisher_cols_df2 = fisher_cols_df2[!fisher_cols_df2%in%drop_cols]
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#fisher_cols_df2
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# quick test
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tab_f = table(stats_df$obesity, stats_df$smoking); tab_f
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test1_f = fisher.test(tab); test1_f
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test2_f = fisher_test(tab); test2_f #rstatix
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sum(tab_f)
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test1_f$estimate
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fisher_df2 = data.frame()
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for (i in fisher_cols_df2){
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cat(i, "\n===============\n")
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df = data.frame(clinical_categ_params = NA, n_obs = NA, method = NA, test_statistic = NA , p = NA)
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my_param_val = (get(i, stats_df))
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tab = table(stats_df$obesity, my_param_val)
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print(tab)
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my_fisher_test = fisher.test(tab)
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print(my_fisher_test)
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#================================================================================
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#--------------------------------
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# Combining chisq and fishers df
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#---------------------------------
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if ( all(colnames(chisq_df) == colnames(fisher_df)) && all(dim(chisq_df) == dim(fisher_df)) ){
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cat("Colnames AND dim match for both chisq and fisher df"
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, "\nNo. of rows:", nrow(chisq_df)
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, "\nNo. of cols:", ncol(chisq_df)
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, "\nProceeding to rbind")
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# extracting results
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my_param_name = i
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my_n_obs = sum(tab)
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my_method = my_fisher_test$method
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my_test_statistic = ">2 categories"
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my_pval = my_fisher_test$p.value
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comb_stats_categ_df = rbind(chisq_df, fisher_df)
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# assiging to loop df
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df$clinical_categ_params = my_param_name
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df$n_obs = my_n_obs
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df$method = my_method
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df$test_statistic = my_test_statistic
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df$p = my_pval
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print(df)
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fisher_df2 = rbind(fisher_df2, df)
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cat("\nCombined df dim:")
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print(dim(comb_stats_categ_df))
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}
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# sort df according to p_signif AND clinical_categ_params
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comb_stats_categ_df_f = comb_stats_categ_df[order(comb_stats_categ_df$p_signif
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, comb_stats_categ_df$clinical_categ_params),]
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########################################################################
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#******************
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# write output file
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#******************
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cat("Chisq and fishers test results in:", outfile_clin_categ)
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#write.csv(stats_categ_df_f, outfile_clin_categ, row.names = FALSE)
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write.csv(comb_stats_categ_df_f, outfile_clin_categ, row.names = FALSE)
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