#!/usr/bin/Rscript   
getwd()
setwd("~/git/mosaic_2020/")
getwd()
############################################################
# TASK: Contingency table analysis i.e chisq and fishers
# data: clincial data of flu positive adult patients
# group: obesity

# Chisq test
#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
############################################################

#=============
# Input
#=============
source("data_extraction_formatting_clinical.R")
str(clinical_df_ics)

clinical_df_ics$sfluv = as.integer(clinical_df_ics$sfluv)
clinical_df_ics$h1n1v  = as.integer(clinical_df_ics$h1n1v)

#=============
# Output
#=============

########################################################################
# Fisher's test for clinical data b/w obesity groups
########################################################################
categorical_cols = c( "death"
                      #, "obesity"
                      #, "flustat"
                      , "sfluv"
                      , "h1n1v"
                      , "gender"
                      , "asthma"
                      #, "o2_sat_suppl" ---> not recoded!?
                      , "ethnicity"
                      , "smoking"
                      , "ia_cxr"
                      , "max_resp_score"
                      , "T1_resp_score"
                      , "com_noasthma"
                      , "T2_resp_score"
                      , "inresp_sev"
                      , "steroid"
                      , "age_bins"
                      , "o2_sat_bin"
                      , "onset_initial_bin"
                      , "t1_resp_recoded"
                      , "steroid_ics")

metadata_cols = c("mosaic", "obesity")

categ_df = clinical_df_ics[, c(metadata_cols, categorical_cols)]

my_categ_cols = colnames(categ_df)[!colnames(categ_df)%in%metadata_cols]

if ( length(my_categ_cols) == ncol(categ_df) - length(metadata_cols) ){
  cat("PASS: variables for chisq test successfully extracted")
}else{
  cat("FAIL: length mismatch when extracting variables for chisq")
  quit()
}

#========================================================
# Data: for fisher:
#2 levels: with OR
#3 levels: without OR
#========================================================
stats_df = subset(categ_df, select = -c(mosaic))

stats_df_copy = stats_df
int_vars <- lapply(stats_df_copy, class)%in%c("integer", "numeric")
int_vars
stats_df_copy[, int_vars] <- lapply(stats_df_copy[, int_vars], as.factor)
str(stats_df_copy)

#-----------
# 2 levels
#-----------
two_lev = lapply(stats_df_copy, nlevels) == 2
fisher_cols_df1  = names(two_lev)[two_lev == TRUE]
fisher_cols_df1

cat("\nTotal no. of cols:", ncol(stats_df_copy)
    , "\nNo. of vars with 2 factor levels:", length(fisher_cols_df1)
    , "\nThese are:\n"
    , fisher_cols_df1)

fisher_cols_df1 = fisher_cols_df1[!fisher_cols_df1%in%metadata_cols]
fisher_cols_df1

fisher_df1 = data.frame()

for (i in fisher_cols_df1){
  cat(i, "\n===============\n")
  df = data.frame(clinical_categ_params = NA, n_obs = NA, method = NA, test_statistic = NA, p = NA, ci_low = NA, ci_high = NA)
  my_param_val = (get(i, stats_df))
  tab = table(stats_df$obesity, my_param_val)
  print(tab)
  my_fisher_test = fisher.test(tab)
  print(my_fisher_test)
  
  # extracting results
  my_param_name = i
  my_n_obs = sum(tab)
  my_method = my_fisher_test$method
  my_test_statistic = my_fisher_test$estimate[[1]] # FIXME|
  my_pval = my_fisher_test$p.value
  my_ci_low = my_fisher_test$conf.int[[1]]
  my_ci_hi = my_fisher_test$conf.int[[2]]
  
  # assiging to loop df
  df$clinical_categ_params = my_param_name
  df$n_obs = my_n_obs
  df$method = my_method 
  df$test_statistic = my_test_statistic
  df$p = my_pval
  df$ci_low = my_ci_low
  df$ci_high = my_ci_hi
  
  print(df)
  fisher_df1 = rbind(fisher_df1, df) # FIXME, test_statistic col not getting created
}

#-----------
# >2 levels
#-----------
multi_lev = lapply(stats_df_copy, nlevels) > 2
fisher_cols_df2  = names(multi_lev)[multi_lev == TRUE]
fisher_cols_df2

cat("\nTotal no. of cols:", ncol(stats_df_copy)
    , "\nNo. of vars with >2 factor levels:", length(fisher_cols_df2)
    , "\nThese are:\n"
    , fisher_cols_df2)


fisher_cols_df2 = fisher_cols_df2[!fisher_cols_df2%in%metadata_cols]
fisher_cols_df2

fisher_df2 = data.frame()

for (i in fisher_cols_df2){
  cat(i, "\n===============\n")
  df = data.frame(clinical_categ_params = NA, n_obs = NA, method = NA, test_statistic = NA , p = NA)
  my_param_val = (get(i, stats_df))
  tab = table(stats_df$obesity, my_param_val)
  print(tab)
  my_fisher_test = fisher.test(tab)
  print(my_fisher_test)
  
  # extracting results
  my_param_name = i
  my_n_obs = sum(tab)
  my_method = my_fisher_test$method
  my_test_statistic = "NA i.e >2 categories"
  my_pval = my_fisher_test$p.value
  
  # assiging to loop df
  df$clinical_categ_params = my_param_name
  df$n_obs = my_n_obs
  df$method = my_method 
  df$test_statistic = my_test_statistic
  df$p = my_pval
  
  print(df)
  fisher_df2 = rbind(fisher_df2, df)
}
#=======================================================================
# quick tests
tab2 = table(stats_df$obesity, stats_df$smoking)
tab2
test2 = fisher_test(tab2); test2 # rstatix, gives n but no other estimates
test3 = fisher.test(stats_df$obesity, stats_df$com_noasthma); test3

########################################################################
#******************
# write output file
#******************