diff --git a/scripts/plotting/combining_two_df.R b/scripts/plotting/combining_two_df.R
new file mode 100644
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+++ b/scripts/plotting/combining_two_df.R
@@ -0,0 +1,421 @@
+#!/usr/bin/env Rscript       
+getwd()
+setwd("~/git/LSHTM_analysis/scripts/plotting/")
+getwd()
+
+#########################################################
+# TASK: To combine struct params and meta data for plotting
+# Input csv files:
+# 1) <gene>_all_params.csv
+# 2) <gene>_meta_data.csv
+
+# Output: 
+# 1) muts with opposite effects on stability
+# 2) large combined df including NAs for AF, OR,etc
+# 		Dim: same no. of rows as gene associated meta_data_with_AFandOR
+# 3) small combined df including NAs for AF, OR, etc.
+# 		Dim: same as mcsm data
+# 4) large combined df excluding NAs 
+# 		Dim: dim(#1) - no. of NAs(AF|OR) + 1
+# 5) small combined df excluding NAs
+# 		Dim: dim(#2) - no. of unique NAs - 1
+# This script is sourced from other .R scripts for plotting
+#########################################################
+
+##########################################################
+# 				Installing and loading required packages 			 
+##########################################################
+#source("Header_TT.R")
+require(data.table)
+require(arsenal)
+require(compare)
+library(tidyverse)
+
+source("plotting_data.R")
+
+# should return the following dfs, directories and variables
+# my_df
+# my_df_u
+# my_df_u_lig
+# dup_muts
+
+cat(paste0("Directories imported:"
+           , "\ndatadir:", datadir
+           , "\nindir:", indir
+           , "\noutdir:", outdir
+           , "\nplotdir:", plotdir))
+
+cat(paste0("Variables imported:"
+           , "\ndrug:", drug
+           , "\ngene:", gene
+           , "\ngene_match:", gene_match
+           , "\nLength of upos:", length(upos)
+           , "\nAngstrom symbol:", angstroms_symbol))       
+
+# clear excess variable
+rm(my_df, upos, dup_muts, my_df_u_lig)
+#========================================================
+
+#========================================================
+#%% variable assignment: input and output paths & filenames
+drug = "pyrazinamide"
+gene = "pncA"
+gene_match = paste0(gene,"_p.")
+cat(gene_match)
+
+#=============
+# directories
+#=============
+datadir = paste0("~/git/Data")
+indir = paste0(datadir, "/", drug, "/input")
+outdir = paste0("~/git/Data", "/", drug, "/output")
+plotdir = paste0("~/git/Data", "/", drug, "/output/plots")
+#===========
+# input
+#===========
+#in_file1: output of plotting_data.R
+
+
+# infile 2: gene associated meta data
+#in_filename_gene_metadata = paste0(tolower(gene),  "_meta_data_with_AFandOR.csv")
+in_filename_gene_metadata = paste0(tolower(gene),  "_metadata.csv")
+infile_gene_metadata = paste0(outdir, "/", in_filename_gene_metadata)
+cat(paste0("Input infile 2:", infile_gene_metadata))
+
+#===========
+# output
+#===========
+# mutations with opposite effects
+out_filename_opp_muts = paste0(tolower(gene), "_muts_opp_effects.csv")
+outfile_opp_muts = paste0(outdir, "/", out_filename_opp_muts)
+
+
+#%%===============================================================
+table(my_df_u$duet_outcome); sum(table(my_df_u$duet_outcome) )
+
+# spelling Correction 1: DUET incase American spelling needed!
+#my_df_u$duet_outcome[my_df_u$duet_outcome=="Stabilising"] <- "Stabilizing"
+#my_df_u$duet_outcome[my_df_u$duet_outcome=="Destabilising"] <- "Destabilizing"
+
+
+# spelling Correction 2: Ligand incase American spelling needed!
+table(my_df_u$ligand_outcome); sum(table(my_df_u$ligand_outcome) )
+#my_df_u$ligand_outcome[my_df_u$ligand_outcome=="Stabilising"] <- "Stabilizing"
+#my_df_u$ligand_outcome[my_df_u$ligand_outcome=="Destabilising"] <- "Destabilizing"
+
+
+# muts with opposing effects on protomer and ligand stability
+table(my_df_u$duet_outcome != my_df_u$ligand_outcome)
+changes = my_df_u[which(my_df_u$duet_outcome != my_df_u$ligand_outcome),]
+
+# sanity check: redundant, but uber cautious!
+dl_i = which(my_df_u$duet_outcome != my_df_u$ligand_outcome)
+ld_i = which(my_df_u$ligand_outcome != my_df_u$duet_outcome)
+
+cat("Identifying muts with opposite stability effects")
+if(nrow(changes) == (table(my_df_u$duet_outcome != my_df_u$ligand_outcome)[[2]]) & identical(dl_i,ld_i)) {
+  cat("PASS: muts with opposite effects on stability and affinity correctly identified"
+        , "\nNo. of such muts: ", nrow(changes))
+}else {
+  cat("FAIL: unsuccessful in extracting muts with changed stability effects")
+}
+
+#***************************
+# write file: changed muts
+write.csv(changes, outfile_opp_muts)
+
+cat("Finished writing file for muts with opp effects:"
+    , "\nFilename: ", outfile_opp_muts
+    , "\nDim:", dim(changes))
+
+# clear variables
+rm(out_filename_opp_muts, outfile_opp_muts)
+rm(changes, dl_i, ld_i)
+
+# count na in each column
+na_count = sapply(my_df_u, function(y) sum(length(which(is.na(y))))); na_count
+df_ncols = ncol(my_df_u)
+
+###########################
+# 2: Read file: <gene>_meta data.csv
+###########################
+cat("Reading meta data file:", infile_gene_metadata)
+
+gene_metadata <- read.csv(infile_gene_metadata
+                      , stringsAsFactors = F
+                      , header = T)
+cat("Dim:", dim(gene_metadata))
+
+#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+# FIXME: remove
+# counting NAs in AF, OR cols: 
+if (identical(sum(is.na(my_df_u$or_mychisq))
+              , sum(is.na(my_df_u$pval_fisher))
+              , sum(is.na(my_df_u$af)))){
+  cat("\nPASS: NA count match for OR, pvalue and AF\n")
+  na_count = sum(is.na(my_df_u$af))
+  cat("\nNo. of NAs: ", sum(is.na(my_df_u$or_mychisq)))
+} else{
+  cat("\nFAIL: NA count mismatch"
+      , "\nNA in OR: ", sum(is.na(my_df_u$or_mychisq))
+      , "\nNA in pvalue: ", sum(is.na(my_df_u$pval_fisher))
+      , "\nNA in AF:", sum(is.na(my_df_u$af)))
+}
+
+
+if (identical(sum(is.na(my_df_u$or_kin))
+              , sum(is.na(my_df_u$pwald_kin))
+              , sum(is.na(my_df_u$af_kin)))){
+  cat("\nPASS: NA count match for OR, pvalue and AF\n from Kinship matrix calculations")
+  na_count = sum(is.na(my_df_u$af_kin))
+  cat("\nNo. of NAs: ", sum(is.na(my_df_u$or_kin)))
+} else{
+  cat("\nFAIL: NA count mismatch"
+      , "\nNA in OR: ", sum(is.na(my_df_u$or_kin))
+      , "\nNA in pvalue: ", sum(is.na(my_df_u$pwald_kin))
+      , "\nNA in AF:", sum(is.na(my_df_u$af_kin)))
+}
+
+#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+# clear variables
+rm(in_filename_gene_metadata, infile_gene_metadata)
+
+str(gene_metadata)
+
+# sort by position (same as my_df)
+# earlier it was mutationinformation
+#head(gene_metadata$mutationinformation)
+#gene_metadata = gene_metadata[order(gene_metadata$mutationinformation),]
+##head(gene_metadata$mutationinformation)
+
+head(gene_metadata$position)
+gene_metadata = gene_metadata[order(gene_metadata$position),]
+head(gene_metadata$position)
+
+###########################
+# Merge 1: two dfs with NA
+# merged_df2
+###########################
+head(my_df_u$mutationinformation)
+head(gene_metadata$mutationinformation)
+
+# Find common columns b/w two df
+# FIXME: mutation has empty cell for some muts
+merging_cols = intersect(colnames(my_df_u), colnames(gene_metadata))
+
+cat(paste0("Merging dfs with NAs: big df (1-many relationship b/w id & mut)"
+           , "\nNo. of merging cols:", length(merging_cols)
+           , "\nMerging columns identified:"))
+print(merging_cols)
+
+# important checks!
+table(nchar(my_df_u$mutationinformation))
+table(nchar(my_df_u$wild_type))
+table(nchar(my_df_u$mutant_type))
+table(nchar(my_df_u$position))
+
+#=============
+# merged_df2: gene_metadata + my_df
+#==============
+# all.y  because x might contain non-structural positions!
+merged_df2 = merge(x = gene_metadata
+                  , y = my_df_u
+                  , by = merging_cols
+                  , all.y = T)
+
+cat("Dim of merged_df2: ", dim(merged_df2))
+head(merged_df2$position)
+
+#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+# FIXME: count how many unique muts have entries in meta data
+# should PASS 
+cat("Checking nrows in merged_df2")
+if(nrow(gene_metadata) == nrow(merged_df2)){
+  cat("PASS: nrow(merged_df2) = nrow (gene associated gene_metadata)"
+      ,"\nExpected no. of rows: ",nrow(gene_metadata) 
+      ,"\nGot no. of rows: ", nrow(merged_df2))
+} else{
+  cat("FAIL: nrow(merged_df2)!= nrow(gene associated gene_metadata)"
+      , "\nExpected no. of rows after merge: ", nrow(gene_metadata)
+      , "\nGot no. of rows: ", nrow(merged_df2)
+      , "\nFinding discrepancy")
+  merged_muts_u = unique(merged_df2$mutationinformation)
+  meta_muts_u = unique(gene_metadata$mutationinformation)
+    # find the index where it differs
+  unique(meta_muts_u[! meta_muts_u %in% merged_muts_u])
+}
+
+#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+# sort by position
+head(merged_df2$position)
+merged_df2 = merged_df2[order(merged_df2$position),]
+head(merged_df2$position)
+
+merged_df2v3 = merge(x = gene_metadata
+                   , y = my_df_u
+                   , by = merging_cols
+                   , all = T) 
+
+merged_df2v2 = merge(x = gene_metadata
+                     , y = my_df_u
+                     , by = merging_cols
+                     , all.x = T) 
+#!=!=!=!=!=!=!=!
+#identical(merged_df2, merged_df2v2)
+
+nrow(merged_df2[merged_df2$position==186,])
+#!=!=!=!=!=!=!=!
+
+# should  be False
+identical(merged_df2, merged_df2v2)
+table(merged_df2$position%in%merged_df2v2$position)
+
+#!!!!!!!!!!! check why these differ
+
+#########
+# merge 3b (merged_df3):remove duplicated mutations
+cat("Merging dfs without NAs: small df (removing muts with no AF|OR associated)"
+    ,"\nCannot trust lineage info from this"
+    ,"\nlinking col: mutationinforamtion"
+    ,"\nfilename: merged_df3")
+
+#==#=#=#=#=#=#
+# Cannot trust lineage, country from this df as the same mutation
+# can have many different lineages
+# but this should be good for the numerical corr plots
+#=#=#=#=#=#=#=
+merged_df3 = merged_df2[!duplicated(merged_df2$mutationinformation),] 
+head(merged_df3$position); tail(merged_df3$position) # should be sorted
+
+# sanity check
+cat("Checking nrows in merged_df3")
+if(nrow(my_df_u) == nrow(merged_df3)){
+  cat("PASS: No. of rows match with my_df"
+      ,"\nExpected no. of rows: ", nrow(my_df_u)
+      ,"\nGot no. of rows: ", nrow(merged_df3))
+} else {
+  cat("FAIL: No. of rows mismatch"
+      , "\nNo. of rows my_df: ", nrow(my_df_u)
+      , "\nNo. of rows merged_df3: ", nrow(merged_df3))
+  quit()
+}
+
+# counting NAs in AF, OR cols in merged_df3
+# this is because mcsm has no AF, OR cols,
+# so you cannot count NAs
+if (identical(sum(is.na(merged_df3$or_kin))
+              , sum(is.na(merged_df3$pwald_kin))
+              , sum(is.na(merged_df3$af_kin)))){
+  cat("PASS: NA count match for OR, pvalue and AF\n")
+  na_count_df3 = sum(is.na(merged_df3$af_kin))
+  cat("No. of NAs: ", sum(is.na(merged_df3$or_kin)))
+} else{
+  cat("FAIL: NA count mismatch"
+      , "\nNA in OR: ", sum(is.na(merged_df3$or_kin))
+      , "\nNA in pvalue: ", sum(is.na(merged_df3$pwald_kin))
+      , "\nNA in AF:", sum(is.na(merged_df3$af_kin)))
+}
+
+# check if the same or and afs are missing for 
+if ( identical( which(is.na(merged_df2$or_mychisq)), which(is.na(merged_df2$or_kin)))
+  && identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin)))
+  && identical( which(is.na(merged_df2$pval_fisher)), which(is.na(merged_df2$pwald_kin))) ){
+  cat('PASS: Indices match for mychisq and kin ors missing values')
+} else{
+  cat('Index mismatch: mychisq and kin ors missing indices match')
+  quit()
+}
+
+###########################
+# 4: merging two dfs: without NA
+###########################
+#########
+# merge 4a (merged_df2_comp): same as merge 1 but excluding NA
+#########
+cat("Merging dfs without any NAs: big df (1-many relationship b/w id & mut)"
+    ,"\nlinking col: Mutationinforamtion"
+    ,"\nfilename: merged_df2_comp")
+
+if ( identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin))) ){
+  print('mychisq and kin ors missing indices match. Procedding with omitting NAs')
+  na_count_df2 = sum(is.na(merged_df2$af))
+  merged_df2_comp = merged_df2[!is.na(merged_df2$af),] 
+  # sanity check: no +-1 gymnastics
+  cat("Checking nrows in merged_df2_comp")
+  if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count_df2)){
+    cat("\nPASS: No. of rows match"
+        ,"\nDim of merged_df2_comp: "
+        ,"\nExpected no. of rows: ", nrow(merged_df2) - na_count_df2
+        , "\nNo. of rows: ", nrow(merged_df2_comp)
+        , "\nNo. of cols: ", ncol(merged_df2_comp))
+  }else{
+    cat("FAIL: No. of rows mismatch"
+        ,"\nExpected no. of rows: ", nrow(merged_df2) - na_count_df2
+        ,"\nGot no. of rows: ", nrow(merged_df2_comp))
+  }
+}else{
+  print('Index mismatch for mychisq and kin ors. Aborting NA ommission')
+}
+
+#########
+# merge 4b (merged_df3_comp): remove duplicate mutation information
+#########
+if ( identical( which(is.na(merged_df3$af)), which(is.na(merged_df3$af_kin))) ){
+  print('mychisq and kin ors missing indices match. Procedding with omitting NAs')
+  na_count_df3 = sum(is.na(merged_df3$af))
+  #merged_df3_comp = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),] # a way
+  merged_df3_comp = merged_df3[!is.na(merged_df3$af),] # another way
+  cat("Checking nrows in merged_df3_comp")
+  if(nrow(merged_df3_comp) == (nrow(merged_df3) - na_count_df3)){
+    cat("\nPASS: No. of rows match"
+        ,"\nDim of merged_df3_comp: "
+        ,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
+        , "\nNo. of rows: ", nrow(merged_df3_comp)
+        , "\nNo. of cols: ", ncol(merged_df3_comp))
+    }else{
+    cat("FAIL: No. of rows mismatch"
+        ,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
+        ,"\nGot no. of rows: ", nrow(merged_df3_comp))
+   }
+} else{
+  print('Index mismatch for mychisq and kin ors. Aborting NA ommission')
+} 
+  
+# alternate way of deriving merged_df3_comp
+foo = merged_df3[!is.na(merged_df3$af),]
+bar = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),]
+# compare dfs: foo and merged_df3_com
+all.equal(foo, bar)
+#summary(comparedf(foo, bar))
+
+#=============== end of combining df
+#==============================================================
+#################
+# OPTIONAL: write ALL 4 output files 
+#################
+#outvars = c("merged_df2"
+#             , "merged_df3"
+#             , "merged_df2_comp"
+#             , "merged_df3_comp")
+
+#cat("Writing output files: "
+#    , "\nPath:", outdir)
+
+#for (i in outvars){
+#  out_filename = paste0(i, ".csv")
+#  outfile = paste0(outdir, "/", out_filename)
+#  cat("Writing output file:"
+#      ,"\nFilename: ", out_filename,"\n")
+#  write.csv(get(i), outfile, row.names = FALSE)
+#  cat("Finished writing: ", outfile
+#      , "\nNo. of rows: ", nrow(get(i))
+#      , "\nNo. of cols: ", ncol(get(i)), "\n")
+#}
+
+#*************************
+# clear variables
+rm(foo, bar, gene_metadata
+   , merged_df2v2, merged_df2v3)
+
+#============================= end of script
+