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reran to output merged_df3 and merged_df2 csvs from count_vars.ML

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Tanushree Tunstall 2022-05-29 03:10:51 +01:00
parent f41cd0082e
commit 650d357afc
1 changed files with 33 additions and 33 deletions
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66
scripts/count_vars_ML.R
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@ -4,7 +4,7 @@
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/katg.R")
source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/rpob.R")
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
@ -80,35 +80,35 @@ write.csv(merged_df2, outfile_merged_df2)
###################################################
###################################################
###################################################
source("~/git/LSHTM_analysis/config/alr.R")
source("~/git/LSHTM_analysis/config/embb.R")
source("~/git/LSHTM_analysis/config/gid.R")
source("~/git/LSHTM_analysis/config/katg.R")
source("~/git/LSHTM_analysis/config/pnca.R")
source("~/git/LSHTM_analysis/config/rpob.R")
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)
# mutationinformation
length(unique((df3$mutationinformation)))
#dm _om
table(df3$mutation_info)
table(df3$mutation_info_labels)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)
# test_set
na_count <-sapply(df3, function(y) sum(length(which(is.na(y)))))
na_count[drug]
# training set
table(df3[drug])
# drtype: MDR and XDR
#table(df3$drtype) orig i.e. incorrect ones!
table(df3$drtype_mode_labels)
#
# source("~/git/LSHTM_analysis/config/alr.R")
# source("~/git/LSHTM_analysis/config/embb.R")
# source("~/git/LSHTM_analysis/config/gid.R")
# source("~/git/LSHTM_analysis/config/katg.R")
# source("~/git/LSHTM_analysis/config/pnca.R")
# source("~/git/LSHTM_analysis/config/rpob.R")
#
# df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
# df3 = read.csv(df3_filename)
#
# # mutationinformation
# length(unique((df3$mutationinformation)))
#
# #dm _om
# table(df3$mutation_info)
# table(df3$mutation_info_labels)
# table(df3$mutation_info_orig)
# table(df3$mutation_info_labels_orig)
#
# # test_set
# na_count <-sapply(df3, function(y) sum(length(which(is.na(y)))))
# na_count[drug]
#
# # training set
# table(df3[drug])
#
# # drtype: MDR and XDR
# #table(df3$drtype) orig i.e. incorrect ones!
# table(df3$drtype_mode_labels)
#
#