114 lines
3.1 KiB
R
114 lines
3.1 KiB
R
# count numbers for ML
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#source("~/git/LSHTM_analysis/config/alr.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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gene
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gene_match
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nrow(merged_df3)
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##############################################
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#=============
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# mutation_info: revised labels
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#==============
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table(merged_df3$mutation_info)
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sum(table(merged_df3$mutation_info))
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table(merged_df3$mutation_info_orig)
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##############################################
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#=============
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# <drug>, dst_mode: revised labels
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#==============
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table(merged_df3$dst) # orig
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sum(table(merged_df3$dst))
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table(merged_df3$dst_mode)
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#table(merged_df3[dr_muts_col])
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sum(table(merged_df3$drtype_mode))
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##############################################
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#=============
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# drtype: revised labels
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#==============
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table(merged_df3$drtype) #orig
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table(merged_df3$drtype_mode)
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# mapping 2.1: numeric
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# drtype_map = {'XDR': 5
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# , 'Pre-XDR': 4
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# , 'MDR': 3
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# , 'Pre-MDR': 2
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# , 'Other': 1
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# , 'Sensitive': 0}
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# create a labels col that is mapped based on drtype_mode
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merged_df3$drtype_mode_labels = merged_df3$drtype_mode
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merged_df3$drtype_mode_labels = as.factor(merged_df3$drtype_mode)
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levels(merged_df3$drtype_mode_labels)
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levels(merged_df3$drtype_mode_labels) <- c('Sensitive', 'Other'
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, 'Pre-MDR', 'MDR'
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, 'Pre-XDR', 'XDR')
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levels(merged_df3$drtype_mode_labels)
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# check
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#table(merged_df3$drtype)
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table(merged_df3$drtype_mode)
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table(merged_df3$drtype_mode_labels)
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sum(table(merged_df3$drtype_mode_labels))
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##############################################
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# lineage
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table(merged_df3$lineage)
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sum(table(merged_df3$lineage_labels))
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# write file
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outfile_merged_df3 = paste0(outdir, '/', tolower(gene), '_merged_df3.csv')
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outfile_merged_df3
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write.csv(merged_df3, outfile_merged_df3)
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outfile_merged_df2 = paste0(outdir, '/', tolower(gene), '_merged_df2.csv')
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outfile_merged_df2
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write.csv(merged_df2, outfile_merged_df2)
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###################################################
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###################################################
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###################################################
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#
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# source("~/git/LSHTM_analysis/config/alr.R")
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# source("~/git/LSHTM_analysis/config/embb.R")
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# source("~/git/LSHTM_analysis/config/gid.R")
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# source("~/git/LSHTM_analysis/config/katg.R")
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# source("~/git/LSHTM_analysis/config/pnca.R")
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# source("~/git/LSHTM_analysis/config/rpob.R")
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#
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# df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
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# df3 = read.csv(df3_filename)
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#
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# # mutationinformation
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# length(unique((df3$mutationinformation)))
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#
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# #dm _om
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# table(df3$mutation_info)
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# table(df3$mutation_info_labels)
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# table(df3$mutation_info_orig)
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# table(df3$mutation_info_labels_orig)
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#
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# # test_set
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# na_count <-sapply(df3, function(y) sum(length(which(is.na(y)))))
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# na_count[drug]
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#
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# # training set
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# table(df3[drug])
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#
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# # drtype: MDR and XDR
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# #table(df3$drtype) orig i.e. incorrect ones!
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# table(df3$drtype_mode_labels)
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#
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#
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