generated ggpairs plots finally
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11 changed files with 657 additions and 939 deletions
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@ -109,137 +109,137 @@ merged_df3 = all_plot_dfs[[2]]
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####################################################################
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# Data for logoplots
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####################################################################
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source(paste0(plot_script_path, "logo_data_msa.R"))
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s1 = c("\nSuccessfully sourced logo_data_msa.R")
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cat(s1)
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####################################################################
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# Data for DM OM Plots: WF and LF dfs
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# My function: dm_om_wf_lf_data()
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# location: scripts/functions/dm_om_data.R
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#source("other_plots_data.R")
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####################################################################
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#source(paste0(plot_script_path, "dm_om_data.R")) # calling the function directly instead
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geneL_normal = c("pnca")
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geneL_na = c("gid", "rpob")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
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wf_duet = all_dm_om_df[['wf_duet']]
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lf_duet = all_dm_om_df[['lf_duet']]
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wf_mcsm_lig = all_dm_om_df[['wf_mcsm_lig']]
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lf_mcsm_lig = all_dm_om_df[['lf_mcsm_lig']]
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wf_foldx = all_dm_om_df[['wf_foldx']]
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lf_foldx = all_dm_om_df[['lf_foldx']]
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wf_deepddg = all_dm_om_df[['wf_deepddg']]
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lf_deepddg = all_dm_om_df[['lf_deepddg']]
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wf_dynamut2 = all_dm_om_df[['wf_dynamut2']]
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lf_dynamut2 = all_dm_om_df[['lf_dynamut2']]
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wf_consurf = all_dm_om_df[['wf_consurf']]
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lf_consurf = all_dm_om_df[['lf_consurf']]
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wf_snap2 = all_dm_om_df[['wf_snap2']]
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lf_snap2 = all_dm_om_df[['lf_snap2']]
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wf_provean = all_dm_om_df[['wf_provean']]
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lf_provean = all_dm_om_df[['lf_provean']]
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# NEW
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wf_dist_gen = all_dm_om_df[['wf_dist_gen']]
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lf_dist_gen = all_dm_om_df[['lf_dist_gen']]
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if (tolower(gene)%in%geneL_na){
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wf_mcsm_na = all_dm_om_df[['wf_mcsm_na']]
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lf_mcsm_na = all_dm_om_df[['lf_mcsm_na']]
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}
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if (tolower(gene)%in%geneL_ppi2){
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wf_mcsm_ppi2 = all_dm_om_df[['wf_mcsm_ppi2']]
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lf_mcsm_ppi2 = all_dm_om_df[['lf_mcsm_ppi2']]
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}
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s2 = c("\nSuccessfully sourced other_plots_data.R")
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cat(s2)
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####################################################################
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# Data for Lineage barplots: WF and LF dfs
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# My function: lineage_plot_data()
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# location: scripts/functions/lineage_plot_data.R
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####################################################################
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#source(paste0(plot_script_path, "lineage_data.R"))
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# converted to a function. Moved lineage_data.R to redundant/
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lineage_dfL = lineage_plot_data(merged_df2
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, lineage_column_name = "lineage"
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, remove_empty_lineage = F
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, lineage_label_col_name = "lineage_labels"
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, id_colname = "id"
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, snp_colname = "mutationinformation"
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)
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lin_wf = lineage_dfL[['lin_wf']]
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lin_lf = lineage_dfL[['lin_lf']]
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s3 = c("\nSuccessfully sourced lineage_data.R")
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cat(s3)
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####################################################################
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# Data for corr plots:
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# My function: corr_data_extract()
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# location: scripts/functions/corr_plot_data.R
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####################################################################
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# make sure the above script works because merged_df2_combined is needed
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merged_df3 = as.data.frame(merged_df3)
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corr_df_m3_f = corr_data_extract(merged_df3
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, gene = gene
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, drug = drug
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, extract_scaled_cols = F)
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head(corr_df_m3_f)
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# corr_df_m2_f = corr_data_extract(merged_df2
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#
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# source(paste0(plot_script_path, "logo_data_msa.R"))
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# s1 = c("\nSuccessfully sourced logo_data_msa.R")
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# cat(s1)
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#
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# ####################################################################
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# # Data for DM OM Plots: WF and LF dfs
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# # My function: dm_om_wf_lf_data()
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# # location: scripts/functions/dm_om_data.R
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# #source("other_plots_data.R")
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# ####################################################################
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#
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# #source(paste0(plot_script_path, "dm_om_data.R")) # calling the function directly instead
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# geneL_normal = c("pnca")
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# geneL_na = c("gid", "rpob")
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# geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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#
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# all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
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#
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# wf_duet = all_dm_om_df[['wf_duet']]
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# lf_duet = all_dm_om_df[['lf_duet']]
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#
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# wf_mcsm_lig = all_dm_om_df[['wf_mcsm_lig']]
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# lf_mcsm_lig = all_dm_om_df[['lf_mcsm_lig']]
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#
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# wf_foldx = all_dm_om_df[['wf_foldx']]
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# lf_foldx = all_dm_om_df[['lf_foldx']]
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#
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# wf_deepddg = all_dm_om_df[['wf_deepddg']]
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# lf_deepddg = all_dm_om_df[['lf_deepddg']]
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#
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# wf_dynamut2 = all_dm_om_df[['wf_dynamut2']]
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# lf_dynamut2 = all_dm_om_df[['lf_dynamut2']]
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#
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# wf_consurf = all_dm_om_df[['wf_consurf']]
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# lf_consurf = all_dm_om_df[['lf_consurf']]
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#
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# wf_snap2 = all_dm_om_df[['wf_snap2']]
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# lf_snap2 = all_dm_om_df[['lf_snap2']]
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#
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# wf_provean = all_dm_om_df[['wf_provean']]
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# lf_provean = all_dm_om_df[['lf_provean']]
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#
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# # NEW
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# wf_dist_gen = all_dm_om_df[['wf_dist_gen']]
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# lf_dist_gen = all_dm_om_df[['lf_dist_gen']]
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#
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# if (tolower(gene)%in%geneL_na){
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# wf_mcsm_na = all_dm_om_df[['wf_mcsm_na']]
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# lf_mcsm_na = all_dm_om_df[['lf_mcsm_na']]
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# }
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#
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# if (tolower(gene)%in%geneL_ppi2){
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# wf_mcsm_ppi2 = all_dm_om_df[['wf_mcsm_ppi2']]
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# lf_mcsm_ppi2 = all_dm_om_df[['lf_mcsm_ppi2']]
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# }
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#
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# s2 = c("\nSuccessfully sourced other_plots_data.R")
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# cat(s2)
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#
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# ####################################################################
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# # Data for Lineage barplots: WF and LF dfs
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# # My function: lineage_plot_data()
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# # location: scripts/functions/lineage_plot_data.R
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# ####################################################################
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#
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# #source(paste0(plot_script_path, "lineage_data.R"))
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# # converted to a function. Moved lineage_data.R to redundant/
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# lineage_dfL = lineage_plot_data(merged_df2
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# , lineage_column_name = "lineage"
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# , remove_empty_lineage = F
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# , lineage_label_col_name = "lineage_labels"
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# , id_colname = "id"
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# , snp_colname = "mutationinformation"
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# )
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#
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# lin_wf = lineage_dfL[['lin_wf']]
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# lin_lf = lineage_dfL[['lin_lf']]
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#
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# s3 = c("\nSuccessfully sourced lineage_data.R")
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# cat(s3)
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#
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# ####################################################################
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# # Data for corr plots:
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# # My function: corr_data_extract()
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# # location: scripts/functions/corr_plot_data.R
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# ####################################################################
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# # make sure the above script works because merged_df2_combined is needed
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# merged_df3 = as.data.frame(merged_df3)
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#
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# corr_df_m3_f = corr_data_extract(merged_df3
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# , gene = gene
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# , drug = drug
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# , extract_scaled_cols = F)
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# head(corr_df_m2_f)
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s4 = c("\nSuccessfully sourced Corr_data.R")
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cat(s4)
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########################################################################
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# End of script
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########################################################################
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if ( all( length(s1), length(s2), length(s3), length(s4) ) > 0 ){
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cat(
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"\n##################################################"
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, "\nSuccessful: get_plotting_dfs.R worked!"
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, "\n###################################################\n")
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} else {
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cat(
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"\n#################################################"
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, "\nFAIL: get_plotting_dfs.R didn't complete fully!Please check"
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, "\n###################################################\n" )
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}
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########################################################################
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# clear excess variables: from the global enviornment
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vars0 = ls(envir = .GlobalEnv)[grepl("curr_*", ls(envir = .GlobalEnv))]
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vars1 = ls(envir = .GlobalEnv)[grepl("^cols_to*", ls(envir = .GlobalEnv))]
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vars2 = ls(envir = .GlobalEnv)[grepl("pivot_cols_*", ls(envir = .GlobalEnv))]
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vars3 = ls(envir = .GlobalEnv)[grepl("expected_*", ls(envir = .GlobalEnv))]
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rm( infile_metadata
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, infile_params
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, vars0
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, vars1
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, vars2
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, vars3)
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# head(corr_df_m3_f)
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#
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# # corr_df_m2_f = corr_data_extract(merged_df2
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# # , gene = gene
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# # , drug = drug
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# # , extract_scaled_cols = F)
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# # head(corr_df_m2_f)
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#
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# s4 = c("\nSuccessfully sourced Corr_data.R")
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# cat(s4)
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#
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# ########################################################################
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# # End of script
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# ########################################################################
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# if ( all( length(s1), length(s2), length(s3), length(s4) ) > 0 ){
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# cat(
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# "\n##################################################"
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# , "\nSuccessful: get_plotting_dfs.R worked!"
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# , "\n###################################################\n")
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# } else {
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# cat(
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# "\n#################################################"
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# , "\nFAIL: get_plotting_dfs.R didn't complete fully!Please check"
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# , "\n###################################################\n" )
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# }
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#
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# ########################################################################
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# # clear excess variables: from the global enviornment
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#
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# vars0 = ls(envir = .GlobalEnv)[grepl("curr_*", ls(envir = .GlobalEnv))]
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# vars1 = ls(envir = .GlobalEnv)[grepl("^cols_to*", ls(envir = .GlobalEnv))]
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# vars2 = ls(envir = .GlobalEnv)[grepl("pivot_cols_*", ls(envir = .GlobalEnv))]
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# vars3 = ls(envir = .GlobalEnv)[grepl("expected_*", ls(envir = .GlobalEnv))]
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#
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# rm( infile_metadata
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# , infile_params
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# , vars0
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# , vars1
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# , vars2
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# , vars3)
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