Substitute tidyverse with dplyr,purr,tibble and tidyr

- Remove the dependency of the (huge) tidyverse packages and call only the necessary (sub-)packages. This includes:
// purrr: for functional programming: https://purrr.tidyverse.org/articles/other-langs.html
// tibble: for the function 'enframe' in the tidy-format
// tidyr: for the function 'separate' in the tidy-format

DESCRIPTION

@@ -12,14 +12,15 @@ LazyData: true
 Imports: 
     doParallel,
     igraph,
-    tidyverse,
     viper,
     GSEABase,
     Category,
     dplyr,
+    purrr,
+    tibble,
+    tidyr,    
     ggplot2,
-    UniProt.ws,
-    tidyr
+    UniProt.ws
 Suggests: knitr,
     rmarkdown
 VignetteBuilder: knitr

NAMESPACE

@@ -17,5 +17,4 @@ export(runPROGENy)
 export(writeLPFile)
 import(doParallel)
 import(igraph)
-import(tidyverse)
 import(viper)

R/generate_measfile.R

@@ -6,13 +6,12 @@
 #' @param topnumber If given, only the top number of measurements will be written out.
 #' @param write2folder Path to outputfolder
 #'
-#' @import tidyverse
-#'
 #' @return A n*m dataframe describing the inferred TF activities, where n is the number of Tfs and m the number of conditions.
 #'
 #' @export
 
 generate_measfile<-function(measurements, topnumber=NULL, write2folder="./measurements"){
+  library(dplyr)
   if(dir.exists(write2folder)==F){dir.create(write2folder)}
   drugs<-colnames(measurements)
   for (i in 1:ncol(measurements)){

R/genesymbol2uniprot.R

@@ -7,15 +7,12 @@
 #' @param geneID Column index of the gene symbol column in map.
 #' @param uniprotID Column index of the uniprot column in map.
 #'
-#' @import tidyverse
-#'
 #' @return A vector of genes or uniprot IDs.
 #'
 #' @export
 
-
 GeneSymbol2Uniprot=function(df, map, geneID=1, uniprotID=2){
-  library(tidyverse)
+  library(dplyr)
   df$genesymbols_for_mapping<-rownames(df)
   colnames(map)[geneID]<-'genesymbols_for_mapping'
   colnames(map)[uniprotID]<-'uniprotids_for_mapping'

R/runDoRothEA.R

@@ -8,15 +8,19 @@
 #' @param df A n*m dataframe describing the contrast t-statistics, where n is the number of genes and m the number of conditions.
 #' @param write2file Path to outputfile for all TF_activities if desired
 #'
-#' @import tidyverse
 #' @import viper
 #'
 #' @return A n*m dataframe describing the inferred TF activities, where n is the number of Tfs and m the number of conditions.
 #'
 #' @export
 
 runDoRothEA<-function(df, regulon, confidence_level=c('A','B','C'), write2file = NULL){
-  library(tidyverse)
+  # library(tidyverse)
+  library(dplyr)
+  library(purrr)
+  library(viper)
+  library(tibble)
+  library(tidyr)
   names(regulon) <- sapply(strsplit(names(viper_regulon), split = ' - '), head, 1)
   filtered_regulon <- regulon %>%
     map_df(.f = function(i) {

R/runPROGENy.R

@@ -18,7 +18,8 @@
 
 runPROGENy <- function(df,weight_matrix,k = 10000, z_scores = T, get_nulldist = F)
 {
-  library(tidyverse)
+  # library(tidyverse)
+  library(dplyr)
   resList <- list()
   if(get_nulldist)
   {