I updated the code to avoid warnings

DESCRIPTION

@@ -2,7 +2,7 @@ Package: FeatureSelection
 Type: Package
 Title: Feature extraction and selection based on 'glmnet', 'xgboost' and 'ranger'
 Version: 1.0.0
-Date: 2021-05-19
+Date: 2024-08-09
 Authors@R: c( person(given = "Lampros", family = "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")))
 Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>
 BugReports: https://github.com/mlampros/FeatureSelection/issues
@@ -12,7 +12,7 @@ Depends:
     R(>= 3.3.0)
 Imports:
     doParallel,
-    dplyr,
+    data.table,
     glmnet,
     ranger,
     xgboost,
@@ -28,6 +28,5 @@ Suggests:
     covr
 SystemRequirements: update: apt-get -y update (deb)
 License: GPL-3
-LazyData: TRUE
 Encoding: UTF-8
-RoxygenNote: 7.1.1
+RoxygenNote: 7.3.0

Dockerfile

@@ -1,19 +1,16 @@
 FROM rocker/rstudio:devel
 
-
 LABEL maintainer='Lampros Mouselimis'
 
-
 RUN export DEBIAN_FRONTEND=noninteractive; apt-get -y update && \
  apt-get install -y make zlib1g-dev libssl-dev libcurl4-openssl-dev && \
  apt-get install -y sudo && \
  apt-get -y update && \
- R -e "install.packages(c( 'doParallel', 'dplyr', 'glmnet', 'ranger', 'xgboost', 'Matrix', 'magrittr', 'utils', 'stats', 'graphics', 'grDevices', 'rlang', 'testthat', 'covr', 'remotes' ), repos =  'https://cloud.r-project.org/' )" && \
+ R -e "install.packages(c( 'doParallel', 'data.table', 'glmnet', 'ranger', 'xgboost', 'Matrix', 'magrittr', 'utils', 'stats', 'graphics', 'grDevices', 'rlang', 'testthat', 'covr', 'remotes' ), repos =  'https://cloud.r-project.org/' )" && \
  R -e "remotes::install_github('mlampros/FeatureSelection', upgrade = 'always', dependencies = TRUE, repos = 'https://cloud.r-project.org/')" && \
  apt-get autoremove -y && \
  apt-get clean
 
-
 ENV USER rstudio
 
 

NAMESPACE

@@ -12,12 +12,8 @@ export(regr_folds)
 export(wrapper_feat_select)
 importFrom(Matrix,Matrix)
 importFrom(Matrix,colSums)
+importFrom(data.table,as.data.table)
 importFrom(doParallel,registerDoParallel)
-importFrom(dplyr,funs)
-importFrom(dplyr,group_by)
-importFrom(dplyr,n)
-importFrom(dplyr,summarize)
-importFrom(dplyr,summarize_each)
 importFrom(glmnet,cv.glmnet)
 importFrom(grDevices,dev.cur)
 importFrom(grDevices,dev.off)

NEWS.md

@@ -1,10 +1,11 @@
 
 ## FeatureSelection
 
-* **18-05-2016**: I added tests and code-coverage
+* **09-08-2024**: I replaced **dplyr** with **data.table** because the **dplyr** functions 'summarise_each_' and 'funs' gave deprecation warnings
+* **19-05-2021**: I replaced **doMC** with **doParallel** because **doMC** does not work on both **Unix** and **Windows** OS (applies only to **'glmnet-lasso'** method if number of threads > 1)
 * **03-02-2020**: 
   + Updated the R files so that *Feature Selection* works with the newest versions of the imported R packages
   + Adjusted the tests
   + Added Dockerfile and docker image
   + Updated the README.md and .travis.yml files
-* **19-05-2021**: I replaced **doMC** with **doParallel** because **doMC** does not work on both **Unix** and **Windows** OS (applies only to **'glmnet-lasso'** method if number of threads > 1)
+* **18-05-2016**: I added tests and code-coverage

R/feature_selection.R

@@ -1,6 +1,9 @@
 
 utils::globalVariables(c("%>%",
                          ".",
+                         ".N",
+                         ".SD",
+                         "coefficients",
                          "predict"))         # Keep 'predict' as a global variable. It appears both in 'stats' and 'glmnet' however I can not specify 'predict.cv.glmnet' because the function does not appear in the >= 3.0.0 version of the package (I receive an error otherwise)
 
 
@@ -32,7 +35,7 @@ utils::globalVariables(c("%>%",
 #'
 #' @export
 #' @importFrom glmnet cv.glmnet
-#' @importFrom dplyr group_by summarize summarize_each funs n
+#' @importFrom data.table as.data.table
 #' @importFrom doParallel registerDoParallel
 #' @importFrom xgboost xgb.DMatrix xgb.train xgb.importance
 #' @importFrom ranger ranger
@@ -120,9 +123,18 @@ utils::globalVariables(c("%>%",
 #' }
 
 
-feature_selection = function(X, y, method = NULL, params_glmnet = NULL, params_xgboost = NULL, params_ranger = NULL, xgb_sort = NULL, CV_folds = 5, stratified_regr = FALSE,
-
-                             scale_coefs_glmnet = FALSE, cores_glmnet = NULL, verbose = FALSE) {
+feature_selection = function(X, 
+                             y,
+                             method = NULL, 
+                             params_glmnet = NULL, 
+                             params_xgboost = NULL, 
+                             params_ranger = NULL,
+                             xgb_sort = NULL,
+                             CV_folds = 5, 
+                             stratified_regr = FALSE,
+                             scale_coefs_glmnet = FALSE, 
+                             cores_glmnet = NULL,
+                             verbose = FALSE) {
 
   if (is.null(method)) stop("use method = .. to select one of the available methods : xgboost, glmnet-lasso, ranger")
   if (CV_folds < 1) stop("CV_folds should be >= 1")
@@ -376,9 +388,14 @@ feature_selection = function(X, y, method = NULL, params_glmnet = NULL, params_x
 
     else {
 
-      all_feat = data.frame(do.call('rbind', get_all_feat))
-
-      tbl_x = data.frame(all_feat %>% dplyr::group_by(.data$Feature) %>% dplyr::summarize(coefficients = mean(.data$coefficients, na.rm = TRUE), Frequency = dplyr::n()))       # for ".data" see: https://community.rstudio.com/t/how-to-solve-no-visible-binding-for-global-variable-note/28887/3
+      all_feat = data.frame(do.call('rbind', get_all_feat)) |>
+        data.table::as.data.table()
+
+      tbl_x = all_feat[, .(coefficients = mean(coefficients, na.rm = TRUE),
+                           Frequency = .N),
+                       by = 'Feature'] |>
+        as.data.frame()
+
       if (scale_coefs_glmnet) tbl_x[, 2] = abs(tbl_x[, 2])
       tbl_x = tbl_x[order(tbl_x$Frequency, tbl_x$coefficients, decreasing = TRUE),]          # the data.frame in 'glmnet-lasso' is sorted by Frequency (default)
     }
@@ -498,9 +515,11 @@ feature_selection = function(X, y, method = NULL, params_glmnet = NULL, params_x
       gc()
     }
 
-    tbl_x = data.frame(do.call('rbind', get_all_feat))
+    tbl_x = data.frame(do.call('rbind', get_all_feat)) |>
+      data.table::as.data.table()
 
-    tbl1 = data.frame(tbl_x %>% dplyr::group_by(.data$Feature) %>% dplyr::summarize_each(dplyr::funs(mean(., na.rm = TRUE))))       # for ".data" see: https://community.rstudio.com/t/how-to-solve-no-visible-binding-for-global-variable-note/28887/3
+    tbl1 = tbl_x[, lapply(.SD, mean, na.rm = TRUE), by = 'Feature'] |>
+      as.data.frame()
 
     if (is.null(xgb_sort) || (xgb_sort == 'Frequency')) {
 
@@ -636,9 +655,11 @@ feature_selection = function(X, y, method = NULL, params_glmnet = NULL, params_x
       gc()
     }
 
-    tbl_x = data.frame(do.call('rbind', get_all_feat))
-
-    tbl1 = data.frame(tbl_x %>% dplyr::group_by(.data$Feature) %>% dplyr::summarize_each(dplyr::funs(mean(., na.rm = TRUE))))       # for ".data" see: https://community.rstudio.com/t/how-to-solve-no-visible-binding-for-global-variable-note/28887/3
+    tbl_x = data.frame(do.call('rbind', get_all_feat)) |>
+      data.table::as.data.table()
+
+    tbl1 = tbl_x[, lapply(.SD, mean, na.rm = TRUE), by = 'Feature'] |>
+      as.data.frame()
 
     tbl1 = tbl1[order(tbl1[, 2], decreasing = TRUE), ]
 

R/wrapper_feature_selection.R

@@ -22,7 +22,7 @@
 #' Furthermore the user can limit the number of features using the keep_number_feat parameter of the params_feature list.
 #' 
 #' @export
-#' @importFrom dplyr group_by summarize n 
+#' @importFrom data.table as.data.table
 #' @importFrom magrittr %>%
 #' @importFrom rlang .data
 #' 
@@ -183,9 +183,13 @@ wrapper_feat_select = function(X, y, params_glmnet = NULL, params_xgboost = NULL
 
     modify_lst = lapply(out_union, function(x) data.frame(feature = x$features, rank = normalized(length(x$features):1)))
 
-    modify_lst1 = data.frame(do.call(rbind, modify_lst))
+    modify_lst1 = data.frame(do.call(rbind, modify_lst)) |>
+      data.table::as.data.table()
 
-    tbl_x = data.frame(modify_lst1 %>% dplyr::group_by(.data$feature) %>% dplyr::summarize(importance = sum(rank, na.rm = TRUE), Frequency = dplyr::n()))
+    tbl_x = modify_lst1[, .(importance = sum(rank, na.rm = TRUE),
+                            Frequency = .N),
+                        by = 'feature'] |>
+      as.data.frame()
 
     tbl1 = tbl_x[order(tbl_x$importance, decreasing = TRUE), ]