Upkeep

.github/workflows/R-CMD-check.yaml

@@ -1,4 +1,4 @@
-# Workflow derived from https://github.com/r-lib/actions/tree/master/examples
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
@@ -18,7 +18,7 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - {os: macOS-latest,   r: 'release'}
+          - {os: macos-latest,   r: 'release'}
           - {os: windows-latest, r: 'release'}
           - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest,   r: 'release'}
@@ -29,30 +29,21 @@ jobs:
       R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
-      - uses: r-lib/actions/setup-pandoc@v1
+      - uses: r-lib/actions/setup-pandoc@v2
 
-      - uses: r-lib/actions/setup-r@v1
+      - uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
           http-user-agent: ${{ matrix.config.http-user-agent }}
           use-public-rspm: true
 
-      - uses: r-lib/actions/setup-r-dependencies@v1
+      - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: rcmdcheck
+          extra-packages: any::rcmdcheck
+          needs: check
 
-      - uses: r-lib/actions/check-r-package@v1
-
-      - name: Show testthat output
-        if: always()
-        run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true
-        shell: bash
-
-      - name: Upload check results
-        if: failure()
-        uses: actions/upload-artifact@main
+      - uses: r-lib/actions/check-r-package@v2
         with:
-          name: ${{ runner.os }}-r${{ matrix.config.r }}-results
-          path: check
+          upload-snapshots: true

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: randomForestExplainer
 Title: Explaining and Visualizing Random Forests in Terms of Variable Importance
-Version: 0.10.1
+Version: 0.10.2
 Authors@R: c(
   person("Aleksandra", "Paluszynska", email = "ola.paluszynska@gmail.com", role = c("aut")),
   person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut","ths")),
@@ -10,22 +10,24 @@ Description: A set of tools to help explain which variables are most important i
 Depends: R (>= 3.0)
 License: GPL
 Encoding: UTF-8
-LazyData: true
 Imports:
   data.table (>= 1.10.4),
   dplyr (>= 0.7.1),
   DT (>= 0.2),
   GGally (>= 1.3.0),
-  ggplot2 (>= 2.2.1),
+  ggplot2 (>= 3.4.0),
   ggrepel (>= 0.6.5),
   randomForest (>= 4.6.12),
   ranger(>= 0.9.0),
-  reshape2 (>= 1.4.2),
-  rmarkdown (>= 1.5)
+  rlang,
+  rmarkdown (>= 1.5),
+  tidyr
 Suggests:
     knitr,
     MASS (>= 7.3.47),
     testthat
 VignetteBuilder: knitr
-RoxygenNote: 7.1.0
+RoxygenNote: 7.3.0
 URL: https://github.com/ModelOriented/randomForestExplainer, https://modeloriented.github.io/randomForestExplainer/
+Config/testthat/edition: 3
+Config/Needs/website: ModelOriented/DrWhyTemplate

NAMESPACE

@@ -26,6 +26,7 @@ import(ggplot2)
 import(ggrepel)
 importFrom(data.table,frankv)
 importFrom(data.table,rbindlist)
+importFrom(rlang,.data)
 importFrom(stats,as.formula)
 importFrom(stats,predict)
 importFrom(stats,terms)

NEWS.md

@@ -1,9 +1,22 @@
+# randomForestExplainer 0.10.2
+
+* Remove dependency on reshape2 in favour of tidyr (@olivroy, #33)
+
+* Silence deprecation warnings from ggplot2 and dplyr (@olivroy, #29)
+
+* Use testthat 3rd edition. (@olivroy, #33)
+
+# randomForestExplainer 0.10.1
+
+* Small tweaks to `explain_forest()`.
+
 # randomForestExplainer 0.10.0
+
 ## New features
 * Added support for ranger forests.
 * Added support for unsupervised randomForest.
 * Added tests for most functions.
 
 ## Bug fixes
-* Fixed bug for explain_forest not finding templates.
-* Added more intuitive error message for explain_forest when local importance is absent.
+* Fixed bug for `explain_forest()` not finding templates.
+* Added more intuitive error message for `explain_forest()` when local `importance` is absent.

R/measure_importance.R

@@ -10,8 +10,7 @@ measure_min_depth <- function(min_depth_frame, mean_sample){
 # randomForest
 measure_no_of_nodes <- function(forest_table){
   `split var` <- NULL
-  frame <- dplyr::group_by(forest_table, `split var`) %>% dplyr::summarize(n())
-  colnames(frame) <- c("variable", "no_of_nodes")
+  frame <- dplyr::group_by(forest_table, variable = `split var`) %>% dplyr::summarize(no_of_nodes = dplyr::n())
   frame <- as.data.frame(frame[!is.na(frame$variable),])
   frame$variable <- as.character(frame$variable)
   return(frame)
@@ -21,8 +20,7 @@ measure_no_of_nodes <- function(forest_table){
 # randomForest
 measure_no_of_nodes_ranger <- function(forest_table){
   splitvarName <- NULL
-  frame <- dplyr::group_by(forest_table, splitvarName) %>% dplyr::summarize(n())
-  colnames(frame) <- c("variable", "no_of_nodes")
+  frame <- dplyr::group_by(forest_table, variable = splitvarName) %>% dplyr::summarize(no_of_nodes = n())
   frame <- as.data.frame(frame[!is.na(frame$variable),])
   frame$variable <- as.character(frame$variable)
   return(frame)
@@ -75,8 +73,7 @@ measure_vimp_ranger <- function(forest){
 measure_no_of_trees <- function(min_depth_frame){
   variable <- NULL
   frame <- dplyr::group_by(min_depth_frame, variable) %>%
-    dplyr::summarize(count = n()) %>% as.data.frame()
-  colnames(frame)[2] <- "no_of_trees"
+    dplyr::summarize(no_of_trees = n()) %>% as.data.frame()
   frame$variable <- as.character(frame$variable)
   return(frame)
 }
@@ -85,8 +82,7 @@ measure_no_of_trees <- function(min_depth_frame){
 measure_times_a_root <- function(min_depth_frame){
   variable <- NULL
   frame <- min_depth_frame[min_depth_frame$minimal_depth == 0, ] %>%
-    dplyr::group_by(variable) %>% dplyr::summarize(count = n()) %>% as.data.frame()
-  colnames(frame)[2] <- "times_a_root"
+    dplyr::group_by(variable) %>% dplyr::summarize(times_a_root = n()) %>% as.data.frame()
   frame$variable <- as.character(frame$variable)
   return(frame)
 }
@@ -329,13 +325,13 @@ plot_multi_way_importance <- function(importance_frame, x_measure = "mean_min_de
     if(size_measure == "p_value"){
       data$p_value <- cut(data$p_value, breaks = c(-Inf, 0.01, 0.05, 0.1, Inf),
                           labels = c("<0.01", "[0.01, 0.05)", "[0.05, 0.1)", ">=0.1"), right = FALSE)
-      plot <- ggplot(data, aes_string(x = x_measure, y = y_measure)) +
-        geom_point(aes_string(color = size_measure), size = 3) +
+      plot <- ggplot(data, aes(x = .data[[x_measure]], y = .data[[y_measure]])) +
+        geom_point(aes(color = .data[[size_measure]]), size = 3) +
         geom_point(data = data_for_labels, color = "black", stroke = 2, aes(alpha = "top"), size = 3, shape = 21) +
         geom_label_repel(data = data_for_labels, aes(label = variable), show.legend = FALSE) +
         theme_bw() + scale_alpha_discrete(name = "variable", range = c(1, 1))
     } else {
-      plot <- ggplot(data, aes_string(x = x_measure, y = y_measure, size = size_measure)) +
+      plot <- ggplot(data, aes(x = .data[[x_measure]], y = .data[[y_measure]], size = .data[[size_measure]])) +
         geom_point(aes(colour = "black")) + geom_point(data = data_for_labels, aes(colour = "blue")) +
         geom_label_repel(data = data_for_labels, aes(label = variable, size = NULL), show.legend = FALSE) +
         scale_colour_manual(name = "variable", values = c("black", "blue"), labels = c("non-top", "top")) +
@@ -345,7 +341,7 @@ plot_multi_way_importance <- function(importance_frame, x_measure = "mean_min_de
       }
     }
   } else {
-    plot <- ggplot(data, aes_string(x = x_measure, y = y_measure)) +
+    plot <- ggplot(data, aes(x = .data[[x_measure]], y = .data[[y_measure]])) +
       geom_point(aes(colour = "black")) + geom_point(data = data_for_labels, aes(colour = "blue")) +
       geom_label_repel(data = data_for_labels, aes(label = variable, size = NULL), show.legend = FALSE) +
       scale_colour_manual(name = "variable", values = c("black", "blue"), labels = c("non-top", "top")) +

R/min_depth_distribution.R

@@ -175,7 +175,7 @@ plot_min_depth_distribution <- function(min_depth_frame, k = 10, min_no_of_trees
   plot <- ggplot(data, aes(x = variable, y = count)) +
     geom_col(position = position_stack(reverse = TRUE), aes(fill = as.factor(minimal_depth))) + coord_flip() +
     scale_x_discrete(limits = rev(levels(data$variable))) +
-    geom_errorbar(aes(ymin = mean_minimal_depth_label, ymax = mean_minimal_depth_label), size = 1.5) +
+    geom_errorbar(aes(ymin = mean_minimal_depth_label, ymax = mean_minimal_depth_label), linewidth = 1.5) +
     xlab("Variable") + ylab("Number of trees") + guides(fill = guide_legend(title = "Minimal depth")) +
     theme_bw() + geom_label(data = data_for_labels,
                             aes(y = mean_minimal_depth_label, label = mean_minimal_depth))