[R] replace uses of T and F with TRUE and FALSE (#5778)

* [R-package] replace uses of T and F with TRUE and FALSE

* enable linting

* Remove skip

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>

R-package/demo/basic_walkthrough.R

@@ -100,7 +100,7 @@ print(paste("test-error=", err))
 
 # You can dump the tree you learned using xgb.dump into a text file
 dump_path = file.path(tempdir(), 'dump.raw.txt')
-xgb.dump(bst, dump_path, with_stats = T)
+xgb.dump(bst, dump_path, with_stats = TRUE)
 
 # Finally, you can check which features are the most important.
 print("Most important features (look at column Gain):")

R-package/demo/caret_wrapper.R

@@ -9,7 +9,7 @@ require(e1071)
 # Load Arthritis dataset in memory.
 data(Arthritis)
 # Create a copy of the dataset with data.table package (data.table is 100% compliant with R dataframe but its syntax is a lot more consistent and its performance are really good).
-df <- data.table(Arthritis, keep.rownames = F)
+df <- data.table(Arthritis, keep.rownames = FALSE)
 
 # Let's add some new categorical features to see if it helps. Of course these feature are highly correlated to the Age feature. Usually it's not a good thing in ML, but Tree algorithms (including boosted trees) are able to select the best features, even in case of highly correlated features.
 # For the first feature we create groups of age by rounding the real age. Note that we transform it to factor (categorical data) so the algorithm treat them as independant values.

R-package/demo/create_sparse_matrix.R

@@ -19,7 +19,7 @@ if (!require(vcd)) {
 data(Arthritis)
 
 # create a copy of the dataset with data.table package (data.table is 100% compliant with R dataframe but its syntax is a lot more consistent and its performance are really good).
-df <- data.table(Arthritis, keep.rownames = F)
+df <- data.table(Arthritis, keep.rownames = FALSE)
 
 # Let's have a look to the data.table
 cat("Print the dataset\n")

R-package/demo/interaction_constraints.R

@@ -19,18 +19,18 @@ treeInteractions <- function(input_tree, input_max_depth){
     setorderv(parents_left, 'ID_merge')
     setorderv(parents_right, 'ID_merge')
 
-    trees <- merge(trees, parents_left, by='ID_merge', all.x=T)
+    trees <- merge(trees, parents_left, by='ID_merge', all.x=TRUE)
     trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
     trees[, c('i.id','i.feature'):=NULL]
 
-    trees <- merge(trees, parents_right, by='ID_merge', all.x=T)
+    trees <- merge(trees, parents_right, by='ID_merge', all.x=TRUE)
     trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
     trees[, c('i.id','i.feature'):=NULL]
   }
 
   # Extract nodes with interactions
   interaction_trees <- trees[!is.na(Split) & !is.na(parent_1), 
-                             c('Feature',paste0('parent_feat_',1:(input_max_depth-1))), with=F]
+                             c('Feature',paste0('parent_feat_',1:(input_max_depth-1))), with=FALSE]
   interaction_trees_split <- split(interaction_trees, 1:nrow(interaction_trees))
   interaction_list <- lapply(interaction_trees_split, as.character)
 
@@ -96,7 +96,7 @@ x1 <- sort(unique(x[['V1']]))
 for (i in 1:length(x1)){
   testdata <- copy(x[, -c('V1')])
   testdata[['V1']] <- x1[i]
-  testdata <- testdata[, paste0('V',1:10), with=F]
+  testdata <- testdata[, paste0('V',1:10), with=FALSE]
   pred <- predict(bst3, as.matrix(testdata))
   
   # Should not print out anything due to monotonic constraints

R-package/demo/tweedie_regression.R

@@ -13,7 +13,7 @@ exclude <-  c('POLICYNO', 'PLCYDATE', 'CLM_FREQ5', 'CLM_AMT5', 'CLM_FLAG', 'IN_Y
 # retains the missing values
 # NOTE: this dataset is comes ready out of the box
 options(na.action = 'na.pass')
-x <- sparse.model.matrix(~ . - 1, data = dt[, -exclude, with = F])
+x <- sparse.model.matrix(~ . - 1, data = dt[, -exclude, with = FALSE])
 options(na.action = 'na.omit')
 
 # response