[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

R-package/tests/testthat/test_helpers.R

@@ -12,7 +12,7 @@ flag_32bit = .Machine$sizeof.pointer != 8
 
 set.seed(1982)
 data(Arthritis)
-df <- data.table(Arthritis, keep.rownames = F)
+df <- data.table(Arthritis, keep.rownames = FALSE)
 df[,AgeDiscret := as.factor(round(Age / 10,0))]
 df[,AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]
 df[,ID := NULL]
@@ -47,7 +47,7 @@ test_that("xgb.dump works", {
   if (!flag_32bit)
     expect_length(xgb.dump(bst.Tree), 200)
   dump_file = file.path(tempdir(), 'xgb.model.dump')
-  expect_true(xgb.dump(bst.Tree, dump_file, with_stats = T))
+  expect_true(xgb.dump(bst.Tree, dump_file, with_stats = TRUE))
   expect_true(file.exists(dump_file))
   expect_gt(file.size(dump_file), 8000)
 
@@ -160,7 +160,7 @@ test_that("SHAPs sum to predictions, with or without DART", {
           objective = "reg:squarederror",
           eval_metric = "rmse"),
         if (booster == "dart")
-          list(rate_drop = .01, one_drop = T)),
+          list(rate_drop = .01, one_drop = TRUE)),
       data = d,
       label = y,
       nrounds = nrounds)
@@ -168,8 +168,8 @@ test_that("SHAPs sum to predictions, with or without DART", {
     pr <- function(...)
       predict(fit, newdata = d, ...)
     pred <- pr()
-    shap <- pr(predcontrib = T)
+    shap <- pr(predcontrib = TRUE)
-    shapi <- pr(predinteraction = T)
+    shapi <- pr(predinteraction = TRUE)
     tol = 1e-5
 
     expect_equal(rowSums(shap), pred, tol = tol)

R-package/tests/testthat/test_interactions.R

@@ -107,7 +107,7 @@ test_that("SHAP contribution values are not NAN", {
 
   shaps <- as.data.frame(predict(fit,
     newdata = as.matrix(subset(d, fold == 1)[, ivs]),
-    predcontrib = T))
+    predcontrib = TRUE))
   result <- cbind(shaps, sum = rowSums(shaps), pred = predict(fit,
       newdata = as.matrix(subset(d, fold == 1)[, ivs])))
 

R-package/tests/testthat/test_lint.R

@@ -1,8 +1,6 @@
 context("Code is of high quality and lint free")
 test_that("Code Lint", {
   skip_on_cran()
-  skip_on_travis()
-  skip_if_not_installed("lintr")
   my_linters <- list(
     absolute_paths_linter=lintr::absolute_paths_linter,
     assignment_linter=lintr::assignment_linter,
@@ -21,7 +19,8 @@ test_that("Code Lint", {
     spaces_inside_linter=lintr::spaces_inside_linter,
     spaces_left_parentheses_linter=lintr::spaces_left_parentheses_linter,
     trailing_blank_lines_linter=lintr::trailing_blank_lines_linter,
-    trailing_whitespace_linter=lintr::trailing_whitespace_linter
+    trailing_whitespace_linter=lintr::trailing_whitespace_linter,
+    true_false=lintr::T_and_F_symbol_linter
   )
-  # lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
+  lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
 })

R-package/vignettes/discoverYourData.Rmd

@@ -63,7 +63,7 @@ The first step is to load `Arthritis` dataset in memory and wrap it with `data.t
 
 ```{r, results='hide'}
 data(Arthritis)
-df <- data.table(Arthritis, keep.rownames = F)
+df <- data.table(Arthritis, keep.rownames = FALSE)
 ```
 
 > `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](http://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.

R-package/vignettes/xgboostPresentation.Rmd

@@ -363,7 +363,7 @@ xgb.plot.importance(importance_matrix = importance_matrix)
 You can dump the tree you learned using `xgb.dump` into a text file.
 
 ```{r dump, message=T, warning=F}
-xgb.dump(bst, with_stats = T)
+xgb.dump(bst, with_stats = TRUE)
 ```
 
 You can plot the trees from your model using ```xgb.plot.tree``

demo/data/gen_autoclaims.R

@@ -14,5 +14,5 @@ data$STATE = as.factor(data$STATE)
 data$CLASS = as.factor(data$CLASS)
 data$GENDER = as.factor(data$GENDER)
 
-data.dummy <- dummy.data.frame(data, dummy.class='factor', omit.constants=T);
+data.dummy <- dummy.data.frame(data, dummy.class='factor', omit.constants=TRUE);
 write.table(data.dummy, 'autoclaims.csv', sep=',', row.names=F, col.names=F, quote=F)

demo/kaggle-otto/otto_train_pred.R

@@ -1,8 +1,8 @@
 require(xgboost)
 require(methods)
 
-train = read.csv('data/train.csv',header=TRUE,stringsAsFactors = F)
+train = read.csv('data/train.csv',header=TRUE,stringsAsFactors = FALSE)
-test = read.csv('data/test.csv',header=TRUE,stringsAsFactors = F)
+test = read.csv('data/test.csv',header=TRUE,stringsAsFactors = FALSE)
 train = train[,-1]
 test = test[,-1]
 

demo/kaggle-otto/understandingXGBoostModel.Rmd

@@ -30,8 +30,8 @@ require(xgboost)
 require(methods)
 require(data.table)
 require(magrittr)
-train <- fread('data/train.csv', header = T, stringsAsFactors = F)
+train <- fread('data/train.csv', header = T, stringsAsFactors = FALSE)
-test <- fread('data/test.csv', header=TRUE, stringsAsFactors = F)
+test <- fread('data/test.csv', header=TRUE, stringsAsFactors = FALSE)
 ```
 > `magrittr` and `data.table` are here to make the code cleaner and much more rapid.
 
@@ -42,13 +42,13 @@ Let's explore the dataset.
 dim(train)
 
 # Training content
-train[1:6,1:5, with =F]
+train[1:6,1:5, with =FALSE]
 
 # Test dataset dimensions
 dim(test)
 
 # Test content
-test[1:6,1:5, with =F]
+test[1:6,1:5, with =FALSE]
 ```
 > We only display the 6 first rows and 5 first columns for convenience
 
@@ -70,7 +70,7 @@ According to its description, the **Otto** challenge is a multi class classifica
 
 ```{r searchLabel}
 # Check the content of the last column
-train[1:6, ncol(train), with  = F]
+train[1:6, ncol(train), with  = FALSE]
 # Save the name of the last column
 nameLastCol <- names(train)[ncol(train)]
 ```
@@ -86,7 +86,7 @@ For that purpose, we will:
 
 ```{r classToIntegers}
 # Convert from classes to numbers
-y <- train[, nameLastCol, with = F][[1]] %>% gsub('Class_','',.) %>% {as.integer(.) -1}
+y <- train[, nameLastCol, with = FALSE][[1]] %>% gsub('Class_','',.) %>% {as.integer(.) -1}
 
 # Display the first 5 levels
 y[1:5]
@@ -95,7 +95,7 @@ y[1:5]
 We remove label column from training dataset, otherwise **XGBoost** would use it to guess the labels!
 
 ```{r deleteCols, results='hide'}
-train[, nameLastCol:=NULL, with = F]
+train[, nameLastCol:=NULL, with = FALSE]
 ```
 
 `data.table` is an awesome implementation of data.frame, unfortunately it is not a format supported natively by **XGBoost**. We need to convert both datasets (training and test) in `numeric` Matrix format.
@@ -163,7 +163,7 @@ Each *split* is done on one feature only at one value.
 Let's see what the model looks like.
 
 ```{r modelDump}
-model <- xgb.dump(bst, with.stats = T)
+model <- xgb.dump(bst, with.stats = TRUE)
 model[1:10]
 ```
 > For convenience, we are displaying the first 10 lines of the model only.

doc/R-package/discoverYourData.md

@@ -52,7 +52,7 @@ The first step is to load `Arthritis` dataset in memory and wrap it with `data.t
 
 ```r
 data(Arthritis)
-df <- data.table(Arthritis, keep.rownames = F)
+df <- data.table(Arthritis, keep.rownames = FALSE)
 ```
 
 > `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](http://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.

doc/R-package/xgboostPresentation.md

@@ -489,7 +489,7 @@ You can dump the tree you learned using `xgb.dump` into a text file.
 
 
 ```r
-xgb.dump(bst, with_stats = T)
+xgb.dump(bst, with_stats = TRUE)
 ```
 
 ```