[R] resolve brace_linter warnings (#8564)

R-package/R/callbacks.R

@@ -615,9 +615,11 @@ cb.gblinear.history <- function(sparse=FALSE) {
   coefs <- NULL
 
   init <- function(env) {
-    if (!is.null(env$bst)) { # xgb.train:
+    # xgb.train(): bst will be present
-    } else if (!is.null(env$bst_folds)) { # xgb.cv:
+    # xgb.cv(): bst_folds will be present
-    } else stop("Parent frame has neither 'bst' nor 'bst_folds'")
+    if (is.null(env$bst) && is.null(env$bst_folds)) {
+        stop("Parent frame has neither 'bst' nor 'bst_folds'")
+    }
   }
 
   # convert from list to (sparse) matrix

R-package/R/utils.R

@@ -251,8 +251,7 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
 # Creates CV folds stratified by the values of y.
 # It was borrowed from caret::createFolds and simplified
 # by always returning an unnamed list of fold indices.
-xgb.createFolds <- function(y, k = 10)
+xgb.createFolds <- function(y, k = 10) {
-{
   if (is.numeric(y)) {
     ## Group the numeric data based on their magnitudes
     ## and sample within those groups.

R-package/R/xgb.importance.R

@@ -104,7 +104,11 @@ xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
       XGBoosterFeatureScore_R, model$handle, jsonlite::toJSON(args, auto_unbox = TRUE, null = "null")
     )
     names(results) <- c("features", "shape", "weight")
-    n_classes <-  if (length(results$shape) == 2) { results$shape[2] } else { 0 }
+    if (length(results$shape) == 2) {
+        n_classes <- results$shape[2]
+    } else {
+        n_classes <- 0
+    }
     importance <- if (n_classes == 0) {
       data.table(Feature = results$features, Weight = results$weight)[order(-abs(Weight))]
     } else {

R-package/R/xgb.plot.importance.R

@@ -102,7 +102,9 @@ xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure
     original_mar <- par()$mar
 
     # reset margins so this function doesn't have side effects
-    on.exit({par(mar = original_mar)})
+    on.exit({
+        par(mar = original_mar)
+    })
 
     mar <- original_mar
     if (!is.null(left_margin))

R-package/demo/cross_validation.R

@@ -12,7 +12,7 @@ cat('running cross validation\n')
 # do cross validation, this will print result out as
 # [iteration]  metric_name:mean_value+std_value
 # std_value is standard deviation of the metric
-xgb.cv(param, dtrain, nrounds, nfold = 5, metrics = {'error'})
+xgb.cv(param, dtrain, nrounds, nfold = 5, metrics = 'error')
 
 cat('running cross validation, disable standard deviation display\n')
 # do cross validation, this will print result out as

R-package/tests/testthat/test_helpers.R

@@ -170,8 +170,9 @@ test_that("SHAPs sum to predictions, with or without DART", {
       label = y,
       nrounds = nrounds)
 
-    pr <- function(...)
+    pr <- function(...) {
       predict(fit, newdata = d, ...)
+    }
     pred <- pr()
     shap <- pr(predcontrib = TRUE)
     shapi <- pr(predinteraction = TRUE)

demo/kaggle-otto/understandingXGBoostModel.Rmd

@@ -86,7 +86,10 @@ For that purpose, we will:
 
 ```{r classToIntegers}
 # Convert from classes to numbers
-y <- train[, nameLastCol, with = FALSE][[1]] %>% gsub('Class_','',.) %>% {as.integer(.) -1}
+y <- train[, nameLastCol, with = FALSE][[1]] %>%
+    gsub('Class_','',.) %>%
+    as.integer %>%
+    subtract(., 1)
 
 # Display the first 5 levels
 y[1:5]