[R] address some lintr warnings (#8609)

R-package/R/xgb.Booster.R

@@ -629,7 +629,7 @@ xgb.attributes <- function(object) {
 #' @export
 xgb.config <- function(object) {
   handle <- xgb.get.handle(object)
-  .Call(XGBoosterSaveJsonConfig_R, handle);
+  .Call(XGBoosterSaveJsonConfig_R, handle)
 }
 
 #' @rdname xgb.config

R-package/tests/testthat/test_basic.R

@@ -404,7 +404,7 @@ test_that("Configuration works", {
   config <- xgb.config(bst)
   xgb.config(bst) <- config
   reloaded_config <- xgb.config(bst)
-  expect_equal(config, reloaded_config);
+  expect_equal(config, reloaded_config)
 })
 
 test_that("strict_shape works", {

R-package/vignettes/discoverYourData.Rmd

@@ -28,7 +28,9 @@ Package loading:
 require(xgboost)
 require(Matrix)
 require(data.table)
-if (!require('vcd')) install.packages('vcd')
+if (!require('vcd')) {
+  install.packages('vcd')
+}
 ```
 
 > **VCD** package is used for one of its embedded dataset only.

R-package/vignettes/xgboostfromJSON.Rmd

@@ -163,9 +163,10 @@ We were able to get the log-odds to agree, so now let's manually calculate the s
 bst_preds <- predict(bst, as.matrix(data$dates))
 
 # calculate the predictions casting doubles to floats
-bst_from_json_preds <- ifelse(fl(data$dates)<fl(node$split_condition),
-                              as.numeric(1/(1+exp(-1*fl(node$children[[1]]$leaf)))),
-                              as.numeric(1/(1+exp(-1*fl(node$children[[2]]$leaf))))
+bst_from_json_preds <- ifelse(
+  fl(data$dates) < fl(node$split_condition)
+  , as.numeric(1 / (1 + exp(-1 * fl(node$children[[1]]$leaf))))
+  , as.numeric(1 / (1 + exp(-1 * fl(node$children[[2]]$leaf))))
 )
 
 # test that values are equal
@@ -177,9 +178,10 @@ None are exactly equal again.  What is going on here?  Well, since we are using
 How do we fix this?  We have to ensure we use the correct data types everywhere and the correct operators.  If we use only floats, the float library that we have loaded will ensure the 32-bit float exponentiation operator is applied. 
 ```{r}
 # calculate the predictions casting doubles to floats
-bst_from_json_preds <- ifelse(fl(data$dates)<fl(node$split_condition),
-                              as.numeric(fl(1)/(fl(1)+exp(fl(-1)*fl(node$children[[1]]$leaf)))),
-                              as.numeric(fl(1)/(fl(1)+exp(fl(-1)*fl(node$children[[2]]$leaf))))
+bst_from_json_preds <- ifelse(
+  fl(data$dates) < fl(node$split_condition)
+  , as.numeric(fl(1) / (fl(1) + exp(fl(-1) * fl(node$children[[1]]$leaf))))
+  , as.numeric(fl(1) / (fl(1) + exp(fl(-1) * fl(node$children[[2]]$leaf))))
 )
 
 # test that values are equal

demo/data/gen_autoclaims.R

@@ -1,8 +1,10 @@
 site <- 'http://cran.r-project.org'
-if (!require('dummies'))
+if (!require('dummies')) {
     install.packages('dummies', repos = site)
-if (!require('insuranceData'))
+}
+if (!require('insuranceData')) {
     install.packages('insuranceData', repos = site)
+}
 
 library(dummies)
 library(insuranceData)
@@ -14,5 +16,16 @@ 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=TRUE);
-write.table(data.dummy, 'autoclaims.csv', sep=',', row.names=F, col.names=F, quote=F)
+data.dummy <- dummy.data.frame(
+    data
+    , dummy.class = 'factor'
+    , omit.constants = TRUE
+)
+write.table(
+    data.dummy
+    , 'autoclaims.csv'
+    , sep = ','
+    , row.names = FALSE
+    , col.names = FALSE
+    , quote = FALSE
+)

demo/kaggle-higgs/higgs-train.R

@@ -25,7 +25,7 @@ param <- list("objective" = "binary:logitraw",
 watchlist <- list("train" = xgmat)
 nrounds <- 120
 print ("loading data end, start to boost trees")
-bst <- xgb.train(param, xgmat, nrounds, watchlist );
+bst <- xgb.train(param, xgmat, nrounds, watchlist)
 # save out model
 xgb.save(bst, "higgs.model")
 print ('finish training')

demo/kaggle-higgs/speedtest.R

@@ -40,7 +40,7 @@ for (i in 1:length(threads)){
     watchlist <- list("train" = xgmat)
     nrounds <- 120
     print ("loading data end, start to boost trees")
-    bst <- xgb.train(param, xgmat, nrounds, watchlist );
+    bst <- xgb.train(param, xgmat, nrounds, watchlist)
     # save out model
     xgb.save(bst, "higgs.model")
     print ('finish training')
@@ -67,4 +67,3 @@ xgboost.time
 # [[5]]
 # user  system elapsed
 # 157.390   5.988  40.949
-

demo/kaggle-otto/otto_train_pred.R

@@ -24,8 +24,13 @@ param <- list("objective" = "multi:softprob",
 
 # Run Cross Validation
 cv.nrounds <- 50
-bst.cv <- xgb.cv(param=param, data = x[trind,], label = y, 
-                nfold = 3, nrounds=cv.nrounds)
+bst.cv <- xgb.cv(
+    param = param
+    , data = x[trind, ]
+    , label = y
+    , nfold = 3
+    , nrounds = cv.nrounds
+)
 
 # Train the model
 nrounds <- 50
@@ -37,7 +42,7 @@ pred <- matrix(pred,9,length(pred)/9)
 pred <- t(pred)
 
 # Output submission
-pred <- format(pred, digits=2,scientific=F) # shrink the size of submission
+pred <- format(pred, digits = 2, scientific = FALSE) # shrink the size of submission
 pred <- data.frame(1:nrow(pred), pred)
 names(pred) <- c('id', paste0('Class_', 1:9))
 write.csv(pred, file = 'submission.csv', quote = FALSE, row.names = FALSE)