[R-package] added R linting and changed R code to comma-first (fixes #2373) (#2437)

R-package/tests/testthat/test_custom_objective.R

 context('Test models with custom objective')
 
-data(agaricus.train, package='lightgbm')
-data(agaricus.test, package='lightgbm')
+data(agaricus.train, package = 'lightgbm')
+data(agaricus.test, package = 'lightgbm')
 dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)
 dtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label)
 watchlist <- list(eval = dtest, train = dtrain)
@@ -17,11 +17,19 @@ logregobj <- function(preds, dtrain) {
 evalerror <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
   err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
-  return(list(name = "error", value = err, higher_better=FALSE))
+  return(list(
+    name = "error"
+    , value = err
+    , higher_better = FALSE
+  ))
 }
 
-param <- list(num_leaves=8, learning_rate=1,
-              objective=logregobj, metric="auc")
+param <- list(
+  num_leaves = 8
+  , learning_rate = 1
+  , objective = logregobj
+  , metric = "auc"
+)
 num_round <- 10
 
 test_that("custom objective works", {

R-package/tests/testthat/test_dataset.R

@@ -3,15 +3,15 @@ require(Matrix)
 
 context("testing lgb.Dataset functionality")
 
-data(agaricus.test, package='lightgbm')
+data(agaricus.test, package = 'lightgbm')
 test_data <- agaricus.test$data[1:100,]
 test_label <- agaricus.test$label[1:100]
 
 test_that("lgb.Dataset: basic construction, saving, loading", {
   # from sparse matrix
-  dtest1 <- lgb.Dataset(test_data, label=test_label)
+  dtest1 <- lgb.Dataset(test_data, label = test_label)
   # from dense matrix
-  dtest2 <- lgb.Dataset(as.matrix(test_data), label=test_label)
+  dtest2 <- lgb.Dataset(as.matrix(test_data), label = test_label)
   expect_equal(getinfo(dtest1, 'label'), getinfo(dtest2, 'label'))
 
   # save to a local file
@@ -40,7 +40,7 @@ test_that("lgb.Dataset: getinfo & setinfo", {
 })
 
 test_that("lgb.Dataset: slice, dim", {
-  dtest <- lgb.Dataset(test_data, label=test_label)
+  dtest <- lgb.Dataset(test_data, label = test_label)
   lgb.Dataset.construct(dtest)
   expect_equal(dim(dtest), dim(test_data))
   dsub1 <- slice(dtest, 1:42)
@@ -50,7 +50,7 @@ test_that("lgb.Dataset: slice, dim", {
 })
 
 test_that("lgb.Dataset: colnames", {
-  dtest <- lgb.Dataset(test_data, label=test_label)
+  dtest <- lgb.Dataset(test_data, label = test_label)
   expect_equal(colnames(dtest), colnames(test_data))
   lgb.Dataset.construct(dtest)
   expect_equal(colnames(dtest), colnames(test_data))
@@ -62,7 +62,7 @@ test_that("lgb.Dataset: colnames", {
 
 test_that("lgb.Dataset: nrow is correct for a very sparse matrix", {
   nr <- 1000
-  x <- Matrix::rsparsematrix(nr, 100, density=0.0005)
+  x <- Matrix::rsparsematrix(nr, 100, density = 0.0005)
   # we want it very sparse, so that last rows are empty
   expect_lt(max(x@i), nr)
   dtest <- lgb.Dataset(x)
@@ -70,15 +70,17 @@ test_that("lgb.Dataset: nrow is correct for a very sparse matrix", {
 })
 
 test_that("lgb.Dataset: Dataset should be able to construct from matrix and return non-null handle", {
-  rawData <- matrix(runif(1000),ncol=10)
+  rawData <- matrix(runif(1000), ncol = 10)
   handle <- NA_real_
   ref_handle <- NULL
-  handle <- lightgbm:::lgb.call("LGBM_DatasetCreateFromMat_R"
+  handle <- lightgbm:::lgb.call(
+    "LGBM_DatasetCreateFromMat_R"
     , ret = handle
     , rawData
     , nrow(rawData)
     , ncol(rawData)
-                                , lightgbm:::lgb.params2str(params=list())
-                                , ref_handle)
+    , lightgbm:::lgb.params2str(params = list())
+    , ref_handle
+  )
   expect_false(is.na(handle))
 })

R-package/tests/testthat/test_parameters.R

-data(agaricus.train, package='lightgbm')
-data(agaricus.test, package='lightgbm')
+
+context("feature penalties")
+
+data(agaricus.train, package = 'lightgbm')
+data(agaricus.test, package = 'lightgbm')
 train <- agaricus.train
 test <- agaricus.test
 
@@ -12,15 +15,15 @@ test_that("Feature penalties work properly", {
     feature_penalties <- rep(1, ncol(train$data))
     feature_penalties[var_index] <- x
     lightgbm(
-      data = train$data,
-      label = train$label,
-      num_leaves = 5,
-      learning_rate = 0.05,
-      nrounds = 20,
-      objective = "binary",
-      feature_penalty = paste0(feature_penalties, collapse = ","),
-      metric="binary_error",
-      verbose = -1
+      data = train$data
+      , label = train$label
+      , num_leaves = 5
+      , learning_rate = 0.05
+      , nrounds = 20
+      , objective = "binary"
+      , feature_penalty = paste0(feature_penalties, collapse = ",")
+      , metric = "binary_error"
+      , verbose = -1
     )
   })