Add Github Action for R. (#5911)

* Fix lintr errors.

R-package/tests/testthat/test_interaction_constraints.R

@@ -5,20 +5,20 @@ context("interaction constraints")
 set.seed(1024)
 x1 <- rnorm(1000, 1)
 x2 <- rnorm(1000, 1)
-x3 <- sample(c(1,2,3), size=1000, replace=TRUE)
-y <- x1 + x2 + x3 + x1*x2*x3 + rnorm(1000, 0.001) + 3*sin(x1)
-train <- matrix(c(x1,x2,x3), ncol = 3)
+x3 <- sample(c(1, 2, 3), size = 1000, replace = TRUE)
+y <- x1 + x2 + x3 + x1 * x2 * x3 + rnorm(1000, 0.001) + 3 * sin(x1)
+train <- matrix(c(x1, x2, x3), ncol = 3)
 
 test_that("interaction constraints for regression", {
   # Fit a model that only allows interaction between x1 and x2
   bst <- xgboost(data = train, label = y, max_depth = 3,
                  eta = 0.1, nthread = 2, nrounds = 100, verbose = 0,
-                 interaction_constraints = list(c(0,1)))
+                 interaction_constraints = list(c(0, 1)))
 
   # Set all observations to have the same x3 values then increment
   #  by the same amount
-  preds <- lapply(c(1,2,3), function(x){
-    tmat <- matrix(c(x1,x2,rep(x,1000)), ncol=3)
+  preds <- lapply(c(1, 2, 3), function(x){
+    tmat <- matrix(c(x1, x2, rep(x, 1000)), ncol = 3)
     return(predict(bst, tmat))
   })
 
@@ -40,16 +40,16 @@ test_that("interaction constraints scientific representation", {
   rows <- 10
   ## When number exceeds 1e5, R paste function uses scientific representation.
   ## See: https://github.com/dmlc/xgboost/issues/5179
-  cols <- 1e5+10
+  cols <- 1e5 + 10
 
-  d <- matrix(rexp(rows, rate=.1), nrow=rows, ncol=cols)
+  d <- matrix(rexp(rows, rate = .1), nrow = rows, ncol = cols)
   y <- rnorm(rows)
 
-  dtrain <- xgb.DMatrix(data=d, info = list(label=y))
+  dtrain <- xgb.DMatrix(data = d, info = list(label = y))
   inc <- list(c(seq.int(from = 0, to = cols, by = 1)))
 
-  with_inc <- xgb.train(data=dtrain, tree_method='hist',
-                        interaction_constraints=inc, nrounds=10)
-  without_inc <- xgb.train(data=dtrain, tree_method='hist', nrounds=10)
+  with_inc <- xgb.train(data = dtrain, tree_method = 'hist',
+                        interaction_constraints = inc, nrounds = 10)
+  without_inc <- xgb.train(data = dtrain, tree_method = 'hist', nrounds = 10)
   expect_equal(xgb.save.raw(with_inc), xgb.save.raw(without_inc))
 })