[SPARK-12327][SPARKR] fix code for lintr warning for commented code

shivaram

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #10408 from felixcheung/rcodecomment.

9 files changed, 88 insertions, 11 deletions

diff --git a/R/pkg/.lintr b/R/pkg/.lintr
index 39c872663a..038236fc14 100644
--- a/R/pkg/.lintr
+++ b/R/pkg/.lintr
@@ -1,2 +1,2 @@
-linters: with_defaults(line_length_linter(100), camel_case_linter = NULL, open_curly_linter(allow_single_line = TRUE), closed_curly_linter(allow_single_line = TRUE), commented_code_linter = NULL)
+linters: with_defaults(line_length_linter(100), camel_case_linter = NULL, open_curly_linter(allow_single_line = TRUE), closed_curly_linter(allow_single_line = TRUE))
 exclusions: list("inst/profile/general.R" = 1, "inst/profile/shell.R")
diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R
index 00c40c38ca..a78fbb714f 100644
--- a/R/pkg/R/RDD.R
+++ b/R/pkg/R/RDD.R
@@ -180,7 +180,7 @@ setMethod("getJRDD", signature(rdd = "PipelinedRDD"),
             }
             # Save the serialization flag after we create a RRDD
             rdd@env$serializedMode <- serializedMode
-            rdd@env$jrdd_val <- callJMethod(rddRef, "asJavaRDD") # rddRef$asJavaRDD()
+            rdd@env$jrdd_val <- callJMethod(rddRef, "asJavaRDD")
             rdd@env$jrdd_val
           })
 
@@ -225,7 +225,7 @@ setMethod("cache",
 #'
 #' Persist this RDD with the specified storage level. For details of the
 #' supported storage levels, refer to
-#' http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence.
+#'\url{http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence}.
 #'
 #' @param x The RDD to persist
 #' @param newLevel The new storage level to be assigned
@@ -382,11 +382,13 @@ setMethod("collectPartition",
 #' \code{collectAsMap} returns a named list as a map that contains all of the elements
 #' in a key-value pair RDD.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)), 2L)
 #' collectAsMap(rdd) # list(`1` = 2, `3` = 4)
 #'}
+# nolint end
 #' @rdname collect-methods
 #' @aliases collectAsMap,RDD-method
 #' @noRd
@@ -442,11 +444,13 @@ setMethod("length",
 #' @return list of (value, count) pairs, where count is number of each unique
 #' value in rdd.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, c(1,2,3,2,1))
 #' countByValue(rdd) # (1,2L), (2,2L), (3,1L)
 #'}
+# nolint end
 #' @rdname countByValue
 #' @aliases countByValue,RDD-method
 #' @noRd
@@ -597,11 +601,13 @@ setMethod("mapPartitionsWithIndex",
 #' @param x The RDD to be filtered.
 #' @param f A unary predicate function.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, 1:10)
 #' unlist(collect(filterRDD(rdd, function (x) { x < 3 }))) # c(1, 2)
 #'}
+# nolint end
 #' @rdname filterRDD
 #' @aliases filterRDD,RDD,function-method
 #' @noRd
@@ -756,11 +762,13 @@ setMethod("foreachPartition",
 #' @param x The RDD to take elements from
 #' @param num Number of elements to take
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, 1:10)
 #' take(rdd, 2L) # list(1, 2)
 #'}
+# nolint end
 #' @rdname take
 #' @aliases take,RDD,numeric-method
 #' @noRd
@@ -824,11 +832,13 @@ setMethod("first",
 #' @param x The RDD to remove duplicates from.
 #' @param numPartitions Number of partitions to create.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, c(1,2,2,3,3,3))
 #' sort(unlist(collect(distinct(rdd)))) # c(1, 2, 3)
 #'}
+# nolint end
 #' @rdname distinct
 #' @aliases distinct,RDD-method
 #' @noRd
@@ -974,11 +984,13 @@ setMethod("takeSample", signature(x = "RDD", withReplacement = "logical",
 #' @param x The RDD.
 #' @param func The function to be applied.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(1, 2, 3))
 #' collect(keyBy(rdd, function(x) { x*x })) # list(list(1, 1), list(4, 2), list(9, 3))
 #'}
+# nolint end
 #' @rdname keyBy
 #' @aliases keyBy,RDD
 #' @noRd
@@ -1113,11 +1125,13 @@ setMethod("saveAsTextFile",
 #' @param numPartitions Number of partitions to create.
 #' @return An RDD where all elements are sorted.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(3, 2, 1))
 #' collect(sortBy(rdd, function(x) { x })) # list (1, 2, 3)
 #'}
+# nolint end
 #' @rdname sortBy
 #' @aliases sortBy,RDD,RDD-method
 #' @noRd
@@ -1188,11 +1202,13 @@ takeOrderedElem <- function(x, num, ascending = TRUE) {
 #' @param num Number of elements to return.
 #' @return The first N elements from the RDD in ascending order.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7))
 #' takeOrdered(rdd, 6L) # list(1, 2, 3, 4, 5, 6)
 #'}
+# nolint end
 #' @rdname takeOrdered
 #' @aliases takeOrdered,RDD,RDD-method
 #' @noRd
@@ -1209,11 +1225,13 @@ setMethod("takeOrdered",
 #' @return The top N elements from the RDD.
 #' @rdname top
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7))
 #' top(rdd, 6L) # list(10, 9, 7, 6, 5, 4)
 #'}
+# nolint end
 #' @aliases top,RDD,RDD-method
 #' @noRd
 setMethod("top",
@@ -1261,6 +1279,7 @@ setMethod("fold",
 #' @rdname aggregateRDD
 #' @seealso reduce
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(1, 2, 3, 4))
@@ -1269,6 +1288,7 @@ setMethod("fold",
 #' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) }
 #' aggregateRDD(rdd, zeroValue, seqOp, combOp) # list(10, 4)
 #'}
+# nolint end
 #' @aliases aggregateRDD,RDD,RDD-method
 #' @noRd
 setMethod("aggregateRDD",
@@ -1367,12 +1387,14 @@ setMethod("setName",
 #' @return An RDD with zipped items.
 #' @seealso zipWithIndex
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L)
 #' collect(zipWithUniqueId(rdd))
 #' # list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2))
 #'}
+# nolint end
 #' @rdname zipWithUniqueId
 #' @aliases zipWithUniqueId,RDD
 #' @noRd
@@ -1408,12 +1430,14 @@ setMethod("zipWithUniqueId",
 #' @return An RDD with zipped items.
 #' @seealso zipWithUniqueId
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L)
 #' collect(zipWithIndex(rdd))
 #' # list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4))
 #'}
+# nolint end
 #' @rdname zipWithIndex
 #' @aliases zipWithIndex,RDD
 #' @noRd
@@ -1454,12 +1478,14 @@ setMethod("zipWithIndex",
 #' @return An RDD created by coalescing all elements within
 #'         each partition into a list.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, as.list(1:4), 2L)
 #' collect(glom(rdd))
 #' # list(list(1, 2), list(3, 4))
 #'}
+# nolint end
 #' @rdname glom
 #' @aliases glom,RDD
 #' @noRd
@@ -1519,6 +1545,7 @@ setMethod("unionRDD",
 #' @param other Another RDD to be zipped.
 #' @return An RDD zipped from the two RDDs.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, 0:4)
@@ -1526,6 +1553,7 @@ setMethod("unionRDD",
 #' collect(zipRDD(rdd1, rdd2))
 #' # list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004))
 #'}
+# nolint end
 #' @rdname zipRDD
 #' @aliases zipRDD,RDD
 #' @noRd
@@ -1557,12 +1585,14 @@ setMethod("zipRDD",
 #' @param other An RDD.
 #' @return A new RDD which is the Cartesian product of these two RDDs.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, 1:2)
 #' sortByKey(cartesian(rdd, rdd))
 #' # list(list(1, 1), list(1, 2), list(2, 1), list(2, 2))
 #'}
+# nolint end
 #' @rdname cartesian
 #' @aliases cartesian,RDD,RDD-method
 #' @noRd
@@ -1587,6 +1617,7 @@ setMethod("cartesian",
 #' @param numPartitions Number of the partitions in the result RDD.
 #' @return An RDD with the elements from this that are not in other.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, list(1, 1, 2, 2, 3, 4))
@@ -1594,6 +1625,7 @@ setMethod("cartesian",
 #' collect(subtract(rdd1, rdd2))
 #' # list(1, 1, 3)
 #'}
+# nolint end
 #' @rdname subtract
 #' @aliases subtract,RDD
 #' @noRd
@@ -1619,6 +1651,7 @@ setMethod("subtract",
 #' @param numPartitions The number of partitions in the result RDD.
 #' @return An RDD which is the intersection of these two RDDs.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5))
@@ -1626,6 +1659,7 @@ setMethod("subtract",
 #' collect(sortBy(intersection(rdd1, rdd2), function(x) { x }))
 #' # list(1, 2, 3)
 #'}
+# nolint end
 #' @rdname intersection
 #' @aliases intersection,RDD
 #' @noRd
@@ -1653,6 +1687,7 @@ setMethod("intersection",
 #'         Assumes that all the RDDs have the *same number of partitions*, but
 #'         does *not* require them to have the same number of elements in each partition.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, 1:2, 2L)  # 1, 2
@@ -1662,6 +1697,7 @@ setMethod("intersection",
 #'                       func = function(x, y, z) { list(list(x, y, z))} ))
 #' # list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6)))
 #'}
+# nolint end
 #' @rdname zipRDD
 #' @aliases zipPartitions,RDD
 #' @noRd
diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index f7e56e4301..d8a0393275 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -17,6 +17,7 @@
 
 # Utility functions to deserialize objects from Java.
 
+# nolint start
 # Type mapping from Java to R
 #
 # void -> NULL
@@ -32,6 +33,8 @@
 #
 # Array[T] -> list()
 # Object -> jobj
+#
+# nolint end
 
 readObject <- function(con) {
   # Read type first
diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R
index 334c11d2f8..f7131140fe 100644
--- a/R/pkg/R/pairRDD.R
+++ b/R/pkg/R/pairRDD.R
@@ -30,12 +30,14 @@ NULL
 #' @param key The key to look up for
 #' @return a list of values in this RDD for key key
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' pairs <- list(c(1, 1), c(2, 2), c(1, 3))
 #' rdd <- parallelize(sc, pairs)
 #' lookup(rdd, 1) # list(1, 3)
 #'}
+# nolint end
 #' @rdname lookup
 #' @aliases lookup,RDD-method
 #' @noRd
@@ -58,11 +60,13 @@ setMethod("lookup",
 #' @param x The RDD to count keys.
 #' @return list of (key, count) pairs, where count is number of each key in rdd.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(c("a", 1), c("b", 1), c("a", 1)))
 #' countByKey(rdd) # ("a", 2L), ("b", 1L)
 #'}
+# nolint end
 #' @rdname countByKey
 #' @aliases countByKey,RDD-method
 #' @noRd
@@ -77,11 +81,13 @@ setMethod("countByKey",
 #'
 #' @param x The RDD from which the keys of each tuple is returned.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)))
 #' collect(keys(rdd)) # list(1, 3)
 #'}
+# nolint end
 #' @rdname keys
 #' @aliases keys,RDD
 #' @noRd
@@ -98,11 +104,13 @@ setMethod("keys",
 #'
 #' @param x The RDD from which the values of each tuple is returned.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)))
 #' collect(values(rdd)) # list(2, 4)
 #'}
+# nolint end
 #' @rdname values
 #' @aliases values,RDD
 #' @noRd
@@ -348,6 +356,7 @@ setMethod("reduceByKey",
 #' @return A list of elements of type list(K, V') where V' is the merged value for each key
 #' @seealso reduceByKey
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
@@ -355,6 +364,7 @@ setMethod("reduceByKey",
 #' reduced <- reduceByKeyLocally(rdd, "+")
 #' reduced # list(list(1, 6), list(1.1, 3))
 #'}
+# nolint end
 #' @rdname reduceByKeyLocally
 #' @aliases reduceByKeyLocally,RDD,integer-method
 #' @noRd
@@ -412,6 +422,7 @@ setMethod("reduceByKeyLocally",
 #' @return An RDD where each element is list(K, C) where C is the combined type
 #' @seealso groupByKey, reduceByKey
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
@@ -420,6 +431,7 @@ setMethod("reduceByKeyLocally",
 #' combined <- collect(parts)
 #' combined[[1]] # Should be a list(1, 6)
 #'}
+# nolint end
 #' @rdname combineByKey
 #' @aliases combineByKey,RDD,ANY,ANY,ANY,integer-method
 #' @noRd
@@ -473,6 +485,7 @@ setMethod("combineByKey",
 #' @return An RDD containing the aggregation result.
 #' @seealso foldByKey, combineByKey
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4)))
@@ -482,6 +495,7 @@ setMethod("combineByKey",
 #' aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L)
 #'   # list(list(1, list(3, 2)), list(2, list(7, 2)))
 #'}
+# nolint end
 #' @rdname aggregateByKey
 #' @aliases aggregateByKey,RDD,ANY,ANY,ANY,integer-method
 #' @noRd
@@ -509,11 +523,13 @@ setMethod("aggregateByKey",
 #' @return An RDD containing the aggregation result.
 #' @seealso aggregateByKey, combineByKey
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4)))
 #' foldByKey(rdd, 0, "+", 2L) # list(list(1, 3), list(2, 7))
 #'}
+# nolint end
 #' @rdname foldByKey
 #' @aliases foldByKey,RDD,ANY,ANY,integer-method
 #' @noRd
@@ -540,12 +556,14 @@ setMethod("foldByKey",
 #' @return a new RDD containing all pairs of elements with matching keys in
 #'         two input RDDs.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
 #' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3)))
 #' join(rdd1, rdd2, 2L) # list(list(1, list(1, 2)), list(1, list(1, 3))
 #'}
+# nolint end
 #' @rdname join-methods
 #' @aliases join,RDD,RDD-method
 #' @noRd
@@ -578,6 +596,7 @@ setMethod("join",
 #'         all pairs (k, (v, w)) for (k, w) in rdd2, or the pair (k, (v, NULL))
 #'         if no elements in rdd2 have key k.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
@@ -585,6 +604,7 @@ setMethod("join",
 #' leftOuterJoin(rdd1, rdd2, 2L)
 #' # list(list(1, list(1, 2)), list(1, list(1, 3)), list(2, list(4, NULL)))
 #'}
+# nolint end
 #' @rdname join-methods
 #' @aliases leftOuterJoin,RDD,RDD-method
 #' @noRd
@@ -616,6 +636,7 @@ setMethod("leftOuterJoin",
 #'         all pairs (k, (v, w)) for (k, v) in x, or the pair (k, (NULL, w))
 #'         if no elements in x have key k.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3)))
@@ -623,6 +644,7 @@ setMethod("leftOuterJoin",
 #' rightOuterJoin(rdd1, rdd2, 2L)
 #' # list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4)))
 #'}
+# nolint end
 #' @rdname join-methods
 #' @aliases rightOuterJoin,RDD,RDD-method
 #' @noRd
@@ -655,6 +677,7 @@ setMethod("rightOuterJoin",
 #'         (k, w) in y, or the pair (k, (NULL, w))/(k, (v, NULL)) if no elements
 #'         in x/y have key k.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3), list(3, 3)))
@@ -664,6 +687,7 @@ setMethod("rightOuterJoin",
 #'                               #      list(2, list(NULL, 4)))
 #'                               #      list(3, list(3, NULL)),
 #'}
+# nolint end
 #' @rdname join-methods
 #' @aliases fullOuterJoin,RDD,RDD-method
 #' @noRd
@@ -688,6 +712,7 @@ setMethod("fullOuterJoin",
 #' @return a new RDD containing all pairs of elements with values in a list
 #' in all RDDs.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
@@ -695,6 +720,7 @@ setMethod("fullOuterJoin",
 #' cogroup(rdd1, rdd2, numPartitions = 2L)
 #' # list(list(1, list(1, list(2, 3))), list(2, list(list(4), list()))
 #'}
+# nolint end
 #' @rdname cogroup
 #' @aliases cogroup,RDD-method
 #' @noRd
@@ -740,11 +766,13 @@ setMethod("cogroup",
 #' @param numPartitions Number of partitions to create.
 #' @return An RDD where all (k, v) pair elements are sorted.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd <- parallelize(sc, list(list(3, 1), list(2, 2), list(1, 3)))
 #' collect(sortByKey(rdd)) # list (list(1, 3), list(2, 2), list(3, 1))
 #'}
+# nolint end
 #' @rdname sortByKey
 #' @aliases sortByKey,RDD,RDD-method
 #' @noRd
@@ -805,6 +833,7 @@ setMethod("sortByKey",
 #' @param numPartitions Number of the partitions in the result RDD.
 #' @return An RDD with the pairs from x whose keys are not in other.
 #' @examples
+# nolint start
 #'\dontrun{
 #' sc <- sparkR.init()
 #' rdd1 <- parallelize(sc, list(list("a", 1), list("b", 4),
@@ -813,6 +842,7 @@ setMethod("sortByKey",
 #' collect(subtractByKey(rdd1, rdd2))
 #' # list(list("b", 4), list("b", 5))
 #'}
+# nolint end
 #' @rdname subtractByKey
 #' @aliases subtractByKey,RDD
 #' @noRd
diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R
index 17082b4e52..095ddb9aed 100644
--- a/R/pkg/R/serialize.R
+++ b/R/pkg/R/serialize.R
@@ -17,6 +17,7 @@
 
 # Utility functions to serialize R objects so they can be read in Java.
 
+# nolint start
 # Type mapping from R to Java
 #
 # NULL -> Void
@@ -31,6 +32,7 @@
 # list[T] -> Array[T], where T is one of above mentioned types
 # environment -> Map[String, T], where T is a native type
 # jobj -> Object, where jobj is an object created in the backend
+# nolint end
 
 getSerdeType <- function(object) {
   type <- class(object)[[1]]
diff --git a/R/pkg/inst/tests/testthat/test_rdd.R b/R/pkg/inst/tests/testthat/test_rdd.R
index 7423b4f2be..1b3a22486e 100644
--- a/R/pkg/inst/tests/testthat/test_rdd.R
+++ b/R/pkg/inst/tests/testthat/test_rdd.R
@@ -223,14 +223,14 @@ test_that("takeSample() on RDDs", {
     s <- takeSample(data, TRUE, 100L, seed)
     expect_equal(length(s), 100L)
     # Chance of getting all distinct elements is astronomically low, so test we
-    # got < 100
+    # got less than 100
     expect_true(length(unique(s)) < 100L)
   }
   for (seed in 4:5) {
     s <- takeSample(data, TRUE, 200L, seed)
     expect_equal(length(s), 200L)
     # Chance of getting all distinct elements is still quite low, so test we
-    # got < 100
+    # got less than 100
     expect_true(length(unique(s)) < 100L)
   }
 })
diff --git a/R/pkg/inst/tests/testthat/test_shuffle.R b/R/pkg/inst/tests/testthat/test_shuffle.R
index adf0b91d25..d3d0f8a24d 100644
--- a/R/pkg/inst/tests/testthat/test_shuffle.R
+++ b/R/pkg/inst/tests/testthat/test_shuffle.R
@@ -176,8 +176,8 @@ test_that("partitionBy() partitions data correctly", {
 
   resultRDD <- partitionBy(numPairsRdd, 2L, partitionByMagnitude)
 
-  expected_first <- list(list(1, 100), list(2, 200)) # key < 3
-  expected_second <- list(list(4, -1), list(3, 1), list(3, 0)) # key >= 3
+  expected_first <- list(list(1, 100), list(2, 200)) # key less than 3
+  expected_second <- list(list(4, -1), list(3, 1), list(3, 0)) # key greater than or equal 3
   actual_first <- collectPartition(resultRDD, 0L)
   actual_second <- collectPartition(resultRDD, 1L)
 
diff --git a/R/pkg/inst/tests/testthat/test_sparkSQL.R b/R/pkg/inst/tests/testthat/test_sparkSQL.R
index 7b508b860e..9e5d0ebf60 100644
--- a/R/pkg/inst/tests/testthat/test_sparkSQL.R
+++ b/R/pkg/inst/tests/testthat/test_sparkSQL.R
@@ -498,9 +498,11 @@ test_that("table() returns a new DataFrame", {
   expect_equal(count(tabledf), 3)
   dropTempTable(sqlContext, "table1")
 
+  # nolint start
   # Test base::table is working
   #a <- letters[1:3]
   #expect_equal(class(table(a, sample(a))), "table")
+  # nolint end
 })
 
 test_that("toRDD() returns an RRDD", {
@@ -766,8 +768,10 @@ test_that("sample on a DataFrame", {
   sampled3 <- sample_frac(df, FALSE, 0.1, 0) # set seed for predictable result
   expect_true(count(sampled3) < 3)
 
+  # nolint start
   # Test base::sample is working
   #expect_equal(length(sample(1:12)), 12)
+  # nolint end
 })
 
 test_that("select operators", {
@@ -1052,8 +1056,8 @@ test_that("string operators", {
   df2 <- createDataFrame(sqlContext, l2)
   expect_equal(collect(select(df2, locate("aa", df2$a)))[1, 1], 1)
   expect_equal(collect(select(df2, locate("aa", df2$a, 1)))[1, 1], 2)
-  expect_equal(collect(select(df2, lpad(df2$a, 8, "#")))[1, 1], "###aaads")
-  expect_equal(collect(select(df2, rpad(df2$a, 8, "#")))[1, 1], "aaads###")
+  expect_equal(collect(select(df2, lpad(df2$a, 8, "#")))[1, 1], "###aaads") # nolint
+  expect_equal(collect(select(df2, rpad(df2$a, 8, "#")))[1, 1], "aaads###") # nolint
 
   l3 <- list(list(a = "a.b.c.d"))
   df3 <- createDataFrame(sqlContext, l3)
@@ -1259,7 +1263,7 @@ test_that("filter() on a DataFrame", {
   expect_equal(count(filtered6), 2)
 
   # Test stats::filter is working
-  #expect_true(is.ts(filter(1:100, rep(1, 3))))
+  #expect_true(is.ts(filter(1:100, rep(1, 3)))) # nolint
 })
 
 test_that("join() and merge() on a DataFrame", {
@@ -1659,7 +1663,7 @@ test_that("cov() and corr() on a DataFrame", {
   expect_true(abs(result - 1.0) < 1e-12)
 
   # Test stats::cov is working
-  #expect_true(abs(max(cov(swiss)) - 1739.295) < 1e-3)
+  #expect_true(abs(max(cov(swiss)) - 1739.295) < 1e-3) # nolint
 })
 
 test_that("freqItems() on a DataFrame", {
diff --git a/R/pkg/inst/tests/testthat/test_utils.R b/R/pkg/inst/tests/testthat/test_utils.R
index 12df4cf4f6..56f14a3bce 100644
--- a/R/pkg/inst/tests/testthat/test_utils.R
+++ b/R/pkg/inst/tests/testthat/test_utils.R
@@ -95,7 +95,9 @@ test_that("cleanClosure on R functions", {
   # TODO(shivaram): length(ls(env)) is 4 here for some reason and `lapply` is included in `env`.
   # Disabling this test till we debug this.
   #
+  # nolint start
   # expect_equal(length(ls(env)), 3)  # Only "g", "l" and "f". No "base", "field" or "defUse".
+  # nolint end
   expect_true("g" %in% ls(env))
   expect_true("l" %in% ls(env))
   expect_true("f" %in% ls(env))