diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 523343ea9f4f32d01bcfbe69247c6cc18335d71a..bfec3245cf52ac905e55d87665f4601f5a1202a6 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1831,6 +1831,8 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #' Return subsets of SparkDataFrame according to given conditions
 #' @param x a SparkDataFrame.
 #' @param i,subset (Optional) a logical expression to filter on rows.
+#'                 For extract operator [[ and replacement operator [[<-, the indexing parameter for
+#'                 a single Column.
 #' @param j,select expression for the single Column or a list of columns to select from the SparkDataFrame.
 #' @param drop if TRUE, a Column will be returned if the resulting dataset has only one column.
 #'             Otherwise, a SparkDataFrame will always be returned.
@@ -1841,6 +1843,7 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #' @export
 #' @family SparkDataFrame functions
 #' @aliases subset,SparkDataFrame-method
+#' @seealso \link{withColumn}
 #' @rdname subset
 #' @name subset
 #' @family subsetting functions
@@ -1858,6 +1861,10 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #'   subset(df, df$age %in% c(19, 30), 1:2)
 #'   subset(df, df$age %in% c(19), select = c(1,2))
 #'   subset(df, select = c(1,2))
+#'   # Columns can be selected and set
+#'   df[["age"]] <- 23
+#'   df[[1]] <- df$age
+#'   df[[2]] <- NULL # drop column
 #' }
 #' @note subset since 1.5.0
 setMethod("subset", signature(x = "SparkDataFrame"),
@@ -1982,7 +1989,7 @@ setMethod("selectExpr",
 #' @aliases withColumn,SparkDataFrame,character-method
 #' @rdname withColumn
 #' @name withColumn
-#' @seealso \link{rename} \link{mutate}
+#' @seealso \link{rename} \link{mutate} \link{subset}
 #' @export
 #' @examples
 #'\dontrun{
@@ -1993,6 +2000,10 @@ setMethod("selectExpr",
 #' # Replace an existing column
 #' newDF2 <- withColumn(newDF, "newCol", newDF$col1)
 #' newDF3 <- withColumn(newDF, "newCol", 42)
+#' # Use extract operator to set an existing or new column
+#' df[["age"]] <- 23
+#' df[[2]] <- df$col1
+#' df[[2]] <- NULL # drop column
 #' }
 #' @note withColumn since 1.4.0
 setMethod("withColumn",
diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R
index 8da84499dfc097016316c44286387a9787b9f863..fee4a4cc9ff276e9e4f037deda51c1dc91bb961f 100644
--- a/R/pkg/R/mllib_classification.R
+++ b/R/pkg/R/mllib_classification.R
@@ -41,7 +41,7 @@ setClass("NaiveBayesModel", representation(jobj = "jobj"))
 
 #' Logistic Regression Model
 #'
-#' Fits an logistic regression model against a Spark DataFrame. It supports "binomial": Binary logistic regression
+#' Fits an logistic regression model against a SparkDataFrame. It supports "binomial": Binary logistic regression
 #' with pivoting; "multinomial": Multinomial logistic (softmax) regression without pivoting, similar to glmnet.
 #' Users can print, make predictions on the produced model and save the model to the input path.
 #'
diff --git a/R/pkg/R/mllib_clustering.R b/R/pkg/R/mllib_clustering.R
index 05bbab680d2796c31395eab0eae97fd4f4b02b70..e384c7398b22f4b675c0df0df27aba80c00bd74f 100644
--- a/R/pkg/R/mllib_clustering.R
+++ b/R/pkg/R/mllib_clustering.R
@@ -47,7 +47,7 @@ setClass("LDAModel", representation(jobj = "jobj"))
 
 #' Bisecting K-Means Clustering Model
 #'
-#' Fits a bisecting k-means clustering model against a Spark DataFrame.
+#' Fits a bisecting k-means clustering model against a SparkDataFrame.
 #' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
 #' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'
@@ -189,7 +189,7 @@ setMethod("write.ml", signature(object = "BisectingKMeansModel", path = "charact
 
 #' Multivariate Gaussian Mixture Model (GMM)
 #'
-#' Fits multivariate gaussian mixture model against a Spark DataFrame, similarly to R's
+#' Fits multivariate gaussian mixture model against a SparkDataFrame, similarly to R's
 #' mvnormalmixEM(). Users can call \code{summary} to print a summary of the fitted model,
 #' \code{predict} to make predictions on new data, and \code{write.ml}/\code{read.ml}
 #' to save/load fitted models.
@@ -314,7 +314,7 @@ setMethod("write.ml", signature(object = "GaussianMixtureModel", path = "charact
 
 #' K-Means Clustering Model
 #'
-#' Fits a k-means clustering model against a Spark DataFrame, similarly to R's kmeans().
+#' Fits a k-means clustering model against a SparkDataFrame, similarly to R's kmeans().
 #' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
 #' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'
diff --git a/R/pkg/R/mllib_regression.R b/R/pkg/R/mllib_regression.R
index 0e07d3bfd899c39932c4f0e5cd745014973a7b87..79086003463e7e6b50324a5ea78c6b0ab1307dd8 100644
--- a/R/pkg/R/mllib_regression.R
+++ b/R/pkg/R/mllib_regression.R
@@ -41,7 +41,7 @@ setClass("IsotonicRegressionModel", representation(jobj = "jobj"))
 
 #' Generalized Linear Models
 #'
-#' Fits generalized linear model against a Spark DataFrame.
+#' Fits generalized linear model against a SparkDataFrame.
 #' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
 #' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'
@@ -259,7 +259,7 @@ setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", pat
 
 #' Isotonic Regression Model
 #'
-#' Fits an Isotonic Regression model against a Spark DataFrame, similarly to R's isoreg().
+#' Fits an Isotonic Regression model against a SparkDataFrame, similarly to R's isoreg().
 #' Users can print, make predictions on the produced model and save the model to the input path.
 #'
 #' @param data SparkDataFrame for training.
diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd
index 9b0ded3b8d38d4c91dff3e1296dcac45bdb7d2b8..36a78477dc261205b9ad2c7a428c6ef9500ffc37 100644
--- a/R/pkg/vignettes/sparkr-vignettes.Rmd
+++ b/R/pkg/vignettes/sparkr-vignettes.Rmd
@@ -923,9 +923,9 @@ The main method calls of actual computation happen in the Spark JVM of the drive
 
 Two kinds of RPCs are supported in the SparkR JVM backend: method invocation and creating new objects. Method invocation can be done in two ways.
 
-* `sparkR.invokeJMethod` takes a reference to an existing Java object and a list of arguments to be passed on to the method.
+* `sparkR.callJMethod` takes a reference to an existing Java object and a list of arguments to be passed on to the method.
 
-* `sparkR.invokeJStatic` takes a class name for static method and a list of arguments to be passed on to the method.
+* `sparkR.callJStatic` takes a class name for static method and a list of arguments to be passed on to the method.
 
 The arguments are serialized using our custom wire format which is then deserialized on the JVM side. We then use Java reflection to invoke the appropriate method.