[SPARKR][DOCS] fix broken url in doc

R/pkg/R/DataFrame.R

@@ -35,7 +35,7 @@ setOldClass("structType")
 #' @slot env An R environment that stores bookkeeping states of the SparkDataFrame
 #' @slot sdf A Java object reference to the backing Scala DataFrame
 #' @seealso \link{createDataFrame}, \link{read.json}, \link{table}
-#' @seealso \url{https://spark.apache.org/docs/latest/sparkr.html#sparkdataframe}
+#' @seealso \url{https://spark.apache.org/docs/latest/sparkr.html#sparkr-dataframes}
 #' @export
 #' @examples
 #'\dontrun{

R/pkg/R/sparkR.R

@@ -28,14 +28,6 @@ connExists <- function(env) {
   })
 }
 
-#' @rdname sparkR.session.stop
-#' @name sparkR.stop
-#' @export
-#' @note sparkR.stop since 1.4.0
-sparkR.stop <- function() {
-  sparkR.session.stop()
-}
-
 #' Stop the Spark Session and Spark Context
 #'
 #' Stop the Spark Session and Spark Context.
@@ -90,6 +82,14 @@ sparkR.session.stop <- function() {
   clearJobjs()
 }
 
+#' @rdname sparkR.session.stop
+#' @name sparkR.stop
+#' @export
+#' @note sparkR.stop since 1.4.0
+sparkR.stop <- function() {
+  sparkR.session.stop()
+}
+
 #' (Deprecated) Initialize a new Spark Context
 #'
 #' This function initializes a new SparkContext.

docs/sparkr.md

@@ -322,8 +322,59 @@ head(ldf, 3)
 Apply a function to each group of a `SparkDataFrame`. The function is to be applied to each group of the `SparkDataFrame` and should have only two parameters: grouping key and R `data.frame` corresponding to
 that key. The groups are chosen from `SparkDataFrame`s column(s).
 The output of function should be a `data.frame`. Schema specifies the row format of the resulting
-`SparkDataFrame`. It must represent R function's output schema on the basis of Spark data types. The column names of the returned `data.frame` are set by user. Below is the data type mapping between R
-and Spark.
+`SparkDataFrame`. It must represent R function's output schema on the basis of Spark [data types](#data-type-mapping-between-r-and-spark). The column names of the returned `data.frame` are set by user.
+
+<div data-lang="r"  markdown="1">
+{% highlight r %}
+
+# Determine six waiting times with the largest eruption time in minutes.
+schema <- structType(structField("waiting", "double"), structField("max_eruption", "double"))
+result <- gapply(
+    df,
+    "waiting",
+    function(key, x) {
+        y <- data.frame(key, max(x$eruptions))
+    },
+    schema)
+head(collect(arrange(result, "max_eruption", decreasing = TRUE)))
+
+##    waiting   max_eruption
+##1      64       5.100
+##2      69       5.067
+##3      71       5.033
+##4      87       5.000
+##5      63       4.933
+##6      89       4.900
+{% endhighlight %}
+</div>
+
+##### gapplyCollect
+Like `gapply`, applies a function to each partition of a `SparkDataFrame` and collect the result back to R data.frame. The output of the function should be a `data.frame`. But, the schema is not required to be passed. Note that `gapplyCollect` can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.
+
+<div data-lang="r"  markdown="1">
+{% highlight r %}
+
+# Determine six waiting times with the largest eruption time in minutes.
+result <- gapplyCollect(
+    df,
+    "waiting",
+    function(key, x) {
+        y <- data.frame(key, max(x$eruptions))
+        colnames(y) <- c("waiting", "max_eruption")
+        y
+    })
+head(result[order(result$max_eruption, decreasing = TRUE), ])
+
+##    waiting   max_eruption
+##1      64       5.100
+##2      69       5.067
+##3      71       5.033
+##4      87       5.000
+##5      63       4.933
+##6      89       4.900
+
+{% endhighlight %}
+</div>
 
 #### Data type mapping between R and Spark
 <table class="table">
@@ -394,58 +445,6 @@ and Spark.
 </tr>
 </table>
 
-<div data-lang="r"  markdown="1">
-{% highlight r %}
-
-# Determine six waiting times with the largest eruption time in minutes.
-schema <- structType(structField("waiting", "double"), structField("max_eruption", "double"))
-result <- gapply(
-    df,
-    "waiting",
-    function(key, x) {
-        y <- data.frame(key, max(x$eruptions))
-    },
-    schema)
-head(collect(arrange(result, "max_eruption", decreasing = TRUE)))
-
-##    waiting   max_eruption
-##1      64       5.100
-##2      69       5.067
-##3      71       5.033
-##4      87       5.000
-##5      63       4.933
-##6      89       4.900
-{% endhighlight %}
-</div>
-
-##### gapplyCollect
-Like `gapply`, applies a function to each partition of a `SparkDataFrame` and collect the result back to R data.frame. The output of the function should be a `data.frame`. But, the schema is not required to be passed. Note that `gapplyCollect` can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.
-
-<div data-lang="r"  markdown="1">
-{% highlight r %}
-
-# Determine six waiting times with the largest eruption time in minutes.
-result <- gapplyCollect(
-    df,
-    "waiting",
-    function(key, x) {
-        y <- data.frame(key, max(x$eruptions))
-        colnames(y) <- c("waiting", "max_eruption")
-        y
-    })
-head(result[order(result$max_eruption, decreasing = TRUE), ])
-
-##    waiting   max_eruption
-##1      64       5.100
-##2      69       5.067
-##3      71       5.033
-##4      87       5.000
-##5      63       4.933
-##6      89       4.900
-
-{% endhighlight %}
-</div>
-
 #### Run local R functions distributed using `spark.lapply`
 
 ##### spark.lapply