Github user olarayej commented on a diff in the pull request:
https://github.com/apache/spark/pull/11569#discussion_r57343915
--- Diff: R/pkg/R/functions.R ---
@@ -2638,3 +2638,81 @@ setMethod("sort_array",
jc <- callJStatic("org.apache.spark.sql.functions",
"sort_array", x@jc, asc)
column(jc)
})
+
+#' This function computes a histogram for a given SparkR Column.
+#'
+#' @name histogram
+#' @title Histogram
+#' @param nbins the number of bins (optional). The default is 10.
+#' @param df the DataFrame containing the Column to build the histogram
from.
+#' @param colname the name of the column to build the histogram from.
+#' @return a data.frame with the histogram statistics, i.e., counts and
centroids.
+#' @examples \dontrun{
+#'
+#' # Create a DataFrame from the Iris dataset
+#' irisDF <- createDataFrame(sqlContext, iris)
+#'
+#' # Compute histogram statistics
+#' histData <- histogram(df, "colname"Sepal_Length", nbins = 12)
+#'
+#' # Once SparkR has computed the histogram statistics, it would be very
easy to
+#' # render the histogram using R's visualization packages such as ggplot2.
+#'
+#' }
+setMethod("histogram",
+ signature(df = "DataFrame"),
+ function(df, colname, nbins = 10) {
--- End diff --
Yeah, but then what if the user wants to do:
`hist(irisDF, irisDF$Sepal_Length + 1)`
describe() would fail as this column doesn't belong to df.
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