Github user shivaram commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11569#discussion_r60278119
  
    --- Diff: R/pkg/R/functions.R ---
    @@ -2638,3 +2638,100 @@ 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). Default value 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.
    +#' @rdname histogram
    +#' @family agg_funcs
    +#' @export
    +#' @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, the histogram can 
be
    +#' # rendered using the ggplot2 library:
    +#'
    +#' require(ggplot2)
    +#' plot <- ggplot(histStats, aes(x = centroids, y = counts))
    +#' plot <- plot + geom_histogram(data = histStats, stat = "identity", 
binwidth = 100)
    +#' plot <- plot + xlab("Sepal_Length") + ylab("Frequency")   
    +#' } 
    +setMethod("histogram",
    +          signature(df = "DataFrame", col = "characterOrColumn"),
    +          function(df, col, nbins = 10) {
    +            # Validate nbins
    +            if (nbins < 2) {
    +              stop("The number of bins must be a positive integer number 
greater than 1.")
    +            }
    +
    +            # Round nbins to the smallest integer
    +            nbins <- floor(nbins)
    +
    +            # Validate col
    +            if (is.null(col)) {
    +              stop("col must be specified.")
    +            }
    +
    +            colname <- col
    +            x <- if (class(col) == "character") {
    +              if (!colname %in% names(df)) {
    +                stop("Specified colname does not belong to the given 
DataFrame.")
    +              }
    +
    +              # Filter NA values in the target column
    +              df <- na.omit(df[, colname])
    --- End diff --
    
    If we are filtering NA values, we should also do it for the other case ? 


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