Github user felixcheung commented on a diff in the pull request: https://github.com/apache/spark/pull/11569#discussion_r60318933 --- 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]) + + # TODO: This will be when improved SPARK-9325 or SPARK-13436 are fixed + eval(parse(text = paste0("df$", colname))) + } else if (class(col) == "Column") { + # Append the given column to the dataset + df$x <- col + colname <- "x" + col + } + + stats <- collect(describe(df[, colname])) + min <- as.numeric(stats[4, 2]) + max <- as.numeric(stats[5, 2]) + + # Normalize the data + xnorm <- (x - min) / (max - min) + + # Round the data to 4 significant digits. This is to avoid rounding issues. + xnorm <- cast(xnorm * 10000, "integer") / 10000.0 + + # Since min = 0, max = 1 (data is already normalized) + normBinSize <- 1 / nbins + binsize <- (max - min) / nbins + approxBins <- xnorm / normBinSize + + # Adjust values that are equal to the upper bound of each bin + bins <- cast(approxBins - + ifelse(approxBins == cast(approxBins, "integer") & x != min, 1, 0), + "integer") + + df$bins <- bins --- End diff -- perhaps swap these two lines? ``` + df$x <- col + df <- na.omit(df) ``` to ``` + df <- na.omit(df) + df$x <- col ``` that would make it more clear?
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