geom_histogram: Histograms.

In SKKU-SKT/ggplot2.SparkR: Big Data Visualization for R and Spark

Description

Display a 1d distribution by dividing into bins and counting the number of observations in each bin. Histograms use bars.

stat_bin is suitable only for continuous x data. If your x data is discrete, you probably want to use stat_count.

Usage

geom_histogram(mapping = NULL, data = NULL, stat = "bin",
  binwidth = NULL, bins = NULL, origin = NULL, right = FALSE,
  position = "stack", na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, ...)

Arguments

mapping	Set of aesthetic mapping created by aes or aes_.
data	The data to be displayed in this layer.
stat	Use override the default connection between geom_histogram, geom_freqpoly and stat_bin
binwidth	???
bins	Does not use in ggplot2.SparkR
origin	Does not use in ggplot2.SparkR
right	Does not use in ggplot2.SparkR
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
na.rm	Not used in ggplot2.SparkR
show.legend	logical. Should this layer be included in the legends?
inherit.aes	If FALSE, overrides the default aesthetics, rather ehan combining with them.
...	other arguments passed on to layer.

Examples

## Not run: 
ggplot(faithful, aes(eruptions)) + geom_histogram()

df <- createDataFrame(sqlContext, faithful)
ggplot(df, aes(eruptions)) + geom_histogram()
ggplot(df, aes(eruptions)) + geom_histogram(binwidth = 5)

ggplot(df, aes(eruptions, fill = waiting)) + geom_histogram()

## End(Not run)

SKKU-SKT/ggplot2.SparkR documentation built on May 9, 2019, 11:13 a.m.