geom_freqpoly: Histograms and frequency polygons.

Description

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

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

Usage

geom_freqpoly(mapping = NULL, data = NULL, stat = "bin",
  position = "identity", na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, ...)
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, ...)
stat_bin(mapping = NULL, data = NULL, geom = "bar", position = "stack",
  width = 0.9, drop = FALSE, right = FALSE, binwidth = NULL,
  bins = NULL, origin = NULL, breaks = NULL, na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE, ...)

Arguments

mapping

Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), is combined with the default mapping at the top le

data

A data frame. If specified, overrides the default data frame defined at the top level of the plot.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

na.rm

If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g.

...

other arguments passed on to layer. There are three types of arguments you can use here:

Aesthetics: to set an aesthetic to a fixed value, likecolor = "red"orsize = 3.

binwidth

Bin width to use. Defaults to 1/bins of the range of the data

bins

Number of bins. Overridden by binwidth or breaks. Defaults to 30

origin

Origin of first bin

right

If TRUE, right-closed, left-open, if FALSE, the default, right-open, left-closed.

geom, stat

Use to override the default connection between geom_histogram/geom_freqpoly and stat_bin.

width

Width of bars when used with categorical data

drop

If TRUE, remove all bins with zero counts

breaks

Actual breaks to use. Overrides bin width, bin number and origin

Aesthetics

geom_histogram uses the same aesthetics as geom_bar; geom_freqpoly uses the same aesthetics as geom_line.

Details

By default, stat_bin uses 30 bins - this is not a good default, but the idea is to get you experimenting with different binwidths. You may need to look at a few to uncover the full story behind your data.

Examples

Run this code

ggplot(diamonds, aes(carat)) +
  geom_histogram()
ggplot(diamonds, aes(carat)) +
  geom_histogram(binwidth = 0.01)
ggplot(diamonds, aes(carat)) +
  geom_histogram(bins = 200)

# Rather than stacking histograms, it's easier to compare frequency
# polygons
ggplot(diamonds, aes(price, fill = cut)) +
  geom_histogram(binwidth = 500)
ggplot(diamonds, aes(price, colour = cut)) +
  geom_freqpoly(binwidth = 500)

# To make it easier to compare distributions with very different counts,
# put density on the y axis instead of the default count
ggplot(diamonds, aes(price, ..density.., colour = cut)) +
  geom_freqpoly(binwidth = 500)

if (require("ggplot2movies")) {
# Often we don't want the height of the bar to represent the
# count of observations, but the sum of some other variable.
# For example, the following plot shows the number of movies
# in each rating.
m <- ggplot(movies, aes(rating))
m + geom_histogram(binwidth = 0.1)

# If, however, we want to see the number of votes cast in each
# category, we need to weight by the votes variable
m + geom_histogram(aes(weight = votes), binwidth = 0.1) + ylab("votes")

# For transformed scales, binwidth applies to the transformed data.
# The bins have constant width on the transformed scale.
m + geom_histogram() + scale_x_log10()
m + geom_histogram(binwidth = 0.05) + scale_x_log10()

# For transformed coordinate systems, the binwidth applies to the
# raw data. The bins have constant width on the original scale.

# Using log scales does not work here, because the first
# bar is anchored at zero, and so when transformed becomes negative
# infinity. This is not a problem when transforming the scales, because
# no observations have 0 ratings.
m + geom_histogram(origin = 0) + coord_trans(x = "log10")
# Use origin = 0, to make sure we don't take sqrt of negative values
m + geom_histogram(origin = 0) + coord_trans(x = "sqrt")

# You can also transform the y axis.  Remember that the base of the bars
# has value 0, so log transformations are not appropriate
m <- ggplot(movies, aes(x = rating))
m + geom_histogram(binwidth = 0.5) + scale_y_sqrt()
}
rm(movies)