Histograms and frequency polygons
Description
Visualise the distribution of a single continuous variable by dividing
the x axis into bins and counting the number of observations in each bin.
Histograms (geom_histogram
) display the count with bars; frequency
polygons (geom_freqpoly
), display the counts with lines. Frequency
polygons are more suitable when you want to compare the distribution
across a the levels of a categorical variable.
stat_bin
is suitable only for continuous x data. If your x data is
discrete, you probably want to use stat_count
.
Usage
1 2 3 4 5 6 7 8 9 10 11 12  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",
position = "stack", ..., binwidth = NULL, bins = NULL, na.rm = FALSE,
show.legend = NA, inherit.aes = TRUE)
stat_bin(mapping = NULL, data = NULL, geom = "bar", position = "stack",
..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL,
breaks = NULL, closed = c("right", "left"), pad = FALSE,
na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Arguments
mapping 
Set of aesthetic mappings created by 
data 
The data to be displayed in this layer. There are three options: If A A 
position 
Position adjustment, either as a string, or the result of a call to a position adjustment function. 
... 
other arguments passed on to 
na.rm 
If 
show.legend 
logical. Should this layer be included in the legends?

inherit.aes 
If 
binwidth 
The width of the bins. The default is to use The bin width of a date variable is the number of days in each time; the bin width of a time variable is the number of seconds. 
bins 
Number of bins. Overridden by 
geom, stat 
Use to override the default connection between

center 
The center of one of the bins. Note that if center is above or
below the range of the data, things will be shifted by an appropriate
number of 
boundary 
A boundary between two bins. As with 
breaks 
Alternatively, you can supply a numeric vector giving
the bin boundaries. Overrides 
closed 
One of 
pad 
If 
Details
By default, the underlying computation (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.
Aesthetics
geom_histogram
uses the same aesthetics as geom_bar
;
geom_freqpoly
uses the same aesthetics as geom_line
.
Computed variables
 count
number of points in bin
 density
density of points in bin, scaled to integrate to 1
 ncount
count, scaled to maximum of 1
 ndensity
density, scaled to maximum of 1
See Also
stat_count
, which counts the number of cases at each x
posotion, without binning. It is suitable for both discrete and continuous
x data, whereas stat_bin is suitable only for continuous x data.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52  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(boundary = 0) + coord_trans(x = "log10")
# Use boundary = 0, to make sure we don't take sqrt of negative values
m + geom_histogram(boundary = 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()
}
