R/geomfreqpoly.r
, R/geomhistogram.r
, R/statbin.r
geom_histogram.Rd
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 the levels of a categorical variable.
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)
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. Can be specified as a numeric value,
or a function that calculates width from x.
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 
stat_bin
is suitable only for continuous x data. If your x data is
discrete, you probably want to use stat_count()
.
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 bin widths. You may need to look at a few to uncover the full
story behind your data.
geom_histogram
uses the same aesthetics as geom_bar()
;
geom_freqpoly
uses the same aesthetics as geom_line()
.
number of points in bin
density of points in bin, scaled to integrate to 1
count, scaled to maximum of 1
density, scaled to maximum of 1
stat_count()
, which counts the number of cases at each x
position, without binning. It is suitable for both discrete and continuous
x data, whereas stat_bin is suitable only for continuous x data.
#># Rather than stacking histograms, it's easier to compare frequency # polygons ggplot(diamonds, aes(price, fill = cut)) + geom_histogram(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, stat(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() }# You can specify a function for calculating binwidth, # particularly useful when faceting along variables with # different ranges mtlong < reshape2::melt(mtcars)#>ggplot(mtlong, aes(value)) + facet_wrap(~variable, scales = 'free_x') + geom_histogram(binwidth = function(x) 2 * IQR(x) / (length(x)^(1/3)))