geom_histogram {ggplot2}R Documentation

geom_histogram

Description

Histogram

Usage

geom_histogram(mapping=NULL, data=NULL, stat="bin", position="stack", ...)

Arguments

mapping mapping between variables and aesthetics generated by aes
data dataset used in this layer, if not specified uses plot dataset
stat statistic used by this layer
position position adjustment used by this layer
... ignored

Details

geom_histogram is an alias for geom_bar + stat_bin so you will need to look at the documentation for those objects to get more information about the parameters.

This page describes geom_histogram, see layer and qplot for how to create a complete plot from individual components.

Value

A layer

Aesthetics

The following aesthetics can be used with geom_histogram. Aesthetics are mapped to variables in the data with the aes function: geom\_histogram(\code{aes}(x = var))

Advice

geom_histogram only allows you to set the width of the bins (with the binwidth parameter), not the number of bins, and it certainly does not suport the use of common heuristics to select the number of bins. In practice, you will need to use multiple bin widths to discover all the signal in the data, and having bins with meaningful widths (rather than some arbitrary fraction of the range of the data) is more interpretable.

Author(s)

Hadley Wickham, http://had.co.nz/

See Also

Examples

## Not run: 

# Simple examles
qplot(rating, data=movies, geom="histogram")
qplot(rating, data=movies, weight=votes, geom="histogram")
qplot(rating, data=movies, weight=votes, geom="histogram", binwidth=1)
qplot(rating, data=movies, weight=votes, geom="histogram", binwidth=0.1)

# More complex
m <- ggplot(movies, aes(x=rating))
m + geom_histogram()
m + geom_histogram(aes(y = ..density..)) + geom_density()

m + geom_histogram(binwidth = 1)
m + geom_histogram(binwidth = 0.5)
m + geom_histogram(binwidth = 0.1)

# Add aesthetic mappings
m + geom_histogram(aes(weight = votes))
m + geom_histogram(aes(y = ..count..))
m + geom_histogram(aes(fill = ..count..))

# Change scales
m + geom_histogram(aes(fill = ..count..)) + 
  scale_fill_gradient("Count", low = "green", high = "red")

# 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.
qplot(rating, data=movies, geom="bar", 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
qplot(rating, data=movies, geom="bar", binwidth = 0.1,
  weight=votes, ylab = "votes")

m <- ggplot(movies, aes(x = 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 = 1) + scale_x_log10()
m + geom_histogram() + scale_x_sqrt()
m + geom_histogram(binwidth = 10) + scale_x_sqrt()

# 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() + coord_trans(x = "log10")
m + geom_histogram() + coord_trans(x = "sqrt")
m + geom_histogram(binwidth=1000) + 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()
m + geom_histogram(binwidth = 0.5) + scale_y_reverse()

# Set aesthetics to fixed value
m + geom_histogram(colour = "darkgreen", fill = "white", binwidth = 0.5)

# Use facets
m <- m + geom_histogram(binwidth = 0.5)
m + facet_grid(Action ~ Comedy)

# Often more useful to use density on the y axis when facetting
m <- m + aes(y = ..density..)
m + facet_grid(Action ~ Comedy)
m + facet_wrap(~ mpaa)

# Multiple histograms on the same graph
# see ?position, ?position_fill, etc for more details
ggplot(diamonds, aes(x=price)) + geom_bar()
hist_cut <- ggplot(diamonds, aes(x=price, fill=cut))
hist_cut + geom_bar() # defaults to stacking
hist_cut + geom_bar(position="fill")
hist_cut + geom_bar(position="dodge")
## End(Not run)

[Package ggplot2 version 0.8.2 Index]