ggplot2 : geom_histogram()

geom_histogram()

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, 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, which is
# particularly useful when faceting along variables with
# different ranges because the function will be called once per facet
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)))