geom_boxplot: Box and whiskers plot.

Description

The upper and lower "hinges" correspond to the first and third quartiles (the 25th and 75th percentiles). This differs slightly from the method used by the boxplot function, and may be apparent with small samples. See boxplot.stats for for more information on how hinge positions are calculated for boxplot.

Usage

geom_boxplot(mapping = NULL, data = NULL, stat = "boxplot",
  position = "dodge", outlier.colour = NULL, outlier.shape = NULL,
  outlier.size = NULL, notch = FALSE, notchwidth = 0.5,
  varwidth = FALSE, ...)

Arguments

mapping

The aesthetic mapping, usually constructed with aes or aes_string. Only needs to be set at the layer level if you are overriding the plot defaults.

data

A layer specific dataset - only needed if you want to override the plot defaults.

stat

The statistical transformation to use on the data for this layer.

position

The position adjustment to use for overlapping points on this layer

outlier.colour

colour for outlying points. Uses the default from geom_point().

outlier.shape

shape of outlying points. Uses the default from geom_point().

outlier.size

size of outlying points. Uses the default from geom_point().

notch

if FALSE (default) make a standard box plot. If TRUE, make a notched box plot. Notches are used to compare groups; if the notches of two boxes do not overlap, this is strong evidence that the medians differ.

notchwidth

for a notched box plot, width of the notch relative to the body (default 0.5)

varwidth

if FALSE (default) make a standard box plot. If TRUE, boxes are drawn with widths proportional to the square-roots of the number of observations in the groups (possibly weighted, using the weight aesthetic).

...

other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Aesthetics

[results=rd,stage=build]{ggplot2:::rd_aesthetics("geom", "boxplot")} p <- ggplot(mtcars, aes(factor(cyl), mpg))

p + geom_boxplot() qplot(factor(cyl), mpg, data = mtcars, geom = "boxplot")

p + geom_boxplot() + geom_jitter() p + geom_boxplot() + coord_flip() qplot(factor(cyl), mpg, data = mtcars, geom = "boxplot") + coord_flip()

p + geom_boxplot(notch = TRUE) p + geom_boxplot(notch = TRUE, notchwidth = .3)

p + geom_boxplot(outlier.colour = "green", outlier.size = 3)

# Add aesthetic mappings # Note that boxplots are automatically dodged when any aesthetic is # a factor p + geom_boxplot(aes(fill = cyl)) p + geom_boxplot(aes(fill = factor(cyl))) p + geom_boxplot(aes(fill = factor(vs))) p + geom_boxplot(aes(fill = factor(am)))

# Set aesthetics to fixed value p + geom_boxplot(fill = "grey80", colour = "#3366FF") qplot(factor(cyl), mpg, data = mtcars, geom = "boxplot", colour = I("#3366FF"))

# Scales vs. coordinate transforms ------- # Scale transformations occur before the boxplot statistics are computed. # Coordinate transformations occur afterwards. Observe the effect on the # number of outliers. library(plyr) # to access round_any m <- ggplot(movies, aes(y = votes, x = rating, group = round_any(rating, 0.5))) m + geom_boxplot() m + geom_boxplot() + scale_y_log10() m + geom_boxplot() + coord_trans(y = "log10") m + geom_boxplot() + scale_y_log10() + coord_trans(y = "log10")

# Boxplots with continuous x: # Use the group aesthetic to group observations in boxplots qplot(year, budget, data = movies, geom = "boxplot") qplot(year, budget, data = movies, geom = "boxplot", group = round_any(year, 10, floor))

# Using precomputed statistics # generate sample data abc <- adply(matrix(rnorm(100), ncol = 5), 2, quantile, c(0, .25, .5, .75, 1)) b <- ggplot(abc, aes(x = X1, ymin = `0%`, lower = `25%`, middle = `50%`, upper = `75%`, ymax = `100%`)) b + geom_boxplot(stat = "identity") b + geom_boxplot(stat = "identity") + coord_flip() b + geom_boxplot(aes(fill = X1), stat = "identity")

# Using varwidth p + geom_boxplot(varwidth = TRUE) qplot(factor(cyl), mpg, data = mtcars, geom = "boxplot", varwidth = TRUE)

# Update the defaults for the outliers by changing the defaults for geom_point

p <- ggplot(mtcars, aes(factor(cyl), mpg)) p + geom_boxplot()

update_geom_defaults("point", list(shape = 1, colour = "red", size = 5)) p + geom_boxplot()

McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box plots. The American Statistician 32, 12-16. stat_quantile to view quantiles conditioned on a continuous variable, geom_jitter for another way to look at conditional distributions"

Details

The upper whisker extends from the hinge to the highest value that is within 1.5 * IQR of the hinge, where IQR is the inter-quartile range, or distance between the first and third quartiles. The lower whisker extends from the hinge to the lowest value within 1.5 * IQR of the hinge. Data beyond the end of the whiskers are outliers and plotted as points (as specified by Tukey).

In a notched box plot, the notches extend 1.58 * IQR / sqrt(n). This gives a roughly 95See McGill et al. (1978) for more details.