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

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 95 See McGill et al. (1978) for more details.

Aesthetics

geom_boxplot understands the following aesthetics (required aesthetics are in bold):

• lower

• middle

• upper

• x

• ymax

• ymin

• alpha

• colour

• fill

• linetype

• shape

• size

• weight

References

McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box plots. The American Statistician 32, 12-16.

See Also

stat_quantile to view quantiles conditioned on a continuous variable, geom_jitter for another way to look at conditional distributions"

Examples

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()