Description Usage Arguments Summary statistics Aesthetics Computed variables References See Also Examples
The boxplot compactly displays the distribution of a continuous variable. It visualises five summary statistics (the median, two hinges and two whiskers), and all "outlying" points individually.
1 2 3 4 5 6 7 8 9 10  geom_boxplot(mapping = NULL, data = NULL, stat = "boxplot",
position = "dodge2", ..., outlier.colour = NULL,
outlier.color = NULL, outlier.fill = NULL, outlier.shape = 19,
outlier.size = 1.5, outlier.stroke = 0.5, outlier.alpha = NULL,
notch = FALSE, notchwidth = 0.5, varwidth = FALSE, na.rm = FALSE,
show.legend = NA, inherit.aes = TRUE)
stat_boxplot(mapping = NULL, data = NULL, geom = "boxplot",
position = "dodge2", ..., coef = 1.5, 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 
outlier.colour, outlier.color, outlier.fill, outlier.shape, outlier.size, outlier.stroke, outlier.alpha 
Default aesthetics for outliers. Set to In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence. Sometimes it can be useful to hide the outliers, for example when overlaying
the raw data points on top of the boxplot. Hiding the outliers can be achieved
by setting 
notch 
If 
notchwidth 
For a notched box plot, width of the notch relative to
the body (defaults to 
varwidth 
If 
na.rm 
If 
show.legend 
logical. Should this layer be included in the legends?

inherit.aes 
If 
geom, stat 
Use to override the default connection between

coef 
Length of the whiskers as multiple of IQR. Defaults to 1.5. 
The lower and upper 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()
.
The upper whisker extends from the hinge to the largest value no further than 1.5 * IQR from the hinge (where IQR is the interquartile range, or distance between the first and third quartiles). The lower whisker extends from the hinge to the smallest value at most 1.5 * IQR of the hinge. Data beyond the end of the whiskers are called "outlying" points and are plotted individually.
In a notched box plot, the notches extend 1.58 * IQR / sqrt(n)
.
This gives a roughly 95% confidence interval for comparing medians.
See McGill et al. (1978) for more details.
geom_boxplot()
understands the following aesthetics (required aesthetics are in bold):
x
lower
upper
middle
ymin
ymax
alpha
colour
fill
group
linetype
shape
size
weight
Learn more about setting these aesthetics in vignette("ggplot2specs")
.
width of boxplot
lower whisker = smallest observation greater than or equal to lower hinge  1.5 * IQR
lower hinge, 25% quantile
lower edge of notch = median  1.58 * IQR / sqrt(n)
median, 50% quantile
upper edge of notch = median + 1.58 * IQR / sqrt(n)
upper hinge, 75% quantile
upper whisker = largest observation less than or equal to upper hinge + 1.5 * IQR
McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box plots. The American Statistician 32, 1216.
geom_quantile()
for continuous x
,
geom_violin()
for a richer display of the distribution, and
geom_jitter()
for a useful technique for small data.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  p < ggplot(mpg, aes(class, hwy))
p + geom_boxplot()
p + geom_boxplot() + coord_flip()
p + geom_boxplot(notch = TRUE)
p + geom_boxplot(varwidth = TRUE)
p + geom_boxplot(fill = "white", colour = "#3366FF")
# By default, outlier points match the colour of the box. Use
# outlier.colour to override
p + geom_boxplot(outlier.colour = "red", outlier.shape = 1)
# Remove outliers when overlaying boxplot with original data points
p + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.2)
# Boxplots are automatically dodged when any aesthetic is a factor
p + geom_boxplot(aes(colour = drv))
# You can also use boxplots with continuous x, as long as you supply
# a grouping variable. cut_width is particularly useful
ggplot(diamonds, aes(carat, price)) +
geom_boxplot()
ggplot(diamonds, aes(carat, price)) +
geom_boxplot(aes(group = cut_width(carat, 0.25)))
# Adjust the transparency of outliers using outlier.alpha
ggplot(diamonds, aes(carat, price)) +
geom_boxplot(aes(group = cut_width(carat, 0.25)), outlier.alpha = 0.1)
# It's possible to draw a boxplot with your own computations if you
# use stat = "identity":
y < rnorm(100)
df < data.frame(
x = 1,
y0 = min(y),
y25 = quantile(y, 0.25),
y50 = median(y),
y75 = quantile(y, 0.75),
y100 = max(y)
)
ggplot(df, aes(x)) +
geom_boxplot(
aes(ymin = y0, lower = y25, middle = y50, upper = y75, ymax = y100),
stat = "identity"
)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.