geom_boxplot(mapping = NULL, data = NULL, stat = "boxplot", position = "dodge", ..., outlier.colour = NULL, outlier.color = NULL, outlier.shape = 19, outlier.size = 1.5, outlier.stroke = 0.5, 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 = "dodge", ..., coef = 1.5, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
aes_. If specified and
inherit.aes = TRUE(the default), it is combined with the default mapping at the top level of the plot. You must supply
mappingif there is no plot mapping.
NULL, the default, the data is inherited from the plot data as specified in the call to
data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See
fortifyfor which variables will be created. A
functionwill be called with a single argument, the plot data. The return value must be a
data.frame., and will be used as the layer data.
layer. These are often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"or
size = 3. They may also be parameters to the paired geom/stat.
NULLto inherit from the aesthetics used for the box. In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.
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 suggests that the medians are significantly different.
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
FALSE(the default), removes missing values with a warning. If
TRUEsilently removes missing values.
NA, the default, includes if any aesthetics are mapped.
FALSEnever includes, and
FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g.
The lower and upper "hinges" correspond to the first and third quartiles
(the 25th and 75th percentiles). This differs slightly from the method used
boxplot function, and may be apparent with small samples.
boxplot.stats for for more information on how hinge
positions are calculated for
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.
geom_boxplot understands the following aesthetics (required aesthetics are in bold):
McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box plots. The American Statistician 32, 12-16.
p <- ggplot(mpg, aes(class, hwy)) p + geom_boxplot()
p + geom_boxplot() + geom_jitter(width = 0.2)
p + geom_boxplot() + coord_flip()
p + geom_boxplot(notch = TRUE)notch went outside hinges. Try setting notch=FALSE. notch went outside hinges. Try setting notch=FALSE.
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)
# 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()Warning message: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(diamonds, aes(carat, price)) + geom_boxplot(aes(group = cut_width(carat, 0.25)))
# 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" )