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.
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)
Set of aesthetic mappings created by
The data to be displayed in this layer. There are three options:
Position adjustment, either as a string, or the result of a call to a position adjustment function.
|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
For a notched box plot, width of the notch relative to
the body (defaults to
logical. Should this layer be included in the legends?
Use to override the default connection between
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
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 largest value no further than 1.5 * IQR from 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 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):
Learn more about setting these aesthetics in
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, 12-16.
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()#> Warning: Continuous x aesthetic -- did you forget aes(group=...)?# 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" )