View source: R/geom-sideboxplot.r
geom_xsideboxplot | R Documentation |
The xside and yside variants of geom_boxplot is geom_xsideboxplot and geom_ysideboxplot.
geom_xsideboxplot( 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, orientation = "x", show.legend = NA, inherit.aes = TRUE ) geom_ysideboxplot( 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, orientation = "y", 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 |
stat |
The statistical transformation to use on the data for this
layer, either as a |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
... |
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 |
orientation |
The orientation of the layer. The default ( |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
XLayer or YLayer object to be added to a ggplot object
geom_*sideviolin
df <- expand.grid(UpperCase = LETTERS, LowerCase = letters) df$Combo_Index <- as.integer(df$UpperCase)*as.integer(df$LowerCase) p1 <- ggplot(df, aes(UpperCase, LowerCase)) + geom_tile(aes(fill = Combo_Index)) #sideboxplots p1 + geom_xsideboxplot(aes(y = Combo_Index)) + geom_ysideboxplot(aes(x = Combo_Index)) + #when mixing continuous/discrete scales #use the following helper functions scale_xsidey_continuous() + scale_ysidex_continuous() #sideboxplots with swapped orientation #Note: They order of the layers are affects the default # scale type. If you were to omit the last two scales, the # data labels may be affected ggplot(iris, aes(Sepal.Width, Sepal.Length, color = Species)) + geom_xsideboxplot(aes(y = Species), orientation = "y") + geom_point() + scale_y_continuous() + scale_xsidey_discrete() #If using the scale_(xsidey|ysidex)_* functions are a bit cumbersome, # Take extra care to recast your data types. ggplot(iris, aes(Sepal.Width, Sepal.Length, color = Species))+ geom_point() + geom_xsideboxplot(aes(y = as.numeric(Species)), orientation = "y") + geom_ysideboxplot(aes(x = as.numeric(Species)), orientation = "x")
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