stat_central_tendency: Add Central Tendency Measures to a GGPLot

In ggpubr: 'ggplot2' Based Publication Ready Plots

Add Central Tendency Measures to a GGPLot

Description

Add central tendency measures (mean, median, mode) to density and histogram plots created using ggplots.

Note that, normally, the mode is used for categorical data where we wish to know which is the most common category. Therefore, we can have have two or more values that share the highest frequency. This might be problematic for continuous variable.

For continuous variable, we can consider using mean or median as the measures of the central tendency.

Usage

stat_central_tendency(
  mapping = NULL,
  data = NULL,
  geom = c("line", "point"),
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  type = c("mean", "median", "mode"),
  ...
)

Arguments

mapping	Set of aesthetic mappings created by 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 mapping if there is no plot mapping.
data	The data to be displayed in this layer. There are three options: If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot(). A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created. A function will 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. A function can be created from a formula (e.g. ~ head(.x, 10)).
geom	The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")
position	Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
na.rm	If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
show.legend	logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.
inherit.aes	If 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. borders().
type	the type of central tendency measure to be used. Possible values include: "mean", "median", "mode".
...	other arguments to pass to geom_line.

See Also

ggdensity

Examples

# Simple density plot
data("mtcars")
ggdensity(mtcars, x = "mpg", fill = "red") +
  scale_x_continuous(limits = c(-1, 50)) +
  stat_central_tendency(type = "mean", linetype = "dashed")

# Color by groups
data(iris)
ggdensity(iris, "Sepal.Length", color = "Species") +
  stat_central_tendency(aes(color = Species), type = "median", linetype = 2)

# Use geom = "point" for central tendency
data(iris)
ggdensity(iris, "Sepal.Length", color = "Species") +
  stat_central_tendency(
     aes(color = Species), type = "median",
     geom = "point", size = 4
     )

# Facet
ggdensity(iris, "Sepal.Length", facet.by = "Species") +
  stat_central_tendency(type = "mean", color = "red", linetype = 2) +
  stat_central_tendency(type = "median", color = "blue", linetype = 2)

ggpubr documentation built on Feb. 16, 2023, 7:18 p.m.