geom_boxjitter: A hybrid boxplot.
In ggpol: Visualizing Social Science Data with 'ggplot2'

Half boxplot, half scatterplot with customizable jitter.

geom_boxjitter(
  mapping = NULL,
  data = NULL,
  stat = "BoxJitter",
  position = "dodge",
  ...,
  outlier.colour = NULL,
  outlier.color = NULL,
  outlier.fill = NULL,
  outlier.shape = 19,
  outlier.size = 1.5,
  outlier.stroke = 0.5,
  outlier.alpha = NULL,
  outlier.intersect = FALSE,
  jitter.colour = NULL,
  jitter.color = NULL,
  jitter.fill = NULL,
  jitter.shape = 19,
  jitter.size = 1.5,
  jitter.stroke = 0.5,
  jitter.alpha = NULL,
  jitter.position = ggplot2::PositionJitter,
  jitter.params = list(width = NULL, height = NULL),
  boxplot.expand = FALSE,
  notch = FALSE,
  notchwidth = 0.5,
  varwidth = FALSE,
  errorbar.draw = FALSE,
  errorbar.length = 0.5,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

`mapping`	Set of aesthetic mappings created by `aes()` or `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)`).
`stat`	Use to override the default connection between `geom_boxplot` and `stat_boxplot`.
`position`	Position adjustment, either as a string, or the result of a call to a position adjustment function.
`...`	Other arguments passed on to `layer()`. These are often aesthetics, used to set an aesthetic to a fixed value, like `colour = "red"` or `size = 3`. They may also be parameters to the paired geom/stat.
`outlier.colour`	Default aesthetics for outliers. Set to `NULL` to 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. 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 `outlier.shape = NA`. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.
`outlier.color`	Default aesthetics for outliers. Set to `NULL` to 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. 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 `outlier.shape = NA`. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.
`outlier.fill`	Default aesthetics for outliers. Set to `NULL` to 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. 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 `outlier.shape = NA`. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.
`outlier.shape`	Default aesthetics for outliers. Set to `NULL` to 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. 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 `outlier.shape = NA`. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.
`outlier.size`	Default aesthetics for outliers. Set to `NULL` to 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. 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 `outlier.shape = NA`. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.
`outlier.stroke`	Default aesthetics for outliers. Set to `NULL` to 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. 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 `outlier.shape = NA`. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.
`outlier.alpha`	Default aesthetics for outliers. Set to `NULL` to 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. 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 `outlier.shape = NA`. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.
`outlier.intersect`	Defaults to 'FALSE'. If set to 'TRUE', outliers will be part of the jitter-plot (but keeping the given outlier graphical parameters) rather than plotted vertically above / below the whisker lines.
`jitter.colour, jitter.color, jitter.fill, jitter.shape, jitter.size, jitter.stroke, jitter.alpha`	Default aesthetics for jitter, set to 'NULL' to inherit from the aesthetics used for the box.
`jitter.position`	Position object used for calculating jitter (defaults to 'ggplot2::PositionJitter').
`jitter.params`	Parameters passed to 'jitter.position' (for 'ggplot2::PositionJitter', this is 'width', 'height' and 'seed').
`boxplot.expand`	Defaults to 'FALSE'. If set to 'TRUE', the full boxplots will be plotted.
`notch`	If `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.
`notchwidth`	For a notched box plot, width of the notch relative to the body (defaults to `notchwidth = 0.5`).
`varwidth`	If `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 `weight` aesthetic).
`errorbar.draw`	Draw horizontal whiskers at the top and bottom (the IQR). Defaults to 'FALSE'.
`errorbar.length`	Length of the horizontal whiskers (errorbar). Defaults to half the width of the half-boxplot, or half the width of the entire boxplot if 'boxplot.expand' is set to 'TRUE'.
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
`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()`.

set.seed(221)
df <- data.frame(score = rgamma(150, 4, 1), 
                 gender = sample(c("M", "F"), 150, replace = TRUE), 
                 genotype = factor(sample(1:3, 150, replace = TRUE)))

ggplot(df) + geom_boxjitter(aes(x = gender, y = score, fill = genotype),
                            jitter.shape = 21, jitter.color = NA,
                            outlier.color = NA, errorbar.draw = TRUE) +
  scale_fill_manual(values = c("#CF3721", "#31A9B8", "#258039")) +
  theme_minimal()