ggforce (version 0.1.1)

geom_sina: Sina plot

Description

The sina plot is a data visualization chart suitable for plotting any single variable in a multiclass dataset. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class.

Usage

stat_sina(mapping = NULL, data = NULL, geom = "sina", position = "identity", ..., binwidth = NULL, bins = NULL, scale = TRUE, method = "density", maxwidth = NULL, adjust = 1, bin_limit = 1, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
geom_sina(mapping = NULL, data = NULL, stat = "sina", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Arguments

mapping

Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), is combined with the default mapping at the top level of the plot. You only need to supply mapping if there isn't a mapping defined for the plot.

data

A data frame. If specified, overrides the default data frame defined at the top level of the plot.

geom,

stat Override the default connection between geom_sina and stat_sina.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

...

other arguments passed on to layer. There are three types of arguments you can use here:

• Aesthetics: to set an aesthetic to a fixed value, like color = "red" or size = 3.
• Other arguments to the layer, for example you override the default stat associated with the layer.
• Other arguments passed on to the stat.

binwidth

The width of the bins. The default is to use bins bins that cover the range of the data. You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data.

bins

Number of bins. Overridden by binwidth. Defaults to 50.

scale

Logical. When set to TRUE x-coordinate widths across all groups are scaled based on the densiest area in the plot. Default: TRUE

method

Choose the method to spread the samples within the same bin along the x-axis. Available methods: "density", "counts" (can be abbreviated, e.g. "d"). See Details.

maxwidth

Control the maximum width the points can spread into. Values between 0 and 1.

adjust

Adjusts the bandwidth of the density kernel when method == "density" (see density).

bin_limit

If the samples within the same y-axis bin are more than bin_limit, the samples's X coordinates will be adjusted.

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.

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.

stat

The statistical transformation to use on the data for this layer, as a string.

Aesthetics

geom_point understands the following aesthetics (required aesthetics are in bold):

• x
• y
• alpha
• colour
• fill
• shape
• size
• stroke

Computed variables

Details

There are two available ways to define the x-axis borders for the samples to spread within:

• method == "density"

  A density kernel is estimated along the y-axis for every sample group. The borders are then defined by the density curve. Tuning parameter adjust can be used to control the density bandwidth in the same way it is used in density.

• method == "counts":

  The borders are defined by the number of samples that occupy the same bin.

Examples

ggplot(midwest, aes(state, area)) + geom_point()

# Boxplot and Violin plots convey information on the distribution but not the
# number of samples, while Jitter does the opposite.
ggplot(midwest, aes(state, area)) + geom_violin()
ggplot(midwest, aes(state, area)) + geom_jitter()

# Sina does both!
ggplot(midwest, aes(state, area)) + geom_violin() + geom_sina()

p <- ggplot(midwest, aes(state, popdensity)) + scale_y_log10()
p + geom_sina()

# Colour the points based on the data set's columns
p + geom_sina(aes(colour = inmetro))

# Or any other way
cols <- midwest$popdensity > 10000
p + geom_sina(colour = cols + 1L)

# Sina plots with continuous x:
p <- ggplot(midwest, aes(cut_width(area, 0.02), popdensity)) + scale_y_log10()
p + geom_sina()


###Sample gaussian distributions
# Unimodal
a <- rnorm(500, 6, 1)
b <- rnorm(400, 5, 1.5)

# Bimodal
c <- c(rnorm(200, 3, .7), rnorm(50, 7, 0.4))

# Trimodal
d <- c(rnorm(200, 2, 0.7), rnorm(300, 5.5, 0.4), rnorm(100, 8, 0.4))

df <- data.frame(
  "Distribution" = c(rep("Unimodal 1", length(a)),
                     rep("Unimodal 2", length(b)),
                     rep("Bimodal", length(c)),
                     rep("Trimodal", length(d))),
  "Value" = c(a, b, c, d))

# Reorder levels
df$Distribution <- factor(df$Distribution,
                          levels(df$Distribution)[c(3, 4, 1, 2)])

p <- ggplot(df, aes(Distribution, Value))
p + geom_boxplot()
p + geom_violin() + geom_sina()

# By default, Sina plot scales the width of the class according to the width
# of the class with the highest density. Turn group-wise scaling off with:
p + geom_violin() + geom_sina(scale = FALSE)