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.
stat_sina( mapping = NULL, data = NULL, geom = "point", position = "dodge", scale = "area", method = "density", bw = "nrd0", kernel = "gaussian", maxwidth = NULL, adjust = 1, bin_limit = 1, binwidth = NULL, bins = NULL, seed = NA, jitter_y = TRUE, ..., na.rm = FALSE, orientation = NA, show.legend = NA, inherit.aes = TRUE ) geom_sina( mapping = NULL, data = NULL, stat = "sina", position = "dodge", ..., na.rm = FALSE, orientation = NA, show.legend = NA, inherit.aes = TRUE )
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
The data to be displayed in this layer. There are three options:
NULL, the default, the data is inherited from the plot
data as specified in the call to
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.
function will be called with a single argument,
the plot data. The return value must be a
will be used as the layer data. A
function can be created
~ head(.x, 10)).
The geometric object to use to display the data, either as a
Geom subclass or as a string naming the geom stripped of the
geom_ prefix (e.g.
"point" rather than
Position adjustment, either as a string naming the adjustment
"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.
How should each sina be scaled. Corresponds to the
ggplot2::geom_violin()? Available are:
'area' for scaling by the largest density/bin among the different sinas
'count' as above, but in addition scales by the maximum number of points
in the different sinas.
'width' Only scale according to the
For backwards compatibility it can also be a logical with
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
The smoothing bandwidth to be used.
If numeric, the standard deviation of the smoothing kernel.
If character, a rule to choose the bandwidth, as listed in
Kernel. See list of available kernels in
Control the maximum width the points can spread into. Values between 0 and 1.
A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
adjust = 1/2 means use half of the default bandwidth.
If the samples within the same y-axis bin are more
bin_limit, the samples's X coordinates will be adjusted.
The width of the bins. The default is to use
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.
Number of bins. Overridden by binwidth. Defaults to 50.
A seed to set for the jitter to ensure a reproducible plot
If y is integerish banding can occur and the default is to
jitter the values slightly to make them better distributed. Setting
jitter_y = FALSE turns off this behaviour
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.
FALSE, the default, missing values are removed with
a warning. If
TRUE, missing values are silently removed.
The orientation of the layer. The default (
automatically determines the orientation from the aesthetic mapping. In the
rare event that this fails it can be given explicitly by setting
"y". See the Orientation section for more detail.
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
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.
The statistical transformation to use on the data for this
layer, either as a
Geom subclass or as a string naming the
stat stripped of the
stat_ prefix (e.g.
"count" rather than
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, and
the samples are spread within that curve. In effect this means that points
will be positioned randomly within a violin plot with the same parameters.
method == "counts":The borders are defined by the number of samples that occupy the same bin.
geom_sina understand the following aesthetics (required aesthetics are in bold):
The density or sample counts per bin for each point
density scaled by the maximum density in each group
The number of points in the group the point belong to
This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail. In that case the orientation can be specified directly using the
orientation parameter, which can be either
"y". The value gives the axis that the geom should run along,
"x" being the default orientation you would expect for the geom.
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$popdensity > 10000 p + geom_sina(colour = cols + 1L) # Sina plots with continuous x: ggplot(midwest, aes(cut_width(area, 0.02), popdensity)) + geom_sina() + scale_y_log10() ### Sample gaussian distributions # Unimodal a <- rnorm(500, 6, 1) b <- rnorm(400, 5, 1.5) # Bimodal c <- c(rnorm(200, 3, .7), rnorm(50, 7, 0.4)) # Trimodal d <- c(rnorm(200, 2, 0.7), rnorm(300, 5.5, 0.4), rnorm(100, 8, 0.4)) df <- data.frame( 'Distribution' = c( rep('Unimodal 1', length(a)), rep('Unimodal 2', length(b)), rep('Bimodal', length(c)), rep('Trimodal', length(d)) ), 'Value' = c(a, b, c, d) ) # Reorder levels df$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)