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 = "sina", position = "dodge", scale = "area", method = "density", bw = "nrd0", kernel = "gaussian", maxwidth = NULL, adjust = 1, bin_limit = 1, binwidth = NULL, bins = NULL, seed = NA, ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) geom_sina(mapping = NULL, data = NULL, stat = "sina", position = "dodge", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
Set of aesthetic mappings created by
The data to be displayed in this layer. There are three options:
The geometric object to use display the data
Position adjustment, either as a string, or the result of a call to a position adjustment function.
How should each sina be scaled. Corresponds 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. The kernels are scaled such that this is the standard deviation of the smoothing kernel. (Note this differs from the reference books cited below, and from S-PLUS.)
The specified (or computed) value of
a character string giving the smoothing kernel
to be used. This must partially match one of
Control the maximum width the points can spread into. Values between 0 and 1.
the bandwidth used is actually
If the samples within the same y-axis bin are more
The width of the bins. The default is to use
Number of bins. Overridden by binwidth. Defaults to 50.
A seed to set for the jitter to ensure a reproducible plot
Other arguments passed on to
logical. Should this layer be included in the legends?
The statistical transformation to use on the data for this layer, as a string.
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
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)