geom_sina.Rd
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)
mapping  Set of aesthetic mappings created by 

data  The data to be displayed in this layer. There are three options: If A A 
geom  The geometric object to use display the data 
position  Position adjustment, either as a string, or the result of a call to a position adjustment function. 
scale  How should each sina be scaled. Corresponds to the
For backwards compatibility it can also be a logical with 
method  Choose the method to spread the samples within the same
bin along the xaxis. Available methods: "density", "counts" (can be
abbreviated, e.g. "d"). See 
bw  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 SPLUS.)
The specified (or computed) value of 
kernel  a character string giving the smoothing kernel
to be used. This must partially match one of

maxwidth  Control the maximum width the points can spread into. Values between 0 and 1. 
adjust  the bandwidth used is actually 
bin_limit  If the samples within the same yaxis bin are more
than 
binwidth  The width of the bins. The default is to use 
bins  Number of bins. Overridden by binwidth. Defaults to 50. 
seed  A seed to set for the jitter to ensure a reproducible plot 
...  Other arguments passed on to 
na.rm  If 
show.legend  logical. Should this layer be included in the legends?

inherit.aes  If 
stat  The statistical transformation to use on the data for this layer, as a string. 
There are two available ways to define the xaxis borders for the samples to spread within:
method == "density"
A density kernel is estimated along the yaxis 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):
x
y
color
group
size
alpha
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 groupwise scaling off with: p + geom_violin() + geom_sina(scale = FALSE)