Description Usage Arguments Details Aesthetics Computed variables Author(s) Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  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.
If numeric, the standard deviation of the smoothing kernel.
If character, a rule to choose the bandwidth, as listed in

kernel 
Kernel. See list of available kernels in 
maxwidth 
Control the maximum width the points can spread into. Values between 0 and 1. 
adjust 
A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
For example, 
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
Nikos Sidiropoulos, Claus Wilke, and Thomas Lin Pedersen
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70  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)

Loading required package: ggplot2
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