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 | 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)
|
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. |
... |
other arguments passed on to |
binwidth |
The width of the bins. The default is to use |
bins |
Number of bins. Overridden by binwidth. Defaults to 50. |
scale |
Logical. When set to |
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 |
maxwidth |
Control the maximum width the points can spread into. Values between 0 and 1. |
adjust |
Adjusts the bandwidth of the density kernel when
|
bin_limit |
If the samples within the same y-axis bin are more
than |
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 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 stats::density()
.
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
sample counts per bin per group
adjusted x-coordinates
Nikos Sidiropoulos
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 | 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)
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