% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sina.R
\name{geom_sina}
\alias{geom_sina}
\alias{stat_sina}
\title{Sina plot}
\usage{
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
)
}
\arguments{
\item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and
\code{inherit.aes = TRUE} (the default), it is combined with the default mapping
at the top level of the plot. You must supply \code{mapping} if there is no plot
mapping.}

\item{data}{The data to be displayed in this layer. There are three
options:

If \code{NULL}, the default, the data is inherited from the plot
data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}.

A \code{data.frame}, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
\code{\link[ggplot2:fortify]{fortify()}} for which variables will be created.

A \code{function} will be called with a single argument,
the plot data. The return value must be a \code{data.frame}, and
will be used as the layer data. A \code{function} can be created
from a \code{formula} (e.g. \code{~ head(.x, 10)}).}

\item{geom}{The geometric object to use to display the data, either as a
\code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the
\code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})}

\item{position}{Position adjustment, either as a string naming the adjustment
(e.g. \code{"jitter"} to use \code{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.}

\item{scale}{How should each sina be scaled. Corresponds to the \code{scale}
parameter in \code{\link[ggplot2:geom_violin]{ggplot2::geom_violin()}}? Available are:
\itemize{
\item \code{'area'} for scaling by the largest density/bin among the different sinas
\item \code{'count'} as above, but in addition scales by the maximum number of points
in the different sinas.
\item \code{'width'} Only scale according to the \code{maxwidth} parameter
}

For backwards compatibility it can also be a logical with \code{TRUE} meaning
\code{area} and \code{FALSE} meaning \code{width}}

\item{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 \code{Details}.}

\item{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
\code{\link[stats:bandwidth]{stats::bw.nrd()}}.}

\item{kernel}{Kernel. See list of available kernels in \code{\link[=density]{density()}}.}

\item{maxwidth}{Control the maximum width the points can spread into. Values
between 0 and 1.}

\item{adjust}{A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
For example, \code{adjust = 1/2} means use half of the default bandwidth.}

\item{bin_limit}{If the samples within the same y-axis bin are more
than \code{bin_limit}, the samples's X coordinates will be adjusted.}

\item{binwidth}{The width of the bins. The default is to use \code{bins}
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.}

\item{bins}{Number of bins. Overridden by binwidth. Defaults to 50.}

\item{seed}{A seed to set for the jitter to ensure a reproducible plot}

\item{jitter_y}{If y is integerish banding can occur and the default is to
jitter the values slightly to make them better distributed. Setting
\code{jitter_y = FALSE} turns off this behaviour}

\item{...}{Other arguments passed on to \code{\link[ggplot2:layer]{layer()}}. These are
often aesthetics, used to set an aesthetic to a fixed value, like
\code{colour = "red"} or \code{size = 3}. They may also be parameters
to the paired geom/stat.}

\item{na.rm}{If \code{FALSE}, the default, missing values are removed with
a warning. If \code{TRUE}, missing values are silently removed.}

\item{orientation}{The orientation of the layer. The default (\code{NA})
automatically determines the orientation from the aesthetic mapping. In the
rare event that this fails it can be given explicitly by setting \code{orientation}
to either \code{"x"} or \code{"y"}. See the \emph{Orientation} section for more detail.}

\item{show.legend}{logical. Should this layer be included in the legends?
\code{NA}, the default, includes if any aesthetics are mapped.
\code{FALSE} never includes, and \code{TRUE} always includes.
It can also be a named logical vector to finely select the aesthetics to
display.}

\item{inherit.aes}{If \code{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. \code{\link[ggplot2:borders]{borders()}}.}

\item{stat}{The statistical transformation to use on the data for this
layer, either as a \code{ggproto} \code{Geom} subclass or as a string naming the
stat stripped of the \code{stat_} prefix (e.g. \code{"count"} rather than
\code{"stat_count"})}
}
\description{
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.
}
\details{
There are two available ways to define the x-axis borders for the
samples to spread within:
\itemize{
\item{\code{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.
}
\item{\code{method == "counts"}:

The borders are defined by the number of samples that occupy the same bin.

}
}
}
\section{Aesthetics}{

geom_sina understand the following aesthetics (required aesthetics are in
bold):
\itemize{
\item \strong{x}
\item \strong{y}
\item color
\item group
\item size
\item alpha
}
}

\section{Computed variables}{


\describe{
\item{density}{The density or sample counts per bin for each point}
\item{scaled}{\code{density} scaled by the maximum density in each group}
\item{n}{The number of points in the group the point belong to}
}
}

\section{Orientation}{


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 \code{orientation} parameter, which can be either \code{"x"} or \code{"y"}. The value gives the axis that the geom should run along, \code{"x"} being the default orientation you would expect for the geom.

}

\examples{
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)
}
\author{
Nikos Sidiropoulos, Claus Wilke, and Thomas Lin Pedersen
}
