R: Sina plot

geom_sina {ggforce} | R Documentation

## Sina plot

### 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.

### Usage

    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)

### Arguments

`mapping` | Set of aesthetic mappings created by `aes` or `aes_`. If specified and `inherit.aes = TRUE` (the default), is combined with the default mapping at the top level of the plot. You only need to supply mapping if there isn't a mapping defined for the plot.
`data` | A data frame. If specified, overrides the default data frame defined at the top level of the plot.
`geom,` | stat Override the default connection between `geom_sina` and `stat_sina`.
`position` | Position adjustment, either as a string, or the result of a call to a position adjustment function.
`...` | other arguments passed on to `layer`. There are three types of arguments you can use here: Aesthetics: to set an aesthetic to a fixed value, like `color = "red"` or `size = 3.` Other arguments to the layer, for example you override the default `stat` associated with the layer. Other arguments passed on to the stat.
`binwidth` | The width of the bins. The default is to use `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.
`bins` | Number of bins. Overridden by binwidth. Defaults to 50.
`scale` | Logical. When set to `TRUE` x-coordinate widths across all groups are scaled based on the densiest area in the plot. Default: `TRUE`
`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 `Details`.
`maxwidth` | Control the maximum width the points can spread into. Values between 0 and 1.
`adjust` | Adjusts the bandwidth of the density kernel when `method == "density"` (see `density`).
`bin_limit` | If the samples within the same y-axis bin are more than `bin_limit`, the samples's X coordinates will be adjusted.
`na.rm` | If `FALSE` (the default), removes missing values with a warning. If `TRUE` silently removes missing values.
`show.legend` | logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes.
`inherit.aes` | If `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. borders.
`stat` | The statistical transformation to use on the data for this layer, as a string.

### Details

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 `density`.

- `method == "counts"`:

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

### Aesthetics

@section Aesthetics: `geom_point` understands the following aesthetics (required aesthetics are in bold):

- `x`

- `y`

- `alpha`

- `colour`

- `fill`

- `group`

- `shape`

- `size`

- `stroke`

Learn more about setting these aesthetics in `vignette("ggplot2-specs")`

### Computed variables

bin_counts

  sample counts per bin per group

scaled

  adjusted x-coordinates

### Author(s)

Nikos Sidiropoulos

### 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:
    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)

[Package ggforce version 0.1.2 Index]