ggplot2 geoms with support for pattern fills

Source: R/geom-rect.R, R/geom-bar.R, R/geom-bin2d.R, and 10 more

All geoms in this package are identical to their counterparts in ggplot2 except that they can be filled with patterns.

geom_rect_pattern(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  linejoin = "mitre",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_bar_pattern(
  mapping = NULL,
  data = NULL,
  stat = "count",
  position = "stack",
  ...,
  width = NULL,
  binwidth = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_histogram_pattern(
  mapping = NULL,
  data = NULL,
  stat = "bin",
  position = "stack",
  ...,
  binwidth = NULL,
  bins = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_bin2d_pattern(
  mapping = NULL,
  data = NULL,
  stat = "bin2d",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_boxplot_pattern(
  mapping = NULL,
  data = NULL,
  stat = "boxplot",
  position = "dodge2",
  ...,
  outlier.colour = NULL,
  outlier.color = NULL,
  outlier.fill = NULL,
  outlier.shape = 19,
  outlier.size = 1.5,
  outlier.stroke = 0.5,
  outlier.alpha = NULL,
  notch = FALSE,
  notchwidth = 0.5,
  varwidth = FALSE,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_col_pattern(
  mapping = NULL,
  data = NULL,
  position = "stack",
  ...,
  width = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_crossbar_pattern(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  fatten = 2.5,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_ribbon_pattern(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  outline.type = "both"
)

geom_area_pattern(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "stack",
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  outline.type = "upper"
)

geom_density_pattern(
  mapping = NULL,
  data = NULL,
  stat = "density",
  position = "identity",
  ...,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_polygon_pattern(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  rule = "evenodd",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_map_pattern(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  ...,
  map,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_sf_pattern(
  mapping = aes(),
  data = NULL,
  stat = "sf",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_tile_pattern(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  linejoin = "mitre",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_violin_pattern(
  mapping = NULL,
  data = NULL,
  stat = "ydensity",
  position = "dodge",
  ...,
  draw_quantiles = NULL,
  trim = TRUE,
  scale = "area",
  na.rm = FALSE,
  orientation = NA,
  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), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

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

stat

Override the default connection between geom_bar() and stat_count().

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

linejoin

Line join style (round, mitre, bevel).

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

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. It can also be a named logical vector to finely select the aesthetics to display.

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().

width

Bar width. By default, set to 90% of the resolution of the data.

binwidth

The width of the bins. Can be specified as a numeric value or as a function that calculates width from unscaled x. Here, "unscaled x" refers to the original x values in the data, before application of any scale transformation. When specifying a function along with a grouping structure, the function will be called once per group. The default is to use the number of bins in bins, covering 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.

The bin width of a date variable is the number of days in each time; the bin width of a time variable is the number of seconds.

orientation

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

bins

Number of bins. Overridden by binwidth. Defaults to 30.

outlier.colour

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

Sometimes it can be useful to hide the outliers, for example when overlaying the raw data points on top of the boxplot. Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.

outlier.color

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

Sometimes it can be useful to hide the outliers, for example when overlaying the raw data points on top of the boxplot. Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.

outlier.fill

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

Sometimes it can be useful to hide the outliers, for example when overlaying the raw data points on top of the boxplot. Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.

outlier.shape

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

Sometimes it can be useful to hide the outliers, for example when overlaying the raw data points on top of the boxplot. Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.

outlier.size

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

Sometimes it can be useful to hide the outliers, for example when overlaying the raw data points on top of the boxplot. Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.

outlier.stroke

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

Sometimes it can be useful to hide the outliers, for example when overlaying the raw data points on top of the boxplot. Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.

outlier.alpha

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

Sometimes it can be useful to hide the outliers, for example when overlaying the raw data points on top of the boxplot. Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden.

notch

If FALSE (default) make a standard box plot. If TRUE, make a notched box plot. Notches are used to compare groups; if the notches of two boxes do not overlap, this suggests that the medians are significantly different.

notchwidth

For a notched box plot, width of the notch relative to the body (defaults to notchwidth = 0.5).

varwidth

If FALSE (default) make a standard box plot. If TRUE, boxes are drawn with widths proportional to the square-roots of the number of observations in the groups (possibly weighted, using the weight aesthetic).

fatten

A multiplicative factor used to increase the size of the middle bar in geom_crossbar() and the middle point in geom_pointrange().

outline.type

Type of the outline of the area; "both" draws both the upper and lower lines, "upper"/"lower" draws the respective lines only. "full" draws a closed polygon around the area.

rule

Either "evenodd" or "winding". If polygons with holes are being drawn (using the subgroup aesthetic) this argument defines how the hole coordinates are interpreted. See the examples in grid::pathGrob() for an explanation.

map

Data frame that contains the map coordinates. This will typically be created using fortify() on a spatial object. It must contain columns x or long, y or lat, and region or id.

draw_quantiles

If not(NULL) (default), draw horizontal lines at the given quantiles of the density estimate.

trim

If TRUE (default), trim the tails of the violins to the range of the data. If FALSE, don't trim the tails.

scale

if "area" (default), all violins have the same area (before trimming the tails). If "count", areas are scaled proportionally to the number of observations. If "width", all violins have the same maximum width.

Value

A ggplot2::Geom object.

Pattern Arguments

Not all arguments apply to all patterns.

pattern

Pattern name string e.g. 'stripe' (default), 'crosshatch', 'point', 'circle', 'none'

pattern_alpha

Alpha transparency for pattern. default: 1

pattern_angle

Orientation of the pattern in degrees. default: 30

pattern_aspect_ratio

Aspect ratio adjustment.

pattern_colour

Colour used for strokes and points. default: 'black'

pattern_density

Approximate fill fraction of the pattern. Usually in range [0, 1], but can be higher. default: 0.2

pattern_filename

Image filename/URL.

pattern_fill

Fill colour. default: 'grey80'

pattern_fill2

Second fill colour. default: '#4169E1'

pattern_filter

(Image scaling) filter. default: 'lanczos'

pattern_frequency

Frequency. default: 0.1

pattern_gravity

Image placement. default: 'center'

pattern_grid

Pattern grid type. default: 'square'

pattern_key_scale_factor

Scale factor for pattern in legend. default: 1

pattern_linetype

Stroke linetype. default: 1

pattern_option_1

Generic user value for custom patterns.

pattern_option_2

Generic user value for custom patterns.

pattern_option_3

Generic user value for custom patterns.

pattern_option_4

Generic user value for custom patterns.

pattern_option_5

Generic user value for custom patterns.

pattern_orientation

'vertical', 'horizontal', or 'radial'. default: 'vertical'

pattern_res

Pattern resolution (pixels per inch).

pattern_rot

Rotation angle (shape within pattern). default: 0

pattern_scale

Scale. default: 1

pattern_shape

Plotting shape. default: 1

pattern_size

Stroke line width. default: 1

pattern_spacing

Spacing of the pattern as a fraction of the plot size. default: 0.05

pattern_type

Generic control option

pattern_subtype

Generic control option

pattern_xoffset

Offset the origin of the pattern. Range [0, 1]. default: 0. Use this to slightly shift the origin of the pattern. For most patterns, the user should limit the offset value to be less than the pattern spacing.

pattern_yoffset

Offset the origin of the pattern. Range [0, 1]. default: 0. Use this to slightly shift the origin of the pattern. For most patterns, the user should limit the offset value to be less than the pattern spacing.

Examples

  if (require("ggplot2")) {

    # 'stripe' pattern example
    df <- data.frame(level = c("a", "b", "c", 'd'), outcome = c(2.3, 1.9, 3.2, 1))
    gg <- ggplot(df) +
      geom_col_pattern(
        aes(level, outcome, pattern_fill = level),
        pattern = 'stripe',
        fill    = 'white',
        colour  = 'black'
      ) +
      theme_bw(18) +
      theme(legend.position = 'none') +
      labs(
        title    = "ggpattern::geom_col_pattern()",
        subtitle = "pattern = 'stripe'"
      )
    plot(gg)

    # 'pch' pattern example
    gg <- ggplot(mtcars, aes(as.factor(cyl), mpg)) +
      geom_violin_pattern(aes(fill = as.factor(cyl),
                              pattern_shape = as.factor(cyl)),
        pattern = 'pch',
        pattern_density = 0.3,
        pattern_angle = 0,
        colour  = 'black'
      ) +
      theme_bw(18) +
      theme(legend.position = 'none') +
      labs(
        title    = "ggpattern::geom_violin_pattern()",
        subtitle = "pattern = 'pch'"
      )
    plot(gg)

    # 'polygon_tiling' pattern example
    gg <- ggplot(mtcars) +
       geom_density_pattern(
         aes(
           x            = mpg,
           pattern_fill = as.factor(cyl),
           pattern_type = as.factor(cyl)
         ),
         pattern = 'polygon_tiling',
         pattern_key_scale_factor = 1.2
       ) +
       scale_pattern_type_manual(values = c("hexagonal", "rhombille",
                                  "pythagorean")) +
       theme_bw(18) +
       theme(legend.key.size = unit(2, 'cm')) +
       labs(
         title    = "ggpattern::geom_density_pattern()",
         subtitle = "pattern = 'polygon_tiling'"
       )
    plot(gg)
  }