donut_geom {donutsk}

Create pie or donut chart

Description

Create pie or donut charts while retaining ggplot flexibility, such as leveraging faceting and palettes, and fine-tuning appearance

• The function geom_donut_int() creates visually internal donut layer as aggregation of passed values

• The function geom_donut_ext() creates visually external donut layer of passed values

• geom_donut_int0() and geom_donut_ext() are generic geoms not supporting highlight feature

Usage

geom_donut_int0(
  mapping = NULL,
  data = NULL,
  stat = "donut_int",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r_int = 0,
  r_ext = 1,
  hl_shift = 0.1,
  ...
)

geom_donut_int(..., hl_col = "firebrick")

geom_donut_ext0(
  mapping = NULL,
  data = NULL,
  stat = "donut_ext",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r_int = 1.5,
  r_ext = 2,
  hl_shift = 0.1,
  ...
)

geom_donut_ext(..., hl_col = "firebrick")

Arguments

mapping

Set of aesthetic mappings created by 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

The statistical transformation to use on the data for this layer, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use 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.

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().

r_int

Internal donut radius

r_ext

External pie or donut radius

hl_shift

Sets the spacing to show highlighted segments

...

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.

hl_col

Sets the color for highlighted segments. It's possible to use both simultaneously hl_col and generic colour

Format

An object of class StatDonutInt (inherits from Stat, ggproto, gg) of length 4.

An object of class StatDonutIntHl (inherits from Stat, ggproto, gg) of length 4.

An object of class StatDonutExt (inherits from Stat, ggproto, gg) of length 4.

An object of class StatDonutExtHl (inherits from Stat, ggproto, gg) of length 4.

Details

There are two additional aesthetics possible to use:

• highlight - optional aesthetic which expects logical (TRUE/FALSE) variable in order to highlight particular donut segments

• opacity - operates pretty much the same as alpha but ensure more contrast colors and removes legend. Once alpha is set opacity does not affect a chart

Value

None

Examples

# Create an example
set.seed(1605)
n <- 20
df <- dplyr::tibble(
  lvl1 = sample(LETTERS[1:5], n, TRUE),
  lvl2 = sample(LETTERS[6:24], n, TRUE),
  value = sample(1:20, n, TRUE),
  highlight_ext = sample(c(FALSE,TRUE), n, TRUE, c(.7, .3))) |>
  dplyr::mutate(highlight_int = ifelse(lvl1 == "A", TRUE, FALSE))

# Create a simple pie chart
ggplot(df, aes(value = value, fill=lvl1)) +
  geom_donut_int(alpha=.6) +
  coord_polar(theta = "y")

# Create a simple donut chart that can handle more granular data
# and highlight specific segments
ggplot(df, aes(value = value, fill=lvl2, highlight=highlight_ext)) +
  geom_donut_int(r_int=.5, alpha=.6, linewidth=.2) +
  coord_polar(theta = "y") +
  xlim(0, 1.5)

# Perform data preparation tasks with `packing()`
# and apply specific color
packing(df, value) |>
  ggplot(aes(value = value, fill=lvl2, highlight=highlight_ext)) +
  geom_donut_int(r_int=.5, alpha=.6, linewidth=.2, col = "gray20") +
  coord_polar(theta = "y") +
  xlim(0, 1.5)

# Built combined donut chart with interanl and external layers
dplyr::bind_rows(
# arrange by value
`arrange()` = dplyr::arrange(df, lvl1, lvl2, value),
# pack values for better space management
`packing()` = packing(df, value, lvl1),
.id = "prep_type") |>
 ggplot(aes(value = value, fill=lvl1)) +
 geom_donut_int(aes(highlight=highlight_int), alpha=.6) +
 geom_donut_ext(aes(opacity=lvl2, highlight=highlight_int)) +
 # apply facets
 facet_grid(~prep_type) +
 # style chart with palette and theme
 scale_fill_viridis_d(option = "inferno", begin = .1, end = .7) +
 theme_void() +
 coord_polar(theta = "y") +
 xlim(0, 2.5)