Package 'donutsk' reference manual

Title:	Construct Advanced Donut Charts
Description:	Build donut/pie charts with 'ggplot2' layer by layer, exploiting the advantages of polar symmetry. Leverage layouts to distribute labels effectively. Connect labels to donut segments using pins. Streamline annotation and highlighting.
Authors:	Dmitry Kibalnikov [aut, cre, cph]
Maintainer:	Dmitry Kibalnikov <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.1
Built:	2024-08-21 04:31:06 UTC
Source:	https://github.com/dkibalnikov/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")
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)

# 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)

Create pie or donut label and text annotations

Description

The set of annotation functions utilizes layout functions to effectively distribute labels within the available space Annotations are streamlined by leveraging pre-calculated special variables such as .sum, .mean, and .n (see Details).

The function geom_label_int() creates geom_label-like internal donut layer as aggregation of passed values
The function geom_text_int() creates geom_text-like internal donut layer as aggregation of passed values
The function geom_label_ext() creates geom_label-like external donut layer of passed values
The function geom_text_ext() creates geom_text-like external donut layer of passed values

Usage

geom_label_int(
  mapping = NULL,
  data = NULL,
  stat = "label_int",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r = 1,
  ...
)

geom_text_int(
  mapping = NULL,
  data = NULL,
  stat = "text_int",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r = 1,
  ...
)

geom_label_ext(
  mapping = NULL,
  data = NULL,
  stat = "label_ext",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  layout = circle(),
  ...
)

geom_text_ext(
  mapping = NULL,
  data = NULL,
  stat = "text_ext",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  layout = circle(),
  ...
)
geom_label_int(
  mapping = NULL,
  data = NULL,
  stat = "label_int",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r = 1,
  ...
)

geom_text_int(
  mapping = NULL,
  data = NULL,
  stat = "text_int",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r = 1,
  ...
)

geom_label_ext(
  mapping = NULL,
  data = NULL,
  stat = "label_ext",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  layout = circle(),
  ...
)

geom_text_ext(
  mapping = NULL,
  data = NULL,
  stat = "text_ext",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  layout = circle(),
  ...
)

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, or the result of a call to a position adjustment function. Cannot be jointly specified with `nudge_x` or `nudge_y`.
`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`	Sets the radius to place label or text for internal donut
`...`	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.
`layout`	The layout function to effectively display text and labels

Format

An object of class StatLabelInt (inherits from Stat, ggproto, gg) of length 3.

An object of class StatTextInt (inherits from Stat, ggproto, gg) of length 3.

An object of class StatLabelExt (inherits from Stat, ggproto, gg) of length 3.

An object of class StatTextExt (inherits from Stat, ggproto, gg) of length 3.

Details

The label functions supports glue::glue() for convenient label construction like Total: {.sum}, where .sum is pre-calculated variable. You can still use glue::glue() or paste() functions to pass data.frame fields for label construction.

In addition to generic aesthetics like color, fill, alpha, etc., the following list of pre-calculated variables is available for geom_label_int() and geom_text_int():

.sum: Summation of the value field
.mean: Mean of the value field
.median: Median of the value field
.n: Observation count of the value field
.prc: Percentage of the value field

For geom_label_ext() and geom_text_ext(), which are suitable for external donut labels, the following list of pre-calculated variables is available:

.prc: Percentage of the value field for the entire multiplicity
.prc_grp: Percentage of the value field for the group defined by fill

Value

None

Examples

# Create an example data set
n <- 30
set.seed(2021)
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(.9, .1))) |>
 dplyr::mutate(highlight_int = dplyr::if_else(lvl1 == "A", TRUE, FALSE))

# Starting plot with doubled donuts and annotations for internal one
p <- dplyr::group_by(df, lvl1, lvl2, highlight_ext, highlight_int) |>
 dplyr::summarise(value = sum(value), .groups = "drop") |>
 packing(value, lvl1) |>
 ggplot(aes(value = value, fill = lvl1)) +
 geom_donut_int(aes(highlight = highlight_int), alpha=.5, r_int=.25) +
 geom_text_int(lineheight = .8, r=1.2, show.legend = FALSE,
  aes(label = "Sum {fill}:\n{.sum}-{scales::percent(.prc)}", col=lvl1)) +
 geom_donut_ext(aes(opacity = lvl2, highlight = highlight_ext)) +
 scale_fill_viridis_d(option = "inferno", begin = .1, end = .7) +
 scale_color_viridis_d(option = "inferno", begin = .1, end = .7) +
 guides(alpha=guide_legend(ncol = 2), fill=guide_legend(ncol = 2)) +
 theme_void() +
 theme(legend.position = "inside", legend.position.inside = c(0.1, 0.9))

p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE)

# Add labels to external donut as percent inside group
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
 geom_label_ext(aes(label=paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                show.legend = FALSE, size=3, col="white")

# Leverage ggplot2 feature for labels
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = TRUE) +
 geom_label_ext(aes(label=paste0(lvl2, ": {scales::percent(.prc)}")),
                show.legend = FALSE, size=3, col="white", angle=90,
                layout = circle())

# Leverage another layout
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
 geom_label_ext(aes(label=paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                show.legend = FALSE, size=3, col="white",
                layout = tv(thinner = TRUE, thinner_gap = 0.15))

# Create an example data set
n <- 30
set.seed(2021)
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(.9, .1))) |>
 dplyr::mutate(highlight_int = dplyr::if_else(lvl1 == "A", TRUE, FALSE))

# Starting plot with doubled donuts and annotations for internal one
p <- dplyr::group_by(df, lvl1, lvl2, highlight_ext, highlight_int) |>
 dplyr::summarise(value = sum(value), .groups = "drop") |>
 packing(value, lvl1) |>
 ggplot(aes(value = value, fill = lvl1)) +
 geom_donut_int(aes(highlight = highlight_int), alpha=.5, r_int=.25) +
 geom_text_int(lineheight = .8, r=1.2, show.legend = FALSE,
  aes(label = "Sum {fill}:\n{.sum}-{scales::percent(.prc)}", col=lvl1)) +
 geom_donut_ext(aes(opacity = lvl2, highlight = highlight_ext)) +
 scale_fill_viridis_d(option = "inferno", begin = .1, end = .7) +
 scale_color_viridis_d(option = "inferno", begin = .1, end = .7) +
 guides(alpha=guide_legend(ncol = 2), fill=guide_legend(ncol = 2)) +
 theme_void() +
 theme(legend.position = "inside", legend.position.inside = c(0.1, 0.9))

p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE)

# Add labels to external donut as percent inside group
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
 geom_label_ext(aes(label=paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                show.legend = FALSE, size=3, col="white")

# Leverage ggplot2 feature for labels
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = TRUE) +
 geom_label_ext(aes(label=paste0(lvl2, ": {scales::percent(.prc)}")),
                show.legend = FALSE, size=3, col="white", angle=90,
                layout = circle())

# Leverage another layout
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
 geom_label_ext(aes(label=paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                show.legend = FALSE, size=3, col="white",
                layout = tv(thinner = TRUE, thinner_gap = 0.15))

The World Bank GDP, PPP (current international $)

Description

A pre-processed subset of GDP data from the World Bank
GDP, PPP means gross domestic product based on purchasing power parity
current international $ means actual (not adjusted to inflation) US dollars

Usage

GDP
GDP

Format

`GDP`

A data frame with 6,004 rows and 5 columns:

date: Year
country: Country name
region: Region hierarchy
region_ISO: 3 letter ISO region codes
GDP: GDP, PPP (current international $)

...

Source

https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD

The set of layout functions is designed to effectively display text and labels

Description

The layout functions help to streamline displaying text and labels geoms without overlapping effectively leveraging space available for pie and donut charts

tv() - The function builds layout resembled an old-fashioned TV screen
petal() - The function builds layout resembled flower with petals
circle() - The function builds circle layout
eye() - The function builds two-sided layout

Usage

tv(
  scale_x = 1.5,
  scale_y = 1.5,
  bend_x = 1,
  bend_y = 1,
  thinner = FALSE,
  thinner_gap = 0.1
)

petal(
  rotate = 0,
  n = 4,
  scale = 2.5,
  bend = 0.3,
  thinner = FALSE,
  thinner_gap = 0.1
)

circle(r = 2.5, thinner = FALSE, thinner_gap = 0.1)

eye(scale_x = 2, bend_x = 1, alpha = 90, clove = 0.5)
tv(
  scale_x = 1.5,
  scale_y = 1.5,
  bend_x = 1,
  bend_y = 1,
  thinner = FALSE,
  thinner_gap = 0.1
)

petal(
  rotate = 0,
  n = 4,
  scale = 2.5,
  bend = 0.3,
  thinner = FALSE,
  thinner_gap = 0.1
)

circle(r = 2.5, thinner = FALSE, thinner_gap = 0.1)

eye(scale_x = 2, bend_x = 1, alpha = 90, clove = 0.5)

Arguments

`scale_x`	Scales the layout in horizontal perspective
`scale_y`	Scales the layout in vertical perspective
`bend_x`	Adjusts the bend level in horizontal perspective
`bend_y`	Adjusts the bend level in vertical perspective
`thinner`	Distributes text or label elements across two different levels
`thinner_gap`	Sets the spacing between thinner levels
`rotate`	Rotates the layout clockwise
`n`	Sets the number of petals in the layout
`scale`	Scales the layout
`bend`	Manages the bending level
`r`	Sets the radius of the layout circle
`alpha`	Defines the angle of distribution in horizontal perspective. Pick up value from degree interval (0, 180)
`clove`	Determines the distribution proportion between the left and right-hand parts. Default value is 0.5. There should be numeric value from interval (0, 1) e.g. 0.4 denotes 40% cases on the right hand and 60% cases on the left hand

Value

Layout functions return layout function i.e. a function that takes a vector of angles and returns a numeric radius vector giving a position for each input value.

Layout functions are designed to be used with the layout argument of donutsk functions.

Examples

# Render multiple layouts simultaneously
list(petal_2n = petal(n = 2),
     petal_3n = petal(n = 3, rotate = 180),
     petal_4n = petal(n = 4),
     tv_base = tv(),
     tv_ext = tv(bend_x = 0, bend_y = 0, thinner = TRUE)) |>
  lapply(function(x){
    rlang::exec(x, 1:300/300) |>
      dplyr::tibble(r = _) |>
      dplyr::mutate(theta = 1:300/300)
  }) |>
  dplyr::bind_rows(.id = "layouts") |>
  ggplot(aes(x=r, y=theta, col = layouts)) +
  geom_point(alpha = .3) +
  coord_polar(theta = "y") +
  xlim(0, 3.5)

# The eye() layout generates table as an output
n <- 20
theta <- 1:n/n

dplyr::tibble(
  theta = theta,
  lbl = paste0("sample: ", sample(LETTERS, n, TRUE))
  ) |>
 dplyr::bind_cols(lt = eye()(theta)) |>
 ggplot(aes(x=x, y=y)) +
 geom_point(aes(x=1, y=theta)) +
 geom_point() +
 geom_segment(aes(x=1, xend=x, y=theta, yend=y), linewidth=.2) +
 geom_label(aes(label=lbl, hjust=dplyr::if_else(theta > 0.5, 1, 0)),
  nudge_x =.2) +
 coord_polar(theta = "y") +
 xlim(0, 5) +
 ylim(0, 1)

# Render multiple layouts simultaneously
list(petal_2n = petal(n = 2),
     petal_3n = petal(n = 3, rotate = 180),
     petal_4n = petal(n = 4),
     tv_base = tv(),
     tv_ext = tv(bend_x = 0, bend_y = 0, thinner = TRUE)) |>
  lapply(function(x){
    rlang::exec(x, 1:300/300) |>
      dplyr::tibble(r = _) |>
      dplyr::mutate(theta = 1:300/300)
  }) |>
  dplyr::bind_rows(.id = "layouts") |>
  ggplot(aes(x=r, y=theta, col = layouts)) +
  geom_point(alpha = .3) +
  coord_polar(theta = "y") +
  xlim(0, 3.5)

# The eye() layout generates table as an output
n <- 20
theta <- 1:n/n

dplyr::tibble(
  theta = theta,
  lbl = paste0("sample: ", sample(LETTERS, n, TRUE))
  ) |>
 dplyr::bind_cols(lt = eye()(theta)) |>
 ggplot(aes(x=x, y=y)) +
 geom_point(aes(x=1, y=theta)) +
 geom_point() +
 geom_segment(aes(x=1, xend=x, y=theta, yend=y), linewidth=.2) +
 geom_label(aes(label=lbl, hjust=dplyr::if_else(theta > 0.5, 1, 0)),
  nudge_x =.2) +
 coord_polar(theta = "y") +
 xlim(0, 5) +
 ylim(0, 1)

Arrange data to distribute small values

Description

Arrange data to distribute small values further apart from each other

Usage

packing(.data, value, level = NULL)
packing(.data, value, level = NULL)

Arguments

`.data`	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
`value`	A .data field which contains values to distribute
`level`	A .data grouping field for distribution

Value

An object of the same type as .data.

Examples

# Create an example
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)
 )

# Arrange all values
packing(df, value)

# Arrange values within values
packing(df, value, lvl1)
# Create an example
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)
 )

# Arrange all values
packing(df, value)

# Arrange values within values
packing(df, value, lvl1)

Connecting labels with donut segments

Description

The set of functions served to connect text or labels with donut segments

geom_pin_line() - builds curved line to linl label with donut segment
geom_pin_head() - builds stylish point heads for pins
geom_pin() - handy wrapper for geom_pin_line() and geom_pin_head()

Usage

geom_pin_line(
  mapping = NULL,
  data = NULL,
  stat = "pin",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r = 1.5,
  cut = 0.1,
  layout = circle(),
  ...
)

geom_pin_head(
  mapping = NULL,
  data = NULL,
  stat = "point",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r = 1.5,
  cut = 0.1,
  layout = circle(),
  ...
)

geom_pin(..., head = TRUE)
geom_pin_line(
  mapping = NULL,
  data = NULL,
  stat = "pin",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r = 1.5,
  cut = 0.1,
  layout = circle(),
  ...
)

geom_pin_head(
  mapping = NULL,
  data = NULL,
  stat = "point",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  r = 1.5,
  cut = 0.1,
  layout = circle(),
  ...
)

geom_pin(..., head = TRUE)

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`	The radius where donut is placed
`cut`	Sets additional two-sided gap for pins
`layout`	The layout function to effectively display text and labels. Obviously it's better to have the same as for `geom_label_ext` or `geom_text_ext`
`...`	Parameters to be passed to `geom_pin_line()` and `geom_pin_head()`
`head`	Boolean - defines whether to add pin head

Format

An object of class StatPinLine (inherits from Stat, ggproto, gg) of length 3.

An object of class StatPinHead (inherits from Stat, ggproto, gg) of length 3.

Value

None

Examples

n <- 30
set.seed(2021)
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(.9, .1))) |>
  dplyr::mutate(highlight_int = dplyr::if_else(lvl1 == "A", TRUE, FALSE))

# Starting plot with doubled donuts and annotations for internal one
p <- dplyr::group_by(df, lvl1, lvl2, highlight_ext, highlight_int) |>
  dplyr::summarise(value = sum(value), .groups = "drop") |>
  packing(value, lvl1) |>
  ggplot(aes(value = value, fill = lvl1)) +
  geom_donut_int(aes(highlight = highlight_int), alpha=.5, r_int = .25) +
  geom_label_int(aes(label = "Sum {fill}:\n{.sum}-{scales::percent(.prc)}"),
                 alpha = .6, col = "white", size = 3, r=1.2) +
  geom_donut_ext(aes(opacity = lvl2, highlight = highlight_ext)) +
  scale_fill_viridis_d(option = "inferno", begin = .1, end = .7) +
  guides(alpha = guide_legend(ncol = 2), fill = guide_legend(ncol = 2)) +
  theme_void() +
  theme(legend.position = "none")

p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE)

# Add labels to external donut as percent inside group
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
  geom_label_ext(aes(label = paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                 show.legend = FALSE, size=3, col="white") +
  geom_pin(size = .5, linewidth=.1, show.legend = FALSE, cut = .2)

# Leverage tv() layout
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
  geom_label_ext(aes(label = paste0(lvl2, ":{scales::percent(.prc_grp)}")),
                 show.legend = FALSE, size=3, col="white",
                 layout = tv(thinner = TRUE, thinner_gap = .15)) +
  geom_pin(size = .5, linewidth=.1, show.legend = FALSE, cut = .2,
           layout = tv(thinner = TRUE, thinner_gap = .15))

# Leverage another layout
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
  geom_label_ext(aes(label = paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                 show.legend = FALSE, size=3, col="white", layout = eye()) +
  geom_pin(size = .5, linewidth=.1, show.legend = FALSE, layout = eye())

n <- 30
set.seed(2021)
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(.9, .1))) |>
  dplyr::mutate(highlight_int = dplyr::if_else(lvl1 == "A", TRUE, FALSE))

# Starting plot with doubled donuts and annotations for internal one
p <- dplyr::group_by(df, lvl1, lvl2, highlight_ext, highlight_int) |>
  dplyr::summarise(value = sum(value), .groups = "drop") |>
  packing(value, lvl1) |>
  ggplot(aes(value = value, fill = lvl1)) +
  geom_donut_int(aes(highlight = highlight_int), alpha=.5, r_int = .25) +
  geom_label_int(aes(label = "Sum {fill}:\n{.sum}-{scales::percent(.prc)}"),
                 alpha = .6, col = "white", size = 3, r=1.2) +
  geom_donut_ext(aes(opacity = lvl2, highlight = highlight_ext)) +
  scale_fill_viridis_d(option = "inferno", begin = .1, end = .7) +
  guides(alpha = guide_legend(ncol = 2), fill = guide_legend(ncol = 2)) +
  theme_void() +
  theme(legend.position = "none")

p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE)

# Add labels to external donut as percent inside group
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
  geom_label_ext(aes(label = paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                 show.legend = FALSE, size=3, col="white") +
  geom_pin(size = .5, linewidth=.1, show.legend = FALSE, cut = .2)

# Leverage tv() layout
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
  geom_label_ext(aes(label = paste0(lvl2, ":{scales::percent(.prc_grp)}")),
                 show.legend = FALSE, size=3, col="white",
                 layout = tv(thinner = TRUE, thinner_gap = .15)) +
  geom_pin(size = .5, linewidth=.1, show.legend = FALSE, cut = .2,
           layout = tv(thinner = TRUE, thinner_gap = .15))

# Leverage another layout
p + coord_radial(theta = "y", expand = FALSE, rotate_angle = FALSE) +
  geom_label_ext(aes(label = paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                 show.legend = FALSE, size=3, col="white", layout = eye()) +
  geom_pin(size = .5, linewidth=.1, show.legend = FALSE, layout = eye())

Package 'donutsk'

Help Index

Create pie or donut chart

Description

Usage

Arguments

Format

Details

Value

Examples

Create pie or donut label and text annotations

Description

Usage

Arguments

Format

Details

Value

See Also

Examples

The World Bank GDP, PPP (current international $)

Description

Usage

Format

GDP

Source

The set of layout functions is designed to effectively display text and labels

Description

Usage

Arguments

Value

See Also

Examples

Arrange data to distribute small values

Description

Usage

Arguments

Value

Examples

Connecting labels with donut segments

Description

Usage

Arguments

Format

Value

See Also

Examples

`GDP`