- ggcirclepack
- status quo without {ggcirclepack}: precomputation required to create two more data frames
- Proposed UI
- Package functions
- Package the functions
- Issues
circle pack is an experimental package that uses the {packcircles} package to handle circle packing computation.
Your feedback is on this work is greatly appreciated.
Beyond the descriptions of our work, we interject comments on our hesitations 🤔 and areas that need some work 🚧, for your consideration marked with emoji.
Your help and feedback would be greatly appreciated on any of the questions…
- Are functions named intuitively? ‘According to IBM studies, intuitive variable naming contributes more to code readability than comments, or for that matter, any other factor’ McConnell, S. Code complete
- Do functions work as you expect?
- Is there rewriting that could make the code more concise?
- What tests should be performed?
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.4 ✔ readr 2.1.5
#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.5.1.9000 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
gapminder::gapminder %>%
filter(continent == "Americas") %>%
filter(year == 2002) %>%
select(country, pop) %>%
mutate(id = row_number()) ->
df_w_id
packcircles::circleProgressiveLayout(df_w_id$pop,
sizetype = 'area') ->
x0y0radius
x0y0radius %>%
packcircles::circleLayoutVertices(npoints = 50) ->
circle_outlines
circle_outlines %>%
left_join(df_w_id) %>%
ggplot() +
aes(x = x, y = y) +
geom_polygon(colour = "black", alpha = 0.6) +
aes(group = id) +
aes(fill = pop) +
geom_text(data = cbind(df_w_id, x0y0radius),
aes(x, y, size = pop, label = country,
group = NULL, fill = NULL)) +
theme(legend.position = "none") +
coord_equal()
#> Joining with `by = join_by(id)`library(tidyverse)
library(ggcirclepack)
gapminder::gapminder %>%
filter(year == 2002) %>%
ggplot() +
aes(id = country, area = pop) +
geom_circlepack() + # draws packed circles
geom_circlepack_text() + # labels at the center
coord_fixed(ratio = 1)compute_panel_aggregation <- function(data, scales, fun = sum){
if(is.null(data$slice)){data$slice <- TRUE}
data %>%
dplyr::filter(.data$slice) ->
data
grp_cols <- c("id", "fill", "alpha",
"colour", "group", "linewidth",
"label", "size",
"linetype", "render")
# Thanks June! https://github.com/teunbrand/ggplot-extension-club/discussions/15
data %>%
group_by(group_by(pick(any_of(grp_cols)))) ->
data
if(is.null(data$area)){data <- mutate(data, area = 1)}
if(is.null(data$wt)){data$wt <- 1}
data %>%
summarize(area = fun(.data$area*.data$wt), .groups = 'drop') ->
data
if(is.null(data$within)){data$within <- 1}
data %>%
group_by(.data$within) %>%
mutate(prop = .data$area/sum(.data$area)) %>%
mutate(percent = round(.data$prop*100)) ->
data
data
}
#' compute_panel_circlepack_center
#'
#' @return
#' @export
#'
#' @examples
compute_panel_circlepack_center <- function(data, scales, fun = sum){
# get aes names as they appear in the data
data <- data |> compute_panel_aggregation(fun = fun)
data %>%
arrange(id) -> # this doesn't feel very principled; motivation is when you go from no fill to color, preserves circle position...
data
data %>%
pull(area) %>%
packcircles::circleProgressiveLayout(
sizetype = 'area') %>%
cbind(data) ->
data
if(!is.null(data$render)){
data %>%
filter(.data$render) ->
data
}
data
}gapminder::gapminder %>%
filter(continent == "Americas") %>%
filter(year == 2002) %>%
# input must have required aesthetic inputs as columns
select(area = pop, id = country) %>%
compute_panel_circlepack_center() %>%
head()
#> x y radius id area within prop percent
#> 1 -3493.018 0.000 3493.018 Argentina 38331121 1 0.045107495 5
#> 2 1639.564 0.000 1639.564 Bolivia 8445134 1 0.009938109 1
#> 3 2732.774 -9142.026 7567.594 Brazil 179914212 1 0.211720380 21
# > 4 1150.752 4801.407 3186.661 Canada 31902268 1 0.037542116 4
#> 5 5273.817 1302.381 2221.005 Chile 15497046 1 0.018236694 2
#> 6 10562.330 -1160.651 3612.938 Colombia 41008227 1 0.048257874 5
gapminder::gapminder %>%
filter(year == 2002) %>%
select(id = continent) %>%
compute_panel_circlepack_center()
#> x y radius id area within prop percent
#> 1 -4.0684289 0.000000 4.0684289 Africa 52 1 0.36619718 37
#> 2 2.8209479 0.000000 2.8209479 Americas 25 1 0.17605634 18
#> 3 0.5868621 -5.635277 3.2410224 Asia 33 1 0.23239437 23
#> 4 0.5595510 5.461472 3.0901936 Europe 30 1 0.21126761 21
#> 5 3.8910939 3.456984 0.7978846 Oceania 2 1 0.01408451 1
gapminder::gapminder %>%
filter(year == 2002) %>%
mutate( render = country == "Argentina") %>%
select(id = continent, render) %>%
compute_panel_circlepack_center()
#> x y radius id render area within prop percent
#> 1 0.1077182 -2.005231 0.5641896 Americas TRUE 1 1 0.007042254 1
gapminder::gapminder %>%
# filter(year == 2002) %>%
mutate( render = country == "Argentina") %>%
select(id = continent, render, area = pop) %>%
compute_panel_circlepack_center(fun = mean)
#> x y radius id render area within prop
#> 1 -666.0431 -4663.549 3017.346 Americas TRUE 28602240 1 0.1723697
#> percent
#> 1 17StatCirclepackcenter <- ggplot2::ggproto(`_class` = "StatCirclepackcenter",
`_inherit` = ggplot2::Stat,
required_aes = c("id"),
compute_panel = compute_panel_circlepack_center,
default_aes = ggplot2::aes(group = after_stat(id),
size = after_stat(area),
label = after_stat(id))
)
#' Title
#'
#' @param mapping
#' @param data
#' @param position
#' @param na.rm
#' @param show.legend
#' @param inherit.aes
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
stat_circlepack_center <- function(mapping = NULL, data = NULL,
geom = "text",
position = "identity", na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE, ...) {
ggplot2::layer(
geom = geom, # inherit other behavior
stat = StatCirclepackcenter, # proto object from Step 2
data = data,
mapping = mapping,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
#' Title
#'
#' @param mapping
#' @param data
#' @param position
#' @param na.rm
#' @param show.legend
#' @param inherit.aes
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
geom_circlepack_text <- stat_circlepack_centergapminder::gapminder %>%
filter(year == 2002) %>%
ggplot() +
aes(id = country, area = pop) +
geom_circlepack_text(alpha = .5) +
coord_equal() +
labs(title = "gapminder 2002 countries")last_plot() +
aes(render = pop > 20000000)layer_data()
#> size group label PANEL render id
#> 1 1.305966 Afghanistan Afghanistan 1 TRUE Afghanistan
#> 2 1.461572 Algeria Algeria 1 TRUE Algeria
#> 3 1.593993 Argentina Argentina 1 TRUE Argentina
#> 4 2.511332 Bangladesh Bangladesh 1 TRUE Bangladesh
#> 5 2.778300 Brazil Brazil 1 TRUE Brazil
#> 6 1.474611 Canada Canada 1 TRUE Canada
#> 7 6.000000 China China 1 TRUE China
#> 8 1.637143 Colombia Colombia 1 TRUE Colombia
#> 9 1.831346 Congo, Dem. Rep. Congo, Dem. Rep. 1 TRUE Congo, Dem. Rep.
#> 10 2.023224 Egypt Egypt 1 TRUE Egypt
#> 11 1.969799 Ethiopia Ethiopia 1 TRUE Ethiopia
#> 12 1.883928 France France 1 TRUE France
#> 13 2.107400 Germany Germany 1 TRUE Germany
#> 14 1.000000 Ghana Ghana 1 TRUE Ghana
#> 15 5.484858 India India 1 TRUE India
#> 16 2.944324 Indonesia Indonesia 1 TRUE Indonesia
#> 17 1.959111 Iran Iran 1 TRUE Iran
#> 18 1.261690 Iraq Iraq 1 TRUE Iraq
#> 19 1.861209 Italy Italy 1 TRUE Italy
#> 20 2.453839 Japan Japan 1 TRUE Japan
#> 21 1.463711 Kenya Kenya 1 TRUE Kenya
#> 22 1.181747 Korea, Dem. Rep. Korea, Dem. Rep. 1 TRUE Korea, Dem. Rep.
#> 23 1.737618 Korea, Rep. Korea, Rep. 1 TRUE Korea, Rep.
#> 24 1.204700 Malaysia Malaysia 1 TRUE Malaysia
#> 25 2.275058 Mexico Mexico 1 TRUE Mexico
#> 26 1.459000 Morocco Morocco 1 TRUE Morocco
#> 27 1.705004 Myanmar Myanmar 1 TRUE Myanmar
#> 28 1.325009 Nepal Nepal 1 TRUE Nepal
#> 29 2.404093 Nigeria Nigeria 1 TRUE Nigeria
#> 30 2.623663 Pakistan Pakistan 1 TRUE Pakistan
#> 31 1.351285 Peru Peru 1 TRUE Peru
#> 32 2.113159 Philippines Philippines 1 TRUE Philippines
#> 33 1.598898 Poland Poland 1 TRUE Poland
#> 34 1.191786 Romania Romania 1 TRUE Romania
#> 35 1.279996 Saudi Arabia Saudi Arabia 1 TRUE Saudi Arabia
#> 36 1.688421 South Africa South Africa 1 TRUE South Africa
#> 37 1.623675 Spain Spain 1 TRUE Spain
#> 38 1.572892 Sudan Sudan 1 TRUE Sudan
#> 39 1.194351 Taiwan Taiwan 1 TRUE Taiwan
#> 40 1.527887 Tanzania Tanzania 1 TRUE Tanzania
#> 41 1.915704 Thailand Thailand 1 TRUE Thailand
#> 42 1.963251 Turkey Turkey 1 TRUE Turkey
#> 43 1.288318 Uganda Uganda 1 TRUE Uganda
#> 44 1.883787 United Kingdom United Kingdom 1 TRUE United Kingdom
#> 45 3.302331 United States United States 1 TRUE United States
#> 46 1.272312 Venezuela Venezuela 1 TRUE Venezuela
#> 47 2.094400 Vietnam Vietnam 1 TRUE Vietnam
#> area x y radius within prop percent
#> 1 25268405 -2836.051 0.0000 2836.051 1 0.004292254 0
#> 2 31287142 1442.390 -4194.8885 3155.789 1 0.005314636 1
#> 3 38331121 5786.203 838.7913 3493.018 1 0.006511172 1
#> 4 135656790 -10562.150 5366.9438 6571.217 1 0.023043538 2
#> 5 179914212 6304.785 -13752.5065 7567.594 1 0.030561389 3
#> 6 31902268 12117.233 2968.6196 3186.661 1 0.005419125 1
#> 7 1280400000 -25094.786 -17102.3603 20188.214 1 0.217497006 22
#> 8 41008227 5474.339 11629.9084 3612.938 1 0.006965922 1
#> 9 55379852 -1355.285 -22683.6785 4198.566 1 0.009407179 1
#> 10 73312559 -21725.233 7688.6560 4830.747 1 0.012453344 1
#> 11 67946797 -10136.746 17045.0432 4650.606 1 0.011541881 1
#> 12 59925035 19670.475 2853.2253 4367.463 1 0.010179253 1
#> 13 82350671 5427.583 20362.5882 5119.866 1 0.013988616 1
#> 14 20550751 10750.391 14829.7966 2557.637 1 0.003490883 0
#> 15 1034172547 31588.647 -18420.2356 18143.521 1 0.175671222 18
#> 16 211060000 8213.969 -30561.9288 8196.492 1 0.035852014 4
#> 17 66907826 21083.729 11723.7263 4614.913 1 0.011365395 1
#> 18 24001816 26740.332 1917.4309 2764.058 1 0.004077103 0
#> 19 57926999 -19059.189 16415.2732 4294.035 1 0.009839854 1
#> 20 127065841 16534.838 23262.6391 6359.742 1 0.021584224 2
#> 21 31386842 -3792.076 25052.4818 3160.814 1 0.005331572 1
#> 22 22215365 -15272.351 22246.8597 2659.205 1 0.003773645 0
#> 23 47969150 -10619.817 26881.1207 3907.564 1 0.008148349 1
#> 24 22662365 10947.596 30376.3580 2685.825 1 0.003849576 0
#> 25 102479927 -5308.329 -33814.2536 5711.425 1 0.017407902 2
#> 26 31167783 -29680.800 7058.1768 3149.764 1 0.005294361 1
#> 27 45598081 -26922.413 18375.1972 3809.766 1 0.007745584 1
#> 28 25873917 27970.896 8793.1457 2869.830 1 0.004395111 0
#> 29 119901274 -22500.653 27330.6653 6177.844 1 0.020367204 2
#> 30 153403524 -4755.728 37001.8314 6987.836 1 0.026058112 3
#> 31 26769436 33228.351 2578.4345 2919.071 1 0.004547229 0
#> 32 82995088 -16882.452 37155.4418 5139.860 1 0.014098081 1
#> 33 38625976 5004.131 33145.0563 3506.427 1 0.006561258 1
#> 34 22404337 18803.808 32003.1696 2670.491 1 0.003805745 0
#> 35 24501530 25197.763 26215.7083 2792.683 1 0.004161988 0
#> 36 44433622 -34121.795 16033.7296 3760.806 1 0.007547782 1
#> 37 40152517 36639.232 8104.6836 3575.044 1 0.006820566 1
#> 38 37090298 -18047.874 -39651.0916 3436.016 1 0.006300397 1
#> 39 22454239 -35259.895 -37579.8322 2673.463 1 0.003814222 0
#> 40 34593779 -40567.220 -34798.7725 3318.364 1 0.005876323 1
#> 41 62806748 -46358.794 -29589.5839 4471.242 1 0.010668760 1
#> 42 67308928 -49790.993 -14657.3976 4628.725 1 0.011433529 1
#> 43 24739869 -46259.905 -8114.4605 2806.233 1 0.004202474 0
#> 44 59912431 -43918.269 -1334.1902 4367.003 1 0.010177112 1
#> 45 287675526 -46911.443 12276.8062 9569.220 1 0.048866421 5
#> 46 24287670 32717.147 15684.1439 2780.469 1 0.004125660 0
#> 47 80908147 19176.278 -38042.2826 5074.826 1 0.013743580 1
#> colour family angle hjust vjust alpha fontface lineheight
#> 1 black 0 0.5 0.5 0.5 1 1.2
#> 2 black 0 0.5 0.5 0.5 1 1.2
#> 3 black 0 0.5 0.5 0.5 1 1.2
#> 4 black 0 0.5 0.5 0.5 1 1.2
#> 5 black 0 0.5 0.5 0.5 1 1.2
#> 6 black 0 0.5 0.5 0.5 1 1.2
#> 7 black 0 0.5 0.5 0.5 1 1.2
#> 8 black 0 0.5 0.5 0.5 1 1.2
#> 9 black 0 0.5 0.5 0.5 1 1.2
#> 10 black 0 0.5 0.5 0.5 1 1.2
#> 11 black 0 0.5 0.5 0.5 1 1.2
#> 12 black 0 0.5 0.5 0.5 1 1.2
#> 13 black 0 0.5 0.5 0.5 1 1.2
#> 14 black 0 0.5 0.5 0.5 1 1.2
#> 15 black 0 0.5 0.5 0.5 1 1.2
#> 16 black 0 0.5 0.5 0.5 1 1.2
#> 17 black 0 0.5 0.5 0.5 1 1.2
#> 18 black 0 0.5 0.5 0.5 1 1.2
#> 19 black 0 0.5 0.5 0.5 1 1.2
#> 20 black 0 0.5 0.5 0.5 1 1.2
#> 21 black 0 0.5 0.5 0.5 1 1.2
#> 22 black 0 0.5 0.5 0.5 1 1.2
#> 23 black 0 0.5 0.5 0.5 1 1.2
#> 24 black 0 0.5 0.5 0.5 1 1.2
#> 25 black 0 0.5 0.5 0.5 1 1.2
#> 26 black 0 0.5 0.5 0.5 1 1.2
#> 27 black 0 0.5 0.5 0.5 1 1.2
#> 28 black 0 0.5 0.5 0.5 1 1.2
#> 29 black 0 0.5 0.5 0.5 1 1.2
#> 30 black 0 0.5 0.5 0.5 1 1.2
#> 31 black 0 0.5 0.5 0.5 1 1.2
#> 32 black 0 0.5 0.5 0.5 1 1.2
#> 33 black 0 0.5 0.5 0.5 1 1.2
#> 34 black 0 0.5 0.5 0.5 1 1.2
#> 35 black 0 0.5 0.5 0.5 1 1.2
#> 36 black 0 0.5 0.5 0.5 1 1.2
#> 37 black 0 0.5 0.5 0.5 1 1.2
#> 38 black 0 0.5 0.5 0.5 1 1.2
#> 39 black 0 0.5 0.5 0.5 1 1.2
#> 40 black 0 0.5 0.5 0.5 1 1.2
#> 41 black 0 0.5 0.5 0.5 1 1.2
#> 42 black 0 0.5 0.5 0.5 1 1.2
#> 43 black 0 0.5 0.5 0.5 1 1.2
#> 44 black 0 0.5 0.5 0.5 1 1.2
#> 45 black 0 0.5 0.5 0.5 1 1.2
#> 46 black 0 0.5 0.5 0.5 1 1.2
#> 47 black 0 0.5 0.5 0.5 1 1.2# Step 1
#' compute_panel_circlepack
#'
#' @param data
#' @param scales
#'
#' @return
#' @export
#'
#' @examples
compute_panel_circlepack <- function(data, scales, npoints = 50, fun = sum){
data <- data |> compute_panel_aggregation(scales, fun)
data %>%
arrange(id) -> # this doesn't feel very principled; motivation is when you go from no fill to color, preserves circle position...
data
data$id = 1:nrow(data)
data %>%
pull(area) %>%
packcircles::circleProgressiveLayout(
sizetype = 'area') %>%
packcircles::circleLayoutVertices(npoints = npoints) %>%
left_join(data, by = join_by(id))
}gapminder::gapminder %>%
filter(continent == "Americas") %>%
filter(year == 2002) %>%
# input must have required aesthetic inputs as columns
rename(id = country, area = pop) %>%
compute_panel_circlepack() %>%
head()
#> x y id area within prop percent
#> 1 0.00000 0.0000 1 38331121 1 0.0451075 5
#> 2 -27.54349 437.7912 1 38331121 1 0.0451075 5
#> 3 -109.73958 868.6783 1 38331121 1 0.0451075 5
#> 4 -245.29200 1285.8657 1 38331121 1 0.0451075 5
#> 5 -432.06299 1682.7743 1 38331121 1 0.0451075 5
#> 6 -667.10708 2053.1445 1 38331121 1 0.0451075 5StatCirclepack <- ggplot2::ggproto(`_class` = "StatCirclepack",
`_inherit` = ggplot2::Stat,
required_aes = c("id"),
compute_panel = compute_panel_circlepack,
default_aes = ggplot2::aes(group = after_stat(id))
)
#' Title
#'
#' @param mapping
#' @param data
#' @param position
#' @param na.rm
#' @param show.legend
#' @param inherit.aes
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
geom_circlepack <- function(mapping = NULL, data = NULL,
position = "identity", na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE, ...) {
ggplot2::layer(
stat = StatCirclepack, # proto object from Step 2
geom = ggplot2::GeomPolygon, # inherit other behavior
data = data,
mapping = mapping,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}gapminder::gapminder %>%
filter(year == 2002) %>%
ggplot() +
aes(id = country) +
geom_circlepack(alpha = .5) +
geom_circlepack_text() +
coord_equal() +
labs(title = "gapminder 2002 countries")last_plot() +
aes(fill = continent) +
labs(title = "from 5 continents")last_plot() +
aes(area = pop) +
labs(title = "with very different populations")last_plot() +
facet_wrap(facets = vars(continent)) +
labs(title = "faceting")last_plot() +
scale_size_continuous(range = c(0, 4)) +
theme(legend.position = "none") +
labs(title = "remove legends")last_plot() +
aes(area = gdpPercap*pop) +
labs(title = "and very different GDPs")last_plot() +
aes(area = gdpPercap) +
labs(title = "and per capita GDPs 2002")last_plot() +
aes(slice = continent == "Europe") +
facet_null() +
aes(fill = after_stat(area)) +
aes(label = paste0(country, "\n$", round(gdpPercap)))gapminder::gapminder %>%
filter(year == 2002,
continent == "Europe") %>%
ggplot() +
aes(id = country, area = pop*gdpPercap) +
geom_circlepack() +
geom_circlepack_text(vjust = 0, color = "grey75",
lineheight = .7) +
aes(label = str_wrap(country, 10)) +
coord_equal() +
scale_size(range = c(.5,4.5)) +
aes(slice = continent == "Europe") +
labs(title = "Percent of European Economy 2002") +
aes(fill = after_stat(area)) +
geom_circlepack_text(vjust = 1.2, color = "grey65",
aes(label = paste(after_stat(percent),
"percent"),
size = stage(after_stat = area,
after_scale = size*.85))) +
ggstamp::theme_void_fill("whitesmoke") +
theme(legend.position = "none")
pride_index <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-06-11/pride_index.csv')
#> Rows: 238 Columns: 5
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): campus_name, campus_location, community_type
#> dbl (2): rating, students
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
comm_types <- c("large urban city",
"medium city",
"small city",
"small town",
"very small town",
"rural community")
pride_it <- pride_index %>%
# fix a typo
mutate(campus_location = str_replace(campus_location, "Swarrthmore", "Swarthmore")) %>%
mutate(community_type = fct_relevel(community_type, comm_types)) %>%
mutate(state = str_sub(campus_location, -2, -1)) %>%
mutate(campus_name = str_replace(campus_name, "University", "U"))
ggplot(pride_it) +
aes(id = campus_name) +
aes(area = students) +
aes(fill = rating == 5) +
geom_circlepack(linewidth = 0.2, color = "grey99") +
aes(label = str_wrap(after_stat(id), 10)) +
stat_circlepack_center(geom = GeomText,
lineheight = .8) +
scale_size(range = c(0,2)) +
aes(size = students*as.numeric(rating == 5)) + # freshly working
facet_wrap(facets = vars(community_type))gapminder::gapminder %>%
filter(year == 2002) %>%
ggplot() +
aes(id = country) +
geom_circlepack(alpha = .5) +
coord_equal() +
aes(area = pop) +
geom_circlepack_text(aes(label = after_stat(
paste(id, "\n",
round(area/1000000, 1), "mil."))), lineheight = .8)gapminder::gapminder %>%
filter(year == 2002) %>%
ggplot() +
aes(id = continent) +
geom_circlepack() +
geom_circlepack_text(alpha = .5) +
coord_equal() +
aes(fill = continent)last_plot() +
aes(id = country)last_plot() +
aes(area = pop)
last_plot() +
facet_wrap(~continent)gapminder::gapminder %>%
filter(year == 2002) %>%
ggplot() +
aes(id = country) +
geom_circlepack() +
geom_circlepack_text(alpha = .5) +
aes(area = pop) +
coord_equal() +
aes(fill = continent) +
aes(render = pop > 20000000)prop_by <- function(...) {
area <- evalq(area, parent.frame())
if (length(list(...)) == 0) {
area / sum(area)
} else {
area / ave(area, ..., FUN = function(x) sum(abs(x)))
}
}
last_plot() +
aes(label = after_stat(scales::percent(prop_by("All"), 2)))last_plot() +
aes(label = after_stat(scales::percent(prop_by(fill), 2)))# GeomTextRepel
gapminder::gapminder %>%
filter(year == 2002) %>%
ggplot() +
aes(id = country) +
geom_circlepack() +
layer(geom = ggrepel::GeomTextRepel,
stat = StatCirclepackcenter,
position = "identity") +
aes(area = pop) +
coord_equal() +
aes(fill = continent) +
aes(render = pop > 20000000)gapminder::gapminder %>%
ggplot() +
aes(id = country, area = pop) +
geom_circlepack(fun = mean) +
geom_circlepack_text(fun = mean) +
aes(label = paste(after_stat(id), "\n",
round(after_stat(area)/ 1000000), "million")) +
labs(title = "Average Population - Millions from 1952-2012") +
aes(fill = I("plum2")) +
coord_equal()mpg %>%
ggplot() +
aes(id = cyl, area = hwy) +
geom_circlepack(fun = mean) +
geom_circlepack_text(fun = mean, lineheight = .8) +
scale_size(range = c(4,5)) +
labs(title = "Average highway MPG by number of cyl") +
coord_equal() +
aes(label =
after_stat(paste0(id,"cyl\n",
round(area), "mpg"))) +
aes(fill = after_stat(area)) +
scale_fill_viridis_c(begin = .3, end = .7) knitrExtra:::chunk_to_r(chunk_name = "compute_panel_circlepack")
#> It seems you are currently knitting a Rmd/Qmd file. The parsing of the file will be done in a new R session.
knitrExtra:::chunk_to_r(chunk_name = "geom_circlepack")
#> It seems you are currently knitting a Rmd/Qmd file. The parsing of the file will be done in a new R session.
knitrExtra:::chunk_to_r(chunk_name = "compute_panel_circlepack_center")
#> It seems you are currently knitting a Rmd/Qmd file. The parsing of the file will be done in a new R session.
knitrExtra:::chunk_to_r(chunk_name = "geom_circlepack_text")
#> It seems you are currently knitting a Rmd/Qmd file. The parsing of the file will be done in a new R session.devtools::document()
devtools::check()
dev
717D
tools::install(pkg = ".", upgrade = "never") Wish list for ggcirclepack:
tidytitanic::tidy_titanic %>%
head()tidytitanic::tidy_titanic %>%
ggplot() +
aes(id = "all") +
geom_circlepack() +
geom_circlepack_text(aes(label = after_stat(area)), color = "gray50") +
coord_equal() +
labs(title = "Titanic Passengers")layer_data(i = 2)
#> size label group PANEL id x y radius area within prop percent
#> 1 4.535534 2201 all 1 all -26.46885 0 26.46885 2201 1 1 100
#> colour family angle hjust vjust alpha fontface lineheight
#> 1 gray50 0 0.5 0.5 NA 1 1.2
last_plot() +
aes(fill = sex) +
scale_size(range = c(3, 6))last_plot() +
aes(alpha = survived) +
scale_alpha_discrete(range = c(.6,.9))last_plot() +
facet_wrap(~class)last_plot() +
facet_grid(age ~ class)