trying to make some Stats…
compute_panel_edgelist_nodes <- function(data = edgelist, scales, layout = "stress", seed = 12345){
set.seed(seed)
node_names <- unique(c(data$from, data$to))
data %>% # edgelist
select(from, to, everything()) %>%
tidygraph::as_tbl_graph() ->
graph
graph %>%
ggraph::create_layout(layout = layout) ->
node_layout
node_layout$name <- node_names
node_layout$is_from <- node_names %in% data$from
node_layout$is_to <- node_names %in% data$to
node_layout$is_from_and_to <- node_layout$is_from & node_layout$is_to
node_layout$node_type <- case_when(node_layout$is_from_and_to ~ "both",
node_layout$is_from ~ "from",
node_layout$is_to ~ "to", TRUE ~ "neither")
node_layout
}
StatEdgelistNodes <- ggproto("StatEdgelistNodes",
Stat,
compute_panel = compute_panel_edgelist_nodes,
required_aes = c("from", "to"),
default_aes = aes(label = after_stat(name)))
edgelist %>%
ggplot() +
aes(from = from, to = to) +
geom_point(stat = StatEdgelistNodes, color = "grey") +
geom_text(stat = StatEdgelistNodes) +
aes(size = after_stat(node_type) == "both")
## Warning: Using size for a discrete variable is not advised.
compute_panel_edgelist_edges <- function(data = edgelist, scales, layout = "stress", seed = 12345){
set.seed(seed)
data %>% # edgelist
select(from, to, everything()) %>%
tidygraph::as_tbl_graph() %>%
ggraph::create_layout(layout = layout) ->
node_layout
data %>%
select(from, to, everything()) %>%
# group_by(PANEL, group) %>%
tidygraph::as_tbl_graph() %>%
activate("edges") %>%
as_tibble() %>%
left_join(node_layout %>%
select(from = .ggraph.orig_index,
x, y)) %>%
left_join(node_layout %>%
select(to = .ggraph.orig_index,
xend = x,
yend = y)) %>%
mutate(group = 1,
PANEL = 1) %>%
mutate(node_type = "both")
}
edgelist %>%
compute_panel_edgelist_edges()
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## # A tibble: 243 × 10
## from to year x y xend yend group PANEL node_type
## <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 13 1957 9.64 2.09 11.2 2.27 1 1 both
## 2 1 14 1957 9.64 2.09 10.3 0.889 1 1 both
## 3 1 20 1957 9.64 2.09 10.0 2.41 1 1 both
## 4 1 52 1957 9.64 2.09 8.82 2.89 1 1 both
## 5 1 53 1957 9.64 2.09 9.27 3.08 1 1 both
## 6 2 20 1957 10.8 3.22 10.0 2.41 1 1 both
## 7 2 21 1957 10.8 3.22 10.5 2.13 1 1 both
## 8 3 9 1957 10.4 0 10.0 1.20 1 1 both
## 9 3 14 1957 10.4 0 10.3 0.889 1 1 both
## 10 4 5 1957 0.770 4.37 0.305 3.94 1 1 both
## # ℹ 233 more rows
StatEdgelistEdges <- ggproto("StatEdgelistEdges",
Stat,
compute_panel = compute_panel_edgelist_edges,
required_aes = c("from", "to"))
last_plot() +
geom_segment(stat = StatEdgelistEdges, size = .5, color = "grey") #uh oh!
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## Warning: Using size for a discrete variable is not advised.
layer_data(i = 2) %>% head()
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## Warning: Using size for a discrete variable is not advised.
## size label PANEL x y circular .ggraph.orig_index
## 1 2 1 1 9.6365178 2.093482 FALSE 1
## 2 2 2 1 10.7685831 3.220272 FALSE 2
## 3 2 3 1 10.3913611 0.000000 FALSE 3
## 4 6 4 1 0.7701130 4.371856 FALSE 4
## 5 6 5 1 0.3051852 3.936482 FALSE 5
## 6 6 6 1 11.7324035 1.864297 FALSE 6
## .ggraph.index name is_from is_to is_from_and_to node_type colour family angle
## 1 1 1 TRUE FALSE FALSE from black 0
## 2 2 2 TRUE FALSE FALSE from black 0
## 3 3 3 TRUE FALSE FALSE from black 0
## 4 4 4 TRUE TRUE TRUE both black 0
## 5 5 5 TRUE TRUE TRUE both black 0
## 6 6 6 TRUE TRUE TRUE both black 0
## hjust vjust alpha fontface lineheight
## 1 0.5 0.5 NA 1 1.2
## 2 0.5 0.5 NA 1 1.2
## 3 0.5 0.5 NA 1 1.2
## 4 0.5 0.5 NA 1 1.2
## 5 0.5 0.5 NA 1 1.2
## 6 0.5 0.5 NA 1 1.2
user_repo_fun <- read_csv("https://raw.githubusercontent.com/EvaMaeRey/mytidytuesday/refs/heads/main/2024-11-19-gg-prefixes/exported_funs_exts_ggplot2_tidyverse_org.csv")
## Rows: 5527 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): user, repo, fun_exported
##
## 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.
user_repo_fun |>
mutate(prefix_short = fun_exported %>% str_extract(".*?_")) %>%
mutate(prefix_long = fun_exported %>% str_extract(".+_")) ->
user_repo_fun_prefix
user_repo_fun_prefix |>
filter(!is.na(prefix_short)) |>
filter(n() > 20, .by = prefix_short) |>
filter(prefix_short != "easy_") |>
ggplot() +
aes(from = repo, to = prefix_short) +
geom_point(stat = StatEdgelistNodes, color = "grey") +
geom_segment(stat = StatEdgelistEdges, color = "grey", size = .2) +
geom_text(stat = StatEdgelistNodes) +
aes(size = after_stat(node_type) == "to") +
theme(legend.position = "none")
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## Warning: Using size for a discrete variable is not advised.
last_plot() +
aes(from = user)
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## Warning: Using size for a discrete variable is not advised.
last_plot() +
aes(id = fun_exported %>% str_extract(".+_"))
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## Warning: Using size for a discrete variable is not advised.
last_plot()
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## Warning: Using size for a discrete variable is not advised.
cran_20230905 <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-19/cran_20230905.csv')
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 19838 Columns: 67
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (51): Package, Version, Priority, Depends, Imports, LinkingTo, Suggests...
## lgl (15): License_is_FOSS, License_restricts_use, BuildKeepEmpty, BuildManu...
## date (1): Published
##
## 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.
package_authors <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-19/package_authors.csv')
## Rows: 51281 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Package, authorsR
##
## 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.
cran_20230905 %>%
filter(Package %>%
str_detect("^gg|^GG")) %>%
filter(Imports %>%
str_detect("ggplot2")|
Depends %>% str_detect("ggplot2")) %>%
select(Package) %>%
left_join(package_authors %>%
mutate(authorsR = authorsR %>%
str_replace(" ", "\n"))) %>%
ggplot() +
aes(from = Package, to = authorsR) +
geom_point(stat = StatEdgelistNodes) +
geom_segment(stat = StatEdgelistEdges)
## Joining with `by = join_by(Package)`
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
gapminder::gapminder %>%
ggplot() +
aes(from = continent, to = country) +
geom_point(stat = StatEdgelistNodes) +
geom_segment(stat = StatEdgelistEdges)
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
layer_data() %>% head()
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## label PANEL x y circular .ggraph.orig_index
## 1 Asia 1 11.9216781 1.38805633 FALSE 1
## 2 Europe 1 8.1613728 1.41185069 FALSE 2
## 3 Africa 1 15.8090130 1.47845457 FALSE 3
## 4 Americas 1 4.3701744 1.39917414 FALSE 4
## 5 Oceania 1 0.9995374 0.05483522 FALSE 5
## 6 Afghanistan 1 12.1840925 2.41220939 FALSE 6
## .ggraph.index name is_from is_to is_from_and_to node_type shape colour
## 1 1 Asia TRUE FALSE FALSE from 19 black
## 2 2 Europe TRUE FALSE FALSE from 19 black
## 3 3 Africa TRUE FALSE FALSE from 19 black
## 4 4 Americas TRUE FALSE FALSE from 19 black
## 5 5 Oceania TRUE FALSE FALSE from 19 black
## 6 6 Afghanistan FALSE TRUE FALSE to 19 black
## size fill alpha stroke
## 1 1.5 NA NA 0.5
## 2 1.5 NA NA 0.5
## 3 1.5 NA NA 0.5
## 4 1.5 NA NA 0.5
## 5 1.5 NA NA 0.5
## 6 1.5 NA NA 0.5
gapminder::gapminder %>%
ggplot() +
aes(from = continent, to = country) +
geom_point(stat = StatEdgelistNodes, layout = "circlepack") +
geom_segment(stat = StatEdgelistEdges, layout = "circlepack")
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
layer_data()
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
## label PANEL x y r circular
## 1 Asia 1 -1.93886330 -6.64720574 3.5496000 FALSE
## 2 Europe 1 -1.93886329 -6.64720576 0.5641896 FALSE
## 3 Africa 1 -2.14400192 -7.75678112 0.5641896 FALSE
## 4 Americas 1 -1.08051212 -7.37964871 0.5641896 FALSE
## 5 Oceania 1 -3.00235309 -7.02433818 0.5641896 FALSE
## 6 Afghanistan 1 -2.79721444 -5.91476279 0.5641896 FALSE
## 7 Albania 1 -0.87537351 -6.27007331 0.5641896 FALSE
## 8 Algeria 1 -1.73372468 -5.53763037 0.5641896 FALSE
## 9 Angola 1 -2.59207583 -4.80518739 0.5641896 FALSE
## 10 Argentina 1 -3.20749170 -8.13391357 0.5641896 FALSE
## 11 Australia 1 -1.28565076 -8.48922410 0.5641896 FALSE
## 12 Austria 1 -0.01702233 -7.00251626 0.5641896 FALSE
## 13 Bahrain 1 -0.67023489 -5.16049792 0.5641896 FALSE
## 14 Bangladesh 1 -3.86070424 -6.29189520 0.5641896 FALSE
## 15 Belgium 1 -2.34914053 -8.86635652 0.5641896 FALSE
## 16 Benin 1 -1.49078938 -9.59879949 0.5641896 FALSE
## 17 Bolivia 1 -4.06584287 -7.40147062 0.5641896 FALSE
## 18 Bosnia and Herzegovina 1 -0.22216097 -8.11209168 0.5641896 FALSE
## 19 Botswana 1 -1.52858607 -4.42805497 0.5641896 FALSE
## 20 Brazil 1 -3.65556559 -5.18231981 0.5641896 FALSE
## 21 Bulgaria 1 -4.92419403 -6.66902765 0.5641896 FALSE
## 22 Burkina Faso 1 0.18811628 -5.89294087 0.5641896 FALSE
## 23 Burundi 1 0.39325489 -4.78336547 0.5641896 FALSE
## 24 Cambodia 1 0.84132882 -7.73495923 0.5641896 FALSE
## 25 Cameroon 1 -3.45042698 -4.07274442 0.5641896 FALSE
## 26 Canada 1 -2.38693722 -3.69561200 0.5641896 FALSE
## 27 Central African Republic 1 -3.41263031 -9.24348897 0.5641896 FALSE
## 28 Chad 1 -0.42729961 -9.22166707 0.5641896 FALSE
## 29 Chile 1 -4.27098149 -8.51104602 0.5641896 FALSE
## 30 China 1 -0.46509628 -4.05092252 0.5641896 FALSE
## 31 Colombia 1 -4.71905539 -5.55945223 0.5641896 FALSE
## 32 Comoros 1 -5.08274434 0.90691308 4.6326185 FALSE
## 33 Congo, Dem. Rep. 1 -5.08274433 0.90691311 0.5641896 FALSE
## 34 Congo, Rep. 1 -4.65232422 1.94997489 0.5641896 FALSE
## 35 Costa Rica 1 -5.77085231 1.80119876 0.5641896 FALSE
## 36 Cote d'Ivoire 1 -3.96421625 1.05568924 0.5641896 FALSE
## 37 Croatia 1 -4.39463638 0.01262744 0.5641896 FALSE
## 38 Cuba 1 -5.51316447 -0.13614869 0.5641896 FALSE
## 39 Czech Republic 1 -6.20127241 0.75813695 0.5641896 FALSE
## 40 Denmark 1 -6.63169254 -0.28492485 0.5641896 FALSE
## 41 Djibouti 1 -3.53379615 2.09875105 0.5641896 FALSE
## 42 Dominican Republic 1 -5.34043217 2.84426056 0.5641896 FALSE
## 43 Ecuador 1 -3.27610830 0.16140357 0.5641896 FALSE
## 44 Egypt 1 -4.82505652 -1.03043436 0.5641896 FALSE
## 45 El Salvador 1 -6.88938039 1.65242260 0.5641896 FALSE
## 46 Equatorial Guinea 1 -4.22190412 2.99303670 0.5641896 FALSE
## 47 Eritrea 1 -4.91001207 3.88732237 0.5641896 FALSE
## 48 Ethiopia 1 -6.45896026 2.69548443 0.5641896 FALSE
## 49 Finland 1 -2.84568817 1.20446540 0.5641896 FALSE
## 50 France 1 -7.57748833 2.54670827 0.5641896 FALSE
## 51 Gabon 1 -5.94358460 -1.17921049 0.5641896 FALSE
## 52 Gambia 1 -3.70652843 -0.88165823 0.5641896 FALSE
## 53 Germany 1 -2.58800035 -0.73288210 0.5641896 FALSE
## 54 Ghana 1 -2.15758022 0.31017973 0.5641896 FALSE
## 55 Greece 1 -7.31980049 0.60936078 0.5641896 FALSE
## 56 Guatemala 1 -7.75022062 -0.43370101 0.5641896 FALSE
## 57 Guinea 1 -7.06211268 -1.32798665 0.5641896 FALSE
## 58 Guinea-Bissau 1 -3.10337604 3.14181287 0.5641896 FALSE
## 59 Haiti 1 -6.02854012 3.73854623 0.5641896 FALSE
## 60 Honduras 1 -2.41526807 2.24752722 0.5641896 FALSE
## 61 Hong Kong, China 1 -4.13694857 -1.92472003 0.5641896 FALSE
## 62 Hungary 1 -8.00790846 1.50364643 0.5641896 FALSE
## 63 Iceland 1 -5.25547665 -2.07349616 0.5641896 FALSE
## 64 India 1 -1.98484797 3.29058903 0.5641896 FALSE
## 65 Indonesia 1 -5.59812002 4.78160804 0.5641896 FALSE
## 66 Iran 1 -1.46947227 -0.58410594 0.5641896 FALSE
## 67 Iraq 1 -8.69601641 2.39793210 0.5641896 FALSE
## 68 Ireland 1 -4.56736870 -2.96778183 0.5641896 FALSE
## 69 Israel 1 -8.18064076 -1.47676281 0.5641896 FALSE
## 70 Italy 1 -3.79148402 4.03609851 0.5641896 FALSE
## 71 Jamaica 1 -4.47959197 4.93038418 0.5641896 FALSE
## 72 Japan 1 -7.14706820 3.58977010 0.5641896 FALSE
## 73 Jordan 1 -1.72716009 1.35324153 0.5641896 FALSE
## 74 Kenya 1 -1.29673999 2.39630335 0.5641896 FALSE
## 75 Korea, Dem. Rep. 1 -8.26559628 3.44099394 0.5641896 FALSE
## 76 Korea, Rep. 1 -3.01842049 -1.77594390 0.5641896 FALSE
## 77 Kuwait 1 -1.89989240 -1.62716777 0.5641896 FALSE
## 78 Lebanon 1 -8.43832856 0.46058462 0.5641896 FALSE
## 79 Lesotho 1 -6.37400473 -2.22227228 0.5641896 FALSE
## 80 Liberia 1 -8.86874870 -0.58247718 0.5641896 FALSE
## 81 Libya 1 -7.49253281 -2.37104845 0.5641896 FALSE
## 82 Madagascar 1 -2.67295594 4.18487468 0.5641896 FALSE
## 83 Malawi 1 -1.03905214 0.45895586 0.5641896 FALSE
## 84 Malaysia 1 -6.71664807 4.63283190 0.5641896 FALSE
## 85 Mali 1 2.78315658 -1.34615217 3.5496000 FALSE
## 86 Mauritania 1 2.78315656 -1.34615217 0.5641896 FALSE
## 87 Mauritius 1 1.95551368 -2.11312452 0.5641896 FALSE
## 88 Mexico 1 3.03355269 -2.44639814 0.5641896 FALSE
## 89 Mongolia 1 1.70511755 -1.01287855 0.5641896 FALSE
## 90 Montenegro 1 2.53276048 -0.24590620 0.5641896 FALSE
## 91 Morocco 1 3.61079948 -0.57917982 0.5641896 FALSE
## 92 Mozambique 1 3.86119557 -1.67942579 0.5641896 FALSE
## 93 Myanmar 1 4.11159170 -2.77967176 0.5641896 FALSE
## 94 Namibia 1 2.20590977 -3.21337050 0.5641896 FALSE
## 95 Nepal 1 0.87747466 -1.77985095 0.5641896 FALSE
## 96 Netherlands 1 1.45472147 0.08736742 0.5641896 FALSE
## 97 New Zealand 1 3.36040340 0.52106616 0.5641896 FALSE
## 98 Nicaragua 1 4.68883849 -0.91245344 0.5641896 FALSE
## 99 Niger 1 1.12787079 -2.88009691 0.5641896 FALSE
## 100 Nigeria 1 1.37826688 -3.98034289 0.5641896 FALSE
## 101 Norway 1 0.62707853 -0.67960498 0.5641896 FALSE
## 102 Oman 1 3.28394877 -3.54664412 0.5641896 FALSE
## 103 Pakistan 1 2.28236439 0.85433978 0.5641896 FALSE
## 104 Panama 1 1.20432539 1.18761340 0.5641896 FALSE
## 105 Paraguay 1 0.37668245 0.42064099 0.5641896 FALSE
## 106 Peru 1 4.43844241 0.18779254 0.5641896 FALSE
## 107 Philippines 1 4.93923458 -2.01269941 0.5641896 FALSE
## 108 Poland 1 5.51648142 -0.14548108 0.5641896 FALSE
## 109 Portugal 1 5.18963071 -3.11294538 0.5641896 FALSE
## 110 Puerto Rico 1 4.36198778 -3.87991774 0.5641896 FALSE
## 111 Reunion 1 -6.21148593 -4.74242638 1.1283791 FALSE
## 112 Romania 1 -6.21129546 -4.17823686 0.5641896 FALSE
## 113 Rwanda 1 -6.21167639 -5.30661590 0.5641896 FALSE
## 114 Sao Tome and Principe 1 1.80202014 6.17805217 4.0383035 FALSE
## 115 Saudi Arabia 1 1.37897452 5.88805493 0.5641896 FALSE
## 116 Senegal 1 2.30967488 6.52604882 0.5641896 FALSE
## 117 Serbia 1 1.29180577 7.01306205 0.5641896 FALSE
## 118 Sierra Leone 1 2.39684363 5.40104170 0.5641896 FALSE
## 119 Singapore 1 0.36110540 6.37506813 0.5641896 FALSE
## 120 Slovak Republic 1 0.44827416 5.25006101 0.5641896 FALSE
## 121 Slovenia 1 1.46614328 4.76304781 0.5641896 FALSE
## 122 Somalia 1 2.48401239 4.27603459 0.5641896 FALSE
## 123 South Africa 1 3.32754400 6.03903562 0.5641896 FALSE
## 124 Spain 1 2.22250615 7.65105594 0.5641896 FALSE
## 125 Sri Lanka 1 0.53544291 4.12505390 0.5641896 FALSE
## 126 Sudan 1 0.27393664 7.50007525 0.5641896 FALSE
## 127 Swaziland 1 -0.56959496 5.73707422 0.5641896 FALSE
## 128 Sweden 1 3.24037524 7.16404274 0.5641896 FALSE
## 129 Switzerland 1 3.15320651 8.28904986 0.5641896 FALSE
## 130 Syria 1 1.20463704 8.13806917 0.5641896 FALSE
## 131 Taiwan 1 3.41471275 4.91402850 0.5641896 FALSE
## 132 Tanzania 1 -0.65676372 6.86208133 0.5641896 FALSE
## 133 Thailand 1 -0.48242621 4.61206710 0.5641896 FALSE
## 134 Togo 1 -0.39525745 3.48705998 0.5641896 FALSE
## 135 Trinidad and Tobago 1 1.55331204 3.63804069 0.5641896 FALSE
## 136 Tunisia 1 0.18676791 8.62508237 0.5641896 FALSE
## 137 Turkey 1 -0.74393248 7.98708845 0.5641896 FALSE
## 138 Uganda 1 2.57118115 3.15102747 0.5641896 FALSE
## 139 United Kingdom 1 3.50188151 3.78902139 0.5641896 FALSE
## 140 United States 1 4.25824436 6.67702954 0.5641896 FALSE
## 141 Uruguay 1 2.13533742 8.77606306 0.5641896 FALSE
## 142 Venezuela 1 4.34541312 5.55202242 0.5641896 FALSE
## 143 Vietnam 1 0.62261167 3.00004678 0.5641896 FALSE
## 144 West Bank and Gaza 1 -1.58746409 6.22408742 0.5641896 FALSE
## 145 Yemen, Rep. 1 -1.50029533 5.09908030 0.5641896 FALSE
## 146 Zambia 1 5.27611349 6.19001634 0.5641896 FALSE
## 147 Zimbabwe 1 3.06603778 9.41405698 0.5641896 FALSE
## leaf depth .ggraph.orig_index .ggraph.index name
## 1 FALSE 0 2 1 Asia
## 2 TRUE 1 7 2 Europe
## 3 TRUE 1 12 3 Africa
## 4 TRUE 1 15 4 Americas
## 5 TRUE 1 18 5 Oceania
## 6 TRUE 1 21 6 Afghanistan
## 7 TRUE 1 37 7 Albania
## 8 TRUE 1 39 8 Algeria
## 9 TRUE 1 40 9 Angola
## 10 TRUE 1 49 10 Argentina
## 11 TRUE 1 50 11 Australia
## 12 TRUE 1 53 12 Austria
## 13 TRUE 1 55 13 Bahrain
## 14 TRUE 1 62 14 Bangladesh
## 15 TRUE 1 63 15 Belgium
## 16 TRUE 1 68 16 Benin
## 17 TRUE 1 70 17 Bolivia
## 18 TRUE 1 90 18 Bosnia and Herzegovina
## 19 TRUE 1 96 19 Botswana
## 20 TRUE 1 101 20 Brazil
## 21 TRUE 1 108 21 Bulgaria
## 22 TRUE 1 109 22 Burkina Faso
## 23 TRUE 1 112 23 Burundi
## 24 TRUE 1 117 24 Cambodia
## 25 TRUE 1 120 25 Cameroon
## 26 TRUE 1 121 26 Canada
## 27 TRUE 1 124 27 Central African Republic
## 28 TRUE 1 128 28 Chad
## 29 TRUE 1 129 29 Chile
## 30 TRUE 1 137 30 China
## 31 TRUE 1 139 31 Colombia
## 32 FALSE -1 3 32 Comoros
## 33 TRUE -1 8 33 Congo, Dem. Rep.
## 34 TRUE -1 9 34 Congo, Rep.
## 35 TRUE -1 16 35 Costa Rica
## 36 TRUE -1 19 36 Cote d'Ivoire
## 37 TRUE -1 22 37 Croatia
## 38 TRUE -1 23 38 Cuba
## 39 TRUE -1 25 39 Czech Republic
## 40 TRUE -1 27 40 Denmark
## 41 TRUE -1 28 41 Djibouti
## 42 TRUE -1 32 42 Dominican Republic
## 43 TRUE -1 33 43 Ecuador
## 44 TRUE -1 34 44 Egypt
## 45 TRUE -1 36 45 El Salvador
## 46 TRUE -1 41 46 Equatorial Guinea
## 47 TRUE -1 44 47 Eritrea
## 48 TRUE -1 46 48 Ethiopia
## 49 TRUE -1 47 49 Finland
## 50 TRUE -1 48 50 France
## 51 TRUE -1 51 51 Gabon
## 52 TRUE -1 52 52 Gambia
## 53 TRUE -1 54 53 Germany
## 54 TRUE -1 57 54 Ghana
## 55 TRUE -1 58 55 Greece
## 56 TRUE -1 74 56 Guatemala
## 57 TRUE -1 79 57 Guinea
## 58 TRUE -1 80 58 Guinea-Bissau
## 59 TRUE -1 81 59 Haiti
## 60 TRUE -1 82 60 Honduras
## 61 TRUE -1 83 61 Hong Kong, China
## 62 TRUE -1 85 62 Hungary
## 63 TRUE -1 86 63 Iceland
## 64 TRUE -1 87 64 India
## 65 TRUE -1 91 65 Indonesia
## 66 TRUE -1 92 66 Iran
## 67 TRUE -1 94 67 Iraq
## 68 TRUE -1 99 68 Ireland
## 69 TRUE -1 100 69 Israel
## 70 TRUE -1 111 70 Italy
## 71 TRUE -1 113 71 Jamaica
## 72 TRUE -1 114 72 Japan
## 73 TRUE -1 116 73 Jordan
## 74 TRUE -1 118 74 Kenya
## 75 TRUE -1 122 75 Korea, Dem. Rep.
## 76 TRUE -1 123 76 Korea, Rep.
## 77 TRUE -1 126 77 Kuwait
## 78 TRUE -1 127 78 Lebanon
## 79 TRUE -1 132 79 Lesotho
## 80 TRUE -1 134 80 Liberia
## 81 TRUE -1 136 81 Libya
## 82 TRUE -1 138 82 Madagascar
## 83 TRUE -1 146 83 Malawi
## 84 TRUE -1 147 84 Malaysia
## 85 FALSE -1 4 85 Mali
## 86 TRUE -1 10 86 Mauritania
## 87 TRUE -1 17 87 Mauritius
## 88 TRUE -1 20 88 Mexico
## 89 TRUE -1 26 89 Mongolia
## 90 TRUE -1 29 90 Montenegro
## 91 TRUE -1 31 91 Morocco
## 92 TRUE -1 35 92 Mozambique
## 93 TRUE -1 38 93 Myanmar
## 94 TRUE -1 42 94 Namibia
## 95 TRUE -1 43 95 Nepal
## 96 TRUE -1 45 96 Netherlands
## 97 TRUE -1 56 97 New Zealand
## 98 TRUE -1 59 98 Nicaragua
## 99 TRUE -1 60 99 Niger
## 100 TRUE -1 71 100 Nigeria
## 101 TRUE -1 88 101 Norway
## 102 TRUE -1 98 102 Oman
## 103 TRUE -1 104 103 Pakistan
## 104 TRUE -1 105 104 Panama
## 105 TRUE -1 106 105 Paraguay
## 106 TRUE -1 110 106 Peru
## 107 TRUE -1 135 107 Philippines
## 108 TRUE -1 140 108 Poland
## 109 TRUE -1 141 109 Portugal
## 110 TRUE -1 142 110 Puerto Rico
## 111 FALSE -1 5 111 Reunion
## 112 TRUE -1 11 112 Romania
## 113 TRUE -1 97 113 Rwanda
## 114 FALSE -1 1 114 Sao Tome and Principe
## 115 TRUE -1 6 115 Saudi Arabia
## 116 TRUE -1 13 116 Senegal
## 117 TRUE -1 14 117 Serbia
## 118 TRUE -1 24 118 Sierra Leone
## 119 TRUE -1 30 119 Singapore
## 120 TRUE -1 61 120 Slovak Republic
## 121 TRUE -1 64 121 Slovenia
## 122 TRUE -1 65 122 Somalia
## 123 TRUE -1 66 123 South Africa
## 124 TRUE -1 67 124 Spain
## 125 TRUE -1 69 125 Sri Lanka
## 126 TRUE -1 72 126 Sudan
## 127 TRUE -1 73 127 Swaziland
## 128 TRUE -1 75 128 Sweden
## 129 TRUE -1 76 129 Switzerland
## 130 TRUE -1 77 130 Syria
## 131 TRUE -1 78 131 Taiwan
## 132 TRUE -1 84 132 Tanzania
## 133 TRUE -1 89 133 Thailand
## 134 TRUE -1 93 134 Togo
## 135 TRUE -1 95 135 Trinidad and Tobago
## 136 TRUE -1 102 136 Tunisia
## 137 TRUE -1 103 137 Turkey
## 138 TRUE -1 107 138 Uganda
## 139 TRUE -1 115 139 United Kingdom
## 140 TRUE -1 119 140 United States
## 141 TRUE -1 125 141 Uruguay
## 142 TRUE -1 130 142 Venezuela
## 143 TRUE -1 131 143 Vietnam
## 144 TRUE -1 133 144 West Bank and Gaza
## 145 TRUE -1 143 145 Yemen, Rep.
## 146 TRUE -1 144 146 Zambia
## 147 TRUE -1 145 147 Zimbabwe
## is_from is_to is_from_and_to node_type shape colour size fill alpha stroke
## 1 TRUE FALSE FALSE from 19 black 1.5 NA NA 0.5
## 2 TRUE FALSE FALSE from 19 black 1.5 NA NA 0.5
## 3 TRUE FALSE FALSE from 19 black 1.5 NA NA 0.5
## 4 TRUE FALSE FALSE from 19 black 1.5 NA NA 0.5
## 5 TRUE FALSE FALSE from 19 black 1.5 NA NA 0.5
## 6 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 7 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 8 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 9 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 10 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 11 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 12 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 13 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 14 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 15 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 16 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 17 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 18 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 19 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 20 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 21 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 22 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 23 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 24 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 25 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 26 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 27 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 28 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 29 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 30 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 31 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 32 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 33 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 34 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 35 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 36 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 37 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 38 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 39 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 40 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 41 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 42 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 43 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 44 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 45 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 46 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 47 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 48 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 49 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 50 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 51 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 52 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 53 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 54 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 55 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 56 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 57 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 58 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 59 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 60 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 61 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 62 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 63 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 64 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 65 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 66 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 67 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 68 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 69 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 70 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 71 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 72 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 73 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 74 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 75 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 76 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 77 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 78 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 79 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 80 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 81 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 82 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 83 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 84 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 85 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 86 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 87 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 88 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 89 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 90 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 91 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 92 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 93 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 94 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 95 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 96 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 97 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 98 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 99 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 100 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 101 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 102 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 103 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 104 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 105 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 106 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 107 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 108 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 109 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 110 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 111 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 112 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 113 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 114 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 115 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 116 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 117 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 118 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 119 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 120 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 121 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 122 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 123 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 124 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 125 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 126 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 127 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 128 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 129 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 130 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 131 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 132 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 133 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 134 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 135 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 136 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 137 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 138 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 139 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 140 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 141 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 142 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 143 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 144 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 145 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 146 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
## 147 FALSE TRUE FALSE to 19 black 1.5 NA NA 0.5
graph <- tbl_graph(flare$vertices, flare$edges)
set.seed(1)
ggraph(graph, 'circlepack', weight = size) +
geom_node_circle(aes(fill = depth), size = 0.25, n = 50) +
coord_fixed()
gapminder::gapminder %>%
select(continent, country) %>%
tidygraph::as_tbl_graph() %>%
ggraph(layout = "circlepack") +
geom_node_circle()
library(tidyverse)
pkgs <- as_tibble(tools::CRAN_package_db())
gg_pkgs <- pkgs %>%
select(Package, Depends, Imports, Suggests) %>%
filter(
str_detect(Package, "^gg"),
if_any(-Package, ~ str_detect(.x, "ggplot2"))
) %>%
mutate(
dep_grid = str_detect(Depends, "\\bgrid\\b") | str_detect(Imports, "\\bgrid\\b"),
dep_dplyr = str_detect(Depends, "\\bdplyr\\b") | str_detect(Imports, "\\bdplyr\\b")
) %>%
drop_na()
gg_pkgs %>%
pivot_longer(cols = dep_grid:dep_dplyr) %>%
filter(value == T) %>%
ggplot() +
aes(from = Package, to = name) +
geom_point(stat = StatEdgelistNodes) +
geom_segment(stat = StatEdgelistEdges)
## Joining with `by = join_by(from)`
## Joining with `by = join_by(to)`
Experiment
