Untitled

Step 0: Use base ggplo2 to get the job done

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.8
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## Warning: package 'ggplot2' was built under R version 3.6.2

## Warning: package 'tibble' was built under R version 3.6.2

## Warning: package 'purrr' was built under R version 3.6.2

## Warning: package 'dplyr' was built under R version 3.6.2

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(gapminder)
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep  

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack  

cbind(prep, pack) %>%
  mutate(id = row_number())

##                country       pop           x          y    radius id
## 1            Argentina  38331121  -3493.0180     0.0000 3493.0180  1
## 2              Bolivia   8445134   1639.5639     0.0000 1639.5639  2
## 3               Brazil 179914212   2732.7743 -9142.0260 7567.5936  3
## 4               Canada  31902268   1150.7522  4801.4069 3186.6608  4
## 5                Chile  15497046   5273.8171  1302.3806 2221.0049  5
## 6             Colombia  41008227  10562.3302 -1160.6508 3612.9384  6
## 7           Costa Rica   3834934  -4573.1172 -4469.2048 1104.8518  7
## 8                 Cuba  11226999  -7453.2262 -3646.6547 1890.4139  8
## 9   Dominican Republic   8650322  -8636.6596  -299.9554 1659.3622  9
## 10             Ecuador  12921234  -7908.2513  3314.7888 2028.0425 10
## 11         El Salvador   6353681  -4769.2005  4746.5765 1422.1250 11
## 12           Guatemala  11178650  -3084.4567  7593.9564 1886.3390 12
## 13               Haiti   7607651   -133.6785  9366.9797 1556.1460 13
## 14            Honduras   6677328   2869.9023  9116.0827 1457.8956 14
## 15             Jamaica   2664659   4409.2334  7302.3944  920.9708 15
## 16              Mexico 102479927  10986.7220  8154.0495 5711.4249 16
## 17           Nicaragua   5146848  -5728.2786 -6555.5701 1279.9580 17
## 18              Panama   2990875  -7982.0775 -6463.5726  975.7177 18
## 19            Paraguay   5884491  -6893.5704  6556.3419 1368.6094 19
## 20                Peru  26769436  -6631.4420 10836.0023 2919.0711 20
## 21         Puerto Rico   3859606  -2614.0309 10551.5165 1108.4001 21
## 22 Trinidad and Tobago   1101832  -1083.9386 11293.7595  592.2196 22
## 23       United States 287675526 -18526.1513 -6598.5236 9569.2196 23
## 24             Uruguay   3363085 -11041.9619   913.3993 1034.6512 24
## 25           Venezuela  24287670   4129.4010 13162.9819 2780.4686 25

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines  


circle_outlines %>%  
  ggplot() +  
  aes(x = x, y = y) +  
  geom_polygon(colour = "black", alpha = 0.6) +  
  aes(group = id) +  
  aes(fill = factor(id)) +  
  geom_text(data = cbind(prep, pack),  
            aes(x, y, size = pop, label = country,  
                group = NULL, fill = NULL)) +  
  theme(legend.position = "none") +  
  coord_equal()

---

Step 1: computation

• define computation that ggplot2 should do for you, before plotting
  • here it’s computing a variable with labels for each observation
• test that functionality!

# you won't use the scales argument, but ggplot will later
compute_group_circle_pack <- function(data, scales){
 
  data %>%
    mutate(id = row_number()) ->
  data1
 
  data1 %>%  
    pull(area) %>%
    packcircles::circleProgressiveLayout(
                                         sizetype = 'area') %>%
    packcircles::circleLayoutVertices(npoints = 300) %>%
    left_join(data1) #%>%
    # rename(group = id)
   
}


# step 1b test the computation function
gapminder::gapminder %>%
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  # input must have required aesthetic inputs as columns
  rename(area = pop) %>%
  compute_group_circle_pack() %>%
  head()

## Joining, by = "id"

##             x         y id   country continent year lifeExp     area gdpPercap
## 1   0.0000000   0.00000  1 Argentina  Americas 2002   74.34 38331121  8797.641
## 2  -0.7660766  73.15225  1 Argentina  Americas 2002   74.34 38331121  8797.641
## 3  -3.0639703 146.27241  1 Argentina  Americas 2002   74.34 38331121  8797.641
## 4  -6.8926731 219.32842  1 Argentina  Americas 2002   74.34 38331121  8797.641
## 5 -12.2505058 292.28821  1 Argentina  Americas 2002   74.34 38331121  8797.641
## 6 -19.1351181 365.11980  1 Argentina  Americas 2002   74.34 38331121  8797.641

# step 1b test the computation function
gapminder::gapminder %>%
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  # input must have required aesthetic inputs as columns
  rename(area = pop) %>%
  compute_group_circle_pack() %>% 
  ggplot() + 
  aes(x = x, y = y, fill = country) + 
  geom_polygon()

## Joining, by = "id"

my_setup_data <- function(data, params){
                                    if(data$group[1] == -1){
                                      nrows <- nrow(data)
                                      data$group <- seq_len(nrows)
                                    }
                                    data
                                  }

---

Step 2: define ggproto

• what’s the naming convention for the proto object?
• which aesthetics are required as inputs?
• where does the function from above go?

StatCirclepack <- ggplot2::ggproto(`_class` = "StatCirclepack",
                                  `_inherit` = ggplot2::Stat,
                                  # setup_data = my_setup_data,
                                  required_aes = c("area"),
                                  compute_group = compute_group_circle_pack#,
                                  # default_aes = aes(fill = after_stat(area))
                                  )

---

Step 3: define geom_* function

geom_polygon_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, ...)
  )
}

---

Step 4: Enjoy! Use your function

gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(area = pop) +
  geom_polygon_circlepack(alpha = .5)

## Joining, by = "id"

last_plot() +
  facet_wrap(facets = vars(continent)) 

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last_plot() +
  aes(fill = country) + 
  guides(fill = "none")

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