How to adjust ggrepel label on pie chart?

Question

I am trying to create a pie chart to visualize percent abundance of 9 genera. However, the labels are all clumping together. How do I remedy this? Code included below:

generaabundance2020 <- c(883, 464, 1948, 1177, 2607, 962, 2073, 620, 2670)

genera2020 <-  c("Andrena", "Ceratina", "Halictus", 
                 "Hesperapis", "Lasioglossum", "Melissodes", 
                 "Osmia", "Panurginus", "Other")

generabreakdown2020 <- data.frame(group = genera2020, value = generaabundance2020)

gb2020label <- generabreakdown2020 %>% 
  group_by(value) %>% # Variable to be transformed
  count() %>% 
  ungroup() %>% 
  mutate(perc = `value` / sum(`value`)) %>% 
  arrange(perc) %>%
  mutate(labels = scales::percent(perc))

generabreakdown2020 %>%
  ggplot(aes(x = "", y = value, fill = group)) +
  geom_col() + 
  coord_polar("y", start = 0) +
  theme_void() +
  geom_label_repel(aes(label = gb2020label$labels), position = position_fill(vjust = 0.5), 
                   size = 5, show.legend = F, max.overlaps = 50) +
  guides(fill = guide_legend(title = "Genera")) +
  scale_fill_manual(values = c("brown1", "chocolate1",
                               "darkgoldenrod1", "darkgreen",
                               "deepskyblue", "darkslateblue",
                               "darkorchid4", "hotpink1",
                               "lightpink"))

Which produces the following:

Answer 1

Thanks for adding your data.

There are a few errors in your code. The main one is that you didn't precalculate where to place the labels (done here in the text_y variable). That variable needs to be passed as the y aesthetic for geom_label_repel.

The second is that you no longer need group_by(value) %>% count() %>% ungroup() because the data you provided is already aggregated.

library(tidyverse)
library(ggrepel)

generaabundance2020 <- c(883, 464, 1948, 1177, 2607, 962, 2073, 620, 2670)
genera2020 <-  c("Andrena", "Ceratina", "Halictus", "Hesperapis", "Lasioglossum", "Melissodes", "Osmia", "Panurginus", "Other")
generabreakdown2020 <- data.frame(group = genera2020, value = generaabundance2020)

gb2020label <- 
  generabreakdown2020 %>% 
  mutate(perc = value/ sum(value)) %>% 
  mutate(labels = scales::percent(perc)) %>% 
  arrange(desc(group)) %>% ## arrange in the order of the legend
  mutate(text_y = cumsum(value) - value/2) ### calculate where to place the text labels

gb2020label %>%
  ggplot(aes(x = "", y = value, fill = group)) + 
  geom_col() +
  coord_polar(theta = "y") +
  geom_label_repel(aes(label = labels, y = text_y), 
                   nudge_x = 0.6, nudge_y = 0.6,
                   size = 5, show.legend = F) +
  guides(fill = guide_legend(title = "Genera")) +
  scale_fill_manual(values = c("brown1", "chocolate1",
                               "darkgoldenrod1", "darkgreen",
                               "deepskyblue", "darkslateblue",
                               "darkorchid4", "hotpink1",
                               "lightpink"))

If you want to arrange in descending order of frequency, you should remember to also set the factor levels of the group variable to the same order.

gb2020label <- 
  generabreakdown2020 %>% 
  mutate(perc = value/ sum(value)) %>% 
  mutate(labels = scales::percent(perc)) %>% 
  arrange(desc(perc)) %>% ## arrange in descending order of frequency
  mutate(group = fct_rev(fct_inorder(group))) %>% ## also arrange the groups in descending order of freq
  mutate(text_y = cumsum(value) - value/2) ### calculate where to place the text labels

gb2020label %>%
  ggplot(aes(x = "", y = value, fill = group)) + 
  geom_col() +
  coord_polar(theta = "y") +
  geom_label_repel(aes(label = labels, y = text_y), 
                   nudge_x = 0.6, nudge_y = 0.6,
                   size = 5, show.legend = F) +
  guides(fill = guide_legend(title = "Genera")) +
  scale_fill_manual(values = c("brown1", "chocolate1",
                               "darkgoldenrod1", "darkgreen",
                               "deepskyblue", "darkslateblue",
                               "darkorchid4", "hotpink1",
                               "lightpink"))

^{Created on 2021-10-27 by the reprex package (v2.0.1)}

Answer 2

You didn't provide us with your data to work with so I'm using ggplot2::mpg here.

library(tidyverse)
library(ggrepel)

mpg_2 <- 
  mpg %>% 
  slice_sample(n = 20) %>% 
  count(manufacturer) %>% 
  mutate(perc = n / sum(n)) %>% 
  mutate(labels = scales::percent(perc)) %>% 
  arrange(desc(manufacturer)) %>% 
  mutate(text_y = cumsum(n) - n/2)

Chart without polar coordinates

mpg_2 %>%    
  ggplot(aes(x = "", y = n, fill = manufacturer)) + 
  geom_col() +
  geom_label(aes(label = labels, y = text_y))

Chart with polar coordinates and geom_label_repel

mpg_2 %>%    
  ggplot(aes(x = "", y = n, fill = manufacturer)) + 
  geom_col() +
  geom_label_repel(aes(label = labels, y = text_y), 
                   force = 0.5,nudge_x = 0.6, nudge_y = 0.6) +
  coord_polar(theta = "y")

But maybe your data isn’t dense enough to need repelling?

mpg_2 %>%    
  ggplot(aes(x = "", y = n, fill = manufacturer)) + 
  geom_col() +
  geom_label(aes(label = labels, y = text_y), nudge_x = 0.6) +
  coord_polar(theta = "y")

^{Created on 2021-10-26 by the reprex package (v2.0.1)}