r - 如何将 geom_text 标签正确定位到这些 geom_col 数据？

Question

我无法让标签正确地适合此图表中每个躲避的条：

我觉得我快到了，但不太清楚如何让标签完美地定位在每个躲避的条上。

代码：

ggplot() +
  geom_col(data = leads_over_chats, aes(x = date, y = count, fill = type),
           colour = "black",
           position = "dodge") +
  labs(title = "Leads Over Chats\n(May 2018)",
       x = "Type",
       y = "Count") +
  geom_text(data = leads_over_chats, aes(x = date, y = count, label = count),
            hjust = -1.5,
            vjust = -0.5,
            size = 4,
            angle = 90,
            position=position_dodge(width = 2.25),
            colour = "black")

我正在尝试复制这个（来自 Kibana）：

可重现的数据框

structure(list(type = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 1L, 1L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("aborted-live-lead", 
"conversation-claimed", "conversation-created", "lead-created"
), class = "factor"), date = structure(c(1525129200, 1525215600, 
1525302000, 1525388400, 1525474800, 1525561200, 1525647600, 1525734000, 
1525820400, 1525906800, 1525993200, 1526079600, 1526166000, 1526252400, 
1526338800, 1526425200, 1526511600, 1526598000, 1526684400, 1526770800, 
1526857200, 1526943600, 1527030000, 1527116400, 1527202800, 1527289200, 
1527375600, 1527462000, 1527548400, 1527634800, 1527721200, 1526166000, 
1526252400, 1526338800, 1526425200, 1526598000, 1526684400, 1526770800, 
1526857200, 1526943600, 1527030000, 1527116400, 1527202800, 1527289200, 
1527375600, 1527462000, 1527548400, 1527634800, 1527721200, 1526252400, 
1526338800, 1526425200, 1526511600, 1526598000, 1526684400, 1526770800, 
1526857200, 1526943600, 1527030000, 1527116400, 1527202800, 1527289200, 
1527375600, 1527462000, 1527548400, 1527634800, 1527721200, 1526252400, 
1526338800, 1526425200, 1526511600, 1526598000, 1526684400, 1526770800, 
1526857200, 1526943600, 1527030000, 1527116400, 1527202800, 1527289200, 
1527375600, 1527462000, 1527548400, 1527634800, 1527721200, 1525129200, 
1525215600, 1525302000, 1525388400, 1525474800, 1525561200, 1525647600, 
1525734000, 1525820400, 1525906800, 1525993200, 1526079600, 1526166000, 
1526252400, 1526338800, 1526425200, 1526511600, 1526598000, 1526684400, 
1526770800, 1526857200, 1526943600, 1527030000, 1527116400, 1527202800, 
1527289200, 1527375600, 1527462000, 1527548400, 1527634800, 1527721200, 
1526166000, 1526252400, 1526338800, 1526425200, 1526511600, 1526598000, 
1526684400, 1526770800, 1526857200, 1526943600, 1527030000, 1527116400, 
1527202800, 1527289200, 1527375600, 1527462000, 1527548400, 1527634800, 
1527721200, 1526252400, 1526338800, 1526425200, 1526511600, 1526598000, 
1526684400, 1526770800, 1526857200, 1526943600, 1527030000, 1527116400, 
1527202800, 1527289200, 1527375600, 1527462000, 1527548400, 1527634800, 
1527721200, 1526252400, 1526338800, 1526425200, 1526511600, 1526598000, 
1526684400, 1526770800, 1526857200, 1526943600, 1527030000, 1527116400, 
1527202800, 1527289200, 1527375600, 1527462000, 1527548400, 1527634800, 
1527721200, 1525129200, 1525215600, 1525302000, 1525388400, 1525474800, 
1525561200, 1525647600, 1525734000, 1525820400, 1525906800, 1525993200, 
1526079600, 1526166000, 1526252400, 1526338800, 1526425200, 1526511600, 
1526598000, 1526684400, 1526770800, 1526857200, 1526943600, 1527030000, 
1527116400, 1527202800, 1527289200, 1527375600, 1527462000, 1527548400, 
1527634800, 1527721200, 1525129200, 1525215600, 1525302000, 1525388400, 
1525474800, 1525561200, 1525647600, 1525820400, 1525906800, 1526079600, 
1526166000, 1526252400, 1526338800, 1526425200, 1526511600, 1526598000, 
1526684400, 1526857200, 1526943600, 1527030000, 1527116400, 1527289200, 
1527462000, 1527548400, 1527634800, 1526252400, 1526338800, 1526425200, 
1526511600, 1526598000, 1526684400, 1526857200, 1526943600, 1527030000, 
1527116400, 1527202800, 1527289200, 1527462000, 1527548400, 1527634800, 
1525129200, 1525215600, 1525302000, 1525388400, 1525734000, 1525820400, 
1525906800, 1525993200, 1526252400, 1526425200, 1526511600, 1526598000, 
1526684400, 1526857200, 1527030000, 1527116400, 1527202800, 1527289200, 
1527548400, 1527721200, 1525129200, 1525215600, 1525302000, 1525388400, 
1525474800, 1525647600, 1525820400, 1525906800, 1525993200, 1526079600, 
1526252400, 1526857200, 1526943600, 1527030000, 1527116400, 1527202800, 
1527462000, 1527548400, 1527634800, 1527721200, 1526857200, 1526943600, 
1527030000, 1527116400, 1527202800, 1527462000, 1527548400, 1527634800, 
1527721200, 1526252400, 1526684400, 1526857200, 1526943600, 1527030000, 
1527202800, 1527462000, 1527548400, 1526252400, 1526857200, 1526943600, 
1527030000, 1527548400, 1525215600, 1525302000, 1525388400, 1525474800, 
1525647600, 1525820400, 1525993200, 1526079600, 1526252400, 1526338800, 
1526511600, 1526598000, 1526684400, 1526770800, 1526857200, 1526943600, 
1527030000, 1527116400, 1527289200, 1527462000, 1527634800, 1527721200, 
1526425200, 1526857200, 1526943600, 1527202800, 1526425200, 1527202800, 
1526425200, 1526943600, 1527202800, 1526857200, 1527202800, 1526338800, 
1526425200, 1526943600, 1527116400, 1527289200, 1527721200, 1525734000, 
1526338800, 1526425200, 1526943600, 1527116400, 1527202800, 1527289200, 
1527202800, 1525302000, 1525388400, 1525734000, 1525820400, 1525993200, 
1526511600, 1526857200, 1526943600, 1527116400, 1527462000, 1527548400, 
1525129200, 1525215600, 1525561200, 1525734000, 1525906800, 1526079600, 
1526252400, 1526857200, 1526943600, 1527375600, 1525302000, 1525474800, 
1525993200, 1526425200, 1527030000, 1525215600, 1525734000, 1526425200, 
1526857200, 1527548400, 1525734000, 1526943600, 1525906800, 1526943600, 
1526252400, 1526338800, 1525215600, 1525820400, 1526252400, 1527202800, 
1525215600, 1526338800, 1526511600, 1526857200, 1525129200, 1527116400
), class = c("POSIXct", "POSIXt"), tzone = ""), count = c(76L, 
82L, 64L, 59L, 70L, 30L, 42L, 54L, 61L, 77L, 83L, 79L, 92L, 120L, 
99L, 145L, 2L, 70L, 88L, 79L, 119L, 101L, 133L, 147L, 177L, 96L, 
93L, 108L, 137L, 112L, 107L, 15L, 89L, 68L, 85L, 34L, 45L, 44L, 
64L, 54L, 54L, 62L, 114L, 52L, 31L, 54L, 56L, 57L, 45L, 27L, 
20L, 33L, 1L, 25L, 29L, 21L, 29L, 26L, 41L, 54L, 51L, 32L, 33L, 
33L, 51L, 33L, 36L, 23L, 28L, 46L, 2L, 24L, 24L, 18L, 38L, 36L, 
47L, 44L, 39L, 24L, 37L, 27L, 50L, 29L, 39L, 6L, 10L, 2L, 11L, 
1L, 5L, 9L, 8L, 21L, 17L, 18L, 24L, 8L, 20L, 19L, 22L, 22L, 20L, 
21L, 16L, 26L, 23L, 23L, 19L, 36L, 17L, 14L, 31L, 33L, 28L, 28L, 
3L, 18L, 7L, 9L, 12L, 13L, 1L, 4L, 13L, 8L, 8L, 6L, 18L, 8L, 
4L, 3L, 15L, 13L, 16L, 5L, 11L, 9L, 5L, 10L, 9L, 6L, 7L, 10L, 
14L, 8L, 11L, 9L, 9L, 18L, 9L, 8L, 6L, 2L, 5L, 6L, 7L, 3L, 11L, 
5L, 11L, 6L, 5L, 10L, 8L, 3L, 3L, 12L, 14L, 11L, 13L, 12L, 19L, 
24L, 15L, 17L, 15L, 19L, 19L, 18L, 10L, 15L, 23L, 30L, 20L, 21L, 
10L, 21L, 24L, 17L, 14L, 14L, 12L, 14L, 22L, 22L, 32L, 38L, 9L, 
23L, 12L, 16L, 1L, 1L, 2L, 5L, 1L, 1L, 2L, 4L, 3L, 5L, 3L, 1L, 
3L, 1L, 2L, 5L, 2L, 5L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 6L, 2L, 
4L, 7L, 4L, 6L, 2L, 1L, 3L, 1L, 2L, 1L, 3L, 1L, 4L, 7L, 6L, 1L, 
3L, 6L, 4L, 10L, 4L, 5L, 9L, 4L, 1L, 2L, 4L, 3L, 9L, 1L, 3L, 
4L, 1L, 2L, 1L, 2L, 2L, 6L, 1L, 1L, 3L, 1L, 2L, 4L, 4L, 1L, 3L, 
5L, 6L, 3L, 1L, 1L, 2L, 1L, 1L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 
1L, 2L, 3L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 5L, 3L, 2L, 4L, 8L, 1L, 
4L, 2L, 1L, 1L, 16L, 1L, 16L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 3L, 
7L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 
1L, 5L, 6L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L)), class = "data.frame", row.names = c(NA, 
-398L))

score 2 · Accepted Answer

首先，您需要在type+date分组中汇总您的数据。您目前count每个日期都有多条记录。然后，对于这种类型的视觉效果，我可能会推荐在闪避的条上使用折线图：

library(tidyverse)

df <- leads_over_chats

df %>%
  mutate(date = as.Date(date)) %>%
  group_by(type, date) %>%
  summarise(total_count = sum(count)) %>%
  ggplot(., aes(date, total_count, color = type)) +
  geom_line()

如果您真的想要一个闪避的条形图，您需要先转换type为 afactor然后利用杠杆tidyr::complete，以便所有条形保持相同的宽度：

df %>%
  mutate(date = as.Date(date),
         type = as.factor(type)) %>%
  group_by(type, date) %>%
  complete(type, date) %>%
  summarise(total_count = sum(count)) %>%
  ggplot(., aes(date, total_count, fill = type)) +
  geom_col(position = position_dodge())

鉴于您的评论，您可能还需要考虑利用patchwork“总和”图表然后突破。就像是：

library(patchwork)

df_grouped_and_summed <-
  df %>%
  mutate(date = as.Date(date),
         type = as.factor(type)) %>%
  group_by(type, date) %>%
  complete(type, date) %>%
  summarise(total_count = sum(count))

p_created <- 
  ggplot(df_grouped_and_summed, aes(date, total_count)) +
  geom_col() +
  labs(x = "", y = "")

p_splits <-
  ggplot(df_grouped_and_summed %>% filter(type != "conversation-created"),
         aes(date, total_count, fill = type)) +
  geom_col() +
  facet_wrap(~ type, ncol = 1) +
  labs(x = "", y = "") +
  guides(fill = FALSE)

p_created + p_splits

最后，如果它只是一个突破，那么您也可以使用堆叠条 - 但是，您会注意到（至少在提供的数据中）部分的总和不等于总数：

df_grouped_and_summed %>%
  filter(type != "conversation-created") %>%
  ggplot(., aes(date, total_count, fill = type)) +
  geom_col()

r - 如何将 geom_text 标签正确定位到这些 geom_col 数据？

1 回答 1

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