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rpositiongeom-textgeom-col

How can I correctly position the geom_text labels to these geom_col data?


I am unable to get the labels to fit properly over each dodged bar in this chart:

enter image description here

I feel that I am almost there, but can't quite figure out how to get the labels perfectly positioned over each respective dodged bar.

Code:

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")

I am trying to replicate this (from Kibana):

enter image description here

Reproducible data frame

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, 
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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))

Solution

  • First, you're going to want to summarize your data at the type + date grouping. You currently have multiple count records for each date. Then, I would probably recommend a line chart over a dodged bar for this type of visual:

    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()
    

    enter image description here

    If you really want a dodged bar chart, you'll want to first convert type to a factor and then leverage tidyr::complete so that all the bars stay the same width:

    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())
    

    enter image description here

    Given your comment, you may also want to consider leveraging patchwork with a "sum" chart and then the breakout. Something like:

    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
    

    enter image description here

    Finally, if it's just a breakout, then you could also use a stacked bar -- however, you'll notice (at least in the data provided) that the sum of the parts does not equal the total:

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

    enter image description here