Stacked barplots: how can I add sample size number and statistical test results?

user_14425537 0
Viewed 34

I have a data similar to this one:

my_data <- tibble(flower_color = c(rep("blue", 12), rep("red", 34), rep("pink", 19)), 
                  flower_length = c(rep("short", 4), rep("medium", 15), rep("long", 12),
                                    rep("very long", 34)))

My aim is to create a stacked barplot representation, like this one:

table(my_data$flower_color, my_data$flower_length) %>%
      as.data.frame() %>%
      filter(Freq > 0) %>%
      ggplot(aes(x = Var1, y = Freq, fill = Var2)) +
      geom_bar(position = "fill", stat = "identity") + 
      scale_y_continuous(labels = scales::percent_format()) +
      theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
      panel.background = element_blank(), axis.line = element_line(colour = "black")) 
      set_palette(q, "jco") + 
      #stat_compare_means() +                   
      # Global p-value
      stat_compare_means(ref.group = "0.5", label = "p.signif",
                         label.y = c(22, 29))

My questions are:

  1. I would like to show on top of each bar the sample size for each blue, pink and red (e.g. on top of blue flower bar adding 12).
  2. Any suggestion about which statistical test I should use for checking if significant differences are present? And How can I add these results on my plot?
geom-bar ggplot2 r
1 Answers

I can't say whether this is the appropriate statistical test, but here is a potential solution:

library(tidyverse)
library(ggpubr)
library(ggsci)

my_data <- tibble(flower_color = c(rep("blue", 12), 
                                   rep("red", 34), 
                                   rep("pink", 19)), 
                  flower_length = c(rep("short", 4), 
                                    rep("medium", 15), 
                                    rep("long", 12),
                                    rep("very long", 34)))

my_data %>%
  group_by(flower_color, flower_length) %>%
  summarise(Freq = n()) %>%
  group_by(flower_color) %>%
  mutate(Total = sum(Freq)) %>%
  ggplot(aes(x = flower_color, y = Freq, fill = flower_length)) +
  geom_bar(position = "fill", stat = "identity") + 
  geom_text(aes(label = paste0("n=", Total), y = 0.9), check_overlap = TRUE) +
  scale_y_continuous(labels = scales::percent_format(),
                     breaks = c(0.25, 0.5, 0.75, 1)) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.line = element_line(colour = "black")) +
  fill_palette(palette = "jco") +
  stat_compare_means(comparisons = list(1:2, 2:3, c(1, 3), c(1, 2, 3)),
                     ref.group = 0.5, label.y = c(0.15, 0.2, 0.25, 0.325),
                     tip.length = 0.001, label = "p.signif")
#> `summarise()` has grouped output by 'flower_color'. You can override using the
#> `.groups` argument.

Created on 2022-08-24 by the reprex package (v2.0.1)

user_129573400
Related