如何将统计测试的结果作为 ggplot2 facet 中的 plotmath 表达式包含在内

复仇封印

我希望在分面 ggplot 图表中包含多个统计测试的结果。

我已经找到了很多关于如何在标题或注释中包含类似内容的优秀示例（如this），但是，我的兴趣在于将其包含为文本注释，以便我可以在一个图上显示许多测试的结果。

我已经能够使用标准文本注释来做到这一点，但是我想使用polymath/显示我的结果，expressions以便我可以生成一个注释，该注释遵循包[ggstatsplot]1 中实现的 APA 样式指南，请参见下面的示例：

我已经使用diamonds来自ggplot2. 我尝试过的一些事情包括：

尝试存储bquote和expression对象为在列wilcox_stats对象-但dplyr似乎并不喜欢它
试图将这一切从ggplot- 但是它变得非常混乱，试图排除所有geom_text想要打印的注释

您可以提供的任何帮助或指示将不胜感激。

# LOAD REQUIRED PACKAGES

library(ggplot2)
library(tidyverse)
library(rstatix)

# CREATE SAMPLE DATA

sample_data <- diamonds %>%
  select(cut, color, table) %>%
  filter(color == c("E","J")) %>%
  mutate(time = factor(case_when(
    table %% 2 == 0 ~ "Before",
    TRUE ~ "After"))) %>%
  group_by(color, time) %>%
  sample_n(100) %>%
  ungroup() %>%
  mutate(numeric_cut = case_when(
    cut == "Ideal" ~ 1, 
    cut == "Premium" ~ 2,     
    cut == "Very Good" ~ 3,
    cut == "Good" ~ 4,
    cut == "Fair" ~ 5))

# STAT TESTS

wilcox_test <- sample_data %>%
  group_by(color) %>%
  wilcox_test(numeric_cut ~ time, paired = TRUE, detailed = TRUE) %>%
  select(color, statistic, p, n1)

wilcox_es <- sample_data %>%
  group_by(color) %>%
  wilcox_effsize(numeric_cut ~ time, paired = TRUE, ci = TRUE) %>%
  select(color, effsize, conf.low, conf.high)

## EXTRACT ELEMENTS OF STAT TESTS AND USE THEM TO CREATE ANNOTATION

wilcox_stats <- left_join(wilcox_test, wilcox_es) %>%
  mutate(statistic = round(statistic, 1)) %>%
  mutate(effsize = round(effsize, 2)) %>%
  mutate(p = round(p, 3)) %>%
  mutate(result = deparse(bquote(
    V[Wilcoxon]==.(statistic)~ #this code does not work
    italics(p)==.p~ 
    hat(r) == .effsize~
    "CI"["95%"]~
    .conf.low~.conf.high~
    n[pairs]==.n1)))

## PREPARE PLOT DATA

plot_data <- sample_data %>%
  group_by(time, cut, color) %>%
  tally() %>%
  ungroup() %>%
  group_by(color) %>%
  mutate(total_n = sum(n)) %>%
  mutate(percent = (n/total_n)*100) %>%
  mutate(percent = round(percent, 1)) %>%
  ungroup() %>%
  left_join(wilcox_stats) %>%
  mutate(result = case_when(
    time == "Before" & cut == "Ideal" ~ "",
    time == "After" & cut == "Ideal" ~ "",
    time == "Before" & cut == "Premium" ~ "",
    time == "After" & cut == "Premium" ~ "",
    time == "Before" & cut == "Very Good" ~ "",
    time == "After" & cut == "Very Good" ~ result,
    time == "Before" & cut == "Good" ~ "",
    time == "After" & cut == "Good" ~ "",
    time == "Before" & cut == "Fair" ~ "",
    time == "After" & cut == "Fair" ~ "")) %>%
  mutate(time = factor(time, levels = c("Before", "After", ordered = TRUE)))

## PLOT RESULTS

plot <- plot_data %>%
  ggplot() +
  aes(x = cut, y = percent, fill = cut) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = result, y = 30), size = 5, parse = TRUE) +
  facet_grid(color ~ time)

下图显示了我希望创建的输出的要点...

杰博

我可能会使用 paste 创建表达式，（tbh，因为我发现包含变量更容易）。

我稍微缩短了代码，也没有使用你的完整表达，但我认为它应该足以让你明白这个想法。

library(tidyverse)

sample_data <- diamonds %>%
  select(cut, color, table) %>%
  filter(color == c("E","J")) %>%
  mutate(time = if_else(table %% 2 == 0, "Before", "After")) %>%
  group_by(color, time) %>%
  sample_n(100) %>%
  ungroup() %>%
  mutate(numeric_cut = as.numeric(cut))

wilcox_test <- sample_data %>%
  group_by(color) %>%
  rstatix::wilcox_test(numeric_cut ~ time, paired = TRUE, detailed = TRUE) %>%
  select(color, statistic, p, n1)

wilcox_es <- sample_data %>%
  group_by(color) %>%
  rstatix::wilcox_effsize(numeric_cut ~ time, paired = TRUE, ci = TRUE) %>%
  select(color, effsize, conf.low, conf.high)

这里是关键的一点

wilcox_stats <- left_join(wilcox_test, wilcox_es) %>%
  mutate(statistic = round(statistic, 1),
         effsize = round(effsize, 2),
         p = round(p, 3),
         label = paste('V[Wilcoxon]==', statistic, '~italic(p)==~', p))
#> Joining, by = "color"

plot_data <- sample_data %>%
  count(time, cut, color) %>%
  group_by(color) %>%
  mutate(total_n = sum(n),
         percent = round((n/total_n)*100,1)) %>%
  ungroup() %>%
  left_join(wilcox_stats) %>%
  mutate(result = if_else(time == "After" & cut == "Very Good", label, ""))
#> Joining, by = "color"

plot_data %>%
  ggplot() +
  aes(x = cut, y = percent, fill = cut) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = result, y = 30), parse = TRUE) +
  facet_grid(color ~ time)