基于iris数据集实现变量统计汇总与ggplot直方图嵌套存储

基于现有代码的修改方案 #

你只需要在原有管道逻辑后追加两步操作即可实现需求：

library(tidyverse)
iris %>%
  pivot_longer(-Species, 
               names_to = "Vars", 
               values_to = "Values") %>%
  group_by(Vars) %>%
  nest() %>%
  mutate(metrics = lapply(data, function(df) df %>% summarise(obs = n(), mean = round(mean(Values),2), median = round(median(Values),2))),
         plot = lapply(data, function(df) df %>% ggplot(aes(Values)) + geom_histogram(binwidth = 0.2, fill = "skyblue", color = "black") + labs(title = first(df$Vars)))) %>%
  unnest(metrics) %>% # 拆分metrics嵌套列为独立列
  select(-data) # 删除不需要的原始嵌套数据列

更简洁的优化写法 #

无需嵌套后再拆分，直接在summarise中生成所有需要的列，代码逻辑更清晰：

library(tidyverse)
iris %>%
  pivot_longer(-Species, names_to = "Vars", values_to = "Values") %>%
  group_by(Vars) %>%
  summarise(
    obs = n(),
    mean = round(mean(Values), 2),
    median = round(median(Values), 2),
    plot = list(ggplot(cur_data(), aes(Values)) + 
                  geom_histogram(binwidth = 0.2, fill = "skyblue", col = "black") +
                  labs(title = cur_group()$Vars))
  )

两种写法最终输出的tibble结构都和你期望的完全一致。

内容的提问来源于stack exchange，提问作者Alexis