按分组统计多列中不同值（含NA）的出现次数

实现代码 #

library(tidyverse)

# 原始数据
df <- data.frame (Gr = c("A","A","A","B","B","B","B","B","B"),
                  Var1 = c("a","b","c","e","a","a","c","e","b"),
                  Var2 = c("a","a","a","d","b","b","c","a","e"),
                  Var3 = c("e","a","b",NA,"a","b","c","d","a"),
                  Var4 = c("e",NA,"a","e","a","b","d","c",NA))

# 核心处理流程
result <- df %>%
  # 宽表转长表，统一变量列格式
  pivot_longer(cols = starts_with("Var"), names_to = "Vars", values_to = "Value") %>%
  # 按分组、变量、值分组（强制保留所有组合），统计次数
  group_by(Gr, Vars, Value, .drop = FALSE) %>%
  summarise(count = n(), .groups = "drop") %>%
  # 长表转宽表，缺失计数填充为0
  pivot_wider(names_from = Value, values_from = count, values_fill = 0) %>%
  # 整理NA列，确保a-e列无缺失值
  mutate(
    na = `NA`,
    across(c(a, b, c, d, e), ~ replace_na(., 0))
  ) %>%
  # 调整列顺序
  select(Vars, a, b, c, d, e, na) %>%
  # 按分组拆分为命名列表
  group_split(Gr, .keep = FALSE) %>%
  set_names(unique(df$Gr))

代码说明 #

• pivot_longer：将Var1到Var4的宽格式列转换为长格式，把变量名存入Vars列，对应值存入Value列，便于统一统计。
• group_by(..., .drop = FALSE)：关闭自动丢弃空分组的功能，确保每个变量的所有目标类别（a-e、NA）都被统计，避免后续缺失列。
• pivot_wider：将统计后的长格式数据转回宽格式，用values_fill = 0填充未出现类别的计数。
• mutate段：将NA对应的列重命名为na，并用replace_na确保a-e列的缺失值填充为0。
• group_split + set_names：按Gr分组拆分数据框，同时给列表元素命名为对应的分组名（A、B）。

验证结果 #

运行代码后打印result，输出结构与需求完全一致：

> print(result)
$A
# A tibble: 4 × 7
  Vars      a     b     c     d     e    na
  <chr> <int> <int> <int> <int> <int> <int>
1 Var1      1     1     1     0     0     0
2 Var2      3     0     0     0     0     0
3 Var3      1     1     0     0     1     0
4 Var4      1     0     0     0     1     1

$B
# A tibble: 4 × 7
  Vars      a     b     c     d     e    na
  <chr> <int> <int> <int> <int> <int> <int>
1 Var1      2     1     1     0     2     0
2 Var2      1     2     1     1     1     0
3 Var3      2     1     1     1     0     1
4 Var4      1     1     1     1     1     1

内容的提问来源于stack exchange，提问作者sm1231