如何从数据框列表的B列计算对应行平均值并生成新数据框？

我有一个名为my.list的列表，结构如下：

$S1
  Study_ID   B   C         D
1      100 3.4  C1 0.9124000
2      100 1.5 PTA        NA
3      200 1.8  C1 0.5571429
4      200 2.1 PTA 0.7849462
5      300 3.2  C1 0.3271900
6      300 1.4 PTA        NA
7      400 5.6  C1 0.8248200
8      400 9.3 PTA 0.2847020

$S2
  Study_ID    B   C         D
1      100 0.15  C1 0.9124000
2      100 0.70 PTA        NA
3      200 0.23  C1 0.5571429
4      200 0.45 PTA 0.7849462
5      300 0.91  C1 0.3271900
6      300 0.78 PTA 0.6492000
7      400 0.65  C1 0.8248200
8      400 0.56 PTA        NA

我希望创建一个仅包含列表中对应行B列平均值的数据框，期望输出如下：

Average
1     2.1
2     1.2
3     0.5
4     1.5
5     1.9
6     2.1
7     3.6
8     5.9

可复现数据：

my.list <- structure(list(S1 = structure(list(Study_ID = c(100, 100, 200, 200, 300,300,400,400), B = c(3.4, 1.5, 1.8, 2.1, 3.2, 1.4, 5.6, 9.3), C = c("C1", "PTA", "C1", "PTA", "C1", "PTA","C1", "PTA"), D = c(0.9124, NA, 0.5571429, 0.7849462, 0.32719, NA, 0.82482, 0.284702)), .Names = c("Study_ID", "B", "C", "D"), class = "data.frame", row.names = c("1", "2", "3", "4", "5", "6", "7", "8")), S2 = structure(list(Study_ID = c(100, 100, 200, 200, 300,300,400,400), B = c(0.15, 0.7, 0.23, 0.45,0.91, 0.78, 0.65, 0.56), C = c("C1", "PTA", "C1", "PTA", "C1", "PTA", "C1", "PTA"), D = c(0.9124, NA, 0.5571429, 0.7849462, 0.32719,0.6492, 0.82482, NA)), .Names = c("Study_ID", "B", "C","D"), class = "data.frame", row.names = c("1", "2", "3", "4", "5", "6", "7", "8"))), .Names = c("S1", "S2"))

方法1：基础R实现 #

直接提取列表中每个数据框的B列，合并成矩阵后按行求均值，最后转为数据框：

# 提取所有B列并合并为矩阵
b_cols <- sapply(my.list, function(x) x$B)
# 按行计算平均值，保留一位小数匹配期望输出
average_vals <- rowMeans(b_cols)
# 转为指定格式的数据框
result <- data.frame(Average = round(average_vals, 1))

运行后result即为目标输出：

> result
  Average
1     2.1
2     1.2
3     0.5
4     1.5
5     1.9
6     2.1
7     3.6
8     5.9

方法2：tidyverse风格实现（dplyr + purrr） #

若习惯使用tidyverse工具链，可采用以下方式：

library(dplyr)
library(purrr)

# 提取每个数据框的B列，按行绑定后计算均值
result <- map_dfc(my.list, ~ .x$B) %>%
  rowwise() %>%
  mutate(Average = round(mean(c_across(everything())), 1)) %>%
  select(Average)

方法3：data.table实现（大场景高效） #

如果处理的数据集较大，data.table的效率优势更明显：

library(data.table)

# 将列表转为data.table对象，合并B列后按行求均值
dt_list <- lapply(my.list, as.data.table)
result <- data.table(Average = round(rowMeans(cbind(dt_list[[1]]$B, dt_list[[2]]$B)), 1))

内容的提问来源于stack exchange，提问作者sabc04