如何用简便方法在R中实现分组行的均值变换及标准差计算？

我有如下R语言数据框：

df<-structure(list(R1 = c(512L, 44620L, 69500L, 91120L, 98870L), 
    R2 = c(587L, 38500L, 67370L, 94870L, 88120L), R3 = c(587L, 
    39370L, 57500L, 96870L, 85370L), R1.1 = c(737L, 2812L, 4050L, 
    6400L, 4762L), R2.1 = c(450L, 2587L, 3900L, 7287L, 5550L), 
    R3.1 = c(712L, 2175L, 4675L, 6687L, 4125L)), class = "data.frame", row.names = c(NA, 
-5L))

数据展示：

R1    R2    R3 R1.1 R2.1 R3.1
1   512   587   587  737  450  712
2 44620 38500 39370 2812 2587 2175
3 69500 67370 57500 4050 3900 4675
4 91120 94870 96870 6400 7287 6687
5 98870 88120 85370 4762 5550 4125

需要完成的计算：

• 将数据分为两组列：第一组R1、R2、R3，第二组R1.1、R2.1、R3.1
• 对每组的第2至5行，执行计算：((该行三列均值 - 组内第一行三列均值) - 1822.9)/4167.5
• 对每组每行的三列标准差执行计算：(标准差 - 1822.9)/4167.5

希望找到更简便的实现方式，最终得到包含变换后数据及对应标准差的结果。

方案一：用tidyverse工具链（简洁易读） #

先加载依赖包：

library(dplyr)
library(tidyr)

执行数据处理与计算：

# 定义固定参数
const1 <- 1822.9
const2 <- 4167.5

# 转长格式并分组统计均值、标准差
df_processed <- df %>%
  mutate(row_id = row_number()) %>%
  pivot_longer(-row_id, names_to = "group", names_pattern = "(R\\d(?:\\.1)?)", 
               values_to = "value") %>%
  mutate(group = ifelse(grepl("\\.1$", group), "group2", "group1")) %>%
  group_by(row_id, group) %>%
  summarise(
    row_mean = mean(value),
    row_sd = sd(value),
    .groups = "drop"
  )

# 提取每组第一行的基准均值
base_means <- df_processed %>%
  filter(row_id == 1) %>%
  select(group, base_mean = row_mean)

# 合并基准值并完成最终变换
final_result <- df_processed %>%
  left_join(base_means, by = "group") %>%
  mutate(
    transformed_mean = ifelse(row_id >= 2, ((row_mean - base_mean) - const1)/const2, NA),
    transformed_sd = (row_sd - const1)/const2
  ) %>%
  pivot_wider(
    names_from = group,
    values_from = c(transformed_mean, transformed_sd),
    names_glue = "{group}_{.value}"
  )

# 查看结果
print(final_result)

方案二：基础R实现（无额外依赖） #

# 定义两组列的索引
groups <- list(group1 = 1:3, group2 = 4:6)

# 批量处理每组数据
result_list <- lapply(groups, function(cols) {
  row_means <- rowMeans(df[, cols])
  row_sds <- apply(df[, cols], 1, sd)
  base_mean <- row_means[1]
  
  # 计算变换后的值
  trans_mean <- ifelse(1:nrow(df) >=2, ((row_means - base_mean) - 1822.9)/4167.5, NA)
  trans_sd <- (row_sds - 1822.9)/4167.5
  
  data.frame(trans_mean, trans_sd)
})

# 合并结果并命名列
final_result <- do.call(cbind, result_list)
colnames(final_result) <- c("group1_transformed_mean", "group1_transformed_sd",
                            "group2_transformed_mean", "group2_transformed_sd")

# 查看结果
print(final_result)

内容的提问来源于stack exchange，提问作者nik