select列starts_with a或c，然后使用split.default拆分列，并对每个组应用rowMeans：

library(dplyr)
library(purrr)
select(df, starts_with(cols)) %>% 
  split.default(gsub("\\d", "", names(.))) %>% 
  map_dfc(rowMeans)

         a      c
1 -0.450    0.811
2  1.05    -0.169
3 -0.00495 -0.224
4 -0.0725   1.45 
5 -0.355    0.378

请注意，根据列名的结构，可能需要更改gsub部分。

计算所有列组的 * 平均值 *，然后计算子集：

data.frame(lapply(split.default(df, gsub("\\d", "", colnames(df))), rowMeans))[, cols]

根据实际数据，获取均值然后获取子集可能更有效。

**编辑：**对于较大的数据，先子集化再取平均值的速度更快。

ix <- gsub("\\d", "", colnames(df)) %in% cols
x1 <- data.frame(lapply(split.default(df[, ix], gsub("\\d", "", colnames(df)[ ix ])), rowMeans))

您可以在mutate()调用中迭代选择器值：

library(dplyr)
library(purrr)

df %>%
  mutate(map_df(set_names(cols), ~ rowMeans(across(starts_with(.x)))), .keep = "none")

             a          c
1 -0.449558319  0.8105241
2  1.052292204 -0.1692037
3 -0.004953185 -0.2243752
4 -0.072480153  1.4457733
5 -0.354655514  0.3781747

基准测试

数据：

library(dplyr)
library(purrr)

n = 100000 * 10 * 10
set.seed(12345); df <- data.frame(matrix(runif(n), ncol = 100))
colnames(df) <- make.unique(rep(letters[1:10], each = 10), sep = "")
cols <- letters[c(1,3,7,9)]

基准：

microbenchmark::microbenchmark(
  base1 = {
    ix <- gsub("\\d", "", colnames(df)) %in% cols
    data.frame(lapply(split.default(df[, ix], gsub("\\d", "", colnames(df)[ ix ])), rowMeans))
  },
  base2 = {
    data.frame(lapply(split.default(df, gsub("\\d", "", colnames(df))), rowMeans))[, cols]    
  },
  tidy1 = {
    select(df, starts_with(cols)) %>% 
      split.default(gsub("\\d", "", names(.))) %>% 
      map_dfc(rowMeans)
  },
  tidy2 = {
    split.default(df, gsub("\\d", "", names(df))) %>% 
      map_dfc(rowMeans) %>% 
      select({{cols}})
  },
  tidy3 = {
    df %>%
      mutate(map_df(set_names(cols), ~ rowMeans(across(starts_with(.x)))), .keep = "none")
  },
  check = "equivalent"
)
  
# Unit: milliseconds
#  expr     min       lq      mean   median       uq      max neval
# base1 26.8613 29.42070  36.75314 33.28410  40.9296 114.7524   100
# base2 67.9460 80.95455  94.08483 86.47900 100.4189 200.9891   100
# tidy1 33.8004 36.53475  44.94198 44.44600  49.8111  76.5831   100
# tidy2 73.4420 89.79525 101.52422 97.48275 108.5292 294.6857   100
# tidy3 39.8900 44.37110  53.25316 52.23770  56.4041  99.9845   100

cbind.data.frame(df %>% select(starts_with("a")) %>% 
  rowMeans() %>% 
  cbind.data.frame() %>% 
  rename("a"="."),
  df %>% select(starts_with("b")) %>% 
                       rowMeans() %>% 
                       cbind.data.frame()%>% 
                     rename("b"="."))