此解决方案按cyl分组，然后使用dplyr::cur_group_rows()索引到mtcars$qsec：

library(dplyr)

mtcars %>%
  group_by(cyl) %>%
  mutate(qsec_control = mean(
    mtcars$qsec[-cur_group_rows()]
  )) %>%
  ungroup()

# A tibble: 32 × 12
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb qsec_cont…¹
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>       <dbl>
 1  21       6  160    110  3.9   2.62  16.5     0     1     4     4        17.8
 2  21       6  160    110  3.9   2.88  17.0     0     1     4     4        17.8
 3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1        17.2
 4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1        17.8
 5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2        18.7
 6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1        17.8
 7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4        18.7
 8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2        17.2
 9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2        17.2
10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4        17.8
# … with 22 more rows, and abbreviated variable name ¹​qsec_control

在data.table中复制zephryl的答案：

library(data.table)

data(mtcars)
setDT(mtcars)

mtcars[, qsec_control := mtcars[-.I, mean(qsec)], by = .(cyl)]

head(mtcars)
    mpg cyl disp  hp drat    wt  qsec vs am gear carb cyl2 qsec_control
1: 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4    6     17.81280
2: 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4    6     17.81280
3: 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1    4     17.17381
4: 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1    6     17.81280
5: 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2    8     18.68611
6: 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1    6     17.81280