我有一个包含四个变量的大型数据集，如下所示：
| 公共问题 | 选民_党派之争 | 人口 | 价值 |
|---|---|---|---|
| 2 | 无党派 | 政治家 | 40 |
| 2 | 温和党派 | 选民 | 42 |
| 2 | 温和党派 | 政治家 | 56 |
| 2 | 高度党派倾向 | 选民 | 72 |
| 4 | 无党派 | 选民 | 24 |
| 4 | 无党派 | 政治家 | 13 |
| 4 | 温和党派 | 选民 | 行 |
| 4 | 高度党派倾向 | 选民 | 82 |
| 4 | 高度党派倾向 | 政治家 | 88 |
| 6 | 无党派 | 选民 | 36 |
| 6 | 温和党派 | 政治家 | 61 |
| 6 | 高度党派倾向 | 政治家 | 67 |
我正在尝试创建一个具有如下所示均值和标准差的 DF：
| 人口 | 选民_党派之争 | PI2_组平均值 | PI2_GroupSD | PI4_组平均值 | PI4_GroupSD | PI6_组平均值 | PI6_GroupSD |
|---|---|---|---|---|---|---|---|
| 政治家 | 无党派 | # | # | # | # | # | # |
| 政治家 | 温和党派 | # | # | # | # | # | # |
| 政治家 | 高度党派倾向 | # | # | # | # | # | # |
| 选民 | 无党派 | # | # | # | # | # | # |
| 选民 | 温和党派 | # | # | # | # | # | # |
| 选民 | 高度党派倾向 | # | # | # | # | # | # |
任何帮助将不胜感激。
我包含了以下数据的子集：
structure(list(Public_Issues = c(4, 20, 18, 4, 10, 4, 8, 10,
16, 16, 16, 14, 18, 6, 2, 18, 4, 10, 12, 8, 8, 2, 4, 4, 12, 6,
10, 20, 6, 14, 8, 10, 6, 20, 10, 4, 10, 10, 4, 2, 20, 8, 16,
4, 6, 2, 14, 2, 8, 4, 18, 4, 18, 2, 4, 4, 4, 16, 16, 2, 12, 10,
6, 12, 2, 2, 8, 18, 14, 2, 2, 2, 2, 8, 18, 14, 10, 6, 2, 20,
4, 8, 18, 16, 2, 6, 18, 20, 14, 14, 14, 16, 12, 20, 6, 20, 14,
20, 16, 20, 20, 6, 8, 10, 8, 10, 16, 12, 2, 20, 20, 20, 6, 14,
6, 10, 4, 20, 16, 8, 6, 10, 14, 4, 20, 12, 10, 4, 14, 8, 4, 10,
8, 14, 16, 12, 16, 12, 16, 10, 2, 18, 6, 2, 14, 10, 6, 2, 10,
4, 18, 20, 8, 10, 6, 16, 6, 16, 10, 14, 18, 4, 8, 16, 6, 14,
20, 18, 16, 14, 8, 4, 20, 10, 12, 16, 8, 10, 18, 2, 18, 16, 10,
14, 8, 4, 2, 14, 12, 16, 16, 14, 20, 12, 8, 16, 10, 14, 20, 14,
18, 2, 2, 6, 12, 2, 20, 6, 18, 8, 18, 14, 10, 8, 18, 10, 16,
4, 20, 2, 12, 8, 2, 18, 18, 18, 2, 14, 8, 16, 6, 18, 8, 12, 10,
20, 2, 18, 8, 14, 14, 8, 8, 18, 14, 20, 16, 20, 12, 8, 18, 16,
14, 2, 16, 12, 4, 2, 20, 14, 10, 2, 18, 18, 2, 8, 20, 4, 20,
12, 10, 2, 16, 4, 4, 20, 18, 4, 8, 16, 10, 18, 10, 4, 8, 12,
12, 12, 6, 4, 14, 18, 20, 6, 8, 18, 8, 20, 8, 20, 16, 8, 8, 10,
16, 8, 12, 18, 20, 20, 18, 12, 12, 14, 16, 12, 4, 18, 16, 10,
6, 8, 6, 10, 8, 12, 2, 10, 16, 16, 16, 12, 10, 2, 18, 4, 20,
2, 4, 10, 6, 14, 8, 8, 4, 4, 8, 20, 12, 6, 16, 16, 6, 6, 10,
6, 16, 18, 18, 14, 4, 16, 2, 6, 14, 6, 8, 18, 8, 18, 4, 4, 16,
8, 18, 16, 18, 2, 10, 2, 8, 6, 2, 8, 10, 14, 20, 4, 20, 18, 6,
6, 6, 2, 12, 8, 2, 8, 10, 14, 16, 12, 12, 16, 2, 6, 18, 16, 8,
4, 10, 16, 18, 10, 8, 18, 8, 12, 2, 16, 10, 8, 10, 12, 20, 12,
2, 14, 16, 6, 8, 10, 6, 14, 18, 12, 2, 4, 20, 6, 14, 2, 20, 6,
2, 18, 10, 10, 12, 6, 10, 16, 4, 14, 6, 14, 2, 18, 10, 6, 2,
18, 8, 12, 12, 12, 16, 8, 10, 12, 18, 8, 4, 16, 16, 10, 4, 2,
6, 12, 18, 12, 4, 6, 20, 16, 8, 10, 10, 12, 10, 20, 18, 16, 12,
2, 16, 10, 14, 18, 16, 4, 8, 14, 18, 16, 20, 8, 20, 8, 4, 14,
8, 16, 12, 16, 20, 12, 18, 18, 6, 6, 6, 20, 6, 6, 8, 14, 20,
10, 6, 4, 6, 6, 16, 6, 4, 12, 2, 6, 16, 4, 18, 8, 2, 8, 4, 18,
2, 14, 18, 8, 14, 2, 10, 20, 8, 10, 18, 4, 6, 18, 10, 10, 14,
6, 18, 14, 10, 20, 4, 16, 14, 16, 10, 16, 4, 16, 14, 4, 12, 14,
4, 20, 8, 20, 18, 8, 18, 20, 4, 18, 12, 8, 18, 14, 8, 18, 18,
2, 2, 8, 14, 16, 14, 18, 20, 12, 16, 14, 8, 12, 20, 14, 6, 16,
6, 14, 20, 6, 18, 14, 14, 16, 10, 4, 12, 4, 8, 18, 14, 6, 8,
12, 8, 2, 8, 6, 4, 2, 8, 18, 14, 6, 18, 16, 4, 10, 2, 16, 14,
18, 18, 20, 20, 16, 4, 10, 4, 6, 18, 6, 18, 18, 10, 18, 18, 8,
4, 6, 20, 6, 12, 12, 12, 10, 20, 8, 20, 18, 18, 10, 2, 18, 2,
2, 14, 16, 10, 2, 8, 14, 8, 20, 8, 8, 20, 10, 16, 2, 6, 6, 16,
18, 2, 20, 14, 6, 2, 4, 16, 18, 8, 16, 12, 14, 12, 20, 2, 16,
2, 8, 6, 16, 18, 18, 10, 18, 18, 20, 6, 8, 20, 10, 8, 8, 4, 2,
18, 18, 14, 10, 8, 10, 12, 8, 16, 2, 16, 12, 6, 14, 16, 6, 10,
16, 6, 20, 10, 18, 12, 16, 20, 8, 8, 18, 18, 2, 20, 20, 6, 6,
8, 16, 2, 14, 8, 18, 20, 12, 10, 8, 10, 4, 20, 6, 6, 16, 10,
4, 16, 12, 14, 14, 10, 14, 12, 4, 12, 8, 4, 10, 16, 16, 12, 6,
12, 4, 8, 2, 16, 8, 16, 14, 20, 6, 4, 8, 14, 6, 6, 6, 16, 10,
6, 12, 16, 2, 12, 8, 4, 8, 20, 2, 16, 18, 8, 18, 10, 8, 6, 4,
14, 8, 6, 2, 18, 8, 14, 10, 12, 20, 16, 14, 20, 8, 8, 14, 20,
4, 12, 6, 8, 12, 20, 16, 20, 4, 16, 14, 16, 14, 10, 2, 18, 18,
8, 18, 18, 14, 8, 4, 18, 12, 14, 18, 14, 14, 18, 20, 10, 4, 4,
14, 12, 16, 6, 10, 14, 18, 2, 8, 8, 2, 2, 8, 8, 6, 18, 4, 10,
10, 2, 20, 20, 16, 10, 16, 14, 18, 4, 10, 2, 4, 4, 20, 16, 4,
10, 14, 12, 14, 20, 14, 6, 20, 14, 14, 2, 14, 10, 16, 18, 16,
6, 20, 4, 18, 18, 12, 16, 14, 14, 16, 14, 12, 14, 20, 16, 20,
14, 4, 20, 8, 16, 14, 8, 18, 14, 20, 10, 10, 16, 2, 6, 2, 4,
16, 8, 10, 16, 4, 8, 4, 2, 16, 10, 4, 18, 18, 8, 16, 6, 2, 20,
12, 2, 4, 6, 16), Voter_Partisanship = structure(c(3L, 2L, 3L,
2L, 2L, 3L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 1L, 2L,
1L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 2L, 1L,
2L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 3L,
3L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 3L, 2L, 1L, 1L, 3L, 1L, 1L, 3L,
3L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 2L, 1L, 1L,
3L, 2L, 2L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 2L,
3L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 3L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 1L, 3L, 3L, 2L, 3L, 2L,
1L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 3L,
2L, 3L, 2L, 3L, 1L, 1L, 1L, 3L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 2L,
2L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 3L,
1L, 1L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 2L, 3L, 3L, 3L, 1L, 3L,
3L, 2L, 1L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 1L,
3L, 3L, 3L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 2L,
2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 2L,
3L, 1L, 3L, 2L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 2L,
1L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 2L, 3L, 3L, 2L, 2L, 3L,
1L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 3L,
2L, 3L, 1L, 1L, 3L, 2L, 3L, 2L, 3L, 1L, 2L, 1L, 1L, 1L, 3L, 3L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 2L,
2L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
3L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 1L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L,
1L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L,
2L, 1L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 3L,
2L, 3L, 2L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 1L, 1L,
3L, 1L, 2L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L,
1L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 1L, 2L, 3L, 1L, 1L,
1L, 3L, 1L, 3L, 2L, 1L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 3L, 2L, 1L, 1L, 1L, 3L,
3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 1L, 1L,
3L, 3L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 1L, 1L,
3L, 1L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 2L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 1L,
1L, 1L, 3L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 3L, 1L, 3L, 2L, 3L,
2L, 1L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 1L, 3L,
1L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 1L,
2L, 1L, 3L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 3L, 1L,
3L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 3L,
1L, 3L, 1L, 3L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 2L, 3L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 1L,
1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 1L, 3L, 2L, 3L,
2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 2L, 1L, 2L, 1L,
3L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 1L, 3L, 2L, 2L, 3L, 1L, 3L, 2L,
3L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 1L, 3L, 3L,
1L, 1L, 1L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 1L, 1L, 2L, 3L, 2L, 1L,
3L, 3L, 2L, 3L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 2L, 3L, 2L,
2L, 1L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, 3L,
3L, 3L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 3L, 1L, 2L, 1L, 2L, 1L, 3L,
2L, 1L, 1L, 3L, 1L), levels = c("Non-Partisan", "Moderately Partisan",
"Strongly Partisan"), class = "factor"), Population = structure(c(2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
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443L, 22934L, 26882L, 801L, 1697L, 4760L, 15032L, 13700L, 19286L,
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15014L, 4282L, 11663L, 28203L, 7495L, 27623L, 9556L, 8602L, 3458L,
19672L, 16786L, 16615L, 3070L, 9978L, 10774L, 27889L, 21425L,
26888L, 12942L, 29902L, 18799L, 19979L, 15698L, 28740L, 3484L,
18980L, 23151L, 7404L, 20241L, 9048L, 9032L, 16532L, 29471L,
29419L, 18410L, 11855L, 5740L, 29351L, 22986L, 3282L, 29439L,
3257L, 3438L, 19364L, 19889L, 17136L, 790L, 15789L, 13187L, 27627L,
8799L, 1377L, 22489L, 3035L, 29243L, 5451L, 21638L, 11730L, 5029L,
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691L, 2007L, 20229L, 25769L, 5608L, 19819L, 28934L, 25523L, 17373L,
13251L, 27625L, 3459L, 19935L, 18375L, 6273L, 29899L, 23116L,
8289L, 20144L, 13354L, 7003L, 18571L, 19371L, 8605L, 11245L,
9574L, 775L, 29129L, 14417L, 22875L, 28510L, 18360L, 13232L,
3785L, 3034L, 27847L, 18392L, 4923L, 18932L, 29911L, 23165L,
25872L, 9895L, 6676L, 18974L, 23113L, 6633L, 24026L, 28476L,
4775L, 22418L, 27449L, 8952L, 20317L, 21498L, 15025L, 25670L,
13525L, 26601L, 12151L, 28846L, 15553L, 1519L, 10572L, 20494L,
28531L, 17247L, 15876L, 29378L, 19052L, 21807L, 18781L, 6048L,
15075L, 29971L, 22713L, 50L, 680L, 11341L, 18872L), class = "data.frame")
>
尝试这样的事情：
图书馆（tidyverse）
df2 <- df %>%
group_by(Voter_Partisanship, Population, Public_Issues) %>%
summarise(mean = mean(value, na.rm = T),
sd = sd(value, na.rm = T)) %>%
pivot_wider(id_cols = c(Population,Voter_Partisanship),
names_from = c(Public_Issues),
values_from = c(mean, sd))