Converting Dataframe with Group Means and Standard Deviations in R

I have a large dataset with four variables, like this:

I am trying to create a DF with the means and standard deviations that would look something like this:

Any help would be greatly appreciated.

I've included a subset of the data below:

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, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 
2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 
1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 
2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 
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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")
> 

Try something like this:

• I'm assuming you df object is called df

library(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))