Mutate a transposed column into new columns row-wise by group-CodePudding

I have grouped data like follows:

  group id var
1     1  1   1
2     1  2   0
3     1  3   1
4     2  1   0
5     2  2   1
6     3  1   0

I'd like to create N new var columns (var1, var2,...) where N is the max number of individuals in a group (in this case N = 3 as largest group is group 1 which has three ids.

var1 should contain the var value for id = 1 in each group; likewise var2 should contain the var value for each id = 2 in each group. Where the number of columns exceeds the number in a group it should return NA.

The sought output for the above example would be:

  group id var var1 var2 var3
1     1  1   1    1    0    1
2     1  2   0    1    0    1
3     1  3   1    1    0    1
4     2  1   0    0    1   NA
5     2  2   1    0    1   NA
6     3  1   0    0   NA   NA

Example Code

a <- data.frame(group = c(1,1,1,2,2,3), 
                id = c(1,2,3,1,2,1), 
                var = c(1,0,1,0,1,0))

CodePudding user response：

Looks like you want to pivot the dataframe wider: you can use pivot_wider(). If you want to keeps the original rows, you can also use right_join().

library(tidyverse)
a %>% right_join(
  pivot_wider(a,values_from = var, names_from = id, names_prefix = "var"),
  by='group'
)

Output:

  group id var var1 var2 var3
1     1  1   1    1    0    1
2     1  2   0    1    0    1
3     1  3   1    1    0    1
4     2  1   0    0    1   NA
5     2  2   1    0    1   NA
6     3  1   0    0   NA   NA

However, maybe is this a bit redundant? Just using pivot_wider() keeps the same info because varx are the original id column:

library(tidyverse)
a %>% 
  pivot_wider(values_from = var, names_from = id, names_prefix = "var")

# A tibble: 3 x 4
  group  var1  var2  var3
  <dbl> <dbl> <dbl> <dbl>
1     1     1     0     1
2     2     0     1    NA
3     3     0    NA    NA