Add column with row values based on a category to R dataframe

so I have a dataframe of the following structure, let's call this one df0:

year	category	a	b	c	d
1989	1	0.3	0.7	0.43	321
1989	1	0.3	0.7	0.43	321
1989	2	0.2	0.4	0.5	174
1989	2	0.2	0.4	0.5	174
1989	2	0.2	0.4	0.5	174
1989	3	0.6	0.2	3.0	224
1990	1	0.6	0.2	3.0	93
1990	1	0.6	0.2	3.0	93
1990	2	0.3	0.7	4.0	293
1990	3	0.9	0.6	2.0	13

What I need to turn this into is the following. Basically, I want to add for each year a column with the a c value for each category. Like this:

year	category	a	b	c	d	c1	c2	c3
1989	1	0.3	0.7	0.43	321	0.43	0.5	3.0
1989	1	0.3	0.7	0.43	321	0.43	0.5	3.0
1989	2	0.2	0.4	0.5	174	0.43	0.5	3.0
1989	2	0.2	0.4	0.5	174	0.43	0.5	3.0
1989	2	0.2	0.4	0.5	174	0.43	0.5	3.0
1989	3	0.6	0.2	3.0	224	0.43	0.5	3.0
1990	1	0.6	0.2	3.0	93	3.0	4.0	2.0
1990	1	0.6	0.2	3.0	93	3.0	4.0	2.0
1990	2	0.3	0.7	4.0	293	3.0	4.0	2.0
1990	3	0.9	0.6	2.0	13	3.0	4.0	2.0

I cannot figure out how to compute this. My first Idea would be to create sub dataframes for each year and then create a vector of each c value from this, but this seems very tedious and I cannot get it to work.

Does anyone have input or a solution on this?

Solution

Using tidyr and dplyr, pivot then join:

library(tidyr)
library(dplyr)

cvals <- df0 %>%
  distinct(year, category, c) %>%
  pivot_wider(
    names_from = category,
    names_prefix = "c",
    values_from = c
  )

left_join(df0, cvals, join_by(year))

Result:

# A tibble: 10 × 9
    year category     a     b     c     d    c1    c2    c3
   <dbl>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  1989        1   0.3   0.7  0.43   321  0.43   0.5     3
 2  1989        1   0.3   0.7  0.43   321  0.43   0.5     3
 3  1989        2   0.2   0.4  0.5    174  0.43   0.5     3
 4  1989        2   0.2   0.4  0.5    174  0.43   0.5     3
 5  1989        2   0.2   0.4  0.5    174  0.43   0.5     3
 6  1989        3   0.6   0.2  3      224  0.43   0.5     3
 7  1990        1   0.6   0.2  3       93  3      4       2
 8  1990        1   0.6   0.2  3       93  3      4       2
 9  1990        2   0.3   0.7  4      293  3      4       2
10  1990        3   0.9   0.6  2       13  3      4       2