If you want to do this in base R, you can use reshape:
reshape(asd, direction = "wide", idvar = "week", timevar = "year", sep = "_")
#> week val_2019 cap_2019 val_2020 cap_2020
#> 1 1 1 3 2 4
#> 3 2 3 6 4 7
Note that it is best not to start your new column names with the year, since variable names beginning with numbers are not legal in R, and therefore always need to be quoted. It becomes quite tiresome to write asd$'2020_val' rather than asd$val_2020 and can often lead to errors when one forgets the quotes.
With tidyr::pivot_wider you could do:
asd <- data.frame(week = c(1,1,2,2), year = c("2019","2020","2019","2020"), val = c(1,2,3,4), cap = c(3,4,6,7))
tidyr::pivot_wider(asd, names_from = year, values_from = c(val, cap), names_glue = "{year}_{.value}")
#> # A tibble: 2 × 5
#> week `2019_val` `2020_val` `2019_cap` `2020_cap`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 2 3 4
#> 2 2 3 4 6 7
For completion, here is data.table option -
library(data.table)
dcast(setDT(asd), week~year, value.var = c('val', 'cap'))
# week val_2019 val_2020 cap_2019 cap_2020
#1: 1 1 2 3 4
#2: 2 3 4 6 7
Slightly different approach using pivot_longer and pivot_wider together:
library(tidyr)
library(dplyr)
asd %>%
pivot_longer(
cols = -c(week, year)
) %>%
pivot_wider(
names_from = c(year, name)
)
week `2019_val` `2019_cap` `2020_val` `2020_cap`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 3 2 4
2 2 3 6 4 7
