R Mutating multiple columns with matching - r

Question

I am processing a large dataset adapted to my research. Suppose that I have 4 observations (records) and 5 columns as follows:
x <- data.frame("ID" = c(1, 2, 3, 4),
"group1" = c("A", NA, "B", NA),
"group2" = c("B", "A", NA, "C"),
"hours1" = c(3, NA, 5, NA),
"hours2" = c(1, 2, NA, 5))
> x
ID group1 group2 hours1 hours2
1 A B 3 1
2 <NA> A NA 2
3 B <NA> 5 NA
4 <NA> C NA 5
The "group1" and "group2" are reference columns containing the character values of A, B, and C, and the last two columns, "hours1" and "hours2," are numeric indicating hours obviously.
The column "group1" is corresponding to the column "hours1"; likewise, "group2" is corresponding to "hours 2."
I want to create multiple columns according to the values, A, B, and C, of the reference columns matching to values of "hours1" and "hours2" as follows:
ID group1 group2 hours1 hours2 A B C
1 A B 3 1 3 1 NA
2 <NA> A NA 2 2 NA NA
3 B <NA> 5 NA NA 5 NA
4 <NA> C NA 5 NA NA 5
For example, ID 1 has A in "group1," corresponding to 3 in "hours1" which is found under the column "A." ID 3 has B in "group1," corresponding to 5 in "hours1" which is found under the columns "B." In "group 2," ID 4 has C, corresponding to 5 in hours2 which is found under column "C."
Is there a way to do it using R?

Answer 1

One way would be to combine all the "hour" column in one column and "group" columns in another column. This can be done using pivot_longer. After that we can get data in wide format and join it with original data.
library(dplyr)
library(tidyr)
x %>%
pivot_longer(cols = -ID,
names_to = c('.value'),
names_pattern = '(.*?)\\d+',
values_drop_na = TRUE) %>%
pivot_wider(names_from = group, values_from = hours) %>%
left_join(x, by = 'ID') %>%
select(ID, starts_with('group'), starts_with('hour'), everything())
# A tibble: 4 x 8
# ID group1 group2 hours1 hours2 A B C
# <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 1 A B 3 1 3 1 NA
#2 2 NA A NA 2 2 NA NA
#3 3 B NA 5 NA NA 5 NA
#4 4 NA C NA 5 NA NA 5
For OP's dataset we can slightly modify the code to achieve the desired result.
zz %>%
pivot_longer(cols = -id,
names_to = c('.value'),
names_pattern = '(.*)_',
values_drop_na = TRUE) %>%
arrange(fu2a) %>%
pivot_wider(names_from = fu2a, values_from = fu2b) %>%
left_join(zz, by = 'id') %>%
select(id, starts_with('fu2a'), starts_with('fu2b'), everything())

Answer 2

Another approach using dplyr could be done separating group and hours variables to compute the desired variables and then merge with the original x:
library(tidyverse)
#Data
x <- data.frame("ID" = c(1, 2, 3, 4),
"group1" = c("A", NA, "B", NA),
"group2" = c("B", "A", NA, "C"),
"hours1" = c(3, NA, 5, NA),
"hours2" = c(1, 2, NA, 5),stringsAsFactors = F)
#Reshape
x %>%
left_join(x %>% select(1:3) %>%
pivot_longer(cols = -ID) %>%
group_by(ID) %>% mutate(id=1:n()) %>%
left_join(x %>% select(c(1,4:5)) %>%
pivot_longer(cols = -ID) %>%
rename(name2=name,value2=value) %>%
group_by(ID) %>% mutate(id=1:n())) %>%
filter(!is.na(value)) %>% select(ID,value,value2) %>%
pivot_wider(names_from = value,values_from=value2))
Output:
ID group1 group2 hours1 hours2 A B C
1 1 A B 3 1 3 1 NA
2 2 <NA> A NA 2 2 NA NA
3 3 B <NA> 5 NA NA 5 NA
4 4 <NA> C NA 5 NA NA 5