Appending a value from one table into another based on a criteria - r

Question

I have two tables that I'm trying to join in a particular way. One is a simple tibble that provides a HEX color and its category that it is associated with:
library(tibble)
library(dplyr)
colors <- tibble(Category = c("A", "B", "C", "D"),
Colors = c("#0079c0", "#cc9900", "#252525", "#c5120e"))
# A tibble: 4 × 2
Category Colours
<chr> <chr>
1 A #0079c0
2 B #cc9900
3 C #252525
4 D #c5120e
I have another tibble that lists the categories both as rows and columns, and those appear in a specific way:
Main_Table <- tibble(Category = c("A", "B", "C", "D"),
A = c(NA, "A", NA, NA),
B = c(NA, NA, NA, NA),
C = c(NA, "C", NA, NA),
D = c("D", "D", NA, NA))
# A tibble: 4 × 5
Category A B C D
<chr> <chr> <lgl> <chr> <chr>
1 A <NA> NA <NA> D
2 B A NA C D
3 C <NA> NA <NA> <NA>
4 D <NA> NA <NA> <NA>
I want to join the color into the main table based on whether its corresponding category is present under the variable that bears its name. For example, let's say that if I want category D's color to be included, I'd end up with the below:
Main_Table_Goal <- tibble(Category = c("A", "B", "C", "D"),
A = c(NA, "A", NA, NA),
B = c(NA, NA, NA, NA),
C = c(NA, "C", NA, NA),
D = c("D", "D", NA, NA),
color = c("#c5120e", "#c5120e", NA, NA))
# A tibble: 4 × 6
Category A B C D color
<chr> <chr> <lgl> <chr> <chr> <chr>
1 A <NA> NA <NA> D #c5120e
2 B A NA C D #c5120e
3 C <NA> NA <NA> <NA> <NA>
4 D <NA> NA <NA> <NA> <NA>
How do I achieve this using dplyr? I've been trying with *_join and other tricks, but I've not gotten anywhere.
EDIT: I should have mentioned that I'd like to eventually include this in a function, so ideally the code can be flexible to accommodate any number of categories.

Answer 1

Here is an option using match
Main_Table %>%
mutate(color = colors$Colors[match(D, colors$Category)])
# A tibble: 4 × 6
# Category A B C D color
# <chr> <chr> <lgl> <chr> <chr> <chr>
#1 A <NA> NA <NA> D #c5120e
#2 B A NA C D #c5120e
#3 C <NA> NA <NA> <NA> <NA>
#4 D <NA> NA <NA> <NA> <NA>

Answer 2

I am not sure how many categories you have in your data. But if you have only four (i.e., A, B, C, and D), the following would be one way. I wanted to work with one data frame. So I initially merged the two data frames. I converted B in logical to character since I wanted to use mutate_at(). Then, I replaced the four categories with the four colors. Finally, I removed Colors and converted B to logical.
library(dplyr)
left_join(Main_Table, colors) %>%
mutate(B = as.character(B)) %>%
mutate_at(vars(A:D),
funs(color = recode(., A = Colors[1],
B = Colors[2],
C = Colors[3],
D = Colors[4]))) %>%
select(-Colors) %>%
mutate(B = as.logical(B))
Given akrun's idea, you can do the following. As long as you can tell how many category you have, you just specify the columns in vars(). If all columns are in character, no need to convert logical to character.
left_join(Main_Table, colors) %>%
mutate(B = as.character(B)) %>%
mutate_at(vars(A:D),funs(color = Colors[match(., Category)])) %>%
select(-Colors) %>%
mutate(B = as.logical(B))
# Category A B C D A_color B_color C_color D_color
# <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <chr> <chr>
#1 A <NA> NA <NA> D <NA> <NA> <NA> #c5120e
#2 B A NA C D #0079c0 <NA> #252525 #c5120e
#3 C <NA> NA <NA> <NA> <NA> <NA> <NA> <NA>
#4 D <NA> NA <NA> <NA> <NA> <NA> <NA> <NA>

Answer 3

This is a dynamic solution where you set the color parameter once at the top:
target_category <- 'D' # set color
target_category_table <- Main_Table %>%
select_(target_category) %>%
left_join(colors %>%
filter(Category == target_category) %>%
setNames(c(target_category, 'color')))
goal_table <- Main_Table %>%
bind_cols(select(target_category_table, color))
goal_table
Result:
# A tibble: 4 × 6
Category A B C D color
<chr> <chr> <lgl> <chr> <chr> <chr>
1 A <NA> NA <NA> D #c5120e
2 B A NA C D #c5120e
3 C <NA> NA <NA> <NA> <NA>
4 D <NA> NA <NA> <NA> <NA>