Conditionally mutate multiple columns in R

I have a dataframe with a factor column with j levels, as well as j vectors of length k. I would like to populate k columns in the former dataframe with values from the latter vectors, conditional on the factor.

Simplified example (three levels, three vectors, two values):

df1 <- data.frame("Factor" = rep(c("A", "B", "C"), times = 5))
vecA <- c(1, 2)
vecB <- c(2, 1)
vecC <- c(3, 3)

Here is a solution using nested ifelse statements:

library(tidyverse)
df1 %>%
  mutate(V1 = ifelse(Factor == "A", vecA[1], 
                     ifelse(Factor == "B", vecB[1], vecC[1])),
         V2 = ifelse(Factor == "A", vecA[2], 
                     ifelse(Factor == "B", vecB[2], vecC[2])))

I would like to avoid the nested ifelse statements. Ideally, I would also like to avoid mutating each column separately.

Here is one idea. In the global environment, get all objects that begin with "vec", which is done by mget(). This creates a list. For each element in the list, paste the numbers with "_" in between. Then, arrange names in the vector for the following join process. After join, split the column, values with cSplit(). I hope this approach will be applicable to your real situation.

library(tidyverse)
library(splitstackshape)

# Create a character vector.
mychr <- map_chr(.x = mget(ls(pattern = "vec")),
                 .f = function(x) {paste0(x, collapse = "_")})

# Remove "vec" in names.
names(mychr) <- sub(x = names(mychr), pattern = "vec", replacement = "")

#   A     B     C 
#"1_2" "2_1" "3_3"

# stack() creates a data frame. Use it in left_join().
# Then, split the column, values into two columns. You probably have more than
# two. So I decided to use cSplit() here.

left_join(df1, stack(mychr), by = c("Factor" = "ind")) %>%
cSplit(splitCols = "values", sep = "_", direction = "wide", type.convert = FALSE)

#    Factor values_1 values_2
# 1:      A        1        2
# 2:      B        2        1
# 3:      C        3        3
# 4:      A        1        2
# 5:      B        2        1
# 6:      C        3        3
# 7:      A        1        2
# 8:      B        2        1
# 9:      C        3        3
#10:      A        1        2
#11:      B        2        1
#12:      C        3        3
#13:      A        1        2
#14:      B        2        1
#15:      C        3        3

Here is a base R option

df1[c('V1', 'V2')] <- do.call(Map, c(f = c, mget(ls(pattern="^vec[A-C]$"))))
df1
#    Factor V1 V2
#1       A  1  2
#2       B  2  1
#3       C  3  3
#4       A  1  2
#5       B  2  1
#6       C  3  3
#7       A  1  2
#8       B  2  1
#9       C  3  3
#10      A  1  2
#11      B  2  1
#12      C  3  3
#13      A  1  2
#14      B  2  1
#15      C  3  3

Or with transpose from purrr

library(dplyr)
library(purrr)
mget(ls(pattern="^vec[A-C]$")) %>% 
     transpose %>% 
     setNames(c('V1', 'V2')) %>% 
     cbind(df1, .)

Here's a way to do:

# modify the vectors
l <- list('A' = vecA, 'B' = vecB, 'C' = vecC)

# create df with mapping
df2 = data.frame(t(sapply(df1$Factor, function(x) l[[x]])))
colnames(df2) <- c('V1', 'V2')

new_df = cbind(df1, df2)

   Factor V1 V2
1       A  1  2
2       B  2  1
3       C  3  3
4       A  1  2
5       B  2  1
6       C  3  3
7       A  1  2
8       B  2  1
9       C  3  3
10      A  1  2
11      B  2  1
12      C  3  3
13      A  1  2
14      B  2  1
15      C  3  3