Using apply() to select specific variables by name

Ok, basically I have a dataset of households that looks like this:

household_data <- data.frame(
                                id = 1:4,
                                gender_component_1 = c(1,2,2,2),
                                gender_component_2 = c(2,1,1,2),
                                bread_winner      = c(1,1,2,1)
)

I want to construct a variable ('gender_bread_winner') which reports the sex of the breadwinner in the family - whether component 1 or 2 , which is reported in a separate variable as a numeric.

I've come up with the following loop:

var_max <- paste("gender_component", household_data$bread_winner, sep = "_")

for (i in 1:nrow(household_data)) {
  household_data$gender_bread_winner[i] <- select(household_data[i,], var_max[i])
 }

Unfortunately, the real dataset is huge and this solution is not at all optimal, I was wondering whether is it possible to do the same thing using apply or similar? I've not been able to though.

Thanks in advance

EDIT : Thank you all for your answers! In the end I found easier to use a score of ifelses like this:

dataset$sesso_max <- NA
dataset$sesso_max <- ifelse(dataset$max_percettore == 1, dataset$sesso_1, dataset$sesso_max)
dataset$sesso_max <- ifelse(dataset$max_percettore == 2, dataset$sesso_2, dataset$sesso_max)
dataset$sesso_max <- ifelse(dataset$max_percettore == 3, dataset$sesso_3, dataset$sesso_max)
dataset$sesso_max <- ifelse(dataset$max_percettore == 4, dataset$sesso_4, dataset$sesso_max)
dataset$sesso_max <- ifelse(dataset$max_percettore == 5, dataset$sesso_5, dataset$sesso_max)
dataset$sesso_max <- ifelse(dataset$max_percettore == 6, dataset$sesso_6, dataset$sesso_max)

If there are only 2 gender_component columns a simple ifelse would do.

household_data <- transform(household_data, gender_bread_winner  = 
        ifelse(bread_winner == 1, gender_component_1, gender_component_2))

This says that when bread_winner has value 1 take the value from gender_component_1 or else take it from gender_component_2 column.

For more than 2 columns we may use max.col as follows -

gender_cols <- grep('gender_component', names(household_data), value = TRUE)
household_data$gender_bread_winner <- household_data[gender_cols]
             [cbind(1:nrow(household_data), household_data$bread_winner)]
household_data

#  id gender_component_1 gender_component_2 bread_winner gender_bread_winner
#1  1                  1                  2            1                   1
#2  2                  2                  1            1                   2
#3  3                  2                  1            2                   1
#4  4                  2                  2            1                   2

Explanation for the answer -

gender_cols has all the columns that have "gender_component" in them.

gender_cols
#[1] "gender_component_1" "gender_component_2"

We create a matrix with row and column index to subset from the dataframe household_data.

cbind(1:nrow(household_data), household_data$bread_winner)
#     [,1] [,2]
#[1,]    1    1
#[2,]    2    1
#[3,]    3    2
#[4,]    4    1

This basically says that get 1st value from 1st row, 1st value from 2nd row and so on. This matrix is used to subset the data from the dataframe.

One way is using mutate() function:

library(tidyverse)
household_data %>%  mutate(gender_bread_winner=ifelse(bread_winner==1,gender_component_1
,gender_component_2))

  id gender_component_1 gender_component_2 bread_winner gender_bread_winner
1  1                  1                  2            1                   1
2  2                  2                  1            1                   2
3  3                  2                  1            2                   1
4  4                  2                  2            1                   2