CODEWITHSUNDEEP
rdummy-variable
GFG
GFG

Reputation: 15

Converting multiple dummy variables that are not mutually exclusive into single categorical variable, adding new rows

I have the following data frame:

data <- data.frame(id=c(1, 1, 2, 2), 
task=c(1, 2, 1, 2),
strategy1=c("1", "1", "0", "1"),
strategy2=c("0", "0", "1", "1"),
strategy3=c("0", "1", "0", "1"))

My aim is to combine the dummy variables for the different strategies into a single categorical variable, 'strategy'. If multiple strategies were used by a participant during a task, new rows with the same 'id' and 'task' numbers must be created accordingly, as there should be only one 'strategy' variable.

For the given example, the data frame should finally look like this:

data_single <- data.frame(id=c(1, 1, 1, 2, 2, 2, 2),
task=c(1, 2, 2, 1, 2, 2, 2),
strategy=c("1", "1", "3", "2", "1", "2", "3"))

Can anyone show me how I can achieve this?

Upvotes: 0

Views: 376

Answers (2)

akrun
akrun

Reputation: 886938

We can also use data.table

library(data.table)
melt(setDT(data), measure = patterns('^strategy'), 
     variable.name = 'strategy')[value == 1, .(id, task, strategy)]

Upvotes: 0

Gregor Thomas
Gregor Thomas

Reputation: 145755

library(tidyr)
library(dplyr)
tidyr::pivot_longer(
  data, 
  cols = starts_with("strategy"),
  names_prefix = "strategy", 
  names_to = "strategy"
) %>%
  filter(value == 1) %>%
  select(-value)
# # A tibble: 7 x 3
#      id  task strategy
#   <dbl> <dbl> <chr>   
# 1     1     1 1       
# 2     1     2 1       
# 3     1     2 3       
# 4     2     1 2       
# 5     2     2 1       
# 6     2     2 2       
# 7     2     2 3

Upvotes: 1

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