Cfrgtkky

I have data about different study strategies for individuals (stored in columns labeled StrategyA, StrategyB, StrategyC. The strategies are coded 1-15. I want to create a dummy for each strategy (e.g. strategy1, strategy2, etc) because each student can list up to 3 strategies.

Example Data

   ID = c(1, 2, 3, 4, 5)

   Strategy_A = c(10, 12, 13, 1, 2)

   Strategy_B = c(1, 2, 1, 4, 5)

   Strategy_C = c(2, 3, 6, 8, 15)

   all = data.frame(ID, Strategy_A, Strategy_B, Strategy_C)

I thought about using apply and creating a function linked to the fastDummies package.

     dummies = function(x){

     dummy_cols(x)

     }



    new = apply(all [,-1], 2, dummies)

    new = as.data.frame(new)

However, this creates dummies for StrategyA_1 StrategyA_2 StrategyA_3 rather than summarizing the dummies as Strategy1 Strategy2 Strategy3. Any ideas how to fix this?

After a small transformation of all, you can use dummy.data.frame() from dummies (you can also use dummy_cols() from fastDummies) and then aggregate per ID.

all <- data.frame(ID = rep(all$ID, 3),

                  Strategy = c(all$Strategy_A, all$Strategy_B, all$Strategy_C)) # data frame "all" with one column Strategy

library(dummies)

all <- dummy.data.frame(all, "Strategy") # or fastDummies::dummy_cols(all, "Strategy")

aggregate(. ~ ID, all, sum) # since strategies are now dummies, the sum will always be 0 or 1

# output

  ID Strategy1 Strategy2 Strategy3 Strategy4 Strategy5 Strategy6 Strategy8 Strategy10 Strategy12 Strategy13 Strategy15

1  1         1         1         0         0         0         0         0          1          0          0          0

2  2         0         1         1         0         0         0         0          0          1          0          0

3  3         1         0         0         0         0         1         0          0          0          1          0

4  4         1         0         0         1         0         0         1          0          0          0          0

5  5         0         1         0         0         1         0         0          0          0          0          1

I provide a method with the tidyverse way.

library(tidyverse)



new <- all %>% gather(select = -ID) %>%

               mutate(key = NULL, num = 1) %>%

               spread(value, num)



#   ID  1  2  3  4  5  6  8 10 12 13 15

# 1  1  1  1 NA NA NA NA NA  1 NA NA NA

# 2  2 NA  1  1 NA NA NA NA NA  1 NA NA

# 3  3  1 NA NA NA NA  1 NA NA NA  1 NA

# 4  4  1 NA NA  1 NA NA  1 NA NA NA NA

# 5  5 NA  1 NA NA  1 NA NA NA NA NA  1



new[is.na(new)] <- 0

new



#   ID 1 2 3 4 5 6 8 10 12 13 15

# 1  1 1 1 0 0 0 0 0  1  0  0  0

# 2  2 0 1 1 0 0 0 0  0  1  0  0

# 3  3 1 0 0 0 0 1 0  0  0  1  0

# 4  4 1 0 0 1 0 0 1  0  0  0  0

# 5  5 0 1 0 0 1 0 0  0  0  0  1