How to create columns from a list in a for loop using mutate

Question

I was wondering if there was a way to create multiple columns from a list in R using the mutate() function within a for loop.

Here is an example of what I mean:

The Problem:

I have a data frame df that has 2 columns: category and rating. I want to add a column for every element of df$category and in that column, I want a 1 if the category column matches the iterator.

library(dplyr)

df <- tibble(
  category = c("Art","Technology","Finance"),
  rating = c(100,95,50)
)

Doing it manually, I could do:

df <-
  df %>% 
  mutate(art = ifelse(category == "Art", 1,0))

However, what happens when I have 50 categories? (Which is close to what I have in my original problem. That would take a lot of time!)

What I tried:

category_names <- df$category

for(name in category_names){

  df <-
    df %>% 
    mutate(name = ifelse(category == name, 1,0))

}

Unfortunately, It doesn't seem to work.

I'd appreciate any light on the subject!

Full Code:

library(dplyr)

#Creates tibble
df <- tibble(
  category = c("Art","Technology","Finance"),
  rating = c(100,95,50)
)

#Showcases the operation I would like to loop over df
df <-
  df %>% 
  mutate(art = ifelse(category == "Art", 1,0))

#Creates a variable for clarity
category_names <- df$category

#For loop I tried
for(name in category_names){

  df <-
    df %>% 
    mutate(name = ifelse(category == name, 1,0))

}

I am aware that what I am essentially doing is a form of model.matrix(); however, before I found out about that function I was still perplexed why what I was doing before wasn't working.

akrun · Accepted Answer · 2020-04-28 02:05:28Z

4

We can use pivot_wider after creating a sequence column

library(dplyr)
library(tidyr)
df %>% 
    mutate(rn = row_number(), n = 1) %>% 
    pivot_wider(names_from = category, values_from = n, 
             values_fill = list(n = 0)) %>%
    select(-rn)
# A tibble: 3 x 4
#  rating   Art Technology Finance
#   <dbl> <dbl>      <dbl>   <dbl>
#1    100     1          0       0
#2     95     0          1       0
#3     50     0          0       1

Or another option is map

library(purrr)
map_dfc(unique(df$category),  ~  df %>%
                                 transmute(!! .x := +(category == .x))) %>% 
     bind_cols(df, .)
# A tibble: 3 x 5
#  category   rating   Art Technology Finance
#* <chr>       <dbl> <int>      <int>   <int>
#1 Art           100     1          0       0
#2 Technology     95     0          1       0
#3 Finance        50     0          0       1

If we need a for loop

for(name in category_names) df <- df %>% mutate(!! name := +(category == name))

Or in base R with table

cbind(df, as.data.frame.matrix(table(seq_len(nrow(df)), df$category)))
#    category rating Art Finance Technology
#1        Art    100   1       0          0
#2 Technology     95   0       0          1
#3    Finance     50   0       1          0

edited Apr 28, 2020 at 2:05

answered Apr 28, 2020 at 1:29

akrun

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6 Comments

spookywagons Over a year ago

My first thought was to use pivot_wider() but was unable to really wrap my head around it; however, with the original question being using mutate and for-loops this seems like a workaround. I'm curious to see an answer within the realm of the question. Thank you for your speedy response!

spookywagons Over a year ago

I edited my previous comment to reflect the reasoning. I was so excited about a response and saw pivot_wider and hastily made the decision to accept it. My apologies for that.

akrun Over a year ago

@spookywagons not clear where it is not working. I didn't see any new data on your post

akrun Over a year ago

@spookywagons let me know if you have an issue with using := instead of ==

akrun Over a year ago

@spookywagons it's okay. Usually people wanted to avoid for loop when using tidyverse. So, I was thinking about answering the other options instead of for loop

|

David Wit · Accepted Answer · 2022-02-23 07:48:02Z

Wanted to throw something in for anyone who stumbles across this question. The problem in the OP is that the "name" column name gets re-used during each iteration of the loop: you end up with only one new column, when you really wanted three (or 50). I consistently find myself wanting to create multiple new columns within loops, and I recently found out that mutate can now take "glue"-like inputs to do this. The following code now also solves the original question:

for(name in category_names){
  df <-
    df %>%
    mutate("{name}" := ifelse(category == name, 1, 0))
}

This is equivalent to akrun's answer using a for loop, but it doesn't involve the !! operator. Note that you still need the "walrus" := operator, and that the column name needs to be a string (I think since it's using "glue" in the background). I'm thinking some people might find this format easier to understand.

Reference: https://www.tidyverse.org/blog/2020/02/glue-strings-and-tidy-eval/

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