Mutate variable in main df from conditional in nested df

How can one create a new variable in the main dataframe using a conditional (if else) statement based on nested data in a list-column?

If we take the cars dataset:

library(dplyr)

cars_nest <- mtcars %>%
  group_by(cyl) %>%
  nest()

And want to create a binary variable where any value of carb > 2 equals 1, otherwise 0. I tried the following but get an error.

cars_nest  <- cars_nest |> 
  mutate(test = ifelse(any(cars_nest$carb) > 2, 1, 0))

>Solution :

A few problems in your code:

1. Your any should include the condition (i.e. any(... > 2))
2. You don’t have a cars_nest variable to use in mutate, that is the name of the dataframe object, not the column (it should be data column)
3. You need rowwise for this type of operation

One suggestion:

1. Since the output of a logical comparison (... > 2) is already logical, you can take advantage of as.integer to corce it into integers without using ifelse

So the code should be:

library(tidyverse)

cars_nest <- mtcars %>%
  group_by(cyl) %>%
  nest() 
  
cars_nest %>% 
  rowwise() %>% 
  mutate(test = as.integer(any(data$carb > 2)))
  # or mutate(test = ifelse(any(data$carb > 2), 1, 0))

# A tibble: 3 × 3
# Rowwise:  cyl
    cyl data                test
  <dbl> <list>             <dbl>
1     6 <tibble [7 × 10]>      1
2     4 <tibble [11 × 10]>     0
3     8 <tibble [14 × 10]>     1