Long to Wide Dataset while adding new variable

[hehaoyue]

Hello, I am trying to change my data from long to wide. I have an example of what it could look like

Instead, I want to see it as a wide and I want to create a new column T/F column to see if they have ever traveled to a certain continent and I want to make sure I can count how many Ts and Fs, like this:

I tried to do something like with group_by and mutate but I was definitely doing something wrong.
A = original long wide dataset

A$Asia = asia %>%
group_by(ID) %>%
mutate(Asia = {if(Continent='Asia') T else F})

What is the best way to do this?

~~ Reproducible example ~~

data.frame(
  stringsAsFactors = FALSE,
                               ID = c(1L,1L,1L,2L,2L,2L,2L,3L,3L,4L,4L,
                                      4L,4L,4L),
                        Continent = c("Asia","Asia","Africa","South America",
                                      "Asia","Africa","Europe",
                                      "North America","Europe","Africa","South America",
                                      "Europe","Africa","Asia")
               )

[williaml]

Can you provide a reproducible example instead of a screenshot?

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[hehaoyue]

data.frame(
  stringsAsFactors = FALSE,
                               ID = c(1L,1L,1L,2L,2L,2L,2L,3L,3L,4L,4L,
                                      4L,4L,4L),
                        Continent = c("Asia","Asia","Africa","South America",
                                      "Asia","Africa","Europe",
                                      "North America","Europe","Africa","South America",
                                      "Europe","Africa","Asia")
               )

(hope I'm doing this right)

[williaml]

This works:

library(tidyverse)

df %>% 
  mutate(visited = TRUE) %>% 
  pivot_wider(ID, names_from = Continent, values_from = visited) %>% 
  unnest() %>% 
  distinct(ID, .keep_all = TRUE) %>% 
  replace(is.na(.), FALSE)

# A tibble: 4 x 6
     ID Asia  Africa `South America` Europe `North America`
  <int> <lgl> <lgl>  <lgl>           <lgl>  <lgl>          
1     1 TRUE  TRUE   FALSE           FALSE  FALSE          
2     2 TRUE  TRUE   TRUE            TRUE   FALSE          
3     3 FALSE FALSE  FALSE           TRUE   TRUE           
4     4 TRUE  TRUE   TRUE            TRUE   FALSE  

[arthur.t]

df %>% 
  distinct() %>%
  mutate(visited = TRUE) %>%
  pivot_wider(names_from = Continent, values_from = visited) %>%
  mutate_all(replace_na, FALSE)