Creating a new column when two columns satisfy certain conditions in R - r

Question

My data is like this:
country supporter1 supporter2 supporter3 supporter4 supporter5
USA Albania Germany USA NA NA
France USA France NA NA NA
UK UK Chile Peru NA NA
Germany USA Iran Mexico India Pakistan
USA China Spain NA NA NA
Cuba Cuba UK Germany South Korea NA
China Russia NA NA NA NA
What I want to do is to create a new variable when the country column and one of the remaining supporter columns (supporter 1, supporter 2, supporter 3, supporter 4, and supporter 5) are the same (for instance country France and supporter2 France are the same). In this case, the new variable should take 1, 0 otherwise.
I expect to have this:
country supporter1 supporter2 supporter3 supporter4 supporter5 new variable
USA Albania Germany USA NA NA 1
France USA France NA NA NA 1
UK UK Chile Peru NA NA 1
Germany USA Iran Mexico India Pakistan 0
USA China Spain NA NA NA 0
Cuba Cuba UK Germany South Korea NA 1
China Russia NA NA NA NA 0

Answer 1

Update dplyr only solution Using if_any:
library(dplyr)
df %>%
rowwise() %>%
mutate(new_var = as.integer(as.logical(if_any(starts_with("supporter"), ~ . %in% country))))
country supporter1 supporter2 supporter3 supporter4 supporter5 new_var
<chr> <chr> <chr> <chr> <chr> <chr> <int>
1 USA Albania Germany USA NA NA 1
2 France USA France NA NA NA 1
3 UK UK Chile Peru NA NA 1
4 Germany USA Iran Mexico India Pakistan 0
5 USA China Spain NA NA NA 0
6 Cuba Cuba UK Germany South Korea NA 1
7 China Russia NA NA NA NA 0
First answer: also correct:
Here is one possible solution:
calculate rowwise
check in cols supporter1 to supporter5 if country is included
unite all new columns to one and with an ifelse statement take 1 or 0
library(dplyr)
library(stringr)
library(tidyr)
df %>%
rowwise() %>%
mutate(across(supporter1:supporter5, ~ifelse(. %in% country, 1,0), .names = "new_{col}")) %>%
unite(New_Col, starts_with('new'), na.rm = TRUE, sep = ' ') %>%
mutate(New_Col = ifelse(str_detect(New_Col, "1"), 1,0))
country supporter1 supporter2 supporter3 supporter4 supporter5 New_Col
<chr> <chr> <chr> <chr> <chr> <chr> <dbl>
1 USA Albania Germany USA NA NA 1
2 France USA France NA NA NA 1
3 UK UK Chile Peru NA NA 1
4 Germany USA Iran Mexico India Pakistan 0
5 USA China Spain NA NA NA 0
6 Cuba Cuba UK Germany South Korea NA 1
7 China Russia NA NA NA NA 0

Answer 2

Here is a base R solution.
First mapply checks for equality of suporter* and country. NA's are considered to return FALSE. Then as.integer/rowSums transforms rows with at least one TRUE into 1, otherwise 0.
eq <- mapply(\(x, y){x == y & !is.na(x)}, df1[-1], df1[1])
as.integer(rowSums(eq) != 0)
#[1] 1 1 1 0 0 1 0
df1$new_variable <- as.integer(rowSums(eq) != 0)
Data
df1 <- read.table(text = "
country supporter1 supporter2 supporter3 supporter4 supporter5
USA Albania Germany USA NA NA
France USA France NA NA NA
UK UK Chile Peru NA NA
Germany USA Iran Mexico India Pakistan
USA China Spain NA NA NA
Cuba Cuba UK Germany 'South Korea' NA
China Russia NA NA NA NA
", header = TRUE)

Answer 3

Another solution is checking per row whether country is present in one of the columns:
df <- data.frame(country=c("USA","France","UK","Germany","USA","Cuba","China"),
supporter1=c("Albania","USA","UK","USA","China","Cuba","Russia"),
supporter2=c("Germany","France","Chile","Iran","Spain","UK","NA"),
supporter3=c("USA","NA","Peru","Mexico","NA","Germany","NA"),
supporter4=c("NA","NA","NA","India","NA","South Korea","NA"),
supporter5=c("NA","NA","NA","Pakistan","NA","NA","NA"))
That would give:
df$new <- sapply(seq(1,nrow(df)), function(x) ifelse(df$country[x] %in% df[x,2:6],1,0))
> df$new
[1] 1 1 1 0 0 1 0