Find number of matches in data frames with different categories in the same column-CodePudding

I have two data frames with a column each having different or same categorical values as coded and illustrated below.

DF1 <- data.frame(Firm1 = c("A", "B", "C", "L", "M", "L"))

DF2 <- data.frame(Firm2 = c("L", "M", "N", "P"))

library(dplyr)
y <- DF1 %>% group_by(Firm1) %>% 
  dplyr:: summarise(n = n())

z <- DF2 %>% group_by(Firm2) %>% 
  dplyr:: summarise(n = n())

I tried using %in% logic but it isn't working. Some of the other pages that I checked on columns/data frames match also couldn't help. Kindly suggest how to get this coded. Thanks.

CodePudding user response：

You can use union and intersect and %in%.

. <- union(DF1$Firm1, DF2$Firm2)
data.frame(Fimr = ., Match = c("No", "Yes")[1   . %in% intersect(DF1$Firm1, DF2$Firm2)])
#  Fimr Match
#1    A    No
#2    B    No
#3    C    No
#4    L   Yes
#5    M   Yes
#6    N    No
#7    P    No

CodePudding user response：

Similar approach to @GKi, but result is always sorted by firm.

x <- list(DF2$Firm2, DF1$Firm1)
data.frame(Firm=sort(do.call(union, x))) |> 
  transform(Match=c("No", "Yes")[Firm %in% do.call(intersect, x)   1L])
#   Firm Match
# 1    A    No
# 2    B    No
# 3    C    No
# 4    L   Yes
# 5    M   Yes
# 6    N    No
# 7    P    No