How to join/ merge two tables using character values? - r

Question

I would like to combine two tables based on first name, last name, and year, and create a new binary variable indicating whether the row from table 1 was present in the 2nd table.
First table is a panel data set of some attributes of NBA players during a season:
firstname<-c("Michael","Michael","Michael","Magic","Magic","Magic","Larry","Larry")
lastname<-c("Jordan","Jordan","Jordan","Johnson","Johnson","Johnson","Bird","Bird")
year<-c("1991","1992","1993","1991","1992","1993","1992","1992")
season<-data.frame(firstname,lastname,year)
firstname lastname year
1 Michael Jordan 1991
2 Michael Jordan 1992
3 Michael Jordan 1993
4 Magic Johnson 1991
5 Magic Johnson 1992
6 Magic Johnson 1993
7 Larry Bird 1992
8 Larry Bird 1992
The second data.frame is a panel data set of some attributes of NBA players selected to the All-Star game:
firstname<-c("Michael","Michael","Michael","Magic","Magic","Magic")
lastname<-c("Jordan","Jordan","Jordan","Johnson","Johnson","Johnson")
year<-c("1991","1992","1993","1991","1992","1993")
ALLSTARS<-data.frame(firstname,lastname,year)
firstname lastname year
1 Michael Jordan 1991
2 Michael Jordan 1992
3 Michael Jordan 1993
4 Magic Johnson 1991
5 Magic Johnson 1992
6 Magic Johnson 1993
My desired result looks like:
firstname lastname year allstars
1 Michael Jordan 1991 1
2 Michael Jordan 1992 1
3 Michael Jordan 1993 1
4 Magic Johnson 1991 1
5 Magic Johnson 1992 1
6 Magic Johnson 1993 1
7 Larry Bird 1992 0
8 Larry Bird 1992 0
I tried to use a left join. But not sure whether that makes sense:
test<-join(season, ALLSTARS, by =c("lastname","firstname","year") , type = "left", match = "all")

Answer 1

Here's a simple solution using data.table binary join which allows you to update a column by reference while joing
library(data.table)
setkey(setDT(season), firstname, lastname, year)[ALLSTARS, allstars := 1L]
season
# firstname lastname year allstars
# 1: Larry Bird 1992 NA
# 2: Larry Bird 1992 NA
# 3: Magic Johnson 1991 1
# 4: Magic Johnson 1992 1
# 5: Magic Johnson 1993 1
# 6: Michael Jordan 1991 1
# 7: Michael Jordan 1992 1
# 8: Michael Jordan 1993 1
Or using dplyr
library(dplyr)
ALLSTARS %>%
mutate(allstars = 1L) %>%
right_join(., season)
# firstname lastname year allstars
# 1 Michael Jordan 1991 1
# 2 Michael Jordan 1992 1
# 3 Michael Jordan 1993 1
# 4 Magic Johnson 1991 1
# 5 Magic Johnson 1992 1
# 6 Magic Johnson 1993 1
# 7 Larry Bird 1992 NA
# 8 Larry Bird 1992 NA

Answer 2

In base R:
ALLSTARS$allstars <- 1L
newdf <- merge(season, ALLSTARS, by=c('firstname', 'lastname', 'year'), all.x=TRUE)
newdf$allstars[is.na(newdf$allstars)] <- 0L
newdf
Or one I like for a different approach:
season$allstars <- (apply(season, 1, function(x) paste(x, collapse='')) %in%
apply(ALLSTARS, 1, function(x) paste(x, collapse='')))+0L
#
# firstname lastname year allstars
# 1 Michael Jordan 1991 1
# 2 Michael Jordan 1992 1
# 3 Michael Jordan 1993 1
# 4 Magic Johnson 1991 1
# 5 Magic Johnson 1992 1
# 6 Magic Johnson 1993 1
# 7 Larry Bird 1992 0
# 8 Larry Bird 1992 0

Answer 3

It looks like you are using join() from the plyr package. You were almost there: just preface your command with ALLSTARS$allstars <- 1. Then do your join as it is written and finally convert the NA values to 0. So:
ALLSTARS$allstars <- 1
test <- join(season, ALLSTARS, by =c("lastname","firstname","year") , type = "left", match = "all")
test$allstars[is.na(test$allstars)] <- 0
Result:
firstname lastname year allstars
1 Michael Jordan 1991 1
2 Michael Jordan 1992 1
3 Michael Jordan 1993 1
4 Magic Johnson 1991 1
5 Magic Johnson 1992 1
6 Magic Johnson 1993 1
7 Larry Bird 1992 0
8 Larry Bird 1992 0
Though I personally would use left_join or right_join from the dplyr package, as in David's answer, instead of plyr's join(). Also note that you don't actually need the by argument of join() in this case as by default the function will try to join on all fields with common names, which is what you want here.