Add new column to long dataframe from another dataframe?

Question

Say that I have two dataframes. I have one that lists the names of soccer players, teams that they have played for, and the number of goals that they have scored on each team. Then I also have a dataframe that contains the soccer players ages and their names. How do I add an "names_age" column to the goal dataframe that is the age column for the players in the first column "names", not for "teammates_names"? How do I add an additional column that is the teammates' ages column? In short, I'd like two age columns: one for the first set of players and one for the second set.

> AGE_DF

  names age
1   Sam  20
2   Jon  21
3  Adam  22
4  Jason 23
5  Jones 24
6  Jermaine 25

> GOALS_DF
   names goals      team teammates_names teammates_goals teammates_team
1    Sam     1       USA           Jason               1        HOLLAND
2    Sam     2   ENGLAND           Jason               2       PORTUGAL
3    Sam     3    BRAZIL           Jason               3          GHANA
4    Sam     4   GERMANY           Jason               4       COLOMBIA
5    Sam     5 ARGENTINA           Jason               5         CANADA
6    Jon     1       USA           Jones               1        HOLLAND
7    Jon     2   ENGLAND           Jones               2       PORTUGAL
8    Jon     3    BRAZIL           Jones               3          GHANA
9    Jon     4   GERMANY           Jones               4       COLOMBIA
10   Jon     5 ARGENTINA           Jones               5         CANADA
11  Adam     1       USA        Jermaine               1        HOLLAND
12  Adam     1   ENGLAND        Jermaine               1       PORTUGAL
13  Adam     4    BRAZIL        Jermaine               4          GHANA
14  Adam     3   GERMANY        Jermaine               3       COLOMBIA
15  Adam     2 ARGENTINA        Jermaine               2         CANADA

What I have tried: I've successfully got this to work using a for loop. The actual data that I am working with have thousands of rows, and this takes a long time. I would like a vectorized approach but I'm having trouble coming up with a way to do that.

Answer 1

Try merge or match .

1. Here's merge (which is likely to screw up your row ordering and can sometimes be slow):

    merge(AGE_DF, GOALS_DF, all = TRUE) 

2. Here's match , which makes use of basic indexing and subsetting. Assign the result to a new column, of course.

    AGE_DF$age[match(GOALS_DF$names, AGE_DF$names)] 

---

Here's another option to consider: Convert your dataset into a long format first, and then do the merge. Here, I've done it with melt and "data.table":

library(reshape2)
library(data.table)
setkey(melt(as.data.table(GOALS_DF, keep.rownames = TRUE), 
            measure.vars = c("names", "teammates_names"), 
            value.name = "names"), names)[as.data.table(AGE_DF)]
#     rn goals      team teammates_goals teammates_team        variable    names age
#  1:  1     1       USA               1        HOLLAND           names      Sam  20
#  2:  2     2   ENGLAND               2       PORTUGAL           names      Sam  20
#  3:  3     3    BRAZIL               3          GHANA           names      Sam  20
#  4:  4     4   GERMANY               4       COLOMBIA           names      Sam  20
#  5:  5     5 ARGENTINA               5         CANADA           names      Sam  20
#  6:  6     1       USA               1        HOLLAND           names      Jon  21
## <<SNIP>>
# 28: 13     4    BRAZIL               4          GHANA teammates_names Jermaine  25
# 29: 14     3   GERMANY               3       COLOMBIA teammates_names Jermaine  25
# 30: 15     2 ARGENTINA               2         CANADA teammates_names Jermaine  25
#     rn goals      team teammates_goals teammates_team        variable    names age

I've added the rownames so you can you can use dcast to get back to the wide format and retain the row ordering if it's important.