How to merge two data frames on common columns in R with sum of others?

R Version 2.11.1 32-bit on Windows 7

I got two data sets: data_A and data_B:

data_A

USER_A USER_B ACTION
1      11     0.3
1      13     0.25
1      16     0.63
1      17     0.26
2      11     0.14
2      14     0.28

data_B

USER_A USER_B ACTION
1      13     0.17
1      14     0.27
2      11     0.25

Now I want to add the ACTION of data_B to the data_A if their USER_A and USER_B are equal. As the example above, the result would be:

data_A

USER_A USER_B ACTIO开发者_C百科N
1      11     0.3
1      13     0.25+0.17
1      16     0.63
1      17     0.26
2      11     0.14+0.25
2      14     0.28

So how could I achieve it?

You can use ddply in package plyr and combine it with merge:

library(plyr)
ddply(merge(data_A, data_B, all.x=TRUE), 
  .(USER_A, USER_B), summarise, ACTION=sum(ACTION))

Notice that merge is called with the parameter all.x=TRUE - this returns all of the values in the first data.frame passed to merge, i.e. data_A:

  USER_A USER_B ACTION
1      1     11   0.30
2      1     13   0.25
3      1     16   0.63
4      1     17   0.26
5      2     11   0.14
6      2     14   0.28

This sort of thing is quite easy to do with a database-like operation. Here I use package sqldf to do a left (outer) join and then summarise the resulting object:

require(sqldf)
tmp <- sqldf("select * from data_A left join data_B using (USER_A, USER_B)")

This results in:

> tmp
  USER_A USER_B ACTION ACTION
1      1     11   0.30     NA
2      1     13   0.25   0.17
3      1     16   0.63     NA
4      1     17   0.26     NA
5      2     11   0.14   0.25
6      2     14   0.28     NA

Now we just need sum the two ACTION columns:

data_C <- transform(data_A, ACTION = rowSums(tmp[, 3:4], na.rm = TRUE))

Which gives the desired result:

> data_C
  USER_A USER_B ACTION
1      1     11   0.30
2      1     13   0.42
3      1     16   0.63
4      1     17   0.26
5      2     11   0.39
6      2     14   0.28

This can be done using standard R function merge:

> merge(data_A, data_B, by = c("USER_A","USER_B"), all.x = TRUE)
  USER_A USER_B ACTION.x ACTION.y
1      1     11     0.30       NA
2      1     13     0.25     0.17
3      1     16     0.63       NA
4      1     17     0.26       NA
5      2     11     0.14     0.25
6      2     14     0.28       NA

So we can replace the sqldf() call above with:

tmp <- merge(data_A, data_B, by = c("USER_A","USER_B"), all.x = TRUE)

whilst the second line using transform() remains the same.

We can use {powerjoin}:

library(powerjoin)
power_left_join(
  data_A,  data_B, by = c("USER_A", "USER_B"), 
  conflict = ~ .x + ifelse(is.na(.y), 0, .y)
)
#>   USER_A USER_B ACTION
#> 1      1     11   0.30
#> 2      1     13   0.42
#> 3      1     16   0.63
#> 4      1     17   0.26
#> 5      2     11   0.39
#> 6      2     14   0.28

In case of conflict, the function fed to the conflict argument will be used on pairs of conflicting columns.

We can also use sum(, na.rm = TRUE) row-wise for the same effect :

power_left_join(data_A,data_B, by = c("USER_A", "USER_B"), 
                conflict = rw ~ sum(.x, .y, na.rm = TRUE))