Joining tables based on different column names

Question

I was watching a video[1] by Greg Reda about Pandas to see what Pandas can do how it compares with data.table. I was surprised to learn how difficult it was to join tables in d

Answer 1

Normally, when joining data.tables the column names don't actually matter. You just need to make sure both tables have a compatible key.

library(data.table)
dt1<-data.table(a=letters[1:10], b=1:10)
setkey(dt1,a)
dt2<-data.table(x=letters[1:10], y=10:1)
setkey(dt2,x)

dt1[dt2]

Basically it will join on all the key columns. Normally you are joining on a key. If you really need to specify non-key columns, you can always cast the data.table to a data.frame and use the standard merge() function

merge(as.data.frame(dt1),dt2, by.x="a", by.y="x")
merge(as.data.frame(dt1),dt2, by.x="b", by.y="y")

Answer 2

With reference to the Rdatatable github page, if you want to perform functions on your join rather than just merge tables, you can also do d1[d2, somefunc, on = "A==W"], where A is your column in d1 and W is your column in d2.

Answer 3

Update: All the features listed below are implemented and is available in the current stable version of data.table v1.9.6 on CRAN.

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There are at least these improvements possible for joins in data.tables.

• merge.data.table gaining by.x and by.y arguments

• Using secondary keys to join using both forms discussed above without need to set keys, but rather by specifying columns on x and i.

The simplest reason is that we've not managed to get to it yet.