I got tableA, which contains a numeric variable, CdTaller (e.g. 1948675).

I also got tableB, which contains two variables: Id (e.g. 2513978) and Name (e.g. "JOSE MANUEL PEREZ")

I want to add a new column to tableA, Name_Taller, which contains a name for those CdTaller which correspond to the Id variable of tableB, so I do the following:

tableA[, Name_Taller := tableB[Id==tableA\$CdTaller]$Name]

However, for those CdTaller of tableA that are not in tableB, R recycles the new variable Name_Taller:

Warning message:
In \`[.data.table\`(tableA, , \`:=\`(Name, tableB[Id ==  :
  Supplied 867324 items to be assigned to 947871 items of column 'Name'
  (recycled leaving remainder of 80547 items).

So for the tableA$CdTaller == '1320402', which can't be found in tableB, R still adds a Name_Taller which actually corresponds to CdTaller == '4430219', because of this recycling.

The question is: how can I avoid this recycling, so that R only adds a NA or empty string to those rows containing a CdTaller not found in tableB? Is there any parameter in data table I can use for this purpose?


1 Answer 1


What you want to do is join-like behaviour that is among others documented here: joins and others

To make things readable here a version with dplyr

ex1 <- data.frame(id = 1:10, name = replicate(10, paste(sample(letters[1:5], size = 2), collapse = "")))

ex2 <- data.frame(otherinfo = (1:5)+50, othername = replicate(5, paste(sample(letters[1:5], size = 2), collapse = "")))

dplyr::left_join(ex1, ex2, by = c(name = "othername"))

   id name otherinfo
1   1   ac        NA
2   2   ac        NA
3   3   ab        NA
4   4   da        53
5   5   ab        NA
6   6   cb        NA
7   7   eb        NA
8   8   de        NA
9   9   ad        NA
10 10   ec        52

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