I have two data frames that I want to join using dplyr. One is a data frame containing first names.
test_data <- data.frame(first_name = c("john", "bill", "madison", "abby", "zzz"), stringsAsFactors = FALSE)
The other data frame contains a cleaned up version of the Kantrowitz names corpus, identifying gender. Here is a minimal example:
kantrowitz <- structure(list(name = c("john", "bill", "madison", "abby", "thomas"), gender = c("M", "either", "M", "either", "M")), .Names = c("name", "gender"), row.names = c(NA, 5L), class = c("tbl_df", "tbl", "data.frame"))
I essentially want to look up the gender of the name from the test_data
table using the kantrowitz
table. Because I'm going to abstract this into a function encode_gender
, I won't know the name of the column in the data set that's going to be used, and so I can't guarantee that it will be name
, as in kantrowitz$name
.
In base R I would perform the merge this way:
merge(test_data, kantrowitz, by.x = "first_names", by.y = "name", all.x = TRUE)
That returns the correct output:
first_name gender 1 abby either 2 bill either 3 john M 4 madison M 5 zzz <NA>
But I want to do this in dplyr because I'm using that package for all my other data manipulation. The dplyr by
option to the various *_join
functions only lets me specify one column name, but I need to specify two. I'm looking for something like this:
library(dplyr) # either left_join(test_data, kantrowitz, by.x = "first_name", by.y = "name") # or left_join(test_data, kantrowitz, by = c("first_name", "name"))
What is the way to perform this kind of join using dplyr?
(Never mind that the Kantrowitz corpus is a bad way to identify gender. I'm working on a better implementation, but I want to get this working first.)
Joins with dplyr. dplyr uses SQL database syntax for its join functions. A left join means: Include everything on the left (what was the x data frame in merge() ) and all rows that match from the right (y) data frame. If the join columns have the same name, all you need is left_join(x, y) .
To pick out single or multiple columns use the select() function. The select() function expects a dataframe as it's first input ('argument', in R language), followed by the names of the columns you want to extract with a comma between each name.
This feature has been added in dplyr v0.3. You can now pass a named character vector to the by
argument in left_join
(and other joining functions) to specify which columns to join on in each data frame. With the example given in the original question, the code would be:
left_join(test_data, kantrowitz, by = c("first_name" = "name"))
This is more a workaround than a real solution. You can create a new object test_data
with another column name:
left_join("names<-"(test_data, "name"), kantrowitz, by = "name") name gender 1 john M 2 bill either 3 madison M 4 abby either 5 zzz <NA>
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