lookup data in a datatable and add it to a new column - r

Question

I have two data tables as shown below:
bigrams
w1w2 freq w1 w2
common names 1 common names
department of 4 department of
family name 6 family name
bigrams = setDT(structure(list(w1w2 = c("common names", "department of", "family name"
), freq = c(1L, 4L, 6L), w1 = c("common", "department", "family"
), w2 = c("names", "of", "name")), .Names = c("w1w2", "freq",
"w1", "w2"), row.names = c(NA, -3L), class = "data.frame"))
unigrams
w1 freq
common 2
department 3
family 4
name 5
names 1
of 9
unigrams = setDT(structure(list(w1 = c("common", "department", "family", "name",
"names", "of"), freq = c(2L, 3L, 4L, 5L, 1L, 9L)), .Names = c("w1",
"freq"), row.names = c(NA, -6L), class = "data.frame"))
desired output
w1w2 freq w1 w2 w1freq w2freq
common names 1 common names 2 1
department of 4 department of 3 9
family name 6 family name 4 5
What I have done so far
setkey(bigrams, w1)
setkey(unigrams, w1)
result <- bigrams[unigrams]
This gives me the i.freq column for w1 but when I try to do the same for w2 the i.freq column is updated to reflect the freq of w2.
How can I get freq for both w1 and w2 in separate columns?
Note: I have already seen solutions to data.table Lookup value and translate and Modify column of a data.table based on another column and add the new column

Answer 1

You can do two joins, and in v1.9.6 of data.table you can specify the on= argument for differing column names.
library(data.table)
bigrams[unigrams, on=c("w1"), nomatch = 0][unigrams, on=c(w2 = "w1"), nomatch = 0]
w1w2 freq w1 w2 i.freq i.freq.1
1: family name 6 family name 4 5
2: common names 1 common names 2 1
3: department of 4 department of 3 9

Answer 2

You can do this with a bit of reshaping.
library(dplyr)
library(tidyr)
bigrams %>%
rename(w1w2_string = w1w2,
w1w2_freq = freq) %>%
gather(order, string,
w1, w2) %>%
left_join(unigrams %>%
rename(string = w1) ) %>%
gather(type, value,
string, freq) %>%
unite(order_type, order, type) %>%
spread(order_type, value)
Edit: Explanation
The first observation you can make is that bigrams contains in fact information about three different units of analysis: a bigram and two unigrams. Convert to long form so that the unit of analysis is a unigram. Then we can merge in the other unigram data. Now note that your unigram has two different pieces of information per row: the frequency for the unigram, and the text of the unigram. Convert to long form again so that the unit of analysis is a piece of information about a unigram. Now spread, so that each new column is a type of information about a unigram.