How to stem all words in an ngram, using quanteda?

Question

I'm working with the Quanteda package in R at the moment, and I'd like to calculate the ngrams of a set of stemmed words to get a quick-and-dirty estimate of what content words tend to be near each other. If I try:

twitter.files <- textfile(files)
twitter.docs <- corpus(twitter.files)
twitter.semantic <- twitter.docs %>%
  dfm(removeTwitter = TRUE, ignoredFeatures = stopwords("english"),
      ngrams = 2, skip = 0:3, stem = TRUE) %>%
  trim(minCount = 50, minDoc = 2)

It only stems the final word in the bigrams. However, if I try to stem first:

twitter.files <- textfile(files)
twitter.docs <- corpus(twitter.files)
stemmed_no_stops <- twitter.docs %>%
   toLower %>%
   tokenize(removePunct = TRUE, removeTwitter = TRUE) %>%
   removeFeatures(stopwords("english")) %>%
   wordstem

 twitter.semantic <- stemmed_no_stops %>%
   skipgrams(n = 2, skip = 0:2) %>%
   dfm %>%
   trim(minCount=25, minDoc = 2)

Then Quanteda doesn't know how to work with the stemmed list; I'll get the error:

assignment of an object of class “NULL” is not valid for @‘ngrams’ 
in an object of class “dfmSparse”; is(value, "integer") is not TRUE

Is there an intermediate step I can do to use a dfm on the stemmed words, or to tell dfm to stem first and do ngrams second?

Answer 1

I tried reproducing your example with the inaugural texts. Using a reproducible example from the package data, your code worked for me:

twitter.docs <- corpus(data_corpus_inaugural[1:5])
stemmed_no_stops <- twitter.docs %>%
    tokens(remove_punct = TRUE, remove_twitter = TRUE) %>%
    tokens_tolower() %>%
    tokens_remove(stopwords("english")) %>%
    tokens_wordstem()
lapply(stemmed_no_stops, head)
## $`1789-Washington`
## [1] "fellow-citizen" "senat"          "hous"           "repres"         "among"         
## [6] "vicissitud"    
## 
## $`1793-Washington`
## [1] "fellow"  "citizen" "call"    "upon"    "voic"    "countri"
## 
## $`1797-Adams`
## [1] "first"   "perceiv" "earli"   "time"    "middl"   "cours"  
## 
## $`1801-Jefferson`
## [1] "friend"   "fellow"   "citizen"  "call"     "upon"     "undertak"
## 
## $`1805-Jefferson`
## [1] "proceed"   "fellow"    "citizen"   "qualif"    "constitut" "requir"  

twitter.semantic <- stemmed_no_stops %>%
    tokens_skipgrams(n = 2, skip = 0:2) %>%
    dfm %>%
    dfm_trim(min_count = 5, min_doc = 2)
twitter.semantic[1:5, 1:4]
# Document-feature matrix of: 5 documents, 4 features.
# 5 x 4 sparse Matrix of class "dfmSparse"
#                  features
# docs              fellow_citizen let_u unit_state foreign_nation
#   1789-Washington              2     0          2              0
#   1793-Washington              1     0          0              0
#   1797-Adams                   0     0          3              5
#   1801-Jefferson               5     5          0              0
#   1805-Jefferson               8     2          1              1