Compute chi square value between ngrams and documents with Quanteda - r

Question

I use Quanteda R package in order to extract ngrams (here 1grams and 2grams) from text Data_clean$Review, but I am looking for a way with R to compte Chi-square between document and the extracted ngrams :
Here the R code that I did to clean Up text (revoiew) and generate the n-grams.
Any idea please?
thank you
#delete rows with empty value columns
Data_clean <- Data[Data$Note!="" & Data$Review!="",]
Data_clean$id <- seq.int(nrow(Data_clean))
train.index <- 1:50000
test.index <- 50001:nrow(Data_clean)
#clean up
# remove grammar/punctuation
Data_clean$Review.clean <- tolower(gsub('[[:punct:]0-9]', ' ', Data_clean$Review))
train <- Data_clean[train.index, ]
test <- Data_clean[test.index, ]
temp.tf <- Data_clean$Raison.Reco.clean %>% tokens(ngrams = 1:2) %>% # generate tokens
dfm # generate dfm

Answer 1

You would not use ngrams for this, but rather a function called textstat_collocations().
It's a bit hard to follow your exact example since none of those objects are explained or supplied, but let's try it with some of quanteda's built-in data. I'll get the texts from the inaugural corpus and apply some filters similar to what you have above.
So to score bigrams for chi^2, you would use:
# create the corpus, subset on some conditions (could be Note != "" for instance)
corp_example <- data_corpus_inaugural
corp_example <- corpus_subset(corp_example, Year > 1960)
# this will remove punctuation and numbers
toks_example <- tokens(corp_example, remove_punct = TRUE, remove_numbers = TRUE)
# find and score chi^2 bigrams
coll2 <- textstat_collocations(toks_example, method = "chi2", max_size = 2)
head(coll2, 10)
# collocation count X2
# 1 reverend clergy 2 28614.00
# 2 Majority Leader 2 28614.00
# 3 Information Age 2 28614.00
# 4 Founding Fathers 3 28614.00
# 5 distinguished guests 3 28614.00
# 6 Social Security 3 28614.00
# 7 Chief Justice 9 23409.82
# 8 middle class 4 22890.40
# 9 Abraham Lincoln 2 19075.33
# 10 society's ills 2 19075.33
Added:
# needs to be a list of the collocations as separate character elements
coll2a <- sapply(coll2$collocation, strsplit, " ", USE.NAMES = FALSE)
# compound the tokens using top 100 collocations
toks_example_comp <- tokens_compound(toks_example, coll2a[1:100])
toks_example_comp[[1]][1:20]
# [1] "Vice_President" "Johnson" "Mr_Speaker" "Mr_Chief" "Chief_Justice"
# [6] "President" "Eisenhower" "Vice_President" "Nixon" "President"
# [11] "Truman" "reverend_clergy" "fellow_citizens" "we" "observe"
# [16] "today" "not" "a" "victory" "of"