CODEWITHSUNDEEP
rnlpquanteda
MCC89
MCC89

Reputation: 67

Quanteda group documents by multiple variables

I would like to be able to group documents in my dfm by two variables - speaker and week_start. I was previously able to do this using dfm(corpus, groups=c("speaker","week_start"). This worked fine and grouped documents by speaker-week.

However, with the recent updates to the quanteda package I seem to be running into a few problems. So I now create the dfm first then I try to group. Below is the code

dfm <- dfm(corpus)
dfm <- dfm_group(dfm, groups = c(speaker, week_start))

However, when I do this I get the error:

Error: groups must have length ndoc(x)

I have also tried to put the docvars in quotations but this generates the same error.

Upvotes: 0

Views: 858

Answers (1)

Ken Benoit
Ken Benoit

Reputation: 14902

We changed the usage of the groups argument in v3 to make it more standard.

From news(Version >= "3.0", package = "quanteda"):

We have added non-standard evaluation for by and groups arguments to access object docvars:

  • The *_sample() functions' argument by, and groups in the *_group() functions, now take unquoted document variable (docvar) names directly, similar to the way the subset argument works in the *_subset() functions.
  • Quoted docvar names no longer work, as these will be evaluated literally.
  • The by = "document" formerly sampled from docid(x), but this functionality is now removed. Instead, use by = docid(x) to replicate this functionality.
  • For groups, the default is now docid(x), which is now documented more completely. See ?groups and ?docid.

So, to get the previous behaviour, you would want to use:

groups = interaction(speaker, week_start)

Here's an example:

library("quanteda")
## Package version: 3.0
## Unicode version: 13.0
## ICU version: 69.1
## Parallel computing: 12 of 12 threads used.
## See https://quanteda.io for tutorials and examples.

corp <- corpus(c(
  "a b c",
  "a c d",
  "c d d",
  "d d e"
),
docvars = data.frame(
  var1 = c("a", "a", "b", "b"),
  var2 = c(1, 2, 1, 1)
)
)
corp %>%
  tokens() %>%
  dfm() %>%
  dfm_group(groups = interaction(var1, var2))
## Document-feature matrix of: 3 documents, 5 features (40.00% sparse) and 2 docvars.
##      features
## docs  a b c d e
##   a.1 1 1 1 0 0
##   b.1 0 0 1 4 1
##   a.2 1 0 1 1 0

Upvotes: 1

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