Fast Calculation of Readability Measures — calculate

Compute readability measures in parallel using backends from the parallel package. By default, computation is parallelized with PSOCK clusters (parallel::clusterApplyLB()) for cross-platform stability. Optionally, FORK-based parallelism (parallel::mclapply()) may be requested on Linux/macOS.

Usage

calculate_readability(
  x,
  ncores = 1,
  nchunks = ncores,
  socket = c("PSOCK", "FORK"),
  ...
)

Arguments

x: A quanteda corpus or a character vector containing the documents to process.
ncores: Integer. Number of CPU cores to use for parallel processing. Defaults to 1 (sequential).
nchunks: Integer. Number of chunks to split the corpus into. Defaults to ncores. Setting nchunks > ncores can improve load balancing when documents vary in size. See Details.
socket: Character. Parallel backend to use. One of "PSOCK" (default, recommended) or "FORK". On Windows, "FORK" is not supported and will trigger an error.
...: Additional arguments passed to textstat_readability.

Value

A data.table with as many columns as passed to measure and doc_id as the document identifier.

Author

Francesco Grossetti francesco.grossetti@unibocconi.it

Examples

texts <- c(
  doc1 = "This is a short and very simple document.",
  doc2 = "This second document contains slightly longer sentences for illustration."
)

calculate_readability(
  texts,
  measure = c("Flesch", "FOG")
)
#> 
#> ── Calculating readability ──
#> 
#> ℹ quanteda.textstats::textstat_readability() has been called with the following parameters
#> ℹ measure = Flesch, FOG
#> ℹ Computing readability sequentially
#> ✔ Done
#>    doc_id Flesch      FOG
#>    <char>  <num>    <num>
#> 1:   doc1 71.815  8.20000
#> 2:   doc2  9.700 16.93333