A practical walkthrough of the key functions in writeAlizer: importing analysis outputs (ReaderBench, Coh‑Metrix, GAMET), and running predictive models with
predict_quality().
The writeAlizer package downloads predictive models for writing quality and written-expression CBM scores and applies these models to your data. More details on model development can be found in the writeAlizer wiki.
writeAlizer accepts the following output files as inputs: 1. ReaderBench: writeAlizer supports output files (.csv format) generated from the Java version of ReaderBench. Source Code Windows Binaries 2. Coh-Metrix: writeAlizer supports output files from Coh-Metrix version 3.0 (.csv format). 3. GAMET: writeAlizer supports output files from GAMET version 1.0 (.csv format).
The writeAlizer scoring models assume that column names in the output
files have been unchanged (exactly the same as generated from the
program). For programs that list file paths in the first column, the
writeAlizer file import functions will parse the file names from the
file paths and store the file names as an identification variable (ID).
import_rb() (ReaderBench) and import_coh()
(Coh-Metrix) keep IDs as character. For ReaderBench
CSVs, the original File.name column is renamed to
ID and stored as character. Numeric IDs are fine too, but
they are not coerced to numeric to avoid losing leading zeros or other
formatting.
writeAlizer is available on CRAN.
To install the development version of writeAlizer:
This minimal example shows how to import a small sample dataset that ships with the package and (optionally) run a model you have available locally.
# Load a small ReaderBench sample shipped with the package
rb_path <- system.file("extdata", "sample_rb.csv", package = "writeAlizer")
rb <- import_rb(rb_path)
head(rb)
# Example: run a ReaderBench predictive model (model artifacts will be downloaded on the first run)
quality <- predict_quality("rb_mod3all", rb)
head(quality)About the examples below: each code snippet loads a
small example CSV that ships with the package using
system.file(...). Replace with your own files when running
analyses on your data. writeAlizer expects tidy CSV outputs
from common text analysis tools. Use the matching import helper for each
format:
# ReaderBench CSV rb_path <- system.file("extdata", "sample_rb.csv", package = "writeAlizer")
rb <- import_rb(rb_path)
# Coh‑Metrix CSV coh_path <- system.file("extdata", "sample_coh.csv", package = "writeAlizer")
coh <- import_coh(coh_path)
# GAMET CSV gam_path <- system.file("extdata", "sample_gamet.csv", package = "writeAlizer")
gam <- import_gamet(gam_path)
# Peek at structure
str(rb)
str(coh)
str(gam)All three imports return a data.frame with an
ID column; predict_quality() relies on that
ID to keep rows aligned in outputs.
Use predict_quality(model, data) to run one of the
built‑in model families:
rb_mod1,
rb_mod2, rb_mod3narr, rb_mod3exp,
rb_mod3per, rb_mod3allcoh_mod1,
coh_mod2, coh_mod3narr,
coh_mod3exp, coh_mod3per,
coh_mod3allgamet_cws1example# ReaderBench -> holistic quality
rb_quality <- predict_quality("rb_mod3all", rb)
head(rb_quality)
# Coh‑Metrix -> holistic quality
coh_quality <- predict_quality("coh_mod3all", coh)
head(coh_quality)
# GAMET -> CWS and CIWS (two prediction columns)
gamet_scores <- predict_quality("gamet_cws1", gam)
head(gamet_scores)Return value. A data.frame with
ID plus one column per sub‑model prediction (prefixed
pred_). When there are multiple numeric prediction columns
(and the model isn’t gamet_cws1), a row‑wise mean column
(e.g., score_mean) is added to summarize overall
quality.
Use this table to pick a model and keep track of published uses. Fill the References column with citations (e.g., “Smith & Lee, 2022; doi:…”) as you go.
| Model key | Data source / import | Target(s) predicted | Output columns (typical) | Notes/References (published uses) |
|---|---|---|---|---|
rb_mod1 |
ReaderBench → import_rb() |
Holistic writing quality | ID, pred_rb_mod1,
score_mean |
(Keller-Margulis et al., 2021; Matta et al., 2022; Mercer & Cannon, 2022) |
rb_mod2 |
ReaderBench → import_rb() |
Holistic writing quality | ID, pred_rb_mod2,
score_mean |
This is a simplified version of rb_mod1 that handles errors on multi-paragraph compositions. (Matta et al., 2023) |
rb_mod3narr |
ReaderBench → import_rb() |
Narrative genre quality | ID, pred_rb_mod3narr,
score_mean |
— |
rb_mod3exp |
ReaderBench → import_rb() |
Expository genre quality | ID, pred_rb_mod3exp,
score_mean |
— |
rb_mod3per |
ReaderBench → import_rb() |
Persuasive genre quality | ID, pred_rb_mod3per,
score_mean |
— |
rb_mod3all |
ReaderBench → import_rb() |
Holistic (all‑genre) quality | ID, pred_rb_mod3all,
score_mean |
*Recommended ReaderBench model for use |
coh_mod1 |
Coh‑Metrix → import_coh() |
Holistic writing quality | ID, pred_coh_mod1,
score_mean |
(Keller-Margulis et al., 2021; Matta et al., 2022) |
coh_mod2 |
Coh‑Metrix → import_coh() |
Holistic writing quality | ID, pred_coh_mod2,
score_mean |
This is a simplified version of coh_mod1 |
coh_mod3narr |
Coh‑Metrix → import_coh() |
Narrative genre quality | ID, pred_coh_mod3narr,
score_mean |
— |
coh_mod3exp |
Coh‑Metrix → import_coh() |
Expository genre quality | ID, pred_coh_mod3exp,
score_mean |
— |
coh_mod3per |
Coh‑Metrix → import_coh() |
Persuasive genre quality | ID, pred_coh_mod3per,
score_mean |
— |
coh_mod3all |
Coh‑Metrix → import_coh() |
Holistic (all‑genre) quality | ID, pred_coh_mod3all,
score_mean |
*Recommended Coh-Metrix model for use |
gamet_cws1 |
GAMET → import_gamet() |
CWS and CIWS | ID, pred_cws, pred_ciws |
(Matta et al., 2025; Mercer et al., 2021) |
example |
Any (demo) | Minimal demo score(s) | ID, pred_example,
score_mean? |
Offline, CRAN‑safe mock; seeded via
wa_seed_example_models("example") |
The package downloads and caches model artifacts the first time you use a model.
# See where model artifacts are cached
writeAlizer::wa_cache_dir()
# Clear cache if needed
writeAlizer::wa_cache_clear()