{"Package":"textrecipes","Title":"Extra 'Recipes' for Text Processing","Version":"1.1.0","Authors@R":"c(\nperson(\"Emil\", \"Hvitfeldt\", , \"emil.hvitfeldt@posit.co\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0679-1945\")),\nperson(given = \"Michael W.\", \"Kearney\",\nemail = \"kearneymw@missouri.edu\",\nrole = \"cph\",\ncomment = \"author of count_functions\"),\nperson(given = \"Posit Software, PBC\", role = c(\"cph\", \"fnd\"))\n)","Description":"Converting text to numerical features requires specifically\ncreated procedures, which are implemented as steps according to the\n'recipes' package. These steps allows for tokenization, filtering,\ncounting (tf and tfidf) and feature hashing.","License":"MIT + file LICENSE","URL":"https://github.com/tidymodels/textrecipes,\nhttps://textrecipes.tidymodels.org/","BugReports":"https://github.com/tidymodels/textrecipes/issues","Depends":"R (>= 3.6), recipes (>= 1.2.0)","Imports":"cli, lifecycle, dplyr, generics (>= 0.1.0), magrittr, Matrix,\npurrr, rlang (>= 1.1.0), SnowballC, sparsevctrs (>= 0.3.0),\ntibble, tokenizers, vctrs, glue","Suggests":"covr, data.table, dials (>= 1.2.0), hardhat, janitor, knitr,\nmodeldata, reticulate, rmarkdown, sentencepiece, spacyr,\nstopwords, stringi, testthat (>= 3.0.0), text2vec,\ntokenizers.bpe, udpipe, wordpiece","VignetteBuilder":"knitr","Config/Needs/website":"tidyverse/tidytemplate, reticulate","Config/testthat/edition":"3","Encoding":"UTF-8","LazyData":"true","RoxygenNote":"7.3.2","SystemRequirements":"\"GNU make\"","NeedsCompilation":"yes","Packaged":"2025-03-18 15:37:27 UTC; emilhvitfeldt","Author":"Emil Hvitfeldt [aut, cre] (),\nMichael W. Kearney [cph] (author of count_functions),\nPosit Software, PBC [cph, fnd]","Maintainer":"Emil Hvitfeldt ","Repository":"CRAN","Date/Publication":"2025-03-18 16:10:02 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/textrecipes_1.1.0.tar.gz","jsonAuthors":[{"name":"Emil Hvitfeldt","email":"emil.hvitfeldt@posit.co","maintainer":true},{"name":"Michael W. Kearney","email":"kearneymw@missouri.edu","maintainer":false},{"name":"Posit Software, PBC ","email":null,"maintainer":false}],"readme":"\n\n\n# textrecipes

\n\n\n\n[![R-CMD-check](https://github.com/tidymodels/textrecipes/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidymodels/textrecipes/actions/workflows/R-CMD-check.yaml)\n[![Codecov test\ncoverage](https://codecov.io/gh/tidymodels/textrecipes/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/textrecipes?branch=main)\n[![CRAN\nstatus](http://www.r-pkg.org/badges/version/textrecipes)](https://CRAN.R-project.org/package=textrecipes)\n[![Downloads](http://cranlogs.r-pkg.org/badges/textrecipes)](https://CRAN.R-project.org/package=textrecipes)\n[![Lifecycle:\nmaturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)\n\n\n## Introduction\n\n**textrecipes** contain extra steps for the\n[`recipes`](https://CRAN.R-project.org/package=recipes) package for\npreprocessing text data.\n\n## Installation\n\nYou can install the released version of textrecipes from\n[CRAN](https://CRAN.R-project.org) with:\n\n``` r\ninstall.packages(\"textrecipes\")\n```\n\nInstall the development version from GitHub with:\n\n``` r\n# install.packages(\"pak\")\npak::pak(\"tidymodels/textrecipes\")\n```\n\n## Example\n\nIn the following example we will go through the steps needed, to convert\na character variable to the TF-IDF of its tokenized words after removing\nstopwords, and, limiting ourself to only the 10 most used words. The\npreprocessing will be conducted on the variable `medium` and `artist`.\n\n``` r\nlibrary(recipes)\nlibrary(textrecipes)\nlibrary(modeldata)\n\ndata(\"tate_text\")\n\nokc_rec <- recipe(~ medium + artist, data = tate_text) %>%\n step_tokenize(medium, artist) %>%\n step_stopwords(medium, artist) %>%\n step_tokenfilter(medium, artist, max_tokens = 10) %>%\n step_tfidf(medium, artist)\n\nokc_obj <- okc_rec %>%\n prep()\n\nstr(bake(okc_obj, tate_text))\n#> tibble [4,284 × 20] (S3: tbl_df/tbl/data.frame)\n#> $ tfidf_medium_colour : num [1:4284] 2.31 0 0 0 0 ...\n#> $ tfidf_medium_etching : num [1:4284] 0 0.86 0.86 0.86 0 ...\n#> $ tfidf_medium_gelatin : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_medium_lithograph : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_medium_paint : num [1:4284] 0 0 0 0 2.35 ...\n#> $ tfidf_medium_paper : num [1:4284] 0 0.422 0.422 0.422 0 ...\n#> $ tfidf_medium_photograph : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_medium_print : num [1:4284] 0 0 0 0 0 ...\n#> $ tfidf_medium_screenprint: num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_medium_silver : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_artist_akram : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_artist_beuys : num [1:4284] 0 0 0 0 0 ...\n#> $ tfidf_artist_ferrari : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_artist_john : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_artist_joseph : num [1:4284] 0 0 0 0 0 ...\n#> $ tfidf_artist_león : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_artist_richard : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_artist_schütte : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_artist_thomas : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n#> $ tfidf_artist_zaatari : num [1:4284] 0 0 0 0 0 0 0 0 0 0 ...\n```\n\n## Breaking changes\n\nAs of version 0.4.0, `step_lda()` no longer accepts character variables\nand instead takes tokenlist variables.\n\nthe following recipe\n\n``` r\nrecipe(~text_var, data = data) %>%\n step_lda(text_var)\n```\n\ncan be replaced with the following recipe to achive the same results\n\n``` r\nlda_tokenizer <- function(x) text2vec::word_tokenizer(tolower(x))\nrecipe(~text_var, data = data) %>%\n step_tokenize(text_var,\n custom_token = lda_tokenizer\n ) %>%\n step_lda(text_var)\n```\n\n## Contributing\n\nThis project is released with a [Contributor Code of\nConduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).\nBy contributing to this project, you agree to abide by its terms.\n\n- For questions and discussions about tidymodels packages, modeling, and\n machine learning, please [post on RStudio\n Community](https://forum.posit.co/new-topic?category_id=15&tags=tidymodels,question).\n\n- If you think you have encountered a bug, please [submit an\n issue](https://github.com/tidymodels/textrecipes/issues).\n\n- Either way, learn how to create and share a\n [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html)\n (a minimal, reproducible example), to clearly communicate about your\n code.\n\n- Check out further details on [contributing guidelines for tidymodels\n packages](https://www.tidymodels.org/contribute/) and [how to get\n help](https://www.tidymodels.org/help/).","jobInfo":{"package":"textrecipes","version":"1.1.0","parsingStatus":"success","parserVersion":2,"parsedAt":"2025-03-18T17:29:49+0000"}}

Description

Usage

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