Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
c(
    person("Gabriela", "De Queiroz", , "gabidequeiroz@gmail.com", role = "ctb"),
    person("Colin", "Fay", , "contact@colinfay.me", role = "ctb",
           comment = c(ORCID = "0000-0001-7343-1846")),
    person("Emil", "Hvitfeldt", , "emilhhvitfeldt@gmail.com", role = "ctb"),
    person("Os", "Keyes", , "ironholds@gmail.com", role = "ctb",
           comment = c(ORCID = "0000-0001-5196-609X")),
    person("Kanishka", "Misra", , "kmisra@purdue.edu", role = "ctb"),
    person("Tim", "Mastny", , "tim.mastny@gmail.com", role = "ctb"),
    person("Jeff", "Erickson", , "jeff@erick.so", role = "ctb"),
    person("David", "Robinson", , "admiral.david@gmail.com", role = "aut"),
    person("Julia", "Silge", , "julia.silge@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-3671-836X"))
  )
Using tidy data principles can make many text mining tasks
    easier, more effective, and consistent with tools already in wide use.
    Much of the infrastructure needed for text mining with tidy data
    frames already exists in packages like 'dplyr', 'broom', 'tidyr', and
    'ggplot2'. In this package, we provide functions and supporting data
    sets to allow conversion of text to and from tidy formats, and to
    switch seamlessly between tidy tools and existing text mining
    packages.
cli, dplyr, generics, janeaustenr, lifecycle, Matrix, methods,
purrr (>= 0.1.1), rlang (>= 0.4.10), stringr, tibble,
tokenizers, vctrs
broom, covr, data.table, ggplot2, hunspell, knitr, mallet,
NLP, quanteda, readr, reshape2, rmarkdown, scales, stm,
stopwords, testthat (>= 2.1.0), textdata, tidyr, tm,
topicmodels, vdiffr, wordcloud
Gabriela De Queiroz [ctb],
  Colin Fay [ctb] (<https://orcid.org/0000-0001-7343-1846>),
  Emil Hvitfeldt [ctb],
  Os Keyes [ctb] (<https://orcid.org/0000-0001-5196-609X>),
  Kanishka Misra [ctb],
  Tim Mastny [ctb],
  Jeff Erickson [ctb],
  David Robinson [aut],
  Julia Silge [aut, cre] (<https://orcid.org/0000-0002-3671-836X>)