Package - cran/quanteda

quanteda: quantitative analysis of textual data

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An R package for managing and analyzing text, created by Kenneth Benoit in collaboration with a team of core contributors: Kohei Watanabe, Paul Nulty, Adam Obeng, Haiyan Wang, Ben Lauderdale, and Will Lowe. Supported by the European Research Council grant ERC-2011-StG 283794-QUANTESS.

For more details, see

How to cite the package:

Benoit K (2017). _quanteda: Quantitative Analysis of Textual
Data_. doi: 10.5281/zenodo.1004683 (URL:, R package version 0.99.22,

A BibTeX entry for LaTeX users is

    title = {quanteda: Quantitative Analysis of Textual Data},
    author = {Kenneth Benoit},
    year = {2017},
    doi = {10.5281/zenodo.1004683},
    url = {},
    note = {R package version 0.99.22},

How to Install

  1. From CRAN: Use your GUI's R package installer, or execute:

  2. From GitHub, using:

    # devtools packaged required to install quanteda from Github 

    Because this compiles some C++ source code, you will need a compiler installed. If you are using a Windows platform, this means you will need also to install the Rtools software available from CRAN. If you are using macOS, you will need to to install XCode, available for free from the App Store, or if you prefer a lighter footprint set of tools, just the Xcode command line tools, using the command xcode-select --install from the Terminal.

    Also, you might need to upgrade your compiler. @kbenoit found that his macOS build only worked reliably after upgrading the default Xcode compiler to clang4, following these instructions.

  3. Additional recommended packages:

    The following packages work well with or extend quanteda and we recommend that you also install them:

    • readtext: An easy way to read text data into R, from almost any input format.

    • spacyr: NLP using the spaCy library, including part-of-speech tagging, entity recognition, and dependency parsing.

    • quantedaData: Additional textual data for use with quanteda.

    • LIWCalike: An R implementation of the Linguistic Inquiry and Word Count approach to text analysis.


Leaving feedback

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Contributions in the form of feedback, comments, code, and bug reports are most welcome. How to contribute:


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