Introduction to Text Analysis Using R
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Introduction to Text Analysis Using R

A Three-Day tutorial

Kenneth Benoit, Department of Methodology, LSE

Date: Updated for newer versions (> 1.0.0) of quanteda in March 2018 quanteda version: 1.1.0 (CRAN)

This repository contains the workshop materials for a one-day version of a workshop [Introduction to Text Analysis Using R](link here) taught by Kenneth Benoit. This workshop and the materials it contains are funded by by the European Research Council grant ERC-2011-StG 283794-QUANTESS: Quantitative Analysis of Text for Social Science.

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This workshop covers how to perform common text analysis and natural language processing tasks using R. Contrary to a belief popular among some data scientists, when used properly, R is a fast and powerful tool for managing even very large text analysis tasks.

The course comprises six mini "modules", each consisting of guided instruction in the form of presentation of methods followed by student implementation of those methods prior to advancing to the next module. Only after we have completed the practical exercises will we advance to the next stage.

We will cover how to format and input source texts, how to structure their metadata, and how to prepare them for analysis. This includes common tasks such as tokenisation, including constructing ngrams and "skip-grams", removing stopwords, stemming words, and other forms of feature selection. We show how to: get summary statistics from text, search for and analyse keywords and phrases, analyse text for lexical diversity and readability, detect collocations, apply dictionaries, and measure term and document associations using distance measures. Our analysis covers basic text-related data processing in the R base language, but most relies on the quanteda package ( for the quantitative analysis of textual data. We also cover how to pass the structured objects from quanteda into other text analytic packages for doing topic modelling, latent semantic analysis, regression models, and other forms of machine learning.


While it is designed for those who have used R in some form previously, expertise in R is not required, and even those with no previous knowledge of R are welcome.

Reading materials

Designed to be done before the course or after, to augment what is presented during the course. These are just suggestions -- no reading before the course is required.


The course will taught interactively, as a series of "mini-modules" consisting of presentations of different aspects of quantitative text analysis using R, followed by practical exercises.

Module 0: Installation and setup of R and relevant packages

  • CRAN for downloading and installing R
  • GitHub page for the quanteda package
  • Exercise:
    • Make sure you have at least R 3.1.0 installed.
    • Make sure your packages are up-to-date. From the command line, run
      update.packages(ask = FALSE)
    • Install quanteda from CRAN. From the "Packages" pane in RStudio, or from the command line:
    • Install readtext from GitHub, following these instructions.
    • Try "knitting" this RMarkdown file: test_setup.Rmd. If it builds without error and looks like this, then you have successfully configured your system.

Module 1: Overview and demonstration of text analysis using R

Module 2: Basic text data types and functions for text

Module 3: Getting textual data into R

Install the readtext package for this section.

Module 4: Processing and preparing texts for analysis

Module 5: Descriptive analysis

Module 6: Advanced analysis and working with other text packages

Advanced Analysis

Integration with other packages

  • stm. See the convert() function, but also I have an outstanding pull request with the stm package maintainer to work directly on a quanteda dfm-class object.
  • tokenizers. A package for tokenizing text in many different and flexible ways.
  • readtext: Import and handling for plain and formatted text files.
  • spacyr: an R wrapper to the spaCy "industrial strength natural language processing" Python library from
  • tidytext: Text mining using dplyr, ggplot2, and other tidy tools.

Module 7: Tell us about your problems

  • This session is intended for students to describe their own challenges and for the instructors to describe how to solve them. If you have some data you'd like us to work on live, as part of our interactive answers to your problems, you are encouraged to put them somewhere that can be accessed online, so that we will be able to access them in the class. Feel free to file issues at or even fork the repository, make a change, and issue a pull request to correct it!