• You should have a basic knowledge of R, and be familiar with the topics covered in the Introduction to R.

  • It is also recommended you have a recent version of R and RStudio installed.

  • Packages needed:

    • tidyverse
    • tidytext
    • readtext
    • sotu
    • SnowballC
    • widyr
    • igraph
    • ggraph
    • tm

Make sure that you not only install, but also load the packages, to confirm the respective versions get along with your R version.


Feinerer, I., Hornik, K., and Meyer, D. (2008). Text Mining Infrastructure in R. Journal of Statistical Software, 25(5), 1 - 54. doi:

Gries, Stefan Thomas, 2009: Quantitative Corpus Linguistics with R: A Practical Introduction. Routledge.

Silge, J and D. Robinson, 2017: Text Mining with R: A Tidy Approach

Niekler, A. and G. Wiedemann 2020: Text mining in R for the social sciences and digital humanities

Kasper Welbers, Wouter Van Atteveldt & Kenneth Benoit (2017) Text Analysis in R. Communication Methods and Measures, 11:4, 245-265 doi: 10.1080/19312458.2017.1387238

Scott Chamberlain (2019). fulltext: Full Text of ‘Scholarly’ Articles Across Many Data Sources

CRAN Task View: Natural Language Processing