# Text Analysis with R

*Last updated: May 12, 2022*

# Prerequisites

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.

## References

Feinerer, I., Hornik, K., and Meyer, D. (2008). Text Mining Infrastructure in R. Journal of Statistical Software, 25(5), 1 - 54. doi: dx.doi.org/10.18637/jss.v025.i05

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