Introduction When I was in grad school at Emory, I had a favorite desk in the library. The desk wasn’t particularly cozy or private, but what it lacked in comfort it made up for in real estate. My books and I needed room to operate. Students of the ancient world require many tools, and when jumping between commentaries, lexicons, and interlinears, additional clutter is additional “friction”, i.e., lapses in thought due to frustration.

One of the greatest assets human beings possess is the power of speech and language, from which almost all our other accomplishments flow. To be able to analyse communication offers us a chance to gain a greater understanding of one another.

On 21-22 April, the London School of Economics hosted the Text Analysis Package Developers’ Workshop , a two-day event held in London that brought together developers of R packages for working with text and text-related data.

The R package ecosystem for natural language processing has been flourishing in recent days. R packages for text analysis have usually been based on the classes provided by the NLP or tm packages. Many of them depend on Java. But recently there have been a number of new packages for text analysis in R, most notably text2vec, quanteda, and tidytext.