Text Mining in R: A Tidy Approach

January 13, 2017
By

(This article was first published on Rstats on Julia Silge, and kindly contributed to R-bloggers)

I spoke on approaching text mining tasks using tidy data principles at rstudio::conf yesterday. I was so happy to have the opportunity to speak and the conference has been a great experience.

If you want to catch up on what has been going on at rstudio::conf, Karl Broman put together a GitHub repo of slides and Sharon Machlis has been live-blogging the conference at Computerworld. A highlight for me was Andrew Flowers’ talk on data journalism and storytelling; I don’t work in data journalism but I think I can apply almost everything he said to how I approach what I do.

To leave a comment for the author, please follow the link and comment on their blog: Rstats on Julia Silge.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)