Text Mining in R: A Tidy Approach
[This article was first published on Rstats on Julia Silge, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
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.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
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 about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.