The End To End Data Science With R is a new book by Rene Essomba. The book, as the name implies, focuses on the core data science applications using R ❤️. This book covers the following topics:
✅ Exploratory data analysis
✅ Data visualization
✅ Supervised learning
✅ Unsupervised learning
✅ Time series
✅ Natural language processing
✅ Image classification
🚀 Elevate Your R Programming Skills: Removing Elements from Vectors
Want to level up your R programming game? Let's talk about removing specific elements from vectors! It's a fundamental skill.
But here's the real fun: try it yourself! Experiment with your own data and see which method resonates with you. To get yourself familiar with what's happening, you have to experiment.
Check out another fireside chat hosted by Audrey Yeo, featuring Heather Turner and Abhishek Ulayil. This week's chat is on building foundations for R’s future as an accessible and diverse collaboration.
Heather is an R Foundation member with a strong track record promoting diversity in contributions to R, while Abhishek has recently converted the R Journal content to more accessible web content, and keynote speaker at useR! 2024.
You need to address the accessibility of the UserR! Conferences. I've worked on accessibility with R FORWARDS for five years & was part of the groundbreaking organization of UseR 2021. This year I volunteered to help with accessibility. I reviewed the website & made recommendations about both the website & overall event, including choosing an accessible hybrid option. My work was not acknowledged at all. This is both systemic and interpersonal discrimination. Have a lovely conference.
@anze3db@jimbob@adamhsparks@TimTeaFan@smach Thanks heaps! I have also updated the search engine indexing - that explains why I've never seen anything show up in google. I just assumed it was a blanket thing for mastodon 🤦
The newest version of my #R#package TidyDensity really took off for me. Now wait until the next release which introduces 39 new functions. #R#RStats#RProgramming
A new release of #rstats broom is on CRAN! v1.0.6 includes several changes to well-used tidiers from the package, e.g. for lm(), gam(), and survfit() output.
If you work with text data in R, the gregexpr() function is essential for pattern matching. It finds all occurrences of a pattern within a string. Key parameters include pattern, text, ignore.case, perl, fixed, and useBytes. You can match characters, ignore case, use advanced regex, and search fixed strings.
Next Tuesday I'll be part of a Fireside Chat alongside #useR2024 keynote Abhishek Ulayil on the topic of "Building foundations for R’s future as an accessible and diverse collaboration".