Always looking to learn more R so my latest project was building a weather app for the terminal. Quite simple app to look at but is more efficient than opening a browser. Had fun making this one.

R programming is something I fall in love with constantly for past 7 years; it’s data handling, statistics, web framework (Shiny) is what I’m enjoying with, if you are a data loving person or an aspiring data scientist you must fall in love with R as well 🤍

Just stop

Hi everyone! As a Data Science PhD student, I’ve been working on a project to bring the best features of Positron directly into RStudio.

I recently launched a new Data Explorer that offers a significantly richer view of your data compared to the standard RStudio Environment tab. It shows an interactive data view, summary statistics for each variable, the percentage of missing values, and distributions.

I’ve also created a context-aware AI that is more accurate, stable, and token-efficient than existing alternatives such as Ellmer and Positron. After a few updates to it over the past few months, people are absolutely loving it!

If you want all the features of Positron and don’t want to switch IDEs, I’d love for you to check this out. Your feedback would be appreciated as I want to keep improving RStudio! More info here.

Will be adding it to my github as soon as I clean up some bugs. If anyone has feedback it would be much appreciated!

This is a genuine curiosity of mine as someone who uses R for the fact it was the first one I became good at extremely quickly after not coding in Python for 2 yrs.

Which programming language is better? Is R better at Python than anything else? Is it that R is used in educational research more?

Inspired by Rust's equivalent https://old.reddit.com/r/rust/comments/1qr5r47/tech_companies_hiring_rust_developers_in_2026

Since https://www.r-users.com pretty much stopped posting new jobs, I thought it's a timely need.

I think it would be bonus points if they're hiring for R and not Python.

• Posit (US)

• Appsilon (PL)

• Cynkra (CH)

• Mirai Solutions (CH)

• Novartis (CH)

• Roche (CH)

• Genentech (US)

• GlaxoSmithKline (UK)

• Cytel (US)

• Johnson & Johnson (US)

• ANZ Bank (NZ)

• Blackrock (US)

• Lloyds of London (UK)

• Two Sigma (US)

• Nordstrom (US)

• Microsoft (US)

• Mozilla (US)

• Foursquare (US)

• Uber (US)

• Airbnb (US)

• NextEra Energy (US)

• Shell (NL/UK)

• Equinor (NO)

• Orsted (DK)

• The World Bank (US)

• International Monetary Fund (US)

• Urban Institute (US)

• HRDAG (US)

• OECD (FR)

• BBC (UK)

• Financial Times (UK/US)

• The New York Times (US)

• The Economist (UK)

• Novo Nordisk (DK)

• Astrazeneca (UK/SE)

• IQVIA (US)

• EU agencies add the European Centre for Disease prevention and control (EU)

I put together a free beginner R course: 15 short video lessons that go from using R as a calculator to data frames, reading a CSV, writing functions, packages, dplyr and the pipe.

What might make it useful vs. the usual videos: each lesson pairs the screencast with the exact code, a downloadable .R script, and a live webR console right on the page — so you can edit and run everything in the browser, nothing to install. All lessons have English subtitles.

The videos are going up on YouTube, and the full course with the interactive consoles is on the site:

- Course: https://rverseanalytics.com/courses/r-foundations.html

- YouTube: https://youtube.com/@rverseanalytics

It's all free and open. Happy to hear feedback on the lessons — and what topics you'd want covered next.

I used to hate coding anything and relied on SQL, Excel, Power BI, Tableau and other software like JASP, jamovi etc. for doing anything with data. I didn't like the way Python dealt with data analysis and it seemed unintuitive.

Then I found R, RStudio and CRAN. That was the turning point. I actually started enjoying writing code and I could handle the whole pipeline myself, from data cleaning, ETL to beautiful plots, .qmd reports, Shiny dashboards. R4DS did more for my statistical thinking than any course I've taken, mostly because the libraries made it so easy to just try things.

However, due to recent requirements (specifically having to work in the quant field), Python has become more of a necessity, while R is used mainly for one-off analysis and limited statistical modelling. The main heavy lifting is done in Python and many of my co-workers also prefer it to R.

I've been able to suck it up a bit and use Claude/ChatGPT to help me code. While I do try to understand what the code is doing, having spent so long learning to code in R and knowing the ease with which it can be done there makes me reluctant to learn Python.

Now, coming to the question: any R users who've pivoted to Python and consider themselves competent in it, how did you learn it having used R before? What would you tell someone like me so I can pick it up quickly and get the benefit of knowing both languages (and also not feel left out when it comes to coding in Python... machine learning and deep learning have a more mature ecosystem there and I don't want to be left out of it if I have to start using them in my current work)?

Thanks!

My background is in Math/Stats fyi

Edit: I don't usually use reddit but damn I actually didnt expect so many helpful tips....many thanks!
Perhaps joining this subreddit is actually helpful afterall :)

Springer is giving free access to 408 scientific books until the end of July (official announcement), including several on statistics and R, such as:

Introductory Statistics with R

Introductory Time Series with R

A Beginner’s Guide to R

Understanding Statistics Using R

Bayesian Essentials with R

A full list can be found here (.xlsx) or in this post.A torrent to download everything (if that's your thing) in this comment.

*Edited to update the xlsx link

This dude tapped me on the shoulder and asked about it. He said it was "super cool". I think it was just my Tomorrow Night theme that was cool, but hey, apparently RStudio is cool guys!

Hi all,

I stumbled across a very beautiful data visualization, however my lack of dataviz vocabulary makes it hard for to explain the terminology for the visualization. The visualization is as attached.

Is there any R package to create such visualization?