Rtist apparel combine compact and readable R-code, and an aesthetic it creates. I crafted the artpieces with base R, while avoiding responsibilities during my first PhD year.

I though people in this group might like the concept and give useful feedback! Rtist currently delivers to EU countries.

https://shoprtist.com/

There are currently eight R-related subreddits that I know of:

• /r/Rlanguage

• /r/rstats

• /r/RStudio

• /r/rprogramming

• /r/R_Programming

• /r/RStatsProgram

• /r/rshiny

• /r/learnR

This is ridiculous. And there is no real difference in their topics: Stats related questions get asked in r/Rlanguage, programming questions get asked in r/Rstudio and questions about RStudio get asked in r/rstats.

Here is why I think we should unify:

• The current situation leads to a fragmentation of the community.

• Questions could receive more and maybe better help.

• One subreddit would make it easier to follow the news.

• The combined traffic (posts and comments per day) is low even for one subreddit alone.

Let's discuss how we could improve the situation.

edit: Oh no, by crossposting I accidentally created three separate discussions...

Anyone else think that R should change name to something else and contain more letters? Finding relevant jobs would be easier and also when searching online.

I'm currently looking for R specific jobs and I get so much nonsense when typing in "R"

It might be an exaggeration, not trying to downplay others’ contributions. Just my feeling about it as a beginner.

Hello, World!

A short post to recommend Harvard’s new offer on R: CS50R. The course is a standalone offshoot of CS50 which, for those unfamiliar, is pretty much the gold standard introduction to programming MOOC.

Lectures

The course is free, comprehensive, structured and well-produced. At its core are seven lectures (each around 1.5h). The lectures span representing, transforming, tidying, and visualising data through to testing and packaging programs. Lectures are supplemented by notes, downloadable source code, and ‘shorts’ - 5m videos explaining standalone topics in a little more detail. To get a sense of the tone, pace, production quality, etc., watch the first five minutes of lecture one HERE.

Assignments

The course also sets ~15 graded assignments. Some can be completed in a few hours and some over the course of several days. The assignments are completed using a browser-based version of RStudio and tested with preinstalled functions. Assignments often require multiple steps and are described as "challenging but doable". ’On Time’ for example has participants working with public transport data from Boston to calculate service punctuality. 

Final Project

For the course’s final project, participants are tasked to develop a substantial package on a subject that interests them. I wrote a package that extracts all written evidence from Parliamentary inquiries, exporting it to a CSV file of raw text for further analysis. Participants are encouraged to upload a short walkthrough of their code to YouTube - mine can be found HERE (feedback welcome!)*

Audience

The course is designed as an introduction to R and/or those new (or newish) to programming in general. I had programmed a bit in the past (though never professionally) but was entirely new to R and keen to pick up the language due to a new, fairly data-heavy role. It brought me up to speed quickly (it certainly feels different to other languages I’ve used in the past!) but I’m confident it would be a superb introduction to programming for newcomers, or equally a helpful primer to those fairly comfortable with the core concepts. Like others in the CS50 family, the course has an active online community (including on Reddit).

TL;DR

CS50R: a superb introduction to R and programming in general. Many thanks to the course organisers - u/davidjmalan, u/carterzenke, and colleagues - for such a such a fantastic course on an important language.

Anyone else taken the course or its predecessors?

*Aside: My code is available on GitHub but I'd be keen to publish it more formally (perhaps on the CRAN?). I think there is a niche audience for it (political / Parliamentary researchers and those working in scrutiny) but I'm sure as a one-man newcomer to R, there will be some semi-questionable code in there!

I swore I would never again use R after my university course was over. I vowed the alphabet could even lose the letter. But here I am again. Pushing that it be used above the company's standard.

It just works, man. I mean, it's preceded by torturous hours of pre-processing, processing, crashing the program, setting the computer on fire, trying VS code, switching back to RStudio beacuase rmd is the goat, processing again,...

But after all that, it just works, man.

It's open-source, cross-platform, AND THE LIBRARIES AAAAA. Imagine having thousands upon thousands of (OPEN SOURCE) tools readily available, accessibly through one line of code run in the console.

Yeah, love R (R is not holding me at gunpoint, I swear).

I learnt Python first and consider myself somewhat proficient in statistics, ML, and deep learning frameworks.

I've started learning R a while ago and since then every problem that was presented to me I preferred Python always.

Are there any problems or scenarios that R performs better than Python? Across all measures ofc, specially development time.

After years of R programming, I've noticed most intermediate users get stuck writing code that works but isn't optimal. We learn the basics, get comfortable, but miss the workflow improvements that make the biggest difference.

I just wrote up the handful of changes that transformed my R experience - things like:

• Why DuckDB (and data.table) can handle datasets larger than your RAM

• How renv solves reproducibility issues

• When vectorization actually matters (and when it doesn't)

• The native pipe |> vs %>% debate

These aren't advanced techniques - they're small workflow improvements that compound over time. The kind of stuff I wish someone had told me sooner.

Read the full article here.

What workflow changes made the biggest difference for you?

I know this is an R sub but I thought I'd share here. I've been writing primarily R code for nearly 20 years but recently needed to get back into Python for several maintenance and development projects. I put together a set of resources for getting up to speed in Python as an experienced R developer.

https://blog.stephenturner.us/p/python-for-r-users

something I’ve noticed after using R for a while is that it kind of changes the way you think about data. when I started programming, I mostly used languages where the mindset was that “write loops, build logic, process things step by step.” but with R, especially once you get comfortable with things like dplyr and pipes, the mindset becomes more like :- "describe what you want the data to become.”

Instead of:-

- iterate through rows

- manually track variables

- build a lot of control flow

you just write something like:

data %>%
  filter(score > 80) %>%
  group_by(class) %>%
  summarize(avg = mean(score))

and suddenly the code reads almost like a sentence.iIt feels less like programming and more like having a conversation with your dataset. but the weird part is that when i go back to other languages after using R for a while, my brain still tries to think in that same pipeline style. im curious if others experienced this too.

did learning R actually change the way you approach data problems or programming in general, or is it just me? also im curious about what was the moment where R suddenly clicked for you?

I've been programming in R for years. I love R. I prefer R. And I'll probably be using R until the day I die.

That doesn't mean I dislike other programming languages. Quite the opposite. I think they're all fascinating in their own ways, and I appreciate that different people have different preferences, backgrounds, and approaches to solving problems.

What I don't understand is why programming language preferences sometimes inspire such bizarre tribalism.

Imagine telling someone you speak Arabic, only for them to interrupt and explain why you should really be speaking Chinese instead. That Chinese is more useful, more widely spoken, and that you've wasted your time learning Arabic.

It sounds absurd, doesn't it?

Yet somehow, when it comes to programming languages, this kind of unsolicited criticism is practically a pastime.

I've spent years learning R, developing my own approaches, and becoming comfortable expressing mathematical and statistical ideas through it. I genuinely enjoy the language. Sometimes I write code just because I find the problem interesting, not because I'm trying to accomplish anything particularly practical.

And I think that's perfectly fine.

Programming languages are tools, but they're also intellectual pursuits. People develop fluency, creativity, and even a certain affection for them.

I don't need my favorite language to be the best. I don't need anyone else's language to be worse.

I just want to enjoy writing code without being told I should be enjoying something else.

Does anyone else feel like programming language debates sometimes lose sight of why we started programming in the first place?

Is my university putting me at a disadvantage to those who have experience doing data science in Python? Looking at job applications, it seems python seems to be the more common language. I am worried R will die off.

I will go first:

Rayrender