[toddm]

From Hadley's video blurb, the ringer is when he states "things just work" in the R environment vis-à-vis python (where he tactfully yet implicitly acknowledges the shitshow that is the python library/package/environment management).

Kudos to the R community and supporters for providing a great and useful platform!

[martinsmit]

As a relatively new programmer who entered it through statistics, I've still yet to have a better UX or "it just works" moment than using the tidyverse. Years after moving to Julia, Python, and Rust I still go back to R to do any tabular data work. Speed isn't an issue, I always have data.table, and I'm productive in a way that I could only hope to be while doing non tabular data tasks.

RStudio is the perfect IDE. REPL/command-line + Scripts + Plots. I could not be happier using it and I wish I could get VSCode to be half as good. Julia for VSCode is pretty good, but the Python science tooling goes 100% towards notebook environments which I'm not a huge fan of so the Python Science VScode experience is subpar.

[c7b]

R is great, and so are some of the packages that lead to the tidyverse, but I think the latter was a bit too much. Re-inventing what already worked with new packages, always overloading R syntax in weird ways (looking at you, ggplot2). I've actually found myself moving back to base R for many of the more basic manipulation tasks.

[civilized]

Base R is loved only by those who were unlucky enough to spend years using it when there was no alternative.

[c7b]

Base R is far from perfect, but for many basic manipulation tasks it works just as fine as tidyverse. Maybe not with piping, but that doesn't really save anything if you format it readably.

There's something to be said about code that just works out of the box. I don't see the need to maximize dependence on third-party libraries as long as the gains are purely "ergonomic". Especially when the creators have a somewhat mixed record regarding long-term commitment vs re-inventing their own wheel.

The real selling point of R imho aren't the data science tools anyway - for that we already have the amazing Python ecosystem (which also the RStudio guys have tacitly admitted with their rebranding) - but the pure statistics packages. Especially if you need something more niche, to the point that you'd use any language just to get an implementation of a specific model, you'll find yourself coming back to R more than half the time. It's simply the language of choice where most statisticians publish their code.

[civilized]

R has some superior data science tools. For example, the tabular data packages dplyr and data.table have no adequate parallels in the Python world. There are many also-rans but no real rivals.

[_Wintermute]

Or anyone who has tried to re-run R code that was written more 6 months ago.

[civilized]

I write R full-time using the full suite of tidyverse packages and that's just not an issue these days. Maybe a few years ago.

And anyway, you'll hit the same issue using third-party packages in any language.

[lordgroff]

Data.table has got to be the most underrated library ever. If the problem is not big data, but other libraries struggle, fire up data.table and marvel at the sheer speed of it all. On rare tasks when everything was too slow, I'd drop down to c++. Data table essentially made sure that was pretty much never required.

[trts]

My workflow for the past year has been develop/analyze in Rstudio and port to Python when ready to deploy to production. Notebooks and VSCode still feel cumbersome to me and not designed as an analytics-first solution.

The time needed to re-write a script in another language, and often using different packages, seems more than made up for by the ease of use of Rstudio.

[lordgroff]

I honestly don't understand why we all went this route of "port to Python". I mean we do it too (not my choice) but it really makes no sense to me.

[disgruntledphd2]

Because software engineers hate R, and nobody really hates Python that much.

[modriano]

I've found the Jupiter lab IDE to be ideal for my DS and EDA workflows, which typically involves having several notebooks, scripts, and terminal windows open in the IDE. I switched from preferring R to python because having a global shared state across everything in RStudio kept switching the working directory or loading a different file than expected, and I just had very little confidence that things I wrote in R would be reproducible a few years on (which appears to have been a correct concern [0]).

[0] https://datacolada.org/100

[civilized]

You can have multiple RStudio instances open for different projects with different states, and you can use renv to manage package versions reproducibly. None of this is different or worse than python, which is well-known to have its own environment difficulties.

[bscphil]

> acknowledges the shitshow that is the python library/package/environment management

I'm puzzled by this and wonder if you can provide some examples. The scientists I know tend to have incredibly disorganized R code, with a bunch of hard-coded paths and a single global environment in their home directory that all their R packages get installed to. Even stuff that seems critically important like reproducible science can be much harder than you'd expect in a lot of fields because questions like "what version of the libraries did you use" has to be answered (if it can be answered at all) by looking at the references in the paper.

Whereas in Python, I don't know how things could be any simpler. Creating an individualized environment for your project is one command. Installing packages that only live inside that environment is one `pip install` away. Most scientific work is not "distributed" in the sense of having users, but if you do ship a product to users, Python gives you the option of either relying on distribution provided packages (my preferred approach most of the time) or shipping a single binary created with something like PyInstaller.

[Lyngbakr]

I've also seen my fair share of garbage R code and I think Gordon Shotwell's comment that "There really are no production languages – only production engineers" speaks to this.[0] A big problem in the scientific community is that scientists aren't trained to write code like production engineers. I don't see it necessarily as being an issue that is endemic to R, though.

Packrat[1] — an RStudio package — can be used to easily avoid the library versioning issues you describe. The problem isn't that the tooling isn't there or that it isn't easy to use. It's that some folks simply don't use it and are perhaps oblivious as to /why/ they should even use it, anyway.

[0] https://shotwell.ca/posts/2019-12-30-why-i-use-r/ [1] https://rstudio.github.io/packrat/

[Gimpei]

Maybe, but I wonder if it is especially easy to produce horrific code in R. For example, I remember trying to refactor an R codebase that made ample use `load`, leading to all these mysterious variables appearing from nowhere.

[dash2]

packrat is a little old. You want renv, which is the iteration of the same idea and I've found works very simply and nicely.

[tetris11]

Also R really didn't play well with conda for a while. It seems to be ironed out in recent years, but I remember the issues of previous years where trying to set up a reproducible R environment in conda was an unreliable endeavour.

[goosedragons]

I think that has more to do with the fact that most scientists are not trained programmers! Plus a lot of data analysis work doesn't lend itself to the same style of programming IMO.

While there could be more effort in getting things like library versions out there a lot of journals don't care so there's no pressure on scientists to provide it.

[toddm]

Most of the replies to your query have addressed the big issue, which is that "data scientists" are almost universally mediocre (at best) coders. It's endemic to that job position and is guaranteed to be language-invariant.

One factor that isn't helping generations younger than mine (mid-50's) is the continual evolution of tools that remove the user from all the underlying parts. I recently worked with someone who told me they "only know Databricks on Azure" and "don't know python." Their self-assessment was accurate, and the utility of that individual was essentially zero.

The problem with python is that people like myself - non-engineers, and mostly end users of software - spend an inordinate amount of time dealing with mismatched library dependencies, deprecated features, rolling-back python versions to get a working kernel and so on.

The fact that the business model of at least two companies (Enthought and Anaconda) is predicated on the difficulty of getting a functioning python environment to work in this day and age speaks volumes about the problem.

If we can't get past "which pip?," how can we expect the other stuff to "just work?"

[ensan]

If you’re just an end user and a non-engineer, how can you (1) universally judge the level of programming of data scientists to be (2) mediocre?

[lordgroff]

The SCIENTISTS have very disorganized R code and also on my experience even worse Python code where they learn inheritance and make some absolutely head scratching choices. It makes me weep from time to time.

Here's the thing, programming is a skill. If people think it's the "not important thing" only the result (seen this often in some of my previous positions), you're going to get disasters yeah.

As for package management in R you can use either Renv or conda. Been coding R for a decade and have always pinned down packages and you could do so well before tooling made it simple as pie.

[bscphil]

> As for package management in R you can use either Renv or conda. Been coding R for a decade and have always pinned down packages and you could do so well before tooling made it simple as pie.

Right, I get that - but OP was claiming that package management in Python was a "shitshow". It's interesting that a lot of people are responding to my comment by saying "actually you can make package management in R just as easy as in Python, it's just that R programmers tend not to be professionals." Doesn't that just confirm my belief that Python's package management story is actually pretty good?

[bobbruno]

While I tend to agree with most of the arguments that DS code is usually of low quality, and that DS are not well-trained in good development practices, I wonder if making them better coders is an attainable goal, or even a proper one. My reasons for that questioning: - Data science requires a significant stack of knowledge beyond coding - in fact, to be a useful DS in a company, you already have to learn about maths, business domains, keep up with the latest algorithms, know how to manipulate data, present, run experiments, analyse them, know deep statistics and some others I am probably forgetting. Adding the SW dev skills on top of that and expecting them to become good developers is a tall order, and only a small percentage of the DS community will achieve it. With the level of demand for ML, I don’t know if this will deliver on the market needs - it’s not that it’s not attainable, I think it’s not scalable; - People coming from a SW dev background tend to think DS is the same, just done by people who don’t code well. That is not true: code is the final product of software development, while it is but a tool for reaching the goal of finding a good ML approach for a DS. The consequence here is that SW dev has a much stronger reason for wanting good quality, maintainable code than DS does. When researching for a solution, many iterations of code written by DS will be discarded without ever having to go to production, and I don’t know if the overhead of keeping good tests, structuring the code, making small commits, etc., is justifiable in this scenario - the goal is not to have maintainable code, it is to see if the model+features has potential for solving the problem. - Evolution and maintenance are also a problem, because the structure that’s good for operations doesn’t help the job of research - it’s not common for a DS to work in a pipeline structure (which seems to be the emerging pattern for MLOps), and forcing them to use that structure on all iterations after the first will have significant productivity issues, to the point of putting success at risk;

I don’t have a solution for the points above, and I understand that, once a promising approach has been found, the code starts to matter much more, because Ops will require it to be automated and executed in a reliable way. For now, what I do is to do the research in a very loose way, not caring about good SW practices. When I find something good, I start refactoring the code to meet the Ops expectations. But I’m a CS major with decades of experience in coding and ML - it’s not reasonable to expect the entire DS community to develop the same skills, it takes too long.

Any ideas out there?

[werewolf]

I have had similar experience. For me the most annoying was the work they put in making it difficult (nearly impossible) to use with conda environments.

[caseyf7]

Before Hadley, the R environment was also a shitshow. His contributions raised the level of the whole community

[fbdab103]

>...yet implicitly acknowledges the shitshow that is the python library/package/environment management)

(Disclosure, I am a Python programmer who has suffered through the trash packaging situation since forever)

Since when has R been in a position to cast shade on the reproducible environment of another language? Anytime I dip my toe into the R ecosystem, it feels anathema to development practices to find anyone using renv or equivalent to try and vendor dependencies. Enormous pain to try to try and get old R code running again.