111–120 of 129 posts
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But Rmd already supported python chunks (as well as other languages such as ruby, which seem to be missing from Qmd)
Quarto supports any language and works just fine. I have quarto blog posts for using APL as an example of a somewhat niche language.
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>ported to python are just objectively worse in that language This is absolutely the case. Dplyr syntax is much more intuitive for many use cases than Pandas or Polars equivalents. One thing I miss from RStudio is the Rmarkdown documents with inline outputs. Jupyter notebooks, even in VSCode, are so needlessly over-engineered and under-featured compared to the elegance of RMarkdown. So I am excited to see what Posit…
I don't even know why people need to use dpyler and the tidyverse, in my opinion R is very comfortable for data wrangling and making all kinds of plots out of the box. Its able to handle huge amounts of data as well especially if you adopt a functional programming approach vs object oriented (what I see with a lot of the classic "academic" brittle hardcoded slop that R gets a bad rap for). Very fast if you keep in mi…
It also provides guardrails and encourages best practices which I find a bit to paternalistic and annoying but again I can see the value.
I think most R users would be surprised and just how much tidyverse functionality is hidden in base R but majority of the dplyr versions of functions have at least some intended improvement over the base R versions, and some are a massive improvement in functionality.
For example in a typical script the only tidyverse package I may load besides ggplot2 is tidyr, because the pivot_ wider/longer() functions really do solve a problem that was not fun in base R.
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I don't even know why people need to use dpyler and the tidyverse, in my opinion R is very comfortable for data wrangling and making all kinds of plots out of the box. Its able to handle huge amounts of data as well especially if you adopt a functional programming approach vs object oriented (what I see with a lot of the classic "academic" brittle hardcoded slop that R gets a bad rap for). Very fast if you keep in mi…
The base graphics packages make the plots as ugly as the ones generated by gnuplot though. ggplot2 on the other hand has very pretty output. And the concept of grammar of plots just makes so much sense to me.
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Maybe it is for you, but the success of Dplyr and ggplot suggests a lot of others disagree.
I wonder how much of this is just a feedback loop; were people taught both tools and then chose the one that works best, or was one more heavily promoted than the other, so people went with what was easiest to get started?
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Not to dispute because I have no idea so I'll assume you're correct. But how many metrics did you find and how were they obtained? And how would you know they are representative of all R users?
For whatever it is worth, the TIOBE index lists Python as #1, R at #21. Python is the first language many people are exposed to today. It has a library and tooling for every use case. https://www.tiobe.com/tiobe-index/
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No. R fundamentally has not really improved in the past ~10 years. Do you know much about how R works? Also try: gsub('serious', 'hyped', x)
Maybe because it already does what it intends to do reasonably well? I mean, what do you think needs to be improved?
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For whatever it is worth, the TIOBE index lists Python as #1, R at #21. Python is the first language many people are exposed to today. It has a library and tooling for every use case. https://www.tiobe.com/tiobe-index/
R has a pretty particular use case though, Python use for statistical programming/data analysis would be an apples to apples comparison. People doing a coding 101 course in Python don't really count against the R user base.
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Maybe because it already does what it intends to do reasonably well? I mean, what do you think needs to be improved?
If youre unironically asserting that R already does everything well enough Im not going to take you seriously.
That's a normal follow-up question that you should be able to answer. Otherwise, why are you even commenting?
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Quarto supports any language and works just fine. I have quarto blog posts for using APL as an example of a somewhat niche language.
I guess you have to add some plugin? I mean in Rmarkdown in Rstudio I just go ```{ruby} and I have a ruby block with nothing special installed. That doesn't work by default with Quarto.
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So, (speaking as someone who started with R and now predominantly writes Python), I think there's a bunch of things going on here. 1. R is 100% better for analytics work and statistical modelling. There's just no contest. 2. Python is much, much better for data getting (APIs/scraping etc) and dealing with non table-like data. Again, there's basically no contest here. 3. Software engineers hate R (in most cases), whic…
One of the better comments in this thread, I would only qualify that different levels of ability mediate much of the "how hard is it to make an unmaintainable mess" dimension. Dplyr/tidy code can be pasta, as can pandas, and there is really a whole new level of that given llm generated nonesense edited/tweaked by novices masquerading as seniors. Apropos this idea of a vs code competitor, I wish they would spend more…
Oh definitely, but at least Python's stdlib is relatively consistent, which helps packages be a little more so.
My favourite example is t.test, which is not a t method for the test class, unlike summary.lm which is.
And there's like 4 different styles of function naming in base & stats alone.
Python has problems (for gods sake, why isn't len a method?) but it's a little more consistent.
I used to think that R was responsible for a lot more of the mess than I now do, having seen the same kind of DS code (and I am a DS) written in both Python and R.
And it would be sweet if R had a pytest equivalent, if I never have to write self.assertEqual again, it'll be too soon.