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!
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.
> 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.
>...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.
I first thought this was an announcement about how R now uses Posits instead of IEEE-754 floats… I wonder if the rebrand will cause any confusion for either party down the line.
The rebrand makes a lot of sense, as the interest and support for Python in the DS/ML community keeps growing. I prefer R for data exploration and visualization, but knowing and leveraging both languages seems to be the way forward. Shiny for Python is a very interesting development.
Kudos to RStudio (Posit) for delivering great product over the last decade+ and growing a kind, helpful community!
I'd be shocked if they drop R but I'd dance a jig if they can have the Python experience be as good in their IDE. I despise notebooks (i could write an essay), and developing with vs code is still very very clunky in comparison.
Have used Rstudio IDE extensively for past 3 years but recently switched to VScode.
VScode feels more refreshing as compared to RStudio. I love the extensions within VScode that allows it more flexibility as compared to RStudio. Also ability to view hex code as colors in the editor itself. Plus the ability to sync settings using GitHub is so convenient when using multiple computers. On the flip side, Rstudio is more convenient for beginners and being very R focused helps to focus on the "Statistics and data munging".
As for the Rstudio as a company, they have supported Python in the past but with the Quarto they went to extend beyond that. I feel Quarto is still work in progress and has more ambitious outlook as compared to RMarkdown. RStudio cloud is a good option when one have to use specific version of R and alleviates the "Reproducibility" issue to some extent. Especially, when someone does not want to deal with Docker or similar platform. I think RStudio cloud is one of my favorite offering from the company.
I'm currently rebuilding my personal website with it and I'm really impressed. As with most RStudio products I've encountered over the years, I find it is intuitive, well documented, and quite powerful. I also think RStudio has played an important role in making the R community quite pleasant and inclusive.
Is it? I liked Rmarkdown until I discovered org-mode and org-babel. It does a lot of stuff better like the option to tangle chunks into multiple files which is killer (last time I looked Rmarkdown still lacked that, not sure about Quarto) and the ability to do stuff like make a table in my text and then USE it in R for calculations is amazing for making examples or grabbing some random HTML table off a site and doing something.
I love Quarto! It's so much more pleasant than writing LaTeX, but you still get professional-looking documents with Python graphs that update themselves!
Yes it is, I’ve looked at various options to publish jupyter notebooks, finally found Quarto, and it’s a full publishing platform with surprisingly decent UX and easy customizability.
I used RStudio in my university stats course, along with the strong recommendation by my professor to get the book "OpenIntro Statistics" (3rd edition back then).
[1]
I really couldn't care less about statistics, which like with many other topics/courses made/makes it incredibly hard for me to concentrate on and actually learn something about it.
I could force the knowledge into my brain to be able to recite and use it in practice over and over again, but the moment the exams come around it's all gone from my head.
That certainly made university very problematic.
[1] Edit to add:
I forgot to say that using RStudio was the only remotely pleasant part of that Stats course and in later courses where some stats work was needed.
I've been a huge fan of the RStudio IDE for its Matlab-like look and feel and its support for R. I hope it continues to improve and continue to be a helpful tool for the community.
RStudio-2022.07.2-576 cannot start without R installed by the look of it:
Error reading R script (), system error 2 (No such file or directory); Unable to find libR.dylib in expected locationswithin R Home directory /Library/Frameworks/R.framework/Resources
toddm|3 years ago
Kudos to the R community and supporters for providing a great and useful platform!
martinsmit|3 years ago
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.
bscphil|3 years ago
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.
caseyf7|3 years ago
fbdab103|3 years ago
(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.
sakras|3 years ago
layer8|3 years ago
kilbuz|3 years ago
Kudos to RStudio (Posit) for delivering great product over the last decade+ and growing a kind, helpful community!
rossdavidh|3 years ago
kingo55|3 years ago
lordgroff|3 years ago
forgotpwd16|3 years ago
kgwgk|3 years ago
Some RStudio products change their name - the “enterprise” offering.
Another RStudio product doest’t - the open-source IDE.
kasperset|3 years ago
VScode feels more refreshing as compared to RStudio. I love the extensions within VScode that allows it more flexibility as compared to RStudio. Also ability to view hex code as colors in the editor itself. Plus the ability to sync settings using GitHub is so convenient when using multiple computers. On the flip side, Rstudio is more convenient for beginners and being very R focused helps to focus on the "Statistics and data munging".
As for the Rstudio as a company, they have supported Python in the past but with the Quarto they went to extend beyond that. I feel Quarto is still work in progress and has more ambitious outlook as compared to RMarkdown. RStudio cloud is a good option when one have to use specific version of R and alleviates the "Reproducibility" issue to some extent. Especially, when someone does not want to deal with Docker or similar platform. I think RStudio cloud is one of my favorite offering from the company.
nojito|3 years ago
Lyngbakr|3 years ago
goosedragons|3 years ago
shepherdjerred|3 years ago
ansgri|3 years ago
mi_lk|3 years ago
Lev1a|3 years ago
[1]
I really couldn't care less about statistics, which like with many other topics/courses made/makes it incredibly hard for me to concentrate on and actually learn something about it. I could force the knowledge into my brain to be able to recite and use it in practice over and over again, but the moment the exams come around it's all gone from my head. That certainly made university very problematic.
[1] Edit to add: I forgot to say that using RStudio was the only remotely pleasant part of that Stats course and in later courses where some stats work was needed.
ipsum2|3 years ago
scottmcdot|3 years ago
d_sem|3 years ago
kelsolaar|3 years ago
Error reading R script (), system error 2 (No such file or directory); Unable to find libR.dylib in expected locationswithin R Home directory /Library/Frameworks/R.framework/Resources
civilized|3 years ago
wodenokoto|3 years ago
https://posit.co/download/rstudio-desktop/
pmarreck|3 years ago
https://en.wikipedia.org/wiki/Unum_(number_format)#Unum_III
chrisgd|3 years ago
gtsnexp|3 years ago
c7b|3 years ago
rsrsrs86|3 years ago