5 ms·

As someone who learned most of my initial coding abilities through R and RStudio in a data science context, and since moved on to more “standard” languages and

by rrjjww 3y ago

As someone who learned most of my initial coding abilities through R and RStudio in a data science context, and since moved on to more “standard” languages and IDEs, I’ve yet to find anything that comes close to the flexibility and integration of RStudio for hacking together data analytics. VS Code/Python has made some major improvements in the past couple years but it’s still very clunky compared to the ease of running R code line by line without having to start up a debug instance. And now with copilot the most frustrating parts of R (such as remembering all the Tidyverse syntax) have been abstracted away.

qudat 3y ago
My partner does a lot of biostats in RStudio and I really think it breds terrible habits. Instead of categorizing code by files, everything is shoved into massive files. Instead of running a file top-to-bottom, code is run out-of-order which makes the code organization and flow of a program a complete disaster. There is something to be said about running and processing large CSVs and keeping that in memory while running other parts of the program as well as having clickable access to all the dataframes loaded into memory.
- mjhay 3y ago
  There's nothing about RStudio that encourages big single files or writing huge unstructured scripts. RStudio is a pretty good IDE, and R is a highly expressive functional-first [0] language. R was heavily influenced by Scheme, and has its own powerful metaprogramming [1] system - which is used to great effect in Tidyverse[2] libraries to make APIs that are nicer and convenient than anything reasonably practical in Python. The problem with a lot of end-user R code is that it is written by statisticians, not programmers. They'd write the same garbage and huge scripts in Python (trust me, I know). [0] http://adv-r.had.co.nz/Functional-programming.html http://adv-r.had.co.nz/Functional-programming.html [1] https://adv-r.hadley.nz/metaprogramming.html https://adv-r.hadley.nz/metaprogramming.html [2] https://www.tidyverse.org/ https://www.tidyverse.org/
  - cameronh90 3y ago
    I agree that RStudio isn't too awful, but the packaging management and reproducibility situation in R is dire, even compared to Python. I have to deal with getting code from data scientists into production, and simply getting it to run outside of their mutant local environment can take days. Things are starting to get a bit better with packrat initially and now renv/pak/rig and the like, but most DS haven't heard of them, and major breakages between minor library versions are still commonplace, as are undocumeted system library dependencies. Then there is the whole stringsAsFactors nightmare, thankfully slowly on its way out but still around causing occasional catastrophic breakage. There are lots of nice things about R, but it makes it very easy to shoot yourself in the foot.
    - mjhay 3y ago
      Yeah, the package management situation is a big weak spot. There are some issues with renv, but it is usable. It definitely helps to keep a lid on the number of dependencies, and for God's sake never pull anything in from Bioconductor. IMO, new code should always prefer Tidyverse libs for basic stuff, and avoid relying on the ancient and warty standard library. All that said, I still greatly prefer it over Python for DS work.
    - levocardia 3y ago
      >I agree that RStudio isn't too awful, but the packaging management and reproducibility situation in R is dire, even compared to Python. I've had exactly the opposite experience. For R, I download R and install it, and download Rstudio and install it. Then when I need a new package I just install.packages("coolnewpackage") and it just works (TM). Occasionally I get info messages about packages being built in newer versions of R, and once a year or so I eventually get around to looking up how to use the updateR() function, but in five years of doing biostats in R I can't remember a single time I had a dependency issue. Python, on the other hand, is a nightmare. Conda makes life a lot easier, but it is not easy to learn if you are not a software engineer (remember, R was made not just by statisticians, but for them as well). For many projects, my Python flow was something like... Try creating a new conda env with the packages I think I need. Try starting the project, oops I don't have spyder-kernels installed. Oh, and my environment isn't compatible with it. How about just running it in VScode? Well now I don't have my variable explorer. How about Jupyter? How do I get Jupyter to find my conda env again? Oh wait I need this other library it's only on conda-forge, and then the conda environment solver fails. I guess I'll start from scratch with a new conda env, and maybe after several trial-and-error sessions of carefully composing the correct "conda create -n ..." incantation in a text editor before copy-pasting them to the command line, I might get the environment I need up and running, after conda finishes its 10-minute compatibility search and downloads 80 GB of python libraries. And using conda is the easy way of doing it! Don't even get me started on pip and venv...
- cjk2 3y ago
  This is the defacto standard way of operating it I understand, which is mostly just hacking at stuff in small chunks until it sort of works and leaving comments throughout it with "run this bit on Tuesdays only". I recently had to inherit someone's R stuff and I had to learn R and fix it all. It now runs from a makefile repeatably. Anyway it could be worse. It could be Minitab.
- ellisv 3y ago
  > Instead of categorizing code by files, everything is shoved into massive files. That's not really RStudio's fault. It is just how many people use R and were taught. > code is run out-of-order which makes the code organization and flow of a program a complete disaster. In my experience, with R Markdown, this is untrue. I see Jupyter Notebooks with cells run out of order much more often.
  - madcaptenor 3y ago
    I have done a lot in R Markdown, and the project I'm currently working on has me mostly working in Databricks notebooks (which are very similar to Jupyter notebooks). My execution gets out of order a lot more often in Databricks.
- bachmeier 3y ago
  > Instead of running a file top-to-bottom, code is run out-of-order which makes the code organization and flow of a program a complete disaster. That's more a REPL issue than specific to a particular language. It's the tradeoff you make. I write my R programs in Geany and then run the whole thing using Rscript. That gives me a clean environment on every run.
goosedragons 3y ago
Emacs + ESS? Way more flexible. Maybe less integration because many of the big R package devs work for Posit. RStudio has a lot of superfluous junk in the UI I just don't need or care about.
- kqr 3y ago
  I've used ESS for the past few years and recently tried using RStudio when I'm on Windows. For my purposes, which is just a little industrial statistics on the side, they are remarkably similar. I feel right at home in either!
lylejantzi3rd 3y ago
> I’ve yet to find anything that comes close to the flexibility and integration of RStudio for hacking together data analytics. Is there a good demo or video you can point to that shows this? I have no experience with R, RStudio, or data science, but you've piqued my interest.
- ellisv 3y ago
  Any of David Robinson's (or anyone else's) Tidy Tuesday videos. https://www.youtube.com/@safe4democracy/featured https://www.youtube.com/@safe4democracy/featured
Kalanos 3y ago
jupyter
silveraxe93 3y ago
This works out of the box in VSCode? Just open a .py file, then select the snippet of code you want to run and cmd+enter It will open a new REPL for you (using your selected interpreter) the first time, and after that all commands are run in that same one.
- wodenokoto 3y ago
  RStudio is just way better at choosing what code to send (if you only send the line the cursor rests on you’re gonna have a bad time. VSCode is a bit better than that but not great. Also, where does your plots get drawn when you use this? RStudio just works in this regards)
ubiquitination 3y ago
I agree - I teach statistics at a University and there is really no alternative to Rstudio for working with R. This is especially true considering that the vast majority of folk using R (in my field) have no prior programming experience. Downloading R, Vscode, downloading some R plugin, getting them to talk to each other, and only then starting to learn R - isn't very straightforward. It's also remarkably consistent on different operating systems - something to consider when half the students are on windows, half on macos...
- bachmeier 3y ago
  RStudio Server on a Digital Ocean instance made my life a lot easier. Students fire up a browser, log in, and they're using R with all the packages. It was horrible when students ran R on their own machines back in the old days. Most of the questions I got were tech support rather than related to the material. And these days it has good Python support too.
dcreater 3y ago
Jupiter (ipynb) notebooks in vs code.
jurimasa 3y ago
If you work with Python, Spyder comes really, really close and is way better than jupyter
RobinL 3y ago
It looks like, as far as I can tell, VS Code doesn't support the interactive window for working in R, which was a bit of a surprise to me when i looked it up. The python interactive window has pretty much fully replaced my use of jupyter, since it gives you notebook-style output without the annoyance of the notebook format. My usual workflow is highlighting lines of code and shift-enter to execute (there's also a cells syntax). I'm surprised by this because it _is_ possible to use R in Jupyter (although I never really liked the experience, R Studio was far superior).
- yabbs 3y ago
  ? Yes it does.
  - aiisjustanif 3y ago
    Please supply references for the audience.
  - RobinL 3y ago
    I'm specifically referring to: https://code.visualstudio.com/docs/python/jupyter-support-py https://code.visualstudio.com/docs/python/jupyter-support-py The support for R looks a bit different (to me at least?): https://code.visualstudio.com/docs/languages/r https://code.visualstudio.com/docs/languages/r In the screenshot the window on the right does not look comparable to the output in a jupyter notebook. It looks more like a standard terminal. e.g. does it support interactive charts, html tables etc? The Python interactive window uses the ipykernel package to allow rich outputs like that. I still might be wrong and would like to be corrected on this, since it would mean R support in VS Code is now better than I thought (I haven't tried it fora. while)
    - ZunarJ5 3y ago
      I use r in a Jupyter Notebook in VS via IRKernel. It's a gem.
      - 3y ago
jakupovic 3y ago
cat, grep, sort and awk come pretty close :)
dcchuck 3y ago
Came here to share that same experience. RStudio truly made me feel "close" to the data.
ivansavz 3y ago
An alternative in the Python world that is definitely worth looking into is the JupyterLab Desktop app, which is a standalone installer that is cross-platform and works great for beginners (no command line needed): https://github.com/jupyterlab/jupyterlab-desktop?tab=readme-ov-file#jupyterlab-desktop https://github.com/jupyterlab/jupyterlab-desktop?tab=readme-... See my other comment in the main thread with more info.

by rrjjww 3y ago

qudat 3y ago

My partner does a lot of biostats in RStudio and I really think it breds terrible habits. Instead of categorizing code by files, everything is shoved into massive files. Instead of running a file top-to-bottom, code is run out-of-order which makes the code organization and flow of a program a complete disaster. There is something to be said about running and processing large CSVs and keeping that in memory while running other parts of the program as well as having clickable access to all the dataframes loaded into memory.

mjhay 3y ago
There's nothing about RStudio that encourages big single files or writing huge unstructured scripts. RStudio is a pretty good IDE, and R is a highly expressive functional-first [0] language. R was heavily influenced by Scheme, and has its own powerful metaprogramming [1] system - which is used to great effect in Tidyverse[2] libraries to make APIs that are nicer and convenient than anything reasonably practical in Python. The problem with a lot of end-user R code is that it is written by statisticians, not programmers. They'd write the same garbage and huge scripts in Python (trust me, I know). [0] http://adv-r.had.co.nz/Functional-programming.html http://adv-r.had.co.nz/Functional-programming.html [1] https://adv-r.hadley.nz/metaprogramming.html https://adv-r.hadley.nz/metaprogramming.html [2] https://www.tidyverse.org/ https://www.tidyverse.org/
- cameronh90 3y ago
  I agree that RStudio isn't too awful, but the packaging management and reproducibility situation in R is dire, even compared to Python. I have to deal with getting code from data scientists into production, and simply getting it to run outside of their mutant local environment can take days. Things are starting to get a bit better with packrat initially and now renv/pak/rig and the like, but most DS haven't heard of them, and major breakages between minor library versions are still commonplace, as are undocumeted system library dependencies. Then there is the whole stringsAsFactors nightmare, thankfully slowly on its way out but still around causing occasional catastrophic breakage. There are lots of nice things about R, but it makes it very easy to shoot yourself in the foot.
  - mjhay 3y ago
    Yeah, the package management situation is a big weak spot. There are some issues with renv, but it is usable. It definitely helps to keep a lid on the number of dependencies, and for God's sake never pull anything in from Bioconductor. IMO, new code should always prefer Tidyverse libs for basic stuff, and avoid relying on the ancient and warty standard library. All that said, I still greatly prefer it over Python for DS work.
  - levocardia 3y ago
    >I agree that RStudio isn't too awful, but the packaging management and reproducibility situation in R is dire, even compared to Python. I've had exactly the opposite experience. For R, I download R and install it, and download Rstudio and install it. Then when I need a new package I just install.packages("coolnewpackage") and it just works (TM). Occasionally I get info messages about packages being built in newer versions of R, and once a year or so I eventually get around to looking up how to use the updateR() function, but in five years of doing biostats in R I can't remember a single time I had a dependency issue. Python, on the other hand, is a nightmare. Conda makes life a lot easier, but it is not easy to learn if you are not a software engineer (remember, R was made not just by statisticians, but for them as well). For many projects, my Python flow was something like... Try creating a new conda env with the packages I think I need. Try starting the project, oops I don't have spyder-kernels installed. Oh, and my environment isn't compatible with it. How about just running it in VScode? Well now I don't have my variable explorer. How about Jupyter? How do I get Jupyter to find my conda env again? Oh wait I need this other library it's only on conda-forge, and then the conda environment solver fails. I guess I'll start from scratch with a new conda env, and maybe after several trial-and-error sessions of carefully composing the correct "conda create -n ..." incantation in a text editor before copy-pasting them to the command line, I might get the environment I need up and running, after conda finishes its 10-minute compatibility search and downloads 80 GB of python libraries. And using conda is the easy way of doing it! Don't even get me started on pip and venv...
cjk2 3y ago
This is the defacto standard way of operating it I understand, which is mostly just hacking at stuff in small chunks until it sort of works and leaving comments throughout it with "run this bit on Tuesdays only". I recently had to inherit someone's R stuff and I had to learn R and fix it all. It now runs from a makefile repeatably. Anyway it could be worse. It could be Minitab.
ellisv 3y ago
> Instead of categorizing code by files, everything is shoved into massive files. That's not really RStudio's fault. It is just how many people use R and were taught. > code is run out-of-order which makes the code organization and flow of a program a complete disaster. In my experience, with R Markdown, this is untrue. I see Jupyter Notebooks with cells run out of order much more often.
- madcaptenor 3y ago
  I have done a lot in R Markdown, and the project I'm currently working on has me mostly working in Databricks notebooks (which are very similar to Jupyter notebooks). My execution gets out of order a lot more often in Databricks.
bachmeier 3y ago
> Instead of running a file top-to-bottom, code is run out-of-order which makes the code organization and flow of a program a complete disaster. That's more a REPL issue than specific to a particular language. It's the tradeoff you make. I write my R programs in Geany and then run the whole thing using Rscript. That gives me a clean environment on every run.

goosedragons 3y ago

Emacs + ESS? Way more flexible. Maybe less integration because many of the big R package devs work for Posit. RStudio has a lot of superfluous junk in the UI I just don't need or care about.

kqr 3y ago
I've used ESS for the past few years and recently tried using RStudio when I'm on Windows. For my purposes, which is just a little industrial statistics on the side, they are remarkably similar. I feel right at home in either!

lylejantzi3rd 3y ago

> I’ve yet to find anything that comes close to the flexibility and integration of RStudio for hacking together data analytics. Is there a good demo or video you can point to that shows this? I have no experience with R, RStudio, or data science, but you've piqued my interest.

ellisv 3y ago
Any of David Robinson's (or anyone else's) Tidy Tuesday videos. https://www.youtube.com/@safe4democracy/featured https://www.youtube.com/@safe4democracy/featured

Kalanos 3y ago

jupyter

silveraxe93 3y ago

This works out of the box in VSCode? Just open a .py file, then select the snippet of code you want to run and cmd+enter It will open a new REPL for you (using your selected interpreter) the first time, and after that all commands are run in that same one.

wodenokoto 3y ago
RStudio is just way better at choosing what code to send (if you only send the line the cursor rests on you’re gonna have a bad time. VSCode is a bit better than that but not great. Also, where does your plots get drawn when you use this? RStudio just works in this regards)

ubiquitination 3y ago

I agree - I teach statistics at a University and there is really no alternative to Rstudio for working with R. This is especially true considering that the vast majority of folk using R (in my field) have no prior programming experience. Downloading R, Vscode, downloading some R plugin, getting them to talk to each other, and only then starting to learn R - isn't very straightforward. It's also remarkably consistent on different operating systems - something to consider when half the students are on windows, half on macos...

bachmeier 3y ago
RStudio Server on a Digital Ocean instance made my life a lot easier. Students fire up a browser, log in, and they're using R with all the packages. It was horrible when students ran R on their own machines back in the old days. Most of the questions I got were tech support rather than related to the material. And these days it has good Python support too.

dcreater 3y ago

Jupiter (ipynb) notebooks in vs code.

jurimasa 3y ago

If you work with Python, Spyder comes really, really close and is way better than jupyter

RobinL 3y ago

It looks like, as far as I can tell, VS Code doesn't support the interactive window for working in R, which was a bit of a surprise to me when i looked it up. The python interactive window has pretty much fully replaced my use of jupyter, since it gives you notebook-style output without the annoyance of the notebook format. My usual workflow is highlighting lines of code and shift-enter to execute (there's also a cells syntax). I'm surprised by this because it _is_ possible to use R in Jupyter (although I never really liked the experience, R Studio was far superior).

yabbs 3y ago
? Yes it does.
- aiisjustanif 3y ago
  Please supply references for the audience.
- RobinL 3y ago
  I'm specifically referring to: https://code.visualstudio.com/docs/python/jupyter-support-py https://code.visualstudio.com/docs/python/jupyter-support-py The support for R looks a bit different (to me at least?): https://code.visualstudio.com/docs/languages/r https://code.visualstudio.com/docs/languages/r In the screenshot the window on the right does not look comparable to the output in a jupyter notebook. It looks more like a standard terminal. e.g. does it support interactive charts, html tables etc? The Python interactive window uses the ipykernel package to allow rich outputs like that. I still might be wrong and would like to be corrected on this, since it would mean R support in VS Code is now better than I thought (I haven't tried it fora. while)
  - ZunarJ5 3y ago
    I use r in a Jupyter Notebook in VS via IRKernel. It's a gem.
    - 3y ago

jakupovic 3y ago

cat, grep, sort and awk come pretty close :)

dcchuck 3y ago

Came here to share that same experience. RStudio truly made me feel "close" to the data.

ivansavz 3y ago

An alternative in the Python world that is definitely worth looking into is the JupyterLab Desktop app, which is a standalone installer that is cross-platform and works great for beginners (no command line needed): https://github.com/jupyterlab/jupyterlab-desktop?tab=readme-ov-file#jupyterlab-desktop https://github.com/jupyterlab/jupyterlab-desktop?tab=readme-... See my other comment in the main thread with more info.