5 ms·

This is a good resource for those new to R. R has some really good GUI layers now. I struggled and struggled for years trying to learn the command line methods

This is a good resource for those new to R. R has some really good GUI layers now. I struggled and struggled for years trying to learn the command line methods, but it was too much for me. The following do a great job (these are alternatives) - Deducer - R Commander - RKWard

earino 11y ago
It seems like this list is incomplete without mentioning that both RStudio[1] and Jupyter[2] notebooks now have really first class support for R. There are also two upstatrs, Rodeo[3] and Beaker[4] are doing cool stuff as well. The company I work for, Domino Data Lab[5], let's you fire up a lot of these notebooks in a nice hosted environment on big cloud servers with minimal cost and effort. It's a fun way to learn how all these new environments can work together. From RStudio for exploratory analysis, to Jupyter notebooks for presenting a topic. The other two I haven't really found the superior use-case. The tools in this space are just getting better and better. 1. https://www.rstudio.com/ https://www.rstudio.com/ 2. http://jupyter.org/ http://jupyter.org/ 3. http://blog.yhat.com/posts/introducing-rodeo.html http://blog.yhat.com/posts/introducing-rodeo.html 4. http://beakernotebook.com/ http://beakernotebook.com/ 5. https://www.dominodatalab.com/ https://www.dominodatalab.com/
- minimaxir 11y ago
  > Jupyter[2] notebooks now have really first class support for R. Jupyter and R is a bit iffy since the R kernel is not native. Although the kernel works fine, setting it up has a ton of manually-installed dependencies, and in-line plots flat-out give unexpected output. (I've had to cheat by embeding charts via Markdown. Although that has the benefit of having the charts be responsive) The important perk is that Jupyter notebooks are now rendered natively on GitHub, which I've made considerable use of: https://github.com/minimaxir/sf-arrests-when-where/blob/master/crime_data_sf.ipynb https://github.com/minimaxir/sf-arrests-when-where/blob/mast...
  - earino 11y ago
    > Jupyter and R is a bit iffy since the R kernel is not native. Although the kernel works fine, setting it up has a ton of manually-installed dependencies, and in-line plots flat-out give unexpected output. (I've had to cheat by embeding charts via Markdown. Although that has the benefit of having the charts be responsive) You know, to be completely honest, I've never used it directly. I've always used it on our platform. It's very possible that our engineers already did all that setup so it "just works." I took the original post: http://r-statistics.co/Statistical-Tests-in-R.html http://r-statistics.co/Statistical-Tests-in-R.html and reimplemented it in an R notebook with some simple plots at the end, but yeah, the plotting just sort of works for me. I didn't realize I had an incomplete view of the complexity of getting that working :( https://app.dominodatalab.com/earino/statistical_tests/view/Statistical+Tests.ipynb https://app.dominodatalab.com/earino/statistical_tests/view/... We also render the notebooks. The difference is that we also let you run them :)
  - IndianAstronaut 11y ago
    Package installation can be a bit of an issue as well, especially if you accidentally install a package twice. But overall, I still prefer notebooks to Rstudio. They are transparent and you can really trace your progress and share the info with others.
    - TheLogothete 11y ago
      And you can't with Rstudio? There's a freaking notebook feature built right in.
      - IndianAstronaut 11y ago
        Much easier to turn the Jupyter noteboks into a clean html format. Also, it's sequential, with Rstudio it's nice, but doesn't have the cell format.
  - stared 11y ago
    Manually setting it is hard (on OS X + Homebrew Python I did it after a long fight; main problem: rmzq library). But... it is super easy with Anaconda: https://www.continuum.io/blog/developer/jupyter-and-conda-r https://www.continuum.io/blog/developer/jupyter-and-conda-r
- hackaflocka 11y ago
  Does R Studio have a bunch of GUI plugins for doing the various common statistics tasks? Because the base R Studio doesn't do much (it's nice for running R, but I don't think that one can do linear regressions etc. via a GUI -- correct me if I'm wrong).
  - jupiter90000 11y ago
    No that's not really what it does. You can edit code separately from the REPL(which is also directly available), view plots, examine some data objects, view help/command history/etc. Essentially it's like an IDE for the R language, it doesn't turn R into like an SPSS GUI type interface. Edit: the closest thing I can think of in RStudio to that is installing the manipulate package which allows adding sliders and such to plots for some custom plotting controls.
    - nafizh 11y ago
      For custom plotting controls, you can just use shiny. It is awesome. Check out the shiny gallery and you will see that.
- sandGorgon 11y ago
  What do you recommend to build business dashboards in R? By pulling data from an api or sb for example
  - earino 11y ago
    Two approaches: 1. If you're interested in running a shiny server, use http://rstudio.github.io/shinydashboard/ http://rstudio.github.io/shinydashboard/! I have used it to build professional high quality dashboards VERY quickly. 2. You can use an API server like Domino's API end points or OpenCPU to expose R APIs and build the interface using JavaScript at plot.ly! This really can be incredibly elegant and you can do really neat dynamic dashboards.
    - sandGorgon 11y ago
      That is great to know! Is opencpu something you recommend for production? I'm just starting to work with analysts who work in R, and I have struggled with the question whether we should wrap existing R code as an api...or port to python. Low volume right now, so not really concerned with performance... But rather that can R deployment play nicely with things like supervisord,etc in production
      - earino 11y ago
        Well, I really don't want to turn this into a sales pitch, but that's the exact use case for Domino's API endpoints. Check out http://support.dominodatalab.com/hc/en-us/articles/204173149-API-Endpoints-Model-Deployment http://support.dominodatalab.com/hc/en-us/articles/204173149... for an explanation. If you're interested, drop me an email and I can work up an example project for you. Exposing R algorithms as REST endpoints is exactly what it does quite well. As for OpenCPU, I know that the guy who wrote it, Jeroen Ooms is genuinely quite brilliant. I know it was his project during his PhD, and I don't know what his plans are for continuing to support it. It's up to you to determine what that means for your "production" needs.
        sandGorgon 11y ago

earino 11y ago

It seems like this list is incomplete without mentioning that both RStudio[1] and Jupyter[2] notebooks now have really first class support for R. There are also two upstatrs, Rodeo[3] and Beaker[4] are doing cool stuff as well. The company I work for, Domino Data Lab[5], let's you fire up a lot of these notebooks in a nice hosted environment on big cloud servers with minimal cost and effort. It's a fun way to learn how all these new environments can work together. From RStudio for exploratory analysis, to Jupyter notebooks for presenting a topic. The other two I haven't really found the superior use-case. The tools in this space are just getting better and better. 1. https://www.rstudio.com/ https://www.rstudio.com/ 2. http://jupyter.org/ http://jupyter.org/ 3. http://blog.yhat.com/posts/introducing-rodeo.html http://blog.yhat.com/posts/introducing-rodeo.html 4. http://beakernotebook.com/ http://beakernotebook.com/ 5. https://www.dominodatalab.com/ https://www.dominodatalab.com/

minimaxir 11y ago
> Jupyter[2] notebooks now have really first class support for R. Jupyter and R is a bit iffy since the R kernel is not native. Although the kernel works fine, setting it up has a ton of manually-installed dependencies, and in-line plots flat-out give unexpected output. (I've had to cheat by embeding charts via Markdown. Although that has the benefit of having the charts be responsive) The important perk is that Jupyter notebooks are now rendered natively on GitHub, which I've made considerable use of: https://github.com/minimaxir/sf-arrests-when-where/blob/master/crime_data_sf.ipynb https://github.com/minimaxir/sf-arrests-when-where/blob/mast...
- earino 11y ago
  > Jupyter and R is a bit iffy since the R kernel is not native. Although the kernel works fine, setting it up has a ton of manually-installed dependencies, and in-line plots flat-out give unexpected output. (I've had to cheat by embeding charts via Markdown. Although that has the benefit of having the charts be responsive) You know, to be completely honest, I've never used it directly. I've always used it on our platform. It's very possible that our engineers already did all that setup so it "just works." I took the original post: http://r-statistics.co/Statistical-Tests-in-R.html http://r-statistics.co/Statistical-Tests-in-R.html and reimplemented it in an R notebook with some simple plots at the end, but yeah, the plotting just sort of works for me. I didn't realize I had an incomplete view of the complexity of getting that working :( https://app.dominodatalab.com/earino/statistical_tests/view/Statistical+Tests.ipynb https://app.dominodatalab.com/earino/statistical_tests/view/... We also render the notebooks. The difference is that we also let you run them :)
- IndianAstronaut 11y ago
  Package installation can be a bit of an issue as well, especially if you accidentally install a package twice. But overall, I still prefer notebooks to Rstudio. They are transparent and you can really trace your progress and share the info with others.
  - TheLogothete 11y ago
    And you can't with Rstudio? There's a freaking notebook feature built right in.
    - IndianAstronaut 11y ago
      Much easier to turn the Jupyter noteboks into a clean html format. Also, it's sequential, with Rstudio it's nice, but doesn't have the cell format.
- stared 11y ago
  Manually setting it is hard (on OS X + Homebrew Python I did it after a long fight; main problem: rmzq library). But... it is super easy with Anaconda: https://www.continuum.io/blog/developer/jupyter-and-conda-r https://www.continuum.io/blog/developer/jupyter-and-conda-r
hackaflocka 11y ago
Does R Studio have a bunch of GUI plugins for doing the various common statistics tasks? Because the base R Studio doesn't do much (it's nice for running R, but I don't think that one can do linear regressions etc. via a GUI -- correct me if I'm wrong).
- jupiter90000 11y ago
  No that's not really what it does. You can edit code separately from the REPL(which is also directly available), view plots, examine some data objects, view help/command history/etc. Essentially it's like an IDE for the R language, it doesn't turn R into like an SPSS GUI type interface. Edit: the closest thing I can think of in RStudio to that is installing the manipulate package which allows adding sliders and such to plots for some custom plotting controls.
  - nafizh 11y ago
    For custom plotting controls, you can just use shiny. It is awesome. Check out the shiny gallery and you will see that.
sandGorgon 11y ago
What do you recommend to build business dashboards in R? By pulling data from an api or sb for example
- earino 11y ago
  Two approaches: 1. If you're interested in running a shiny server, use http://rstudio.github.io/shinydashboard/ http://rstudio.github.io/shinydashboard/! I have used it to build professional high quality dashboards VERY quickly. 2. You can use an API server like Domino's API end points or OpenCPU to expose R APIs and build the interface using JavaScript at plot.ly! This really can be incredibly elegant and you can do really neat dynamic dashboards.
  - sandGorgon 11y ago
    That is great to know! Is opencpu something you recommend for production? I'm just starting to work with analysts who work in R, and I have struggled with the question whether we should wrap existing R code as an api...or port to python. Low volume right now, so not really concerned with performance... But rather that can R deployment play nicely with things like supervisord,etc in production
    - earino 11y ago
      Well, I really don't want to turn this into a sales pitch, but that's the exact use case for Domino's API endpoints. Check out http://support.dominodatalab.com/hc/en-us/articles/204173149-API-Endpoints-Model-Deployment http://support.dominodatalab.com/hc/en-us/articles/204173149... for an explanation. If you're interested, drop me an email and I can work up an example project for you. Exposing R algorithms as REST endpoints is exactly what it does quite well. As for OpenCPU, I know that the guy who wrote it, Jeroen Ooms is genuinely quite brilliant. I know it was his project during his PhD, and I don't know what his plans are for continuing to support it. It's up to you to determine what that means for your "production" needs.
      - sandGorgon 11y ago