10 ms·

Announcing RStudio v1.0

200 points by bsg75 10y ago

carlmcqueen 10y ago
The updates here are massive. I'd been waiting for the official release, instead of preview because of some issues, the official release seems to have ironed out the issues. Excited to code directly into notebooks as reproducible code for fellow workers. Sparklyr is really also changing the game for me in how I can integrate new users into R. It used to take a little work to get people off SAS EG or whatever statistics package they looked for. Not so much anymore.
- blahi 10y ago
  R Notebooks are such a killer feature!
  - gshulegaard 10y ago
    Funny, many people say this, but iPython notebooks have been around for a while (now called Jupyter Notebooks since they are not Python specific anymore) and it didn't spur a massive migration to Python...at least as far as I can tell. Not a bad thing, it's just R Notebooks kind of seem like old news to me. Addendum: I looked it up and it appears that Jupyter Notebooks actually have an R kernel (https://irkernel.github.io/ https://irkernel.github.io/).
    - blahi 10y ago
      Yeah but jupyter leaves bad taste in the mouth. Very frustrating to debug, binary file (no git/diff), much harder to layout than RMarkdown, no variable explorer. Additionally, the kernel idea is kind of lame/not implemented well. And you are getting nowhere in the data world without proper R support. There wasn't migration to python because there's a total lack of anything to migrate to in python. It has only textbook examples as far as statistical models go. That's around 10% coverage and pretty much 0 coverage for all the abstractions and utility packages in R. Pretty much the same with machine learning (minus neural networks where it is strong). Python took some share from matlab and data processing tools but I don't see it retaining that. IMO it will lose it to Julia for the engineering & science people and to Scala for the data processing people. The "second best language for everything" doesn't really work too well in the "data science" world.
      - aeroevan 10y ago
        Jupyter (IPython) notebooks aren't binary, they are JSON. Clearing output before committing in git does show reasonable diffs (including output causes way too much churn). Most of the nice stuff from RStudio is making its way into https://github.com/jupyterlab/jupyterlab https://github.com/jupyterlab/jupyterlab which is starting to look nice.
      - plafl 10y ago
        What machine learning tools are missing from python? I think it's the best supported language but I would like to know what I'm missing
        apathy 10y ago
        GAMs, BARTs, extensions to the Cox model, etc.
      - aheilbut 10y ago
        I'm curious why you view the jupyter kernel idea to be not implemented well?
        blahi 10y ago
        Because it breaks all the time for some esoteric (but always different than the previous) reason.
    - swied 10y ago
      Jupyter notebooks are cool, but they are kind of mysterious in terms of how they work. For example, recently the R kernel started crashing whenever I opened a new R notebook. I finally found a note in a local document that said, "If your R kernel keeps dying, you may have to update pcre by executing 'conda update pcre'." I had never heard of pcre, but I updated it and the R kerel started working again. Go figure... I am really excited about using R Notebooks.
      - IndianAstronaut 10y ago
        Perl compatible regex is essential to R text and string parsing.
    - rcthompson 10y ago
      I think it's the fact that RStudio's notebooks are well-integrated into the IDE: so debugging, profiling, the panel listing the variables in the current workspace, and all the other niceties of the IDE still work with the notebook interface. I don't know of any equivalent to that in Jupyter.
      - michaelsbradley 10y ago
        Exactly right! The environment pane of RStudio and its related dev & debugging tools are available for use whether you're authoring a traditional script file or an R Notebook. It would be awesome if such facilities were available when working with Jupyter notebooks, but that is not presently the case.
    - jbmorgado 10y ago
      iPython notebooks don't have the functionality of R Notebooks. R Notebooks allow you to prepare something for presentation, not just for running/testing. I.e.: You can run code and then choose to present the code and the output, just the code, just the output or none of them (for instance just putting a sentence in the presentation where you say you applied some mathematical function to the data, but show the mathematica function (latex) instead of the code. This is really important to publish and to present data and it's not possible to do in Jupyter/iPhython. Also, it allows you to export directly to HTML/PDF. Something Jupyter doesn't.
minimaxir 10y ago
If you're looking into learning data science/visualization, RStudio is one of the best IDEs out there in that field. One of the reasons I switched to using Jupyter over R/RStudio directly was the native rendering of notebooks on GitHub, which made it pretty (example of mine: https://github.com/minimaxir/stack-overflow-survey/blob/master/stack_overflow_dev_survey.ipynb https://github.com/minimaxir/stack-overflow-survey/blob/mast...) The addition of native R notebooks may make me switch back, although I'll have to experiment on the differences between Jupyter Notebook rendering and .Rmd rendering on GitHub. (and since the notebooks are theoretically language agnostic, it might be fun to experiment with Python code too!) Native sparklyr is something I'll also have to research/experiment with, since according to the official Spark documentation, although R has first class support with Spark, there is not API parity with Python + Spark, for example. (although, sparklyr has most of the important transformers/models so it is definitely worth a look: http://spark.rstudio.com/mllib.html http://spark.rstudio.com/mllib.html)
- trestletech 10y ago
  The older/official Spark integration for R (SparkR) is quite lacking. Sparklyr is newer and makes up much of the ground that was missing on the Python integration. Still a few features that need to be checked off the list, but I think most users will find that sparklyr has the subset of features that they need.
sandGorgon 10y ago
we have recently started migrating to python from R because of notebooks and pyspark. I dont see us moving back anytime soon, because production code in python is orders of magnitude better than R. I just wish there was a decent dplyr for python though :(
- bckygldstn 10y ago
  I haven't used dplyr much, is is not similar to pandas?
  - sandGorgon 10y ago
    I think you should read the discussion here - https://news.ycombinator.com/item?id=11335491 https://news.ycombinator.com/item?id=11335491 It explains it better than i can
  - apathy 10y ago
    dplyr abstracts away so much bullshit... they're not even playing the same sport. Pandas brought R-style data.frames to Python; dplyr makes databases, data.tables, etc. all behave the same. Out-of-memory, in-memory, all the abstractions just work the same. Oh and it's fast as shit (the tight loops are in C++).
- mike_ivanov 10y ago
  There is a HUGE difference between running production code and exploratory analysis. Python with Jupyter (however good in production they are) are just not fit for that task. After all, Python is a general-purpose programming language and R is largely a DSL designed specifically for figuring things out as quick as possible. And R is really good at it.
  - sandGorgon 10y ago
    Not entirely true - I can understand you like R, but Pandas+Numpy is fantastic. "DSL for figuring out things aa quick as possible " is a religious statement. I suggest you spend some time on the Python side to understand how good it is. However I can quantify some of what you said - that tge ecosystem of analytics libraries is bigger in CRAN. I agree with that... however at this point, the only library I really miss is dplyr.
jbmorgado 10y ago
I wish there was something like this for Python. Sure we have Jupyter/iPhyton and sure we have Spyder, but nothing even comes close to RStudio.
- madenine 10y ago
  Check out Rodeo[0] from Yhat. Its not RStudio, but its an IDE with data science in mind. Been getting better and better each release. [0] http://rodeo.yhat.com/ http://rodeo.yhat.com/
  - jbmorgado 10y ago
    I was trying Rodeo these past few days. It still has a lot to improve, I really don't think the autor should have put this out of beta so fast, it's clearly still a 0.x version, not a 2.x. Markdown is extremely limited (what we need is something more like knitr), there are quite a few bugs and it's difficult to access documentation of the packages. I appreciate the work of the author, but all in all, it's nowhere near RStudio and - except for the fancy interface - actually behind Spyder.
    - raizinho 10y ago
      There something close to knitr for Python: https://github.com/pystitch/stitch https://github.com/pystitch/stitch
      - jbmorgado 10y ago
        This is great thank you. It would be great if Spyder would add a plugin to edit these formats or something similar.
- anc84 10y ago
  What are you missing in Spyder compared to RStudio?
  - 10y ago
capnrefsmmat 10y ago
I've been co-teaching a class in computing for statisticians this semester (some details on the previous iteration here https://www.refsmmat.com/posts/2016-01-22-stat-computing.html https://www.refsmmat.com/posts/2016-01-22-stat-computing.htm...) and have mixed feelings about RStudio. Most of our students use RStudio for their work. It's convenient and easy. For developing standalone scripts or functions, rather than notebooks or R Markdown files, the typical workflow is to write code in a file, then run it in the current R session by selecting pieces and hitting "Run". But this encourages terrible practices. After a few iterations, the current environment does not reflect what's written in the file. If students write tests in a separate file, they neglect to source in their functions, because they're already in the workspace and so the tests run fine. Datasets that were loaded in the R console are used in code without being explicitly loaded there. Code gets changed without being run, or variable definitions are changed but old copies in the workspace accidentally used instead. We end up getting many homework submissions that simply don't run if you start them in a new R session. Pieces are missing, code is out of order, tests only run if you manually select the test code and run it. It only worked in the R session of the original author. R Markdown is a decent step, since rendering the HTML should start a session from scratch, but when we're asking them to write well-tested and modular algorithmic code, using R Markdown doesn't really fit. I've begun to appreciate DrRacket's approach, where there is a "Definitions" pane and an "Interactions" window. When you Run the definitions, the current workspace is blown away and everything is defined from scratch from the Definitions, so there's no lingering state from REPL interactions. (Unfortunately your REPL history is lost, which can be annoying.) You can't run into the same inconsistent state as RStudio actively encourages.
- yihui 10y ago
  Isn't this a simple instruction you give students in the very first class like "before you submit your homework, restart R session, and make sure your submission runs in the new session"? This only requires them to click a menu item (Restart R session), and a button (Knit or Source or something). Not really a burden for them, but will save your life as the instructor. As someone who had been a student in statistics for more than 10 years, I confess I had never written a single test for my homework. Frankly I just didn't have the time or interest (too much homework, and becoming a professional software engineer was not the goal of the homework assignments). That said, when I put on my software engineer hat now at work, I'd definitely do what you advertise here and write tests carefully. If you want your students to enjoy the benefits of both R packages and R Markdown, I wrote some thoughts here a couple of years ago: http://yihui.name/rlp/ http://yihui.name/rlp/ Don't get me wrong. I'll all for teaching students good practice of software engineering. I just want to speak from my own memories and experience as a student. Sometimes I feel teachers are like parents: they want kids to learn all possible right things, no matter if they are practically able to swallow all the good stuff (sometimes this has bad psychological consequences, like rebellious children). If I were an instructor in statistics, I'd only require students to submit an R Markdown document. Other things like tests can earn extra credits but not required.
makmanalp 10y ago
The profiler integration is so cool! I've never seen anything like that in a free tool. AFAIK RStudio is now being lead by JJ Allaire, same person who did Coldfusion and stuff. Also in there is Hadley Wickham, of ggplot2 / dplyr fame.
statics2245 10y ago
For all the naysayers... Try installing python/jupyter in a corporate environment. It was a no go from the start at the last 4 companies I have worked at. R and RStudio just installed and worked for 3 of the 4 companies. The 4th required a tweak to one environmental variable and everything installed/worked after that. Corporate IT restrictions can make or break software.
- StefanKarpinski 10y ago
  The flip side is that RStudio is AGPL [1] – although not too surprisingly this is not heavily advertised. It may be easy to setup, but your legal department will have a heart attack if they find out that you're using it. At some point, RStudio will ask you to comply with the AGPL or pay them for a non-AGPL license. [1] https://en.wikipedia.org/wiki/Affero_General_Public_License https://en.wikipedia.org/wiki/Affero_General_Public_License
  - jcheng 10y ago
    We're proud to be AGPL. The AGPL license is pretty clearly stated on both the product page and the download page. We were an AGPL licensed project years before we were a "real" company, because we thought that for server-oriented software (RStudio was originally conceived to be server only), it was the license most aligned with the principles of the R project. It's certainly not our intention to deceive and then submarine our customers, and I sincerely hope that's not what you were implying. IANAL but RStudio users have nothing to fear from the AGPL, as the copyleft provisions are for derivative works of RStudio itself. If OTOH someone is trying to build an R editor interface for their commercial SaaS data science startup, and want to leverage our code to do it, then yeah--the AGPL is going to apply and if that's a problem then we try to work something out. (BTW I'm a fan of the work you're doing with Julia!)
    - StefanKarpinski 10y ago
      That's a fair reason for the AGPL license choice for RStudio. It is a product-like software project after all. My experience with corporate legal is that they are much more wary of GPL software than strictly necessary – hence the warning about AGPL since ease of use != ease of permission. (We've had JuliaBox fire-walled from a few big banks, albeit not for license reasons, but because they don't want private data on the cloud, so it's a familiar issue.) I had a hard time finding any mention of the AGPL on the RStudio site, including some broken links. Congrats on the release! RStudio pushes the envelope on very many data analytics UI features. Excellent work.

200 points by bsg75 10y ago

The updates here are massive. I'd been waiting for the official release, instead of preview because of some issues, the official release seems to have ironed out the issues. Excited to code directly into notebooks as reproducible code for fellow workers. Sparklyr is really also changing the game for me in how I can integrate new users into R. It used to take a little work to get people off SAS EG or whatever statistics package they looked for. Not so much anymore.

blahi 10y ago
R Notebooks are such a killer feature!
- gshulegaard 10y ago
  Funny, many people say this, but iPython notebooks have been around for a while (now called Jupyter Notebooks since they are not Python specific anymore) and it didn't spur a massive migration to Python...at least as far as I can tell. Not a bad thing, it's just R Notebooks kind of seem like old news to me. Addendum: I looked it up and it appears that Jupyter Notebooks actually have an R kernel (https://irkernel.github.io/ https://irkernel.github.io/).
  - blahi 10y ago
    Yeah but jupyter leaves bad taste in the mouth. Very frustrating to debug, binary file (no git/diff), much harder to layout than RMarkdown, no variable explorer. Additionally, the kernel idea is kind of lame/not implemented well. And you are getting nowhere in the data world without proper R support. There wasn't migration to python because there's a total lack of anything to migrate to in python. It has only textbook examples as far as statistical models go. That's around 10% coverage and pretty much 0 coverage for all the abstractions and utility packages in R. Pretty much the same with machine learning (minus neural networks where it is strong). Python took some share from matlab and data processing tools but I don't see it retaining that. IMO it will lose it to Julia for the engineering & science people and to Scala for the data processing people. The "second best language for everything" doesn't really work too well in the "data science" world.
    - aeroevan 10y ago
      Jupyter (IPython) notebooks aren't binary, they are JSON. Clearing output before committing in git does show reasonable diffs (including output causes way too much churn). Most of the nice stuff from RStudio is making its way into https://github.com/jupyterlab/jupyterlab https://github.com/jupyterlab/jupyterlab which is starting to look nice.
    - plafl 10y ago
      What machine learning tools are missing from python? I think it's the best supported language but I would like to know what I'm missing
      - apathy 10y ago
        GAMs, BARTs, extensions to the Cox model, etc.
    - aheilbut 10y ago
      I'm curious why you view the jupyter kernel idea to be not implemented well?
      - blahi 10y ago
        Because it breaks all the time for some esoteric (but always different than the previous) reason.
  - swied 10y ago
    Jupyter notebooks are cool, but they are kind of mysterious in terms of how they work. For example, recently the R kernel started crashing whenever I opened a new R notebook. I finally found a note in a local document that said, "If your R kernel keeps dying, you may have to update pcre by executing 'conda update pcre'." I had never heard of pcre, but I updated it and the R kerel started working again. Go figure... I am really excited about using R Notebooks.
    - IndianAstronaut 10y ago
      Perl compatible regex is essential to R text and string parsing.
  - rcthompson 10y ago
    I think it's the fact that RStudio's notebooks are well-integrated into the IDE: so debugging, profiling, the panel listing the variables in the current workspace, and all the other niceties of the IDE still work with the notebook interface. I don't know of any equivalent to that in Jupyter.
    - michaelsbradley 10y ago
      Exactly right! The environment pane of RStudio and its related dev & debugging tools are available for use whether you're authoring a traditional script file or an R Notebook. It would be awesome if such facilities were available when working with Jupyter notebooks, but that is not presently the case.
  - jbmorgado 10y ago
    iPython notebooks don't have the functionality of R Notebooks. R Notebooks allow you to prepare something for presentation, not just for running/testing. I.e.: You can run code and then choose to present the code and the output, just the code, just the output or none of them (for instance just putting a sentence in the presentation where you say you applied some mathematical function to the data, but show the mathematica function (latex) instead of the code. This is really important to publish and to present data and it's not possible to do in Jupyter/iPhython. Also, it allows you to export directly to HTML/PDF. Something Jupyter doesn't.

minimaxir 10y ago

If you're looking into learning data science/visualization, RStudio is one of the best IDEs out there in that field. One of the reasons I switched to using Jupyter over R/RStudio directly was the native rendering of notebooks on GitHub, which made it pretty (example of mine: https://github.com/minimaxir/stack-overflow-survey/blob/master/stack_overflow_dev_survey.ipynb https://github.com/minimaxir/stack-overflow-survey/blob/mast...) The addition of native R notebooks may make me switch back, although I'll have to experiment on the differences between Jupyter Notebook rendering and .Rmd rendering on GitHub. (and since the notebooks are theoretically language agnostic, it might be fun to experiment with Python code too!) Native sparklyr is something I'll also have to research/experiment with, since according to the official Spark documentation, although R has first class support with Spark, there is not API parity with Python + Spark, for example. (although, sparklyr has most of the important transformers/models so it is definitely worth a look: http://spark.rstudio.com/mllib.html http://spark.rstudio.com/mllib.html)

trestletech 10y ago
The older/official Spark integration for R (SparkR) is quite lacking. Sparklyr is newer and makes up much of the ground that was missing on the Python integration. Still a few features that need to be checked off the list, but I think most users will find that sparklyr has the subset of features that they need.

sandGorgon 10y ago

we have recently started migrating to python from R because of notebooks and pyspark. I dont see us moving back anytime soon, because production code in python is orders of magnitude better than R. I just wish there was a decent dplyr for python though :(

bckygldstn 10y ago
I haven't used dplyr much, is is not similar to pandas?
- sandGorgon 10y ago
  I think you should read the discussion here - https://news.ycombinator.com/item?id=11335491 https://news.ycombinator.com/item?id=11335491 It explains it better than i can
- apathy 10y ago
  dplyr abstracts away so much bullshit... they're not even playing the same sport. Pandas brought R-style data.frames to Python; dplyr makes databases, data.tables, etc. all behave the same. Out-of-memory, in-memory, all the abstractions just work the same. Oh and it's fast as shit (the tight loops are in C++).
mike_ivanov 10y ago
There is a HUGE difference between running production code and exploratory analysis. Python with Jupyter (however good in production they are) are just not fit for that task. After all, Python is a general-purpose programming language and R is largely a DSL designed specifically for figuring things out as quick as possible. And R is really good at it.
- sandGorgon 10y ago
  Not entirely true - I can understand you like R, but Pandas+Numpy is fantastic. "DSL for figuring out things aa quick as possible " is a religious statement. I suggest you spend some time on the Python side to understand how good it is. However I can quantify some of what you said - that tge ecosystem of analytics libraries is bigger in CRAN. I agree with that... however at this point, the only library I really miss is dplyr.

jbmorgado 10y ago

I wish there was something like this for Python. Sure we have Jupyter/iPhyton and sure we have Spyder, but nothing even comes close to RStudio.

madenine 10y ago
Check out Rodeo[0] from Yhat. Its not RStudio, but its an IDE with data science in mind. Been getting better and better each release. [0] http://rodeo.yhat.com/ http://rodeo.yhat.com/
- jbmorgado 10y ago
  I was trying Rodeo these past few days. It still has a lot to improve, I really don't think the autor should have put this out of beta so fast, it's clearly still a 0.x version, not a 2.x. Markdown is extremely limited (what we need is something more like knitr), there are quite a few bugs and it's difficult to access documentation of the packages. I appreciate the work of the author, but all in all, it's nowhere near RStudio and - except for the fancy interface - actually behind Spyder.
  - raizinho 10y ago
    There something close to knitr for Python: https://github.com/pystitch/stitch https://github.com/pystitch/stitch
    - jbmorgado 10y ago
      This is great thank you. It would be great if Spyder would add a plugin to edit these formats or something similar.
anc84 10y ago
What are you missing in Spyder compared to RStudio?
- 10y ago

capnrefsmmat 10y ago

I've been co-teaching a class in computing for statisticians this semester (some details on the previous iteration here https://www.refsmmat.com/posts/2016-01-22-stat-computing.html https://www.refsmmat.com/posts/2016-01-22-stat-computing.htm...) and have mixed feelings about RStudio. Most of our students use RStudio for their work. It's convenient and easy. For developing standalone scripts or functions, rather than notebooks or R Markdown files, the typical workflow is to write code in a file, then run it in the current R session by selecting pieces and hitting "Run". But this encourages terrible practices. After a few iterations, the current environment does not reflect what's written in the file. If students write tests in a separate file, they neglect to source in their functions, because they're already in the workspace and so the tests run fine. Datasets that were loaded in the R console are used in code without being explicitly loaded there. Code gets changed without being run, or variable definitions are changed but old copies in the workspace accidentally used instead. We end up getting many homework submissions that simply don't run if you start them in a new R session. Pieces are missing, code is out of order, tests only run if you manually select the test code and run it. It only worked in the R session of the original author. R Markdown is a decent step, since rendering the HTML should start a session from scratch, but when we're asking them to write well-tested and modular algorithmic code, using R Markdown doesn't really fit. I've begun to appreciate DrRacket's approach, where there is a "Definitions" pane and an "Interactions" window. When you Run the definitions, the current workspace is blown away and everything is defined from scratch from the Definitions, so there's no lingering state from REPL interactions. (Unfortunately your REPL history is lost, which can be annoying.) You can't run into the same inconsistent state as RStudio actively encourages.

yihui 10y ago
Isn't this a simple instruction you give students in the very first class like "before you submit your homework, restart R session, and make sure your submission runs in the new session"? This only requires them to click a menu item (Restart R session), and a button (Knit or Source or something). Not really a burden for them, but will save your life as the instructor. As someone who had been a student in statistics for more than 10 years, I confess I had never written a single test for my homework. Frankly I just didn't have the time or interest (too much homework, and becoming a professional software engineer was not the goal of the homework assignments). That said, when I put on my software engineer hat now at work, I'd definitely do what you advertise here and write tests carefully. If you want your students to enjoy the benefits of both R packages and R Markdown, I wrote some thoughts here a couple of years ago: http://yihui.name/rlp/ http://yihui.name/rlp/ Don't get me wrong. I'll all for teaching students good practice of software engineering. I just want to speak from my own memories and experience as a student. Sometimes I feel teachers are like parents: they want kids to learn all possible right things, no matter if they are practically able to swallow all the good stuff (sometimes this has bad psychological consequences, like rebellious children). If I were an instructor in statistics, I'd only require students to submit an R Markdown document. Other things like tests can earn extra credits but not required.

makmanalp 10y ago

The profiler integration is so cool! I've never seen anything like that in a free tool. AFAIK RStudio is now being lead by JJ Allaire, same person who did Coldfusion and stuff. Also in there is Hadley Wickham, of ggplot2 / dplyr fame.

statics2245 10y ago

For all the naysayers... Try installing python/jupyter in a corporate environment. It was a no go from the start at the last 4 companies I have worked at. R and RStudio just installed and worked for 3 of the 4 companies. The 4th required a tweak to one environmental variable and everything installed/worked after that. Corporate IT restrictions can make or break software.

StefanKarpinski 10y ago
The flip side is that RStudio is AGPL [1] – although not too surprisingly this is not heavily advertised. It may be easy to setup, but your legal department will have a heart attack if they find out that you're using it. At some point, RStudio will ask you to comply with the AGPL or pay them for a non-AGPL license. [1] https://en.wikipedia.org/wiki/Affero_General_Public_License https://en.wikipedia.org/wiki/Affero_General_Public_License
- jcheng 10y ago
  We're proud to be AGPL. The AGPL license is pretty clearly stated on both the product page and the download page. We were an AGPL licensed project years before we were a "real" company, because we thought that for server-oriented software (RStudio was originally conceived to be server only), it was the license most aligned with the principles of the R project. It's certainly not our intention to deceive and then submarine our customers, and I sincerely hope that's not what you were implying. IANAL but RStudio users have nothing to fear from the AGPL, as the copyleft provisions are for derivative works of RStudio itself. If OTOH someone is trying to build an R editor interface for their commercial SaaS data science startup, and want to leverage our code to do it, then yeah--the AGPL is going to apply and if that's a problem then we try to work something out. (BTW I'm a fan of the work you're doing with Julia!)
  - StefanKarpinski 10y ago
    That's a fair reason for the AGPL license choice for RStudio. It is a product-like software project after all. My experience with corporate legal is that they are much more wary of GPL software than strictly necessary – hence the warning about AGPL since ease of use != ease of permission. (We've had JuliaBox fire-walled from a few big banks, albeit not for license reasons, but because they don't want private data on the cloud, so it's a familiar issue.) I had a hard time finding any mention of the AGPL on the RStudio site, including some broken links. Congrats on the release! RStudio pushes the envelope on very many data analytics UI features. Excellent work.