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Announcing RStudio v1.0

blog.rstudio.org

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Re: Announcing RStudio v1.0

#41

Post by statics2245 »

StefanKarpinski wrote: post #30
statics2245 wrote: post #28

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.

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

It passed our legal department's review and now an official software offering for our actuaries. We use the software to analyze data not distribute software derived from RStudio.

While I am a big fan of Julia, in my opinion it has a long way to go to be ready for our version of prime time.

Python on the other hand just doesn't have the breath of statistical models that R has. Generalized Additive Models is just one example.

Re: Announcing RStudio v1.0

#42

Post by raizinho »

jbmorgado wrote: post #18
madenine wrote: post #12

Earlier quoted context omitted.

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/

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 nea…

There something close to knitr for Python: https://github.com/pystitch/stitch

Re: Announcing RStudio v1.0

#43

Post by apathy »

plafl wrote: post #14
blahi wrote: post #5

Earlier quoted context omitted.

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 textboo…

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

GAMs, BARTs, extensions to the Cox model, etc.

Re: Announcing RStudio v1.0

#44

Post by apathy »

bckygldstn wrote: post #11
sandGorgon wrote: post #9

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 :(

I haven't used dplyr much, is is not similar to pandas?

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++).

Re: Announcing RStudio v1.0

#45

Post by apathy »

capnrefsmmat wrote: post #17

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.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 wr…

have them write packages and turn in the vignette. problem solved

Re: Announcing RStudio v1.0

#46

Post by plinkplonk »

StefanKarpinski wrote: post #30
statics2245 wrote: post #28

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.

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

" At some point, RStudio will ask you to comply with the AGPL or pay them for a non-AGPL license."

Did you mean to use "may" instead of "will"? The latter would seem to indicate that the R-Studio makers actually plan to do this.

Re: Announcing RStudio v1.0

#47

Post by sandGorgon »

mike_ivanov wrote: post #29
sandGorgon wrote: post #9

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 :(

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.

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.

Re: Announcing RStudio v1.0

#48

Post by yihui »

capnrefsmmat wrote: post #39
yihui wrote: post #22

Earlier quoted context omitted.

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 f…

Well, it doesn't just require them to click a menu item and hit a button -- then they have to fix all the problems that arose because RStudio encouraged a hackish development style. There's a workaround, but RStudio still actively encourages your workspace to get out of sync from your script. Compare that to DrRacket, where the code is labeled the "Definitions" window, and every time you reload the definitions, your…

It is not that we encourage a "hackish development style", but computer scientists and statisticians/data analysts are solving different problems, and statisticians' primary job is often not software development. There is not a single absolutely correct style for both groups. You should not expect statisticians to be professional software engineers, or vice versa. We can learn good practice from each other. Statisticians and data analysts often use the EDA approach (Exploratory Data Analysis), and it makes sense to "pollute" the workspace temporarily. Running everything from scratch feels like using punch cards, which is related to the history of S (which in turn inspired R). Statisticians at Bell Labs found it tedious to throw a program to a machine, wait for a day, get hundreds of pages of output the next day, read the output by eyes, modify the program, and do it again. They wanted instant feedback (plots/summary tables) as they explore the data.

We take reproducibility very seriously. The fact that RStudio's Knit button uses a new R session, instead of the current R session, to compile R Markdown documents was a deliberate choice to make sure your output is produced from a clean R session. But if you are doing EDA, it may not be very pleasant to click this button over and over again every time you update your code (you can if you want).

If your course is focused on software engineering, everything you said makes perfect sense. Statisticians can learn the good principles in CS, but they are statisticians after all. There must be tradeoffs.

Re: Announcing RStudio v1.0

#49

Post by [deleted] »

sandGorgon wrote: post #47
mike_ivanov wrote: post #29

Earlier quoted context omitted.

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.

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…

[deleted]

Re: Announcing RStudio v1.0

#50

Post by mike_ivanov »

sandGorgon wrote: post #47
mike_ivanov wrote: post #29

Earlier quoted context omitted.

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.

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…

Already doing both R and Python in equal proportions, thank you for the suggestion anyways.
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