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I love R more than any other language I have ever used. Perhaps more than any piece of software I've ever used. All of these points are valid, and yes, it's mes

I love R more than any other language I have ever used. Perhaps more than any piece of software I've ever used. All of these points are valid, and yes, it's messy, and if you try to write the same type of code that you would in Python, it will frustrate you. And yet.. it somehow works. It makes data analysis and statistical modelling a pleasure. It somehow gives off a sense of lightness, and makes it easy to investigate and explore. I would guess I am genuinely 2x as productive in R as I would be in Python on similar tasks. I know it's not a "proper" language, but I think that, maybe, not everything has to be exactly like "proper" software engineering?

Breza 4y ago
I feel the exact same way! I've used R for the past decade. Once you learn the philosophy behind it, it just works. Yesterday my boss asked me a question about a dataset and I wrote code to analyze it while talking through the problem in real time.
ourlordcaffeine 4y ago
The main issue I've had is speed. As soon as you have problems that can't be vectorized, models that take 30 hours to run in R take 30 minutes in python.
- yarky 4y ago
  Mind giving an example ? The only time I faced this was due to an autoregressive model, which was super easy to delegate to c++. I've been working with Python for the last year and appreciate how much it helps with general IT problems, but I would still stick to R for statistical/data analysis.
- CornCobs 4y ago
  In my limited experience, problems that cannot be vectorized really shouldn't be written in python either (assuming you mean python loops). But indeed the edge that Python has is the ease of use of drop-in solutions like Numba allowing you to continue to write in Python but not Python
- 1980phipsi 4y ago
  In both languages, you can write/use C extensions.
- ekianjo 4y ago
  you can insert C code very easily in R for when you need more speed.
- mellavora 4y ago
  Example, please? This seems highly unlikely, based on my 20+ years with R. Yes, using wrong data structures/algorithms can lead to slow code, but switching languages won't fix this. rprof and microbenchmark are your friends if you really need to optimize your code. and (as in python, and as several others have pointed out), if you have something especially challenging, write it in C/C++/fortran instead, and link it to R.
hadley 4y ago
100% this :)
- ekianjo 4y ago
  Thanks for all your work on the tidyverse!
- toto444 4y ago
  Before downvoting this short one liner make sure you to check who wrote it!
- dm319 4y ago
  Ha ha, I love that this is your only comment here! Thanks for all your work on R. I came here with sleeves rolled up to defend the language, but was pleasantly surprised to find it was already being done much better than I could have. It's interesting to see how R elicits such a reaction to some programmers. I think it's frequently misunderstood, and R needs to be used in a particular way to allow it to fly. When I've tried to recreate analyses in Python or Julia, they have nowhere near the fluency of R. It isn't possible to know this if you're messing around with if statements and other procedural methods of achieving things which are better suited to other languages, but rather when crunching data for analysis and graphically visualising the results. I also understand that it's due to R's lisp-y-ness that allows us to have tidyverse in the first place. Question for Hadley - there have been a couple of projects to fuse the speed of data.table and tidyverse. What do you think of this aim and are you tempted to change tidyverse to get to the speeds of data.table, or would that require too much of a fundamental change?
  - hadley 4y ago
    Have you seen https://dtplyr.tidyverse.org https://dtplyr.tidyverse.org? It gives you the syntax of dtplyr and (almost all of) the speed of data.table.
    - laughy 4y ago
      There is also the tidytable package. But dtplyr works really well. Have used it in a couple of shiny apps that wrangle some heavy input files.
azalemeth 4y ago
I very much agree with this. I use python for (different types of) data analysis too, and in python in particular it feels like the "boilerplate" to "science" ratio is rather high in the direction of "boilerplate". R manages to abstract this away very effectively, as the article highlights. The beauty of R is that you can write one line of code and use some hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis in your totally different, completely whacky problem, and it's fast, and (by and large) works. Of course, that's its biggest problem as well. Scientifically, it will quite happily give you a 150 mm howitzer to aim at your foot, assuming you know best.
- hervature 4y ago
  > hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis I think you mean "poorly-documented-cobbled-together-under-deadlines-never-to-be-maintained by someone who has no idea of software principles". Very few labs have a dedicated software engineer to actually turn this software into a usable/hackable tool let alone maintain it.
  - ekianjo 4y ago
    thats an unnecessary negative stance. not every algorithm needs to be scalable and over optimized to be useful in most cases. and if something becomes really useful in R it ends up being reimplemented in more effe five ways down the road.
    - milliams 4y ago
      No, but it does need to be tested and reliable.
horsawlarway 4y ago
Coming from Matlab, I have the opposite feeling. I truly, genuinely dislike the language. I think it's very productive, and I appreciate that Matlab costs an arm and a leg (and god help you once you start paying for some of the nicer packages on top) - but Matlab has spoiled me immensely on the language front. To me, Matlab feels like a language that was designed with an intent to appeal to folks with some understanding of traditional procedural programming, but nudged into treating matrices as first class citizens. R feels like a language that was built for people who were using excel, and have never written a line of code in their life - it's riddled with completely unintuitive, frustrating, intentionally obtuse operators and terms for things that have perfectly fine definitions in normal programming. The difference is that I have 20+ years of programming experience (including quite a bit of functional programming) that I can easily port over to Matlab, and which becomes literal baggage trying to use R. The end result is that I will use R, but I basically always walk away frustrated and infuriated, even when the problem is solved.
- ekianjo 4y ago
  > R feels like a language that was built for people who were using excel, I don't think so. Most people who come to R after years of Excel find it just as alien as you do.
  - jghn 4y ago
    I recall when the pipe operator was first being proposed the argument for it was that it'd enable workflows that felt more like Excel. The implication being that indeed, base R is alien to an Excel user. I also recall my pushback was along the lines of "who on earth would want that". Yeah, it's a good thing I'm not the person coming up with these things :)
    - ekianjo 4y ago
      > I recall when the pipe operator was first being proposed the argument for it was that it'd enable workflows that felt more like Excel. I have no idea where you get that impression, most Excel power users I have met take a long time to understand how to use the pipe operator in R.
SubiculumCode 4y ago
Thanks for expressing how I feel about R so succinctly.
Gatsky 4y ago
Yeah, and the growing user base, widening ecosystem, and continual stream of analysis packages being written only in R suggests that lots of others agree. An important factor not often mentioned is that I think R really helps individual developers/very small teams to be productive.

Breza 4y ago

I feel the exact same way! I've used R for the past decade. Once you learn the philosophy behind it, it just works. Yesterday my boss asked me a question about a dataset and I wrote code to analyze it while talking through the problem in real time.

ourlordcaffeine 4y ago

The main issue I've had is speed. As soon as you have problems that can't be vectorized, models that take 30 hours to run in R take 30 minutes in python.

yarky 4y ago
Mind giving an example ? The only time I faced this was due to an autoregressive model, which was super easy to delegate to c++. I've been working with Python for the last year and appreciate how much it helps with general IT problems, but I would still stick to R for statistical/data analysis.
CornCobs 4y ago
In my limited experience, problems that cannot be vectorized really shouldn't be written in python either (assuming you mean python loops). But indeed the edge that Python has is the ease of use of drop-in solutions like Numba allowing you to continue to write in Python but not Python
1980phipsi 4y ago
In both languages, you can write/use C extensions.
ekianjo 4y ago
you can insert C code very easily in R for when you need more speed.
mellavora 4y ago
Example, please? This seems highly unlikely, based on my 20+ years with R. Yes, using wrong data structures/algorithms can lead to slow code, but switching languages won't fix this. rprof and microbenchmark are your friends if you really need to optimize your code. and (as in python, and as several others have pointed out), if you have something especially challenging, write it in C/C++/fortran instead, and link it to R.

hadley 4y ago

100% this :)

ekianjo 4y ago
Thanks for all your work on the tidyverse!
toto444 4y ago
Before downvoting this short one liner make sure you to check who wrote it!
dm319 4y ago
Ha ha, I love that this is your only comment here! Thanks for all your work on R. I came here with sleeves rolled up to defend the language, but was pleasantly surprised to find it was already being done much better than I could have. It's interesting to see how R elicits such a reaction to some programmers. I think it's frequently misunderstood, and R needs to be used in a particular way to allow it to fly. When I've tried to recreate analyses in Python or Julia, they have nowhere near the fluency of R. It isn't possible to know this if you're messing around with if statements and other procedural methods of achieving things which are better suited to other languages, but rather when crunching data for analysis and graphically visualising the results. I also understand that it's due to R's lisp-y-ness that allows us to have tidyverse in the first place. Question for Hadley - there have been a couple of projects to fuse the speed of data.table and tidyverse. What do you think of this aim and are you tempted to change tidyverse to get to the speeds of data.table, or would that require too much of a fundamental change?
- hadley 4y ago
  Have you seen https://dtplyr.tidyverse.org https://dtplyr.tidyverse.org? It gives you the syntax of dtplyr and (almost all of) the speed of data.table.
  - laughy 4y ago
    There is also the tidytable package. But dtplyr works really well. Have used it in a couple of shiny apps that wrangle some heavy input files.

azalemeth 4y ago

I very much agree with this. I use python for (different types of) data analysis too, and in python in particular it feels like the "boilerplate" to "science" ratio is rather high in the direction of "boilerplate". R manages to abstract this away very effectively, as the article highlights. The beauty of R is that you can write one line of code and use some hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis in your totally different, completely whacky problem, and it's fast, and (by and large) works. Of course, that's its biggest problem as well. Scientifically, it will quite happily give you a 150 mm howitzer to aim at your foot, assuming you know best.

hervature 4y ago
> hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis I think you mean "poorly-documented-cobbled-together-under-deadlines-never-to-be-maintained by someone who has no idea of software principles". Very few labs have a dedicated software engineer to actually turn this software into a usable/hackable tool let alone maintain it.
- ekianjo 4y ago
  thats an unnecessary negative stance. not every algorithm needs to be scalable and over optimized to be useful in most cases. and if something becomes really useful in R it ends up being reimplemented in more effe five ways down the road.
  - milliams 4y ago
    No, but it does need to be tested and reliable.

horsawlarway 4y ago

Coming from Matlab, I have the opposite feeling. I truly, genuinely dislike the language. I think it's very productive, and I appreciate that Matlab costs an arm and a leg (and god help you once you start paying for some of the nicer packages on top) - but Matlab has spoiled me immensely on the language front. To me, Matlab feels like a language that was designed with an intent to appeal to folks with some understanding of traditional procedural programming, but nudged into treating matrices as first class citizens. R feels like a language that was built for people who were using excel, and have never written a line of code in their life - it's riddled with completely unintuitive, frustrating, intentionally obtuse operators and terms for things that have perfectly fine definitions in normal programming. The difference is that I have 20+ years of programming experience (including quite a bit of functional programming) that I can easily port over to Matlab, and which becomes literal baggage trying to use R. The end result is that I will use R, but I basically always walk away frustrated and infuriated, even when the problem is solved.

ekianjo 4y ago
> R feels like a language that was built for people who were using excel, I don't think so. Most people who come to R after years of Excel find it just as alien as you do.
- jghn 4y ago
  I recall when the pipe operator was first being proposed the argument for it was that it'd enable workflows that felt more like Excel. The implication being that indeed, base R is alien to an Excel user. I also recall my pushback was along the lines of "who on earth would want that". Yeah, it's a good thing I'm not the person coming up with these things :)
  - ekianjo 4y ago
    > I recall when the pipe operator was first being proposed the argument for it was that it'd enable workflows that felt more like Excel. I have no idea where you get that impression, most Excel power users I have met take a long time to understand how to use the pipe operator in R.

SubiculumCode 4y ago

Thanks for expressing how I feel about R so succinctly.

Gatsky 4y ago

Yeah, and the growing user base, widening ecosystem, and continual stream of analysis packages being written only in R suggests that lots of others agree. An important factor not often mentioned is that I think R really helps individual developers/very small teams to be productive.