If you already know another dynamic language and want to understand R, I would recommend skipping all the intros to R based around data analysis and start with Advanced R by Hadley Wickham. It will explain all the weirdness you'll encounter right up front before it confuses you. Then you can read the data analysis tutorials and focus on the content rather than the R weirdness.
I agree that Advanced R is a fantastic resource; however I do not think reading it is a good way to learn R. Advanced R explains the whys not the whats, and someone coming into R, who hasn't encountered any of the stuff Wickham gives intuitions for will likely find it an inexplicable truck of concepts.
Instead I would propose using R in some capacity, encountering it's weird quirks (why sometimes evaluating an expression prints something to console and sometimes it doesn't? How is dplyr using my column names as variables? Why do I get warnings when using & instead of &&?) And then turning to Advanced R as a source of sanity
Another source I would recommend is in fact the R language definition. It's very approachable and you quickly realize that R is pretty simple at the core, buried under piles of cruft
I consider myself a pretty experienced developer/software engineer/whatever. Decade into my career, started in iOS in Obj-C, learned Swift along the way, eventually migrated to the backend with Java/SQL, and finally found myself where I really wanted to be, embedded doing C/C++ work for a household name.
That said, about halfway through my master's about two years ago, I found myself in an intro to data mining course that was sold as an "we will teach you R". I had heard non-programmer math friends talk about what they had accomplished in R, and was excited to dive in.
Now, it didn't help that the class ended up being heavy on the statistics side (which despite a math/cs double major, stats was never my thing), but the actually learning R part was 99% left as an exercise to us alongside of the classwork required.
I can say without a doubt, learning R is the worst programming experience I've ever had. Our assignments would give some high level direction on which libraries to use, but getting the right libraries setup and in the environment was just an absolute nightmare. All I remember from that class is hours every week googling unreadable python pukes from R studio (because apparently everything data mining/ML related in R is actually just python), and then spending an hour or less actually doing the statistics work.
I feel bad because I feel like I was setup to not be able to give it a fair chance, but if that's what non-programmer math types are subjected to when told "you need to do some programming for your job", I can understand the apprehension.
> All I remember from that class is hours every week googling unreadable python pukes from R studio (because apparently everything data mining/ML related in R is actually just python)
I’m curious what libraries you were using, I’ve had to go into the source of quite a few popular libraries and I don’t think I’ve ever encountered Python. Lots of C and it’s derivatives, lots of Stan and FORTRAN, but I don’t think I’ve seen Python yet.
What you described is a common phenomenon in stats / data mining / psychometrics / econometrics. They all need students to use certain programing languages, such as SAS / Stata / R / Python, but they don't really spend time to teach those programing languages. I guess this could also be the same for MATLAB in math?
The problem is that there are no incentives in learning how to program for people in those fields. Most people just get their code to "work" (i.e., output the analyses that they want) without really wanting to know how it works. Most of the time code is passed from grad student to grad student and modified to make it work for the specific analysis. As a result, you get Frankenstein code that somewhat works but that is good enough for writing a results section of a paper.
There are people that know how to code but those are few and in-between. Usually they are pushed out of academic positions because there are very few ways to fund work to develop scientific code.
I pretty much had the same experience with R. I've never been able to get really productive with it as a software developer. I feel like I know too much and can't break from old habbits.
It's very academic which I feel really holds it back from software devs and also captures non developers in it's web.
Whilst it's certainly got some runs on the board. I think having data science folk work in more standard languages would actually make supporting them and their needs easier.
Funny, I'll never say that R itself is very academic. The environment maybe, the users sure but the language is very much an mutated Lisp with vectorized operation. Same for the python stuff, I never hit that problem working a lot with it but most of the time when I wanted to check a lib, I landed in C++ aka I am not sure to understand how to read the code.
> because apparently everything data mining/ML related in R is actually just python
Everything? I'm just wondering what you've been doing exactly. I know that Tensorflow in R is just a wrapper on top of Python, not sure what else. If you're doing any deep learning, then going straight to Python is certainly much better than using R. For most other things it's not quite as clear of a decision.
> because apparently everything data mining/ML related in R is actually just python
I think it is apparent only to you. The most popular package for ML in R is `caret` and it has nothing to do with Python. Similarly, `mlr` also has no Python. In fact, except for tensorflow and keras, I can't think of any major ML package that needs Python. Even torch package which brings pytorch to R doesn't need Python (https://cran.r-project.org/web/packages/torch/index.html).
What confuses me even more is that you were learning statistics but using R packages that use Python as backend. In my experience, almost all the new statistics and econometrics methods are first released as R packages by the researchers. Can you name any data mining R packages that you used that required Python? I am really curious to know.
I recently tried to migrate my R code to Julia. Even though I already knew R data.table is faster than DataFrames.jl, I was totally blown away by how slow Julia is. So I quickly gave up. I think I will have to write unavoidable hard loop in cpp, which I really don't want to do...
In order to get those so much vaunted C-like speeds the Julia fanboys claim, you need lots of contortions and hacks. Off the bat, Julia speeds are mediocre.
There are a few tricks to getting Julia to be actually fast, and while it's not hard per se if you know them all (at least for numerical work), it's definitely not trivial.
IMHO, you really have to embrace dispatch-oriented programming, and that includes being scrupulous about avoiding type instability. You also have to be a bit conscious about allocations, since it's easy to write Julia code (especially if you're trying to write in a "vectorized" style as is common in R, Python, Matlab) that generates absurd numbers of allocations, which must then be garbage-collected. But also easy to avoid those allocations if you know.
It took about two years, but after picking up more of this, I was eventually able to switch everything my group does from a two-language solution of matlab for scripts and plotting and C (with MPI) for HPC to all-Julia. This [1] was originally targeted at academics making the same switch, but much of it could be relevant to those with an R background as well.
Lot's of negative comments in here about learning R from experienced programmers. I've found this is largely because experienced programmers have this unjustified bias that R is some toy language that should be easy to learn and has nothing to teach them. If you approached a language like Rust in the same way you would likely be just as frustrated with it.
Certainly R has its quirks, but most of this comes from being one of the oldest continuing existing programming languages there is. It derives from S which was written 46 years ago. Because of this it has multiple object/class systems reflecting the changing standards for OOP. It's most dominant one, S3, predates Java and therefore uses the Generic Function paradigm of OOP similar to Common Lisp's ClOS. If you're experienced but have never worked with non-Java style OOP you're going to be a bit confused.
R's most important feature, which is well worth studying and mastering for any serious programmer, is that it is a completely vectorized programming language. It borrows this style from APL (though is a million times more readable). Every value in R is a vector and for the vast majority of operations the best approach to solve your problem is by thinking in vector operations. This makes simple things like string formatting with `paste` seem like a confusing nightmare, but there is a real logic there. Functions like `ifelse` can seem strange, and writing C-style code in R, while possible will result in horrible performance.
Once you do learn to think in vectors you realize that R isn't just popular in the stats world because most statisticians haven't seen a "real" programming language, but because you can very rapidly iterate on models. Translating mathematical notation into R, for the experience R programmer, is easier than any other language I've worked with by a long shot.
My advice to any experienced programmer approaching R is to have some respect for the language. Most of the frustrations you'll have aren't because R is a bad language, but because you have less experience than you think and learning R well can expand your programming views in a similar way to Haskell.
To add: in my experience, programmers who denigrate R think of it as a software engineering language. Not all programming languages are meant to be languages for building large scale applications. Programming is not just about building business applications, it's about getting a computer to do things.
And R excels at doing statistics and data science. If you keep that mindset, I believe many will find that its an excellent programming language.
Honestly, I'd argue that the issue I had in my experience in R wasn't that I myself wasn't giving it the respect it deserved, but rather that the course constructors for my degree didn't give it that respect.
We were essentially told "Install R Studio, then just copy and paste these library imports and you're good to go". Your description of a vectorized language makes total sense, and that single paragraph is more of an intro to R than we ever got in class.
That said, I think the reason this happened with this class in particular is the viewpoint of (what I perceive) as the majority user's of R. Mathematics focused researchers who never learned the language, they just have done enough to get by and don't really appreciate the underpinnings, or the nuances of running an environment on a machine that isn't theres.
I can't entirely absolve myself of blame though, once I realized what was happening I should have gone and done some more foundational R learning, but at that point I just wanted to be done with the class.
I wouldn't say this is true for "the majority user's of R" at all.
But for "the majority of professors who tangentially use R code in classes on statistics/bioinformatics/economics/finance (anything not explicitly about R and/or Data Science best practices)"? Absolutely.
The R code you see in industry (or academic labs where someone cares about modern R) looks vastly different from those script examples in college that are most people's first impression of the language.
I find it highly unlikely that learning R will expand your programming views anywhere near Haskell.
Haskell is an advanced functional programming language. Most R stuff seems to be incoherent, hard to verify correctness, hacky. It does not seem built on a solid foundation like Haskell. Truly everything being a vector is not a huge take away.
As for "here's just a bunch of examples", well that seems sort of a brute force way to learn something. I agree examples are important, but they are usually to back something up. Having to reverse engineer some fundamental ideas out of just examples is more work. Seems like this is just promoting more hackyness. Seems like its training a neural network instead of actually understanding something.
This sounds like a highly biased perspective of both R, and what it means for a language to be respectable.
If you define a language that "will expand your programming views" as one that can verify correctness easily then yeah, R is terrible at that. But so are many languages that are as flexible as R. Would you have the same opinion of FORTH? Or LISP? or TCL? I think these languages definitely count as "hacky" languages and yet they don't seem to draw the same derision as R (in my experience)
R certainly expanded my programming views! Haskell did too, but the lessons of Haskell didn't stick the way that R's lessons did. Here are some of the things I learnt from R (though they can be found in other languages of course).
* Multiple dispatch. Before learning R, I knew about polymorphism in Java and C++, and multiple dispatch in R broadened my mind and turns out to be very handy.
* The idea of "frames". In R, when you invoke `lm(height~sex*age, data=mydataframe)`, the first argument (the formula) doesn't get evaluated until the lm command asks it to be evaluated, and lm can set up the "frame" for that evaluation, i.e. the place where variables are looked up, however it likes. In fact, lm sets it up to include variables from both the scope in which you invoked lm, and also from mydataframe. This is what makes R so wonderfully concise for modelling in data science, compared to e.g. Python + pandas. I knew about frames from interactive debuggers, but until R it never occurred to me that the programming language could manipulate them.
* "Held" arguments. In R, when you invoke `plot(x, y1+y2)`, it doesn't just evaluate the arguments and then call the plot function -- it leaves the arguments unevaluated, and invokes plot. Plot then (1) decides when to evaluate them, (2) gets access to the language expression `y1+y2`, which means that it can print "y1+y2" on the plot label, (3) it can even define extra variables to include in the scope when y1+y2 gets evaluated. (I knew about held arguments earlier, from Mathematica, but they only clicked when I read the R documentation.)
I've read that R is a descendent of Scheme, and that that's where it gets all its "manipulate language expressions" from. I don't know any Scheme, nor Lisp, and I should definitely learn them -- but in the meantime, my experience has been that R's ability to manipulate language expressions is what makes it such a wonderful sweet spot as a data modelling language. I mostly use Python + pandas nowadays, but it feels such a slog in comparison.
Syntactic forms ('frames', 'held arguments') are reasonably useful, but have two flaws:
A. Understanding how to implement functions using syntactic forms is a steep learning curve. I remember running out of dplyr and having to implement a udf. Fairly unpleasant experience (enquo, !!, perhaps other unusual constructs). Felt like programming C macros.
B. "A function can decide where variables are looked up however it likes" is a significant obstacle in understanding how even basic constructs like function calls actually work. There is a non-trivial amount of hard-to-debug dark magic lurking behind every corner.
A middle ground has never been achieved. For example, `plot(expr(x), expr(y1 + y2))`, where the system limits the dark magic to explicit uses of the `expr()` construct, and `expr(x)` always means `{vars: vars(x), expr: (vars(x)) => x}`. Instead of patching interpreter environments, simply call a lambda function.
I completely agree about the steep learning curve and the feeling of dark magic -- how many times have I had to relearn what deparse(substitute(x)) means -- but oh the satisfaction of broadening my programming horizons. For me it didn't feel like C macros, it felt like "This must be what it feels like to have the power of Lisp"!
That's the weird thing about R. All this dark magic is hiding under the hood, but the core R team hid it so deftly that to the casual statistician it's a straightforward data modelling language that "just works". I'm not sure that it's possible to get rid of the dark magic and retain that data-modeller friendliness.
I have used R a few times now, and I definitely agree with the statement that thinking in vectors is central to writing good R scripts. However, as a computer engineer and performance junkie, its unfortunate that it doesn't get as much attention as other "STEM DSLs" (Julia, MATLAB) when it comes to performance.
The same could technically be argued for Python; The current approaches to dealing with high-performance compute workloads either rely on JITing (e.g. Numba, Tensorflow/JAX's XLA) or bridging over to giant binary blobs through the CPython's well-supported C interop.
There are few comments here complaining about R performance just like the parent comment. It's a shame that R does not have industrial strength industrial compiler based on this blog article unlike Python [1].
If you want to see the performance benchmark of R against major programming languages for data analysis please check this excellent keynote speech on the R compilation or more accurately on failures of R compilation effort [2].
As mentioned in [1], the main reason people do not focus on providing industrial strength compiler for R is probably because most of R program’s time are spent in the library codes that are written in a compiled language (e.g., C or Fortran). As you can see from [2], even though this is the case (computation intensive being delegated to proper compiled languages), the R programs running time still suffers due to the "impedance mismatch" because of the look up overheads, indirections, etc.
Perhaps someone should try to compile, transpile and/or embed R inside D language similar to the efforts provided in [3][4]. D now supports C compiler internally, has DasBetterC and D interface to C++ (DPP) is second to none. Additionally, since D can also perform better than Fortran codes for numerical computing perhaps Fortran codes can eventually be replaced by existing high performance D library like Mir[5]. I can foresee this symbiotic relationship can be beneficial for R and D (R on D?). D can becomes very popular with extra humongous data analysis and statistics libraries from CRAN, and R can get the run time and compilation performance improvement it badly needs.
There’s some overlap, but vectors are essential to the language. Every type of data in R is a vector. There are no scalars, just vectors of length 1. Instead of dictionaries, it’s idiomatic in R to use “lists”, which are vectors of vectors. Data frames are lists (vectors of vectors) constrained to have equal length element vectors (ie columns). Classes are defined as lists with some metadata (stored in a vector) to direct method dispatch.
It’s not just vectorizing mathematical operations a la numpy.
Okay I’m not sure why R is getting a lot of Hate.
After all its a programming language that gets the job done and its very popular in finacial industry especially among Risk Modelers, Quants and i have even seen this being used in analytics space within financial industry
Object-Oriented Programming, Functional Programming and R (John M. Chambers)
"Chambers and Hastie (1992), in the discussion of classes and methods, noted that S differed from other OOP languages because of its functional programming style. In fact, this version of functional OOP finessed the resulting distinction from encapsulated OOP in two ways. First, the methods were dispatched according to a single argument, the first formal argument of the generic function in principle. As a result, the methods were unambiguously associated with a single class, as they would be in encapsulated OOP. Methods were actually dispatched on either argument to the usual binary operators, but a number of encapsulated OOP languages do the same, under the euphemism of operator overloading.
"Second, the question of whether methods belonged to a class or a function was avoided by not having them belong to either. Methods were assigned as ordinary functions and identified by the pattern of their name: “function.class”. In any case, there were no class objects and generic functions were ordinary functions that invoked UseMethod() to select and call the appropriate method. Neither the function nor the class was able to own the methods."
R plus the tidyverse is what makes it a great language. Some tidyverse concepts are being baked into base R, like the pipe, but base R by itself feels hollow.
R’s future is inseparable from the tidyverse. We need to just lump them together in any serious discussion of R.
I teach a graduate level R course mainly for economics and statistics majors. (My educational background and career are computer science and technical; while that may stereotype me into Python, I just love R.) I spend the first three weeks on base R, to convey language-essential concepts, like vectorized objects, then the rest of the course is tidyverse-centric.
I spend the first three weeks on base R, to convey language-essential concepts ...
Awesome. When I was at Berkeley, Linear Systems had Matlab assignments. The real engineers (ME, CE, NE, ...) had taken a Matlab class and knew the language. We computer scientists hadn't and suffered horribly as a consequence. Your three weeks of learning base R instead of sink or swim will pay dividends.
Got the reverse experience. Trained in Matlab, R and Python, we had to follow a database/application class with computer scientist and software engineers where the big project was to make a basic Android application with sqlite database. That was painfully to be dropped in Android Studio without any Java knowledge. And because the class was focused on database, we had no Java introduction or whatever. We were able to team up with students from the other cursus that already had multiple Java projects and classes under their belt but the pill was bitter swallow.
Personally I much prefer data.table. The syntax is a bit harder to get a handle on, but you can do just about anything with it, and it's much faster at runtime than tidyverse.
hard second. ggplot2 was the last (only) good package hadley ever wrote; the rest is more about reinventing the wheel and viral-marketing with hex-stickers than about getting work done. the newer stuff in particular is atrocious; heaven forfend function calls that don't require understanding the lore of a package (`recipes` has stupid functions like `recipe`, `bake` and similar nonsense).
at a push, base + data.table + ggplot2 get everything done. tinyverse ftw.
I currently do lots of data analysis in excel and know basic Python. I would be interested to get opinions on if R is better suited to data analysis than python if that’s all I was doing.
Python is certainly more popular and for job prospects I always tell that to newer data folks. That being said if you want to load in some data do some SQL like manipulation, run some stats and make a graph or output a report I would argue R is way better experience than Python but that’s much more about the package ecosystem and less a comment on the language. Dplyr is just more friendly to use than pandas (often 3-5 ways to do something and as a beginner this can be disorienting) and ggplot2 vs matlibplot. For interactive graphs you are probably going to use plotless anyway from both languages.
One other thing I would mention is knowing SQL well is the most translatable skill. A lot of dplyr and pandas are doing SQL like operations (in fact dbplyr will generate SQL equivalent commands for your dplyr code for various backends).
In summary know how to manipulate data in SQL then pick a language (because you will need to do some IO/reporting stuff outside just data work) where the ecosystem of packages feels user friendly to you and your work flow and roll with that.
These days I usually prefer to use sqldf. It's so powerful how it let's you reference existing dataframes within the SQL command. It's like spark.sql in Python and temporary views but much more simple.
I'm strong in R, Python and Excel. I'd say anyone transitioning from Excel would be better off using R first. Because the R Integrated Development Environment (IDE) RStudio is fantastic compared to any Python IDEs that you can actually figure out how to install. The IDE makes it easy to visualise what you're actually doing to your dataframes by using multiple table tabs.
I am currently a data scientist. Educational background in cs, few hobby web projects and currently updating my skills in java/ kotlin with the idea to go in mobile dev.
I use only Python in my work. I learnt R and honestly, it's the same thing as using Python scientific packages. It's mostly vectorized operations, spaghetti functional, if people know how to write functions, code just to get it done. To make graphs, web dashboard(no, we are not doing web dev, it's dark magic frameworks), build machine learning and eventually some reports. Stuff like that.
I do some software engineering but that s optional and I do it because I can. Most data scientists/ ml engineers can't. So you guys are not fair. R and Python in these environments are not even being used for building stuff. This language is not build for that. Hence it's not good from the perspective you look at it from(software engineers).
Unlike Python , R is solely for statistics, data science and probably some basic ml( I haven't tried tho). Also Shiny for building web dashboards. But don't look at the code for dashboards, it's bad, with 'get it done and forget' approach.
That being said. Good luck scrapping, mining, cleaning data with something not called R/Python. Good luck with data engineering. Exploring and visualizing trends. Creating dashboards even. Machine learning. Monitoring and reporting in scientific manner.
Try This type of work with your favorite languages. Then see how quickly and easily it's done with R/Python . Come back and say it's bad language.
It's the same thing as embedded dev complaining about how bad js is for his job. You just totally ignore the context.
Here is my take on R as a guy who does stats as well as some software engineering in more mainstream languages like Python.
R is a fantastic DSL for data manipulation and statistical analysis, with both traditional and modern tools, on datasets up to the gigabyte scale. It has great, easy-to-use data structures and unparalleled APIs in the tidyverse. It is not the thing for the latest deep learning implementation on petascale data, but most data science work doesn't need or benefit from that. Surprisingly, some machine learning methods have their nicest APIs in R, because that's where the users of those methods are.
It has its warts, but so do JavaScript and SQL, and I think few people dispute that these are very powerful DSLs. Statistical analysis is just as legitimate a computing task as building webpages or querying databases. It is not the same as general-purpose programming, and it needs a good DSL.
Agree with this. Nvim-R plus dplyr and the plotting libraries are the best tools I've found for manipulating and understanding the characteristics of a dataset quickly. Eventually I moved to Python for the specific reason that the R packages for interacting with cloud platforms couldn't really keep up with the development of those platforms, and I got tired of having projects that mashed together both languages. I haven't checked, maybe things have stabilized enough now that I would be happy going back to R.
That makes sense. I don't much like crossing streams between languages on a project. It's great if you can keep R for analysis and Python for production, but if R can't keep up with the wrangling part, you're a bit up a creek. I'm lucky in that most of my analysis only needs to query fairly well-behaved SQL databases.
As an experienced R programmer that doesn’t do much statistics or have a background in computer science. What language characteristics does R lack that makes it a DSL instead of GPL?
Edit: I found an interesting quote from a guy named Martin Fowler about the subject.
“Languages can have a domain focus but still be general-purpose languages. A good example of this is R, a language and platform for statistics; it is very much targeted at statistics work, but has all the expressiveness of a general-purpose programming language. Thus, despite its domain focus, I would not call it a DSL.”
I work using R almost everyday and I think many of the problems that are unique to R could be solved by having a couple of experienced SWE in the core team to point R in the right direction. As it stands, I think R will be left behind until it fixes things like performance and scalability (e.g. intuitive byref semantics and a faster runtime) and a consistent scoping model and OOP.
Apart from that, I think there's a bigger challenge which still needs to be addressed is that analysis/modelling projects tend to be worked on by individuals and/or thrown away after the initial value is pulled out of them.
Going forward, I think we need to start identifying design methodologies that would make collaborating on this sort of work pain-free and more agile. Doing so should give us more value and sooner and for longer.
Coming from writing OOP-style code in Python and C++, I initially disliked R quite a lot. My code is becoming more functional-style by the year and I'm now finding myself enjoying R more and more.
There are issues with R: lots of weird quirks; many different ways of doing things, often just supported for legacy reasons; poor error messages; to name just a few. But on the positive side, it really encourages functional-style programming through the use of apply functions and many of the new tidyverse packages. The resulting code can be very neat and less error-prone than equivalent python code.
The S3 system of OOP initially struck me as very weird, but now I see it as essentially just single dispatch, not really much of OOP at all. It works quite well and is extremely simple.
I guess my thoughts are: approach R as a functional language, and I think you'll find much to like. Try to write C++ or python-style OOP in it, and you'll just find it very strange.
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[ 2.7 ms ] story [ 117 ms ] threadInstead I would propose using R in some capacity, encountering it's weird quirks (why sometimes evaluating an expression prints something to console and sometimes it doesn't? How is dplyr using my column names as variables? Why do I get warnings when using & instead of &&?) And then turning to Advanced R as a source of sanity
Another source I would recommend is in fact the R language definition. It's very approachable and you quickly realize that R is pretty simple at the core, buried under piles of cruft
That said, about halfway through my master's about two years ago, I found myself in an intro to data mining course that was sold as an "we will teach you R". I had heard non-programmer math friends talk about what they had accomplished in R, and was excited to dive in.
Now, it didn't help that the class ended up being heavy on the statistics side (which despite a math/cs double major, stats was never my thing), but the actually learning R part was 99% left as an exercise to us alongside of the classwork required.
I can say without a doubt, learning R is the worst programming experience I've ever had. Our assignments would give some high level direction on which libraries to use, but getting the right libraries setup and in the environment was just an absolute nightmare. All I remember from that class is hours every week googling unreadable python pukes from R studio (because apparently everything data mining/ML related in R is actually just python), and then spending an hour or less actually doing the statistics work.
I feel bad because I feel like I was setup to not be able to give it a fair chance, but if that's what non-programmer math types are subjected to when told "you need to do some programming for your job", I can understand the apprehension.
I’m curious what libraries you were using, I’ve had to go into the source of quite a few popular libraries and I don’t think I’ve ever encountered Python. Lots of C and it’s derivatives, lots of Stan and FORTRAN, but I don’t think I’ve seen Python yet.
readr, caret, lattice, ggplot2, RColorBrewer, mlbench, ElemStatLearn, klaR, dplyr, arules, arulesViz, tensorflow
https://torch.mlverse.org/
That's not Rs fault though
There are people that know how to code but those are few and in-between. Usually they are pushed out of academic positions because there are very few ways to fund work to develop scientific code.
Whilst it's certainly got some runs on the board. I think having data science folk work in more standard languages would actually make supporting them and their needs easier.
What in R made it feel academic for you?
Secondly, it seems optimised for producing one off figures and results, not production systems generally desired by industry.
Everything? I'm just wondering what you've been doing exactly. I know that Tensorflow in R is just a wrapper on top of Python, not sure what else. If you're doing any deep learning, then going straight to Python is certainly much better than using R. For most other things it's not quite as clear of a decision.
I think it is apparent only to you. The most popular package for ML in R is `caret` and it has nothing to do with Python. Similarly, `mlr` also has no Python. In fact, except for tensorflow and keras, I can't think of any major ML package that needs Python. Even torch package which brings pytorch to R doesn't need Python (https://cran.r-project.org/web/packages/torch/index.html).
What confuses me even more is that you were learning statistics but using R packages that use Python as backend. In my experience, almost all the new statistics and econometrics methods are first released as R packages by the researchers. Can you name any data mining R packages that you used that required Python? I am really curious to know.
I do however recommend picking up data.table along the way because that is easily one of the best reasons to continue using R today.
https://sebkrantz.github.io/collapse/
In order to get those so much vaunted C-like speeds the Julia fanboys claim, you need lots of contortions and hacks. Off the bat, Julia speeds are mediocre.
IMHO, you really have to embrace dispatch-oriented programming, and that includes being scrupulous about avoiding type instability. You also have to be a bit conscious about allocations, since it's easy to write Julia code (especially if you're trying to write in a "vectorized" style as is common in R, Python, Matlab) that generates absurd numbers of allocations, which must then be garbage-collected. But also easy to avoid those allocations if you know.
It took about two years, but after picking up more of this, I was eventually able to switch everything my group does from a two-language solution of matlab for scripts and plotting and C (with MPI) for HPC to all-Julia. This [1] was originally targeted at academics making the same switch, but much of it could be relevant to those with an R background as well.
[1] https://github.com/brenhinkeller/JuliaAdviceForMatlabProgram...
Certainly R has its quirks, but most of this comes from being one of the oldest continuing existing programming languages there is. It derives from S which was written 46 years ago. Because of this it has multiple object/class systems reflecting the changing standards for OOP. It's most dominant one, S3, predates Java and therefore uses the Generic Function paradigm of OOP similar to Common Lisp's ClOS. If you're experienced but have never worked with non-Java style OOP you're going to be a bit confused.
R's most important feature, which is well worth studying and mastering for any serious programmer, is that it is a completely vectorized programming language. It borrows this style from APL (though is a million times more readable). Every value in R is a vector and for the vast majority of operations the best approach to solve your problem is by thinking in vector operations. This makes simple things like string formatting with `paste` seem like a confusing nightmare, but there is a real logic there. Functions like `ifelse` can seem strange, and writing C-style code in R, while possible will result in horrible performance.
Once you do learn to think in vectors you realize that R isn't just popular in the stats world because most statisticians haven't seen a "real" programming language, but because you can very rapidly iterate on models. Translating mathematical notation into R, for the experience R programmer, is easier than any other language I've worked with by a long shot.
My advice to any experienced programmer approaching R is to have some respect for the language. Most of the frustrations you'll have aren't because R is a bad language, but because you have less experience than you think and learning R well can expand your programming views in a similar way to Haskell.
And R excels at doing statistics and data science. If you keep that mindset, I believe many will find that its an excellent programming language.
We were essentially told "Install R Studio, then just copy and paste these library imports and you're good to go". Your description of a vectorized language makes total sense, and that single paragraph is more of an intro to R than we ever got in class.
That said, I think the reason this happened with this class in particular is the viewpoint of (what I perceive) as the majority user's of R. Mathematics focused researchers who never learned the language, they just have done enough to get by and don't really appreciate the underpinnings, or the nuances of running an environment on a machine that isn't theres.
I can't entirely absolve myself of blame though, once I realized what was happening I should have gone and done some more foundational R learning, but at that point I just wanted to be done with the class.
But for "the majority of professors who tangentially use R code in classes on statistics/bioinformatics/economics/finance (anything not explicitly about R and/or Data Science best practices)"? Absolutely.
The R code you see in industry (or academic labs where someone cares about modern R) looks vastly different from those script examples in college that are most people's first impression of the language.
Haskell is an advanced functional programming language. Most R stuff seems to be incoherent, hard to verify correctness, hacky. It does not seem built on a solid foundation like Haskell. Truly everything being a vector is not a huge take away.
As for "here's just a bunch of examples", well that seems sort of a brute force way to learn something. I agree examples are important, but they are usually to back something up. Having to reverse engineer some fundamental ideas out of just examples is more work. Seems like this is just promoting more hackyness. Seems like its training a neural network instead of actually understanding something.
If you define a language that "will expand your programming views" as one that can verify correctness easily then yeah, R is terrible at that. But so are many languages that are as flexible as R. Would you have the same opinion of FORTH? Or LISP? or TCL? I think these languages definitely count as "hacky" languages and yet they don't seem to draw the same derision as R (in my experience)
* Multiple dispatch. Before learning R, I knew about polymorphism in Java and C++, and multiple dispatch in R broadened my mind and turns out to be very handy.
* The idea of "frames". In R, when you invoke `lm(height~sex*age, data=mydataframe)`, the first argument (the formula) doesn't get evaluated until the lm command asks it to be evaluated, and lm can set up the "frame" for that evaluation, i.e. the place where variables are looked up, however it likes. In fact, lm sets it up to include variables from both the scope in which you invoked lm, and also from mydataframe. This is what makes R so wonderfully concise for modelling in data science, compared to e.g. Python + pandas. I knew about frames from interactive debuggers, but until R it never occurred to me that the programming language could manipulate them.
* "Held" arguments. In R, when you invoke `plot(x, y1+y2)`, it doesn't just evaluate the arguments and then call the plot function -- it leaves the arguments unevaluated, and invokes plot. Plot then (1) decides when to evaluate them, (2) gets access to the language expression `y1+y2`, which means that it can print "y1+y2" on the plot label, (3) it can even define extra variables to include in the scope when y1+y2 gets evaluated. (I knew about held arguments earlier, from Mathematica, but they only clicked when I read the R documentation.)
I've read that R is a descendent of Scheme, and that that's where it gets all its "manipulate language expressions" from. I don't know any Scheme, nor Lisp, and I should definitely learn them -- but in the meantime, my experience has been that R's ability to manipulate language expressions is what makes it such a wonderful sweet spot as a data modelling language. I mostly use Python + pandas nowadays, but it feels such a slog in comparison.
A. Understanding how to implement functions using syntactic forms is a steep learning curve. I remember running out of dplyr and having to implement a udf. Fairly unpleasant experience (enquo, !!, perhaps other unusual constructs). Felt like programming C macros.
B. "A function can decide where variables are looked up however it likes" is a significant obstacle in understanding how even basic constructs like function calls actually work. There is a non-trivial amount of hard-to-debug dark magic lurking behind every corner.
A middle ground has never been achieved. For example, `plot(expr(x), expr(y1 + y2))`, where the system limits the dark magic to explicit uses of the `expr()` construct, and `expr(x)` always means `{vars: vars(x), expr: (vars(x)) => x}`. Instead of patching interpreter environments, simply call a lambda function.
That's the weird thing about R. All this dark magic is hiding under the hood, but the core R team hid it so deftly that to the casual statistician it's a straightforward data modelling language that "just works". I'm not sure that it's possible to get rid of the dark magic and retain that data-modeller friendliness.
The same could technically be argued for Python; The current approaches to dealing with high-performance compute workloads either rely on JITing (e.g. Numba, Tensorflow/JAX's XLA) or bridging over to giant binary blobs through the CPython's well-supported C interop.
If you want to see the performance benchmark of R against major programming languages for data analysis please check this excellent keynote speech on the R compilation or more accurately on failures of R compilation effort [2].
As mentioned in [1], the main reason people do not focus on providing industrial strength compiler for R is probably because most of R program’s time are spent in the library codes that are written in a compiled language (e.g., C or Fortran). As you can see from [2], even though this is the case (computation intensive being delegated to proper compiled languages), the R programs running time still suffers due to the "impedance mismatch" because of the look up overheads, indirections, etc.
Perhaps someone should try to compile, transpile and/or embed R inside D language similar to the efforts provided in [3][4]. D now supports C compiler internally, has DasBetterC and D interface to C++ (DPP) is second to none. Additionally, since D can also perform better than Fortran codes for numerical computing perhaps Fortran codes can eventually be replaced by existing high performance D library like Mir[5]. I can foresee this symbiotic relationship can be beneficial for R and D (R on D?). D can becomes very popular with extra humongous data analysis and statistics libraries from CRAN, and R can get the run time and compilation performance improvement it badly needs.
[1]https://www.r-bloggers.com/2021/05/where-are-the-industrial-...
[2]https://www.youtube.com/watch?v=VdD0nHbcyk4
[3]https://dlang.org/blog/2018/06/20/how-an-engineering-company...
[4]https://theartofmachinery.com/2021/01/01/djinn.html
[5]http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/...
It’s not just vectorizing mathematical operations a la numpy.
S3 appeared a few years before Java but there were other OOP languages like C++ around at the time.
[0] There's an old article from Robert Gentleman named something like "S4 objects in 5 pages, more or less" but I can't find it. However, there's a mention of Dylan and CLOS here: https://genomebiology.biomedcentral.com/articles/10.1186/gb-...
EDIT: Here's the document I was looking to cite: https://www.stat.auckland.ac.nz/S-Workshop/Gentleman/S4Objec...
https://arxiv.org/pdf/1409.3531.pdf
Object-Oriented Programming, Functional Programming and R (John M. Chambers)
"Chambers and Hastie (1992), in the discussion of classes and methods, noted that S differed from other OOP languages because of its functional programming style. In fact, this version of functional OOP finessed the resulting distinction from encapsulated OOP in two ways. First, the methods were dispatched according to a single argument, the first formal argument of the generic function in principle. As a result, the methods were unambiguously associated with a single class, as they would be in encapsulated OOP. Methods were actually dispatched on either argument to the usual binary operators, but a number of encapsulated OOP languages do the same, under the euphemism of operator overloading.
"Second, the question of whether methods belonged to a class or a function was avoided by not having them belong to either. Methods were assigned as ordinary functions and identified by the pattern of their name: “function.class”. In any case, there were no class objects and generic functions were ordinary functions that invoked UseMethod() to select and call the appropriate method. Neither the function nor the class was able to own the methods."
R’s future is inseparable from the tidyverse. We need to just lump them together in any serious discussion of R.
I teach a graduate level R course mainly for economics and statistics majors. (My educational background and career are computer science and technical; while that may stereotype me into Python, I just love R.) I spend the first three weeks on base R, to convey language-essential concepts, like vectorized objects, then the rest of the course is tidyverse-centric.
https://www.tidyverse.org/ There is also a free ebook that’s a good reference.
ggplot2 the plotting package included is pretty awesome
I took a biostatistics class and after the basic examples in R using the tidyverse to analyze data for projects was very helpful.
R by itself is nice. But the tidyverse is creeping in and bringing dependency hell with it.
http://www.tinyverse.org/
at a push, base + data.table + ggplot2 get everything done. tinyverse ftw.
One other thing I would mention is knowing SQL well is the most translatable skill. A lot of dplyr and pandas are doing SQL like operations (in fact dbplyr will generate SQL equivalent commands for your dplyr code for various backends).
In summary know how to manipulate data in SQL then pick a language (because you will need to do some IO/reporting stuff outside just data work) where the ecosystem of packages feels user friendly to you and your work flow and roll with that.
I use only Python in my work. I learnt R and honestly, it's the same thing as using Python scientific packages. It's mostly vectorized operations, spaghetti functional, if people know how to write functions, code just to get it done. To make graphs, web dashboard(no, we are not doing web dev, it's dark magic frameworks), build machine learning and eventually some reports. Stuff like that.
I do some software engineering but that s optional and I do it because I can. Most data scientists/ ml engineers can't. So you guys are not fair. R and Python in these environments are not even being used for building stuff. This language is not build for that. Hence it's not good from the perspective you look at it from(software engineers).
Unlike Python , R is solely for statistics, data science and probably some basic ml( I haven't tried tho). Also Shiny for building web dashboards. But don't look at the code for dashboards, it's bad, with 'get it done and forget' approach.
That being said. Good luck scrapping, mining, cleaning data with something not called R/Python. Good luck with data engineering. Exploring and visualizing trends. Creating dashboards even. Machine learning. Monitoring and reporting in scientific manner.
Try This type of work with your favorite languages. Then see how quickly and easily it's done with R/Python . Come back and say it's bad language.
It's the same thing as embedded dev complaining about how bad js is for his job. You just totally ignore the context.
R is a fantastic DSL for data manipulation and statistical analysis, with both traditional and modern tools, on datasets up to the gigabyte scale. It has great, easy-to-use data structures and unparalleled APIs in the tidyverse. It is not the thing for the latest deep learning implementation on petascale data, but most data science work doesn't need or benefit from that. Surprisingly, some machine learning methods have their nicest APIs in R, because that's where the users of those methods are.
It has its warts, but so do JavaScript and SQL, and I think few people dispute that these are very powerful DSLs. Statistical analysis is just as legitimate a computing task as building webpages or querying databases. It is not the same as general-purpose programming, and it needs a good DSL.
Edit: I found an interesting quote from a guy named Martin Fowler about the subject.
“Languages can have a domain focus but still be general-purpose languages. A good example of this is R, a language and platform for statistics; it is very much targeted at statistics work, but has all the expressiveness of a general-purpose programming language. Thus, despite its domain focus, I would not call it a DSL.”
Apart from that, I think there's a bigger challenge which still needs to be addressed is that analysis/modelling projects tend to be worked on by individuals and/or thrown away after the initial value is pulled out of them.
Going forward, I think we need to start identifying design methodologies that would make collaborating on this sort of work pain-free and more agile. Doing so should give us more value and sooner and for longer.
There are issues with R: lots of weird quirks; many different ways of doing things, often just supported for legacy reasons; poor error messages; to name just a few. But on the positive side, it really encourages functional-style programming through the use of apply functions and many of the new tidyverse packages. The resulting code can be very neat and less error-prone than equivalent python code.
The S3 system of OOP initially struck me as very weird, but now I see it as essentially just single dispatch, not really much of OOP at all. It works quite well and is extremely simple.
I guess my thoughts are: approach R as a functional language, and I think you'll find much to like. Try to write C++ or python-style OOP in it, and you'll just find it very strange.