14 ms·

Why I use R

185 points by cardosof 7y ago

creed98 7y ago
How does R work for text? Easy to batch process words in data frames?
- twelfthnight 7y ago
  Here's a great book on this: https://www.tidytextmining.com/ https://www.tidytextmining.com/. Incidentally, the author is joining RStudio next month. However, in my experience sklearn's feature extract methods [1] are more straightforward and NLP libraries like Pytorch/Tensorflow, Spacy, NLTK, Gensim and Snorkel are more geared toward python as well. [1] https://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction https://scikit-learn.org/stable/modules/feature_extraction.h...
bayesian_horse 7y ago
Some good points. Certain things are easier in R and often you can find code snippets that perform certain statistical tasks "the right way" and they are more concise than they would be in Python. To me, R always felt a bit quirky. I don't think R is much better at functional programming than Python. I found R to be limiting in terms of general programming. Also, we now have type checking. I'm positive you could combine type checking and clever type declarations to handle application state like in elm.
- Cosi1125 7y ago
  > I don't think R is much better at functional programming than Python. R is a functional programming language. Does Python treat functions as first-class citizens? Pass arguments by value? Store expression trees as data structures?
  - bayesian_horse 7y ago
    Python can do all that. It doesn't force you to do it, and handling expression trees may be a bit complicated (using ast). For me the main defining feature of functional programming is the ability to pass functions and the ability to avoid side effects. If I remember correctly, I often had trouble with side effects in R.
    - Cosi1125 7y ago
      Could you elaborate on that? R is not a pure functional language (like, e.g., Haskell), so it's possible to produce side effects, or even "leak state", but it's hard to do it accidentally.
  - Sean1708 7y ago
    First class functions and pass-by-value have been in basically every language that was designed in the last 30 years, and have been added to most of the popular ones that existed before that. Homoiconicity is less common, but is still present in some form in most recent languages that were originally designed to be interpreted or JIT-compiled (Python is no exception here).
psv1 7y ago
If you're: - working interactively (i.e. your code isn't part of a larger application) - working with relatively small datasets that fit into memory - don't need any deep learning libraries then both R and Python can do a great job and choosing one over the other is simply a matter of preference. I might even lean slightly towards R because its data frames are a bit easier to use than pandas and RStudio's REPL is the best. But if you need to deploy your code somewhere, or high performance, or the latest deep learning libraries, then Python absolutely crushes R. And it's not even close.
- fock 7y ago
  while with high-performance you mean documented FFI and friends? this should be possible with R as well. It also seems that the actual open-source ML-community (vs. Google: we want you to use our software to ensure you can't ever own your stuff) supports R just fine: https://mxnet.apache.org/api/r https://mxnet.apache.org/api/r
- ekianjo 7y ago
  Note that you can call Python from R, and vice versa too.
  - rubatuga 7y ago
    For the record, the last time I called python from R its memory usage ballooned to 10 times its normal size.
  - psv1 7y ago
    That's one of those things that make for a nice Medium article, but in practice work horribly or not at all.
- WhompingWindows 7y ago
  How are you defining 'high performance'? I think R's data.table is quicker on a number of metrics than comparable packages from Python.
OldGuyInTheClub 7y ago
Insightful article on many fronts. I haven't had time to learn R but have been impressed by what it offers especially RStudio as an environment. I use Matlab from time-to-time and like having equivalent features that are of great help in initial data exploration and code experimentation. I haven't found anything fully equivalent in the Python world.
bransonf 7y ago
As a very frequent R user, I actually find non-standard evaluation to be more hassle than good in most situations. If you want to program with most of the tidyverse libraries, you are forced to implement a bunch of non-sense into your function to properly evaluate arguments within a function. Sure, NSE may be useful in some circumstances, but more often than not, it just increases the likelihood of introducing a bug. Especially for new programmers, NSE is a huge leap and very confusing. The old solution was exporting underscore ie: ‘mutate_()’ suffixed functions that used standard evaluation. And this was fine. But, then RStudio decides to deprecate these functions and force NSE on users. I’m not happy about that, and I often avoid using libraries like dplyr when writing functions so that I don’t have to deal with it.
- meanmrmustard92 7y ago
  Agreed. Recent updates to rlang have made programming dplyr/ggplot2 functions a bit better, but it still feels super clunky. I use data.table for most things for programmability and speed reasons. As much as I like ggplot2, I find the rest of the tidyverse to be solving problems it invents (e.g. quosures to fix the problem of not permitting string arguments for dplyr verbs) and monopolising an open source ecosystem.
  - lottin 7y ago
    I steer clear of tidyverse as well. At some point I used to like ggplot but this is because I didn't know lattice yet. lattice is a lot like ggplot but it doesn't come with "strings attached" and the plots look a little nicer.
  - wodenokoto 7y ago
    I struggle to find the logic in the data.table interface, whereas dplyr is a joy to use, at least interactive. I agree that it is slow, and when things break apart, heavy the NSE use in dplyr really comes back to bite you.
- naiveprogrammer 7y ago
  Same. The moment I felt that my brain was melting over NSE and dplyr was the moment I started to phase out most tidyverse stuff from my work. I've switched to data.table and plain R for most of my stuff now.
bayesian_horse 7y ago
One advantage of R is that it is easier or faster to teach enough R to non-programmer scientists such that they can do their own statistical stuff.
ggm 7y ago
I tried R with JSON and R with Elastic Search and both interfaces were 'abandonware' with 'just use the HTTP paradigm and deal with the data frame afterward' outcomes. Felt like a really low bar, to get real-world data forms imported, and find the <- deplyr functional bindings pretty much 'not there' I liked Shiny, because of the low barrier to deployment of a GUI inside the company leveraging R for the graphing. But integration is .. painful.
- meztez 7y ago
  Were you using jsonlite? Great library that can parse directly from a URL and transform into data.frame depending on JSON representation. I agree it is not clear from the get go which library should be used when you do something outside of base R. You kind of need to be in the known
  - ggm 7y ago
    I think I was, yes. This is 2+ years ago. I haven't gone back because I got there with REST fetches and internalized re-framing of the JSON. I was just amazed how 'crude' it was, and that the dplyer bindings for ElasticSearch were non-functional (for me at least)
- psv1 7y ago
  The elastic library on CRAN got a major update a few months ago which made it much more powerful and easier to use. Makes uploading easy too, especially if there's a direct relationship between the structure of your dataframe and the Elastic json. Uploading nested structures is still a bit of a pain though.
  - ggm 7y ago
    Thanks. Good to know, we may re-visit things. Elastic itself is a pain to manage, and with data growth we're re-examining what we get from it. For time series data, there is an element of "why?" about it: PostGres as a time series JSON blob store, might work better for us. The sharding by time is easy to understand and it has more affinity to the underlying host OS. Elastic, you have to understand a LOT of moving parts to make it work well (or be a Kubernetes expert, or pay somebody else) -if you already have an investment in PG then its possibly better use of your time. But its good to know the interface got some eyeballs. I retract the accusation of abandonware.
danielecook 7y ago
R is the right tool in many cases but I've observed several cases where people have become overreliant on it and hacked together things that would be better written in bash, python, or other languages.
- epistasis 7y ago
  Is there any language for which the same could not be said?
twelfthnight 7y ago
I'm happy to have read this article as it has a different perspective on R than I am used to. My general take is that Python really is a better suited language for production workflows, while R is superb at interactive workflows. I love R, but chiefly for RMarkdown, Shiny, ggplot and obscure stats packages rather than large machine learning codebases. Here's why: > Native data science structures. DataFrames are often easier to use than Pandas. However, in production workflows we're often using more datatypes than DataFrames, and R is weaker there. For example: * Lists must be accessed with `[[ ]]`, instead of `[ ]`. I've seen many silent bugs slip through due to this. * There are 3 competing implementation of classes. This results in classes being mystical and rarely understood. * R is a Lisp 2. Variables and functions may share the same name. This leads to confusing errors. * Catching specific types of errors can be awkward. * Adding elements to a list iteratively is slow [1] > Non-Standard Evaluation This can be handy while quickly working in RStudio, but it's not easy to maintain. I've seen code that failed because it specified `f(!!variable)` instead of `f(!!!variable)`. I like R's formula notation, but I'm happy enough with sklearn's API that I don't miss it. > The glory of CRAN CRAN is not set up for production. It makes pinning versions very difficult [2]. Many people resort to using MRAN, which is a Microsoft supported snapshot of CRAN at a specific time, so a dev can just pretend they are installing software as if it were 6 months ago. I have seen MRAN go down multiple times [3]. Not to mention, the owner of CRAN is notoriously prickly [4] and packages will not be accepted to CRAN unless the maintainer ensures their software runs on Solaris [5]. Hadley Wickham has done so much for the community with `devtools` and his books. He gets a lot of praise, but it's not misplaced. > Functional programming Okay, this is actually pretty great. Hooray functional programming! Not totally related, but R has a great polymorphic dispatch of functions, which really can't be undersold (the way automatic documentation generates for this is kisses fingers) Ultimately, R is a cool language. In interactive settings, I would rather work in RStudio than Jupyter any day. I like RMarkdown better than Notebooks for sharing analysis, too. If there is a specific Bayesian model necessary only available in R, that's fine, wrap it in a container. But the rest of the ETL and pipeline code feels easier to write and maintain in Python. [1] https://stackoverflow.com/questions/17046336/here-we-go-again-append-an-element-to-a-list-in-r https://stackoverflow.com/questions/17046336/here-we-go-agai... [2] https://stackoverflow.com/questions/17082341/installing-older-version-of-r-package https://stackoverflow.com/questions/17082341/installing-olde... [3] https://github.com/Microsoft/microsoft-r-open/issues/51 https://github.com/Microsoft/microsoft-r-open/issues/51 [4] https://www.reddit.com/r/rstats/comments/2t5oqp/dont_use_the_mailing_lists_a_love_letter_to_the_r/ https://www.reddit.com/r/rstats/comments/2t5oqp/dont_use_the... [5] http://www.deanbodenham.com/learn/r-package-submission-to-cran.html http://www.deanbodenham.com/learn/r-package-submission-to-cr...
thetwentyone 7y ago
To give a quick comparison with Julia: 1. Native Data types - this is one of the things that Julia was designed to do very well. That is, native-like-treatment for all data without needing to have a C-family underbelly like Python does for its high performance code. 2. Non-Standard Evaluation - Julia has Metaprogramming[1] and Symbols[2] which provide similar ideas in a different way. It uses abstract syntax trees and is very lisp-like in that way if you wanted to get into writing Macros and such. 3. Package Management - Julia has a best-in-class built in package management system with versioning. Julia also has first-class support for documentation, so its very easy for developers to write relevant documentation. As an R user before RStudio, package management was a pain but RStudio hides the manual work that used to be searching for, downloading, and unpacking packages. Packages usually work really well together, usually automatically so you can often get really cool results[3] where other languages would require a lot of coordination (like Tidyverse). 4. Function paradigm - Julia is multi-paradigm and is conducive to functional, imperative, object-oriented, among others. I'm a big Julia fan, after having gone R -> Python -> Julia. Not to make this totally in favor, I still like R for plotting because it's more mature. RStudio also is very nice for dynamically interacting with datasets, but Juno comes pretty close there too. 1: https://docs.julialang.org/en/v1/manual/metaprogramming/# https://docs.julialang.org/en/v1/manual/metaprogramming/# 2: https://stackoverflow.com/questions/23480722/what-is-a-symbol-in-julia https://stackoverflow.com/questions/23480722/what-is-a-symbo... 3: https://www.youtube.com/watch?v=HAEgGFqbVkA https://www.youtube.com/watch?v=HAEgGFqbVkA
- kwertzzz 7y ago
  Actually, for plotting I prefer to use PyPlot in Julia which is based on matplotlib which is very mature and complete in my opinion. I tried to use other (more native) plotting packages like GR, Plots and Makie but they did not provide all the plotting types I needed or where to rough around the edges. In any case, I am looking forward to new julia versions which should address the delay in plotting (as far as I known).
  - fock 7y ago
    why do you have to look forward to new versions of the base language if you have a problem with plotting?
LyndsySimon 7y ago
This mostly strikes me as ”I know R better than I know Python” - which is fair for deciding what to use, but is obviously not an objective comparison. Hopefully I’ll have time tomorrow to write a rebuttal for some his arguments. Particularly, the preference for CRAN and code longevity strikes me as being shortsighted.
- missosoup 7y ago
  R is objectively worse than Python for almost all data science tasks, and R is a huge PITA to productionise compared to Python. I've yet to see any argument for R that doesn't boil down to 'well, I know it better' or 'well, I prefer the syntax'. R to data science is as Matlab is to engineering. It's a stopgap 'non programmer' language that thrived at a time when most academics didn't know any programming. Now school children learn programming. There is no use case for these languages anymore.
  - exdsq 7y ago
    > There is no use case for these languages anymore. There are entire ecosystems of academic libraries built around Matlab that can’t all just be picked up and moved to Python. This argument probably doesn’t realise just how ingrained Matlab is in STEM non-CS academic departments. Example: my girlfriends department writes a world-leading MRI analysis library in Matlab. They offer training courses on it (so departments around the world now know it) and it’s frequently used within academic papers (so there are now resources available on it). Why would they move to Python?
    - hatmatrix 7y ago
      Also, Simulink - there is no equivalent in Python and many industries use this.
      - exdsq 7y ago
        https://uk.mathworks.com/solutions/aerospace-defense/standards/nasa.html https://uk.mathworks.com/solutions/aerospace-defense/standar... Yep, NASA used it alongside Matlab for the Orion's Guidance and Navigation Control systems. I've never had the chance to use it though, it looks pretty interesting.
tbenst 7y ago
The part on “Make code more concise“ struck me as an anti-pattern that should instead be a simple function composition. Or the functional programmer in me notes that the author re-invented the Maybe monad. I do like the point in Learn the user’s language, as friendlier error messages is something we should all strive for, although I’ve never had an issue with particular problem in Python’s stacktraces, and actually having types like Julia or at least annotations via mypy seems a better solution. CRAN is a great point, and pythons packaging is in a sorry state with a crazy number of approaches and undeclared dependencies. R does a great job here. Functional programming section is ironic given the lack of functional patterns in the post. R has even fewer higher order functions than the python standard library. It’s hard for me to see how R is better for production than python, and the argument against pandas seems a bit strawman considering that numpy/scipy are quite stable and more central to the ecosystem than DataFrame. R is fantastic for data science and highly productive, until you need to do data mugging or anything else that involves a general purpose language.
- cameronh90 7y ago
  "CRAN is a great point, and pythons packaging is in a sorry state with a crazy number of approaches and undeclared dependencies. R does a great job here" But for production usage, again it's a huge pain. It's difficult to keep version stability with developer machines since there's no standard lock file, and the CRAN servers often delete or silently update old versions of packages. Packrat and Microsoft's MRAN really helps, but another curious issue is that it and other CRAN servers seem to have terrible stability - often going down for hours at a time (or worse). Python's packaging is ~impossible to understand for new developers (and really absolutely needs improving), but in an organisation you just pick an approach suited for your use case.
  - tbenst 7y ago
    Nix really shines here, has ~17000 R packages pinned and reproducibly built: https://hydra.nixos.org/eval/1560470#tabs-still-succeed https://hydra.nixos.org/eval/1560470#tabs-still-succeed
- zamalek 7y ago
mikorym 7y ago
For those interested, there is a nice Vim plugin for R called Nvim-R.
uptownfunk 7y ago
R was that language I learnt so many years ago. I love it actually, have so many fond memories. It’s hard to find something better especially for prototyping. I will admit productionalizing has almost drag and drop simplicity in python compared to R. I love love love what Hadley has done with Dplyr for the most part, at least in spirit, though I think the implementation could have been done better so as not to be so clunky, esp wrt NSE. But I think he is just trying to work within the current R ecosystem. Which makes me ask.. is it then time for R2? (Like a Python 3). Before you shoot me.. Do we need to save the good things we have innovated from within the R ecosystem over the years and consider doing things from scratch? Is this what Julia tried to do? I haven’t gotten around to trying it yet. That said, I think R is always going to be there and have it’s place. Frankly if they could just make an IDE like Rstudio that ran python, I’d probably be happy enough with that. I heard with reticulate you can run both, curious to hear of others experience with this..
- bayesian_horse 7y ago
  You should try Jupyter lab. It has notebooks, text editors and more.
  - hatmatrix 7y ago
    The whole problem with notebooks is that it's not a simple text file. On the other hand has anyone tried Rodeo for Python?
    - subroutine 7y ago
      I've used it. It does have the feel of RStudio IDE if that's what you're getting at.
    - uptownfunk 7y ago
      I wanted to like it but it kept crashing when I tried it a year or two back, I may give it another shot. I do recall it was closest to what i wanted..
    - bayesian_horse 7y ago
Gatsky 7y ago
R is interesting in that it has one library which has zero real competitors to the point where it becomes a justification for using the entire language: ggplot2. It's almost like ggplot2 crosses over from a package to an application, and R is the user interface. Any examples like this in other languages? I can't think of any, maybe Python & scikit-learn 5 years ago.
- alexilliamson 7y ago
  Ruby and Rails?
- nwallin 7y ago
  Can you compare ggplot2 to matplotlib? 90 seconds of googling didn't seem to indicate to me that ggplot2 is particularly different, either in terms of its power or expressivity, than matplotlib. Those 90 seconds of googling constitute my entire knowledge of ggplot2.
  - Gatsky 7y ago
    From people that have used both extensively (which does not include you or me, it would seem), I always hear that ggplot2 is unsurpassed. Admittedly, that isn't a satisfactory answer to your question.
wjak 7y ago
R is good for machine learning and for production. We have helped big orgs to incorporate this technology in their it ecosystem. We used our open-source product called R Suite to manage deployment issues. https://github.com/WLOGSolutions/RSuite https://github.com/WLOGSolutions/RSuite
not_the_nsa 7y ago
Not mentioned are the fantastic user communities, especially the culture of inclusiveness and openness fostered by RStudio ([code of conduct](https://github.com/tidyverse/dev-day-2019/blob/master/CODE_OF_CONDUCT.md) https://github.com/tidyverse/dev-day-2019/blob/master/CODE_O...) and [rOpenSci](https://ropensci.org/community/ https://ropensci.org/community/). Basically the inverse of SnarkOverflow. Especially rOpenSci's peer review process ([more here](https://devguide.ropensci.org/softwarereviewintro.html) https://devguide.ropensci.org/softwarereviewintro.html)) for R packages is fantastic. I do most data engineering in R (RMarkdown workbooks), and most software engineering in Python/Django. It took three separate, dedicated attempts to get warm with R (pre-tidyverse, showing my age), now I'm interrupting work on an RShiny app to write this comment. The ecosystem around the tidyverse helps immensely to convert my colleague's workflows from Excel to R. Clarity and simplicity wins over purity here (you may now light your pitchforks). And NSE still breaks my brain.
- FranzFerdiNaN 7y ago
  I think the community that R users have managed to create is it's strength. Python has a community of software developers, while R has a community of people who use programming to solve their problems. It's a fundamental difference in mindset, one which really shapes the community. What also helps is that R is so focused on data and statistics. It gives a focus to the users that really helps when it comes to finding help. Python is famously second best at everything, but that also means it's community is spread thinner over more subjects.
  - halbritt 7y ago
    It's this same reason that running anything that depends on R in production is a PITA.
    - FranzFerdiNaN 7y ago
      I run R in production and its absolutely fine and wasnt harder than pretty much any other thing in software development.
darkhorn 7y ago
It is wrong to compare R with Python. R should be compared with NumPy + Jupyter.
j7ake 7y ago
I use R for the statistical packages, handling data frames, and plotting. For other types of work I would switch to python.
stoops 7y ago
How do I apply a function to every element of a data frame where that function takes as input the i,j indices of the element along with its value? This is a problem I struggled on for weeks in college. Eventually having to hack something together that relied on modifying the underlying data frame. I've not return to R since as python has always had better libraries and easier to deploy.
- ksevastyanenko 7y ago
  In R you'd typically want to operate over vectors (rows or columns, with columns being the faster option) rather than on individual values. This requires a bit of a mental shift when you come to R from a C/Cuda background or even python. You can find the man pages right in R console - look up `?lapply` for column-wise operations and `?apply` for row-wise. When it comes to data.frame transformations you are typically better off using packages from `hadleyverse` - check https://github.com/hadley/reshape https://github.com/hadley/reshape and https://tidyr.tidyverse.org/ https://tidyr.tidyverse.org/ Of course, what's important is not the technology used, but the problem solved. Fantastic that python works for you.
- anthony_doan 7y ago
  You use one of the family of `apply` function.
- mpjdem 7y ago
  If you need i,j indices to solve your problem you should probably not be using a data.frame but a matrix type.
  - anthony_doan 7y ago
    If he/she chose to use dataframe then the data is probably multiple type. Matrix data structure can only have one type.
tomerbd 7y ago
I used to use R because of RStudio until JupyterLab came and flipped it all over now I use only bash and python for small data analysis as well.
superMayo 7y ago
As an applied economist, I can assure you that R is wayyy better in terms of statistics than Python. Doing causal models in Python is a real pain because its data science community is mostly focused on machine learning. Even a robust linear model with IV or fixed effect (which is standard in causal models) is really hard to achieve in Python. Of course this argument is about the libraries and note the core language, but it's a strong argument to stay with R. This argument is also valid outside academics, as more and more data scientists try to adress causality.
sqrt17 7y ago
Python as a text processing language has been less convenient than Perl for a long time Python+(Django, Flask) as a Web service language hasn't been as convenient as Ruby on Rails for some time Python+numpy as a numeric computation language hasn't had all the features of Matlab Python+pandas+matplotlib as a data science language hasn't had all the features of R but using Python throughout, or even Python with a sprinkling of Cython/C++/C for performance, allows for cleaner and faster engineering than using a special language for each niche. I don't think that R has a bigger problem with non-programmers being bad software engineers than Python has - there are plenty of people who know Python passably and are quite happy that they can be productive without being good software engineers (versus Java where the intent of the language is biased for everyone to write code with a minimum quality standard rather than everyone to write code with focus on being productive). But you can find decent Python software engineers, and more recently you can find decent software engineers who also know enough of the niche in question to produce high-quality production code in that niche from the get-go rather than throwing models over a data science - engineering wall that exists between two departments.
- PerryCox 7y ago
  I think this is a very valid point. There are times when Python is not the absolute best choice for a given problem, but in most cases it can allow you to achieve the desired goal in a way that is easy for newcomers to the particular codebase to grok very quickly. There are times when it may be better to use another language and there is nothing wrong with that. I default to Python and if I think there will be specific issues with it, then I can look at a more specialized language.
CapmCrackaWaka 7y ago
I actually use R mostly because of its data.table package. It is much faster and more concise than pandas, which is a nightmare to work with. Sure, you can get the job done in pandas, but you often have to wait ~10x longer for your commands to run and sometimes, I simply cannot use pandas at all because I run out of memory. https://h2oai.github.io/db-benchmark/ https://h2oai.github.io/db-benchmark/ People are usually pretty surprised when I take the stance that R is faster than Python for the things most people actually care about, which is data manipulation and model building. Python has its datatable library which is approaching data.tables speed however it is very much a work in progress, and does not have very useful features yet.
- qwhelan 7y ago
  FWIW it looks like pandas is slow/OOM-ing because the benchmarks solely use Categoricals, which aren't as heavily used by pandas users compared to R. In particular, I suspect the benchmark sizing is forcing falling back from numpy's int64 to Python ints as categorical labels, which easily could explain a 10x or more differential.
RocketSyntax 7y ago
Shiny
YeGoblynQueenne 7y ago
Look, I use R (for plottng mainly) but it's a hideous abomination. It's like some kind of giant, horrible patchwork chimera put together by evil golems with the word "CONFUSION" carved on their foreheads. Yes, CRAN is magickal and wonderful, a package manager that works 90% of the time and doesn't make you want to eat your computer. Yes, Python's package management is borked with a borky borker. But- Python, like most programming languages this side of Leboge was designed _as a programming language_. Like, _for programming_. From the very start. So all you need to know, once you have it up and running is _what you want to program_ and not H O W to program it. The "H O W" is terribly, awfully important. Because in R, anything you want to do, you have to know the super secret mystical occult incantation that does it (and nothing else will do it). You can't just intuit syntax. Oh no. You _have_ to know _exactly_ what code to write. Otherwise- run, you fools! Here's my latest and greatest. I wanted to automatically adjust the position of a legend in a plot so that it will not overlap the lines in the plot. How do you do that? Well, it turns out that the function legend() returns ... the position and dimensions of the legend's rectangle relative to the plot margins. Wait what? Why would it _do_ that? Why would a function called "legend" not return, oh, I don't know - a _bloody_ _legend_? Well, because it's R, that's why. That's not how most programming languages work, because that's not what most programmers expect, because that's not how most programming languages work. R is far out there in the oubliette of languages used by non-programmers, that work not like programmers would epxect them to work because they're languages not for programmers. No shit non-programmers take to it. Because who else would? Well. Not programmers. /first 2020 programming rant

Why I use R

185 points by cardosof 7y ago

creed98 7y ago

How does R work for text? Easy to batch process words in data frames?

twelfthnight 7y ago
Here's a great book on this: https://www.tidytextmining.com/ https://www.tidytextmining.com/. Incidentally, the author is joining RStudio next month. However, in my experience sklearn's feature extract methods [1] are more straightforward and NLP libraries like Pytorch/Tensorflow, Spacy, NLTK, Gensim and Snorkel are more geared toward python as well. [1] https://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction https://scikit-learn.org/stable/modules/feature_extraction.h...

bayesian_horse 7y ago

Some good points. Certain things are easier in R and often you can find code snippets that perform certain statistical tasks "the right way" and they are more concise than they would be in Python. To me, R always felt a bit quirky. I don't think R is much better at functional programming than Python. I found R to be limiting in terms of general programming. Also, we now have type checking. I'm positive you could combine type checking and clever type declarations to handle application state like in elm.

Cosi1125 7y ago
> I don't think R is much better at functional programming than Python. R is a functional programming language. Does Python treat functions as first-class citizens? Pass arguments by value? Store expression trees as data structures?
- bayesian_horse 7y ago
  Python can do all that. It doesn't force you to do it, and handling expression trees may be a bit complicated (using ast). For me the main defining feature of functional programming is the ability to pass functions and the ability to avoid side effects. If I remember correctly, I often had trouble with side effects in R.
  - Cosi1125 7y ago
    Could you elaborate on that? R is not a pure functional language (like, e.g., Haskell), so it's possible to produce side effects, or even "leak state", but it's hard to do it accidentally.
- Sean1708 7y ago
  First class functions and pass-by-value have been in basically every language that was designed in the last 30 years, and have been added to most of the popular ones that existed before that. Homoiconicity is less common, but is still present in some form in most recent languages that were originally designed to be interpreted or JIT-compiled (Python is no exception here).

psv1 7y ago

If you're: - working interactively (i.e. your code isn't part of a larger application) - working with relatively small datasets that fit into memory - don't need any deep learning libraries then both R and Python can do a great job and choosing one over the other is simply a matter of preference. I might even lean slightly towards R because its data frames are a bit easier to use than pandas and RStudio's REPL is the best. But if you need to deploy your code somewhere, or high performance, or the latest deep learning libraries, then Python absolutely crushes R. And it's not even close.

fock 7y ago
while with high-performance you mean documented FFI and friends? this should be possible with R as well. It also seems that the actual open-source ML-community (vs. Google: we want you to use our software to ensure you can't ever own your stuff) supports R just fine: https://mxnet.apache.org/api/r https://mxnet.apache.org/api/r
ekianjo 7y ago
Note that you can call Python from R, and vice versa too.
- rubatuga 7y ago
  For the record, the last time I called python from R its memory usage ballooned to 10 times its normal size.
- psv1 7y ago
  That's one of those things that make for a nice Medium article, but in practice work horribly or not at all.
WhompingWindows 7y ago
How are you defining 'high performance'? I think R's data.table is quicker on a number of metrics than comparable packages from Python.

OldGuyInTheClub 7y ago

Insightful article on many fronts. I haven't had time to learn R but have been impressed by what it offers especially RStudio as an environment. I use Matlab from time-to-time and like having equivalent features that are of great help in initial data exploration and code experimentation. I haven't found anything fully equivalent in the Python world.

bransonf 7y ago

As a very frequent R user, I actually find non-standard evaluation to be more hassle than good in most situations. If you want to program with most of the tidyverse libraries, you are forced to implement a bunch of non-sense into your function to properly evaluate arguments within a function. Sure, NSE may be useful in some circumstances, but more often than not, it just increases the likelihood of introducing a bug. Especially for new programmers, NSE is a huge leap and very confusing. The old solution was exporting underscore ie: ‘mutate_()’ suffixed functions that used standard evaluation. And this was fine. But, then RStudio decides to deprecate these functions and force NSE on users. I’m not happy about that, and I often avoid using libraries like dplyr when writing functions so that I don’t have to deal with it.

meanmrmustard92 7y ago
Agreed. Recent updates to rlang have made programming dplyr/ggplot2 functions a bit better, but it still feels super clunky. I use data.table for most things for programmability and speed reasons. As much as I like ggplot2, I find the rest of the tidyverse to be solving problems it invents (e.g. quosures to fix the problem of not permitting string arguments for dplyr verbs) and monopolising an open source ecosystem.
- lottin 7y ago
  I steer clear of tidyverse as well. At some point I used to like ggplot but this is because I didn't know lattice yet. lattice is a lot like ggplot but it doesn't come with "strings attached" and the plots look a little nicer.
- wodenokoto 7y ago
  I struggle to find the logic in the data.table interface, whereas dplyr is a joy to use, at least interactive. I agree that it is slow, and when things break apart, heavy the NSE use in dplyr really comes back to bite you.
naiveprogrammer 7y ago
Same. The moment I felt that my brain was melting over NSE and dplyr was the moment I started to phase out most tidyverse stuff from my work. I've switched to data.table and plain R for most of my stuff now.

bayesian_horse 7y ago

One advantage of R is that it is easier or faster to teach enough R to non-programmer scientists such that they can do their own statistical stuff.

ggm 7y ago

I tried R with JSON and R with Elastic Search and both interfaces were 'abandonware' with 'just use the HTTP paradigm and deal with the data frame afterward' outcomes. Felt like a really low bar, to get real-world data forms imported, and find the <- deplyr functional bindings pretty much 'not there' I liked Shiny, because of the low barrier to deployment of a GUI inside the company leveraging R for the graphing. But integration is .. painful.

meztez 7y ago
Were you using jsonlite? Great library that can parse directly from a URL and transform into data.frame depending on JSON representation. I agree it is not clear from the get go which library should be used when you do something outside of base R. You kind of need to be in the known
- ggm 7y ago
  I think I was, yes. This is 2+ years ago. I haven't gone back because I got there with REST fetches and internalized re-framing of the JSON. I was just amazed how 'crude' it was, and that the dplyer bindings for ElasticSearch were non-functional (for me at least)
psv1 7y ago
The elastic library on CRAN got a major update a few months ago which made it much more powerful and easier to use. Makes uploading easy too, especially if there's a direct relationship between the structure of your dataframe and the Elastic json. Uploading nested structures is still a bit of a pain though.
- ggm 7y ago
  Thanks. Good to know, we may re-visit things. Elastic itself is a pain to manage, and with data growth we're re-examining what we get from it. For time series data, there is an element of "why?" about it: PostGres as a time series JSON blob store, might work better for us. The sharding by time is easy to understand and it has more affinity to the underlying host OS. Elastic, you have to understand a LOT of moving parts to make it work well (or be a Kubernetes expert, or pay somebody else) -if you already have an investment in PG then its possibly better use of your time. But its good to know the interface got some eyeballs. I retract the accusation of abandonware.

danielecook 7y ago

R is the right tool in many cases but I've observed several cases where people have become overreliant on it and hacked together things that would be better written in bash, python, or other languages.

epistasis 7y ago
Is there any language for which the same could not be said?

twelfthnight 7y ago

I'm happy to have read this article as it has a different perspective on R than I am used to. My general take is that Python really is a better suited language for production workflows, while R is superb at interactive workflows. I love R, but chiefly for RMarkdown, Shiny, ggplot and obscure stats packages rather than large machine learning codebases. Here's why: > Native data science structures. DataFrames are often easier to use than Pandas. However, in production workflows we're often using more datatypes than DataFrames, and R is weaker there. For example: * Lists must be accessed with `[[ ]]`, instead of `[ ]`. I've seen many silent bugs slip through due to this. * There are 3 competing implementation of classes. This results in classes being mystical and rarely understood. * R is a Lisp 2. Variables and functions may share the same name. This leads to confusing errors. * Catching specific types of errors can be awkward. * Adding elements to a list iteratively is slow [1] > Non-Standard Evaluation This can be handy while quickly working in RStudio, but it's not easy to maintain. I've seen code that failed because it specified `f(!!variable)` instead of `f(!!!variable)`. I like R's formula notation, but I'm happy enough with sklearn's API that I don't miss it. > The glory of CRAN CRAN is not set up for production. It makes pinning versions very difficult [2]. Many people resort to using MRAN, which is a Microsoft supported snapshot of CRAN at a specific time, so a dev can just pretend they are installing software as if it were 6 months ago. I have seen MRAN go down multiple times [3]. Not to mention, the owner of CRAN is notoriously prickly [4] and packages will not be accepted to CRAN unless the maintainer ensures their software runs on Solaris [5]. Hadley Wickham has done so much for the community with `devtools` and his books. He gets a lot of praise, but it's not misplaced. > Functional programming Okay, this is actually pretty great. Hooray functional programming! Not totally related, but R has a great polymorphic dispatch of functions, which really can't be undersold (the way automatic documentation generates for this is kisses fingers) Ultimately, R is a cool language. In interactive settings, I would rather work in RStudio than Jupyter any day. I like RMarkdown better than Notebooks for sharing analysis, too. If there is a specific Bayesian model necessary only available in R, that's fine, wrap it in a container. But the rest of the ETL and pipeline code feels easier to write and maintain in Python. [1] https://stackoverflow.com/questions/17046336/here-we-go-again-append-an-element-to-a-list-in-r https://stackoverflow.com/questions/17046336/here-we-go-agai... [2] https://stackoverflow.com/questions/17082341/installing-older-version-of-r-package https://stackoverflow.com/questions/17082341/installing-olde... [3] https://github.com/Microsoft/microsoft-r-open/issues/51 https://github.com/Microsoft/microsoft-r-open/issues/51 [4] https://www.reddit.com/r/rstats/comments/2t5oqp/dont_use_the_mailing_lists_a_love_letter_to_the_r/ https://www.reddit.com/r/rstats/comments/2t5oqp/dont_use_the... [5] http://www.deanbodenham.com/learn/r-package-submission-to-cran.html http://www.deanbodenham.com/learn/r-package-submission-to-cr...

thetwentyone 7y ago

To give a quick comparison with Julia: 1. Native Data types - this is one of the things that Julia was designed to do very well. That is, native-like-treatment for all data without needing to have a C-family underbelly like Python does for its high performance code. 2. Non-Standard Evaluation - Julia has Metaprogramming[1] and Symbols[2] which provide similar ideas in a different way. It uses abstract syntax trees and is very lisp-like in that way if you wanted to get into writing Macros and such. 3. Package Management - Julia has a best-in-class built in package management system with versioning. Julia also has first-class support for documentation, so its very easy for developers to write relevant documentation. As an R user before RStudio, package management was a pain but RStudio hides the manual work that used to be searching for, downloading, and unpacking packages. Packages usually work really well together, usually automatically so you can often get really cool results[3] where other languages would require a lot of coordination (like Tidyverse). 4. Function paradigm - Julia is multi-paradigm and is conducive to functional, imperative, object-oriented, among others. I'm a big Julia fan, after having gone R -> Python -> Julia. Not to make this totally in favor, I still like R for plotting because it's more mature. RStudio also is very nice for dynamically interacting with datasets, but Juno comes pretty close there too. 1: https://docs.julialang.org/en/v1/manual/metaprogramming/# https://docs.julialang.org/en/v1/manual/metaprogramming/# 2: https://stackoverflow.com/questions/23480722/what-is-a-symbol-in-julia https://stackoverflow.com/questions/23480722/what-is-a-symbo... 3: https://www.youtube.com/watch?v=HAEgGFqbVkA https://www.youtube.com/watch?v=HAEgGFqbVkA

kwertzzz 7y ago
Actually, for plotting I prefer to use PyPlot in Julia which is based on matplotlib which is very mature and complete in my opinion. I tried to use other (more native) plotting packages like GR, Plots and Makie but they did not provide all the plotting types I needed or where to rough around the edges. In any case, I am looking forward to new julia versions which should address the delay in plotting (as far as I known).
- fock 7y ago
  why do you have to look forward to new versions of the base language if you have a problem with plotting?

LyndsySimon 7y ago

This mostly strikes me as ”I know R better than I know Python” - which is fair for deciding what to use, but is obviously not an objective comparison. Hopefully I’ll have time tomorrow to write a rebuttal for some his arguments. Particularly, the preference for CRAN and code longevity strikes me as being shortsighted.

missosoup 7y ago
R is objectively worse than Python for almost all data science tasks, and R is a huge PITA to productionise compared to Python. I've yet to see any argument for R that doesn't boil down to 'well, I know it better' or 'well, I prefer the syntax'. R to data science is as Matlab is to engineering. It's a stopgap 'non programmer' language that thrived at a time when most academics didn't know any programming. Now school children learn programming. There is no use case for these languages anymore.
- exdsq 7y ago
  > There is no use case for these languages anymore. There are entire ecosystems of academic libraries built around Matlab that can’t all just be picked up and moved to Python. This argument probably doesn’t realise just how ingrained Matlab is in STEM non-CS academic departments. Example: my girlfriends department writes a world-leading MRI analysis library in Matlab. They offer training courses on it (so departments around the world now know it) and it’s frequently used within academic papers (so there are now resources available on it). Why would they move to Python?
  - hatmatrix 7y ago
    Also, Simulink - there is no equivalent in Python and many industries use this.
    - exdsq 7y ago
      https://uk.mathworks.com/solutions/aerospace-defense/standards/nasa.html https://uk.mathworks.com/solutions/aerospace-defense/standar... Yep, NASA used it alongside Matlab for the Orion's Guidance and Navigation Control systems. I've never had the chance to use it though, it looks pretty interesting.

tbenst 7y ago

The part on “Make code more concise“ struck me as an anti-pattern that should instead be a simple function composition. Or the functional programmer in me notes that the author re-invented the Maybe monad. I do like the point in Learn the user’s language, as friendlier error messages is something we should all strive for, although I’ve never had an issue with particular problem in Python’s stacktraces, and actually having types like Julia or at least annotations via mypy seems a better solution. CRAN is a great point, and pythons packaging is in a sorry state with a crazy number of approaches and undeclared dependencies. R does a great job here. Functional programming section is ironic given the lack of functional patterns in the post. R has even fewer higher order functions than the python standard library. It’s hard for me to see how R is better for production than python, and the argument against pandas seems a bit strawman considering that numpy/scipy are quite stable and more central to the ecosystem than DataFrame. R is fantastic for data science and highly productive, until you need to do data mugging or anything else that involves a general purpose language.

cameronh90 7y ago
"CRAN is a great point, and pythons packaging is in a sorry state with a crazy number of approaches and undeclared dependencies. R does a great job here" But for production usage, again it's a huge pain. It's difficult to keep version stability with developer machines since there's no standard lock file, and the CRAN servers often delete or silently update old versions of packages. Packrat and Microsoft's MRAN really helps, but another curious issue is that it and other CRAN servers seem to have terrible stability - often going down for hours at a time (or worse). Python's packaging is ~impossible to understand for new developers (and really absolutely needs improving), but in an organisation you just pick an approach suited for your use case.
- tbenst 7y ago
  Nix really shines here, has ~17000 R packages pinned and reproducibly built: https://hydra.nixos.org/eval/1560470#tabs-still-succeed https://hydra.nixos.org/eval/1560470#tabs-still-succeed
zamalek 7y ago

mikorym 7y ago

For those interested, there is a nice Vim plugin for R called Nvim-R.

uptownfunk 7y ago

R was that language I learnt so many years ago. I love it actually, have so many fond memories. It’s hard to find something better especially for prototyping. I will admit productionalizing has almost drag and drop simplicity in python compared to R. I love love love what Hadley has done with Dplyr for the most part, at least in spirit, though I think the implementation could have been done better so as not to be so clunky, esp wrt NSE. But I think he is just trying to work within the current R ecosystem. Which makes me ask.. is it then time for R2? (Like a Python 3). Before you shoot me.. Do we need to save the good things we have innovated from within the R ecosystem over the years and consider doing things from scratch? Is this what Julia tried to do? I haven’t gotten around to trying it yet. That said, I think R is always going to be there and have it’s place. Frankly if they could just make an IDE like Rstudio that ran python, I’d probably be happy enough with that. I heard with reticulate you can run both, curious to hear of others experience with this..

bayesian_horse 7y ago
You should try Jupyter lab. It has notebooks, text editors and more.
- hatmatrix 7y ago
  The whole problem with notebooks is that it's not a simple text file. On the other hand has anyone tried Rodeo for Python?
  - subroutine 7y ago
    I've used it. It does have the feel of RStudio IDE if that's what you're getting at.
  - uptownfunk 7y ago
    I wanted to like it but it kept crashing when I tried it a year or two back, I may give it another shot. I do recall it was closest to what i wanted..
  - bayesian_horse 7y ago

Gatsky 7y ago

R is interesting in that it has one library which has zero real competitors to the point where it becomes a justification for using the entire language: ggplot2. It's almost like ggplot2 crosses over from a package to an application, and R is the user interface. Any examples like this in other languages? I can't think of any, maybe Python & scikit-learn 5 years ago.

alexilliamson 7y ago
Ruby and Rails?
nwallin 7y ago
Can you compare ggplot2 to matplotlib? 90 seconds of googling didn't seem to indicate to me that ggplot2 is particularly different, either in terms of its power or expressivity, than matplotlib. Those 90 seconds of googling constitute my entire knowledge of ggplot2.
- Gatsky 7y ago
  From people that have used both extensively (which does not include you or me, it would seem), I always hear that ggplot2 is unsurpassed. Admittedly, that isn't a satisfactory answer to your question.

wjak 7y ago

R is good for machine learning and for production. We have helped big orgs to incorporate this technology in their it ecosystem. We used our open-source product called R Suite to manage deployment issues. https://github.com/WLOGSolutions/RSuite https://github.com/WLOGSolutions/RSuite

not_the_nsa 7y ago

Not mentioned are the fantastic user communities, especially the culture of inclusiveness and openness fostered by RStudio ([code of conduct](https://github.com/tidyverse/dev-day-2019/blob/master/CODE_OF_CONDUCT.md) https://github.com/tidyverse/dev-day-2019/blob/master/CODE_O...) and [rOpenSci](https://ropensci.org/community/ https://ropensci.org/community/). Basically the inverse of SnarkOverflow. Especially rOpenSci's peer review process ([more here](https://devguide.ropensci.org/softwarereviewintro.html) https://devguide.ropensci.org/softwarereviewintro.html)) for R packages is fantastic. I do most data engineering in R (RMarkdown workbooks), and most software engineering in Python/Django. It took three separate, dedicated attempts to get warm with R (pre-tidyverse, showing my age), now I'm interrupting work on an RShiny app to write this comment. The ecosystem around the tidyverse helps immensely to convert my colleague's workflows from Excel to R. Clarity and simplicity wins over purity here (you may now light your pitchforks). And NSE still breaks my brain.

FranzFerdiNaN 7y ago
I think the community that R users have managed to create is it's strength. Python has a community of software developers, while R has a community of people who use programming to solve their problems. It's a fundamental difference in mindset, one which really shapes the community. What also helps is that R is so focused on data and statistics. It gives a focus to the users that really helps when it comes to finding help. Python is famously second best at everything, but that also means it's community is spread thinner over more subjects.
- halbritt 7y ago
  It's this same reason that running anything that depends on R in production is a PITA.
  - FranzFerdiNaN 7y ago
    I run R in production and its absolutely fine and wasnt harder than pretty much any other thing in software development.

darkhorn 7y ago

It is wrong to compare R with Python. R should be compared with NumPy + Jupyter.

j7ake 7y ago

I use R for the statistical packages, handling data frames, and plotting. For other types of work I would switch to python.

stoops 7y ago

How do I apply a function to every element of a data frame where that function takes as input the i,j indices of the element along with its value? This is a problem I struggled on for weeks in college. Eventually having to hack something together that relied on modifying the underlying data frame. I've not return to R since as python has always had better libraries and easier to deploy.

ksevastyanenko 7y ago
In R you'd typically want to operate over vectors (rows or columns, with columns being the faster option) rather than on individual values. This requires a bit of a mental shift when you come to R from a C/Cuda background or even python. You can find the man pages right in R console - look up `?lapply` for column-wise operations and `?apply` for row-wise. When it comes to data.frame transformations you are typically better off using packages from `hadleyverse` - check https://github.com/hadley/reshape https://github.com/hadley/reshape and https://tidyr.tidyverse.org/ https://tidyr.tidyverse.org/ Of course, what's important is not the technology used, but the problem solved. Fantastic that python works for you.
anthony_doan 7y ago
You use one of the family of `apply` function.
mpjdem 7y ago
If you need i,j indices to solve your problem you should probably not be using a data.frame but a matrix type.
- anthony_doan 7y ago
  If he/she chose to use dataframe then the data is probably multiple type. Matrix data structure can only have one type.

tomerbd 7y ago

I used to use R because of RStudio until JupyterLab came and flipped it all over now I use only bash and python for small data analysis as well.

superMayo 7y ago

As an applied economist, I can assure you that R is wayyy better in terms of statistics than Python. Doing causal models in Python is a real pain because its data science community is mostly focused on machine learning. Even a robust linear model with IV or fixed effect (which is standard in causal models) is really hard to achieve in Python. Of course this argument is about the libraries and note the core language, but it's a strong argument to stay with R. This argument is also valid outside academics, as more and more data scientists try to adress causality.

sqrt17 7y ago

Python as a text processing language has been less convenient than Perl for a long time Python+(Django, Flask) as a Web service language hasn't been as convenient as Ruby on Rails for some time Python+numpy as a numeric computation language hasn't had all the features of Matlab Python+pandas+matplotlib as a data science language hasn't had all the features of R but using Python throughout, or even Python with a sprinkling of Cython/C++/C for performance, allows for cleaner and faster engineering than using a special language for each niche. I don't think that R has a bigger problem with non-programmers being bad software engineers than Python has - there are plenty of people who know Python passably and are quite happy that they can be productive without being good software engineers (versus Java where the intent of the language is biased for everyone to write code with a minimum quality standard rather than everyone to write code with focus on being productive). But you can find decent Python software engineers, and more recently you can find decent software engineers who also know enough of the niche in question to produce high-quality production code in that niche from the get-go rather than throwing models over a data science - engineering wall that exists between two departments.

PerryCox 7y ago
I think this is a very valid point. There are times when Python is not the absolute best choice for a given problem, but in most cases it can allow you to achieve the desired goal in a way that is easy for newcomers to the particular codebase to grok very quickly. There are times when it may be better to use another language and there is nothing wrong with that. I default to Python and if I think there will be specific issues with it, then I can look at a more specialized language.

CapmCrackaWaka 7y ago

I actually use R mostly because of its data.table package. It is much faster and more concise than pandas, which is a nightmare to work with. Sure, you can get the job done in pandas, but you often have to wait ~10x longer for your commands to run and sometimes, I simply cannot use pandas at all because I run out of memory. https://h2oai.github.io/db-benchmark/ https://h2oai.github.io/db-benchmark/ People are usually pretty surprised when I take the stance that R is faster than Python for the things most people actually care about, which is data manipulation and model building. Python has its datatable library which is approaching data.tables speed however it is very much a work in progress, and does not have very useful features yet.

qwhelan 7y ago
FWIW it looks like pandas is slow/OOM-ing because the benchmarks solely use Categoricals, which aren't as heavily used by pandas users compared to R. In particular, I suspect the benchmark sizing is forcing falling back from numpy's int64 to Python ints as categorical labels, which easily could explain a 10x or more differential.

RocketSyntax 7y ago

Shiny

YeGoblynQueenne 7y ago

Look, I use R (for plottng mainly) but it's a hideous abomination. It's like some kind of giant, horrible patchwork chimera put together by evil golems with the word "CONFUSION" carved on their foreheads. Yes, CRAN is magickal and wonderful, a package manager that works 90% of the time and doesn't make you want to eat your computer. Yes, Python's package management is borked with a borky borker. But- Python, like most programming languages this side of Leboge was designed _as a programming language_. Like, _for programming_. From the very start. So all you need to know, once you have it up and running is _what you want to program_ and not H O W to program it. The "H O W" is terribly, awfully important. Because in R, anything you want to do, you have to know the super secret mystical occult incantation that does it (and nothing else will do it). You can't just intuit syntax. Oh no. You _have_ to know _exactly_ what code to write. Otherwise- run, you fools! Here's my latest and greatest. I wanted to automatically adjust the position of a legend in a plot so that it will not overlap the lines in the plot. How do you do that? Well, it turns out that the function legend() returns ... the position and dimensions of the legend's rectangle relative to the plot margins. Wait what? Why would it _do_ that? Why would a function called "legend" not return, oh, I don't know - a _bloody_ _legend_? Well, because it's R, that's why. That's not how most programming languages work, because that's not what most programmers expect, because that's not how most programming languages work. R is far out there in the oubliette of languages used by non-programmers, that work not like programmers would epxect them to work because they're languages not for programmers. No shit non-programmers take to it. Because who else would? Well. Not programmers. /first 2020 programming rant