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One Year with R

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Re: One Year with R

#61

Post by tel »

R, and by R I mean R+tidyverse, is the world's best graphing calculator attached to an OK scheme.

To which I mean R is a highly optimized, well-oiled machine if you're using it for its highly-optimized, well-oiled purposes. I tend to have notebooks full of tiny fragments like this

    dat_min %>%
      group_by(ymd = make_date(year(date), month(date), day(date))) %>%
      summarize(vol_btc=sum(vol_btc), vol_usdt=sum(vol_usdt), tradecount=sum(tradecount)) %>%
      ungroup() %>%
      pivot_longer(cols=c(-ymd)) %>%
      ggplot(aes(ymd, value)) + 
      geom_line() +
      facet_grid(name ~ ., scales="free_y")
It's madness if you're not familiar with the tidyverse, but 3 dozen fragments like this is enough to eviscerate a fresh data set. Almost any question you can dream of is a 3-20 line set of transforms away from a beautiful plot or analysis answering your question. Very notably, this includes some of the finest modeling tools available today.

Terseness here is a huge advantage as well because in many data analysis workflows you are rerunning that same 10 line snippet over and over, making small changes, adjusting to eventually visualize the thing you're looking for perfectly. Having all of that in the same small block is ideal.

Finally, for the non-trivial number of folks in this specific scenario, the integration between Stan and R/RStudio is top-notch and makes using both tools very pleasant.

You can replicate all of this in Python, but optimal Python/Jupyter is still a far cry away from R/RStudio for these specific sorts of tasks.

Re: One Year with R

#62

Post by tgb »

ggrothendieck wrote: post #21

What needs to be added is that before R the reproducibility problem in science was compounded by the fact that analyses were done with proprietary software limiting communication and replication of those analyses. This was and continues to be a major problem, particular in some fields, but at least now there is a common widely used language that can be used to overcome this. I wouldn't focus on idiosyncrasies but rat…

On the contrary, R packaging system is too broken for R to be reliably reproducible. No one specifies package versions or R versions. Base R has no way to install a specific version of a package. There’s a package that lets you do that, but well, you might need a specific version of it. Particularly if you need to run an old version of R for reproducing an old script it may be impossible to use any standard tool to install the correct packages thanks to this problem - the version of devtools that install.packages gets won’t be compatible with your old R but you need that package to request another version. Instead everyone just ignores it and hopes package versions don’t matter.

Re: One Year with R

#63

Post by hadley »

epistasis wrote: post #53
crispyambulance wrote: post #28

I was expecting a rant, but the OP's article is actually very thoughtful. He definitely knows what he's talking about. The thing about R, for me and many others, is that it's very much an everyday grind language. Especially with Rstudio, its natural domain is as one of "notebook" languages like python, julia, matlab, and mathematica but with a more clear focus towards the tasks of data-analysis. I just tell the BI-to…

The tidyverse docs are the only ones with the super frustrating ... of impenetrable gnostic "documentation" that I know of. In general the tidyverse documentation is horrible, almost as bad as typical Python docs, IMHO. Other parts of base R are wonderfully documented in my opinion.

Do you have any specific examples that illustrate the general problem? I'd love to better understand what you're looking for in docs.

Re: One Year with R

#64

Post by hadley »

indeed30 wrote: post #12

I love R more than any other language I have ever used. Perhaps more than any piece of software I've ever used. All of these points are valid, and yes, it's messy, and if you try to write the same type of code that you would in Python, it will frustrate you. And yet.. it somehow works. It makes data analysis and statistical modelling a pleasure. It somehow gives off a sense of lightness, and makes it easy to investig…

100% this :)

Re: One Year with R

#65

Post by prionassembly »

I only briefly knew R in grad school (circa 2007) but I lived inside Stata for about 7 years, and yeah, while specific frustrations vary, the general tenor...

Then -- I once made a meme to that Oliver Stone Vietnam movie that said "This is my copy of Stata. Without me it is useless. Without it, I am useless. I must cherish it as I cherish my life..." (In the original said of a rifle.) I was good with Stata, fast and precise and never "ugh, okay, let's open a quickie notebook... there goes my morning..."

Re: One Year with R

#66

Post by gadflyinyoureye »

roel_v wrote: post #15

I think (and I usually anger at least some people when I say this) that it's wrong to see R as a 'programming language'. I mean, it looks like one, and it's Turing complete so if you use that as a criterion it is a programming language, but I think it's more useful to see it as a stats software package with a text-only user interface. Approaching it this way instead of as just another language to 'pick up' makes usin…

I just got done making a DockerLambda in R. The lambda itself is simple. It takes a few inputs, gets data from S3/Files into dataframes, and passes it off to the real calculation. The actual math is done by another team.

I approached it like you described. I wouldn't want to do a really complex REST API in it, but as a wrapper for calculations, we've got a repeatable pattern to run them in a cost effective manner.

Re: One Year with R

#67

Post by tomrod »

curiousgal wrote: post #55
tomrod wrote: post #30

Earlier quoted context omitted.

.loc works smashingly. %>%?

Well thats the thing with pandas, which one is it? [], loc, iloc, . ? Why do I have to reset_index so often? I agree with OP R has Pandas beaten when it comes to accessing data.

iloc - numerical location, loc - location in table

I rarely reset index -- perhaps its a difference in familiarity? (I use R but it isn't my background, perhaps there is a forced R pattern that is a general antipattern for indexes?)

Re: One Year with R

#68

Post by toto444 »

hadley wrote: post #64
indeed30 wrote: post #12

I love R more than any other language I have ever used. Perhaps more than any piece of software I've ever used. All of these points are valid, and yes, it's messy, and if you try to write the same type of code that you would in Python, it will frustrate you. And yet.. it somehow works. It makes data analysis and statistical modelling a pleasure. It somehow gives off a sense of lightness, and makes it easy to investig…

100% this :)

Before downvoting this short one liner make sure you to check who wrote it!

Re: One Year with R

#69

Post by CornCobs »

ourlordcaffeine wrote: post #29
indeed30 wrote: post #12

I love R more than any other language I have ever used. Perhaps more than any piece of software I've ever used. All of these points are valid, and yes, it's messy, and if you try to write the same type of code that you would in Python, it will frustrate you. And yet.. it somehow works. It makes data analysis and statistical modelling a pleasure. It somehow gives off a sense of lightness, and makes it easy to investig…

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

In my limited experience, problems that cannot be vectorized really shouldn't be written in python either (assuming you mean python loops). But indeed the edge that Python has is the ease of use of drop-in solutions like Numba allowing you to continue to write in Python but not Python

Re: One Year with R

#70

Post by dash2 »

I think this is really interesting. The author certainly isn't an expert, for example `result[which(result But that's just why it's useful - R is great when you are an expert, but becoming an expert takes years. The perspective of new users is really important. (I've been using R almost 20 years, have written several packages, and still feel like an amateur. Indeed, I'd never heard of `**` as an alias for `^` until today; nor `sequence`, which apparently has always been in base; and I still can't remember what `sweep` does.)

I thought some of these arguments were better than others. True that base R regex is confusing and messy (and that stringi/stringr are improvements). False that allowing string concatenation with `+` would be a good idea. That's just a footgun waiting to go off, given that R also is weakly typed. Expecting `nchar(1000)` to magically work seems naïve. `An awful lot of these problems are fixed, or try to be fixed, in the tidyverse. Not using tidyverse is a bit unusual because most beginners nowadays, I think, start with the tidyverse more than with base R.

For me the worst part of R is simply it fails silently. This is really deadly, especially when you are producing scientific results. There are so many places where R will plug gamely on after you have done something deeply inappropriate. Given how badly scientists code, one has to worry.

I don't agree that "R won’t change" is the base problem. It's not so simple. R is used for science. I like very much that my code from 2008 will probably still work if someone wants to replicate my results. I appreciate the R-core team's work in making this true. There are genuine trade-offs here.

If you want emotional relief, it's worth following https://twitter.com/whydoesR.

Maybe Julia is the way forward? Or is R "worse is better"?

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