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One Year with R

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Re: One Year with R

#51

Post by 1980phipsi »

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 both languages, you can write/use C extensions.

Re: One Year with R

#52

Post by clove »

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…

I've used R for 19 years and do not have any other programming language ability. I am curious: What is frustrating to you about R relative to other languages?

Re: One Year with R

#53

Post by epistasis »

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.

Re: One Year with R

#54

Post by s_Hogg »

VariableStar wrote: post #2

Excellent read. I agree with a lot (after only a cursory read). One thing the author seemingly forgives R by not mentioning is how harsh and discouraging of beginners the community was at some early stage. That was my experience around 2002-2006.

Yeah I tried to publish a package back in like 2015 and was dealt with very harshly, got banned for a week from submitting for asking questions about the process after the first attempt didn't work. It really turned me off R and frankly I haven't looked back.

Re: One Year with R

#55

Post by curiousgal »

tomrod wrote: post #30
beernet wrote: post #20

Long-time R user here. Yes, many of these points are valid but I still think R is unbeaten when it comes to speed in (tabular) data exploration. In the article you mention that you missed using data.table - a significant portion of the problems you named would be solved or at least weakened by using data.table. Started working with it many years ago and never looked back. It's easy, powerful and efficient to use. I a…

.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.

Re: One Year with R

#56

Post by clove »

musingsole wrote: post #10
zhdc1 wrote: post #8

Earlier quoted context omitted.

> For my use case, R is absolutely terrible compared to some for profit statistical package / language. Which one? I've switched most of my work over to Julia, but I'd much rather use R or Python than Stata or SPSS.

I don't understand the use case for SPSS. My local university is training their neuroscience researchers on it, which seems so odd in 2022 with Julia or python sitting right there.

Ease of use. I haven't used it personally, but I'm pretty sure you can do everything with a mouse - no need to learn actual code.

Re: One Year with R

#57

Post by epistasis »

fn-mote wrote: post #46
AuthorizedCust wrote: post #13

I don’t like this. Much of this is: 1. pointing out that, like every other language, base R has idiosyncrasies 2. how use of R is more complex when you’re largely ignorant of the tidyverse, which is crucial for the vast majority of tissue today’s use of R 3. frustration because you’re using a language/ecosystem, that’s targeted for a few specific uses, as a general purpose programming language

> how use of R is more complex when you’re largely ignorant of the tidyverse This. I'm interested in non-flamewar non-religious reasons that the tidyverse is bad. He does give some. I think his complaints about inconsistency and a moving target have some validity. However, the price of not using tidyverse is (roughly) paid in the rest of the article. I would definitely not use R without it. Read his Section 5 on the…

Worst part of the tidyverse is learning it, and then looking up how to use specific functions. The bad documentation is mostly in the ggplot lib though.

It's a pleasure to use, though!

Re: One Year with R

#58

Post by hervature »

azalemeth wrote: post #22
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…

I very much agree with this. I use python for (different types of) data analysis too, and in python in particular it feels like the "boilerplate" to "science" ratio is rather high in the direction of "boilerplate". R manages to abstract this away very effectively, as the article highlights. The beauty of R is that you can write one line of code and use some hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Sta…

> hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis

I think you mean "poorly-documented-cobbled-together-under-deadlines-never-to-be-maintained by someone who has no idea of software principles". Very few labs have a dedicated software engineer to actually turn this software into a usable/hackable tool let alone maintain it.

Re: One Year with R

#59

Post by CapmCrackaWaka »

I would say about 90% of the posts / articles / comments I see on the internet which discuss R are usually of the "meta" format. They talk about R's strengths or weaknesses, about the difference between R and Python, about how much they love or hate R, or any other high level subject.

I can't remember the last time I saw a project someone did in R, or a tutorial on how to do something in R, get very much traction anywhere. It seems the only time people talk about R on the internet is to discuss the language itself (which is definitely frustrating), and it's getting old. Even this awesome, comprehensive document, which I would usually be foaming at the mouth to read, has me going "meh". I'm tired of the subject.

Re: One Year with R

#60

Post by magnio »

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.

> almost as bad as typical Python docs

I found numpy, scipy, pandas, and plotly docs to be quite clear and extensive. The only docs I have found to be confusing are matplotlib's and the Python standard library's. Not sure what packages you are referring to?

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