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R Passes SAS in Scholarly Use

r4stats.com

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Re: R Passes SAS in Scholarly Use

#41

Post by pollitos »

R is really LISP with syntactic sugar and bindings to well respected high-performance FORTRAN matrix and math optimization codes.

http://librestats.com/2011/08/27/how-much-of-r-is-written-in...

It's great for bleeding edge scientific research. The results of many languages don't always match for advanced algorithms, but the open source nature of R, makes it easier to identify the problem areas.

The R-core interpreter does have a number of deficiencies. (R is based on S-language specification that left wiggle room from the 70s.) General purpose programming and data wrangling/engineering is best handled in other programming idioms.

Re: R Passes SAS in Scholarly Use

#42

Post by roel_v »

uptownfunk wrote: post #13

Some reasons I love / use R: Plenty of free high quality documentation and learning materials around R (just read anything by Hadley) Package manager. Super easy to find, install, and start using packages. Open source / Free Large community of users Extensive usage by the stats community. (If a new algorithm comes out, chances R there will be an R implementation) Easy to build and share your own packages via Github.…

A big problem with R is that it's just stats. The other day I wanted to do a simple loan amortization (simple PMT/IPMT in Excel). People say 'use R over Excel!'. Right. There are some clunky barely-working packages in R that do half of what you need and some stack overflow posts that mostly show how to do the other half, but that's no basis to build on.

And don't get me started on string handling in R, or that there's no way to get the path of the currently running script, or a dozen other things that are trivial in a general purpose language but are a major pain in R. R is not 'general purpose' enough, and it doesn't have to be useful to write both kernel drivers and database REST frontends, but being able to do things that are math-related and not purely stats - that's not too much to ask for I'd say. Especially because it's not reasonable to ask people whose main job is not writing software to learn multiple languages/tools.

(Other recent example I remember: how unintuitive I found it to plot a sine wave and its first and second derivative. My Mathematica-oriented colleague did it in 2 minutes.)

Re: R Passes SAS in Scholarly Use

#43

Post by yoplait_ »

jacobolus wrote: post #36
minimaxir wrote: post #25

Earlier quoted context omitted.

If you haven't looked at the R ecosystem in awhile, there is a package for each of those use cases. (scraping/API: rvest; database: dplyr; parsing text: stringr, etc). Yes, Hadley Wickham is primarily responsible for the popularity of R.

We’re talking about an order of magnitude difference in number of packages (82096 on PyPI vs. 8551 on CRAN) and their maturity, and such a naïve metric probably undersells the difference in variety of use cases. If you picked 100 random production python projects out of a hat, no more than a small handful of them would be remotely appropriate to build using R. And that's entirely fine. R is great at being a quick and…

how about quick and friendly?

Re: R Passes SAS in Scholarly Use

#44

Post by return0 »

aabajian wrote: post #15

I think Python is the biggest hidden gem in statistics. It's had a tremendous impact on machine learning and algorithm development, yet traditional statisticians still rely on SAS/R/Stata/MATLAB. All of these languages have libraries that produce the same results, the difficulty is mangling the data into the correct input format. Python's list comprehensions are much, much easier to use than MATLAB matrices, R's data…

Whats the status of matpootlib nowadays? Graphs are a major major attractir/detractor

Re: R Passes SAS in Scholarly Use

#45

Post by gaius »

kpil wrote: post #29
LeifCarrotson wrote: post #19

Earlier quoted context omitted.

No. Some have the benefit of proprietary modules (FPGA toolchains), some have large libraries of pre-entered and organized data (Mathematica), and some have early access to hardware (LabView, CUDA). Programming languages, perhaps, are less vulnerable to these issues. And perhaps open source could beat these applications eventually, given perfect competition. But we're not in that world, unfortunately.

I think that open source programming languages will always win in the long run, since the target customer base knows how to program and extend the tools.

I'm not sure that logically follows. What %age of Python users actually know the underlying C well enough to make changes to the language? Even the number who know how to write bindings is tiny overall.

Re: R Passes SAS in Scholarly Use

#46

Post by boomskats »

oso2k wrote: post #12
detaro wrote: post #10

Earlier quoted context omitted.

I'm not sure if Mathematica is on the radar if you are looking for "just" a stats package, it can do way more. I have no idea how it compares price-wise to SAS?

For a single licensed installation, over $7k per year for SAS. Every add-on/package increases price as well. What we're charged is inline with [0]. [0] https://www.quora.com/How-much-does-SAS-cost

Shameless self promotion, but not every package increases the price. We maintain a free (GPLv3) library for JS/SAS that lets you build nice user interfaces to your programs/workflows using modern frameworks like Angular or React.

Check out github.com/boemska/h54s. I wouldn't normally post it like this, but SAS comes up so rarely that I figured if you're on HN and you use SAS, then you'll probably be interested. The more the merrier.

Re: R Passes SAS in Scholarly Use

#47

Post by ldp01 »

aabajian wrote: post #15

I think Python is the biggest hidden gem in statistics. It's had a tremendous impact on machine learning and algorithm development, yet traditional statisticians still rely on SAS/R/Stata/MATLAB. All of these languages have libraries that produce the same results, the difficulty is mangling the data into the correct input format. Python's list comprehensions are much, much easier to use than MATLAB matrices, R's data…

I'm glad I read this comment. After checking some of the docs I think I will have a go at Python for data wrangling. List comprehensions look... friendly.

R still rules for plotting and running canned statistical procedures but sometimes I feel like if I stop programming R for a week I forget how to use it effectively... E.g. Forgetting to add stringsAsFactor=FALSE to everything, forgetting rbind() can overwrite column names, forgetting I have to define my own string concatenation operator in every script.

If Python can save me some of the frustration involved in manipulating data frames that will be nice.

Re: R Passes SAS in Scholarly Use

#48

Post by lima »

Myrmornis wrote: post #39
blahi wrote: post #31

Earlier quoted context omitted.

If I use python I have to write half of the algorithms I use myself. Worse, I have to write a gazillion helper functions nyself. It just won't do

I think you got downvoted because people didn't know what you meant. You're right though. Although R is laughably inferior to python as a programming language, it is vastly more work to try to do statistical data analysis in python than in R. I recommend using both languages and using csv or whatever format to exchange data sets.

With pandas, this is no longer true.

Re: R Passes SAS in Scholarly Use

#49

Post by LionessLover »

dredmorbius wrote: post #3

1. This is a good place for use of log or semi-log plots. 2. How do the authors unambiguously search for 'R'? Monocharacter language names are difficult search keys. (C, B, S, R)

To 2: When I look for just "R" even in an anonymous window (so it should not use my history) I get as the first suggestion a link to https://www.r-project.org/ - the home of R. What else is there for that letter - that is equally popular? "R" is "hip" and trending. Microsoft not too long ago started a big push into the R space and now regularly generates headlines around the system, accelerating the trend even more.

Re: R Passes SAS in Scholarly Use

#50

Post by ekianjo »

minimaxir wrote: post #25
jacobolus wrote: post #21

Earlier quoted context omitted.

The even bigger advantage of python is everything else : web scraping, interfacing with weird APIs, hitting the database, consuming and emitting obscure formats, parsing text, calling operating system services, and a million other things. That and much much better abstractions for building larger systems out of reusable components and much better tooling for serious software engineering. R or Matlab can be fine at ex…

If you haven't looked at the R ecosystem in awhile, there is a package for each of those use cases. (scraping/API: rvest; database: dplyr; parsing text: stringr, etc). Yes, Hadley Wickham is primarily responsible for the popularity of R.

Dplyr is not for databases at all. Its a piping operator that simplifies complex syntax a lot.
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