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R Passes SAS in Scholarly Use

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Re: R Passes SAS in Scholarly Use

#91

Post by lottin »

kensai wrote: post #30

The success of R in Statistics (in respect to Python, etc) was that it was thought from the beginning with Statisticians and their specific needs and approaches in mind. As much as I appreciate Python, it is a general purpose programming language adapted to Statisticians needs, not the other way around. R has many issues, but if you speak to Statisticians you will hear that its the closest thing they have to their ow…

This is absolutely true, and actually learning R is an excellent way of learning statistics.

Re: R Passes SAS in Scholarly Use

#92

Post by hooloovoo_zoo »

Malarkey73 wrote: post #68

I'm not totally sure whether this analysis captures the true extent which R vs SAS vs SPSS is used. If I use R for a plot, or a simple bit of regression, or anova, or even cross-validation. I don't reference it in a paper. I only cite it if there is a package designed for a particular type of data (e.g. a Bioconductor package) or something a bit more esoteric (e.g. apcluster). About 95% of the work is data munging an…

Why don't you cite the packages you use?

Re: R Passes SAS in Scholarly Use

#93

Post by griffinmichl »

jsprogrammer wrote: post #89
Malarkey73 wrote: post #68

I'm not totally sure whether this analysis captures the true extent which R vs SAS vs SPSS is used. If I use R for a plot, or a simple bit of regression, or anova, or even cross-validation. I don't reference it in a paper. I only cite it if there is a package designed for a particular type of data (e.g. a Bioconductor package) or something a bit more esoteric (e.g. apcluster). About 95% of the work is data munging an…

Shouldn't your full source be available; which would explicitly record your dependencies?

Source code really should be available, but it almost never is. The peer review process is, in my opinion, quite flawed. While your paper's high level content gets reviewed, no one actually looks at your code and data to ensure that you didn't forget to carry the one. Your analysis could be totally wrong, but reviewers only review what you say you did, not what you actually did.

Re: R Passes SAS in Scholarly Use

#95

Post by pollitos »

hadley wrote: post #58
pollitos wrote: post #41

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…

I would love to know what you mean by data wrangling because I think R has a lot of good tools for it.

For example, reshaping JSON to the format an intricate R function expects. Appreciate the great work with (d)plyr and similar packages, but it's still work and overhead. Combined with some inefficiencies/quirks in base r functions (does ifelse() still evaluate twice?) it's easier to go with a widely used and respected package in a general purpose language; Nokogiri for example. For data engineering, consider there is not a maintaned R package for a web-client, and asynchronous programming is weak.

Re: R Passes SAS in Scholarly Use

#96

Post by [deleted] »

hadley wrote: post #58
pollitos wrote: post #41

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…

I would love to know what you mean by data wrangling because I think R has a lot of good tools for it.

[deleted]

Re: R Passes SAS in Scholarly Use

#97

Post by throwawaysocks »

dekhn wrote: post #70
throwawaysocks wrote: post #11

Earlier quoted context omitted.

The interfaces are terrible. The price is high. If you need to call one of the built-in pieces of Magic (TM) then Mathematica is OK, but if you want to build something new that needs to interface with literally anything outside of Mathematica, then Mathematica is a PITA.

Actually, the whole Mathematica kernal is exposed via a C API. I wrote a Python-Mathematica bridge based on this and it was wonderful. You could sit in Python, and send Python expressions with variables, etc, to Mathematica for evaluation, and get the results back as Python objects. The interface was trivial.

I've worked with these types of bridges before. They are terrible if you want to keep the program running and intermittently call Mathematica throughout the course of a multi-hour session.

If you have a small script that makes a single one-off call to Mathematica, and the interface already exists for your language, which it probably doesn't, even if you're using an extremely popular language, and even though you're paying hundreds of dollars a year just for PERSONAL use, then things can be ok. But if you want to make a bunch of calls and keep the program running reliably then you're SOL.

Oh, and don't even think about deploying. It will cost you so much that it's more cost-effective to just rewrite the thing or do the work of switching out with a different library/tool.

Re: R Passes SAS in Scholarly Use

#98

Post by jhbadger »

LionessLover wrote: post #49
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.

Even weirder, a Google search for "xlispstat" seems to bring up more R hits that don't even mention xlispstat than actual xlispstat ones. Some weird algorithm is associating R and xlispstat as relating to statistics and because R is much more popular these days, prioritizing R over xlispstat.

Re: R Passes SAS in Scholarly Use

#99

Post by dekhn »

throwawaysocks wrote: post #97
dekhn wrote: post #70

Earlier quoted context omitted.

Actually, the whole Mathematica kernal is exposed via a C API. I wrote a Python-Mathematica bridge based on this and it was wonderful. You could sit in Python, and send Python expressions with variables, etc, to Mathematica for evaluation, and get the results back as Python objects. The interface was trivial.

I've worked with these types of bridges before. They are terrible if you want to keep the program running and intermittently call Mathematica throughout the course of a multi-hour session. If you have a small script that makes a single one-off call to Mathematica, and the interface already exists for your language, which it probably doesn't, even if you're using an extremely popular language, and even though you're p…

I don't understand why you consider a problem to intermittently call mathematica's kernel in a multi-hour session. There's nothing that would make this not work. The mathematica C interface launches a copy of the kernel and communicates with it over a straightforward protocol.

In this case, I wrote that interface and open sourced it. http://library.wolfram.com/infocenter/MathSource/585/

Re: R Passes SAS in Scholarly Use

#100

Post by noelsusman »

hooloovoo_zoo wrote: post #92
Malarkey73 wrote: post #68

I'm not totally sure whether this analysis captures the true extent which R vs SAS vs SPSS is used. If I use R for a plot, or a simple bit of regression, or anova, or even cross-validation. I don't reference it in a paper. I only cite it if there is a package designed for a particular type of data (e.g. a Bioconductor package) or something a bit more esoteric (e.g. apcluster). About 95% of the work is data munging an…

Why don't you cite the packages you use?

Nobody cites every package they use, it's not feasible. I use a lot of packages, and some journals have a limit on the number of citations you can have. I only cite packages when it provides specialized statistical functionality.

For example, I do a lot of work with data from complex surveys, and I always cite Lumley's survey package because without it I wouldn't be able to do the work. On the flip side, I use Hadley's readr package extensively because I think his I/O functions are more sane than the defaults. I'm not going to cite readr in every paper I write just because I'm too lazy to type stringsAsFactors = FALSE when I read a csv file.

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