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R Passes SAS in Scholarly Use

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Re: R Passes SAS in Scholarly Use

#121

Post by Myrmornis »

hadley wrote: post #57
nol13 wrote: post #22

Earlier quoted context omitted.

Basically this. Certainly not the worst language (SAS), but intangibly less pleasurable than python or most other common languages I've used. Maybe that's because I'm not a 'real' stats person though and ~ notation still takes me a minute to grok. And there is no challenger on he horizon for it's dominance in available packages. And at least it's not SAS. My theories: A) Stat people use R because because they don't k…

No. R is a language that is beautifully well suited to data analysis and interactive computing. Stat people don't use it simply because we don't know better.

As a language, one problem is that it is many people's first programming language, and it's a very bad introduction. In particular it doesn't teach people to think in terms of standard data structures like hash maps / dicts. It's bad for the job prospects of lots of grad students who might hope to leave academia.

Re: R Passes SAS in Scholarly Use

#122

Post by Myrmornis »

lottin wrote: post #91
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.

and a very inadvisable way to learn programming. (But I agree with what you said.)

Re: R Passes SAS in Scholarly Use

#123

Post by ldp01 »

pedrosorio wrote: post #85
ldp01 wrote: post #66

Earlier quoted context omitted.

A reply from the man himself! Thanks for the link. I'll have a go. I do like the look of the dplyr library a lot. Combining functions like select and group_by with the pipe operators creates code that is reminiscent of SQL- very nice for readability.

You're going to love Spark if you haven't tried it yet.

Another powerful tool I will keep in mind!

I think this thread illustrates a kind of tension between those coming from an IT/big-data/web oriented background and the more traditional statistics/science/engineering side.

The IT side bring a lot of very powerful and scalable tools to the table. However there are aspects of traditional work which I suspect are lost on some big-data people.

For example, in my line of work (physical asset mgmt) we deal with a lot of very small datasets, very poor quality datasets (e.g. some guy's favourite spreadsheet) and also cultural issues (some engineers are inherently averse to changing systems, and spending decisions are inherently political). In this situation, there is a limit to the benefit of more powerful/scalable tools, and it is advantageous to use tools which are considered high quality and vetted by the community.

R is in a good position here as it has the pedigree of being accepted by the academic stats community, as well as actually being a great tool.

Re: R Passes SAS in Scholarly Use

#124

Post by pollitos »

hadley wrote: post #119
pollitos wrote: post #118

Earlier quoted context omitted.

Thanks for the link on JSON and your packages, great work as always. I should clarify when I said we-client, I meant websockets client to consume feed. The last time I tried, the only R package (r-websockets) just crashed my Linux box and not maintained for several years. httr doesn't do websockets, as I understand. Seems likely a fundamental way to engineer/wrangle data into R.

You can do it with httpuv, but it might a bit clunky. I think better websockets support, and better async generally, is on the roadmap for the next year.

For httpuv, author Joe Cheng advised cannot do a websocket-client. https://stackoverflow.com/questions/28120307/how-to-interact...

Looking forward to the improvements. Appreciate all your work in the field.

Re: R Passes SAS in Scholarly Use

#125

Post by collyw »

minimaxir wrote: post #24
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…

> 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 frames, Java's ArrayLists, etc. At the end of the day, for machine learning applications, your data is in a tabular format. (in Python, a pandas data frame) Yes, Python has a few tricks like li…

Only learning a single language is a bad idea, but using a single one, it it fits the problem is a good one.

Re: R Passes SAS in Scholarly Use

#126

Post by kpil »

gaius wrote: post #45
kpil wrote: post #29

Earlier quoted context omitted.

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.

I'm fairly sure that most of them would be able to wing it if they had to. C isn't exactly that special.

In all events, the percentage of users skilled in software development must be higher than among users of tools not related to software development.

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