Palimpsest

Evolving notes, images and sounds by Luis Apiolaza

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My R year

End-of-year posts are corny but, what the heck, I think I can let myself delve in to corniness once a year. The following code gives a snapshot of what and how was R for me in 2012.
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R for inquisition

A post on high-dimensional arrays by @isomorphisms reminded me of APL and, more generally, of matrix languages, which took me back to inquisitive computing: computing not in the sense of software engineering, or databases, or formats, but of learning by poking problems through a computer.

I like languages not because I can get a job by using one, but because I can think thoughts and express ideas through them. The way we think about a problem is somehow molded by the tools we use, and if we have loops, loops we use or if we have a terse matrix notation (see my previous post on Matrix Algebra Useful for Statistics), we may use that.
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Matrix Algebra Useful for Statistics

I was having a conversation with an acquaintance about courses that were particularly useful in our work. My forestry degree involved completing 50 compulsory + 10 elective courses; if I had to choose courses that were influential and/or really useful they would be Operations Research, Economic Evaluation of Projects, Ecology, 3 Calculus and 2 Algebras. Subsequently my PhD was almost entirely research based but I sort of did Matrix Algebra: Dorian lent me his copy of Searle’s Matrix Algebra Useful for Statistics and passed me a pile of assignments that Shayle Searle used to give in his course in Cornell. I completed the assignments on my own pace and then sat a crazy take-home exam for 24 hours.

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When R, or any other language, is not enough

This post is tangential to R, although R has a fair share of the issues I mention here, which include research reproducibility, open source, paying for software, multiple languages, salt and pepper.

There is an increasing interest in the reproducibility of research. In many topics we face multiple, often conflicting claims and as researchers we value the ability to evaluate those claims, including repeating/reproducing research results. While I share the interest in reproducibility, some times I feel we are obsessing too much on only part of the research process: statistical analysis. Even here, many people focus not on the models per se, but only on the code for the analysis, which should only use tools that are free of charge.

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