Evolving notes, images and sounds by Luis Apiolaza
A pint of R plotted an interesting dataset: intentional homicides in South America. I thought the graphs were pretty but I was unhappy about the way information was conveyed in the plots; relative risk should be very important but number of homicides is very misleading as it also relates to country population (this problem often comes up in our discussions in Stats Chat).
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After reading David Smith’s tweet on the price of Oracle R Enterprise (actually free, but it requires Oracle Data Mining at $23K/core as pointed out by Joshua Ulrich.) I went to Oracle’s site to see what was all about. Oracle has a very interesting concept of why we use R:
Statisticians and data analysts like R because they typically don’t know SQL and are not familiar with database tasks. R allows them to remain highly productive.
Pardon? It sounds like if we only knew SQL and database tasks we would not need statistical software. File for future reference.
Mike Croucher at Walking Randomly points out an interesting difference in operator precedence for several mathematical packages to evaluate a simple operation 2^3^4. It is pretty much a divide between Matlab and Excel (does the later qualify as mathematical software?) on one side with result 4096 (or (2^3)^4) and Mathematica, R and Python on the other, resulting on 2417851639229258349412352 (or 2^(3^4)). Remember your parentheses…
Corey Chivers, aka Bayesian Biologist, uses R to help students understand the Monty Hall problem. I think a large part of the confusion to grok it stems from a convenient distraction: opening doors. The problem could be reframed as: i- you pick a door (so probability of winning the prize is 1/3) and Monty gets the other two doors (probability of winning is 2/3), ii- Monty is offering to switch all his doors for yours, so switching increases the probability of winning, iii- Monty will never open a winning door to entice the switch, so we should forget about them.
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December and January were crazy months, with a lot of travel and suddenly I found myself in February working in four parallel projects involving quantitative genetics data analyses. (I’ll write about some of them very soon)
Anyhow, as I have pointed out in repeated occasions, I prefer asreml-R for mixed model analyses because I run out of functionality with nlme and lme4 very quickly. Ten-trait multivariate mixed model with a pedigree, anyone? I thought so. Well, there are asreml-R versions for Windows, Linux and OS X; unsurprisingly, I use the latter. Installation in OS X is not particularly complicated (just follow the instructions in this PDF file) and remember to add and export the following environment variables in your .bash_profile:
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A commenter on this blog reminded me of one of the frustrating aspects faced by newbies, not only to R but to any other programming environment (I am thinking of typical students doing stats for the first time). The statement “R is a language” sounds perfectly harmless if you have previous exposure to programming. However, if you come from a zero-programming background the question is What do you really mean?
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