ODV figures in R

Objective

With more and more scientists moving to open source software (i.e. R or Python) to perform their numerical analyses the opportunities for collaboration increase and we may all benefit from this enhanced productivity. At the risk of sounding sycophantic, the future of scientific research truly is in multi-disciplinary work. What then could be inhibiting this slow march towards progress? We tend to like to stick to what is comfortable. Oceanographers in South Africa have been using MATLAB and ODV (Ocean Data View) since about the time that Jesus was lacing up his sandals for his first trip to Palestine. There has been much debate on the future of MATLAB in science, so I won’t get into that here, but I will say that the package oce contains much of the code that one would need for oceanographic work in R, and the package angstroms helps one to work with ROMS (Regional Ocean Modeling System) output. The software that has however largely gone under the radar in these software debates has been ODV. Probably because it is free (after registration) it’s fate has not been sealed by university departments looking to cut costs. The issue with ODV however is the same with all Microsoft products; the sin of having a “pointy clicky” user interface. One cannot perform truly reproducible research with a menu driven user interface. The steps must be written out in code. And so here I will lay out those necessary steps to create an interpolated CTD time series of temperature values that looks as close to the default output of ODV as possible.

Wind Vector Time Series

Objective

As more and more physical scientists (e.g. oceanographers) move to R from other object oriented command line programming languages, such as Matlab, there will be more and more demand for the code that is needed to do some basic things that they may already know how to do in their previous languages that they don’t yet know how to do in R. Surprisingly, there are many things that should be very easy to find how to do in R that are not. Or are at least not widely publicized. One such example is how to plot wind vectors as a time series. This is a very necessary part of any analysis of the wind or currents in a particular area. Making it useful broadly to most climate scientists. Try as I might, I’ve only been able to find one source that gives an example of how to plot wind (or current) vectors as a time series with ggplot2 in R. Having now been asked how to do this by several people I thought it would be useful to write up my workflow and put it on the internet so that there is one more source that people searching for answers may find.

Gender and GDP

Objective

Most people living in the Western World are very quick to extol the virtues of gender equality. There are however many places where this is not so. This then inevitably leads to conflict as cultures and nations are drawn closer together on our ever shrinking earth. Perhaps not the sort of conflict that leads to sabre rattling, but certainly ideological disagreements that affect policy and have real impacts on large swathes of humanity. So what is to be done? How can say how anyone else should be. There are of course all sorts of moral back and forth’s that could be pursued cyclically ad nauseum, but what if we could show quantitatively what the benefit of gender equality was to a nation and the lives of it’s people? That is the exact sort of question I like to answer here at this blog and it is a question that came up in my daily life a couple of weeks ago. Because most metrics for most countries are recorded, this is the sort of thing that can be answered. Indeed, it has been before, so here I add another take on an argument that really shouldn’t still be happening…

Religious sentiment

Objective

Before we begin, I would like to acknowledge that the framework for this analysis was adapted from a blogpost found on the wonderfully interesting R-bloggers website. The objective of this analysis is to use sentiment analysis on different religious texts to visualise the differences/ similarities between them. This concept is of course fraught with a host of issues. Not least of which being detractors who will likely not endeavour to engage in rational debate against the findings of this work. This is of course beyond the control of the experimental design so I rather focus here on the issue that the translations of the texts used are not modern, and none of them were written (and generally not intended to be read in) English. Therefore a sentiment analysis of these works will be given to a certain amount of inaccuracy because the sentiment database is a recent project and one must assume that the emotions attached to the words in the English language reflect modern sentiment, and not the emotive responses that were necessarily desired at the time of writing/ translation. That being said, the sentiment that these texts would elicit in a reader today (assuming that anyone actually still reads the source material for their faith) would be accurately reflected by the sentiment analysis project and so this issue is arguably a minor one. As a control group (non-religious text), we will be using Pride and Prejudice, by Jane Austen simply because this has already been ported into R and is readily accesible in the package janeaustenr.