ODV figures in R with bathymetry

Objective

Nearly four years after writing a blog post about recreating R figures in ODV I had someone reach out to me expressing interest in adding a bathymetry layer over the interpolated data. It’s always nice to know that these blog posts are being found useful for other researchers. And I have to admit I’m a bit surprised that the code still runs 4 years later. Especially considering that it uses the tidyverse which is notorious for breaking backwards compatibility. In order to demonstrate the overlaying of bathymetry data on a CTD transect we will need to use a different dataset than in the previous blog post. One may use any data one would like, but for this blog I went to this shiny app to extract some data from the coast of South Africa. Specifically I filtered for temperature data from November 1990 at all depths. We won’t go back over the theory for recreating the ODV figure in this blog post, so please revisit that for a recap as necessary. Below I will show two of the necessary steps to get interpolated CTD data before we begin on the bathymetry mask.

Polar plot climatologies

Objective

Whilst cruising about on Imgur I found a post about science stuff. Not uncommon, which is nice. These sorts of grab-bag posts about nothing in particular often include some mention of climate science, almost exclusively some sort of clever visualisation of a warming planet. That seems to be what people are most interested in. I’m not complaining though, it keeps me employed. The aforementioned post caught my attention more than usual because it included a GIF, and not just a static picture of some sort of blue thing that is becoming alarmingly red (that was not meant to be a political metaphor). I’m referring to the now famous GIF by climate scientist Ed Hawkins (@ed_hawkins) whose blog may be found here, and the specific post in question here. A quick bit of research on this animation revealed that it has likely been viewed by millions of people, was featured in the opening ceremony of the Rio Olympics, and was created in MATLAB. Those three key points made me decide to do a post on how to re-create this exact figure in R via a bit of reverse engineering. The original GIF in question is below.

Mapping with ggplot2

Objective

There are many different things that require scientists to use programming languages (like R). Far too many to count here. There is however one common use amongst almost all environmental scientists: mapping. Almost every report, research project or paper will have need to refer to a study area. This is almost always “Figure 1”. To this end, whenever I teach R, or run workshops on it, one of the questions I am always prepared for is how to create a map of a particular area. Being a happy convert to the tidyverse I only teach the graphics of ggplot2. I have found that people often prefer to use the ggmap extension to create ggplot quality figures with Google map backgrounds, but I personally think that a more traditional monotone background for maps looks more professional. What I’ve decided to showcase this week is the data and code required to create a publication quality map. Indeed, the following code will create the aforementioned obligatory “Figure 1” in a paper I am currently preparing for submission.

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.