Sequential sites

Preface

The rest of the blog post after this preface section is a copy of the vignette I’ve written for the first function in the new package I am developing: coastR. This package aims to provide functions that are useful for coastal oceanography but that do not yet exist in the R language. It is not my intention to provide algorithms for physical oceanography as these may already be found elsewhere. This post covers how one may determine the correct sequence of sites along a convoluted coastline.

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.

Goats per capita

Objective

A few weeks ago for a post about the relationship between gender equality and GDP/ capita I found a nifty website that has a massive amount of census information for most countries on our planet. Much of this information could be used to answer some very interesting and/ or important questions. But some of the data can be used to answer seemingly pointless questions. And that’s what I intend to do this week. Specifically, which countries in the world have the highest rates of goats/ capita?

Party immigration

Objective

As an immigrant myself, all of the talk of immigration to be found in main stream media outlets today makes me a bit nervous. Whereas most people that speak of the pro’s and con’s of immigration do so from the point of view of how it may affect the country of their birth, I view this issue as something that affects my ability to live outside the country of my birth. I immigrated into the Republic of South Africa in 2013 and have been living here since. I would do a piece on South African immigration but the numbers are difficult to get a hold of and honestly most people are less interest in South Africa than the USA.