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.
Objective Having been working in environmental science for several years now, entirely using R, I’ve come to greatly appreciate environmental data sources that are easy to access. If you are reading this text now however, that probably means that you, like me, have found that this often is not the case. The struggle to get data is real. But it shouldn’t be. Most data hosting organisations do want scientists to use their data and do make it freely available.
Objective In South Africa there are a range of idioms for different time frames in which someone may (or may not) do something. The most common of these are: ‘now’, ‘just now’, and ‘now now’. If one were to Google these sayings one would find that there is general agreements on how long these time frames are, but that agreement is not absolute.
Advice from the internet.
This got me to wondering just how much disagreement there may be around the country.
Preface This week I have expanded the coastR package with the inclusion of a function that calculates the angle of the heading for alongshore or shore-normal transects. The rest of this blog post is the vignette that I’ve written detailing the set of this function. Next week I’ll likely be taking a break from coastR development to finally create a package for the SACTN dataset. That is a project that has been in the works for a loooong time and it will be good to finally see a development release available to the public.
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).
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.
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.
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.
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.
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.