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.

Analysis of Bio-Oracle data

Objective

While running some brief quality control tests on Bio-Oracle layers before using them for a recent project it was detected that some of the layers in the current version of the Bio-Oracle product appear to have very large errors. Specifically the error is that there are layers where the minimum values are greater than the maximum values. It is unclear how this could be possible, so in the following text and code we will look into how we go about investigating these data layers and we will discuss which layers are fine, and which are not. This error was first detected in the current velocity layers but a brief search turned up errors in other layers, too. So in this post we will be going through each individual layer to test for this max less than min error. We will look at all of the different depths as well as the future projections.

Downloading environmental data in R

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. But sometimes it feels like the path to access was designed by crab people, rather than normal topside humans. I recently needed to gather several new data products and in classic ‘cut your nose off to spite your face’ fashion I insisted on doing all of it directly through an R script that could be run in RStudio. Besides being stubborn, one of the main reasons I felt this was necessary is that I wanted these download scripts to be able to be run operationally via a cron job. I think I came out pretty successful in the end so wanted to share the code with the rest of the internet. Enjoy.

South Africa time survey

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.

Transects

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.