OK, just a quickie here. I’m working with a colleague on a manuscript using LaTeX. The citation formatting for the journal we are looking at uses the numerical citations but bibtex will number the citations by the order in which the \bibitem values they occur in the bibliography section.
As part of a class in Landscape Genetics, faculty (mostly done by Melanie Murphy and Jeffrey Evans) compiled an extensive list of spatial data sources. These were made available on the course website we hosted but I wanted to make a more persistent copy of them here so they will not be lost. They are listed below the break.
I just ran across an R package that allows you to integrate your R workflow into the Slack environment. Really cool. Below I show how to set it up and to post output of your analyses to slack channels for your team as well as to register notifications.
OK, so I just ‘ found‘ shiny and it has a lot of cool stuff to it. OK, I’ve known about it for a long time but have just had the opportunity to sit down and work it out and see how it can fit into the presentation and learning I’m trying to develop in my Applied Population Genetics online textbook. Here is a brief overview of how I set up the shiny server on my Ubuntu box that is hosting the book (so I can embed more interactivity in the display).
I just uploaded a new plugin for RStudio called dlab. I’ll be migrating over all the little helper functions I use to this as a general require() on startup. What it has now is an AddIn that allows you to select text and have it wrapped in the r-code markup.
The program STRUCTURE is an ubiquitous feature of many population genetic studies these days—if it is appropriate is another question. Today, while covering model based clustering in population genetics, we ran into a problem where STRUCTURE was unable to run and the OS said it was Corrupted and should be thrown away. Jump below for our fix, it really is an easy one.
An analysis common to modern population genetics is that of finding ecological distances between objects on a landscape. The estimation of pairwise distance derived from spatial data is a computationally intensive thing, one that if you are not careful will bring your laptop to its knees! One way to mitigate this data problem is to use a minimal amount raster area so that the estimation of the underlying distance graph can be done on a smaller set of points. This example provides a simple solution using convex hulls. Jump below for the complete example.