Inspiration for this post Conservation is often the main motivation behind studying where a species lives – having a model of a species’ range can help scientists assess whether it is at risk of extinction, designate protected regions to preserve its habitat, and study potential impacts of human activity. When we create species distribution models using common methods like Maxent, the result is a map of predicted habitat suitability or probability of species presence, such as the one below.
Inspiration for this post In species distribution modeling, one of the key steps requires the researcher to select a “background region” for the species, i.e. a region over which a machine learning model will compare the environment of the “background points” with the environment at points where the species is known to occur. The key to selecting this region is to pick an area where the species could occur but hasn’t necessarily been observed – for example, you don’t want to include an area separated from the rest of the range by a big mountain range that you don’t believe the organism could cross, but you do want to include a range of potential environments.
Using Python in RMarkdown In order to write blog posts using Python code, I wanted to figure out a way to include Python code chunks in RMarkdowns. When you insert a code chunk in RMarkdown, you have the option of specifying the language of that chunk: the default is R, but you can also insert a Bash, SQL, Python, etc. code chunk.
When I attempted to insert a Python code chunk and import libraries, however, I kept getting the error:
Inspiration for this post This post comes from the finishing touches I needed to put on a paper about life history evolution. The paper compares life history traits across the four groups of tetrapods (amphibians, reptiles, mammals, and birds), so when I carried out the nitty gritty phylogenetic analyses, I used four separate phylogenies. When it came time to make a figure to visualize those analyses, however, I ended up with an unwieldy (and unpublishable) 16-panel figure (four phylogenies by four traits).
PhD comics Jorge Cham (www.phdcomics.com) Recently I’ve been part of beta testing a new R package. The package allows users to apply different masks to spcies distribution models to create more ecologically realistic range models. The package itself is very cool, but since it hasn’t been released yet, this post will be short on ecology. Instead, I’m going to focus on the process I’ve been using to debug the package.
Given that the Academic website theme comes with a place to upload talks, I thought I might as well upload the slides for my Senior Honors presentation. Unfortunately, this was a slightly more difficult task than I anticipated. The default format for slides in Hugo appears to be Markdown slides, which is fine, except when your talk was made in Google Slides.
Google Slides to Markdown? My first thought was that there must be some tool to convert a Google slides talk into a Markdown document.
As I continue setting up this site, one of the major things irking me was the order of content on the website. For example, I wasn’t loving that the first thing you saw when visiting the site was the giant picture of my face. I wanted to be able to put content at the top of the site and move information like my bio and work experience towards the bottom.
Getting started I’ve been contemplating the idea of creating a website for sometime, but my initial (mostly half-hearted) attempts ran into some serious technological glitches. I started out trying to create a GitHub site using Jekyll, since I knew I wanted my website to be hosted via GitHub to keep all of my code in the same place. After about an hour of unsuccessful commits that resulted in a lovely white page with my name on it, I scrapped the whole venture until I heard about the blogdown R package.