Introduction to Environmental Studies.
A previous visitor to the laboratory, Philip Bertrand, is taking a trip between his graduate programs to travel the world and report on climate change. Here is ongoing blog, cataloging their travel from across the globe. Definitely worth following.
Here is some interesting data coming out of the Baja Araptus attenuatus project. We looked at methylation variation, localized within the genome and compared the amount of among-population variation present. The underlying idea here is that in insects, methylation is more often encountered in coding regions, and has been shown in many cases to be influencing phenotype.
I am in various stages of writing technical texts using R/RStudio/knitr and have been looking for some methods that help in this process. My goals are to be able to:
- Maintain a single source tree that can produce the text (including graphics, statistical analyses, etc). easily
- Be able to produce high quality typesetting
- Be able to easily make epub
- Include both Code and output in the text.
I’ve just run across Gitbook and it looks like a good option, particularly with the help of the R package Rgitbook. Here is a bit of work that I had to do to get things going on my machine.
Every time I upgrade in any significant way, two R libraries seem to raise their ugly heads and scream like a spoiled child— rgdal and rgeos . Why do these two have to be SOOOO much of a pain? Why can’t we have a auto build of a binary with all the options in it for OSX? Who knows? I always feel like I get the fuzzy end of the lollipop with these two. Here is my latest approach for getting them going.
In R, there is often the need to merge two
data.frame objects (say one with individual samples and the other with population coordinates. The
merge() function is a pretty awesome though it may take a little getting used to.
Here are some things to remember:
- You need to have two data.frame objects to merge
- The first one in the function call will be the one merged on-to the second one is added to the first.
- Each will need a column to use as an index—it is a column that will be used to match rows of data. If they are the same column names then the function will do it automagically, if no common names are found in the names() of either data.frame objects, you can specify the columns using the optional by.x= and by.y= function arguments.
Much of the work in my laboratory uses spatial data in some context. As such it is important to try to be able to grab and use spatial data to in an easy fashion. At present, R is probably the best way to grab, visualize, and analyze spatial data. For this example, I went to http://worldclim.org and downloaded the elevation (altitude) for tile 13 (eastern North America) as a GeoTiff. A GeoTiff is a specific type of image format that has spatial data contained within it. The tile data has a pixel resolution of 30 arc seconds which puts us in the general area of ~ 1km. First, we need to get things set up to work.
# Set the working directory to where you want it.
# load in the raster library
Loading required package: raster
Loading required package: sp
Here is a map of the dogwood we’ve sampled in the Fan region of Richmond Virginia.
Here is a short (39 minute) video of some basic graphics approaches in R I use in a class on population genetics.
We have added a new member to our lab, Jane Remfert. She is an incoming ILS PhD Student who is going to work on pollen movement in dogwood. Very exciting!
Often there comes along a story that you see and think, “I would have been perfect for that study, why didn’t I think of that?” Here is another one.
Congratulations Dr. Chris Hittinger, this is awesome.
Two NSF grants submitted! Taking a bit of time out for some programming and to set up Dyerlab South (in the vicinity of 24.9515812,-80.5807652)…
The default CRAN repository is not the only place that R packages are stored. You can also find them on github. When I develop libraries for R, I typically develop them on http://github.com/dyerlab and then upload them to CRAN when I get to major milestones. The latest versions of all my software will always be found on github. So here is how to install packages directly. Read more
I’ve been working on integrating the Swift language into my analysis workflow but much of what I do involves the GNU Scientific Libraries for matrix analysis and other tools. Here is a quick tutorial on how to install the GSL library on a clean OSX platform.
- It is easiest if you have XCode installed. You can get this from the App Store for free. Go download it and install it.
- Download the latest version of the GSL libraries. You can grab them by:
- Looking for your nearest mirror site listed at http://www.gnu.org/prep/ftp.html and connecting to it.
- Open the directory
gsl/where all the versions will be listed. Scroll down and grab
- Open the terminal (Utilities -> Terminal.app) and type:
- Unpack the archive by:
tar zxvf gsl-latest.tar.gzthen
cd gsl-1.16/(or whatever the version actually was, it will probably be some number larger than 1.16).
- Inside that folder will be a README file (which you probably won’t read) and an INSTALL file (which you should read). In that folder it will tell you to:
sudo make install. This last command will require you to type in your password as it is going to install something into the base system.
- All the libraries and header files will be installed into the
Jameson successfully defended his MS Thesis on eDNA techniques for identification of Atlantic Sturgeon. Jameson is now the twelfth graduate student to pass through the lab. It was great having him here and we look forward to seeing where he goes from here. Way to go!