Table of Contents
A SESYNC Data Skills Workshop provides researchers from the socio-environmental synthesis community with hands-on training in open source tools for collaborative coding, data management, analysis, visualization, and dissemination. The goal of this one-day workshop is to introduce novice and intermediate scientific coders to concepts, skills and approaches for data-driven research, while relying on tools available through the RStudio development environment. The schedule provides an overview of the specific topics we will address through a series of four lessons that integrate live-coding and trainee challenge exercises. Registration is open to graduate students, researchers and faculty working in the environmental sciences at the University of Maryland (e.g. PSLA, MEES, BARC and ENST departments/programs).
Please review the schedule below and follow pre-arrival instructions.
- Ian Carroll, Data Scientist @SESYNC
- Kelly Hondula, Quantitative Researcher @SESYNC/Ph.D. Candidate @MEESProgram
- Alec Armstrong, Ph.D. Candidate @MEESProgram
8:30 am - 4:30 pm on Friday, September 22, 2017
Plant Sciences Building 0104
Participants must bring a laptop with a Mac, Linux, or Windows operating sytem (not a tablet, Surface, Chromebook, etc.), and have installed the software described below the schedule.
Please email email@example.com with any questions, including installation issues, or for information not covered here.
|8:30 am||Help w/ Software Installation|
|10:45||tidy Data and dplyr Transformations|
|12:15 pm||Lunch Break|
|1:30||Visualizations with ggplot2|
|3:45||From Data to Document in RMarkdown|
Pre-Arrival Installations & Downloads
To participate, you will need working copies of the software described below. Please make sure to install and/or download everything before the start of the short course.
If you do not aleady have a GitHub account, please create one at https://www.github.com.
Note that students and educators with a
.edu e-mail address are eligible for some free stuff through GitHub’s Student Developer Pack.
The table below lists software we will use in this short course.
Unless noted (and especially for
git) please use the default installation options.
For Windows users, an installer for each item is available at the given download site.
Mac users are encouraged to use Homebrew – the missing package manager for OS X – via the shell.
Most packages in the list below can be installed by typing
brew install %package% in the Terminal and pressing return, but packages with an * require
brew cask install %package%.
Ubuntu users may install from the shell with
sudo apt-get install %package%, and other Linux users are on their own.
|Software||Download Site||Homebrew Package(s)||Aptitude Package(s)|
The following R packages (i.e. add-on pieces of software) need to be installed. Open RStudio and, for each package listed below, type
install.packages("%package%") in the Console (where you see a
>) and press return. To install the
tidyr package, for example, you type
install.packages("tidyr"), and then follow the instructions given.