Data Analysis in R

Facilitated by: Jennifer Mortensen, University of Arkansas & Jessica Cañizares, Tufts University. E-mails: mortejen@gmail.com, jessicarozek@gmail.com

Purpose: This workshop is intended for participants that seek to increase their data analysis and visualization skills. Our goal is to guide learners who are unfamiliar with or just learning R to be able to process data and produce presentation- and publication-quality figures. No prior experience with R is necessary.

Overview: Whether you’re a conservation practitioner, a scientist, a business owner, a guide, or a birder, we all work with data. Synthesizing and disseminating data is often one of the most important aspects of our jobs. Many of us are at this conference to do just that! And most of us could benefit from having more tools to efficiently and effectively share our data. R is an open-source, freely available, cross-platform statistical programming language. It can handle the large, complex datasets that are becoming more common across all fields and is growing to be the standard software for data science. It lends itself well to both academic and non-academic research and business use because of its depth of topic-specific packages. Despite these benefits of its use, R can seem daunting to those with little programming experience. Our goal is to make this powerful tool seem friendlier by providing an R pipeline for data manipulation and visualization of results.

Objectives: By the end of the workshop participants will have

● Become more comfortable working in R

● Learned R basics, including the R package ecosystem

● Practiced reading files and manipulating data in R

● Generated summary statistics and tables in R

● Been introduced to data visualization and created figures in R

Session structure: Participants will receive hands-on instruction in modern data processing and visualization approaches. The workshop will have four parts: (1) intro to R, (2) intro to data manipulation/processing, (3) intro to data visualization, and (4) exploration of a few interesting packages. Participants will be guided through simple scripts and provide a list of useful resources at the end of each part. At the very end we will have a Q & A related to participants’ own projects. If possible, each participant should bring their own laptop to follow along. Participants are also encouraged to bring one of their own datasets to work with if time permits.