CABW 2018 R training

R is a language for statistical computing as well as a general purpose programming language. Increasingly, it has become one of the primary languages used in data science and for data analysis across many of the natural sciences. This workshop will provide attendees with the foundations for continued learning of R and for analysis of a range of data types.

Course objectives

By the end of this course you should be able to or have the resources to find out how to:

  • Use R to import and organize bioassessment data

  • Create plots using R packages to explore data trends

  • Create maps in R to explore spatial patterns

Agenda

  • 1:30 - 2:30pm: Using R with bioassessment data: what, why, and how

  • 2:30 - 3:30pm: Wrangling and plotting bioassessment data for exploratory analysis

  • 3:30 - 5:00pm: Mapping bioassessment data in R

Instructors

  • Marcus Beck (marcusb@sccwrp.org) - Marcus is a scientist with the Southern California Coastal Water Research Project. His interests include time series analysis of water quality data, freshwater bioassessment, and data visualization.
  • Ryan Peek (rapeek@ucdavis.edu) - Ryan is a post-doctoral researcher at the Center for Watershed Sciences at UC Davis. His interests focus on hydropower regulation and environmental management of rivers and streams in California, as well as integrating the fields of genomics, ecology, and hydrology.

Software requirements

Please bring a laptop with the latest version of RStudio and R installed. Download the compressed data folder and make sure it is accessible on your computer. The setup instructions will guide you through the rest of the process. Take note of the required R packages that you must also download and install. Please contact the instructors with any questions or issues related to setup. If for whatever reason you can’t get RStudio installed with the right packages, click on this link to use R from one of our servers. We will provide you with a login the day of the workshop. These are limited so this is not the preferred option.

Source content

All source materials for this website can be accessed at https://github.com/SCCWRP/CABW2018_R_training

Attribution

Content in these lessons was borrowed, modified, and/or adapted from Marc Weber (USEPA), Ryan Hill (USEPA), Jeffrey Hollister (USEPA), the USGS-R training curriculum here, the NCEAS Open Science for Synthesis workshop here, and the wonderful text R for data science.