R is a language for statistical computing as well as a general purpose programming language. R has become one of the primary languages used in data science and for data analysis across many of the natural sciences. This training workshop will provide attendees with the foundations for continued learning of R and for analysis of a range of data types.
This course is designed to expose you to the fundamentals of using R for reproducible data analysis workflows. A specific focus will be on using monitoring data from the NERRS CDMO for novel analyses and storytelling to increase the impact of these valuable sources of information. You will not be experts by the end of the course, but you will have a solid foundation for continued learning. By the end of this course you will be able to or have the resources to find out how to:
Please fill out the pre-workshop survey to help the organizers with planning.
Training will occur remotely on Monday June 29th and Tuesday June 30th, login information will be sent by email.
Day 1, June 29th:
1:00 - 2:00 The what, why, and how of R
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2:15 - 3:15 Importing and wrangling data with the tidyverse
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3:30 - 4:30 Plotting data with ggplot2
Day 2, June 30th:
1:00 - 2:00 Importing and wrangling CDMO SWMP data into R
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2:15 - 3:15 Analyzing CDMO SWMP data in R
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3:30 - 4:30 Spatial data analysis with simple features
Please make sure you have the required software installed on your computer, specifically RStudio and R. The setup instructions will guide you through the installation process. Take note of the required R packages that you must also download and install. Please contact the instructor with any questions or issues related to setup.
Marcus Beck (mbeck@tbep.org) - Marcus is the Program Scientist for the Tampa Bay Estuary Program and is developing data analysis and visualization methods for Bay health indicators. He received his BS in Zoology from the University of Florida in 2007 and his MSc and PhD in Conservation Biology from the University of Minnesota in 2009 and 2013. Marcus has experience researching environmental indicators and developing open science products to support environmental decision-making. He has over ten years of experience using and developing software in the R Statistical Programming Language and is the lead developer for the SWMPr R package to retrieve, organize, and analyze NERRS data from the SWMP network.
All source materials for this website can be accessed at https://github.com/tbep-tech/rookery-bay-training