NERRS Ecosystem Metabolism R Training

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 foundation for using R to calculate and interpret ecosystem metabolism with NERRS data.

Course objectives

This course is designed to expose you to the fundamentals of using R for estimating ecosystem metabolism with NERRS data. A specific focus will be on using monitoring data from the NERRS CDMO with functions from the SWMPr and WtRegDO packages. SWMPr provides simple functions to retrieve, organize, and analyze SWMP data and WtRegDO provides the functions for estimating metabolism. 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:

  • Use R and SWMPr to import and organize SWMP data
  • Use the WtRegDO package to detide dissolved oxygen time series
  • Use the WtRegDO package to estimate ecosystem metabolism using the Odum open water method
  • Use R to understand and interpret ecosystem metabolism

Pre-workshop survey

Please fill out the pre-workshop survey to help the organizers with planning.


There will be four training events, each two weeks apart. All trainings will be remote using RStudio Cloud and GoToMeeting webinar services. All times are in Eastern.

Training 1, March 1st:

Training 2, March 15th:

  • 1:00 - 2:30 Detiding dissolved oxygen time series, recording

Training 3, March 29th:

  • 1:00 - 2:30 Estimating ecosystem metabolism using the Odum open water method, recording

Training 4, April 12th:

  • 1:00 - 2:30 Understanding and interpreting ecosystem metabolism, recording

Housekeeping for training days

There are a few housekeeping items to go over each training session.

  1. First and most importantly, please mute your microphones. If you have a question, first type it into the chat and one of our moderators should assist you. I will pause at different points in the lecture to request verbal questions.

  2. Please use RStudio Cloud or your own installation of RStudio during this workshop. The setup instructions for RStudio Cloud are here and for RStudio installation here. You may use one or the other, but we recommend RStudio Cloud for the training.

  3. We have a live coding link that we’ll be using as we go through the lessons. If you get lost, you can copy/paste code from this link into RStudio.

  4. All training content is on this website. We will be covering the content directly on the website, so if you get lost you can view the agenda to see which lesson we’re covering and find where we’re at by scrolling through the content.

  5. Each lesson has it’s own R script that is linked at the top. If you are not using the live coding link, you can download the lesson R script and use that directly.

All exercises and breaks will be timed to make sure that we stay on schedule. We’ll display a timer on the screen to track progress. Each lesson will also be recorded so you can review at a later date.

Software requirements

These trainings will use RStudio and R. Please make sure that you are ready to use the required software prior to the workshop. We will not be available for installation issues or questions on training days. There are two options for setup:

  1. Use RStudio Cloud to access from a web browser following the setup instructions.


  2. Install RStudio and R on your personal computer following the setup instructions.

Data and resources

Please view the Data and Resources page for data used in the training and additional links for R learning material. A live coding link is also available.


Marcus Beck ( - 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.

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