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You are here: Home / Resources / Teaching Materials / Investigating human impacts on Southeastern US stream ecology using R / About

Investigating human impacts on Southeastern US stream ecology using R

By Alicia Caughman1, Emily Weigel1

Georgia Institute of Technology

Adaptation of the "Investigating human impacts on stream ecology: Scaling up from Local to National with a focus on the Southeast" specifically to focus on self-paced R code instruction

Listed in Teaching Materials

Additional materials available

Version 1.0 - published on 01 Jan 2021 doi:10.25334/P995-7G91 - cite this

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms

Adapted from: Investigating human impacts on stream ecology: Scaling up from Local to National with a focus on the Southeast v 1.0

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Description

Focus: The students will analyze indicators of stream health from across the United States by plotting data and performing descriptive statistics and linear regression.

Overview: This lesson centers around analyzing the correlation between different indicators of stream health. The students will complete a lesson in R to practice creating scatter plots and performing linear regression. Additionally, students will practice summarizing data, calculating outliers, and creating boxplots. They will apply the methods from prior lessons in the final lesson of the course to analyze stream data from Region 4, which contains Atlanta, more independently. 

 

Learning objectives:

  1. Perform descriptive statistics and linear regression in R
  1. Create boxplots and scatter plots of stream health data from different EPA regions.
  1. Practice coding elements necessary to do basic R calculation operations and perform useful functions: summary(), abline(), lm(), quantile()
  1. Compare, contrast, and summarize patterns of stream health locally and nationally

Contents

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Tags

  1. 1 Hour
  2. Accessibility
  3. Activity Length
  4. Audience Level
  5. Cleaned
  6. Dataset
  7. Developing Self-awareness
  8. Fostering a Sense of Belonging
  9. Instructional Setting
  10. introductory
  11. Lab
  12. Leveraging Networks
  13. Majors
  14. Non-majors
  15. Online course
  16. Reference material
  17. Teaching material
  18. Undergraduate

Notes

R coding in this lab is supported well asynchronously through the use of a swirl module. The module may be used as a stand-alone or in conjunction with materials published in the adapted modules.

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