USING R FOR SOIL AND ENVIRONMENTAL SCIENCES

Prerequisites & course information

Requisites: ( STAT 240 , 301 , 324 , 371 , or F&W ECOL/STAT 571 , or HORT 571 prior to Spring 2025) and ( BSE 301 , ENVIR ST/CIV ENGR/GEOG 377 , LAND ARC/ENVIR ST/G L E/GEOG/GEOSCI 371 , or FW ECOL 371 prior to Summer 2026), or graduate/professional standing Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Breadth - Physical Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Use R software for basic data manipulation, processing, and visualization. Audience: Both Grad & Undergrad 2. Use R software for basic statistical analysis and hypothesis tests. Audience: Both Grad & Undergrad 3. Use R software for spatial and temporal analysis on soil and environmental datasets. Audience: Both Grad & Undergrad 4. Become familiar with the use of electromagnetic induction, portable visible near-infrared spectroscopy, and portal X-ray fluorescence spectroscopy for estimating soil and environmental variables in the lab and in the field. Audience: Both Grad & Undergrad 5. Build machine learning models using R software for mapping and predicting soil and environmental variables in space and time. Audience: Both Grad & Undergrad 6. Access, process, and build models using various open-source soil and environmental datasets from USDA, USGS, NASA, and other sources. Audience: Both Grad & Undergrad 7. Become competent in the oral presentation. Audience: Both Grad & Undergrad 8. Apply the various R packages to your own research datasets for solving problems in your own disciplines. Audience: Graduate 9. Explain the social, economic, and environmental dimensions of the sustainability challenges of soil and environmental monitoring and management. Audience: Both Grad & Undergrad 10. Analyze sustainability issues and/or practices on soil and environment using a systems-based approach. Audience: Both Grad & Undergrad Learning Outcomes: 1. Use R software for basic data manipulation, processing, and visualization. Audience: Both Grad & Undergrad 2. Use R software for basic statistical analysis and hypothesis tests. Audience: Both Grad & Undergrad 3. Use R software for spatial and temporal analysis on soil and environmental datasets. Audience: Both Grad & Undergrad 4. Become familiar with the use of electromagnetic induction, portable visible near-infrared spectroscopy, and portal X-ray fluorescence spectroscopy for estimating soil and environmental variables in the lab and in the field. Audience: Both Grad & Undergrad 5. Build machine learning models using R software for mapping and predicting soil and environmental variables in space and time. Audience: Both Grad & Undergrad 6. Access, process, and build models using various open-source soil and environmental datasets from USDA, USGS, NASA, and other sources. Audience: Both Grad & Undergrad 7. Become competent in the oral presentation. Audience: Both Grad & Undergrad 8. Apply the various R packages to your own research datasets for solving problems in your own disciplines. Audience: Graduate 9. Explain the social, economic, and environmental dimensions of the sustainability challenges of soil and environmental monitoring and management. Audience: Both Grad & Undergrad 10. Analyze sustainability issues and/or practices on soil and environment using a systems-based approach. Audience: Both Grad & Undergrad