EVR 528 / 628

Course description

“Introduction to Data Management and Visualization for Environmental Scientists” is designed for students looking to gain basic data management and visualization skills relevant to careers in environmental science and policy. The course provides an introduction to using R and RStudio to interact with environmental data; No coding experience is required. Students will learn highly marketable skills like visualizing tabular and geospatial data, data management, and reproducibility. All concepts will be introduced using real-world environmental data sets and questions. In-class exercises and homework assignments will mimic the types of tasks and questions that students will encounter in the workforce. By the end of the course, students will be comfortable working in R.

Resources

• Syllabus
• Cheat sheets
  • RStudio IDE
  • Base R
  • {ggplot2}
  • Git and GitHub
  • {readr}
  • {dplyr}
  • {tidyr}
  • {forcats}
  • {lubridate}
  • {stringr}
  • {quarto}
  • {sf}
  • Some available in Spanish
• My code snippets
  • Code documentation

Course schedule

Students must complete any assigned readings and software tests before class. Note that content, and timing of the content, is subject to change. Any changes will be clearly communicated via Canvas. The latest official academic calendar is here (* Week contains holiday).

Week 1 (Aug 18-22)

Introduction to Data Science and RStudio IDE | slides

• Software installation and testing
• Overview of R Studio IDE
• R Studio Projects and project organization
• R scripts
• R packages: {tidyverse} package, {EVR628tools} package

Relevant links for the week

• Instructions to install R and RStudio

Week 2 (Aug 25-29)

Data visualization | slides

• Types of visualization
• Visualization principles
• The grammar of graphics and the {ggplot2} package

Relevant links for the week

• Assigned readings:
  • Jambor, H.K. “A checklist for designing and improving the visualization of scientific data.” Nat Cell Biol 27, 879–883 (2025).
  • R4DS CH1: Data visualization
• Other links:
  • Englander, G. “Timeless principles for better figure design”. The World Bank
  • IPCC Visual Style Guide by the IPCC
  • The R Graph Gallery
  • Data-to-viz
• Link to document from live coding session

*Week3 (Sep 1-5)

Keeping track of your code with Git and GitHub | slides

• Reproducible research
• Introduction to file structure
• Version control with Git and GitHub
• Building your first repository (hello world!)

Relevant links for the week

• Instructions to install git and create GitHub account
• Other links:
  • Pereira-Braga et al “Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution” Methods in Ecology and Evolution
  • Happy Git with R
• Link to document from live coding session

---

First assignment: Setting up your portfolio in GitHub

---

Week 4 (Sep 8-12)

Good coding principles | slides

• Code style and documentation
• Classes, objects, variables, values
• Indexing and subsetting vectors
• Useful functions in {base} and {stats}

Relevant links for the week

• Assigned readings:
  • R4DS CH2: Worflow: basics
  • R4DS CH4: Workflow: code style
• Link to document from live coding session

Week 5 (Sep 15-19)

data.frames | slides

• Indexing and subsetting data.frames
• Useful functions in {base} and {stats}
• Other links:
  • Villaseñor-Derbez et al (2018). A user-friendly tool to evaluate the effectiveness of no-take marine reserves. PLoS One, 13(1), e0191821.
  • Lynham & Villaseñor-Derbez (2024). Evidence of spillover benefits from large-scale marine protected areas to purse seine fisheries. Science, 386(6727), 1276-1281.
• Link to document from live coding session

Week 6 (Sep 22-26)

Data management and transformation | slides

• Relative vs. absolute paths
• Reading and writing tabular data (*csv and *.rds)
• Modifying rows (filter, arrange, distinct) with {dplyr}
• Modifying columns (select, rename) with {dplyr}
• Grouping and summarizing data with {dplyr}

Relevant links for the week:

• Assigned readings:
  • R4DS CH7: Data import
  • R4DS CH3: Data transformation
• Link to document from live coding session

Week 7 Sept 29-Oct 3

Data tidying and wrangling | slides

• Lengthening data with tidyr
• Widening data with tidyr
• Combining multiple sources of data (*_joins)

Relevant links for the week:

• Assigned readings:
  • R4DS CH5: Data tidying
  • R4DS CH19: Joins (sections 19.1 - 19.4 only)
• Other readings:
  • Wickham, Hadley. 2014. “Tidy Data.” The Journal of Statistical Software 59. http://www.jstatsoft.org/v59/i10/
• Link to document from live coding session

Week 8 (Oct 6-10)

Scaling up your code and visualizations | slides

• Refresh on layers: aesthetics and geometries
• Themes with ggplot2
• Position adjustments (jitter and dodge)
• Summarizing data on the fly (stat_summary, geom_smooth)
• Plot extensions ({ggridges} and {cowplot})
• quarto markdown

Relevant links for the week:

• Assigned readings:
  • R4DS CH9: Layers
  • R4DS CH11: Communication

---

Second assignment: Data wrangling due Oct 19

---

*Week 9 (Oct 13-17)

Scaling up your code and visualizations 2 | slides

• Facetting
• Summarizing data on the fly (stat_summary, geom_smooth)
• Plot extensions ({ggridges} and {cowplot})
• quarto markdown

Week 10 (Oct 20-24)

Dealing with text, dates, and factors | slides

• Ordering factors with forcats
• Managing dates and times with lubridate
• Regular expressions with stringr

Relevant links for the week:

• Assigned readings:
  • R4DS CH14: Strings
  • R4DS CH15: Regular expressions
  • R4DS CH16: Factors
  • R4DS CH17: Dates and times
• Link to document from live coding session

---

Third assignment: Data visualization due Nov 2

---

Week 11 (Oct 27-31)

Working with spatial data in R | slides

• Vector data and sf
• Exploratory visualizations with plot and mapview

Relevant links for the week:

• Assigned readings:
  • Geocomputation with R CH1 - Introduction
  • Geocomputation with R CH2 - Geographic data in R (stop at 2.2)
• Additional reading:
  • Towards Data Science: Why you need to use Geopackage files instead of shapefile or GeoJSON
• Link to document for live coding session

Week 12 (Nov 3-7)

Visualizing spatial data | slides

• Raster data and terra
• Attribute operations
• Building maps with ggspatial and tidyterra

Relevant links for the week:

• Assigned readings:
  • Geocomputation with R CH2 - Geographic data in R (2.3-2.5)
• Link to document for live coding session

---

Fourth assignment: Visualizing spatial data

---

Week 13, (Nov 10-14):

Programming | slides

• User-defined functions
• Iteration with loops
• Background jobs
• Standardizing the environment with .Rprofile

Week 14, (Nov 17-21)

Shiny Apps Framework | slides

• Reactive programming
• UI / UX
• Front-end vs back-end

Extensions | slides

• Connecting to external databases with DBI
• Animating with the gganimate package
• Interactive maps with leaflet
• Retrieving environmental datasets from the Internet (gfwr, rerddap)

*Week 15, (Nov 24-28)

Thanksgiving recess

• No class

Week 16: (Dec 1-5)

Final Exams week Dec 4 - Dec 10

• No class, final projects due online

Reading resources

• Books and manuals:
  • R for data science by Hadley Wickham et al
  • Geocomputation with R by Robin Lovelace et al
• Ad hoc peer review literature and blog posts:
  • Brett et al., “Ocean data need a sea change to help navigate the warming world”. Nature 582, no. 7811 (2020): 181-183.
  • Jambor, H.K. “A checklist for designing and improving the visualization of scientific data.” Nat Cell Biol 27, 879–883 (2025).
  • Englander, G. “Timeless principles for better figure design”. The World Bank. (2024).
• Other relevant sources:
  • The Visual Display of Quantitative Information by Edward Tufte