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
• Others
  • I accidentally committed a file > 100 MB to git

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 (Week of Aug 17)

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 (Week of Aug 24)

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
• Assigned readings:
  • Pereira-Braga et al 2023 “Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution” Methods in Ecology and Evolution
  • Stewart Lowndes et al 2017 “Our path to better science in less time using open data science tools” Nature Ecology and Evolution
• Other links:
  • Happy Git with R
• Link to document from live coding session

Week 3 (Week of Aug 31)

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

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First assignment: Setting up your portfolio in GitHub

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*Week 4 (Week of Sep 07)

Labor day on Monday Sept 7

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 (Week of Sep 14)

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 (Week of Sep 21)

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 (Week of Sep 28)

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 (Week of Oct 05)

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

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Second assignment: Data wrangling due Oct 19

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*Week 9 (Week of Oct 12)

Fall Recess is Oct 10-13

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 (Week of Oct 19)

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

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Third assignment: Data visualization due Nov 2

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Week 11 (Week of Oct 26)

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 (Week of Nov 02)

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

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Fourth assignment: Visualizing spatial data

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Week 13 (Week of Nov 09)

Quarto markdown | slides

• Intro to quarto markdown
• Slides
• Documents (html and pdf)
• Build your website

Relevant links for the week:

• Assigned readings
  • R4DS CH28: Quarto
  • R4DS CH29: Quarto formats
• Link to sample slide deck we’ll build
• Link to sample doc we’ll build

Week 14 (Week of Nov 16)

Programming | slides

• Background jobs
• User-defined functions
• Iteration with loops

Relevant links for the week:

• Assigned readings
  • R4DS CH25: Functions
  • R4DS CH27: for loops (Section 27.5 only)
• Link to document for live coding session

Extensions | slides

• Standardizing the environment with .Rprofile
• Shiny Apps Framework
• Connecting to external databases with DBI
• Animating with the gganimate package
• Interactive maps with leaflet
• Retrieving environmental datasets from the Internet (gfwr, rerddap)

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FINAL PROJECT: Communicating your work

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*Week 15 (Week of Nov 23)

Thanksgiving recess is Nov 24-29

• No class

Week 16 (Week of Nov 30)

Final Exams week Dec 3 - Dec 9

Week 17 (Week of Nov 30)

Classes end after lecture on Tuesday Dec 1

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