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

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. (* Week contains holiday)

The latest official academic calendar is here.

Week 1 (Aug 18-22)

Introduction to Data Science and RStudio IDE

Software installation and testing
Overview of R Studio IDE
R Studio Projects and project organization
R scripts
R packages: tidyverse, EVR628

Relevant links for the week

Week 2 (Aug 25-29)

Data visualization

Types of visualization
Visualization principles
Colorblindness, IPCC’s visual style guide, and the viridis package
The grammar of graphics and the ggplot2 package

Relevant links for the week

Slide deck
Assigned readings:
- Jambor, H.K. “A checklist for designing and improving the visualization of scientific data.” Nat Cell Biol 27, 879–883 (2025).
- R4DS CH2
Other links:
- Englander, G. “Timeless principles for better figure design”. The World Ban
- IPCC Visual Style Guide by the IPCC
- The R Graph Gallery
- Data-to-viz

*Week3 (Sep 1-5)

Keeping track of your code with Git and GitHub

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

First assignment: Setting up your portfolio in GitHub

Week 4 (Sep 9-12)

Good coding principles

Code style and documentation
File structure and organization
Classes, objects, variables, values
Indexing and subsetting vectors and data frames
Useful functions in base and stats

Week 5 (Sep 15-19)

Scaling up your code and visualizations

Layers, geometries, and aesthetics in ggplot2
Themes with ggplot2
Other plotting packages (cowplot, GGally)
Creating documents and presentations with Quarto

Second assignment: Data visualization

Week 6 (Sep 22-26)

Data management

Reading and writing tabular data with here
Metadata and documentation
Retrieving environmental datasets from the Internet
Raw data vs processed data

Week 7, Sept 29-Oct 3

Data transformation

data.frames and tibbles in the tidyverse
Rows (filter, arrange, distinct) in dplyr
Columns (select, rename) in dplyr
The pipe (native and magrittr)
Grouping and summarizing data

Week 8 (Oct 6-10)

Data tidying and wrangling

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

Week 9 (Oct 13-17)

Dealing with text, dates, and factors

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

Third assignment: Data wrangling

Week 10 (Oct 20-24)

Working with spatial data in R

Vector data and sf
Raster data and terra
Exploratory visualizations with plot and mapview

Week 11 (Oct 27-31)

Visualizing spatial data

Attribute operations
Building maps with ggplot2 and tmap

Fourth assignment: Visualizing spatial data

Week 12 (Nov 3-7)

Extensions

Connecting to external databases with DBI
Animating with the gganimate package
Interactive maps with leaflet

Week 13, (Nov 10-14):

Programming

User-defined functions
Iteration with loops
Functional programming with the purrr package
Background jobs
Standardizing the environment with .Rprofile

Week 14, (Nov 17-21)

Shiny Apps Framework

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

*Week 15, (Nov 24-28)

Thanksgiving recess

Week 16: (Dec 4-10)

FINAL EXAMS WEEK

Final presentations (egg timers)

What will you learn?

You will learn how to access, work with, and visualize many different types of environmental data. For example:

Access hurricane tracks and build animated visualizations
Track fishing vessels form space and build maps of fishing activity
Develop interactive web-based apps to allow others explore your data
Create slides with interactive content for effective communication
And much more!

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