EVR 628- Intro to Environmental Data Science

Week 1: Introduction to Data Science and RStudio IDE

Juan Carlos Villaseñor-Derbez (JC)

Rules of the Game

Course Materials and Content

Course website at: jcvdav.github.io/EVR_628

Grading

• Assistance and participation: 50% - come prepared to participate
• Assignments: 40% (n = 4; 10% each, graded as completed / not completed)
• Final presentation / Data Science portfolio: 10%

The (General) Instruction Format

Lecture sessions (Tuesdays)

• Slide-based delivery of content
• Emphasis on the why of an approach

Hands-on sessions (Thursdays)

• No slides, mostly live coding (you follow along)
• Emphasis on how of an approach
• Expect the first sessions to be rocky as you get up to speed and we standardize our computers

Important

Make sure to complete assigned readings or software installation before class. This will count towards participation.

Interactions with Me

• Office hours: Thursdays 10:15-12:00
• Via email, please include EVR628 in your subject heading

Adaptive Approach

• About three weeks in I will poll you (anonymously)
• Then again 5 weeks in (anonymously)
• Speed things up? Slow things down?
• Are materials relevant to you?

Quick Notes on Using These Slides

• Navigation bar on the left
• Click and zoom on figures
• f for full screen / Esc to exit full screen
• o for overview
• e for PDF export mode (can’t see code)
• ? for other help
• Navigate with keyboard
• Clicking on links
• Course homepage in footer

plot(mpg ~ disp, data = mtcars)

Things you will be able to build

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

• Access public data repositories 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

How will we approach data science?

How we think about projects

Modified from Wickham, Mine Cetinkaya-Rundel, and Grolemund (2023)

• Maybe also add “collect” before “import”

How we’ll learn things

• Organization (Weeks 1, 3, 4, 6)
• Visualization (Weeks 2, 5, 11)
• Reproducibility (Weeks 3, and throughout)
• Data management, transformation, wrangling (Weeks 6-9, 11)
• Spatial data, programming, and other extensions (Week 10 - 14)
• No modelling!
• No communication(-ish)

Side note #1: Other courses

EVR 509/609

EVR 624

RSM612

About You

• Preferred name
• Year and program
• Interests

Let’s Jump In

Introduction to Data Science

Learning Objectives

By the end of this week, you should be able to:

• Understand the difference between R and RStudio
• Open the RStudio IDE
• Identify the main work areas
• Start a project and a script
• Install R packages

What is Data Science?

library(tidyverse)
library(ggrepel)

tibble(skill = c("Coding", "Stats", "Domain"),
       x = c(-1, 1, 0),
       y = c(1, 1, -1)) |> 
  ggplot(aes(x = x, y = y, label = skill, color = skill)) +
  geom_point(size = 100, alpha = 0.5) +
  geom_text(mapping = aes(label = skill),
            color = "black",
            nudge_x = c(-2, 2, 0),
            nudge_y = c(0.5, 0.5, -2)) +
  geom_text(x = 0, y = 0, label = "Data Science",
            color = "black") +
  coord_equal() +
  lims(x = c(-4, 4),
       y = c(-4, 4)) +
  theme_void() +
  theme(legend.position = "none")

Conceptual model of data science

This course only covers one bubble: Talking to your computer

Side note #2: A word of caution

• This will feel weird
• It will be painful

BUT!

• It should feel weird
• You are not alone; leverage resources:
  • Me
  • Your TA
  • Each other
  • LLMs
• It will be worth it
• If we have to slow things down, we will

Use of LLMs

• You are allowed to use LLMs to troubleshoot your code
• I discourage you from asking your LLM to make the assignments for you
• At the end of this course you should be literate enough in R to do some vibe coding

• Becker et al. (2025):
  • 16 experienced software developers
  • 246 tasks assigned
  • How much time will you save using AI? (-24%)
  • AI randomized
  • How much do you think you saved using AI? (-20%)
  • Actual completion times with AI (+19%!)

Motivation to learn scripting languages

• Reproducibility:Data and code are increasingly required by publishers
  • Economics
  • Natural sciences
• Efficiency and flexibility
• Scale: From small to medium data, it’s all the same
• Responsibility:
  • We spend thousands of hours and dollars collecting data
  • Objective is to understand how the world works / improve the state of the world
  • Bad practices can erode all this away

Our toolkit

• R will be our engine for statistical computing
• RStudio will be our Integrated Development Environment (IDE)
• Git will help us keep track of our code
• GitHub will allow us to share our code with the world
• EVR628tools contains data and a few functions to support the course

You should have already done this

• Download and install the latest version of R (currently v 4.5.x) for MacOS or Windows
• Download and install the latest version of RStudio Desktop
• Full set of instructions available here.

Why an IDE?

Multiple platforms

• Keep data in Excel (needs microsoft license)
• Statistical analyses in some GUI (needs IBM SPSS license)
• Build figures in Tableau (needs license)
• Copy things to Powerpoint / Word (need….)
• Ooops, need to update my data…

R Studio

• Free and open-source
• One single platform to:
  • Talk to R, Python, SQL, html….
  • Maintain data, analysis, figures, and even text
• Cognitive costs of switching between screens
• There are other solutions (posit, VSCode, Cursor, Codium…)

RStudio projects

• RStudio projects keep all files associated with a project together
• 1 folder = 1 project
• In my case, 1 paper = 1 project | 1 class = 1 project
• This entire course will be one project in your computer¹
• A project tells R what the “working directory” is: where to find files
• Needs to live in your computer (not the cloud!)

1. plus one more for your final project

Learning Objectives - Revisited

By the end of this week, you should be able to:

• Understand the difference between R and RStudio
• Open the RStudio IDE
• Identify the main work areas
• Start a project and a script
• Install R packages

Next class

• Introduction to RStudio IDE
• Changing the default settings
• Running your first lines of code
• Creating an RStudio project
• A few basics of project organization

That’s It for Today

(Unless you are having trouble installing R / RStudio)

Getting to Know R Studio

Announcements:

• This week only: Office Hours shifted to 10:15-12:00 FRIDAY
• Software Carpentry date TBD, but keep an eye

Dynamics

• Post it on your screen means “I’m working on it”
• Post it down means “I’m ready to listen”

Hands-on Tasks:

• R vs R GUI vs RStudio
• Open RStudio
• Modify appearance
• Modify global options
• Describe windows
• Console: getwd()
• Console: ?getwd()
• Search for help
• Sort code in console

• Longer code in the console:

plot(mpg ~ disp, data = mtcars, col = "blue", pch = 21)

• Getting the history back (pane, up key, autocomplete)
• Start a project called “EVR628”
• First script
• Running line from a script
• Run entire script

• Install tidyverse
• Fail to install EVR628tools, then succeed (remotes)
• Load packages
• Directory structure
• Start a new project called “temp_project”
• Close and reopen project
• Switch between projects
• Close R Studio and open project

References

Becker, Joel, Nate Rush, Elizabeth Barnes, and David Rein. 2025. “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity.” https://arxiv.org/abs/2507.09089.

Wickham, Hadley, Mine Cetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd ed. Sebastopol, CA: O’Reilly Media.