Wrap-up: Where to go from here

Notes
Modified

May 13, 2026

NoteLearning objectives
  • Reflect on the course learning objectives
  • Build a personal data science stack
  • Identify resources for continuing to develop your skills

What you have learned

The course learning objectives for INFO 3312/5312:

  • Implement principles of designing and creating effective data visualizations
  • Evaluate, critique, and improve upon one’s own and others’ data visualizations based on how well the visualization communicates a message clearly and correctly
  • Post-process and refine plots for effective communication
  • Master using R and a variety of modern data visualization packages to reproducibly create data visualizations
  • Work reproducibly individually and collaboratively using Git and GitHub

Building your personal data science stack

When Posit Workbench access ends after the semester, you will need a local setup. This section walks through the full configuration.

Install R and an IDE

R — download the latest version from https://www.r-project.org/

IDE — choose one: - Positron (recommended — the IDE used in this course) - RStudio (classic, more tutorials available online) - VS Code with the R extension

Reproducibility tools: - Quarto — for documents, reports, and websites - Git — version control

Install core R packages

# major packages from the course, available on CRAN
install.packages(c(
  "tidyverse",
  "tidymodels",
  "devtools",
  "usethis",
  "colorspace",
  "janitor",
  "skimr"
))

# packages hosted on GitHub (not on CRAN)
remotes::install_github("hafen/geofacet")

Set up a GitHub account

Configure Git

usethis::use_git_config(
  user.name  = "Your Name",
  user.email = "email@associated.with.github.com"
)

Authenticate with a Personal Access Token

Personal Access Tokens (PATs) are the recommended way to authenticate Git operations over HTTPS. The {usethis} and {gitcreds} packages handle most of the workflow.

Create a PAT:

usethis::create_github_token(
  scopes      = c("repo", "user", "gist", "workflow"),
  description = "My laptop"
)

This opens GitHub in your browser to generate the token. Copy it before closing the browser window — it will not be shown again.

Store the PAT:

gitcreds::gitcreds_set()
# Paste the token when prompted

For more detail see Happy Git and GitHub for the useR.

{renv} for package reproducibility

{renv} creates a project-local package library so that your code always runs against the same package versions, regardless of what is installed globally.

Benefits: isolated (no global package conflicts), portable (collaborators can reproduce your environment), and reproducible (package versions are locked in renv.lock).

Tradeoffs: requires initialization for each new project, and some packages have installation issues through {renv}. For personal exploratory projects, you may not need it. For shared research or production work, it is strongly recommended.

renv::init()      # initialize a new project library
renv::snapshot()  # save current package versions to renv.lock
renv::restore()   # restore packages from renv.lock

What’s next in data visualization

Generative AI and visualization

AI tools are reshaping how data visualizations are created and interpreted:

New frontiers

The tools covered in this course are a foundation, not a ceiling. Some directions worth exploring:

Find a community

The R community is unusually welcoming. Some places to connect:

Keep your skills fresh

Skills atrophy without practice. A few sustainable strategies:

  • Tidy Tuesday — github.com/rfordatascience/tidytuesday — a weekly data visualization challenge using real-world datasets. Even submitting one every few months keeps your skills sharp.
  • Create a blog — publishing your analyses forces you to explain your thinking, which deepens understanding. Quarto makes this straightforward: quarto.org/docs/websites/website-blog.html
  • Integrate reproducible workflows into other coursework — write every analysis report in Quarto, use Git for every project, and the habits will stick.

Summary

  • After the semester, install R, Positron (or RStudio), Quarto, and Git locally; configure Git with your name and email; authenticate with a Personal Access Token
  • Use {renv} for projects where reproducibility matters to collaborators or future you
  • The R community is active and welcoming — Tidy Tuesday and the Data Science Learning Community are good entry points for staying engaged
  • The skills in this course are a foundation: {ggplot2}, Shiny, Quarto, and Git are all actively developed tools with growing ecosystems