Wrap-up: Where to go from here

Lecture 26

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Spring 2024

May 9, 2023

End-of-semester logistics

Remaining assignments

  • Course evaluations

Build a simple data science stack

RStudio Workbench

  • Access to RStudio Workbench will end after May 26th
  • All INFO 3312/5312 materials remain available in your repos on GitHub
  • Any other work you have done on the server will not be accessible
  • Where will you go from here?

Software installation

Install some core R packages

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

# install a package hosted on GitHub
remotes::install_github(repo = "cis-ds/rcis")

Create a GitHub account

Configure Git

usethis::use_git_config(
  user.name = "Your name", 
  user.email = "Email associated with your GitHub account"
  )

Painless authentication with PAT

Personal Access Token

  • Uses HTTPS protocol
  • More flexible than SSH
  • Integrates with the usethis package to automate some Git workflows
  • Alternative: continue using SSH

Setup PAT authentication

usethis::create_github_token(
  scopes = c("repo", "user", "gist", "workflow"),
  description = "<DESCRIBE YOUR DEVICE>"
)
gitcreds::gitcreds_set()

#> ? Enter password or token: ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
#> -> Adding new credentials...
#> -> Removing credentials from cache...
#> -> Done.
usethis::create_github_token(
  scopes = c("repo", "user", "gist", "workflow"),
  description = "<DESCRIBE YOUR DEVICE>",
  host = "https://github.coecis.cornell.edu/"
)
gitcreds::gitcreds_set(url = "https://github.coecis.cornell.edu/")

#> ? Enter password or token: ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
#> -> Adding new credentials...
#> -> Removing credentials from cache...
#> -> Done.

What have you learned?

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 good a job the visualization does for communicating 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.

Where to go from here

Keep your skills fresh