Maps and Data

Stat 251

2025-05-06

Logistics

Feedback

• Screencasts all look good

• Make sure to fill out the class survey!

• Peer Review is open:

  • make sure that you can access the github repo (you might have to send a comment) and the video
  • watch the video
  • read through the script
  • run the code
  • submit your feedback by Thursday next week

Access to repo

• Go into Canvas

• Follow the link to the youtube video

• Follow the link to the repo - you most likely don’t have access to the repo

  • leave your github handle in the comments of the two screencasts you are reviewing
  • go into your own screencast, get the github handles from your reviewers, add them as collaborators to your repo

Add the screencast to your portfolio

• portfolios are important for getting internships/industry positions

• right now:

  • final project is private (because of FERPA)
  • no good way to make fork of private repo public (because of Github rules, protection of instructor’s IP)

• best solution (right now): make a new repo (public or private), copy the necessary files, and link that to your portfolio

Maps in R and python

• Last homework assignment - essentially attendance-based

• Work on some map-related question, submit by the end of class on Thursday for full credit

Maps in R and python

Maps provide a lot of context

tuesdata <- tidytuesdayR::tt_load(2024, week = 42)

orcas <- tuesdata$orcas

Orca Encounters

usmap <- map_data("state")
usmap |> 
  ggplot(aes(x = long, y = lat)) + 
  geom_path(aes(group = group)) + 
  geom_point(data = orcas, aes(x = begin_longitude, y = begin_latitude), colour = "darkorange")

Closer Orca Encounters

usmap |> dplyr::filter(region=="washington") |>
  ggplot(aes(x = long, y = lat)) + 
  geom_path(aes(group = group)) + 
  geom_point(data = orcas, aes(x = begin_longitude, y = begin_latitude), colour = "darkorange")

Homework 12