Schedule

Course Calendar

Readings in the schedule below will sometimes be marked with abbreviations:

Guiding questions for pre-class readings and videos are available at the bottom of this page.

Week Tuesday Thursday Announcements
1 1/18: Welcome! Getting to know each other, brainstorming project ideas
Before class:
2 1/23: Advanced visualization in ggplot2
Before class:
  • Review the construction of plots from STAT 112 and STAT 155 by answering the Guiding Questions at the bottom of this page.
 1/25: File organization and paths, Git & GitHub
Before class: 		Turn in HW1 by midnight on Monday 1/29.
3 1/30: Advanced map visualization
Before class:
  • Watch this video on Coordinate Reference Systems, and answer the Guiding Questions at the bottom of this page.
  • Run the package installation commands in the Guiding Questions section at the bottom of this page.
 2/1: Advanced map visualization (continued)
Before class: 		Turn in HW2 by midnight on Monday 2/5. Prepare for Skills Session 1 next week (2/5-2/9).
4 2/6: Interactive visualization
Before class: Install the shiny and plotly R packages.
Listen to this podcast (timestamp 18:09-25:27). Reflect on the Guiding Question at the bottom of this page. 		2/8: Interactive visualization (continued)
Before class: 		Skills Session 1 will be happening this week. Turn in HW3 (includes Project Milestone 1) by midnight on Monday 2/12.
5 2/13: Data wrangling
Before class: Read Chapters 12, 13, 16, 17 in R4DS. 		2/15: Data wrangling and project work time
Before class: Read Chapters 14, 15, 19 in R4DS. 		Turn in HW4 and Reflection 1 by midnight on Monday 2/19.
6 2/20: Statistical modeling and missing data
Before class: 		2/22: Writing functions, more version control
Before class: Read R4DS Chapter 26 (Functions) and RPDS Section 13.1 (if-else). 		Turn in HW5 (includes Project Milestone 2) by midnight on Monday 2/26.
7 2/27: Functions and version control(continued)
Before class: 		2/29-3/1: Capstone Days! (No class but please attend talks to support your peers!) Turn in HW5 (includes Project Milestone 2) by midnight on Monday 2/26.
8 3/5: Loops and iteration
Before class: Read R4DS Chapter 27 (Iteration) and this tutorial. 		3/7: Loops and iteration (continued)
Before class: 		Turn in HW6 by midnight on FRIDAY 3/8.
9 3/9-3/17: Spring Break!
10 3/19: Data acquisition: APIs
Before class: 		3/21: Data acquisition: APIs (continued)
Before class: 		Turn in HW7 (includes Project Milestone 3) by midnight on Wednesday 3/27.
11 3/26: Data acquisition: Scraping
Before class: Read the rvest vignette. 		3/28: Reflection, Skills Session 2 practice, ChatGPT
Before class: 		Turn in HW8 by midnight on Wednesday 4/3.
12 4/2: Data acquisition: databases
Before class: Read R4DS Chapter 22 (Databases). 		4/4: Data acquisition: databases (continued) Turn in HW9 by midnight on Wednesday 4/10.
13 4/9: Project work time: peer code review
Before class: Prepare to present your project code to another team. You will be helping each other check the accuracy of your data wrangling. 		4/11: Project work time Schedule Skills Session 2 for next week (4/15-4/19) via Calendly.
14 4/16: Project work time 4/18: Project work time Skills Session 2 will be happening this week.
15 4/23: 4/25: Last day of class

Guiding Questions

Use the guiding questions/exercises below to focus your pre-class preparations.

1/23: Advanced visualization in ggplot2

To review plot creation skills from STAT/COMP 112 and STAT 155, use the diamonds dataset in the ggplot2 package to recreate the following visualizations:

library(ggplot2)
data(diamonds)

Solution 
library(gridExtra)
p1 <- ggplot(diamonds, aes(x = cut)) + geom_bar() + theme_classic()
p2 <- ggplot(diamonds, aes(x = price)) + geom_histogram() + theme_classic()
p3 <- ggplot(diamonds, aes(x = price)) + geom_density() + theme_classic()
grid.arrange(p1, p2, p3, nrow = 1)

p4 <- ggplot(diamonds, aes(x = cut, y = price)) + geom_boxplot() + theme_classic()
p5 <- ggplot(diamonds, aes(x = price, color = cut)) + geom_density() + theme_classic()
grid.arrange(p4, p5, nrow = 1)

p6 <- ggplot(diamonds, aes(x = cut, fill = color)) + geom_bar() + theme_classic()
p7 <- ggplot(diamonds, aes(x = cut, fill = color)) + geom_bar(position = "dodge") + theme_classic()
p8 <- ggplot(diamonds, aes(x = cut, fill = color)) + geom_bar(position = "fill") + theme_classic()
grid.arrange(p6, p7, p8, nrow = 1)

p9 <- ggplot(diamonds, aes(x = carat, y = price)) + geom_point() + theme_classic()
p10 <- ggplot(diamonds, aes(x = carat, y = price, color = cut)) + geom_point() + geom_smooth(method = "lm") + theme_classic()
grid.arrange(p9, p10, nrow = 1)

1/30: Advanced map visualization

After/while watching this video on Coordinate Reference Systems (CRS), answer the following questions:

  • What is the shape of the Earth?
  • What are the two components of a CRS/GCS?
  • Why do we use many different local CRSs rather than just one CRS for the whole earth?
  • Why is it insufficient to identify a location by its latitude and longitude?
  • Why do we need to be mindful about CRSs when working with different spatial datasets?

Run the following package installation commands:

install.packages(c("sf", "elevatr", "terra", "stars", "tidycensus", "remotes"))
install.packages("USAboundariesData", repos = "http://packages.ropensci.org", type = "source")
remotes::install_github("ropensci/USAboundaries")

2/6: Interactive visualization in Shiny

What was new, unexpected, or interesting in the discussion about animations, interactivity, and dashboards?