Week 8 Class Notes

Announcement

• Office hours update:
  • Mondays 11am - noon (316 Thomas)
  • Tuesdays 3pm - 4pm (330 Thomas)
  • Wednesdays 2:30pm - 4pm (316 Thomas)
  • Thursdays 3:30pm - 4:30pm (330 Thomas)
  • Fridays 11am - noon (316 Thomas)
• Feel free to use office hours as a chance to just meet with your group and work together with an expert nearby if needed.
• Homework Solutions, Activity Examples, & R Cheat Sheets: “Canvas >> Files”

Building Graphics

1. Draw (by hand) or imagine the specific plot that you intend to construct
2. Data Wrangling (if needed) - get the data in glyph-ready form, or verify that the current form is glyph-ready for your purposes.
3. Establish the frame using a ggplot() statement
4. Create the intended glyph using geom_[style]() such as
   • geom_point()
   • geom_bar()
   • geom_boxplot()
   • geom_density()
   • geom_vline()
   • geom_segment()
   • geom_histogram()
   • geom_ash()
   • and many more
5. Map variables to the graphical attributes of the glyph using aes( )
6. Add additional layers to the frame using the + symbol

Want some help?

This can all be a lot to keep straight… visit “Canvas >> Files >> Cheatsheets”" for some resources to help you shoulder the burden:

• EnoughDCF: one page summary from the author of Data Computing
• rstudio-ggplot2-cheatsheet: 2 page summary of ggplot2 tools and usage from RStudio
• rstudio-data-wrangling-cheatsheet: 2 page summary of dplyr (& tidyr) tools and usage from RStudio

Also see…

• rstudio-rmarkdown-cheatsheet
• rstudio-rmarkdown-reference
• rstudio-IDE-cheatsheet
• and more…

Remarks about facet_wrap()

The syntax for facets can be a bit complicated for new users. Here are a few pointers:

• facet_wrap() just makes a box for each level of the categorical variable
  • Syntax: facet_wrap( ~ categoricalVariable)
  • For example:

data("NCHS")

# 1is.na(smoker) gets cases that are non-missing for `smoker` (i.e. removes NA's)
Heights <- 
  NCHS %>%
  filter(age > 20, !is.na(smoker)) %>%   
  group_by(sex, smoker, age) %>%
  summarise(height = mean(height, na.rm = TRUE))

Heights %>%
  ggplot(aes(x = age, y = height)) + 
  geom_line(aes(linetype = smoker)) + 
  facet_wrap( ~ sex)

Remarks about facet_grid()

• facet_grid() allows control of row & column facets
• facet_grid() syntax:
  • row & column facets: facet_grid(rows ~ cols)
  • row facets only: facet_grid( rows ~ . ) (note the required “.”)
  • column facets only: facet_grid( ~ cols) (no “.” this time)

Heights %>%
  ggplot(aes(x = age, y = height)) + 
  geom_line(aes(linetype = smoker)) + 
  facet_grid(sex ~ .)

Heights %>%
  ggplot(aes(x = age, y = height)) + 
  geom_line() + 
  facet_grid(sex ~ smoker)

Homework

• Activity: Bicycle Sharing (DC p. 182)
  • Sections A.3 & A.5 are worth extra credit (not required)
  • Make sure you complete all the “your turn” segments in A.1, A.2, A.4
  • The last “your turn (optional)” at the end of A.2 is required
• Homework:
  • DC Ch 8 Exercises: 8.1, 8.2, 8.3 (skip the world map)
  • DC chapter 11 reading quiz on Canvas

Activity: Bicycle Sharing (DC p. 182)

Assignment is worth a total of 10 points.

• [2 point] Turn in HTML with embedded .Rmd file (e.g. “DataComputing simple” template)
• [2 points] A.1 side-by-side boxplots of rental durations
• [4 points total] A.2 plots
  • [0.5 point] density plot: day of the year (1 to 365)
  • [0.5 point] density plot: day of the week (Sunday to Saturday; numeric is fine)
  • [0.5 point] density plot: hour of the day (0 to 24)
  • [0.5 point] density plot: minute in the hour (0 to 60)
  • [1 points] Plot rental frequency by hour, weekday, & client type
  • [1 points] Plot rental density by hour, weekday, & client type
• [2 points] A.4 leaflet

• [EXTRA CREDIT: 2 pts] complete both A.3 & A.5

• General Remarks:
  • Sections A.3 & A.5 are worth extra credit (not required)
  • Make sure you complete all the “your turn” segments in A.1, A.2, A.4
  • The last “your turn (optional)” at the end of A.2 is REQUIRED

---

teaching | stat 184 home | syllabus | piazza | canvas