Data Visualization

STAT 20: Introduction to Probability and Statistics

Warmup Questions

What is the issue in these commands?

Can you spot the bug(s)?

ggplot(penguins, x = bill_length_mm, y = bill_depth_mm) +
  geom_point()

the x and y mappings need to be specified inside aes()

Can you spot the bug(s)?

ggplot(
  data = penguins, 
  mapping = aes(x = bill_length_mm, 
                y = bill_depth_mm,
                color = species))
  geom_point()

the plus sign + is missing after ggplot() and before geom_point()

Can you spot the bug(s)?

ggplot(
  data = penguins, 
  mapping = aes(x = bill_length_mm 
                y = bill_depth_mm,
                color = species) |>
  goem_point()

• missing comma between arguments x and y inside aes()
• missing closing parenthesis of ggplot()
• cannot use pipe operator |>; must be +
• misspelled goem_point()

Agenda

• Announcements

• Review

• Worksheet

• Lab Flights II

Announcements: Retake of Quiz 1

Retake of Quiz-1

• Timeslots: through Sep 25
• Content: Weeks 1-2

Review

Palmer Penguins

# A tibble: 333 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           36.7          19.3               193        3450
 5 Adelie  Torgersen           39.3          20.6               190        3650
 6 Adelie  Torgersen           38.9          17.8               181        3625
 7 Adelie  Torgersen           39.2          19.6               195        4675
 8 Adelie  Torgersen           41.1          17.6               182        3200
 9 Adelie  Torgersen           38.6          21.2               191        3800
10 Adelie  Torgersen           34.6          21.1               198        4400
# ℹ 323 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Question: In terms of the way they are constructed …

What do these plots have in common? How do they differ?

01:30

Common:

• they are univariate visualizations
• graphics to see distribution of a continuous variable
• continuous variable in x-axis

Differences:

• the geometries
• the meaning of the y-axis scale

Question: What do these plots have in common? How do they differ?

01:30

Common:

• same geom: they are both bar charts

Differences:

• type of bar chart (stacked normalized vs standard)
• number of variables (2 vs 1)
• what’s in x-axis, what’s in y-axis

Question: What are the aesthetic mappings and geometries used here?

01:30

Mappings:

• bill_length_mm to x-axis
• flipper_length_mm to y-xias
• species to color
• sex to shape

Geoms:

• points

Analytical Tasks (by Stephen Few)

7-Core Analytical Tasks (by Stephen Few)

• Times-series analysis
• Ranking analysis
• Part-to-Whole analysis
• Deviation & Comparisons against a benchmark
• Distribution analysis
• Correlation & Regression
• Multivariate
• Other*

Your Turn: Worksheet

For each graphic below, answer the following questions:

1. What geometry/geometries are used?
2. What variable is mapped to the x-axis?
3. What variable is mapped to the y-axis?
4. Are other visual attributes used (e.g., size, shape, facets)?
5. How is color used: data-encoding or decorative?
6. How many total variables are represented in the graphic?
7. What primary analytical task(s) (Few) does the plot serve?
8. If annotations are present:
   • do they describe data points?
   • or do they highlight a specific takeaway?

Write your answers in today’s worksheet

NOTE: You do NOT need to write ggplot code today. Today’s worksheet is conceptual and requires you to think through the Grammar of Graphics framework.

Plot 1

Plot 2

Plot 3

Plot 4

Plot 5

Plot 6

Lab 4: Flights II (cont’d)

End of Lecture