Deep dive into ggplot2 layers - I

Lecture 2

Dr. Greg Chism

University of Arizona
INFO 526 - Fall 2023

Warm up

Announcements

A note on readings for this week: Some of it is review so feel free to skim those parts.
Please fill out the class questionnaire, found within the #announcements Slack channel.

Setup

# load packages
library(tidyverse)
library(here)
library(countdown)

# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))

# set width of code output
options(width = 65)

# set figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 7,        # 7" width
  fig.asp = 0.618,      # the golden ratio
  fig.retina = 3,       # dpi multiplier for displaying HTML output on retina
  fig.align = "center", # center align figures
  dpi = 300             # higher dpi, sharper image
)

A/B testing

Data: Sale prices of houses in Tucson

Data on houses for sale
in Tucson, AZ, around July 2023
Scraped from Zillow
Source: tucsonHousing.csv

`slides/data/tucsonHousing.csv`

library(tidyverse)
library(here)

tucsonHousing <- read_csv(here(
  "slides", "02", "data" ,"tucsonHousing.csv"))

glimpse(tucsonHousing)

Rows: 112
Columns: 8
$ address    <chr> "710 E 5th St, Tucson, AZ 85719", "3543 N Fl…
$ year_built <dbl> 1936, 1943, 1948, 1950, 1950, 1951, 1951, 19…
$ price      <dbl> 330000, 260000, 310000, 270000, 270000, 2149…
$ bed        <dbl> 2, 2, 3, 4, 4, 3, 1, 4, 3, 3, 4, 4, 3, 3, 4,…
$ bath       <dbl> 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2,…
$ area       <dbl> 903, 1253, 1256, 1634, 1634, 1070, 766, 1490…
$ type       <chr> "Single Family", "Single Family", "Single Fa…
$ url        <chr> "/homedetails/710-E-5th-St-Tucson-AZ-85719/8…

ggplot(tucsonHousing, aes(x = area, y = price)) +
  geom_point(alpha = 0.7, size = 2) +
  geom_smooth(method = "lm", se = FALSE, size = 0.7) +
  labs(
    x = "Area (square feet)",
    y = "Sale price (USD)",
    title = "Price and area of houses in Tucson"
  )

New variable: `decade_built`

tucsonHousing <- tucsonHousing |>
  mutate(decade_built = (year_built %/% 10) * 10)

tucsonHousing |>
  select(year_built, decade_built)

# A tibble: 112 × 2
   year_built decade_built
        <dbl>        <dbl>
 1       1936         1930
 2       1943         1940
 3       1948         1940
 4       1950         1950
 5       1950         1950
 6       1951         1950
 7       1951         1950
 8       1952         1950
 9       1952         1950
10       1952         1950
# ℹ 102 more rows

New variable: `decade_built_cat`

tucsonHousing <- tucsonHousing |>
  mutate(
    decade_built_cat = case_when(
      decade_built <= 1950 ~ "1950 or before",
      decade_built >= 2000 ~ "2000 or after",
      TRUE ~ as.character(decade_built)
    )
  )

tucsonHousing |>
  count(decade_built_cat)

# A tibble: 6 × 2
  decade_built_cat     n
  <chr>            <int>
1 1950 or before      25
2 1960                 9
3 1970                18
4 1980                 9
5 1990                20
6 2000 or after       31

A slightly more complex visualization

Code
Plot

ggplot(
  tucsonHousing,
  aes(x = area, y = price, color = decade_built_cat)
) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  geom_smooth(method = "lm", se = FALSE, size = 0.5, show.legend = FALSE) +
  facet_wrap(~decade_built_cat) +
  labs(
    x = "Area (square feet)",
    y = "Sale price (USD)",
    color = "Decade built",
    title = "Price and area of houses in Tucson"
  )

A/B testing

In the next two slides, the same plots are created with different “cosmetic” choices. Examine the plots two given (Plot A and Plot B), and indicate your preference by voting for one of them in the Vote tab.

Test 1

Plot A
Plot B

Test 2

Plot A
Plot B

What makes figures bad?

Bad taste

Data-to-ink ratio

Tufte strongly recommends maximizing the data-to-ink ratio this in the Visual Display of Quantitative Information (Tufte, 1983).

Graphical excellence is the well-designed presentation of interesting data—a matter of substance, of statistics, and of design … [It] consists of complex ideas communicated with clarity, precision, and efficiency. … [It] is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space … [It] is nearly always multivariate … And graphical excellence requires telling the truth about the data. (Tufte, 1983, p. 51).

Cover of The Visual Display of Quantitative Information

Which of the plots has higher data-to-ink ratio?

Plot A
Plot B

A deeper look

at the plotting code

Summary statistics

mean_area_decade <- tucsonHousing |>
  group_by(decade_built_cat) |>
  summarise(mean_area = mean(area))

mean_area_decade

# A tibble: 6 × 2
  decade_built_cat mean_area
  <chr>                <dbl>
1 1950 or before       1440.
2 1960                 1506.
3 1970                 1558.
4 1980                 1586.
5 1990                 1570.
6 2000 or after        1795.

Barplot

Code
Plot

ggplot(
  mean_area_decade,
  aes(y = decade_built_cat, x = mean_area)
) +
  geom_col() +
  labs(
    x = "Mean area (square feet)", y = "Decade built",
    title = "Mean area of houses in Tucson, by decade built"
  )

Scatterplot

Code
Plot

ggplot(
  mean_area_decade,
  aes(y = decade_built_cat, x = mean_area)
) +
  geom_point(size = 4) +
  labs(
    x = "Mean area (square feet)", y = "Decade built",
    title = "Mean area of houses in Tucson, by decade built"
  )

Lollipop chart – a happy medium?

Application exercise

Go to the course GitHub organization: https://github.com/INFO526-DataViz
Clone the repo called ae-02 and work on the exercise.
- Note: For today, this is not a personalized repo for you. The repo is public so everyone can clone it, but you won’t be able to push to it. Starting Wednesday (hopefully) you’ll start getting your personalized repos you can push to.
Once you’re done, share your code on Slack in #general.
Label your chunk(s) and pay attention to code style and formatting!

10:00

Bad data

Original
Improved

Faceted plot showing the average importance of democracy in 6 countries over time.

Bad perception

Aspect ratios affect our perception of rates of change, modeled after an example by William S. Cleveland.

Aesthetic mappings in ggplot2

A second look: lollipop chart

Plot
Code

ggplot(
  mean_area_decade,
  aes(y = decade_built_cat, x = mean_area)
) +
  geom_point(size = 4) +
  geom_segment(aes(
    x = 0, xend = mean_area,
    y = decade_built_cat, yend = decade_built_cat
  )) +
  labs(
    x = "Mean area (square feet)",
    y = "Decade built",
    title = "Mean area of houses in Tucson, by decade built"
  )

Activity: Spot the differences |

Plot
Code
Discussion

ggplot(
  mean_area_decade,
  aes(y = decade_built_cat, x = mean_area)
) +
  geom_point(size = 4) +
  geom_segment(aes(
    xend = 0,
    yend = decade_built_cat
  )) +
  labs(
    x = "Mean area (square feet)",
    y = "Decade built",
    title = "Mean area of houses in Tucson, by decade built"
  )

Can you spot the differences between the code here and the one provided in the previous slide? Are there any differences in the resulting plot? Work in a pair (or group) to answer.

03:00

Global vs. layer-specific aesthetics

Aesthetic mappings can be supplied in the initial ggplot() call, in individual layers, or in some combination of both.
Within each layer, you can add, override, or remove mappings.
If you only have one layer in the plot, the way you specify aesthetics doesn’t make any difference. However, the distinction is important when you start adding additional layers.

Activity: Spot the differences II

Do you expect the following plots to be the same or different? If different, how? Discuss in a pair (or group) without running the code and sketch the resulting plots based on what you think the code will produce.

Plot

# Plot A
ggplot(tucsonHousing, aes(x = area, y = price)) +
  geom_point(aes(color = decade_built_cat))

# Plot B
ggplot(tucsonHousing, aes(x = area, y = price)) +
  geom_point(color = "blue")

# Plot C
ggplot(tucsonHousing, aes(x = area, y = price)) +
  geom_point(color = "#a493ba")

03:00

Wrap up

Think back to all the plots you saw in the lecture, without flipping back through the slides. Which plot first comes to mind? Describe it in words.

Deep dive into ggplot2 layers - I

Warm up

Announcements

Setup

A/B testing

Data: Sale prices of houses in Tucson

slides/data/tucsonHousing.csv

A simple visualization

New variable: decade_built

New variable: decade_built_cat

A slightly more complex visualization

A/B testing

Test 1

Test 2

What makes figures bad?

Bad taste

Data-to-ink ratio

A deeper look

Summary statistics

Barplot

Scatterplot

Lollipop chart – a happy medium?

Application exercise

Bad data

Bad perception

Aesthetic mappings in ggplot2

A second look: lollipop chart

Activity: Spot the differences |

Global vs. layer-specific aesthetics

Activity: Spot the differences II

Plot

Wrap up

`slides/data/tucsonHousing.csv`

New variable: `decade_built`

New variable: `decade_built_cat`