Deep dive into ggplot2 layers - I

Lecture 1

Dr. Mine Çetinkaya-Rundel

Duke University
STA 313 - Spring 2023

Warm up

Announcements

Office hours + locations finalized at vizdata.org/course-team.html.
Don’t forget to complete the getting to know you survey by 8pm today at the latest!
A note on readings for this week: Some of it is review so feel free to skim those parts.

Setup

# load packages
library(tidyverse)
library(openintro)
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 Duke Forest

Data on houses that were sold in the Duke Forest neighborhood of Durham, NC around November 2020
Scraped from Zillow
Source: openintro::duke_forest

`openintro::duke_forest`

library(tidyverse)
library(openintro)

glimpse(duke_forest)

Rows: 98
Columns: 13
$ address    <chr> "1 Learned Pl, Durham, NC 27705", "1616 Pine…
$ price      <dbl> 1520000, 1030000, 420000, 680000, 428500, 45…
$ bed        <dbl> 3, 5, 2, 4, 4, 3, 5, 4, 4, 3, 4, 4, 3, 5, 4,…
$ bath       <dbl> 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 5.0, 3.0, 5.0,…
$ area       <dbl> 6040, 4475, 1745, 2091, 1772, 1950, 3909, 28…
$ type       <chr> "Single Family", "Single Family", "Single Fa…
$ year_built <dbl> 1972, 1969, 1959, 1961, 2020, 2014, 1968, 19…
$ heating    <chr> "Other, Gas", "Forced air, Gas", "Forced air…
$ cooling    <fct> central, central, central, central, central,…
$ parking    <chr> "0 spaces", "Carport, Covered", "Garage - At…
$ lot        <dbl> 0.97, 1.38, 0.51, 0.84, 0.16, 0.45, 0.94, 0.…
$ hoa        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ url        <chr> "https://www.zillow.com/homedetails/1-Learne…

ggplot(duke_forest, 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 Duke Forest"
  )

New variable: `decade_built`

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

duke_forest |>
  select(year_built, decade_built)

# A tibble: 98 × 2
   year_built decade_built
        <dbl>        <dbl>
 1       1972         1970
 2       1969         1960
 3       1959         1950
 4       1961         1960
 5       2020         2020
 6       2014         2010
 7       1968         1960
 8       1973         1970
 9       1972         1970
10       1964         1960
# … with 88 more rows

New variable: `decade_built_cat`

duke_forest <- duke_forest |>
  mutate(
    decade_built_cat = case_when(
      decade_built <= 1940 ~ "1940 or before",
      decade_built >= 1990 ~ "1990 or after",
      TRUE ~ as.character(decade_built)
    )
  )

duke_forest |>
  count(decade_built_cat)

# A tibble: 6 × 2
  decade_built_cat     n
  <chr>            <int>
1 1940 or before       8
2 1950                26
3 1960                32
4 1970                11
5 1980                13
6 1990 or after        8

A slightly more complex visualization

Code
Plot

ggplot(
  duke_forest,
  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 Duke Forest"
  )

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
Vote

Test 2

Plot A
Plot B
Vote

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
Vote

A deeper look

at the plotting code

Summary statistics

mean_area_decade <- duke_forest |>
  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 1940 or before       2072.
2 1950                 2545.
3 1960                 2873.
4 1970                 3413.
5 1980                 2889.
6 1990 or after        2822.

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 Duke Forest, by decade built"
  )

Scaterplot

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 Duke Forest, by decade built"
  )

Lollipop chart – a happy medium?

Application exercise

Go to the course GitHub organization: https://github.com/vizdata-s23
Clone the repo called ae-01 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 Thursday 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 Duke Forest, 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 Duke Forest, 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 I|

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
Discussion

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

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

# Plot C
ggplot(duke_forest, 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 Duke Forest

openintro::duke_forest

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

Scaterplot

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 I|

Wrap up

`openintro::duke_forest`

New variable: `decade_built`

New variable: `decade_built_cat`