Deep dive: layers (II)

Lecture 4

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Spring 2024

February 1, 2024

Announcements

Homework 02 will be posted tomorrow
Post questions on the discussion forum

Visualization critique

Distracted boyfriends cause statisticians to move to New Jersey?

What is the story?
Is it believable?
Is it relevant?

Setup

From last time

tompkins <- read_csv("data/tompkins-home-sales.csv") |>
  mutate(decade_built = (year_built %/% 10) * 10) |>
  mutate(
    decade_built_cat = case_when(
      decade_built <= 1940 ~ "1940 or before",
      decade_built >= 1990 ~ "1990 or after",
      .default = as.character(decade_built)
    )
  )

mean_area_decade <- tompkins |>
  group_by(decade_built_cat) |>
  summarize(mean_area = mean(area))

mean_area_decade

# A tibble: 6 × 2
  decade_built_cat mean_area
  <chr>                <dbl>
1 1940 or before       1872.
2 1950                 1645.
3 1960                 1874.
4 1970                 1908.
5 1980                 1852.
6 1990 or after        2226.

Geoms

Geometric objects, or geoms for short, perform the actual rendering of the layer, controlling the type of plot that you create
You can think of them as “the geometric shape used to represent the data”

One variable

Discrete:
- geom_bar(): display distribution of discrete variable.
Continuous
- geom_histogram(): bin and count continuous variable, display with bars
- geom_density(): smoothed density estimate
- geom_dotplot(): stack individual points into a dot plot
- geom_freqpoly(): bin and count continuous variable, display with lines

Aside

Always use “typewriter text” (monospace font) when writing function names, and follow with (), e.g.,

geom_freqpoly()
mean()
lm()

`geom_dotplot()`

What does each point represent? How are their locations determined? What do the \(x\) and \(y\) axes represent?

ggplot(tompkins, aes(x = price)) +
  geom_dotplot(binwidth = 100000, dotsize = 0.2)

Comparing across groups

Which of the following allows for easier comparison across groups?

Histogram
Frequency polygon

ggplot(tompkins, aes(x = price, fill = decade_built_cat)) +
  geom_histogram(binwidth = 100000)

ggplot(tompkins, aes(x = price, color = decade_built_cat)) +
  geom_freqpoly(binwidth = 100000, linewidth = 1)

Application exercise

`ae-02` - Part 1

Go to the course GitHub org and find your ae-02 (repo name will be suffixed with your NetID).
Clone the repo in RStudio Workbench, open the Quarto document in the repo, and follow along and complete the exercises.
Render, commit, and push your edits by the AE deadline – end of tomorrow.

10:00

Two variables

Two variables - both continuous

geom_point(): scatterplot
geom_quantile(): smoothed quantile regression
geom_rug(): marginal rug plots
geom_smooth(): smoothed line of best fit
geom_text(): text labels

Two variables - show density

geom_bin2d(): bin into rectangles and count
geom_density2d(): smoothed 2d density estimate
geom_hex(): bin into hexagons and count

`geom_hex()`

Not so helpful for 156 observations:

tompkins |>
  filter(decade_built == 1950) |>
  ggplot(aes(x = area, y = price)) +
  geom_hex()

`geom_hex()`

More helpful for 1897 observations:

ggplot(tompkins, aes(x = area, y = price)) +
  geom_hex()

`geom_hex()`

Even more helpful for 53940 observations:

ggplot(diamonds, aes(x = carat, y = price)) +
  geom_hex()

`geom_hex()`

(Maybe) even more helpful on the (natural) log scale:

ggplot(diamonds, aes(x = carat, y = price)) +
  geom_hex() +
  scale_fill_gradient(trans = "log")

`geom_hex()` and warnings

Requires installing the hexbin package separately!

install.packages("hexbin")

Otherwise you might see

Warning: Computation failed in `stat_binhex()`

Two variables

At least one discrete
- geom_count(): count number of point at distinct locations
- geom_jitter(): randomly jitter overlapping points
One continuous, one discrete
- geom_col(): a bar chart of pre-computed summaries
- geom_boxplot(): boxplots
- geom_violin(): show density of values in each group

`geom_jitter()`

How are the following three plots different?

Plot A
Plot B
Plot C

ggplot(tompkins, aes(x = beds, y = price)) +
  geom_point()

ggplot(tompkins, aes(x = beds, y = price)) +
  geom_jitter()

ggplot(tompkins, aes(x = beds, y = price)) +
  geom_jitter()

set.seed(1234)

ggplot(tompkins, aes(x = beds, y = price)) +
  geom_jitter()

set.seed(1234)

ggplot(tompkins, aes(x = beds, y = price)) +
  geom_jitter()

Two variables

One time, one continuous
- geom_area(): area plot
- geom_line(): line plot
- geom_step(): step plot
Display uncertainty:
- geom_crossbar(): vertical bar with center
- geom_errorbar(): error bars
- geom_linerange(): vertical line
- geom_pointrange(): vertical line with center
Spatial
- geom_sf(): for map data (more on this later…)

Average price per year built

mean_price_year <- tompkins |>
  group_by(year_built) |>
  summarize(
    n = n(),
    mean_price = mean(price),
    sd_price = sd(price)
  )

mean_price_year

# A tibble: 161 × 4
   year_built     n mean_price sd_price
        <dbl> <int>      <dbl>    <dbl>
 1       1800     2    282500    67175.
 2       1805     1    195000       NA 
 3       1810     1    185000       NA 
 4       1814     1    260000       NA 
 5       1815     2    435000   233345.
 6       1820     3    369167.   37611.
 7       1822     1    100000       NA 
 8       1824     1    580000       NA 
 9       1825     4    372500    85098.
10       1830     1    350000       NA 
# ℹ 151 more rows

`geom_line()`

ggplot(mean_price_year, aes(x = year_built, y = mean_price)) +
  geom_line()

`geom_area()`

ggplot(mean_price_year, aes(x = year_built, y = mean_price)) +
  geom_area()

`geom_step()`

ggplot(mean_price_year, aes(x = year_built, y = mean_price)) +
  geom_step()

Application exercise

`ae-02` - Part 2

10:00

let’s clean things up a bit!

Let’s clean things up a bit!

Plot
Code

ggplot(tompkins, aes(x = area, y = price)) +
  geom_point(alpha = 0.2, size = 2, color = "#B31B1B") +
  scale_x_continuous(labels = label_number(big.mark = ",")) +
  scale_y_continuous(labels = label_dollar(scale_cut = cut_short_scale())) +
  labs(
    x = "Area (square feet)",
    y = "Sale price (USD)",
    title = "Sale prices of homes in Tompkins County, NY",
    subtitle = "2022-23",
    caption = "Source: Redfin.com"
  )

Wrap up

ggplot2 uses geom_*() functions to define types of plots
Select appropriate geom_*() functions based on the number and types of variables you wish to visualize
Consider the number of observations to determine an appropriate chart type and/or adjustments to the chart

Happy birthday!

Deep dive: layers (II)

Announcements

Announcements

Visualization critique

Distracted boyfriends cause statisticians to move to New Jersey?

Setup

From last time

Geoms

Geoms

One variable

One variable

Aside

geom_dotplot()

Comparing across groups

Application exercise

ae-02 - Part 1

Two variables

Two variables - both continuous

Two variables - show density

geom_hex()

geom_hex()

geom_hex()

geom_hex()

geom_hex() and warnings

Two variables

geom_jitter()

geom_jitter() and set.seed()

Two variables

Average price per year built

geom_line()

geom_area()

geom_step()

Application exercise

ae-02 - Part 2

Let’s clean things up a bit!

Wrap up

Wrap up

Happy birthday!

`geom_dotplot()`

`ae-02` - Part 1

`geom_hex()`

`geom_hex()`

`geom_hex()`

`geom_hex()`

`geom_hex()` and warnings

`geom_jitter()`

`geom_jitter()` and `set.seed()`

`geom_line()`

`geom_area()`

`geom_step()`

`ae-02` - Part 2