Data visualization II

POL51

Juan Tellez

jftellez@ucdavis.edu

UC Davis

October 1, 2024

Get ready

Boot up Posit Cloud
Download the script for today’s in-class examples:
- Schedule ➡️ Week 2 ➡️ Example ➡️ Day 2
Upload the script to Posit Cloud

Plan for today

A graph for every season
The five(ish) graphs
Making graphs pretty or ugly

A graph for every season

There are many graphs out there
Each one works best in a specific context
Each one combines different aesthetics and geometries

Key plotting questions

What am I trying to show?
- (a distribution, a relationship, a comparison, an amount)
What kind of variables do I have?
- (continuous, discrete, something in between)
What aesthetics and geometries do I need for this plot?
- (x-axis, y-axis, color, size, shape, etc.)

What kind of variable do I have?

Data from the General Social Survey
id	age	degree	race	num_kids
1	47	Bachelor	White	3
2	61	High School	White	0
3	72	Bachelor	White	2
4	43	High School	White	4
5	55	Graduate	White	2

Continuous variables take on lots of values (GDP, population, income, age, etc.)

What kind of variable do I have?

Data from the General Social Survey
id	age	degree	race	num_kids
1	47	Bachelor	White	3
2	61	High School	White	0
3	72	Bachelor	White	2
4	43	High School	White	4
5	55	Graduate	White	2

Discrete or categorical variables takes on a few values, often “qualitative” (yes/no, 1/0, race, etc.)

Graph 1: the scatterplot

The scatterplot visualizes the relationship between two continuous variables

Shows every point in the data, reveals trends and outliers

The grammar of scatterplots

The data
gdpPercap	lifeExp
974.58	43.83
5937.03	76.42
6223.37	72.30
4797.23	42.73
12779.38	75.32

Mapping the data
Data	Aesthetic	Geometry
gdpPercap	x	geom_point()
lifeExp	y	geom_point()

ggplot(gap_07, aes(x = gdpPercap, y = lifeExp)) + geom_point()

Typically, we put the cause on the x-axis and the effect on the y-axis

Scatterplots are for continuous variables

Plot is uninformative because continent is discrete (i.e., a category)

🇺🇸 The presidents 🇺🇸

Sample of presidential elections
year	winner	win_party	ec_pct	popular_pct	two_term
1824	John Quincy Adams	D.-R.	0.32	0.31	FALSE
1828	Andrew Jackson	Dem.	0.68	0.56	TRUE
1832	Andrew Jackson	Dem.	0.77	0.55	TRUE
1836	Martin Van Buren	Dem.	0.58	0.51	FALSE

🚨 Your turn 🚨

Using the elections_historic dataset:

Make a scatterplot, with:
- % of popular vote (x-axis, popular_pct)
- % of electoral college vote (y-axis, ec_pct)
if successful, add:
- winner’s party to color aesthetic (win_party)
- whether or not president served two terms to shape (two_term)
- add labels to each point (winner_label)

05:00

US Presidents

Code

# libraries
library(tidyverse)
library(socviz)
library(ggrepel)

# plot
ggplot(elections_historic, aes(x = popular_pct, y = ec_pct, label = winner_label,
    color = win_party, shape = two_term, label = winner_label)) + geom_hline(yintercept = 0.5,
    size = 1.4, color = "gray80") + geom_vline(xintercept = 0.5, size = 1.4, color = "gray80") +
    geom_point() + geom_text_repel() + labs(x = "Percent of popular vote", y = "Percent of Electoral College vote",
    title = "Presidential Elections: Popular & Electoral College Margins", subtitle = "1824-2016",
    color = NULL, size = NULL) + scale_y_continuous(labels = scales::percent) + scale_x_continuous(labels = scales::percent) +
    scale_color_manual(values = c(yellow, red, blue, "gray")) + theme(legend.position = "none")

Graph 2: the time series

The time series uses a line to show you how a variable (y-axis) moves over time (x-axis)

The grammar of time series

Gapminder data (summarized).
year	avg_yrs
1952	49.06
1957	51.51
1962	53.61
1967	55.68
1972	57.65

Mapping the data
Data	Aesthetic	Geometry
year	x	geom_line()
avg_yrs	y	geom_line()

ggplot(life_yr, aes(x = year, y = avg_yrs)) + geom_line()

Notice the new geometry, geom_line()

The time series

ggplot(life_yr, aes(x = year, y = avg_yrs)) + geom_line()

🚨 Your turn: 💸Recession💸 🚨

Using the economics dataset:

Make a time series, with:
- date on the x-axis (date)
- unemployment rate on the y-axis (unemploy)
Can you identify the recessions?

05:00

Multiple time series

Sometimes we observe multiple units over time; how can we visualize these?

Gapminder dataset (sample of countries).
country	continent	year	lifeExp	pop	gdpPercap
Bolivia	Americas	1952	40	2883315	2677
Bolivia	Americas	1957	42	3211738	2128
China	Asia	1952	44	556263527	400
China	Asia	1957	51	637408000	576
Denmark	Europe	1952	71	4334000	9692
Denmark	Europe	1957	72	4487831	11100

What we want:

Start from scratch

ggplot(data = gapminder_sample)

Add aesthetics

ggplot(data = gapminder_sample, aes(x = year, y = lifeExp))

Add geometry: 🤢

ggplot(data = gapminder_sample, aes(x = year, y = lifeExp)) + geom_line()

Why?

In each year we have multiple points (because multiple countries), ggplot() tries to connect them all

using `color` to separate lines

ggplot(data = gapminder_sample, aes(x = year, y = lifeExp, color = country)) + geom_line()

Multiple time series

ggplot(data = gapminder_sample, aes(x = year, y = lifeExp, color = country)) + geom_line()

These are useful for comparing trends across units (countries, places, people, etc)

Graph 3: the histogram

A histogram shows you how a continuous variable is distributed

Interpreting histograms

The grammar of histograms

The data
lifeExp
43.83
76.42
72.30
42.73
75.32

Mapping the data
Data	Aesthetic	Geometry
lifeExp	x	geom_histogram()

ggplot(gap_07, aes(x = lifeExp)) + geom_histogram()

Notice the new geometry, geom_histogram(); and that a histogram only uses the x-axis!

The histogram

ggplot(gap_07, aes(x = lifeExp)) + geom_histogram()

🚨 Your turn: organs 🫁🧠 🚨

In some countries, when you die it is assumed you want to donate your organs

To not donate, you have to opt out
In other countries, when you die it is assumed you do not want to donate your organs
To not donate, you have to opt in

Sample from organdata
country	donors	opt
Spain	31.5	Out
United States	20.1	In
Italy	11.6	In
Finland	17.1	NA
Spain	21.7	Out

🚨 Your turn: organs 🫁🧠 🚨

Using the organdata dataset:

Make a histogram of country’s organ donation rate (donors)
Then set the fill aesthetic to opt, whether donors have to opt in or opt out of donating. How does the graph change?

05:00

Graph 4: the barplot

Barplots place a category (place, country, person, etc) on one axis and a quantity (amount, average, median, etc.) on another

Useful for making comparisons, highlighting differences

The grammar of barplots

The data
marital	tv
No answer	2.56
Never married	3.11
Separated	3.55
Divorced	3.09
Widowed	3.91

Mapping the data
Data	Aesthetic	Geometry
tv	x	geom_col()
marital	y	geom_col()

ggplot(tv, aes(y = marital, x = tv)) + geom_col()

The barplot

ggplot(tv, aes(y = marital, x = tv)) + geom_col()

Graph 5: the boxplot

Boxplots compare distributions of continuous variables across groups

Compare distributions: the boxplot

Boxplots contain a lot of info 🥵:

bold line is the median observation
box is the middle 50% of observations
thin lines show you min and max value, except…
the dots, which are outlier observations

The grammar of boxplots

The data
continent	lifeExp
Asia	43.83
Europe	76.42
Africa	72.30
Africa	42.73
Americas	75.32

Mapping the data
Data	Aesthetic	Geometry
contient	y	geom_boxplot()
lifeExp	x	geom_boxplot()

ggplot(gapminder, aes(y = continent, x = lifeExp)) + geom_boxplot()

The boxplot

ggplot(gapminder, aes(y = continent, x = lifeExp)) + geom_boxplot()

The five(-ish) graphs

Graph	aes()	geom_	Purpose
Scatterplot	x = cause, y = effect	point()	Relationships
Time series	x = date, y = variable	line()	Trends
Histogram	x = cont. variable	histogram()	Distributions
Barplot	y = category, x = quantity	col()	Compare amounts
Boxplot	y = category, x = cont. variable	boxplot()	Compare distributions

Know how and when to use which!

Making better graphs

We’ve barely scratched the surface; there’s many more aesthetics, geometries, and layers in ggplot()
Here are some of my favorite ones
And some ideas for making graphs better

Customize geometries

ggplot(gap_07, aes(x = gdpPercap, y = lifeExp)) + geom_point(size = 4, color = "orange",
    shape = 2)

Change your geometries by adding arguments to geom_*(), like size, shape, color, etc.

Use different color and fill scales

ggplot(gapminder, aes(x = lifeExp, fill = continent)) + geom_histogram() + scale_fill_brewer(palette = "Blues")

scale_fill_brewer() for fill, which we use for columns, histograms, 2D stuff

Use different color and fill scales

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, color = continent)) + geom_point() +
    scale_color_brewer(palette = "Dark2")

scale_color_brewer() for color, which we use for points, lines, less than 2D stuff

My favorite scale (right now)

ggplot(gapminder, aes(x = lifeExp, fill = continent)) + geom_histogram() + scale_fill_viridis_d(option = "magma")

scale_fill_viridis_d for discrete variables, scale_fill_viridis_d for continuous

Add themes

ggplot(gapminder, aes(x = lifeExp, fill = continent)) + geom_histogram() + theme_dark()

Preset themes in tidyverse include theme_dark(), theme_light(), theme_minimal(), theme_void()

Many other custom themes

library(tvthemes)
ggplot(gapminder, aes(x = lifeExp, fill = continent)) + geom_histogram() + theme_spongeBob() +
    scale_fill_spongeBob() + labs(title = "So ugly")

theme_spongeBob() from tvthemes package, many more online

🔥 Coding challenge 🔥

Right now you probably can’t make a nice graph, so:

Make the ugliest graph you can
Post it on Slack
Winner will get a shockingly small amount of extra credit

20:00

Data visualization II

Get ready

Plan for today

A graph for every season

Key plotting questions

What kind of variable do I have?

What kind of variable do I have?

Graph 1: the scatterplot

The grammar of scatterplots

Scatterplots are for continuous variables

🇺🇸 The presidents 🇺🇸

🚨 Your turn 🚨

US Presidents

Graph 2: the time series

The grammar of time series

The time series

🚨 Your turn: 💸Recession💸 🚨

Multiple time series

What we want:

Start from scratch

Add aesthetics

Add geometry: 🤢

Why?

using color to separate lines

Multiple time series

Graph 3: the histogram

Interpreting histograms

The grammar of histograms

The histogram

🚨 Your turn: organs 🫁🧠 🚨

🚨 Your turn: organs 🫁🧠 🚨

Graph 4: the barplot

The grammar of barplots

The barplot

Graph 5: the boxplot

Compare distributions: the boxplot

The grammar of boxplots

The boxplot

The five(-ish) graphs

Making better graphs

Customize geometries

Use different color and fill scales

Use different color and fill scales

My favorite scale (right now)

Add themes

Many other custom themes

🔥 Coding challenge 🔥

using `color` to separate lines