The Scientific Study of Politics - Data visualization II

What kind of variable do I have?

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?

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)

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

The data
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()

Notice the new geometry, geom_line()

The time series

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

🚨 Your turn: 💸Recession💸 🚨

Look at the economics dataset from the tidyverse package
Type and run economics to see the data
Type and run ?economics to read about the data
Make a time series of unemployment over time
Can you identify the recessions?

05:00

Multiple time series

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

country	continent	year	lifeExp	pop	gdpPercap
Bolivia	Americas	1977	50.023	5079716	3548.098
Egypt	Africa	1997	67.217	66134291	4173.182
United States	Americas	1977	73.380	220239000	24072.632
United States	Americas	1997	76.810	272911760	35767.433
Germany	Europe	1982	73.800	78335266	22031.533

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()

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()

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
Ireland	18.2	In
Denmark	14.0	In
Germany	13.9	In
Germany	12.8	In
Spain	NA	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()

Note

You could switch the x and y mapping around, but I think categories look better on the y-axis

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()

Note

You could switch the x and y mapping around, but I think categories look better on the y-axis

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!