Exploratory Data Analysis, Part II

Lecture 3

Author

Affiliation

John Zito

Duke University
STA 101 Fall 2026

Published

September 8, 2026

Today’s example

So far…

Data set	Occasion	Source	A row was a…
`age_guesses`	Lecture 0	file	101 student
`card_krueger`	Lecture 1	file	restaurant
`pokemon`	Lab 0	file	Pokémon
`penguins`	Lab 0	package	penguin
`nc_county`	Lab1, HW 1	file	NC county
`bechdel`	Lecture 2+	file	film
`gerrymander`	Today	package	US congressional district

Packages

For the data: usdata

library(usdata)

For the analysis: tidyverse and ggthemes

library(tidyverse)
library(ggthemes)

Data: `gerrymander`

gerrymander

# A tibble: 435 × 12
   district last_name first_name party16 clinton16 trump16 dem16 state party18
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr> <chr>  
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK    R      
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL    R      
 3 AL-02    Roby      Martha     R            33      64.9     0 AL    R      
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL    R      
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL    R      
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL    R      
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL    R      
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL    D      
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR    R      
10 AR-02    Hill      French     R            41.7    52.4     0 AR    R      
# ℹ 425 more rows
# ℹ 3 more variables: dem18 <dbl>, flip18 <dbl>, gerry <fct>

What is gerrymandering?

JZ’s tour of the USA

JZ’s tour of the USA

JZ’s tour of the USA

JZ’s tour of the USA

Data: `gerrymander`

What is a good first function to use to get to know a dataset?

glimpse(gerrymander)

Rows: 435
Columns: 12
$ district   <chr> "AK-AL", "AL-01", "AL-02", "AL-03", "AL-04", "AL-05", "AL-0…
$ last_name  <chr> "Young", "Byrne", "Roby", "Rogers", "Aderholt", "Brooks", "…
$ first_name <chr> "Don", "Bradley", "Martha", "Mike D.", "Rob", "Mo", "Gary",…
$ party16    <chr> "R", "R", "R", "R", "R", "R", "R", "D", "R", "R", "R", "R",…
$ clinton16  <dbl> 37.6, 34.1, 33.0, 32.3, 17.4, 31.3, 26.1, 69.8, 30.2, 41.7,…
$ trump16    <dbl> 52.8, 63.5, 64.9, 65.3, 80.4, 64.7, 70.8, 28.6, 65.0, 52.4,…
$ dem16      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,…
$ state      <chr> "AK", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AR", "AR",…
$ party18    <chr> "R", "R", "R", "R", "R", "R", "R", "D", "R", "R", "R", "R",…
$ dem18      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0,…
$ flip18     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
$ gerry      <fct> mid, high, high, high, high, high, high, high, mid, mid, mi…

Data: `gerrymander`

Rows: Congressional districts
Columns:
- Congressional district and state
- 2016 election: winning party, % for Clinton, % for Trump, whether a Democrat won the House election, name of election winner
- 2018 election: winning party, whether a Democrat won the 2018 House election
- Whether a Democrat flipped the seat in the 2018 election
- Prevalence of gerrymandering: low, mid, and high

Variable types: `district`

Variable	Type
`district`	categorical, ID
`last_name`
`first_name`
`party16`
`clinton16`
`trump16`
`dem16`
`state`
`party18`
`dem18`
`flip18`
`gerry`

Congressional district:

gerrymander |>
  select(district)

# A tibble: 435 × 1
   district
   <chr>   
 1 AK-AL   
 2 AL-01   
 3 AL-02   
 4 AL-03   
 5 AL-04   
 6 AL-05   
 7 AL-06   
 8 AL-07   
 9 AR-01   
10 AR-02   
# ℹ 425 more rows

Variable types: `last_name`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`
`party16`
`clinton16`
`trump16`
`dem16`
`state`
`party18`
`dem18`
`flip18`
`gerry`

Last name of 2016 election winner:

gerrymander |>
  select(last_name)

# A tibble: 435 × 1
   last_name
   <chr>    
 1 Young    
 2 Byrne    
 3 Roby     
 4 Rogers   
 5 Aderholt 
 6 Brooks   
 7 Palmer   
 8 Sewell   
 9 Crawford 
10 Hill     
# ℹ 425 more rows

Variable types: `first_name`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`
`clinton16`
`trump16`
`dem16`
`state`
`party18`
`dem18`
`flip18`
`gerry`

First name of 2016 election winner:

gerrymander |>
  select(first_name)

# A tibble: 435 × 1
   first_name
   <chr>     
 1 Don       
 2 Bradley   
 3 Martha    
 4 Mike D.   
 5 Rob       
 6 Mo        
 7 Gary      
 8 Terri     
 9 Rick      
10 French    
# ℹ 425 more rows

Variable types: `party16`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`
`trump16`
`dem16`
`state`
`party18`
`dem18`
`flip18`
`gerry`

Political party of 2016 election winner:

gerrymander |>
  select(party16)

# A tibble: 435 × 1
   party16
   <chr>  
 1 R      
 2 R      
 3 R      
 4 R      
 5 R      
 6 R      
 7 R      
 8 D      
 9 R      
10 R      
# ℹ 425 more rows

Variable types: `clinton16`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`	numerical, continuous
`trump16`
`dem16`
`state`
`party18`
`dem18`
`flip18`
`gerry`

Percent of vote received by Clinton in 2016 Presidential Election:

gerrymander |>
  select(clinton16)

# A tibble: 435 × 1
   clinton16
       <dbl>
 1      37.6
 2      34.1
 3      33  
 4      32.3
 5      17.4
 6      31.3
 7      26.1
 8      69.8
 9      30.2
10      41.7
# ℹ 425 more rows

Variable types: `trump16`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`	numerical, continuous
`trump16`	numerical, continuous
`dem16`
`state`
`party18`
`dem18`
`flip18`
`gerry`

Percent of vote received by Trump in 2016 Presidential Election:

gerrymander |>
  select(trump16)

# A tibble: 435 × 1
   trump16
     <dbl>
 1    52.8
 2    63.5
 3    64.9
 4    65.3
 5    80.4
 6    64.7
 7    70.8
 8    28.6
 9    65  
10    52.4
# ℹ 425 more rows

Variable types: `dem16`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`	numerical, continuous
`trump16`	numerical, continuous
`dem16`	categorical
`state`
`party18`
`dem18`
`flip18`
`gerry`

Did a Democrat win the 2016 House election. Levels of 1 (yes) and 0 (no):

gerrymander |>
  select(dem16)

# A tibble: 435 × 1
   dem16
   <dbl>
 1     0
 2     0
 3     0
 4     0
 5     0
 6     0
 7     0
 8     1
 9     0
10     0
# ℹ 425 more rows

Variable types: `state`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`	numerical, continuous
`trump16`	numerical, continuous
`dem16`	categorical
`state`	categorical
`party18`
`dem18`
`flip18`
`gerry`

State the Representative is from:

gerrymander |>
  select(state)

# A tibble: 435 × 1
   state
   <chr>
 1 AK   
 2 AL   
 3 AL   
 4 AL   
 5 AL   
 6 AL   
 7 AL   
 8 AL   
 9 AR   
10 AR   
# ℹ 425 more rows

Variable types: `party18`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`	numerical, continuous
`trump16`	numerical, continuous
`dem16`	categorical
`state`	categorical
`party18`	categorical
`dem18`
`flip18`
`gerry`

Political Party of the 2018 election winner:

gerrymander |>
  select(party18)

# A tibble: 435 × 1
   party18
   <chr>  
 1 R      
 2 R      
 3 R      
 4 R      
 5 R      
 6 R      
 7 R      
 8 D      
 9 R      
10 R      
# ℹ 425 more rows

Variable types: `dem18`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`	numerical, continuous
`trump16`	numerical, continuous
`dem16`	categorical
`state`	categorical
`party18`	categorical
`dem18`	categorical
`flip18`
`gerry`

Did a Democrat win the 2018 House election. Levels of 1 (yes) and 0 (no):

gerrymander |>
  select(dem18)

# A tibble: 435 × 1
   dem18
   <dbl>
 1     0
 2     0
 3     0
 4     0
 5     0
 6     0
 7     0
 8     1
 9     0
10     0
# ℹ 425 more rows

Variable types: `flip18`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`	numerical, continuous
`trump16`	numerical, continuous
`dem16`	categorical
`state`	categorical
`party18`	categorical
`dem18`	categorical
`flip18`	categorical
`gerry`

In the 2018 election, did the seat flip from D to R (-1), from R to D (1), or not change (0)?

gerrymander |>
  select(flip18)

# A tibble: 435 × 1
   flip18
    <dbl>
 1      0
 2      0
 3      0
 4      0
 5      0
 6      0
 7      0
 8      0
 9      0
10      0
# ℹ 425 more rows

Variable types: `gerry`

Variable	Type
`district`	categorical, ID
`last_name`	categorical, ID
`first_name`	categorical, ID
`party16`	categorical
`clinton16`	numerical, continuous
`trump16`	numerical, continuous
`dem16`	categorical
`state`	categorical
`party18`	categorical
`dem18`	categorical
`flip18`	categorical
`gerry`	categorical, ordinal

Categorical variable for prevalence of gerrymandering with levels of low, mid and high:

gerrymander |>
  select(gerry)

# A tibble: 435 × 1
   gerry
   <fct>
 1 mid  
 2 high 
 3 high 
 4 high 
 5 high 
 6 high 
 7 high 
 8 high 
 9 mid  
10 mid  
# ℹ 425 more rows

Exploring two categorical variables

Research question

Is a Congressional District more likely to have high prevalence of gerrymandering if a Democrat was able to flip the seat in the 2018 election? Support your answer with a visualization as well as summary statistics.

Step 1

ggplot(gerrymander)

Step 2

ggplot(gerrymander, aes(x = flip18))

Step 3

ggplot(gerrymander, aes(x = flip18)) +
  geom_bar()

Step 4

ggplot(gerrymander, aes(x = flip18, fill = gerry)) +
  geom_bar()

Step 5a

ggplot(gerrymander, aes(x = flip18, fill = gerry)) +
  geom_bar(position = "dodge")

Step 5b

ggplot(gerrymander, aes(x = flip18, fill = gerry)) +
  geom_bar(position = "fill")

What’s the answer?

Is a Congressional District more likely to have high prevalence of gerrymandering if a Democrat was able to flip the seat in the 2018 election?

ggplot(gerrymander, aes(x = flip18, fill = gerry)) +
  geom_bar(position = "fill")

What are the actual numbers?

Is a Congressional District more likely to have high prevalence of gerrymandering if a Democrat was able to flip the seat in the 2018 election?

gerrymander |>
  count(flip18, gerry) |>
  group_by(flip18) |>
  mutate(prop = n / sum(n))

# A tibble: 8 × 4
# Groups:   flip18 [3]
  flip18 gerry     n  prop
   <dbl> <fct> <int> <dbl>
1     -1 low       2 0.4  
2     -1 mid       3 0.6  
3      0 low      52 0.133
4      0 mid     242 0.617
5      0 high     98 0.25 
6      1 low       8 0.211
7      1 mid      25 0.658
8      1 high      5 0.132

Step 1

gerrymander

# A tibble: 435 × 12
   district last_name first_name party16 clinton16 trump16 dem16 state party18
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr> <chr>  
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK    R      
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL    R      
 3 AL-02    Roby      Martha     R            33      64.9     0 AL    R      
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL    R      
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL    R      
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL    R      
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL    R      
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL    D      
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR    R      
10 AR-02    Hill      French     R            41.7    52.4     0 AR    R      
# ℹ 425 more rows
# ℹ 3 more variables: dem18 <dbl>, flip18 <dbl>, gerry <fct>

Step 2

gerrymander |>
  count(flip18, gerry)

# A tibble: 8 × 3
  flip18 gerry     n
   <dbl> <fct> <int>
1     -1 low       2
2     -1 mid       3
3      0 low      52
4      0 mid     242
5      0 high     98
6      1 low       8
7      1 mid      25
8      1 high      5

Recall

The heights of these bars are the counts in the table:

ggplot(gerrymander, aes(x = flip18, fill = gerry)) +
  geom_bar(position = "dodge")

Step 3

gerrymander |>
  count(flip18, gerry) |>
  group_by(flip18)

# A tibble: 8 × 3
# Groups:   flip18 [3]
  flip18 gerry     n
   <dbl> <fct> <int>
1     -1 low       2
2     -1 mid       3
3      0 low      52
4      0 mid     242
5      0 high     98
6      1 low       8
7      1 mid      25
8      1 high      5

Step 4

gerrymander |>
  count(flip18, gerry) |>
  group_by(flip18) |>
  mutate(prop = n / sum(n))

# A tibble: 8 × 4
# Groups:   flip18 [3]
  flip18 gerry     n  prop
   <dbl> <fct> <int> <dbl>
1     -1 low       2 0.4  
2     -1 mid       3 0.6  
3      0 low      52 0.133
4      0 mid     242 0.617
5      0 high     98 0.25 
6      1 low       8 0.211
7      1 mid      25 0.658
8      1 high      5 0.132

The Full Monty

Is a Congressional District more likely to have high prevalence of gerrymandering if a Democrat flipped the seat in the 2018 election? (flip18 = 1: Democrat flipped the seat, 0: No flip, -1: Republican flipped the seat.)

ggplot(
  gerrymander, 
  aes(x = flip18, fill = gerry)
  ) +
  geom_bar(position = "fill")

gerrymander |>
  count(flip18, gerry) |>
  group_by(flip18) |>
  mutate(prop = n / sum(n))

# A tibble: 8 × 4
# Groups:   flip18 [3]
  flip18 gerry     n  prop
   <dbl> <fct> <int> <dbl>
1     -1 low       2 0.4  
2     -1 mid       3 0.6  
3      0 low      52 0.133
4      0 mid     242 0.617
5      0 high     98 0.25 
6      1 low       8 0.211
7      1 mid      25 0.658
8      1 high      5 0.132

New commands

count creates a new data frame that tallies up the number of rows that fall into each bin;
group_by silently groups the rows according to bin, and all subsequent operations are done within group;
mutate either adds new columns that aren’t already there, or modifies existing columns.
- we used it to add a prop column that wasn’t there before;
- because the table of counts is grouped, the proportions were computed within each level of flip18.

That pesky pipe

We teach you to do this

gerrymander |>
  count(flip18, gerry) |>
  group_by(flip18) |>
  mutate(prop = n / sum(n))

# A tibble: 8 × 4
# Groups:   flip18 [3]
  flip18 gerry     n  prop
   <dbl> <fct> <int> <dbl>
1     -1 low       2 0.4  
2     -1 mid       3 0.6  
3      0 low      52 0.133
4      0 mid     242 0.617
5      0 high     98 0.25 
6      1 low       8 0.211
7      1 mid      25 0.658
8      1 high      5 0.132

You could do this instead

Technically equivalent. Gives the same result. Super hard to read:

mutate(group_by(count(gerrymander, flip18, gerry), flip18), prop = n / sum(n))

# A tibble: 8 × 4
# Groups:   flip18 [3]
  flip18 gerry     n  prop
   <dbl> <fct> <int> <dbl>
1     -1 low       2 0.4  
2     -1 mid       3 0.6  
3      0 low      52 0.133
4      0 mid     242 0.617
5      0 high     98 0.25 
6      1 low       8 0.211
7      1 mid      25 0.658
8      1 high      5 0.132

Without the pipe

With the pipe

Drilling down:
`group_by()`,
`summarize()`,
`count()`

What does `group_by()` do?

What does group_by() do in the following pipeline?

gerrymander |>
  count(flip18, gerry) |>
  group_by(flip18) |>
  mutate(prop = n / sum(n))

# A tibble: 8 × 4
# Groups:   flip18 [3]
  flip18 gerry     n  prop
   <dbl> <fct> <int> <dbl>
1     -1 low       2 0.4  
2     -1 mid       3 0.6  
3      0 low      52 0.133
4      0 mid     242 0.617
5      0 high     98 0.25 
6      1 low       8 0.211
7      1 mid      25 0.658
8      1 high      5 0.132

What does `group_by()` do?

What does group_by() do in the following pipeline?

gerrymander |>
  count(flip18, gerry) |>
  #group_by(flip18) |>
  mutate(prop = n / sum(n))

# A tibble: 8 × 4
  flip18 gerry     n    prop
   <dbl> <fct> <int>   <dbl>
1     -1 low       2 0.00460
2     -1 mid       3 0.00690
3      0 low      52 0.120  
4      0 mid     242 0.556  
5      0 high     98 0.225  
6      1 low       8 0.0184 
7      1 mid      25 0.0575 
8      1 high      5 0.0115

Let’s simplify!

What does group_by() do in the following pipeline?

gerrymander |>
  group_by(state) |>
  summarize(mean_trump16 = mean(trump16))

# A tibble: 50 × 2
   state mean_trump16
   <chr>        <dbl>
 1 AK            52.8
 2 AL            62.6
 3 AR            60.9
 4 AZ            46.9
 5 CA            31.7
 6 CO            43.6
 7 CT            41.0
 8 DE            41.9
 9 FL            47.9
10 GA            51.3
# ℹ 40 more rows

Let’s simplify!

What does group_by() do in the following pipeline?

gerrymander |>
  #group_by(state) |>
  summarize(mean_trump16 = mean(trump16))

# A tibble: 1 × 1
  mean_trump16
         <dbl>
1         45.9

`group_by()`

it converts a data frame to a grouped data frame, where subsequent operations are performed once per group
ungroup() removes grouping

gerrymander |>
  group_by(state)

# A tibble: 435 × 12
# Groups:   state [50]
   district last_name first_name party16 clinton16 trump16 dem16 state party18
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr> <chr>  
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK    R      
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL    R      
 3 AL-02    Roby      Martha     R            33      64.9     0 AL    R      
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL    R      
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL    R      
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL    R      
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL    R      
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL    D      
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR    R      
10 AR-02    Hill      French     R            41.7    52.4     0 AR    R      
# ℹ 425 more rows
# ℹ 3 more variables: dem18 <dbl>, flip18 <dbl>, gerry <fct>

`group_by()`

it converts a data frame to a grouped data frame, where subsequent operations are performed once per group
ungroup() removes grouping

gerrymander |>
  group_by(state) |>
  ungroup()

# A tibble: 435 × 12
   district last_name first_name party16 clinton16 trump16 dem16 state party18
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr> <chr>  
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK    R      
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL    R      
 3 AL-02    Roby      Martha     R            33      64.9     0 AL    R      
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL    R      
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL    R      
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL    R      
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL    R      
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL    D      
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR    R      
10 AR-02    Hill      French     R            41.7    52.4     0 AR    R      
# ℹ 425 more rows
# ℹ 3 more variables: dem18 <dbl>, flip18 <dbl>, gerry <fct>

`group_by() |> summarize()`

A common pipeline is group_by() and then summarize() to calculate summary statistics for each group:

gerrymander |>
  group_by(state) |>
  summarize(
    mean_trump16 = mean(trump16),
    median_trump16 = median(trump16)
  )

# A tibble: 50 × 3
   state mean_trump16 median_trump16
   <chr>        <dbl>          <dbl>
 1 AK            52.8           52.8
 2 AL            62.6           64.9
 3 AR            60.9           63.0
 4 AZ            46.9           47.7
 5 CA            31.7           28.4
 6 CO            43.6           41.3
 7 CT            41.0           40.4
 8 DE            41.9           41.9
 9 FL            47.9           49.6
10 GA            51.3           56.6
# ℹ 40 more rows

`group_by() |> summarize()`

This pipeline can also be used to count number of observations for each group:

gerrymander |>
  group_by(state) |>
  summarize(n = n())

# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 AK        1
 2 AL        7
 3 AR        4
 4 AZ        9
 5 CA       53
 6 CO        7
 7 CT        5
 8 DE        1
 9 FL       27
10 GA       14
# ℹ 40 more rows

`summarize()`

... |>
  summarize(
    name_of_summary_statistic = summary_function(variable)
  )

. . .

name_of_summary_statistic: Anything you want to call it!
- Recommendation: Keep it short and evocative
summary_function():
- n(): number of observations
- mean(): mean
- median(): median
- …

Spot the difference

What’s the difference between the following two pipelines?

gerrymander |>
  group_by(state) |>
  summarize(n = n())

# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 AK        1
 2 AL        7
 3 AR        4
 4 AZ        9
 5 CA       53
 6 CO        7
 7 CT        5
 8 DE        1
 9 FL       27
10 GA       14
# ℹ 40 more rows

gerrymander |>
  count(state)

# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 AK        1
 2 AL        7
 3 AR        4
 4 AZ        9
 5 CA       53
 6 CO        7
 7 CT        5
 8 DE        1
 9 FL       27
10 GA       14
# ℹ 40 more rows

`count()`

... |>
  count(variable)

... |>
  count(variable1, variable2)

Count the number of observations in each level of variable(s)
Place the counts in a variable called n

`count()` and `sort`

What does the following pipeline do? Rewrite it with count() instead.

gerrymander |>
  group_by(state) |>
  summarize(n = n()) |>
  arrange(desc(n))

# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 CA       53
 2 TX       36
 3 FL       27
 4 NY       27
 5 IL       18
 6 PA       18
 7 OH       16
 8 GA       14
 9 MI       14
10 NC       13
# ℹ 40 more rows

`count()` and `sort`

What does the following pipeline do? Rewrite it with count() instead.

gerrymander |>
  count(state) |>
  arrange(desc(n))

# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 CA       53
 2 TX       36
 3 FL       27
 4 NY       27
 5 IL       18
 6 PA       18
 7 OH       16
 8 GA       14
 9 MI       14
10 NC       13
# ℹ 40 more rows

`count()` and `sort`

What does the following pipeline do? Rewrite it with count() instead.

gerrymander |>
  count(state, sort = TRUE)

# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 CA       53
 2 TX       36
 3 FL       27
 4 NY       27
 5 IL       18
 6 PA       18
 7 OH       16
 8 GA       14
 9 MI       14
10 NC       13
# ℹ 40 more rows

Summary

“Science”?

We call it “statistical science” or “data science,” but frankly it’s all much closer to art and rhetoric and storytelling;
When you compress a data set down to pictures and summaries, you make countless choices about how to visualize, which summaries to inspect, how much detail to retain or discard, etc;
A choice is good or bad insofar as a skeptical human audience finds it informative and persuasive.

Fine, but what dataviz should I use?

If you want to know where to begin, the number and type of variables typically narrows down the menu of options:

. . .

Variable combo	Options for a first pass
1 numerical	histogram, density, box plot, …
1 categorical	bar chart, ~~pie chart~~
2 numerical	scatterplot
numerical/categorical	side-by-side boxes, stacked densities or histograms, …
2 categorical	stacked bar plot

. . .

For three or more variables, the human mind is limited, and you have to get creative (play with color, shape, texture, etc).

Computational themes

Every coding task in this class will be some combo of these two things:

. . .

Building cakes (ggplot)

Stacking dolls (pipe |>)

. . .

Master these, and everything will be coming up roses!

Where to now?

Where did the line come from? How was it drawn? How do you interpret it? How do you use it? What about more than two variables? What about categorical variables?

Today’s example

So far…

Packages

Data: gerrymander

What is gerrymandering?

JZ’s tour of the USA

JZ’s tour of the USA

JZ’s tour of the USA

JZ’s tour of the USA

Data: gerrymander

Data: gerrymander

Variable types: district

Variable types: last_name

Variable types: first_name

Variable types: party16

Variable types: clinton16

Variable types: trump16

Variable types: dem16

Variable types: state

Variable types: party18

Variable types: dem18

Variable types: flip18

Variable types: gerry

Exploring two categorical variables

Research question

Step 1

Step 2

Step 3

Step 4

Step 5a

Step 5b

What’s the answer?

What are the actual numbers?

Step 1

Step 2

Recall

Step 3

Step 4

The Full Monty

New commands

That pesky pipe

We teach you to do this

You could do this instead

Without the pipe

With the pipe

Drilling down: group_by(), summarize(), count()

What does group_by() do?

What does group_by() do?

Let’s simplify!

Let’s simplify!

group_by()

group_by()

group_by() |> summarize()

group_by() |> summarize()

summarize()

Spot the difference

count()

count() and sort

count() and sort

count() and sort

Summary

“Science”?

Fine, but what dataviz should I use?

Computational themes

Where to now?

Where to now?

Data: `gerrymander`

Data: `gerrymander`

Data: `gerrymander`

Variable types: `district`

Variable types: `last_name`

Variable types: `first_name`

Variable types: `party16`

Variable types: `clinton16`

Variable types: `trump16`

Variable types: `dem16`

Variable types: `state`

Variable types: `party18`

Variable types: `dem18`

Variable types: `flip18`

Variable types: `gerry`

Drilling down:
`group_by()`,
`summarize()`,
`count()`

What does `group_by()` do?

What does `group_by()` do?

`group_by()`

`group_by()`

`group_by() |> summarize()`

`group_by() |> summarize()`

`summarize()`

`count()`

`count()` and `sort`

`count()` and `sort`

`count()` and `sort`