Joining data

Lecture 8

Dr. Mine Çetinkaya-Rundel

Duke University
STA 199 - Fall 2025

September 18, 2025

Warm-up

While you wait: Participate 📱💻

statsci_longer

# A tibble: 17 × 3
   degree_type  year     n
   <chr>       <dbl> <dbl>
 1 AB2          2011     0
 2 AB2          2012     1
 3 AB2          2013     0
 4 AB2          2014     0
 5 AB2          2015     4
 6 AB2          2016     4
 7 AB2          2017     1
 8 AB2          2018     0
 9 AB2          2019     0
10 AB2          2020     1
11 AB2          2021     2
12 AB2          2022     0
13 AB2          2023     3
14 AB2          2024     1
15 AB2          2025     0
16 AB           2011     2
17 AB           2012     2

⟶

# A tibble: 4 × 16
  degree_type `2011` `2012` `2013` `2014` `2015`
  <chr>        <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 AB2              0      1      0      0      4
2 AB               2      2      4      1      3
3 BS2              2      6      1      0      5
4 BS               5      9      4     13     10
# ℹ 10 more variables: `2016` <dbl>,
#   `2017` <dbl>, `2018` <dbl>, `2019` <dbl>,
#   `2020` <dbl>, `2021` <dbl>, `2022` <dbl>,
#   `2023` <dbl>, `2024` <dbl>, `2025` <dbl>

Fill in the blanks to pivot statsci_longer wider.

statsci_longer |>
  pivot_wider(
    names_from = __BLANK_1__,
    values_from = __BLANK_2__,
  )

Scan the QR code or go to app.wooclap.com/sta199. Log in with your Duke NetID.

Pivot wider

statsci_longer

# A tibble: 17 × 3
   degree_type  year     n
   <chr>       <dbl> <dbl>
 1 AB2          2011     0
 2 AB2          2012     1
 3 AB2          2013     0
 4 AB2          2014     0
 5 AB2          2015     4
 6 AB2          2016     4
 7 AB2          2017     1
 8 AB2          2018     0
 9 AB2          2019     0
10 AB2          2020     1
11 AB2          2021     2
12 AB2          2022     0
13 AB2          2023     3
14 AB2          2024     1
15 AB2          2025     0
16 AB           2011     2
17 AB           2012     2

⟶

statsci_longer |>
  pivot_wider(
    names_from = year,
    values_from = n,
  )

# A tibble: 2 × 16
  degree_type `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018`
  <chr>        <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 AB2              0      1      0      0      4      4      1      0
2 AB               2      2     NA     NA     NA     NA     NA     NA
# ℹ 7 more variables: `2019` <dbl>, `2020` <dbl>, `2021` <dbl>,
#   `2022` <dbl>, `2023` <dbl>, `2024` <dbl>, `2025` <dbl>

Announcements

HW 2 due Sunday at 11:59pm
- Do Part 1 first, render, commit, and push
- Do Part 2 second, render, commit, and push + upload to Gradescope
Feedback on the AI feedback tool

Recap: Pivot

When should you pivot? If all of the data you need is in your data frame, but the columns you need don’t exist, there is a good chance it’s time to pivot!
Wide and long: Data sets can’t be labeled as wide or long but they can be made wider or longer for a certain analysis that requires a certain format
Pivot longer - data type: When pivoting longer, variable names that turn into values are characters by default. If you need them to be in another format, you need to explicitly make that transformation, which you can do so within the pivot_longer() function.

Recap: Plotting

You can tweak a plot forever, but at some point the tweaks are likely not very productive.
However, you should always be critical of defaultsand see if you can improve the plot to better portray your data / results / what you want to communicate.

Recoding data

What’s going on in this plot?

Can you guess the variable plotted here?

Sales taxes in US states

sales_taxes

# A tibble: 51 × 7
   state    state_tax_rate state_tax_rank avg_local_tax_rate max_local
   <chr>             <dbl>          <dbl>              <dbl>     <dbl>
 1 Alabama            4                40               5.44     11   
 2 Alaska             0                46               1.82      7.85
 3 Arizona            5.6              28               2.92      5.3 
 4 Arkansas           6.5               9               2.98      6.12
 5 Califor…           7.25              1               1.73      5.25
 6 Colorado           2.9              45               4.96      8.3 
 7 Connect…           6.35             12               0         0   
 8 Delaware           0                46               0         0   
 9 Florida            6                17               1.02      2   
10 Georgia            4                40               3.44      5   
# ℹ 41 more rows
# ℹ 2 more variables: combined_tax_rate <dbl>, combined_rank <dbl>

Participate 📱💻

Put the following steps in order to compare the average state sales tax rates of swing states (Arizona, Georgia, Michigan, Nevada, North Carolina, Pennsylvania, and Wisconsin) vs. non-swing states:

Group by swing_state
Summarize to find the mean sales tax in each type of state
Create a new variable called swing_state with levels "Swing" and "Non-swing"

Scan the QR code or go to app.wooclap.com/sta199. Log in with your Duke NetID.

Participate 📱💻

First, create a vector called list_of_swing_states that contains the names of the swing states.

list_of_swing_states <- c(
  "Arizona", "Georgia", "Michigan", "Nevada", "North Carolina",
  "Pennsylvania", "Wisconsin"
)

Then, fill in the blanks to create a new variable called swing_state with levels "Swing" and "Non-swing":

sales_taxes <- sales_taxes |>
  __BLANK_1__(
    swing_state = __BLANK_2__(
      state __BLANK_3__ list_of_swing_states, "Swing", "Non-swing"
    )
  )

Scan the QR code or go to app.wooclap.com/sta199. Log in with your Duke NetID.

Recap: `if_else()`

if_else(
1  x == y,
2  "x is equal to y",
3  "x is not equal to y"
)

1: Condition
2: Value if condition is TRUE
3: Value if condition is FALSE

Participate 📱💻

Fill in the blank to compare the average state sales tax rates of swing states vs. non-swing states.

sales_taxes |>
  __BLANK__ |>
  summarize(mean_state_tax = mean(state_tax_rate))

arrange(swing_state)
filter(swing_state == "Swing")
group_by(swing_state)
group_by(list_of_swing_states)

Scan the QR code or go to app.wooclap.com/sta199. Log in with your Duke NetID.

Sales tax in swing states

sales_taxes |>
  group_by(swing_state) |>
  summarize(mean_state_tax = mean(state_tax_rate))

# A tibble: 2 × 2
  swing_state mean_state_tax
  <chr>                <dbl>
1 Non-swing             5.05
2 Swing                 5.46

Sales tax in coastal states

Suppose you’re tasked with the following:

Compare the average state sales tax rates of states on the Pacific Coast, states on the Atlantic Coast, and the rest of the states.

How would you approach this task?

Create a new variable called coast with levels "Pacific", "Atlantic", and "Neither"
Group by coast
Summarize to find the mean sales tax in each type of state

Participate 📱💻

First, create two vectors called pacific_coast and atlantic_coast that contain the respective states.

pacific_coast <- c("Alaska", "Washington", "Oregon", "California", "Hawaii")

atlantic_coast <- c(
  "Connecticut", "Delaware", "Georgia", "Florida", "Maine", "Maryland", "Massachusetts",
  "New Hampshire", "New Jersey", "New York", "North Carolina", "Rhode Island", 
  "South Carolina", "Virginia"
)

Then, fill in the blank to create a new variable called coast:

sales_taxes <- sales_taxes |>
  mutate(
    coast = __BLANK__(
      state %in% atlantic_coast ~ "Atlantic",
      state %in% pacific_coast  ~ "Pacific",
      .default = "Neither"
    )
  )

Scan the QR code or go to app.wooclap.com/sta199. Log in with your Duke NetID.

Recap: `case_when()`

case_when(
1  x > y  ~ "x is greater than y",
2  x < y  ~ "x is less than y",
3  .default = "x is equal to y"
)

1: Value if first condition is TRUE
2: Value if second condition is TRUE
3: Value if neither condition is TRUE, i.e., default value

Sales tax in coastal states

Compare the average state sales tax rates of states on the Pacific Coast, states on the Atlantic Coast, and the rest of the states.

sales_taxes |>
  group_by(coast) |>
  summarize(mean_state_tax = mean(state_tax_rate))

# A tibble: 3 × 2
  coast    mean_state_tax
  <chr>             <dbl>
1 Atlantic           4.84
2 Neither            5.46
3 Pacific            3.55

Sales tax in US regions

Suppose you’re tasked with the following:

Compare the average state sales tax rates of states in various regions (Midwest - 12 states, Northeast - 9 states, South - 16 states, West - 13 states).

How would you approach this task?

Create a new variable called region with levels "Midwest", "Northeast", "South", and "West".
Group by region
Summarize to find the mean sales tax in each type of state

`mutate()` with `case_when()`

Who feels like filling in the blanks lists of states in each region? Who feels like it’s simply too tedious to write out names of all states?

list_of_midwest_states <- c(___)
list_of_northeast_states <- c(___)
list_of_south_states <- c(___)
list_of_west_states <- c(___)

sales_taxes <- sales_taxes |>
  mutate(
    coast = case_when(
      state %in% list_of_west_states ~ "Midwest",
      state %in% list_of_northeast_states ~ "Northeast",
      state %in% list_of_south_states ~ "South",
      state %in% list_of_west_states ~ "West"
    )
  )

Joining data

Why join?

Suppose we want to answer questions like:

Is there a relationship between
- number of QS courses taken
- having scored a 4 or 5 on the AP stats exam
- motivation for taking course
- …
and performance in this course?

Each of these would require joining class performance data with an outside data source so we can have all relevant information (columns) in a single data frame.

Why join?

Suppose we want to answer questions like:

Compare the average state sales tax rates of states in various regions (Midwest - 12 states, Northeast - 9 states, South - 16 states, West - 13 states).

This can also be solved with joining region information with the state-level sales tax data.

Setup

For the next few slides…

x <- tibble(
  id = c(1, 2, 3),
  value_x = c("x1", "x2", "x3")
)

x

# A tibble: 3 × 2
     id value_x
  <dbl> <chr>  
1     1 x1     
2     2 x2     
3     3 x3

y <- tibble(
  id = c(1, 2, 4),
  value_y = c("y1", "y2", "y4")
)

y

# A tibble: 3 × 2
     id value_y
  <dbl> <chr>  
1     1 y1     
2     2 y2     
3     4 y4

`left_join()`

left_join(x, y)

Joining with `by = join_by(id)`

# A tibble: 3 × 3
     id value_x value_y
  <dbl> <chr>   <chr>  
1     1 x1      y1     
2     2 x2      y2     
3     3 x3      <NA>

`right_join()`

right_join(x, y)

Joining with `by = join_by(id)`

# A tibble: 3 × 3
     id value_x value_y
  <dbl> <chr>   <chr>  
1     1 x1      y1     
2     2 x2      y2     
3     4 <NA>    y4

`full_join()`

full_join(x, y)

Joining with `by = join_by(id)`

# A tibble: 4 × 3
     id value_x value_y
  <dbl> <chr>   <chr>  
1     1 x1      y1     
2     2 x2      y2     
3     3 x3      <NA>   
4     4 <NA>    y4

`inner_join()`

inner_join(x, y)

Joining with `by = join_by(id)`

# A tibble: 2 × 3
     id value_x value_y
  <dbl> <chr>   <chr>  
1     1 x1      y1     
2     2 x2      y2

`anti_join()`

anti_join(x, y)

Joining with `by = join_by(id)`

# A tibble: 1 × 2
     id value_x
  <dbl> <chr>  
1     3 x3

Summary of joins

Application exercise

Goal

Compare the average state sales tax rates of states in various regions (Midwest, Northeast, South, West), where the input data are:

States and sales taxes
States and regions

ae-06-taxes-join

Go to your ae project in RStudio.
If you haven’t yet done so, make sure all of your changes up to this point are committed and pushed, i.e., there’s nothing left in your Git pane.
If you haven’t yet done so, click Pull to get today’s application exercise file: ae-06-taxes-join.qmd.
Work through the application exercise in class, and render, commit, and push your edits by the end of class.

Joining data

Warm-up

While you wait: Participate 📱💻

Pivot wider

Announcements

Recap: Pivot

Recap: Plotting

Recoding data

What’s going on in this plot?

Sales taxes in US states

Participate 📱💻

Participate 📱💻

Recap: if_else()

Participate 📱💻

Sales tax in swing states

Sales tax in coastal states

Participate 📱💻

Recap: case_when()

Sales tax in coastal states

Sales tax in US regions

mutate() with case_when()

Joining data

Why join?

Why join?

Setup

left_join()

right_join()

full_join()

inner_join()

anti_join()

Summary of joins

Application exercise

Goal

ae-06-taxes-join

Recap: `if_else()`

Recap: `case_when()`

`mutate()` with `case_when()`

`left_join()`

`right_join()`

`full_join()`

`inner_join()`

`anti_join()`