Software Tools for Data Analysis
STA 9750
Michael Weylandt
Week 6 – Tuesday 2026-10-06, Thursday 2026-10-08
Last Updated: 2026-10-06

STA 9750 Week 6

Today: Lecture #05: Multi-Table dplyr Verbs

These slides can be found online at:

https://michael-weylandt.com/STA9750/slides/slides06.html

In-class activities can be found at:

https://michael-weylandt.com/STA9750/labs/lab05.html

Upcoming TODO

Upcoming student responsibilities - Tuesday Section:

Date Time Student Responsibility
2026-10-13
Classes on Monday Schedule – Columbus Day Conversion Day
2026-10-16 11:59pm ET Mini-Project #01 Due
2026-10-20 6:00pm ET Pre-Assignment #06 Due
2026-10-26 11:59pm ET Mini-Project Peer Feedback #01 Due
2026-10-27 6:00pm ET Mid-Semester Check-In Slides Due
2026-11-03 6:00pm ET Pre-Assignment #07 Due
2026-11-03 11:59pm ET Mid-Semester Teammate Peer Evaluations Due

Upcoming TODO

Upcoming student responsibilities - Thursday Section:

Date Time Student Responsibility
2026-10-15 6:00pm ET Pre-Assignment #06 Due
2026-10-16 11:59pm ET Mini-Project #01 Due
2026-10-22 6:00pm ET Mid-Semester Check-In Slides Due
2026-10-26 11:59pm ET Mini-Project Peer Feedback #01 Due
2026-10-29 6:00pm ET Pre-Assignment #07 Due
2026-10-29 11:59pm ET Mid-Semester Teammate Peer Evaluations Due
2026-11-05 6:00pm ET Pre-Assignment #08 Due

Other Important Dates

Other Important Dates:

Date Instructor Action
2026-10-06 Estimated Return of Peer Comments #00
2026-10-13 Estimated Return of Meta-Review Comments #00
2026-10-13 Classes on Monday Schedule – Columbus Day Conversion Day #01
2026-10-15 Mini-Project Released #02
2026-10-19 Mini-Project Peer Feedback Assigned #01
2026-10-27 Estimated Return of Peer Comments #01
2026-11-03 Estimated Return of Meta-Review Comments #01

STA 9750 Week 6

Today: Lecture #05: Multi-Table dplyr Verbs

Today

Today

  • Mini-Projects
  • Review
  • Multi-Table Verbs
  • Pivots and Data Reshaping
  • Wrap-Up

MP#01 - Deadline Soon

Mini-Project #01 due on 2026-10-16 at 11:59pm ET

Topics Covered:

  • Reproducible Research Tooling
  • Packages and Functions
  • Basic Data Manipulation
  • Tables for Data Display

Make sure to complete all instructor-provided EDA questions and writing prompts

Be reasonable, justify, and document your analysis.

  • Do your answers pass the sniff test?
  • At least one sentence of text per short question is a good baseline

MP#01 - What to Write

In MP#01, you are not just submitting code, but a full documented analysis.

  • Code intermixed with text throughout
  • But what to write?

Always explain why you are taking particular steps; sometimes (high-level) what; rarely how

  • Clean code should (generally) be self-documenting

MP#01 - What to Write

Unnecessary:

Next, we rename columns in the data set: 

```{r}
data_set <- data_set |> rename(...)
```

Your target audience is a competent working data scientist, so explaining that rename() renames things is unhelpful.

To be fair, there are some times in life where gnarly non-self-documenting code is required, but you shouldn’t be encountering those in this class

MP#01 - What to Write

Better:

Next, we update column names to include SI units and to more closely reflect the 
terminology of Gorman *et al* (2014):

```{r}
better_penguins <- penguins |> 
  rename(culmen_depth_mm = bill_dep, 
         ...)
```

This explains what you are doing at a level beyond the code (not just renaming, but aligning with other data sources).

EDA Functions

In MP#01, I ask you to run glimpse() and skim() on your data. These functions give summaries of the data set (column types, means, ranges, etc.)

These EDA functions help you get oriented with new data; normally, you wouldn’t document them, but I’m asking you to run and discuss them (in MP#01 only)

EDA Functions

Example:

After an initial survey of our data, 

```{r}
glimpse(penguins)
```

we note that this data has a mix of categorial and numeric columns, as well as 
one column (`year`) that should transformed from numeric to factor (categorical)
for modeling (ANOVA) purposes.

Documentation Sandwich

Documentation sandwich:

  • Before: Why are you doing something?

  • After: What does this tell you? What does it imply about next steps?

The latter is particularly important with tables and figures. You have a story to tell your audience. Don’t just dump a bunch of numbers and expect them to figure it out

Not always necessary to have both. Use your best judgment

Documentation Sandwich

To assess whether automated penguin sexing is possible, we need to compare typical male and female body masses across our three species of interest:

Inter-Sex Differences in Penguin Body Mass by Species
Species Body Mass (Female) Body Mass (Male)
Adelie 3.37 ± 0.27 4.04 ± 0.35
Chinstrap 3.53 ± 0.29 3.94 ± 0.36
Gentoo 4.68 ± 0.28 5.48 ± 0.31
Data originally published in by K.B. Gorman, T.D. Williams, and W. R. Fraser in ’Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pyogscelis). PLoS One 9(3): e90081. https://doi.org/10.1371/journal.pone.0090081. Later popularized via the R package palmerpenguins

We note that typical inter-sex differences range from 1.25 (Chinstrap) to 2.6 (Gentoo) times the intra-sex variability, indicating that a simple sex classifier based on sex and body mass is likely to perform well for Gentoo penguins, but struggle for Chinstraps.

Documentation Sandwich

Examining the same data graphically, we see that it is indeed difficult to distinguish Chinstrap penguins by body mass alone, as the significant overlap in the central panel below confirms.

Review

Single-Table Verbs

dplyr single-table verbs

  • select, filter: Selecting rows and columns
  • rename, mutate: Changing rows and columns
  • summarize, group_by: Combining multiple rows
  • arrange, slice_min/max: Re-ordering

Lab #05 Review Activity

Breakout Rooms

Breakout Rooms
Please join the Breakout Room for your Project Team
Breakout #01: CitiThatNeverSleeps (T) + xG Analytics (R) Breakout #02: Beyond the Scale (T) + King Philip (R)
Breakout #03: Breathe Easy (T) + Emergency Care Bears (R) Breakout #04: CUNY-naughts (T) + Collide-o-Scope (R)
Breakout #05: Table for One (T) + Data Vinci (R) Breakout #06: Beat The Odds (T) + Zipliners (R)
Breakout #07: Team Rocket (T) + Team Nexus (R) Breakout #08: EconoMates (T) + Unemployment Hub (R)
Breakout #09: Crude Awakening (T) + NYC Housing Pulse (R) Breakout #10: Degrees of Freedom (T) + TeamTZ (R)

Go to your team’s breakout room. I will adjust / merge rooms based to ensure every room has enough people.

Multi-Table Verbs

Multi-Table Analysis

Multiple Tables:

  • More insights than from a single table
  • Maintain ‘tidy’ structure throughout

Will create new (compound) rows:

  • Dangers: drops and (over) duplication

Primary Keys

Keys are unique identifiers for individual records

  • Primary (one column) or compound (multiple columns together)

The history of corporate IT is largely one of (failed) primary keys

  • Finance: Tickers, Tickers + Exchange, Tickers + Share Class, CUSIP, ISIN, SEDOL, …

Meaningful true keys are vanishingly rare - cherish them when you find them

Often ‘unique enough’ for an analysis

dplyr::group_by() + dplyr::count() is helpful here

Primary Keys

What primary keys and compound keys are used to identify you?

  • CUNY EmplID
  • Name
  • Name + Birthday
  • Name + Birthday + ???
  • National ID Number (SSN?)

Joins

Joins combine tables by identity - not simple ‘stacking’

Specify a join key - ideally this is an actual key, but doesn’t have to be

In dplyr, we use the join_by function:

dplyr::join_by(table_1_name == table_2_name)

Here table_1_name and table_2_name are column names from two tables

Join rows where these values are equal (advanced joins possible)

Inner and Outer Joins

When tables are perfectly matched, not an issue:

cunys
# A tibble: 4 × 2
  college campus_borough
  <chr>   <chr>         
1 CCNY    Manhattan     
2 Baruch  Manhattan     
3 CSI     Staten Island 
4 York    Queens        
routes
# A tibble: 3 × 2
  borough_name  bus_code
  <chr>         <chr>   
1 Manhattan     M       
2 Staten Island S       
3 Queens        Q

Inner and Outer Joins

When tables are perfectly matched, not an issue:

inner_join(cunys, routes, join_by(campus_borough == borough_name))
# A tibble: 4 × 3
  college campus_borough bus_code
  <chr>   <chr>          <chr>   
1 CCNY    Manhattan      M       
2 Baruch  Manhattan      M       
3 CSI     Staten Island  S       
4 York    Queens         Q

Default to inner but irrelevant

Note automatic repetition of "M" row

Inner and Outer Joins

How to handle ‘unaligned’ values?

cunys <- tribble(~college, ~campus_borough, 
                 "CCNY", "Manhattan",
                 "Baruch", "Manhattan", 
                 "CSI", "Staten Island",
                 "York", "Queens", 
                 "Medgar Evers", "Brooklyn")

inner_join(cunys, routes, join_by(campus_borough == borough_name))
# A tibble: 4 × 3
  college campus_borough bus_code
  <chr>   <chr>          <chr>   
1 CCNY    Manhattan      M       
2 Baruch  Manhattan      M       
3 CSI     Staten Island  S       
4 York    Queens         Q

MEC vanished!

Inner and Outer Joins

left_join(cunys, routes, join_by(campus_borough == borough_name))
# A tibble: 5 × 3
  college      campus_borough bus_code
  <chr>        <chr>          <chr>   
1 CCNY         Manhattan      M       
2 Baruch       Manhattan      M       
3 CSI          Staten Island  S       
4 York         Queens         Q       
5 Medgar Evers Brooklyn       <NA>

MEC stays, but no bus code - NA value

  • inner_join - Keep only matches
  • left_join - Keep all rows in left (first) table even w/o matches
  • right_join - Keep all rows in right (second) table even w/o matches
  • full_join - Keep all rows from both tables, even w/o matches

left_ and right_ are types of ‘outer’ joins

Legos of Data Analysis

These functions are like Legos:

  • Simple individually
  • Combine for complex structures

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

library(dplyr)
library(nycflights13)
flights |> 
    n_distinct()
[1] 336776

Not quite what we wanted…

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

How many unique combinations of carrier + flight (e.g., United 101)?

flights |>
    select(carrier, flight) |>
    n_distinct()
[1] 5725

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

💡 Did airlines re-use flight numbers for different destinations?

flights |>
    distinct(carrier, flight, dest) |>
    # Find reuse of number across different destinations
    # Shorthand for group_by + summarize(n = n())
    count(carrier, flight) 
# A tibble: 5,725 × 3
   carrier flight     n
   <chr>    <int> <int>
 1 9E        2900     1
 2 9E        2901     1
 3 9E        2902     1
 4 9E        2903     2
 5 9E        2904     2
 6 9E        2905     1
 7 9E        2906     1
 8 9E        2907     1
 9 9E        2908     1
10 9E        2909     2
# ℹ 5,715 more rows

Seems so!

BTW: This is bad and dangerous!

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

Find examples of re-use:

flights |>
    distinct(carrier, flight, dest) |>
    count(carrier, flight) |> 
    slice_max(n)
# A tibble: 1 × 3
  carrier flight     n
  <chr>    <int> <int>
1 UA        1162    16

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

Finding most re-used flight number:

flights |>
    distinct(carrier, flight, dest) |>
    count(carrier, flight) |> 
    slice_max(n) |> 
    left_join(flights, join_by(carrier == carrier, flight == flight)) |>
    pull(dest) |> # pull out column as vector
    table() # frequency table

BOS CLE DEN DFW IAH JAC LAS MIA MSY ORD SAN SAT SEA SFO SNA TPA 
 13   1  19   5   8   2   1   2   8  55   4   1  18   2  27   4

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

Finding most re-used flight number:

flights |>
    distinct(carrier, flight, dest) |>
    count(carrier, flight) |> 
    slice_max(n)
# A tibble: 1 × 3
  carrier flight     n
  <chr>    <int> <int>
1 UA        1162    16

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

Seeing where our most reused number went:

flights |>
    distinct(carrier, flight, dest) |>
    count(carrier, flight) |> 
    slice_max(n) |>
    inner_join(flights, join_by(carrier == carrier, flight == flight)) |>
    count(flight, carrier, dest)
# A tibble: 16 × 4
   flight carrier dest      n
    <int> <chr>   <chr> <int>
 1   1162 UA      BOS      13
 2   1162 UA      CLE       1
 3   1162 UA      DEN      19
 4   1162 UA      DFW       5
 5   1162 UA      IAH       8
 6   1162 UA      JAC       2
 7   1162 UA      LAS       1
 8   1162 UA      MIA       2
 9   1162 UA      MSY       8
10   1162 UA      ORD      55
11   1162 UA      SAN       4
12   1162 UA      SAT       1
13   1162 UA      SEA      18
14   1162 UA      SFO       2
15   1162 UA      SNA      27
16   1162 UA      TPA       4

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

Additional join to get airport information + formatting:

flights |>
    distinct(carrier, flight, dest) |>
    count(carrier, flight) |> 
    slice_max(n) |>
    inner_join(flights, join_by(carrier == carrier, flight == flight)) |>
    count(flight, carrier, dest) |> 
    inner_join(airports, join_by(dest == faa)) |>
    select(name, n, carrier, flight) |>
    arrange(desc(n)) |>
    rename(`Destination Airport` = name, 
           `Number of Times Flown` = n, 
           `Carrier Code` = carrier, 
           `Flight Number`= flight)
# A tibble: 16 × 4
   `Destination Airport`   Number of Times Flow…¹ `Carrier Code` `Flight Number`
   <chr>                                    <int> <chr>                    <int>
 1 Chicago Ohare Intl                          55 UA                        1162
 2 John Wayne Arpt Orange…                     27 UA                        1162
 3 Denver Intl                                 19 UA                        1162
 4 Seattle Tacoma Intl                         18 UA                        1162
 5 General Edward Lawrenc…                     13 UA                        1162
 6 George Bush Interconti…                      8 UA                        1162
 7 Louis Armstrong New Or…                      8 UA                        1162
 8 Dallas Fort Worth Intl                       5 UA                        1162
 9 San Diego Intl                               4 UA                        1162
10 Tampa Intl                                   4 UA                        1162
11 Jackson Hole Airport                         2 UA                        1162
12 Miami Intl                                   2 UA                        1162
13 San Francisco Intl                           2 UA                        1162
14 Cleveland Hopkins Intl                       1 UA                        1162
15 Mc Carran Intl                               1 UA                        1162
16 San Antonio Intl                             1 UA                        1162
# ℹ abbreviated name: ¹​`Number of Times Flown`

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

Extra join to match to airlines as well:

head(airlines, 3)
# A tibble: 3 × 2
  carrier name                  
  <chr>   <chr>                 
1 9E      Endeavor Air Inc.     
2 AA      American Airlines Inc.
3 AS      Alaska Airlines Inc.

Also has a column named name - need to disambiguate!

Legos of Data Analysis

Q: How many distinct flights left NYC in 2013?

Additional join to get airport information + formatting:

flights |>
    distinct(carrier, flight, dest) |>
    count(carrier, flight) |> 
    slice_max(n) |>
    inner_join(flights, join_by(carrier == carrier, flight == flight)) |>
    count(flight, carrier, dest) |>  
    inner_join(airports, join_by(dest == faa)) |>
    select(name, n, carrier, flight) |>
    rename(dest_name = name) |> 
    inner_join(airlines, join_by(carrier == carrier)) |> 
    arrange(desc(n)) |>
    select(-carrier) |> 
    rename(`Destination Airport` = dest_name, 
           `Number of Times Flown` = n, 
           `Carrier` = name, 
           `Flight Number`= flight)
# A tibble: 16 × 4
   `Destination Airport`          Number of Times Flow…¹ `Flight Number` Carrier
   <chr>                                           <int>           <int> <chr>  
 1 Chicago Ohare Intl                                 55            1162 United…
 2 John Wayne Arpt Orange Co                          27            1162 United…
 3 Denver Intl                                        19            1162 United…
 4 Seattle Tacoma Intl                                18            1162 United…
 5 General Edward Lawrence Logan…                     13            1162 United…
 6 George Bush Intercontinental                        8            1162 United…
 7 Louis Armstrong New Orleans I…                      8            1162 United…
 8 Dallas Fort Worth Intl                              5            1162 United…
 9 San Diego Intl                                      4            1162 United…
10 Tampa Intl                                          4            1162 United…
11 Jackson Hole Airport                                2            1162 United…
12 Miami Intl                                          2            1162 United…
13 San Francisco Intl                                  2            1162 United…
14 Cleveland Hopkins Intl                              1            1162 United…
15 Mc Carran Intl                                      1            1162 United…
16 San Antonio Intl                                    1            1162 United…
# ℹ abbreviated name: ¹​`Number of Times Flown`

Diving deeper Into Joins

Data Set: nycflights13

Return to your breakout rooms and continue Lab #05

Pivoting

Pivoting

The pivot_* functions change the shape of data

  • Values are not created or destroyed, just moved around
  • wider data sets are formed by forming multiple rows into columns
  • longer data sets are splitting columns from the same row into new rows

These functions come from the tidyr package - not dplyr

library(tidyr) # included in library(tidyverse)

Pivoting

Untidy example from last week:

# A tibble: 12 × 4
   Semester Course     Number Type      
   <chr>    <chr>       <dbl> <chr>     
 1 Fall     Accounting    200 Enrollment
 2 Fall     Accounting    250 Cap       
 3 Fall     Law           100 Enrollment
 4 Fall     Law           125 Cap       
 5 Fall     Statistics    200 Enrollment
 6 Fall     Statistics    200 Cap       
 7 Spring   Accounting    300 Enrollment
 8 Spring   Accounting    350 Cap       
 9 Spring   Law            50 Enrollment
10 Spring   Law           100 Cap       
11 Spring   Statistics    400 Enrollment
12 Spring   Statistics    400 Cap

Pivoting

This data was untidy because it split a single unit (course) across multiple rows

pivot_wider to get to the right format

pivot_wider(BARUCH_UNTIDY, names_from=Type, values_from=Number)
# A tibble: 6 × 4
  Semester Course     Enrollment   Cap
  <chr>    <chr>           <dbl> <dbl>
1 Fall     Accounting        200   250
2 Fall     Law               100   125
3 Fall     Statistics        200   200
4 Spring   Accounting        300   350
5 Spring   Law                50   100
6 Spring   Statistics        400   400

Pivots

pivot_ changes the shape of a data set. Purposes:

  • Get ready for presentation
  • Prep for a join
  • Combine rows before looking at ‘cross-row’ structure

Pivots

Which penguin species has the largest between-sex mass difference?

library(tidyr)
avg_mass_tbl <- penguins |> drop_na() |> 
    group_by(sex, species) |> 
    summarize(avg_mass = mean(body_mass), .groups="drop")
    # .groups="drop" is equivalent to |> ungroup()
avg_mass_tbl
# A tibble: 6 × 3
  sex    species   avg_mass
  <fct>  <fct>        <dbl>
1 female Adelie       3369.
2 female Chinstrap    3527.
3 female Gentoo       4680.
4 male   Adelie       4043.
5 male   Chinstrap    3939.
6 male   Gentoo       5485.

Pivots

We want data that is wider (or at least not longer) than our current data:

species male_avg female_avg
Adelie … …
Chinstrap … …
Gentoo … …

Pivots

For the penguins:

pivot_wider(avg_mass_tbl, 
            id_cols = species, 
            names_from=sex, 
            values_from=avg_mass)
# A tibble: 3 × 3
  species   female  male
  <fct>      <dbl> <dbl>
1 Adelie     3369. 4043.
2 Chinstrap  3527. 3939.
3 Gentoo     4680. 5485.

Put it all together:

pivot_wider(avg_mass_tbl, 
            id_cols = species, 
            names_from=sex, 
            values_from=avg_mass) |>
    mutate(sex_diff = male - female) |>
    slice_max(sex_diff)
# A tibble: 1 × 4
  species female  male sex_diff
  <fct>    <dbl> <dbl>    <dbl>
1 Gentoo   4680. 5485.     805.

Pivots

pivot_wider Arguments:

  • id_cols: kept as ‘keys’ for new table
  • names_from: existing column ‘spread’ to create new columns names
  • values_from: values in new table

pivot_longer:

  • ‘Inverse’ operation
  • Spread one row + multiple columns => one col + multiple rows

pivot_wider and pivot_longer have many additional arguments for dealing with repeats / missing values. The help page (+ experimenting) is your friend

Legos of Data Analysis

Question: What does this do I can’t do in Excel?

Technically, nothing. All programming languages of sufficient complexity are equally powerful (Turing equivalence).


In actuality, quite a lot:

  • filter allows more complex filtering than clicking on values

  • group_by + summarize extend array formulas

  • *_join provides more complex matching than VLOOKUP

  • pivot_* provide general formulation of pivot tables

  • everything else you can do in R.

Ability to script minimizes “hard-coding” of names and values.

But truthfully

fortunes::fortune(59)

Let's not kid ourselves: the most widely used piece of software for statistics
is Excel.
   -- Brian D. Ripley ('Statistical Methods Need Software: A View of
      Statistical Computing')
      Opening lecture RSS 2002, Plymouth (September 2002)
fortunes::fortune(222)

Some people familiar with R describe it as a supercharged version of
Microsoft's Excel spreadsheet software.
   -- Ashlee Vance (in his article "Data Analysts Captivated by R's Power")
      The New York Times (January 2009)

Additional dplyr Tricks

  • Ranking functions
    • row_number, min_rank, dense_rank: differ in ties
    • Use with desc() to flip ordering
    • cum_dist, percent_rank: compute quantiles
  • Cumulative Statistics
    • cummean, cummax, cummin, …

FAQs

Subqueries

[W]ill we be learning how to perform joins within a subquery?

You don’t need subqueries in R since it’s an imperative language. Just create a new variable to represent the result of the subquery and use that in the next command.

SELECT first_name, last_name
FROM collectors
WHERE id IN (
    SELECT collector_id
    FROM sales
);
collector_ids <- sales |> pull(collector_id)
collectors |> filter(id %in% collector_ids) |> select(first_name, last_name)

Data Integrity

[H]ow can we ensure that the information [resulting from a join] is accurate and not repeated?

  1. If you have a true unique ID, you’re usually safe
  2. Pay attention to all warnings
  3. Manually examine the result of any joins

Performance

Will joining large data sets […] affect performance?

Somewhat - larger data sets are always slower.

Bigger danger is “bad joins” creating huge data automatically.

Note that R is less “smart” than SQL, so won’t optimize execution order for you automatically.

dplyr joins vs SQL joins

What is the difference between dplyr and SQL joins?

Not too much - biggest difference is no INDEX or FOREIGN KEY in R so less guarantees of data integrity.

When to use anti_join()?

Rare: looking for unmatched rows.

  • Useful to find data integrity issues or ‘implicit’ missingness.
  • I use an anti_join to find students who haven’t submitted an assignment.

many-to-many Warning

Tricky to address, but fortunately pretty rare.

  • SQL explicitly forbids many-to-many
  • Usually a sign that a “key” isn’t really unique
    • Check for duplicates in x and y tables
    • Can occur with “fancy” joins (rolling, inequality)
  • Add additional join variables to break “duplication”

How to Check Efficiency?

No automatic way. Some rules of thumb:

  • Don’t create large tables just to filter down
    • filter before join when possible
  • full_outer join is a bit dangerous
  • cross_join is rarely the right answer

tidyr vs dplyr

Is tidyr more efficient than dplyr?

Nope - different packages from the same developers.

Designed to work together elegantly.

Rare Joins

What are cross_join, filter joins, and nest_join?

  • cross_join: dangerous.
    • Creates “all pairs” of rows. Useful for ‘design’ problems
  • filter joins (anti_, semi_):
    • Hunting down quietly missing data.
    • Filtering to sub-samples
  • nest_join: beyond this course.
    • left_join with extra structure to output.

Wrap-Up

Review

Multi-Table dplyr:

  • inner_join and left_join
  • join_by specifications
  • pivot_longer and pivot_wider to get data into optimal formats (tidyr)

Additional dplyr:

  • Ranking, cumulative, and shift functions

Orientation

  • Communicating Results (quarto) ✅
  • R Basics ✅
  • Data Manipulation in R ⬅️
    • Single-Table Data Manipulation ✅
    • Multiple-Table Data Manipulation ⬅️
  • Data Visualization in R
  • Getting Data into R
  • Statistical Modeling in R

Next Time

  • Communicating Results (quarto) ✅
  • R Basics ✅
  • Data Manipulation in R ✅
  • Data Visualization in R ⬅️
  • Getting Data into R
  • Statistical Modeling in R

Data Visualization

  • Grammar of Graphics
  • ggplot2
  • EDA Plots vs Publication-Ready Plots

Next Time

Musical Treat