Grammar of data wrangling

Lecture 04

Dr. Benjamin Soltoff

Cornell University
INFO 5001 - Fall 2026

September 3, 2026

Announcements

Announcements

• Homework 01 tomorrow

Learning objectives

• Identify computer programming as a form of problem solving
• Practice decomposing an analytic goal into a set of discrete, computational tasks
• Identify the verbs for a language of data manipulation
• Practice transforming data

Computational problem-solving

Meet the Palmer penguins

Meet the Palmer penguins

Measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.

glimpse(penguins)

Rows: 333
Columns: 8
$ species     <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adeli…
$ island      <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgersen…
$ bill_len    <dbl> 39.1, 39.5, 40.3, 36.7, 39.3, 38.9, 39.2, 41.1, 38.6,…
$ bill_dep    <dbl> 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 17.6, 21.2,…
$ flipper_len <int> 181, 186, 195, 193, 190, 181, 195, 182, 191, 198, 185…
$ body_mass   <int> 3750, 3800, 3250, 3450, 3650, 3625, 4675, 3200, 3800,…
$ sex         <fct> male, female, female, female, male, female, male, fem…
$ year        <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007,…

What is the average body mass of an Adelie penguin?

1. First we need to identify the input, or the data we’re going to analyze.
2. Next we need to select only the observations which are Adelie penguins.
3. Finally we need to calculate the average value, or mean, of body_mass.

penguins |>
  filter(species == "Adelie") |>
  summarize(avg_mass = mean(body_mass))

# A tibble: 1 × 1
  avg_mass
     <dbl>
1    3706.

What is the average body mass of a penguin for each species?

01:00

penguins |>
  group_by(species) |>
  summarize(avg_mass = mean(body_mass))

# A tibble: 3 × 2
  species   avg_mass
  <fct>        <dbl>
1 Adelie       3706.
2 Chinstrap    3733.
3 Gentoo       5092.

What is the average bill length and body mass for each Adelie penguin by sex?

01:00

• Filter, then group by
• Group by, then filter

penguins |>
  filter(species == "Adelie") |>
  group_by(sex) |>
  summarize(
    bill = mean(bill_len),
    avg_mass = mean(body_mass)
  )

# A tibble: 2 × 3
  sex     bill avg_mass
  <fct>  <dbl>    <dbl>
1 female  37.3    3369.
2 male    40.4    4043.

penguins |>
  group_by(sex) |>
  filter(species == "Adelie") |>
  summarize(
    bill = mean(bill_len),
    avg_mass = mean(body_mass)
  )

# A tibble: 2 × 3
  sex     bill avg_mass
  <fct>  <dbl>    <dbl>
1 female  37.3    3369.
2 male    40.4    4043.

The pipe operator |>

Avoids more complex syntax such as:

Nested functions

summarize(
  group_by(
    filter(
      .data = penguins,
      species == "Adelie"
    ),
    sex
  ),
  bill = mean(bill_len),
  avg_mass = mean(body_mass)
)

Intermediate objects

penguins1 <- filter(
  .data = penguins,
  species == "Adelie"
)
penguins2 <- group_by(.data = penguins1, sex)
summarize(
  .data = penguins2,
  bill = mean(bill_len),
  avg_mass = mean(body_mass)
)

Verbiage for data transformation

1. The first argument is a data frame
2. Subsequent arguments describe what to do with the data frame
3. The result is a new data frame

Key functions in {dplyr}

function()	Action performed
filter()	Subsets observations based on their values
arrange()	Changes the order of observations based on their values
select()	Selects a subset of columns from the data frame
rename()	Changes the name of columns in the data frame
mutate()	Creates new columns (or variables)
group_by()	Changes the unit of analysis from the complete dataset to individual groups
summarize()	Collapses the data frame to a smaller number of rows which summarize the larger data

Application exercise

ae-02

Instructions

• Go to the course GitHub org and find your ae-02 (repo name will be suffixed with your GitHub name).
• Clone the repo in Positron, run renv::restore() to install the required packages, open the Quarto document in the repo, and follow along and complete the exercises.
• Render, commit, and push your edits by the AE deadline – end of the day

Wrap up

Recap

• The pipe operator, |>, can be read as “and then”.

• The pipe operator passes what comes before it into the function that comes after it as the first argument in that function.

  sum(1, 2)

  [1] 3

  1 |>
    sum(2)

  [1] 3

• Always use a line break after the pipe, and indent the next line of code.

• Use {dplyr} functions to transform your data