filter()

Extract rows that meet some sort of test

filter(.data = DATA, ...)

filter()

filter(starwars, 
       species == "Droid")

Application Exercise

In the Console, use filter() and logical tests to show…

1. The data for Humans
2. The data for characters with a homeworld Alderaan
3. Rows where the mass is greater than 1000

Common mistakes

filter(starwars, 
       homeworld = "Alderaan")

filter(starwars, 
       homeworld == "Alderaan")

Common mistakes

filter(starwars, 
       homeworld = "Alderaan")

filter(starwars, 
       homeworld == "Alderaan")

filter(starwars, 
       homeworld == Alderaan)

filter(starwars, 
       homeworld == "Alderaan")

filter() with multiple conditions

Extract rows that meet every test

filter(starwars, 
       species == "Droid",
       height > 100)

Boolean operators

Default is “and”

These do the same thing:

filter(starwars, species == "Droid", height > 100)

filter(starwars, species == "Droid" & height > 100)

Application Exercise

Create a new R chunk in your .qmd file. Use filter() and Boolean logical tests to show…

1. Droids more than 70 kg
2. Characters that are born before 56 and have blue eyes
3. Characters that are born before 56 and have blue eyes and are not Human

Common mistakes

filter(starwars, 
       50 < mass < 100)

filter(starwars, 
       mass > 50,
       mass < 100)

filter(starwars, 
       homeworld == "Alderaan",
       homeworld == "Bespin",
       homeworld == "Corellia")

filter(starwars, 
       homeworld %in% c("Alderaan", "Bespin", "Corellia"))

Common syntax

• Every dplyr verb function follows the same pattern
• First argument is a data frame; returns a data frame

VERB(DATA, ...)

mutate()

Create new columns

mutate(.data, ...)

ifelse()

Do conditional tests within mutate()

ifelse(TEST,      
       VALUE_IF_TRUE, 
       VALUE_IF_FALSE)

Application Exercise

Create a new R chunk in your .qmd file. Use mutate() to

1. Add a droid column that is TRUE if the species is a Droid
2. Add a column for the logged weight (hint: use log())
3. Add a human_droid column that says “Human or Droid” if the character is a human or a droid and “Not Human or Droid” if it’s not

What if you have multiple verbs?

Make a dataset of just Droids and calculate their BMI

. . .

Pipes!

The |> operator (pipe) takes an object on the left and passes it as the first argument of the function on the right

. . .

starwars |> filter(_, species == "Droid")

What if you have multiple verbs?

These do the same thing!

filter(starwars, species == "Droid")

starwars |> filter(species == "Droid")

What if you have multiple verbs?

Make a dataset of just Droids and calculate their BMI

starwars |>
  filter(species == "Droid") |>
  mutate(bmi = mass / (height / 100)^2)

summarize()

Compute a table of summaries

starwars |>
  summarize(mean_height = mean(height, na.rm = TRUE))

summarize()

Compute a table of summaries

starwars |>
  summarize(mean_height = mean(height, na.rm = TRUE))

summarize()

starwars |>
  summarize(mean_height = mean(height, na.rm = TRUE),
            max_height = max(height, na.rm = TRUE))

Application Exercise

Create a new R chunk in your .qmd file. Use summarize() to calculate…

1. The first (minimum) birth year in the dataset
2. The last (maximum) birth year in the dataset
3. The number of distinct homeworlds in the dataset (use the cheatsheet)

Application Exercise

Create a new R chunk in your .qmd file. Use filter() and summarize() to calculate

1. The number of unique homeworlds among humans
2. The median height for characters from Tatooine born before the year 100

group_by()

Put rows into groups based on values in a column

starwars |> group_by(species)

. . .

Nothing happens by itself!

. . .

Powerful when combined with summarize()

Application Exercise

Create a new R chunk in your .qmd file.

1. Find the minimum, maximum, and median mass for each species
2. Find the minimum, maximum, and median mass for each species from Tatooine