filter()

This lesson is called filter(), part of the R in 3 Months (Fall 2026) course. This lesson is called filter(), part of the R in 3 Months (Fall 2026) course.

Transcript

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# Load Packages -----------------------------------------------------------

library(tidyverse)

# Import Data -------------------------------------------------------------

penguins <-
  read_csv("penguins.csv")

# filter() ----------------------------------------------------------------

# We use filter() to choose a subset of observations.

# We use == to select all observations that meet the criteria.

penguins |>
  filter(species == "Adelie") 

# We use != to select all observations that don't meet the criteria.

penguins |>
  filter(species != "Adelie") 

# We use filter_out() to do the same thing

penguins |>
  filter_out(species == "Adelie") 

# We can combine comparisons and logical operators.

penguins |>
  filter(species == "Adelie" | species == "Chinstrap") 

# We can use %in% to collapse multiple comparisons into one.

penguins |>
  filter(species %in% c("Adelie", "Chinstrap"))

# We can also use when_any() to do the same thing

penguins |>
  filter(when_any(species == "Adelie", species == "Chinstrap"))

# We can chain together multiple filter functions.
# Doing it this way, we don't have create complex logic in one line.

# Complicated version

penguins |>
  filter(
    species %in% c("Adelie", "Chinstrap") & island == "Torgersen"
  ) 

# Simpler version

penguins |>
  filter(species %in% c("Adelie", "Chinstrap")) |>
  filter(island == "Torgersen")

# when_all() version

penguins |>
  filter(
    when_all(
      species %in% c("Adelie", "Chinstrap"),
      island == "Torgersen"
    )
  )

# We can use <, >, <=, and >= for numeric data.

penguins |>
  filter(body_mass_g > 4000)

# We can drop NAs with !is.na().

penguins |>
  filter(!is.na(sex))

# But the double negative is confusing.
# We can also drop NAs with drop_na().

penguins |>
  drop_na(sex)

Your Turn

Solution

# Load Packages -----------------------------------------------------------

# Load the tidyverse package

library(tidyverse)

# Import Data -------------------------------------------------------------

penguins <- read_csv("penguins.csv")
			
# filter() ----------------------------------------------------------------

# Use filter() to only keep female penguins

penguins |> 
  filter(sex == "female")

# Use filter() to only keep penguins NOT on Torgersen island

penguins |> 
  filter(island != "Torgersen")

# Use filter() to only keep penguins on Torgersen island or Biscoe island
# Use the or logical operator (|) to do this

penguins |> 
  filter(island == "Torgersen" | island == "Biscoe")

# Rewrite your filter() code above to keep the penguins from Torgersen island or Biscoe island
# This time, though, use the %in% operator

penguins |> 
  filter(island %in% c("Torgersen", "Biscoe"))

# Use a comparison operator to keep penguins with flipper lengths greater than or equal to 193 millimeters

penguins |> 
  filter(flipper_length_mm >= 193)

# Drop any rows that have missing data in the flipper_length_mm variable

# Do this first with !is.na()

penguins |> 
  filter(!is.na(flipper_length_mm))

# Do this a second time with drop_na()

penguins |> 
  drop_na(flipper_length_mm)

# Load Packages -----------------------------------------------------------

# Load the tidyverse package

library(tidyverse)

# Import Data -------------------------------------------------------------

penguins <- read_csv("penguins.csv")
			
# filter() ----------------------------------------------------------------

# Use filter() to only keep female penguins

# YOUR CODE HERE

# Use filter() to only keep penguins NOT on Torgersen island

# YOUR CODE HERE

# Use filter() to only keep penguins on Torgersen island or Biscoe island
# Use the or logical operator (|) to do this

# YOUR CODE HERE

# Rewrite your filter() code above to keep the penguins from Torgersen island or Biscoe island
# This time, though, use the %in% operator

# YOUR CODE HERE

# Use a comparison operator to keep penguins with flipper lengths greater than or equal to 193 millimeters

# YOUR CODE HERE

# Drop any rows that have missing data in the flipper_length_mm variable

# Do this first with !is.na()

# YOUR CODE HERE

# Do this a second time with drop_na()

# YOUR CODE HERE

Learn More

To learn more about the filter() function, check out Chapter 3 of R for Data Science.

Have any questions? Put them below and we will help you out!

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Eda Akpek • March 18, 2026

Hi Gracielle, I am trying to use the filter out (!= and filter_out) commands. I am typing in the commands correctly, but the tables do not appear in the console. I'm not getting any error messages either.

Eda Akpek • March 18, 2026

Sorry, again I mistyped my code!

Lilly Yu • September 30, 2026

In the video-shown code, there is => to represent equal to or greater than. In the solution code, this is >= (reversed). The => wouldn't work for me when I used it on my own. Is this an error in the video-shown code?

filter()

Transcript

Your Turn

Solution

Learn More

Have any questions? Put them below and we will help you out!

Eda Akpek • March 18, 2026

Eda Akpek • March 18, 2026

Lilly Yu • September 30, 2026

Gracielle Higino Coach • October 1, 2026

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