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Fundamentals of R

group_by() and summarize()

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View code shown in video 
# Load Packages -----------------------------------------------------------

library(tidyverse)

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

penguins <- read_csv("penguins.csv")

# group_by() and summarize() ----------------------------------------------

# summarize() becomes truly powerful when paired with group_by(), 
# which enables us to perform calculations on multiple groups. 

# Calculate the mean bill length for penguins on different islands.

penguins |> 
  group_by(island) |> 
  summarize(mean_bill_length = mean(bill_length_mm, na.rm = TRUE))

# We can use group_by() with multiple groups.

penguins |> 
  group_by(island, year) |> 
  summarize(mean_bill_length = mean(bill_length_mm, na.rm = TRUE)) 

# Another option is to use the .by argument in summarize().

penguins |> 
  summarize(mean_bill_length = mean(bill_length_mm, na.rm = TRUE),
            .by = c(island, year))

# You can count the number of penguins in each group using the n() summary function.

penguins |> 
  group_by(island) |> 
  summarize(number_of_penguins = n())

# But a simpler way do this is with the count() function.

penguins |> 
  count(island)

# You can also use count() with multiple groups.

penguins |> 
  count(island, year)

Your Turn

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

# Load the tidyverse package

library(tidyverse)

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

# Download data from https://rfor.us/penguins
# Copy the data into the RStudio project
# Create a new R script file and add code to import your data

penguins <- read_csv("penguins.csv")
			
# group_by() and summarize() ----------------------------------------------

# Calculate the weight of the heaviest penguin on each island.

# YOUR CODE HERE

# Calculate the weight of the heaviest penguin on each island for each year.

# YOUR CODE HERE

Learn More

To learn more about the group_by() and summarize() functions, 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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gene trevino

gene trevino • January 30, 2025

When I run the following code:

penguins %>% group_by(island, year) %>%
summarize(Heaviest_Penguins = max(body_mass_g, na.rm = TRUE))

I get the following output:

island year Heaviest_Penguins

Why do I get NA for Biscoe and Torgersen ?

Thanks

David Keyes

David Keyes Founder • January 31, 2025

Hmm, that's strange. I see something different:

# A tibble: 9 × 3
# Groups:   island [3]
  island     year Heaviest_Penguins
  <fct>     <int>             <int>
1 Biscoe     2007              6300
2 Biscoe     2008              6000
3 Biscoe     2009              6000
4 Dream      2007              4650
5 Dream      2008              4800
6 Dream      2009              4475
7 Torgersen  2007              4675
8 Torgersen  2008              4700
9 Torgersen  2009              4300

Can you share the code you used to import the CSV file?

Pepper Phillips

Pepper Phillips • February 11, 2025

Can you use drop_na instead of na.rm = TRUE?

David Keyes

David Keyes Founder • February 11, 2025

Yes, absolutely! I do that quite often, in fact.

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