- Welcome to Fundamentals of R
- Update Everything
- Start a New Project
-
Data Wrangling and Analysis
- The Tidyverse
- Pipes
- select()
- mutate()
- filter()
- summarize()
- group_by() and summarize()
- arrange()
- Create a New Data Frame
- Bring it All Together (Data Wrangling)
-
Data Visualization
- The Grammar of Graphics
- Scatterplots
- Histograms
- Bar Charts
- Setting color and fill Aesthetic Properties
- Setting color and fill Scales
- Setting x and y Scales
- Adding Text to Plots
- Plot Labels
- Themes
- Facets
- Save Plots
- Bring it All Together (Data Visualization)
-
Quarto
- Quarto Overview
- YAML
- Text
- Code Chunks
- Tips for Working with Quarto
- Bring It All Together (Quarto)
-
Wrapping Up
- An Important Workflow Tip
Fundamentals of R
group_by() and summarize()
This lesson is called group_by() and summarize(), part of the Fundamentals of R course. This lesson is called group_by() and summarize(), part of the Fundamentals of R course.
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# 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.
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