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Fundamentals of R (RStudio)

summarize()

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

library(tidyverse)

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

penguins <- read_csv("penguins.csv")

# summarize() -------------------------------------------------------------

# With summarize(), we can go from a complete dataset down to a summary.

# We use any of the summary functions with summarize().
# Here's how we calculate the mean bill length.

penguins |> 
  summarize(mean_bill_length = mean(bill_length_mm))

# This doesn't work! Notice what the result is. 

# We need to add na.rm = TRUE to tell R to drop NA values.

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

# Another option is to drop NA values before calling summarize().

penguins |> 
  drop_na(bill_length_mm) |> 
  summarize(mean_bill_length = mean(bill_length_mm))

# We can have multiple arguments in each usage of summarize().

penguins |> 
  summarize(mean_bill_length = mean(bill_length_mm, na.rm = TRUE),
            max_bill_depth = max(bill_depth_mm, na.rm = TRUE))

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")
			
# Calculate the weight of the heaviest penguin.
# Don't forget to drop NAs!

# YOUR CODE HERE

# Calculate the minimum and maximum weight of penguins in the dataset.

# YOUR CODE HERE

Learn More

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

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