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

library(tidyverse)

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

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

# We use geom_histogram() to make a histogram.

ggplot(data = penguins,
       mapping = aes(x = bill_length_mm)) +
  geom_histogram()

# How does ggplot know what to plot on the y axis? 
# It's using the default statistical transformation for geom_histogram, 
# which is stat = "bin".

# If we add stat = "bin" we get the same thing. 
# Each geom has a default stat.

ggplot(data = penguins,
       mapping = aes(x = bill_length_mm)) +
  geom_histogram(stat = "bin")

# We can adjust the number of bins using the bins argument. 

ggplot(data = penguins,
       mapping = aes(x = bill_length_mm)) +
  geom_histogram(bins = 100)

Your Turn

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

library(tidyverse)

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

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

# Make a histogram that shows the distribution of the body_mass_g variable.

# YOUR CODE HERE

# Adjust your histogram so it has 50 bins.

# YOUR CODE HERE

Learn More

You can find examples of code to make histograms on the Data to Viz website , the R Graph Gallery website , and in Chapter 6 of the R Graphics Cookbook , and Chapter 7 of the Fundamentals of Data Visualization.

To learn about more statistical transformations, Chapter 9 of R for Data Science has a discussion of them.

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

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