Day 5 Notes: Cental Measures, Histograms, and Boxplots

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Today’s Agenda

• Measures of Centrality
• Skewness
• Graphs

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# loading palmer penguin data
library(palmerpenguins)

Measure of Centrality

• mean and median are super easy to calculate in R

# mean and median of penguin body mass

mean(penguins$body_mass_g, na.rm = TRUE)

## [1] 4201.754

median(penguins$body_mass_g, na.rm = TRUE)

## [1] 4050

# mean is sensitive to outliers and the median is not very

Skewness

Below are several distributions, each shows the mean in red and the median in blue.

• A symmetric distribution looks like:

• A right skew in a distribution looks like:

• A left skew in a distribution looks like:

Graphs

# a histogram for penguin weights
hist(penguins$body_mass_g)

# more bins!
hist(penguins$body_mass_g, breaks=100)

# can better see where there is dips in the data
# we can see outliers better
# easier to identify groups/species
# harder to see how big bins are

hist(penguins$body_mass_g, breaks=1000)

# you as the statistician have to play this balancing game of deciding how many bins is best for your data

# other aesthetic improvements
hist(penguins$body_mass_g,
     breaks=50,
     main = "Histogram of Penguins Sizes",
     xlab = "Body Mass (g)",
     col = "blue")

boxplot(penguins$body_mass_g ~ penguins$species,
        xlab = "Species",
        ylab = "Body Mass (g)")