HDS 3.3-3.4

Author

Alana Zarneke

library(tidyverse)
library(palmerpenguins)

Begin by loading the tidyverse and palmerpenguins packages in the code chunk above and adding your name as the author.

Visualizing the `penguins` Data

Two Categorical Variables

Let’s start by making a stacked bar chart of the island variable by sex. Fill in the missing code:

ggplot(
  data = penguins,
  aes(x = "islands", fill = sex)) +
  geom_bar() +
  labs(
    x = "Island",
    y = "Number of Penguins",
    fill = "Sex",
    title = "Distribution of Penguin Species by Island and Sex"
  )

Modify the previous bar chart to make a side-by-side bar chart of the two variables:

ggplot(
  data = penguins,
  aes(x = "islands", fill = sex)) +
  geom_bar(position = "dodge") +
  labs(
    x = "Island",
    y = "Number of Penguins",
    fill = "Sex",
    title = "Distribution of Penguin Species by Island and Sex"
  )

One Quantitative/One Categorical Variables

Now let’s make side-by-side boxplots of body_mass_g by sex. Put the quantitative variable on the y-axis and the categorical variable on the x-axis.

ggplot(
  data = penguins,
  aes(
    y = body_mass_g,
    x = sex)
) +
geom_boxplot()

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).

One Quantitative/Two Categorical Variables

Is there a difference in body_mass_g between sexes? Use facet_wrap() to modify the previous plot to compare the sexes separately for each species. Is the sex difference more or less clear now?

ggplot(
  data = penguins,
  aes(
    y = body_mass_g,
    x = sex)
) +
geom_boxplot() +
  facet_wrap(~ species)

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Two Quantitative Variables

Is there a relationship between flipper_length_mm and set the body_mass_g? Make a scatterplot of the two variables:

ggplot(
  data = penguins,
  aes(
    y = body_mass_g,
    x = flipper_length_mm)
) +
geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Now add species to the plot by using different colored points:

ggplot(
  data = penguins,
  aes(
    y = body_mass_g,
    x = flipper_length_mm)
) +
geom_point() +
  facet_wrap(~ species)

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Now make the size of the points proportional to bill_depth_mm.

ggplot(
  data = penguins,
  aes(
    y = body_mass_g,
    x = flipper_length_mm,
    size = bill_depth_mm)
) +
geom_point() +
  facet_wrap(~ species)

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

What additional insight do you get from this?