HDS 3.1-3.2

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

Categorical Variables

Let’s start by making a bar chart of the species variable. Modify this code by filling in the ______ to do so:

ggplot(data = penguins, mapping = aes(x = species)) +
  geom_bar() +
  labs(
    x = "Penguin Species",
    y = "Number of Penguins",
    title = "Distribution of Penguin Species near Palmer Station, Antarctica"
  )

Make a bar chart showing the number of penguins on each island by using the code above as a template:

ggplot(data = penguins, mapping = aes(x = island)) +
  geom_bar() +
  labs(
    x = "Island",
    y = "Number of Penguins",
    title = "Title Placeholder"
  )

Quantitative Variables

Now let’s make a histogram of the bill_length_mm variable. Modify this code by filling in the ______ to do so:

ggplot(penguins, mapping = aes(x = bill_length_mm)) +
  geom_histogram() +
  labs(
    x = "Penguins",
    y = "Bill Length (mm)",
    title = "Number of Penguins by Island"
  )

Make a histogram of flipper_length_mm and set the binwidth to 4. Use the code above as a template:

ggplot(penguins, mapping = aes(x = flipper_length_mm)) +
  geom_histogram(bindwith = 4) + 
  labs(
    x = "Penguins",
    y = "Flipper Length (mm)",
    title = "Flipper Length (mm) of Penguins"
  )

Warning in geom_histogram(bindwith = 4): Ignoring unknown parameters:
`bindwith`

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

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

Now make a density plot (instead of a histogram) of flipper_length_mm by using the geom_density() function:

ggplot(penguins, mapping = aes(x = flipper_length_mm)) +
  geom_density() + 
  labs(
    x = "Penguins",
    y = "Flipper Length (mm)",
    title = "Flipper Length (mm) of Penguins"
  )

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

Suppose we would like to look at how flipper_length_mm differs across species. Modify this code by filling in the ______ to do so:

ggplot(penguins, mapping = aes(x = flipper_length_mm)) +
  geom_histogram() +
  facet_wrap( ~ species) +
  labs(
    x = "Flipper Length (mm)",
    y = "Species",
    title = "Penguin Flipper Length by Species"
  )

Do different species have distinctly different flipper lengths?

Yes, different species do have different flipper lengths on average.