HDS 3.1-3.2

library(palmerpenguins)
library(tidyverse)

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 = "Penguins by Island",
    y = "Number of Penguins",
    title = "Distribution of Penguin Species near Palmer Station, Antarctica"
  )

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(data = penguins, mapping = aes(x = bill_length_mm)) +
  geom_histogram() +
  labs(
    x = "Penguin Bill length (mm)",
    y = "Number of Penguins",
    title = "Distrbution of Penguins Species near Palmer Station, Antarctica sorted by bill length"
  )

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

ggplot(data = penguins, mapping = aes(x = flipper_length_mm)) +
  geom_histogram(binwidth = 4) +
  labs(
    x = "Penguin Flipper Length (mm)",
    y = "Number of Penguins",
    title = "Distrbution of Penguins Species near Palmer Station, Antarctica sorted by Flipper Length"
  )

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(data = penguins, mapping = aes(x = flipper_length_mm)) +
  geom_density() +
  labs(
    x = "Penguin Flipper Length (mm)",
    y = "Number of Penguins",
    title = "Distrbution of Penguins Species near Palmer Station, Antarctica sorted by Flipper Length"
  )

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(data = penguins, mapping = aes(x = flipper_length_mm)) +
  geom_histogram() +
  facet_wrap( ~ species) +
  labs(
    x = "Flipper Length (mm)",
    y = "Number of Penguins",
    title = "Distribution of Penguins Species near Palmer Station, Antarctica sorted by Flipper Length and Species"
  )

Do different species have distinctly different flipper lengths?

Yes, the Adelie plot shows that the average flipper length is under 200 mm, whereas the chinstrap species is similar to the Adelie with average flipper length but has way less data. The Gento species looks to have an average flipper length close to 220 mm .