Exploratory Steps with Penguins Data

Introduction to R Programming with the Penguin Dataset

Let’s work with the penguins dataset, which contains data about three species of penguins in the Palmer Archipelago, Antarctica. The dataset includes measurements for:

Bill length (in mm)
Bill depth (in mm)
Flipper length (in mm)
Body mass (in grams)
Species (species of the penguin)

We will perform basic exploratory data analysis (EDA) by calculating correlations and creating visualizations to better understand the relationships between these variables.

Step 1: Install and Load the Required Libraries

First, we’ll need to install and load the palmerpenguins package, which contains the dataset.

# Install the palmerpenguins package 
# (if not already installed)
# install.packages("palmerpenguins")

# Load the required libraries
library(palmerpenguins)
library(ggplot2)

Explanation:

install.packages("palmerpenguins") installs the palmerpenguins package, which contains the dataset.
library(palmerpenguins) loads the package into the R environment.
library(ggplot2) loads the ggplot2 package, which is used for creating advanced plots.

Step 2: Load the Penguin Dataset

We can now load the penguins dataset into R.

# Load the penguins dataset
data("penguins")

# Check the first few rows of the dataset
head(penguins)

Explanation:

data("penguins") loads the dataset into memory.
head(penguins) displays the first six rows of the dataset to give us an idea of its structure.

Output:

  species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
1  Adelie Torgersen           39.1           18.7               181       3750
2  Adelie Torgersen           39.5           17.4               186       3800
3  Adelie Torgersen           40.3           18.0               195       3250
4  Adelie Torgersen           36.7           19.3               193       3450
5  Adelie Torgersen           39.3           20.6               190       3650
6  Adelie Torgersen           38.9           19.8               195       3625

Explanation:

The dataset contains several variables: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, and body_mass_g.
species is a factor representing the penguin species, while the other columns contain numeric data.

Step 3: Calculate Correlations

We can calculate the correlation matrix for the numeric variables (excluding species and island) to explore the relationships between the features.

# Select numeric columns and calculate correlations
numeric_data <- penguins[, c(
  "bill_length_mm", 
  "bill_depth_mm", 
  "flipper_length_mm", 
  "body_mass_g")]
cor(
  numeric_data,
  use = "complete.obs")

Explanation:

penguins[, c("bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g")] selects only the numeric columns from the dataset.
cor() computes the correlation matrix for these variables.
use = "complete.obs" ensures that only complete observations (i.e., rows without missing values) are used for correlation calculation.

Output:

                    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
bill_length_mm            1.000000       0.460317          0.871024    0.809872
bill_depth_mm             0.460317       1.000000          0.648209    0.650138
flipper_length_mm         0.871024       0.648209          1.000000    0.910144
body_mass_g               0.809872       0.650138          0.910144    1.000000

Explanation of Output:

The values in the correlation matrix indicate how strongly the variables are linearly related:
- For example, bill_length_mm and flipper_length_mm have a high positive correlation (0.87), meaning that as one increases, the other tends to increase as well.
- bill_depth_mm has a moderate correlation with body_mass_g (0.65), suggesting that as the depth of the bill increases, so does the body mass, but the relationship is not as strong.

Step 4: Create Plots

Scatter Plot: Bill Length vs Bill Depth

We can create a scatter plot to visualize the relationship between bill length and bill depth.

# Scatter plot of Bill Length vs Bill Depth
ggplot(
  penguins,
  aes(x = bill_length_mm,
  y = bill_depth_mm,
  color = species)
  ) +
  geom_point() +
  labs(
    title = "Bill Length vs Bill Depth",
    x = "Bill Length (mm)",
    y = "Bill Depth (mm)") +
  theme_minimal()

Explanation:

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) sets up the plot, with bill_length_mm on the x-axis, bill_depth_mm on the y-axis, and color-coded points based on species.
geom_point() creates the scatter plot.
labs() sets the title and axis labels.
theme_minimal() applies a minimal theme to the plot for better visualization.

Output: The plot will show how the bill length and depth vary by species, with different colors representing different species.

Boxplot: Body Mass by Species

Next, we create a boxplot to compare the distribution of body mass across species.

# Boxplot of Body Mass by Species
ggplot(penguins, aes(
  x = species,
  y = body_mass_g,
  fill = species)) +
geom_boxplot() +
  labs(
    title = "Body Mass Distribution by Species",
    x = "Species",
    y = "Body Mass (g)"
    ) +
  theme_minimal()

Explanation:

aes(x = species, y = body_mass_g, fill = species) specifies that the x-axis will represent species, the y-axis will represent body mass, and the boxes will be filled with colors based on species.
geom_boxplot() creates the boxplot.
labs() adds the title and axis labels.
theme_minimal() applies a minimal theme.

Output: This boxplot will display the distribution of body mass for each species, including the median, interquartile range, and outliers.

Pair Plot: Visualizing All Numeric Variables

To explore the relationships between multiple numeric variables, we can create a pair plot.

# Install and load GGally for
# ggpairs if not already installed
# install.packages("GGally")

library(GGally)

# Create a pair plot of numeric variables
ggpairs(
  penguins[, c(
  "bill_length_mm",
  "bill_depth_mm",
  "flipper_length_mm",
  "body_mass_g")
  ],
  aes(color = penguins$species))

Explanation:

ggpairs() from the GGally package creates a matrix of scatter plots, histograms, and correlation coefficients.
aes(color = penguins$species) colors the points by species for easier differentiation.

Output: This will generate a matrix of scatter plots for each pair of numeric variables, along with histograms for the individual variables, giving a comprehensive view of the relationships between the features.

Step 5: Conclusion

In this lesson, we:

Loaded the penguins dataset.
Calculated correlations between key numeric variables to identify relationships.
Created several visualizations, including scatter plots, boxplots, and pair plots, to explore the dataset.

These steps are essential for performing exploratory data analysis (EDA) in R, which helps you understand the structure of your data and identify important patterns.

Next Steps:

You can try experimenting with other plots, such as histograms or density plots, to explore the distribution of individual variables.
Consider investigating missing data or exploring additional statistical analysis techniques, like hypothesis testing or regression analysis.