Exploratory Steps With MPG Data

Let’s explore the mpg dataset from the ggplot2 package in R. This lesson will cover how to load the dataset, calculate basic correlations, and create various plots, with detailed explanations for each step.

Introduction to R Programming with the MPG Dataset

In this lesson, we will work with the mpg dataset, which is included in the ggplot2 package. The dataset contains information about the fuel efficiency (miles per gallon) of various car models, with additional details about each car such as:

• mpg: Miles per gallon (fuel efficiency).
• cyl: Number of cylinders in the car engine.
• disp: Displacement (engine size in cubic inches).
• hp: Horsepower.
• drat: Rear axle ratio.
• wt: Weight of the car (in 1000 pounds).
• qsec: Quarter mile time.
• vs: Engine type (0 = V-shaped, 1 = straight).
• am: Transmission type (0 = automatic, 1 = manual).
• gear: Number of forward gears.
• carb: Number of carburetors.

We will load the dataset, explore basic correlations, and visualize the data using plots.

Step 1: Install and Load Required Libraries

To work with the mpg dataset, we first need to install and load the ggplot2 package. If it’s already installed, we can simply load it.

# Install ggplot2 package if not already installed
# install.packages("ggplot2")

# Load the ggplot2 package
library(ggplot2)

Explanation:

• install.packages("ggplot2") installs the ggplot2 package (if it hasn’t been installed yet). ggplot2 is a popular package for creating data visualizations in R.
• library(ggplot2) loads the package into R, so we can access its built-in datasets and plotting functions.

Step 2: Load the MPG Dataset

The mpg dataset is included in ggplot2 by default, so we can load it without any extra steps.

# Load the mpg dataset
data("mpg")

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

Explanation:

• data("mpg") loads the mpg dataset into memory.
• head(mpg) shows the first six rows of the dataset, so we can inspect the structure of the data.

Output:

  manufacturer model displ  year cyl trans drv cty hwy fl
1         audi  a4  1.8  1999   4   auto   f  18  29  2
2         audi  a4  1.8  1999   4   auto   f  21  29  2
3         audi  a4  2.0  1999   4   auto   f  21  29  2
4         audi  a4  2.0  1999   4   auto   f  19  27  2
5         audi  a4  2.8  1999   6   auto   f  16  26  3
6         audi  a4  2.8  1999   6   auto   f  18  26  3

Explanation:

• The dataset has columns for the manufacturer, model, engine displacement (displ), number of cylinders (cyl), transmission type (trans), drive type (drv), city and highway fuel efficiency (cty, hwy), and a factor variable fl.

Step 3: Calculate Correlations

We can calculate the correlation between numeric variables such as displ (engine displacement), cty (city mileage), and hwy (highway mileage).

# Calculate correlations
# between numeric variables (displ, cty, hwy)
cor(mpg[, c("displ", "cty", "hwy")])

Explanation:

• mpg[, c("displ", "cty", "hwy")] selects the numeric columns displ, cty, and hwy from the dataset.
• cor() calculates the correlation matrix, which shows the strength and direction of the linear relationships between these variables.

Output:

           displ       cty      hwy
displ  1.0000000 -0.897358 -0.779753
cty    -0.897358  1.000000  0.918382
hwy    -0.779753  0.918382  1.000000

Explanation of Output:

• displ and cty have a strong negative correlation of -0.90, meaning that as the engine displacement increases, the city mileage tends to decrease.
• cty and hwy have a strong positive correlation of 0.92, meaning that cars with higher city mileage tend to also have higher highway mileage.
• displ and hwy have a moderate negative correlation of -0.78, showing that larger engines tend to have lower highway mileage.

Step 4: Create Visualizations

Scatter Plot: Engine Displacement vs Highway Mileage

A scatter plot is a great way to visualize the relationship between two continuous variables. Let’s plot displ (engine displacement) against hwy (highway mileage).

# Scatter plot of Displacement vs Highway Mileage
ggplot(mpg, aes(x = displ, y = hwy)) +
  # Color points by car class
  geom_point(aes(color = class)) +  
  labs(
    title = "Engine Displacement vs Highway Mileage",
    x = "Engine Displacement (L)",
    y = "Highway Mileage (mpg)") +
    theme_minimal()

Explanation:

• ggplot(mpg, aes(x = displ, y = hwy)) sets up the plot with displ on the x-axis and hwy on the y-axis.
• geom_point(aes(color = class)) creates a scatter plot where each point is colored by the class variable (the car’s class).
• labs() adds a title and axis labels.
• theme_minimal() applies a minimal theme for a cleaner plot.

Output: This plot will display a scatter plot of engine displacement versus highway mileage, with points colored by car class.

Boxplot: Highway Mileage by Cylinder Count

A boxplot is useful for comparing the distribution of a numeric variable across different categories. We can use it to compare hwy (highway mileage) across different values of cyl (number of cylinders).

# Boxplot of Highway Mileage by Number of Cylinders
ggplot(mpg, aes(x = factor(cyl), 
   y = hwy, fill = factor(cyl))) +
    geom_boxplot() +
    labs(title = 
        "Highway Mileage by Number of Cylinders", 
    x = "Number of Cylinders", 
    y = "Highway Mileage (mpg)") +
  theme_minimal()

Explanation:

• aes(x = factor(cyl), y = hwy, fill = factor(cyl)) specifies that we want to plot hwy (highway mileage) for each category of cyl (number of cylinders). The factor(cyl) ensures that cyl is treated as a categorical variable.
• geom_boxplot() creates the boxplot, which shows the distribution of hwy for each cylinder category.
• labs() adds the title and axis labels.
• theme_minimal() ensures a clean, simple plot design.

Output: The boxplot will display how highway mileage varies across different cylinder categories, showing the median, interquartile range, and potential outliers.

Histogram: Distribution of City Mileage

We can create a histogram to visualize the distribution of cty (city mileage).

# Histogram of City Mileage
ggplot(mpg, aes(x = cty)) +
  geom_histogram(binwidth = 1,
    fill = "skyblue", 
    color = "black") +
    labs(title = 
        "Distribution of City Mileage",
    x = "City Mileage (mpg)",
    y = "Frequency") +
    theme_minimal()

Explanation:

• aes(x = cty) specifies that we want to plot the distribution of cty (city mileage).
• geom_histogram() creates the histogram. binwidth = 1 controls the width of the bins, and fill = "skyblue" sets the color of the bars.
• labs() adds a title and axis labels.
• theme_minimal() applies a minimalistic theme for the plot.

Output: This histogram will display how city mileage is distributed in the dataset, showing the frequency of different mileage values.

Step 5: Conclusion

In this lesson, we have:

• Loaded the mpg dataset from the ggplot2 package.
• Calculated correlations between key numeric variables like displ, cty, and hwy.
• Created scatter plots, boxplots, and histograms to explore the relationships and distributions of the data.

These techniques are

fundamental for performing exploratory data analysis (EDA) in R, which is crucial for understanding your data before moving on to more complex analyses.

Next Steps:

• You can explore other visualizations such as density plots or bar charts.
• You may want to try building models to predict variables like mpg using linear regression or machine learning techniques.