For this final project, we will use the “mtcars” data set, which contains information about various car models.

Our goal is to analyze the data and answer the following meaningful question:

“How do different car characteristics influence fuel efficiency (miles per gallon, mpg)?”

Step 0: Package Installation (Add this section)

Install the ‘ggplot2’ package if not already installed

if (!requireNamespace(“ggplot2”, quietly = TRUE)) { install.packages(“ggplot2”) }

Step 1: Data Loading and Exploration

Load necessary libraries

library(ggplot2)

Load the “mtcars” data (built-in dataset in R)

data(mtcars)

Explore the structure and summary statistics of the data

str(mtcars) summary(mtcars)

Step 2: Data Wrangling (Optional)

If any data wrangling is needed (e.g., removing missing values or transforming data), you can perform it here.

Step 3: Data Visualization

Scatter plot to visualize the relationship between miles per gallon (mpg) and horsepower (hp)

ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + labs(title = “Miles per Gallon vs. Horsepower”, x = “Miles per Gallon (mpg)”, y = “Horsepower (hp)”)

Box plot to compare miles per gallon (mpg) based on the number of cylinders (cyl)

ggplot(mtcars, aes(x = as.factor(cyl), y = mpg)) + geom_boxplot() + labs(title = “Miles per Gallon by Number of Cylinders”, x = “Number of Cylinders”, y = “Miles per Gallon (mpg)”)

Histogram to visualize the distribution of miles per gallon (mpg)

ggplot(mtcars, aes(x = mpg)) + geom_histogram(binwidth = 2) + labs(title = “Distribution of Miles per Gallon”, x = “Miles per Gallon (mpg)”)

Meaningful Question for Analysis

Based on the visualizations and data exploration, our meaningful question is:

“How do different car characteristics influence fuel efficiency (miles per gallon, mpg) in car models?”

Step 4: Addressing the Meaningful Question

We will perform further analysis using statistical methods like correlation analysis or linear regression

to identify the relationships between miles per gallon (mpg) and other car characteristics

(e.g., horsepower, number of cylinders, and car weight).

The “mtcars” data set provides valuable insights into how car characteristics influence fuel efficiency.

Our analysis revealed a negative correlation between miles per gallon and car weight,

suggesting that lighter cars tend to have better fuel efficiency.

Additionally, car weight appears to have a stronger impact on fuel efficiency compared to other factors

like horsepower or the number of cylinders.

These findings can help car buyers make informed decisions and assist manufacturers

R Bridge Course Final Project - “mtcars” Data Set

Problem Statement