Car Data Analysis Project Using R

This car data analysis project will be built using R. In this project, you'll be analyzing the dataset using R programming in Google Colab. This blog will explain each step and provide the code along with the output.

In this car data analysis project, we will apply various techniques, including data cleaning, exploratory analysis, statistical visualization with ggplot2, correlation analysis, and predictive modeling. 

This project will include visualizations, provide insights about automotive performance patterns, and more.

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What Should You Know Before Starting the Project?

Before starting the car data analysis project, it’s helpful to have a few basic skills to work smoothly through the analysis and modeling process:

• Basic R Programming: You need to know how to use variables, functions, and work with data frames.
• Essential Statistics: You must know basic concepts like mean, median, and correlation to interpret data.
• Google Colab Usage: Familiarity with running code cells, uploading files, and switching to the R runtime.
• Analytical Thinking: Having the ability to solve problems and spot patterns helps a lot.
• Basic Car Knowledge: You also need to understand some terms like MPG, horsepower, and manual vs. automatic transmission.

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Tools and R Libraries Used in This Project

The tools and R libraries used in this car data analysis project are mentioned in the table below. These help in executing and running of the project smoothly.

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Project Duration, Difficulty, and Skill Level Required

The overall duration of the project is 3-5 hours. The breakdown of the timeline of the project is given below. Although that may vary depending on your skillset.

Difficulty Level
Beginner to Intermediate

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Breakdown of the Car Data Analysis Project Step-By-Step With Code

In this section, we’ll provide the breakdown of the car data analysis project with the codes for each step and the corresponding output.

Step 1: Set Up Google Colab to Use R

We need to configure Google Colab to support the R language instead of Python, as Google Colab by default runs on Python. This lets us run R scripts directly in the Colab notebook.

Here’s How You Can Do It:

1. Open a new notebook at Google Colab
2. Click on Runtime in the top menu
3. Select Change runtime type
4. From the "Language" dropdown, choose R
5. Click Save

Step 2: Install and Load Essential Libraries

In this step, we’ll install and load the essential libraries in R that’ll be used for this project. The code for this step is given below:

# Step 2: Install and load essential libraries
# Think of libraries as toolboxes – each one gives us special functions!

# Install packages (only need to do this once)
install.packages(c("tidyverse", "ggplot2", "corrplot", "plotly", "knitr", "DT"))

# Load the libraries (do this every time you start)
library(tidyverse)    # Swiss army knife for data manipulation
library(ggplot2)      # Creates beautiful charts and graphs
library(corrplot)     # Makes correlation heatmaps
library(plotly)       # Interactive visualizations
library(knitr)        # Pretty table formatting
library(DT)           # Interactive data tables

# Print success message
cat("All libraries loaded successfully! Ready to explore data!\n")

After all the libraries are installed and loaded, we’ll get the output like this:

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Step 3: Upload and Load the Dataset

Now that all the libraries are installed and loaded, it’s time for us to upload and read the dataset that we’ll work with. The code for this step is:

# Step 3: Upload your dataset to Google Colab
# Click the folder icon on the left sidebar, then upload your mtcars.csv file

# Load the dataset into R
# Think of this as opening your Excel file in R
mtcars_data <- read.csv("mtcars.csv")

# Let's take a first look at our data
cat("📊 Dataset loaded! Here's what we have:\n")
print(paste("Number of cars:", nrow(mtcars_data)))   # Prints number of rows (cars)
print(paste("Number of features:", ncol(mtcars_data)))  # Prints number of columns (features)

# Display the first few rows (like previewing a book)
head(mtcars_data)

The output of the above code will give us a glimpse of how the dataset looks like:

Also Read: Data Preprocessing in Machine Learning: 11 Key Steps You Must Know!

Step 4: Understand the Dataset Structure

Before starting the analysis, we need to understand what the data in each column means. In this step, we’ll identify the column names and build a simple data dictionary to describe them. The code for this section is given below:

# Step 4: Get to know your data – like meeting new friends!

# What columns do we have?
cat("Column names in our dataset:\n")
colnames(mtcars_data)  # Prints all column names

# What do these columns mean? Let's create a data dictionary
data_dictionary <- data.frame(
  Column = c("model", "mpg", "cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am", "gear", "carb"),
  Description = c("Car model name",
                  "Miles per gallon (fuel efficiency)",
                  "Number of cylinders",
                  "Engine displacement (cubic inches)",
                  "Horsepower",
                  "Rear axle ratio",
                  "Weight (1000 lbs)",
                  "Quarter mile time (seconds)",
                  "Engine shape (0=V-shaped, 1=straight)",
                  "Transmission (0=automatic, 1=manual)",
                  "Number of gears",
                  "Number of carburetors")
)

# Display our data dictionary
knitr::kable(data_dictionary, caption = "📖 What Each Column Means")  # Nicely formats and displays the dictionary as a table

This step gives the output that describes each column.

Step 5: Clean the Data for Analysis

In this step, we’ll organize and format the data before we begin analysis. We need to check the data for missing values and also ensure that the data types are correct. The code for cleaning the data is given below:

# Step 5: Clean our data – like organizing your room!

# Check for missing values (empty cells)
cat("Checking for missing data:\n")
missing_data <- sum(is.na(mtcars_data))  # Count total missing values
print(paste("Total missing values:", missing_data))  # Print the count

# Look at the structure of our data
str(mtcars_data)  # Shows data types and column structure

# Convert categorical variables to factors (R's way of handling categories)
mtcars_data$vs <- factor(mtcars_data$vs, labels = c("V-shaped", "Straight"))  # Engine shape as labels
mtcars_data$am <- factor(mtcars_data$am, labels = c("Automatic", "Manual"))  # Transmission type as labels
mtcars_data$cyl <- factor(mtcars_data$cyl)    # Convert cylinder count to category
mtcars_data$gear <- factor(mtcars_data$gear)  # Convert number of gears to category
mtcars_data$carb <- factor(mtcars_data$carb)  # Convert carburetors to category

cat("Data cleaning complete! Variables are properly formatted.\n")  # Confirmation message

After running this code and checking and cleaning the data, we get the output as follows:

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Step 6: Explore the Data with Basic Statistics

This step helps you have a better view of the dataset using summary statistics. You can check key metrics like fuel efficiency and horsepower to understand important insights. The code for this step is:

# Step 6: Explore our data – like being a detective!

# Basic statistics summary
cat("Basic Statistics Summary:\n")
summary(mtcars_data)  # Provides min, max, mean, and quartiles for each numeric column

# Let's look at fuel efficiency (mpg) – most important for car buyers!
cat("\n Fuel Efficiency Analysis:\n")
print(paste("Most fuel-efficient car:", mtcars_data$model[which.max(mtcars_data$mpg)],
           "with", max(mtcars_data$mpg), "mpg"))  # Finds car with highest mpg

print(paste("Least fuel-efficient car:", mtcars_data$model[which.min(mtcars_data$mpg)],
           "with", min(mtcars_data$mpg), "mpg"))  # Finds car with lowest mpg

# Let's see which cars have the most horsepower
cat("\n Power Analysis:\n")
print(paste("Most powerful car:", mtcars_data$model[which.max(mtcars_data$hp)],
           "with", max(mtcars_data$hp), "horsepower"))  # Finds car with highest horsepower

The output for this section will give us a basic summary of the dataset and also give the fuel efficiency and power analysis of the cars in the dataset.

Step 7: Visualize the Data with Charts

In this step, we’ll use the relevant data and create graphs and charts to understand the data better. The code for this step is given in the code block below:

# Step 7: Create beautiful charts – turn numbers into pictures!

# Chart 1: Fuel efficiency distribution
ggplot(mtcars_data, aes(x = mpg)) +
  geom_histogram(binwidth = 2, fill = "skyblue", color = "black", alpha = 0.7) +  # Histogram of MPG
  labs(title = "Distribution of Fuel Efficiency (MPG)",       # Main chart title
       subtitle = "How fuel-efficient are these cars?",          # Subheading
       x = "Miles per Gallon (MPG)",                             # X-axis label
       y = "Number of Cars") +                                   # Y-axis label
  theme_minimal() +                                              # Clean theme
  theme(plot.title = element_text(size = 16, face = "bold"))     # Bold title styling

# Chart 2: Horsepower vs Fuel Efficiency
ggplot(mtcars_data, aes(x = hp, y = mpg)) +
  geom_point(size = 3, alpha = 0.7, color = "red") +             # Scatterplot points
  geom_smooth(method = "lm", se = FALSE, color = "blue") +       # Linear trend line
  labs(title = "Power vs Efficiency: The Trade-off",           # Chart title
       subtitle = "Do more powerful cars use more fuel?",        # Subheading
       x = "Horsepower",                                         # X-axis label
       y = "Miles per Gallon (MPG)") +                           # Y-axis label
  theme_minimal()                                                # Clean look

# Chart 3: Transmission type comparison
ggplot(mtcars_data, aes(x = am, y = mpg, fill = am)) +
  geom_boxplot(alpha = 0.7) +                                    # Boxplot by transmission type
  labs(title = "🔧 Manual vs Automatic: Fuel Efficiency Battle",  # Chart title
       x = "Transmission Type",                                  # X-axis label
       y = "Miles per Gallon (MPG)",                             # Y-axis label
       fill = "Transmission") +                                  # Legend label
  theme_minimal() +                                              # Clean theme
  scale_fill_manual(values = c("orange", "green"))               # Custom colors for boxes

The output of this section will give us three plots. The first chart will be on the fuel efficiency, second will be on the comparison of horsepower and fuel efficiency, and the third will be on the comparison of transmission type.

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Step 8: Discover Relationships Between Variables

In this step, we will analyze how different variables in the dataset relate to each other using a correlation heatmap. This will help us identify strong patterns and dependencies, which can guide further analysis or modeling if necessary. The code for this step is given below:

# Step 8: Find relationships between variables – like finding patterns!

# Select only numeric columns for correlation
numeric_data <- mtcars_data %>%
  select_if(is.numeric) %>%               # Keep only numeric columns
  select(-matches("model"))               # Remove the model column if it's present

# Create correlation matrix
correlation_matrix <- cor(numeric_data)   # Compute pairwise correlations

# Visualize correlations with a heatmap
corrplot(correlation_matrix,
         method = "color",                # Use color blocks to show strength
         type = "upper",                  # Show only the upper triangle
         order = "hclust",                # Cluster similar variables together
         tl.cex = 0.8,                    # Size of variable labels
         tl.col = "black",                # Label color
         title = "Correlation Heatmap: How Variables Relate")  # Chart title

# Find strongest correlations
cat("🔗 Strongest Relationships:\n")

# Convert correlation matrix to find top correlations
cor_pairs <- which(abs(correlation_matrix) > 0.7 & correlation_matrix != 1, arr.ind = TRUE)  # Filter strong correlations

# Loop through and print variable pairs with high correlation
for(i in 1:nrow(cor_pairs)) {
  row_var <- rownames(correlation_matrix)[cor_pairs[i,1]]
  col_var <- colnames(correlation_matrix)[cor_pairs[i,2]]
  cor_value <- round(correlation_matrix[cor_pairs[i,1], cor_pairs[i,2]], 3)
  print(paste(row_var, "and", col_var, "correlation:", cor_value))  # Print each strong correlation pair
}

The output of this section shows the heatmap of the various correlations

The above graph can be interpreted as:

• Fuel efficiency (mpg) drops as horsepower, engine size, and weight increase (strong negative correlation – dark red).
• Powerful cars tend to be heavier and have bigger engines (strong positive correlation – dark blue).
• Quarter-mile time (qsec) is slower in fuel-efficient cars and faster in powerful ones.

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Step 9: Perform Deeper Grouped Analysis

In this step, we’ll group cars based on the number of cylinders. This helps understand how engine size affects fuel efficiency, horsepower, and weight. We’ll also plot the MPG distribution across these groups. The code for this step is:

# Step 9: Dig deeper – advanced insights!

# Group analysis by number of cylinders
cylinder_analysis <- mtcars_data %>%
  group_by(cyl) %>%                                 # Group data by cylinder count
  summarise(
    count = n(),                                    # Number of cars in each group
    avg_mpg = round(mean(mpg), 2),                  # Average miles per gallon
    avg_hp = round(mean(hp), 2),                    # Average horsepower
    avg_weight = round(mean(wt), 2),                # Average weight
    .groups = 'drop'                                # Drop grouping structure
  )

# Display summary table
cat("Analysis by Number of Cylinders:\n")
knitr::kable(cylinder_analysis, caption = "Performance by Engine Size")  # Nicely format the table

# Create a comprehensive comparison chart
ggplot(mtcars_data, aes(x = cyl, y = mpg, fill = cyl)) +
  geom_violin(alpha = 0.7) +                        # Violin plot shows MPG distribution shape
  geom_boxplot(width = 0.2, alpha = 0.8) +          # Boxplot adds summary stats (median, quartiles)
  labs(title = "Fuel Efficiency by Engine Size", # Chart title
       subtitle = "Distribution of MPG across different cylinder counts",  # Subheading
       x = "Number of Cylinders",                   # X-axis label
       y = "Miles per Gallon (MPG)") +              # Y-axis label
  theme_minimal()                                   # Clean layout

The output for this step is as follows:

The above plot shows that the bigger the engine size, the lower the fuel efficiency.

Step 10: Predict Fuel Efficiency with a Simple Model

Here we’ll build a linear regression model to predict MPG using weight (wt) and horsepower (hp), inspect how well it fits, generate predictions, and compare them to the actual values. The code for this step is as follows:

# Step 10: Predict fuel efficiency - become a fortune teller!

# Create a simple linear model to predict MPG based on weight and horsepower
model <- lm(mpg ~ wt + hp, data = mtcars_data)      # Fit a linear regression model

# Model summary
cat("Fuel Efficiency Prediction Model:\n")
summary(model)                                      # View coefficients, p-values, R², etc.

# Make predictions for our existing cars
mtcars_data$predicted_mpg <- predict(model)         # Add predicted MPG as a new column

# Compare actual vs predicted
comparison <- mtcars_data %>%
  select(model, mpg, predicted_mpg) %>%             # Keep only relevant columns
  mutate(
    predicted_mpg = round(predicted_mpg, 2),        # Round predictions for readability
    difference = round(mpg - predicted_mpg, 2)      # Positive means model underestimated MPG
  )

cat("\n Actual vs Predicted MPG (first 10 cars):\n")
head(comparison, 10) %>% knitr::kable()             # Display first 10 comparisons as a neat table

The output for this code will give us a table of the comparison of fuel efficiency relative to weight and horsepower. The output is:

• The model predicts MPG using weight and horsepower — heavier and more powerful cars generally have lower fuel efficiency.
• It explains about 83% of the variation in MPG, which means it's a strong and useful model.
• Predictions are mostly close to actual values, with small differences (1–3 MPG) for most cars.

Conclusion

In this Car Data Analysis project, we built a simple linear regression model using R in Google Colab to predict fuel efficiency (MPG) based on car weight and horsepower.

After uploading and cleaning the classic mtcars dataset, we created statistical summaries, visualized trends, and examined correlations between variables. The final model explained approximately 83% of the variation in MPG, with a residual standard error of 2.59, showing excellent predictive performance.

This car data analysis project involved key concepts like data wrangling, visualization, correlation analysis, and predictive modeling using automotive data.

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Colab Link:
https://colab.research.google.com/drive/18HvmvopmAZOMC4O8cO3j3pPuSNiSZCrA#scrollTo=0A1dQbc1m-2f