A real-world data analysis project allows you to apply all the skills you have learned in R—from data import to visualization, modeling, and reporting. This case study demonstrates a complete workflow to solve a business problem using R.

1. Project Objective

Suppose a retail company wants to analyze customer purchase behavior to improve sales and target marketing campaigns. The objective is to:

• Understand customer demographics and purchase patterns
• Identify high-value customers
• Visualize trends and relationships
• Provide actionable insights for business decisions

2. Data Collection and Import

The dataset contains customer information, purchase history, and product details.

library(readr)
# Import CSV data
retail_data <- read_csv("customer_purchases.csv")# Inspect data
head(retail_data)
str(retail_data)
summary(retail_data)

3. Data Cleaning

Cleaning ensures accuracy and consistency:

library(dplyr)# Remove duplicates
retail_data <- retail_data %>% distinct()# Handle missing values
retail_data$Age[is.na(retail_data$Age)] <- median(retail_data$Age, na.rm = TRUE)# Rename columns for clarity
retail_data <- retail_data %>% rename(CustomerID = ID, PurchaseAmt = Amount)

4. Exploratory Data Analysis (EDA)

Explore the data to identify patterns and insights:

library(ggplot2)# Age distribution
ggplot(retail_data, aes(x=Age)) +
  geom_histogram(binwidth=5, fill="skyblue", color="black") +
  ggtitle("Customer Age Distribution")# Purchase amount by Gender
ggplot(retail_data, aes(x=Gender, y=PurchaseAmt, fill=Gender)) +
  geom_boxplot() +
  ggtitle("Purchase Amount by Gender")# Correlation between Age and Purchase Amount
cor(retail_data$Age, retail_data$PurchaseAmt)

5. Data Transformation

Prepare data for analysis and modeling:

library(tidyr)# Create AgeGroup variable
retail_data <- retail_data %>%
  mutate(AgeGroup = case_when(
    Age < 30 ~ "Young",
    Age >= 30 & Age < 50 ~ "Adult",
    TRUE ~ "Senior"
  ))# Summarize purchases by AgeGroup
summary_by_age <- retail_data %>%
  group_by(AgeGroup) %>%
  summarise(TotalPurchase = sum(PurchaseAmt),
            AvgPurchase = mean(PurchaseAmt),
            Count = n())

6. Modeling and Analysis

a) Identifying High-Value Customers

# Flag customers with purchases above threshold
retail_data <- retail_data %>%
  mutate(HighValue = ifelse(PurchaseAmt > 1000, "Yes", "No"))# Count high-value customers
table(retail_data$HighValue)

b) Regression Analysis (Predicting Purchase Amount)

# Simple linear regression using Age and Gender
model <- lm(PurchaseAmt ~ Age + Gender, data = retail_data)
summary(model)

7. Visualization and Reporting

Communicate findings visually:

# Scatter plot with regression line
ggplot(retail_data, aes(x=Age, y=PurchaseAmt, color=Gender)) +
  geom_point() +
  geom_smooth(method="lm", se=FALSE) +
  ggtitle("Age vs Purchase Amount by Gender")# Pie chart for high-value customers
high_value_table <- table(retail_data$HighValue)
pie(high_value_table, labels = names(high_value_table), main = "High-Value Customers")

8. Key Insights

• Most purchases are made by adults aged 30–50
• Male customers tend to have slightly higher purchase amounts
• High-value customers represent a small percentage but contribute significantly to revenue
• Age and gender can partially explain purchase behavior

9. Conclusion and Recommendations

• Target marketing campaigns toward high-value customers and adults aged 30–50
• Consider personalized promotions for high-purchase segments
• Use regression models to predict future purchase behavior
• Continuously monitor and clean data to maintain accuracy

10. Advantages of a Complete Case Study

• Integrates all R skills: data import, cleaning, EDA, visualization, and modeling
• Provides hands-on experience with real-world business problems
• Demonstrates how insights from data can guide decision-making
• Enhances portfolio for analytics projects and professional growth

This case study shows the end-to-end workflow of a data analysis project in R, providing practical exposure to solving real business problems.