A real-world data analysis project allows you to apply all the skills you have learned in R\u2014from data import to visualization, modeling, and reporting. This case study demonstrates a complete workflow to solve a business problem using R.<\/p>\n\n\n\n
Suppose a retail company wants to analyze customer purchase behavior to improve sales and target marketing campaigns. The objective is to:<\/p>\n\n\n\n
The dataset contains customer information, purchase history, and product details.<\/p>\n\n\n\n
library(readr)
# Import CSV data
retail_data <- read_csv(\"customer_purchases.csv\")# Inspect data
head(retail_data)
str(retail_data)
summary(retail_data)<\/pre>\n\n\n\n3. Data Cleaning<\/strong><\/h1>\n\n\n\n
Cleaning ensures accuracy and consistency:<\/p>\n\n\n\n
library(dplyr)# Remove duplicates
retail_data <- retail_data %>% distinct()# Handle missing values
retail_data$Age[is.na(retail_data$Age)] <- median(retail_data$Age, na.rm = TRUE)# Rename columns for clarity
retail_data <- retail_data %>% rename(CustomerID = ID, PurchaseAmt = Amount)<\/pre>\n\n\n\n4. Exploratory Data Analysis (EDA)<\/strong><\/h1>\n\n\n\n
Explore the data to identify patterns and insights:<\/p>\n\n\n\n
library(ggplot2)# Age distribution
ggplot(retail_data, aes(x=Age)) +
geom_histogram(binwidth=5, fill=\"skyblue\", color=\"black\") +
ggtitle(\"Customer Age Distribution\")# Purchase amount by Gender
ggplot(retail_data, aes(x=Gender, y=PurchaseAmt, fill=Gender)) +
geom_boxplot() +
ggtitle(\"Purchase Amount by Gender\")# Correlation between Age and Purchase Amount
cor(retail_data$Age, retail_data$PurchaseAmt)<\/pre>\n\n\n\n5. Data Transformation<\/strong><\/h1>\n\n\n\n
Prepare data for analysis and modeling:<\/p>\n\n\n\n
library(tidyr)# Create AgeGroup variable
retail_data <- retail_data %>%
mutate(AgeGroup = case_when(
Age < 30 ~ \"Young\",
Age >= 30 & Age < 50 ~ \"Adult\",
TRUE ~ \"Senior\"
))# Summarize purchases by AgeGroup
summary_by_age <- retail_data %>%
group_by(AgeGroup) %>%
summarise(TotalPurchase = sum(PurchaseAmt),
AvgPurchase = mean(PurchaseAmt),
Count = n())<\/pre>\n\n\n\n6. Modeling and Analysis<\/strong><\/h1>\n\n\n\n
a) Identifying High-Value Customers<\/strong><\/h3>\n\n\n\n
# Flag customers with purchases above threshold
retail_data <- retail_data %>%
mutate(HighValue = ifelse(PurchaseAmt > 1000, \"Yes\", \"No\"))# Count high-value customers
table(retail_data$HighValue)<\/pre>\n\n\n\nb) Regression Analysis (Predicting Purchase Amount)<\/strong><\/h3>\n\n\n\n
# Simple linear regression using Age and Gender
model <- lm(PurchaseAmt ~ Age + Gender, data = retail_data)
summary(model)<\/pre>\n\n\n\n7. Visualization and Reporting<\/strong><\/h1>\n\n\n\n
Communicate findings visually:<\/p>\n\n\n\n
# 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\")<\/pre>\n\n\n\n8. Key Insights<\/strong><\/h1>\n\n\n\n
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- Most purchases are made by adults aged 30\u201350<\/li>\n\n\n\n
- Male customers tend to have slightly higher purchase amounts<\/li>\n\n\n\n
- High-value customers represent a small percentage but contribute significantly to revenue<\/li>\n\n\n\n
- Age and gender can partially explain purchase behavior<\/li>\n<\/ul>\n\n\n\n
9. Conclusion and Recommendations<\/strong><\/h1>\n\n\n\n
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- Target marketing campaigns toward high-value customers and adults aged 30\u201350<\/li>\n\n\n\n
- Consider personalized promotions for high-purchase segments<\/li>\n\n\n\n
- Use regression models to predict future purchase behavior<\/li>\n\n\n\n
- Continuously monitor and clean data to maintain accuracy<\/li>\n<\/ul>\n\n\n\n
10. Advantages of a Complete Case Study<\/strong><\/h1>\n\n\n\n
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- Integrates all R skills: data import, cleaning, EDA, visualization, and modeling<\/li>\n\n\n\n
- Provides hands-on experience with real-world business problems<\/li>\n\n\n\n
- Demonstrates how insights from data can guide decision-making<\/li>\n\n\n\n
- Enhances portfolio for analytics projects and professional growth<\/li>\n<\/ul>\n\n\n\n
This case study shows the end-to-end workflow of a data analysis project in R, providing practical exposure to solving real business problems.<\/p>\n\n\n\n
<\/strong><\/p> <\/div> <\/div>\n\n\n