Project overview
In this project, you’ll step into the role of a data analyst at a book company to evaluate the impact of a new program launched on July 1, 2019 aimed at encouraging customers to buy more books. Your challenge is to analyze the company’s 2019 sales data to determine if the program successfully increased book purchases and improved review quality.
You’ll leverage your data analysis skills in R, using powerful packages like dplyr, stringr, and lubridate to clean and process the sales data. This will involve handling missing values, converting text reviews into positive/negative sentiment, and comparing key sales and review metrics before and after the program start date. Through hands-on practice, you’ll gain experience efficiently analyzing a real-world business dataset to deliver actionable insights.
Objective: Analyze book sales data using R to determine if a newly launched program led to an increase in purchases and improvement in review sentiment.
Key skill required
To complete this project, it's recommended to build these foundational skills in R
- Manipulating strings in R using stringr functions
- Working with dates and times in R using lubridate
- Applying the map function to vectorize custom functions
- Understanding and employing regular expressions for pattern matching
Projects steps
Step 1: Introduction
Step 2: Data Exploration
Step 3: Handling Missing Data
Step 4: Processing Review Data
Step 5: Comparing Book Sales Between Pre- and Post-Program Sales
Step 6: Comparing Book Sales Within Customer Type
Step 7: Comparing Review Sentiment Between Pre- and Post-Program Sales
Step 8: Further Steps
Step 9: Next Steps
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