This project showcases an end-to-end analysis of a retail sales dataset using R. The workflow began with data cleaning and transformation using the janitor::clean_names() function for consistent column formatting. Key steps included: Data Cleaning: Handled missing values, removed duplicates, and dropped irrelevant columns. Feature Engineering: Created new variables such as profit per transaction, profit margin, and customer age groups for deeper insight. Exploratory Data Analysis (EDA): Performed statistical and visual analysis to understand cost and revenue patterns, customer demographics, and product performance. Visual Insights: Utilized ggplot2 to highlight trends in revenue across time, customer segments, product categories, and countries. Correlation Analysis: Explored relationships between numerical variables using a correlation matrix and heatmap. The analysis reveals key business insights such as revenue drivers, customer behavior trends, and product performance patterns, all using tidyverse tools in R.

This report explores two core datasets: one focused on educational attainment and income in the United States, and the other on global annual mean temperatures. Using R, I apply data wrangling, custom function creation, visualization, and classification techniques to uncover trends and insights across both domains.

For Google Data Analytics Case Study 1

Exercises and bits of code that I found useful from the book