The purpose of this short code-through is to provide a simple and friendly tour of the ‘diamonds’ dataset in R. The dataset comes from the ggplot2 package and contains information on nearly 54,000 diamonds, including their price, weight (carat), color, clarity, and cut.

my Data

This Code-Through tutorial demonstrates how to clean, manipulate, and explore a dataset in R using the dplyr package. The goal is to provide a simple, step-by-step walkthrough that a beginner can follow to understand the essential data-wrangling workflow. Using a small student performance dataset, this tutorial explains how to: Inspect and understand raw data Use dplyr verbs such as filter(), mutate(), select(), and arrange() Create new variables to support analysis Clean incomplete or inconsistent observations Summarize results Visualize the cleaned dataset using ggplot2 By the end, readers should feel confident performing basic data cleaning operations in R and understanding why each step matters.

This is the Final Project for the DSA515 course discussing the impact of Free Throw Attempts and Free Throw Percentage on player performance and Team Wins