This is our final dashboard for NBA statistics

This publication presents a spatial analysis of median home-value growth from 2000 to 2012. Using R mapping tools, the project reveals how housing appreciation varies across clusters of census tracts and identifies areas experiencing rapid change.

This code-through tutorial demonstrates how to clean, transform, and summarize data in R using the dplyr package. The goal is to provide a clear, beginner-friendly walkthrough of the core functions used in data wrangling. Using the built-in mtcars dataset, we will walk through filtering rows, selecting variables, creating new columns, sorting data, computing grouped summaries, and visualizing the results. These steps represent a typical workflow that analysts follow when preparing datasets for deeper statistical analysis or modeling. By the end of the tutorial, a new R user should feel confident applying these techniques to their own data. Using the built-in mtcars dataset, this tutorial will show how to: Load data into R Filter and select variables Create new variables using mutate() Sort observations with arrange() Group data and compute summaries Produce a basic plot to visualize results These functions represent the core workflow for data wrangling in R. By the end, a new user should be able to understand how to apply these techniques to any dataset in their own projects.