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.

This project analyzes U.S. small-denomination time deposits using classical models and machine learning methods. It evaluates forecast accuracy, volatility behavior, and the impact of monetary policy, showing that flexible models like XGBoost adapt best to post-2022 structural changes.

Visualisasi data penjualan supermarket yang telah di bersihkan menggunakan RStudio