Data Manipulation and Cleaning in R

Overview

Transform raw data into valuable insights using R's powerful tidyverse tools. This beginner-friendly course introduces you to essential data cleaning and manipulation techniques, making complex data tasks approachable and practical. Learn how to clean messy data, handle missing values, and prepare datasets for analysis using Microsoft's development environment and AI assistance. Through hands-on practice, you'll master fundamental data cleaning skills while building confidence in: - Organizing and structuring data effectively - Handling common data issues - Working with different data formats - Using AI tools to enhance your workflow - Creating reproducible data cleaning processes Each concept is taught step-by-step with extensive examples and guided practice, ensuring you build a strong foundation in data manipulation skills.

Syllabus

Introduction to Data Manipulation with dplyr

In this module, you'll get hands-on experience with dplyr, the powerhouse package for data manipulation in R. We'll work with real retail sales data as you learn to filter, arrange, and transform your data with ease. By the end of this module, you'll be confidently writing clean, efficient code using the pipe operator and essential dplyr functions that professional data analysts use daily.

Reshaping Data with tidyr

Data rarely comes in the perfect format we need - and that's exactly what we'll tackle in this module. Using tidyr, you'll learn to reshape data like a pro, converting between wide and long formats, and handling complex data structures. Through practical exercises with regional sales data, you'll master the tools needed to transform messy data into clean, analysis-ready formats.

String Manipulation with stringr

Text data can be particularly challenging. In this module, you'll work with stringr to clean and standardize text data effectively. Using real product descriptions and customer data, you'll learn pattern matching and advanced string manipulation techniques that make text data cleaning a breeze. You'll see how combining stringr with dplyr creates robust solutions for complex data cleaning challenges.

Handling Missing Values and Duplicates

In this module, you'll learn approaches to handling missing values, outliers, and duplicates. Working with actual order and inventory data, you'll develop strategies for maintaining data quality. You'll discover how modern AI tools can help automate your cleaning processes, making your work more efficient and consistent.

Final Project

The comprehensive project simulates a real-world data cleaning scenario where you'll act as a data specialist tasked with standardizing a critical organizational dataset. You'll apply all the key skills learned throughout the course in a structured, step-by-step approach.