Data Wrangling and Transformation with Tidyverse

Raw data is rarely ready for analysis right out of the box; it often requires significant cleaning and restructuring before it can reveal meaningful insights. This course provides a clear path for turning disorganized information into structured, tidy datasets that are ready for visualization and modeling. You will transition from struggling with inconsistent spreadsheets to confidently manipulating data using a consistent and readable syntax. By focusing on the logic of data transformation, you will learn how to prepare your information for any downstream task, from simple reporting to complex machine learning. What you'll learn: - Understand the fundamental principles of tidy data and how they streamline the entire data science pipeline - Apply core dplyr functions to filter, select, mutate, and summarize complex datasets with ease - Reshape data structures using tidyr to transition seamlessly between long and wide formats - Handle dates, times, and strings effectively using modern tools like lubridate and stringr - Group and aggregate data using the latest syntax for efficient exploratory analysis - Practice data cleaning techniques to manage missing values and inconsistent formatting The course begins with essential terminology and the philosophy of the Tidyverse before moving into the practical application of data manipulation tools. Through written explanations and code-based examples, you will learn to build reproducible workflows for any data project. This course is designed for beginners who have a basic grasp of R and want to develop professional-grade data preparation skills. No prior experience with data wrangling is required. Start reading to build a solid foundation in modern data transformation.