Data Wrangling in R with Essential Tidyverse Functions

Data cleaning and preparation consume the majority of any data analyst's time. To work efficiently in R, you need a robust toolkit that simplifies how you manipulate, filter, and transform raw data into actionable insights. This text-based course guides you through the most powerful functions in the tidyverse ecosystem, helping you write cleaner, faster, and more readable R code. You will transition from writing complex, nested base R code to crafting elegant data pipelines. By focusing on practical data transformation scenarios, you will build the confidence to handle messy datasets, perform complex calculations across rows, and structure your data for immediate analysis. What you'll learn: - Understand foundational tidyverse concepts and how to structure data pipelines using the modern pipe operator. - Isolate and extract specific data points using advanced slicing and filtering techniques. - Compute rolling calculations, offsets, and lead/lag values for time-series and sequential data. - Handle missing values systematically using robust imputation and replacement functions. - Group, count, and rank data efficiently to extract quick summary statistics. - Apply modern data science best practices including clean code styling and reproducible workflows. Starting with basic syntax and core tidyverse philosophy, this course guides you step-by-step through practical data manipulation scenarios. You will read through clear explanations, study structured code examples, and practice solving real-world data challenges. This course is designed for beginners who have a basic familiarity with R and want to build practical data manipulation skills. No advanced programming or statistical background is required. Start writing cleaner R code and streamline your data science workflow today.