This book is a practical introduction to statistics and data analysis using R. It's designed for learners with little to no prior experience in coding or statistics but also serves as a helpful companion for students taking their first statistics course and/or those who are new at coding. The content is structured to guide readers through foundational concepts in R programming and statistical methods. The topics of the book begin with basic familiarity with R-Studio and data sets, calculations of common statistical metrics, basic function definitions, simple linear regression, and basic model appropriateness assessment. The content then progresses into discrete and continuous probability functions (of a single variable), including sampling distributions and basic familiarity with the common normally-related distributions (Z, T, Chi-square, and F). We then transition into hypothesis testing on one-sample and two-sample means, variances, and proportions, as well as testing the independence between two categorical variables. Each chapter introduces a concept, explains the methodology behind it, and provides examples and exercises to help readers apply what they've learned. Solutions to exercises and additional resources are included to support independent learning. This book is ideal for students, professionals, or anyone interested in learning how to analyze data with R, or it can be used as a parallel resource to a first-semester elementary course in statistics.