"Doing Meta-Analysis with R" is a hands-on guide authored by Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, and David D. Ebert. The book aims to provide a comprehensive introduction to conducting meta-analyses using the R programming language. It covers the fundamentals of meta-analysis, including its definition, historical context, and common pitfalls, as well as the basics of R and R Studio. The book is structured into several parts, starting with an introduction to meta-analyses and R, followed by detailed chapters on effect sizes, pooling effect sizes, heterogeneity, forest plots, subgroup analyses, meta-regression, publication bias, advanced methods, and helpful tools. Each chapter includes practical examples and hands-on exercises to reinforce learning. The authors also provide a companion R package, {dmetar}, which simplifies the process of conducting meta-analyses and includes additional data sets for practice. The book is designed for applied researchers, students, and data scientists who want to learn how to perform meta-analyses using R, offering a blend of theoretical concepts and practical guidance."Doing Meta-Analysis with R" is a hands-on guide authored by Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, and David D. Ebert. The book aims to provide a comprehensive introduction to conducting meta-analyses using the R programming language. It covers the fundamentals of meta-analysis, including its definition, historical context, and common pitfalls, as well as the basics of R and R Studio. The book is structured into several parts, starting with an introduction to meta-analyses and R, followed by detailed chapters on effect sizes, pooling effect sizes, heterogeneity, forest plots, subgroup analyses, meta-regression, publication bias, advanced methods, and helpful tools. Each chapter includes practical examples and hands-on exercises to reinforce learning. The authors also provide a companion R package, {dmetar}, which simplifies the process of conducting meta-analyses and includes additional data sets for practice. The book is designed for applied researchers, students, and data scientists who want to learn how to perform meta-analyses using R, offering a blend of theoretical concepts and practical guidance.