The paper introduces the reshape package for R, which provides a framework for reshaping and aggregating data using the 'melting' and 'casting' paradigms. The package aims to simplify the common task of data reshaping, which is often tedious and complex. The authors describe the conceptual framework, practical advice for melting and casting, and a case study using real-life data. **Key Points:** - **Conceptual Framework:** Data is divided into identifier and measured variables. Identifier variables distinguish units of measurement, while measured variables represent the values. - **Melting:** The `melt` function converts data from a wide format to a long format, distinguishing between measured and identifier variables. - **Casting:** The `cast` function rearranges the melted data into a new shape, such as a data frame, list, or high-dimensional array. - **Practical Advice:** The paper provides detailed examples and explanations for using `melt` and `cast`, including handling missing values and aggregation. - **Case Study:** A real-life example using French fries data demonstrates how to investigate balance, means, and inter-rep reliability. The reshape package is designed to be flexible and powerful, making data reshaping more efficient and user-friendly.The paper introduces the reshape package for R, which provides a framework for reshaping and aggregating data using the 'melting' and 'casting' paradigms. The package aims to simplify the common task of data reshaping, which is often tedious and complex. The authors describe the conceptual framework, practical advice for melting and casting, and a case study using real-life data. **Key Points:** - **Conceptual Framework:** Data is divided into identifier and measured variables. Identifier variables distinguish units of measurement, while measured variables represent the values. - **Melting:** The `melt` function converts data from a wide format to a long format, distinguishing between measured and identifier variables. - **Casting:** The `cast` function rearranges the melted data into a new shape, such as a data frame, list, or high-dimensional array. - **Practical Advice:** The paper provides detailed examples and explanations for using `melt` and `cast`, including handling missing values and aggregation. - **Case Study:** A real-life example using French fries data demonstrates how to investigate balance, means, and inter-rep reliability. The reshape package is designed to be flexible and powerful, making data reshaping more efficient and user-friendly.