The split-apply-combine strategy is a common approach in data analysis, where data is divided into manageable pieces, processed independently, and then combined. This strategy is implemented in the R package plyr, which simplifies the process of applying this method without worrying about data structure. The paper presents two case studies: one on batting records of veteran baseball players and another on spatio-temporal ozone measurements. It discusses how plyr provides a more efficient and readable alternative to traditional loops, abstracting away from data structure details. The package supports various data types, including arrays, data frames, and lists, and offers functions for splitting, processing, and combining data. The paper also compares plyr with base R functions, highlighting its advantages in readability and efficiency. It describes the usage of plyr functions, their input and output types, and provides examples of how they can be applied to real-world data. The paper concludes that plyr is a valuable tool for data analysis, offering a structured and efficient way to handle complex data processing tasks.The split-apply-combine strategy is a common approach in data analysis, where data is divided into manageable pieces, processed independently, and then combined. This strategy is implemented in the R package plyr, which simplifies the process of applying this method without worrying about data structure. The paper presents two case studies: one on batting records of veteran baseball players and another on spatio-temporal ozone measurements. It discusses how plyr provides a more efficient and readable alternative to traditional loops, abstracting away from data structure details. The package supports various data types, including arrays, data frames, and lists, and offers functions for splitting, processing, and combining data. The paper also compares plyr with base R functions, highlighting its advantages in readability and efficiency. It describes the usage of plyr functions, their input and output types, and provides examples of how they can be applied to real-world data. The paper concludes that plyr is a valuable tool for data analysis, offering a structured and efficient way to handle complex data processing tasks.