The paper introduces the split-apply-combine strategy for data analysis, a method that breaks down large problems into manageable pieces, operates on each piece independently, and then combines the results. This strategy is implemented in the R package plyr, which simplifies the process of data manipulation and analysis. The paper includes two case studies: one on analyzing batting records of veteran baseball players and another on spatial-temporal ozone measurements. The case studies demonstrate how the split-apply-combine strategy can be applied to real-world data sets, showing the effectiveness of the plyr package in handling complex data structures. The paper also discusses the advantages of using plyr over traditional loop structures and base R functions, highlighting the efficiency and clarity it brings to data analysis tasks.The paper introduces the split-apply-combine strategy for data analysis, a method that breaks down large problems into manageable pieces, operates on each piece independently, and then combines the results. This strategy is implemented in the R package plyr, which simplifies the process of data manipulation and analysis. The paper includes two case studies: one on analyzing batting records of veteran baseball players and another on spatial-temporal ozone measurements. The case studies demonstrate how the split-apply-combine strategy can be applied to real-world data sets, showing the effectiveness of the plyr package in handling complex data structures. The paper also discusses the advantages of using plyr over traditional loop structures and base R functions, highlighting the efficiency and clarity it brings to data analysis tasks.