In this chapter, we took a tour of various database systems and the R packages that allow us to interface with them, and saw how in-database querying and analysis can provide better performance than copying the data into R to do the same analysis. This is especially true for large datasets that cannot be easily processed in R; using a database that is tuned for querying and analysis can help to avoid performance issues in R. As technology improves, more and more advanced analysis and algorithms can be run in databases providing more options for R programmers who face the challenge of analyzing large datasets efficiently. These powerful data processing tools can complement R very nicely—they provide the computing muscle to analyze large datasets, while R provides easy interfaces for data manipulation and analysis. R can also help to bring together different threads of analyses, regardless of the tool used, to present a coherent and compelling picture of the data using tools such as...