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Chapter 10 From Data Management to Advanced Analytical Approaches: Connecting R to the Database Bram Van Moorter Abstract The previous chapters explored the wide set of tools that PostgreSQL and PostGIS offer to manage tracking data. In this chapter, you will expand database functionalities with those provided by dedicated software for statistical computing and graphics: the R programming language and software environment. Specifically, you will use the advanced graphics available through R and its libraries (especially ‘adehabitat’) to perform exploratory analysis of animal tracking data. This data exploration is followed with two short ecological analyses. These ecological anal- yses are discussed in the context of two central concepts in animal space use: geo- graphic versus environmental space, and the spatiotemporal scale of the research question. In the first analysis, you investigate the animal’s home range, which is its use of geographic space. In the second, to explore an animal’s use of environmental space we introduce briefly the study of both use and selection of environmental features by animals. For both demonstrations we consider explicitly the temporal scale of the study through the seasonal changes introduced by seasonal migration. Introduction: From Data Management to Data Analysis In previous chapters, you explored the wide set of tools that PostgreSQL and PostGIS offer to process and analyse tracking data. Nevertheless, a database is not specifically designed to perform advanced statistical analysis or to implement complex analytical algorithms, which are key elements to extract scientific knowledge from the data for both fundamental and applied research. In fact, these functionalities must be part of an information system that aims at a proper handling of wildlife tracking data. The possibility of a tighter integration of analytical functions with the database is B. Van Moorter () Norwegian Ins