Dynamic graphics and reporting for statistics

Statistics as a scientific discipline has a dynamic nature, which can be observed in many statistical algorithms and theories as well as in data analysis. For example, asymptotic theories in statistics are inherently dynamic: they describe how a statistic or an estimator behaves as the sample size increases. Data analysis is almost never a static process. Instead, it is an iterative process involving cleaning, describing, modeling, and re-cleaning the data. Reports may end up being re-written due to changes in the data and analysis. This thesis consists of three parts, addressing the dynamic aspects of statistics and data analysis. In the first part, we show how to explain the ideas behind some statistical methods using animations, followed by an introduction to the design and functionality of the animation package. In the second part, we discuss the design of an interactive statistical graphics system, with an emphasis on the reactive programming paradigm and its connection with the data infrastructure in R, as utilized in the cranvas package. In the third part, we provide a solution to statistical reporting, which is implemented in the knitr package, making use of literate programming. It frees us from the traditional approach of cut-and-paste, and provides a seamless integration of computing and reporting that enhances reproducible research. Demos and examples were given along with the discussion.