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.