Preface
This book is an introduction to the practical tools of exploratory data anal-
ysis. The organization of the book follows the process I use when I start
working with a dataset:
• Importing and cleaning: Whatever format the data is in, it usually
takes some time and effort to read the data, clean and transform it, and
check that everything made it through the translation process intact.
• Single variable explorations: I usually start by examining one variable
at a time, finding out what the variables mean, looking at distributions
of the values, and choosing appropriate summary statistics.
• Pair-wise explorations: To identify possible relationships between
variables, I look at tables and scatter plots, and compute correlations
and linear fits.
• Multivariate analysis: If there are apparent relationships between
variables, I use multiple regression to add control variables and in-
vestigate more complex relationships.
• Estimation and hypothesis testing: When reporting statistical results,
it is important to answer three questions: How big is the effect? How
much variability should we expect if we run the same measurement
again? Is it possible that the apparent effect is due to chance?
• Visualization: During exploration, visualization is an important tool
for finding possible relationships and effects. Then if an apparent ef-
fect holds up to scrutiny, visualization is an effective way to commu-
nicate results.
This book takes a computational approach, which has several advantages
over mathematical approaches:
