Exploratory Data Analysis Using R provides a classroom-tested
introduction to exploratory data analysis (EDA) and introduces the
range of "interesting" - good, bad, and ugly - features that can be
found in data, and why it is important to find them. It also
introduces the mechanics of using R to explore and explain data.
The book begins with a detailed overview of data, exploratory
analysis, and R, as well as graphics in R. It then explores working
with external data, linear regression models, and crafting data
stories. The second part of the book focuses on developing R
programs, including good programming practices and examples,
working with text data, and general predictive models. The book
ends with a chapter on "keeping it all together" that includes
managing the R installation, managing files, documenting, and an
introduction to reproducible computing. The book is designed for
both advanced undergraduate, entry-level graduate students, and
working professionals with little to no prior exposure to data
analysis, modeling, statistics, or programming. it keeps the
treatment relatively non-mathematical, even though data analysis is
an inherently mathematical subject. Exercises are included at the
end of most chapters, and an instructor's solution manual is
available. About the Author: Ronald K. Pearson holds the position
of Senior Data Scientist with GeoVera, a property insurance company
in Fairfield, California, and he has previously held similar
positions in a variety of application areas, including software
development, drug safety data analysis, and the analysis of
industrial process data. He holds a PhD in Electrical Engineering
and Computer Science from the Massachusetts Institute of Technology
and has published conference and journal papers on topics ranging
from nonlinear dynamic model structure selection to the problems of
disguised missing data in predictive modeling. Dr. Pearson has
authored or co-authored books including Exploring Data in
Engineering, the Sciences, and Medicine (Oxford University Press,
2011) and Nonlinear Digital Filtering with Python. He is also the
developer of the DataCamp course on base R graphics and is an
author of the datarobot and GoodmanKruskal R packages available
from CRAN (the Comprehensive R Archive Network).
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