The only how-to guide offering a unified, systemic approach to
acquiring, cleaning, and managing data in R Every experienced
practitioner knows that preparing data for modeling is a
painstaking, time-consuming process. Adding to the difficulty is
that most modelers learn the steps involved in cleaning and
managing data piecemeal, often on the fly, or they develop their
own ad hoc methods. This book helps simplify their task by
providing a unified, systematic approach to acquiring, modeling,
manipulating, cleaning, and maintaining data in R. Starting with
the very basics, data scientists Samuel E. Buttrey and Lyn R.
Whitaker walk readers through the entire process. From what data
looks like and what it should look like, they progress through all
the steps involved in getting data ready for modeling. They
describe best practices for acquiring data from numerous sources;
explore key issues in data handling, including text/regular
expressions, big data, parallel processing, merging, matching, and
checking for duplicates; and outline highly efficient and reliable
techniques for documenting data and recordkeeping, including audit
trails, getting data back out of R, and more. * The only
single-source guide to R data and its preparation, it describes
best practices for acquiring, manipulating, cleaning, and
maintaining data * Begins with the basics and walks readers through
all the steps necessary to get data ready for the modeling process
* Provides expert guidance on how to document the processes
described so that they are reproducible * Written by seasoned
professionals, it provides both introductory and advanced
techniques * Features case studies with supporting data and R code,
hosted on a companion website A Data Scientist's Guide to
Acquiring, Cleaning and Managing Data in R is a valuable working
resource/bench manual for practitioners who collect and analyze
data, lab scientists and research associates of all levels of
experience, and graduate-level data mining students.
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