Discover best practices for data analysis and software development in R and start on the path
to becoming a fully-fledged data scientist. Updated for the R 4.0 release this book teaches
you techniques for both data manipulation and visualization and shows you the best way for
developing new software packages for R. Beginning Data Science in R 4 Second Edition details
how data science is a combination of statistics computational science and machine learning.
You'll see how to efficiently structure and mine data to extract useful patterns and build
mathematical models. This requires computational methods and programming and R is an ideal
programming language for this. Modern data analysis requires computational skills and usually a
minimum of programming. After reading and using this book you'll have what you need to get
started with R programming with data science applications. Source code will be available to
support your next projects as well. Source code is available at github.com Apress
beg-data-science-r4. What You Will Learn Perform data science and analytics using statistics
and the R programming language Visualize and explore data including working with large data
sets found in big data Build an R package Test and check your code Practice version control
Profile and optimize your code Who This Book Is ForThose with some data science or analytics
background but not necessarily experience with the R programming language.
