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- Title Exploratory Data Analysis with R
- Author(s) Roger D. Peng
- Publisher:lulu.com (April 20, 2016)
- Hardcover/Paperback 288 pages
- eBook PDF
- Language: English
- ISBN-10/ASIN: 1365060063
- ISBN-13: 978-1365060069
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Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world.
This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you have. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing informative data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.About the Authors
- Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is also a co-founder of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the general public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui.
- Data Analysis and Data Mining, Big Data
- The R Programming Language
- Statistics, Mathematical Statistics, and SAS Programming