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by Bradley C. Boehmke Ph. D. (Author)
This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.
This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned:
- How to work with different types of data such as numerics, characters, regular expressions, factors, and dates
- The difference between different data structures and how to create, add additional components to, and subset each data structure
- How to acquire and parse data from locations previously inaccessible
- How to develop functions and use loop control structures to reduce code redundancy
- How to use pipe operators to simplify code and make it more readable
- How to reshape the layout of data and manipulate, summarize, and join data sets
Back Jacket
This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.
This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned:
- How to work with different types of data such as numerics, characters, regular expressions, factors, and dates
- The difference between different data structures and how to create, add additional components to, and subset each data structure
- How to acquire and parse data from locations previously inaccessible
- How to develop functions and use loop control structures to reduce code redundancy
- How to use pipe operators to simplify code and make it more readable
- How to reshape the layout of data and manipulate, summarize, and join data sets
In essence, the user will have the data wrangling toolbox required for modern day data analysis.
Brad Boehmke, Ph.D., is an Operations Research Analyst at Headquarters Air Force Materiel Command, Studies and Analyses Division. He is also Assistant Professor in the Operational Sciences Department at the Air Force Institute of Technology. Dr. Boehmke's research interests are in the areas of cost analysis, economic modeling, decision analysis, and developing applied modeling applications through the R statistical language.
Author Biography
Brad Boehmke, Ph.D., is an Operations Research Analyst at Headquarters Air Force Materiel Command, Studies and Analyses Division. He is also Assistant Professor in the Operational Sciences Department at the Air Force Institute of Technology. Dr. Boehmke's research interests are in the areas of cost analysis, economic modeling, decision analysis, and developing applied modeling applications through the R statistical language.
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