BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER
SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS.
Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book:
* Instructs both practitioners and students fluent in one language seeking to learn the other
* Provides command-by-command translations of R to SAS and SAS to R
* Offers examples and applications in both R and SAS
* Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more
* Includes sections on advanced methods and applications
Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics.
Inhaltsverzeichnis
Preface
Scope
Chapter 1 About SAS and R
Chapter 2 Data Input, Import and Print
Chapter 3 Data Inspection and Cleaning
Chapter 4 Handling Dates, Strings, Numbers
Chapter 5 Numerical Summary and Group by Analysis
Chapter 6 Frequency Distributions and Cross Tabulations
Chapter 7 Using SQL with SAS and R
Chapter 8 Functions, Loops, Arrays, Macros
Chapter 9 Data Visualization
Chapter 10 Data Output
Chapter 11 Statistics for Data Scientists
Citations
Über den Autor
AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.
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