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SAS for R Users. A Book for Data Scientists. Edition No. 31

  • ID: 5226590
  • Book
  • October 2019
  • 208 Pages
  • John Wiley and Sons Ltd

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.

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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

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Ajay Ohri
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