SAS Companion for the UNIX Environment and Derivatives


Book Description

You have now read enough about UNIX operating systems to be familiar with some of the commands to hanle files, create shell scripts, and run scripts. To use the SAS systerm, you will need to become familiar with the config.sas and autoexec.sas files described in configuration and autoexec file details.




SAS Companion for UNIX Environments


Book Description

User interface and the SAS system. User interfaces for character-based terminals. Using the OSF/Motif interface to the SAS system. Customizing the OSF/Motif interface to the SAS system. Key labels and key definitions. The SAS resource helper. Defining character-based terminals for the SAS system. Sample terminqpgm program.




SAS Language


Book Description

This chapter introduces you to the SAS system ion general and to base SAS software specifically. It provides an overview of the software, defines basic concepts and terminology, and describes the methods of operation available for using the SAS system.




SAS Software Solutions, Basic Data Processing


Book Description

New and intermediate users will appreciate this easy-to-use guide to the basics of SAS. Arranged in three parts, readers will find this notable book both a highly detailed usage guide as well as a handy reference packed with examples of SAS programs. Solutions for common data processing problems are provided along with an extensive cross-reference to SAS product documentation. The examples are realistic and cover everything from the most basic commands to the more complex. Specific topics, presented in a task-oriented format, include how to enter, read, and edit data using INPUT, INFILE, SET, and MERGE statements; how to manipulate data using DATA step programming methods; how to combine SAS data sets; how to customize reports; and how to create tables, bar charts, line graphs and plots.







SAS/INSIGHT User's Guide


Book Description




SAS and R


Book Description

An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second Edition This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two Systems Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.




Forthcoming Books


Book Description




SAS 9.1.3 Intelligence Platform


Book Description

Explains how to administer the SAS Web applications that run in the middle tier of the SAS Intelligence Platform. The Web applications include the SAS Information Delivery Portal, SAS Web Report Studio, and SAS Web OLAP Viewer for Java.This guide describes the middle-tier environment, provides sample deployment scenarios, and explains how to configure the Web applications for optimal performance. The guide contains instructions for common administrative tasks, such as configuring trusted Web authentication, as well as instructions for administering the individual Web applications. For example, the guide explains how to add content to the SAS Information Delivery Portal and how to control access to that content. This title is also available online.




Python for Data Analysis


Book Description

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples