Ken Kleinman

Nicholas J. Horton

An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent TasksThe 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 p

Data Management, Statistical Analysis, and Graphics, Second Edition

SAS and R

Improve Your Analytical SkillsIncorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more

Using R and RStudio for Data Management, Statistical Analysis, and Graphics

Andrew B. Lawson

Following the events of 9/11 and in the current world climate, there is increasing concern of the impact of potential bioterrorism attacks. Spatial surveillance systems are used to detect changes in public health data, and alert us to possible outbreaks of disease, either from natural resources or from bioterrorism attacks. Statistical methods play a key role in spatial surveillance, as they are used to identify changes in data, and build models of that data in order to make predictions about future activity. This book is the first to provide an overview of all the current key methods in spatial surveillance, and present them in an accessible form, suitable for the public health professional. It features an abundance of examples using real data, highlighting the practical application of the methodology. It is edited and authored by leading researchers and practitioners in spatial surveillance methods. Provides an overview of the current key methods in spatial surveillance of public health data. Includes coverage of both single and multiple disease surveillance. Covers all of the key topics, including syndromic surveillance, spatial cluster detection, and Bayesian data mining.

Spatial and Syndromic Surveillance for Public Health

Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsUsing R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes

Using R for Data Management, Statistical Analysis, and Graphics

An All-in-One Resource for Using SAS and R to Carry out Common TasksProvides a path between languages that is easier than reading complete documentationSAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, id

Data Management, Statistical Analysis, and Graphics

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.

Ken Kleinman

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