Statistics and Computing
Alex Almeida
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Abstract
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This book presents R, a statistical computing environment, designed to enhance statistical analysis and graphical representation of data. It focuses on the elementary concepts of R with practical examples to assist beginners, especially in health sciences and epidemiology. The text addresses the flexibility of R in conducting statistical modeling and its historical development, providing insights for both new and experienced statisticians.
Related papers
Emacs Speaks Statistics: A Multiplatform, Multipackage Development Environment for Statistical Analysis
Journal of Computational and Graphical Statistics, 2004
Computer programming is an important component of statistics research and data analysis. This skill is necessary for using sophisticated statistical packages as well as for writing custom software for data analysis. Emacs Speaks Statistics (ESS) provides an intelligent and consistent interface between the user and software. ESS interfaces with SAS, S-PLUS, R, and other statistics packages under the Unix, Microsoft Windows, and Apple Mac operating systems. ESS extends the Emacs text editor and uses its many features to streamline the creation and use of statistical software. ESS understands the syntax for each data analysis language it works with and provides consistent display and editing features across packages. ESS assists in the interactive or batch execution by the statistics packages of statements written in their languages. Some statistics packages can be run as a subprocess of Emacs, allowing the user to work directly from the editor and thereby retain a consistent and constant lookand-feel. We discuss how ESS works and how it increases statistical programming efficiency.
Emacs Speaks Statistics: A Universal Interface for Statistical Analysis
2001
Emacs Speaks Statistics (ESS) is a user interface for developing statistical applications and performing data analysis using any of several common statistical programming languages. ESS falls in the programming tools category of Integrated Development Environments (IDEs), which are approaches for developing and visualizing computer programs. We discuss how it works, the advantages of using it, and extensions for increasing statistical programming efficiency.
Emacs Speaks Statistics (ESS): A multi-platform, multi-package intelligent environment for statistical analysis
2003
Computer programming is an important component of statistics research and data analysis. This skill is necessary for using sophisticated statistical packages as well as for writing custom software for data analysis. Emacs Speaks Statistics (ESS) provides an intelligent and consistent interface between the user and software. ESS interfaces with SAS, S-PLUS, R, and other statistics packages under the Unix, Microsoft Windows, and Apple Mac operating systems. ESS extends the Emacs text editor and uses its many features to streamline the creation and use of statistical software. ESS understands the syntax for each data analysis language it works with and provides consistent display and editing features across packages. ESS assists in the interactive or batch execution by the statistics packages of statements written in their languages. Some statistics packages can be run as a subprocess of Emacs, allowing the user to work directly from the editor and thereby retain a consistent and constant lookand-feel. We discuss how ESS works and how it increases statistical programming efficiency.
Toward a common framework for statistical analysis and development
2008
We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R's numerous existing packages. This framework offers a simple unified structure and syntax that can encompass a large fraction of existing statistical procedures.
Book Review: The Art of R Programming
2012
The language R has become the statistician's gold standard for data analysis and programming and it is gaining popularity also among econometricians. The main reasons for this popularity are to be found in its being open-source and extremely rich of contributed packages (3686 in the Comprehensive R Archive Network or CRAN).
A First Course in Statistical Programming with R
This is the only introduction you'll need to start programming in R, the opensource language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R core development team, and by an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the groundbreaking R system, this book emphasizes programming, including the principles that apply to most computing languages, and the techniques used to develop more complex projects. Learning the language is made easier by the frequent exercises within chapters which enable you to progress conf idently through the book. More substantial exercises at the ends of chapters help to test your understanding. Solutions, datasets, and any errata will be available from the book's website.
Related papers
R++, User-Friendly Statistical Software
Human-Computer Interaction – INTERACT 2019, 2019
Statistical analysis is gradually entering all areas of society, be in academia or in the private sector. Statistical software is used by statisticians but also by non-experts (medical doctors, psychologists.. .). Unfortunately, this kind of software is integrated into obsolete interfaces that completely ignore the principles of HCI and are poorly adapted to non-expert users. R++ project aims to develop a modern statistical analysis software program integrated into a user-friendly interface. In this paper, we present the methodology that led us to the design of R++. We also give two examples that this methodology allowed us to achieve.
Statistical Computing with R
2009
This course was originally developed jointly with Benjamin Kedem and Paul Smith. It consists of modules as indicated on the Course Syllabus. These fall roughly into three main headings: (A). R (& SAS) language elements and functionality, including computer-science ideas; (B). Numerical analysis ideas and implementation of statistical algorithms, primarily in R; and (C). Data analysis and statistical applications of (A)-(B). The object of the course is to reach a point where students have some facility in generating statistically meaningful models and outputs. Wherever possible, the use of R and numerical-analysis concepts is illustrated in the context of analysis of real or simulated data. The assigned homework problems will have the same flavor. The course formerly introduced Splus, where now we emphasize the use of R. The syntax is very much the same for the two packages, but R costs nothing and by now has much greater capabilities. Also, in past terms SAS has been introduced prim...
Tools of the Stats Trade, Part 2
The Saga Continues! Yesssss, the tale of many Ss continues! If you recall, we discussed SPSS and SAS in our previous article. We noted that a main difference between the two is that SPSS offers a more user-friendly graphical interface, good for beginning users, and SAS caters more to the programmer in you. Underlying both packages there is a powerful programming language. You might be thinking, ssssso many packages! Which one should I use when? Should I explore others programs or stick to what I know? Excellent questions! A salient element is the amount of programming flexibility they allow the user. That is, your ability to modify how the program actually works. And it is on this note is that we start our tour today of two more statistical packages: SYSTAT and Stata. These packages offer the convenience of a graphical interface, but they also emphasize the programming component. They are more challenging to learn, but they also can be much more rewarding.
An evaluation of statistical software for research and instruction
Behavior Research Methods, Instruments, & Computers, 1985
A variety of microcomputer statistics packages were evaluated. The packages were compared on a number of dimensions, including error handling, documentation, statistical capability, and accuracy. Results indicated that there are some very good packages available both for instruction and for analyzing research data. In general, the microcomputer packages were easier to learn and to use than were mainframe packages. Furthermore, output of mainframe packages was found to be less accurate than output of some of the microcomputer packages.
Statistical Software for Today and Tomorrow
Encyclopedia of Statistics, John Wiley, 2005
This article is a relatively short exposition on the current state of the art with regards to statistical software along with some prognostications as to the future trends in the area. The article is somewhat biased in its discussions of the state of the art with a necessitated focus on those packages that seem most promising with regards to their current and future role in the academic research communities.
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