Bioinformatics and R. One step ahead in Bioinformatics using Package Repositories About a year ago I published a post about in-house tools in research and how using this type of software may end up undermining the quality of a manuscript and the reproducibility of its results. While I can certainly relate to someone reluctant to release nasty code (i.e. not commented, not well-tested, not documented), I still think we...
Read more » Little Book of R for Bioinformatics by Avril Coghlan Introduction to bioinformatics, with a focus on genome analysis, using the R statistics software. Read more » Highlights of the Milwaukee Workshop on R and Bioinformatics by Joseph Rickert On May 10th and 11th, in honor of this being the International Year of Statistics, the Milwaukee Chapter of the American Statistical Association (MILWASA) held a workshop on cutting edge uses of R in Bioinformatics. Read more » Automated Archival and Visual Analysis of Tweets Mentioning #bog13, Bioinformatics, #rstats, and Others Read more »
Beta. Introduction to Computational Finance and Financial Econometrics By Eric Zivot (University of Washington) Learn mathematical, programming and statistical tools used in the real world analysis and modeling of financial data. Get an in-depth insight into the mathematical and statistical tools and techniques used in quantitative and computational finance! In this course, you'll make use of R to analyze financial data, estimate statistical models, and construct optimized portfolios. You will learn how to build probability models for assets returns, the way you should apply statistical techniques to evaluate if asset returns are normally distributed, how to use Monte Carlo simulation and bootstrapping techniques to evaluate statistical models, and the usage of optimization methods to construct efficient portfolios.
The material in this course was originally developed as a complement to Prof. Eric Zivot's Coursera lectures. This course is for everyone interested in finance. Online Statistics Education: A Free Resource for Introductory Statistics. Developed by Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University OnlineStatBook Project Home This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study ( Project Leader: David M. If you are an instructor using these materials, I can send you an instructor's manual, PowerPoint Slides, and additional questions that may be helpful to you. Table of Contents Mobile This version uses formatting that works better for mobile devices.
Rice Virtual Lab in Statistics This is the original classic with all the simulations and case studies. Version in PDF e-Pub (e-book) Partial support for this work was provided by the National Science Foundation's Division of Undergraduate Education through grants DUE-9751307, DUE-0089435, and DUE-0919818. An Introduction to R. Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al.
It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). This manual provides information on data types, programming elements, statistical modelling and graphics. This manual is for R, version 3.1.0 (2014-04-10).
Copyright © 1990 W. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Preface This introduction to R is derived from an original set of notes describing the S and S-PLUS environments written in 1990–2 by Bill Venables and David M. Comments and corrections are always welcome. Suggestions to the reader 1.1 The R environment Try ? Statistics, R Language and SAS. The R Programming Language. Using R for Introductory Statistics. The following was posted to the R-mailing list 11/02/2006 <PRE> Using R for Introductory Statistics John Verzani, CUNY, College of Staten Island, New York This reference presents a self-contained treatment of statistical topics and the intricacies of the R software.
The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. It lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. Discounted Price: $35.96/£19.99 For more details and to order: The R programming language for programmers coming from other programming languages. IntroductionAssignment and underscoreVariable name gotchasVectorsSequencesTypesBoolean operatorsListsMatricesMissing values and NaNsCommentsFunctionsScopeMisc.Other resources Ukrainian translation Other languages: Powered by Translate Introduction I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R.
R is more than a programming language. This document is a work in progress. Assignment and underscore The assignment operator in R is <- as in e <- m*c^2. It is also possible, though uncommon, to reverse the arrow and put the receiving variable on the right, as in m*c^2 -> e. It is sometimes possible to use = for assignment, though I don't understand when this is and is not allowed. However, when supplying default function arguments or calling functions with named arguments, you must use the = operator and cannot use the arrow. At some time in the past R, or its ancestor S, used underscore as assignment.
Vectors Sequences. Quick-R: Home Page. An R Introduction to Statistics | R Tutorial. Twotorials by anthony damico.