background preloader

Cookbook for R » Cookbook for R

Related:  RRR-Programming

Axes and Text Many high level plotting functions (plot, hist, boxplot, etc.) allow you to include axis and text options (as well as other graphical paramters). For example # Specify axis options within plot() plot(x, y, main="title", sub="subtitle", xlab="X-axis label", ylab="y-axix label", xlim=c(xmin, xmax), ylim=c(ymin, ymax)) For finer control or for modularization, you can use the functions described below. Titles Use the title( ) function to add labels to a plot. Introduction to R - Training Material USE R AS A CALCULATOR[edit] Use the ‘gets’ (also called the ‘assigns’) operator ( <- ) rather than the equals sign. After assigning a particular value to an object, confirm that the object now has that value by typing in the name of the object.

R tips pages This page introduces the basics of working with data sets having multiple variables, often of several types. The focus here is on data frames, which are the most convenient data objects in R. Matrices and lists are also useful data objects, and these are introduced briefly at the end. Manage data Start with a spreadsheet program R Programming Welcome to the R programming Wikibook This book is designed to be a practical guide to the R programming language[1]. R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN)[2] and many resources in specialized books, forums such as Stackoverflow[3] and personal blogs[4], but all of these resources are scattered and therefore difficult to find and to compare.

Writing R Extensions Table of Contents This is a guide to extending R, describing the process of creating R add-on packages, writing R documentation, R’s system and foreign language interfaces, and the R API. This manual is for R, version 3.1.0 (2014-04-10). Copyright © 1999–2013 R Core Team The Work of Edward Tufte and Graphics Press Edward Tufte is a statistician and artist, and Professor Emeritus of Political Science, Statistics, and Computer Science at Yale University. He wrote, designed, and self-published 4 classic books on data visualization. The New York Times described ET as the "Leonardo da Vinci of data," and Business Week as the "Galileo of graphics." He is now writing a book/film The Thinking Eye and constructing a 234-acre tree farm and sculpture park in northwest Connecticut, which will show his artworks and remain open space in perpetuity.

Statistical Learning About This Course This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). Model visualisation. This page lists my published software for model visualisation. This work forms the basis for the third chapter of my thesis. classifly: Explore classification boundaries in high dimensions. Given p-dimensional training data containing d groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-facted.

Epistemic, ontological and aleatory risk « Critical Uncertainties What do an eighteenth century mathematician and a twentieth century US Secretary of Defence have to do with engineering and risk? The answer is that both thought about uncertainty and risk, and the differing definitions that they arrived at neatly illustrate that there is more to the concept of risk than just likelihood multiplied by consequence. Which in turn has significant implications for engineering risk management. Editorial note. I’ve pretty much completely revised this post since the original, hope you like it The concept of risk allows us to make decisions in an uncertain world where we cannot perfectly predict future outcomes.

Do more with dates and times in R with lubridate 1.3.0 note: This vignette is an updated version of the blog post first published at r-statistics Lubridate is an R package that makes it easier to work with dates and times. Below is a concise tour of some of the things lubridate can do for you. Output to a file Problem You want to save your graph(s) to a file. Solution

Related:  RRR StatR