background preloader

Producing Simple Graphs with R

Producing Simple Graphs with R
Related:  R and Matlab programmingR-Programming

R-bloggers | R news & tutorials from the web wq-package.pdf Data | 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 It is best to enter your data to an ordinary text file, such as a .csv (comma-separated text) file, created with the help of a spreadsheet program such as Excel or the free program Calc. Long vs wide layouts Keep data that you want analyzed together in a single worksheet. Plot Site species1 species2 species3 1 A 0 12 4 2 A 88 2 0 3 B 12 4 1 ... The equivalent long layout will be easier to analyze: Plot Site Species Number 1 A 1 0 1 A 2 12 1 A 3 4 2 A 1 88 2 A 2 2 2 A 3 0 3 B 1 12 3 B 2 4 3 B 3 1 ... Data file tips The following suggestions for your data file will save you frustration when it comes time to read into R. Read data from text file Work with data frames Attach

rgraphics R Graphicsby Paul Murrell The SECOND EDITION of this book is now available, with its own web page. A book on the core graphics facilities of the R language and environment for statistical computing and graphics (Chapman & Hall/CRC, August 2005). A link to the publisher's web page for the book. A list of Errata. PDF version of the preface, table of contents, and Chapters 1, 4, and 5. R code for figures: Chapter 1: An Introduction to R Graphics Chapter 2: Simple Usage of Traditional Graphics Chapter 3: Customising Traditional Graphics Chapter 4: Trellis Graphics: The Lattice Package Chapter 5: The Grid Graphics Model Chapter 6: The Grid Graphics Object Model Chapter 7: Developing New Graphics Functions and Objects Appendix A: A Brief Introduction to R Appendix B: Combining Traditional Graphics and Grid Graphics Extras: Some extra plots not in the book An R add-on package called "RGraphics" is available from CRAN. Figure 3.12 -- uses 'hjust' argument in grid.text()

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

Forecasting: principles and practice Welcome to our online textbook on forecasting. This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details. The book is written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area; (2) undergraduate students studying business; (3) MBA students doing a forecasting elective. We use it ourselves for a second-year subject for students undertaking a Bachelor of Commerce degree at Monash University, Australia. For most sections, we only assume that readers are familiar with algebra, and high school mathematics should be sufficient background. Use the table of contents on the right to browse the book.

Statistical Learning | Stanford Lagunita 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). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). Course Staff Trevor Hastie Trevor Hastie is the John A Overdeck Professor of Statistics at Stanford University. Rob Tibshirani

Percentile The nth percentile of an observation variable is the value that cuts off the first n percent of the data values when it is sorted in ascending order. Problem Find the 32nd, 57th and 98th percentiles of the eruption durations in the data set faithful. Solution We apply the quantile function to compute the percentiles of eruptions with the desired percentage ratios. > duration = faithful$eruptions # the eruption durations > quantile(duration, c(.32, .57, .98)) 32% 57% 98% 2.3952 4.1330 4.9330 Answer The 32nd, 57th and 98th percentiles of the eruption duration are 2.3952, 4.1330 and 4.9330 minutes respectively. Exercise Find the 17th, 43rd, 67th and 85th percentiles of the eruption waiting periods in faithful. Note There are several algorithms for the computation of percentiles.

R Programming - Manuals R Basics The R & BioConductor manual provides a general introduction to the usage of the R environment and its basic command syntax. Code Editors for R Several excellent code editors are available that provide functionalities like R syntax highlighting, auto code indenting and utilities to send code/functions to the R console. Programming in R using Vim or Emacs Programming in R using RStudio Integrating R with Vim and Tmux Users interested in integrating R with vim and tmux may want to consult the Vim-R-Tmux configuration page. Finding Help Reference list on R programming (selection)R Programming for Bioinformatics, by Robert GentlemanAdvanced R, by Hadley WickhamS Programming, by W. Control Structures Conditional Executions Comparison Operators equal: ==not equal: ! Logical Operators If Statements If statements operate on length-one logical vectors. Syntax if(cond1=true) { cmd1 } else { cmd2 } Example if(1==0) { print(1) } else { print(2) } [1] 2 Avoid inserting newlines between '} else'. Loops Syntax

Welcome to a Little Book of R for Time Series! — Time Series 0.2 documentation Cookbook for R