By William N. Venables, David M. Smith
This educational guide presents a entire advent to R, a software program package deal for statistical computing and images. R helps quite a lot of statistical thoughts and is well extensible through user-defined capabilities. certainly one of R's strengths is the benefit with which publication-quality plots could be produced in a wide selection of codecs. this can be a revealed variation of the educational documentation from the R distribution, with extra examples, notes and corrections. it truly is in accordance with R model 2.9.0, published April 2009. R is loose software program, allotted less than the phrases of the GNU normal Public License (GPL). it may be used with GNU/Linux, Unix and Microsoft home windows. all of the funds raised from the sale of this ebook helps the improvement of unfastened software program and documentation.
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Additional resources for An Introduction to R
Each additional line of the file has as its first item a row label and the values for each variable. If the file has one fewer item in its first line than in its second, this arrangement is presumed to be in force. So the first few lines of a file to be read as a data frame might look as follows. ✟ ☛ Input file form with names and row labels: 01 02 03 04 05 ... heat in the example, as factors. This can be changed if necessary. data") Often you will want to omit including the row labels directly and use the default labels.
Chapter 10: Writing your own functions 44 10 Writing your own functions As we have seen informally along the way, the R language allows the user to create objects of mode function. These are true R functions that are stored in a special internal form and may be used in further expressions and so on. In the process, the language gains enormously in power, convenience and elegance, and learning to write useful functions is one of the main ways to make your use of R comfortable and productive. It should be emphasized that most of the functions supplied as part of the R system, such as mean(), var(), postscript() and so on, are themselves written in R and thus do not differ materially from user written functions.
1 R as a set of statistical tables One convenient use of R is to provide a comprehensive set of statistical tables. Functions are provided to evaluate the cumulative distribution function P (X ≤ x), the probability density function and the quantile function (given q, the smallest x such that P (X ≤ x) > q), and to simulate from the distribution. Distribution beta binomial Cauchy chi-squared exponential F gamma geometric hypergeometric log-normal logistic negative binomial normal Poisson signed rank Student’s t uniform Weibull Wilcoxon R name beta binom cauchy chisq exp f gamma geom hyper lnorm logis nbinom norm pois signrank t unif weibull wilcox additional arguments shape1, shape2, ncp size, prob location, scale df, ncp rate df1, df2, ncp shape, scale prob m, n, k meanlog, sdlog location, scale size, prob mean, sd lambda n df, ncp min, max shape, scale m, n Prefix the name given here by ‘d’ for the density, ‘p’ for the CDF, ‘q’ for the quantile function and ‘r’ for simulation (r andom deviates).