By William N. Venables, David M. Smith, R Development Core Team

**Read Online or Download An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics PDF**

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**Additional info for An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics**

**Example text**

Otherwise the same process is recursively applied to each panel. The result is an adaptive integration process that concentrates function evaluations in regions where the integrand is farthest from linear. There is, however, a heavy overhead, and the function is only competitive with other algorithms when the integrand is both smooth and very difficult to evaluate. The example is also given partly as a little puzzle in R programming. 7 Scope The discussion in this section is somewhat more technical than in other parts of this document.

P and the dxxx ones have log. , log = TRUE)), directly. In addition there are functions ptukey and qtukey for the distribution of the studentized range of samples from a normal distribution. 2 Examining the distribution of a set of data Given a (univariate) set of data we can examine its distribution in a large number of ways. The simplest is to examine the numbers. Two slightly different summaries are given by summary and fivenum and a display of the numbers by stem (a “stem and leaf” plot). > attach(faithful) > summary(eruptions) Min.

F <- function(x) { y <- 2*x print(x) print(y) print(z) } In this function, x is a formal parameter, y is a local variable and z is a free variable. In R the free variable bindings are resolved by first looking in the environment in which the function was created. This is called lexical scope. First we define a function called cube. cube <- function(n) { sq <- function() n*n n*sq() } The variable n in the function sq is not an argument to that function. Therefore it is a free variable and the scoping rules must be used to ascertain the value that is to be associated with it.