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Petal W. 5 1 2 3 4 5 6 7 Figure 18: Pairwise plot of the iris data frame Accessing Elements of a Vector or Matrix Accessing elements is achieved through a process called indexing. Indexing may be done by • a vector of positive integers: to indicate inclusion c CSIRO Australia, 2005 Course Materials and Exercises R Objects 54 • a vector of negative integers: to indicate exclusion • a vector of logical values: to indicate which are in and which are out • a vector of names: if the object has a names attribute For the latter, if a zero index occurs on the right, no element is selected.

Mode(value) [1] "numeric" > length(value) [1] 1 > mode(string) [1] "character" > length(string) [1] 1 > mode(2<4) [1] "logical" > mode(cn) [1] "complex" > length(cn) [1] 1 c CSIRO Australia, 2005 Course Materials and Exercises R and the Tinn-R Editor 37 > mode(sin) [1] "function" NULL objects are empty objects with no assigned mode. They have a length of zero. > names(value) [1] NULL The R Language: Missing, Indefinite and Infinite Values In many practical examples, some of the data elements will not be known and will therefore be assigned a missing value.

These can be passed to the plot routine to produce a line graph of the density. A rug plot is produced beneath the graph to indicate actual data values. A legend describing the two lines on the plot is produced for clarity. Figure 19 displays the density plot produced from the script below. 5, xlab="Weight",ylab="Density") > lines(dw5,lty=1,col="blue") > lines(dw10,lty=2,col="red") > pu <- par("usr")[3:4] # actual y limits > segments(Weight,pu[1],Weight,0,col="green") > legend(locator(1),c("500kg window", "1000kg window"),lty=1:2) 6 e−04 > detach("Cars93") Density 0 e+00 1 e−04 2 e−04 3 e−04 4 e−04 5 e−04 500kg window 1000kg window 1000 2000 3000 4000 5000 Weight Figure 19: Density plot of vehicle weight from the Cars93 dataset.

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