Download e-book for kindle: Advanced R by Hadley Wickham.

By Hadley Wickham.

ISBN-10: 1466586974

ISBN-13: 9781466586970

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Data structures 25 • names() generalises to rownames() and colnames() for matrices, and dimnames(), a list of character vectors, for arrays. length(a) #> [1] 6 nrow(a) #> [1] 2 ncol(a) #> [1] 3 rownames(a) <- c("A", "B") colnames(a) <- c("a", "b", "c") a #> a b c #> A 1 3 5 #> B 2 4 6 length(b) #> [1] 12 dim(b) #> [1] 2 3 2 dimnames(b) <- list(c("one", "two"), c("a", "b", "c"), c("A", "B")) b #> , , A #> #> a b c #> one 1 3 5 #> two 2 4 6 #> #> , , B #> #> a b c #> one 7 9 11 #> two 8 10 12 c() generalises to cbind() and rbind() for matrices, and to abind() (provided by the abind package) for arrays.

What makes them different to 1:5? pdf) makes data analysis easier. Under the hood, a data frame is a list of equal-length vectors. This makes it a 2-dimensional structure, so it shares properties of both the matrix and the list. This means that a data frame has names(), colnames(), and rownames(), although names() and colnames() are the same thing. The length() of a data frame is the length of the underlying list and so is the same as ncol(); nrow() gives the number of rows. As described in Chapter 3, you can subset a data frame like a 1d structure (where it behaves like a list), or a 2d structure (where it behaves like a matrix).

4 teaches you about the data frame, the most important data structure for storing data in R. Data frames combine the behaviour of lists and matrices to make a structure ideally suited for the needs of statistical data. 1 Vectors The basic data structure in R is the vector. Vectors come in two flavours: atomic vectors and lists. They have three common properties: • Type, typeof(), what it is. Data structures 15 • Length, length(), how many elements it contains. • Attributes, attributes(), additional arbitrary metadata.

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Advanced R by Hadley Wickham.

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