By Wickham, Hadley

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**Sample text**

The diﬀerences aren’t too important, but it’s useful to know they exist in case you get strange output from a function (tapply() is a frequent oﬀender). As always, use str() to reveal the diﬀerences. 0) dim(l) <- c(2, 2) l #> [,1] [,2] #> [1,] Integer,3 TRUE #> [2,] "a" 1 These are relatively esoteric data structures, but can be useful if you want to arrange objects into a grid-like structure. For example, if you’re running models on a spatio-temporal grid, it might be natural to preserve the grid structure by storing the models in a 3d array.

Some of the most important are described below. , subset(), merge(), plyr::arrange()), but it is useful to understand how they are implemented with basic subsetting. This will allow you to adapt to new situations that are not dealt with by existing functions. 1 Lookup tables (character subsetting) Character matching provides a powerful way to make lookup tables.

What does dim() return when applied to a vector? 2. array(x) return? 3. How would you describe the following three objects? 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.