By Hadley Wickham.
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Additional resources for Advanced R
1 introduces you to atomic vectors and lists, R’s 1d data structures. 2 takes a small detour to discuss attributes, R’s ﬂexible metadata speciﬁcation. Here you’ll learn about factors, an important data structure created by setting attributes of an atomic vector. 3 introduces matrices and arrays, data structures for storing 2d and higher dimensional data. 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.
List(x) to test if an object is actually a vector. 1 Atomic vectors There are four common types of atomic vectors that I’ll discuss in detail: logical, integer, double (often called numeric), and character. There are two rare types that I will not discuss further: complex and raw. 5) L suffix, you get an integer rather than a double c(1L, 6L, 10L) and FALSE (or T and F) to create logical vectors c(TRUE, FALSE, T, F) c("these are", "some strings") Atomic vectors are always ﬂat, even if you nest c()’s: c(1, c(2, c(3, 4))) #>  1 2 3 4 # the same as c(1, 2, 3, 4) #>  1 2 3 4 Missing values are speciﬁed with NA, which is a logical vector of length 1.
Frame': 2 obs. frame': 2 obs. of 2 variables: #> $ a: int 1 2 #> $ b: chr "a" "b" The conversion rules for cbind() are complicated and best avoided by ensuring all inputs are of the same type. frame': 3 obs. - attr(*, "class")= chr "AsIs" dfl[2, "y"] #> [] #>  1 2 3 I() adds the AsIs class to its input, but this can usually be safely ignored. frame': 3 obs. of 2 variables: #> $ x: int 1 2 3 #> $ y: 'AsIs' int [1:3, 1:3] 1 2 3 4 5 6 7 8 9 dfm[2, "y"] #> [,1] [,2] [,3] #> [1,] 2 5 8 Use list and array columns with caution: many functions that work with data frames assume that all columns are atomic vectors.
Advanced R by Hadley Wickham.