By Hadley Wickham.
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Extra info for Advanced R
Com/hadley/adv-r). html). com/type/myfonts/ Part I Foundations 2 Data structures This chapter summarises the most important data structures in base R. You’ve probably used many (if not all) of them before, but you may not have thought deeply about how they are interrelated. In this brief overview, I won’t discuss individual types in depth. Instead, I’ll show you how they ﬁt together as a whole. If you need more details, you can ﬁnd them in R’s documentation. R’s base data structures can be organised by their dimensionality (1d, 2d, or nd) and whether they’re homogeneous (all contents must be of the same type) or heterogeneous (the contents can be of diﬀerent types).
What’s the diﬀerence between [, [[, and $ when applied to a list? 3. When should you use drop = FALSE? 4. If x is a matrix, what does x <- 0 do? How is it diﬀerent to x <- 0? 5. How can you use a named vector to relabel categorical variables? 1 starts by teaching you about [. You’ll start by learning the six types of data that you can use to subset atomic vectors. You’ll then learn how those six data types act when used to subset lists, matrices, data frames, and S3 objects. 2 expands your knowledge of subsetting operators to include [[ and $, focussing on the important principles of simplifying vs.
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. NA will always be coerced to the correct type if used inside c(), or you can create NAs of a speciﬁc type with NA_real_ (a double vector), NA_integer_ and NA_character_.