You can use these logical vectors very efficiently to select some values from a vector. Indexing works similarly as it does with vectors, apart from that here you have to state an index for more than 1-dimension. select. Elements from a vector, matrix, or data frame can be extracted using numeric indexing, or by using a boolean vector of the appropriate length. It’s easiest to learn how subsetting works for atomic vectors, and then how it generalises to higher dimensions and other more complicated objects. select. To get the 7th element of the colors vector: colors[7]. You want to get part of a data structure. Thelonger form is appropriate for programming control-flow and typicallypreferred in ifclauses. To remove them, we’ll use logical indexing to change the invalid values (999 and -2) to NA. Logical index vectors. Its members are TRUE if the corresponding members in the original vector are to be included in the slice, and FALSE if otherwise. These operators are generic. If you want to keep only the values larger than 2 in the vector x, you could do that with the following code: Wait — what is that NA value doing there? Note that brackets [] are used for indexing, whereas parentheses are used to call a function. R’s default behavior might not be what you expect: valueMatrix <-matrix (LETTERS [1: 15], ncol = 3) valueMatrix isTRUE(x) is the same as{ is.log… TRUE. passed on to [indexing operator. Logical indexing on the left assigns these values to the elements that were singled out in column major order. The longerform evaluates left to right examining only the first element of eachvector. Logical index vectors We can use a vector of logical values to index another vector of the same length. If you use a logical vector to index, R returns a vector with only the values for which the logical vector is TRUE. & and && indicate logical AND and | and ||indicate logical OR. corresponding members in the original vector are to be included in the slice, and When used with the indexing notation the items within a vector that are NAcan be easily removed: > a <-c(1,2,3,4,NA)> is.na(a) FALSE FALSE FALSE FALSE TRUE> !is.na(a) TRUE TRUE TRUE TRUE FALSE> a[!is.na(a)] 1 2 3 4> b <-a[!is.na(a)]> b Let's talk about the basic rules of logical indexing, and then we'll reexamine the expression B(isnan(B)). Using logical indexing to categorize a variable into mutually exclusive groups. 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