Subset Vectors in R
By: Karthik Janar
In this tutorial, weâ€™ll see how to extract elements from a vector based on some conditions that we specify. For example, we may only be interested in the first 20 elements of a vector, or only the elements that are not NA, or only those that are positive or correspond to a specific variable of interest. By the end of this tutorial, youâ€™ll know how to handle each of these scenarios.
First create a vector called x that contains a random ordering of 20 numbers (from a standard normal distribution) and 20 NAs.
y <- rnorm(10)
z <- rep(NA, 10)
x <- sample(c(y,z),20)
x
## [1] -1.2731728 NA 0.6789480 NA -0.4966300 NA
## [7] NA NA -0.4829580 NA -0.4601567 -0.4970408
## [13] -0.3120564 -0.2704296 NA NA NA NA
## [19] -0.5934239 -0.6427310
The way you tell R that you want to select some particular elements (i.e.Â a â€˜subsetâ€™) from a vector is by placing an â€˜index vectorâ€™ in square brackets immediately following the name of the vector.
For a simple example, try x[1:10] to view the first ten elements of x.
x[1:10]
## [1] -1.273173 NA 0.678948 NA -0.496630 NA NA
## [8] NA -0.482958 NA
Index vectors come in four different flavors â€“ logical vectors, vectors of positive integers, vectors of negative integers, and vectors of character strings â€“ each of which weâ€™ll cover in this tutorial.
Letâ€™s start by indexing with logical vectors. One common scenario when working with real-world data is that we want to extract all elements of a vector that are not NA (i.e.Â missing data). Recall that is.na(x) yields a vector of logical values the same length as x, with TRUEs corresponding to NA values in x and FALSEs corresponding to non-NA values in x.
What do you think x[is.na(x)] will give you? It will return a vector of all NAs
x[is.na(x)]
## [1] NA NA NA NA NA NA NA NA NA NA
!
gives us the negation of a logical expression, so !is.na(x) can be read as â€˜is not NAâ€™. Therefore, if we want to create a vector called y that contains all of the non-NA values from x, we can use y <- x[!is.na(x)]. .
y <- x[!is.na(x)]
y
## [1] -1.2731728 0.6789480 -0.4966300 -0.4829580 -0.4601567 -0.4970408
## [7] -0.3120564 -0.2704296 -0.5934239 -0.6427310
Now that weâ€™ve isolated the non-missing values of x and put them in y, we can subset y as we please. Recall that the expression y > 0 will give us a vector of logical values the same length as y, with TRUEs corresponding to values of y that are greater than zero and FALSEs corresponding to values of y that are less than or equal to zero. What do you think y[y > 0] will give you? A vector of all the positive elements of y.
Type y[y > 0] to see that we get all of the positive elements of y, which are also the positive elements of our original vector x.
y[y>0]
## [1] 0.678948
You might wonder why we didnâ€™t just start with x[x > 0] to isolate the positive elements of x. Try that now to see why.
x[x>0]
## [1] NA 0.678948 NA NA NA NA NA
## [8] NA NA NA NA
Since NA is not a value, but rather a placeholder for an unknown quantity, the expression NA > 0 evaluates to NA. Hence we get a bunch of NAs mixed in with our positive numbers when we do this.
Combining our knowledge of logical operators with our new knowledge of subsetting, we could do this â€“ x[!is.na(x) & x > 0].
x[!is.na(x) & x>0]
## [1] 0.678948
In this case, we request only values of x that are both non-missing AND greater than zero.
Earlier we saw how to subset just the first ten values of x using x[1:10]. In this case, weâ€™re providing a vector of positive integers inside of the square brackets, which tells R to return only the elements of x numbered 1 through 10.
Many programming languages use whatâ€™s called â€˜zero-based indexingâ€™, which means that the first element of a vector is considered element 0. R uses â€˜one-based indexingâ€™, which (you guessed it!) means the first element of a vector is considered element 1.
Can you figure out how weâ€™d subset the 3rd, 5th, and 7th elements of x? Hint â€“ Use the c() function to specify the element numbers as a numeric vector.
x[c(3,5,7)]
## [1] 0.678948 -0.496630 NA
Itâ€™s important that when using integer vectors to subset our vector x, we stick with the set of indexes {1, 2, â€¦, 20} since x only has 20 elements. What happens if we ask for the zeroth element of x (i.e.Â x[0])?
x[0]
## numeric(0)
As you might expect, we get nothing useful. Unfortunately, R doesnâ€™t prevent us from doing this. What if we ask for the 3000th element of x?
x[3000]
## [1] NA
Again, nothing useful, but R doesnâ€™t prevent us from asking for it. This should be a cautionary note. You should always make sure that what you are asking for is within the bounds of the vector youâ€™re working with.
What if weâ€™re interested in all elements of x EXCEPT the 2nd and 10th? It would be pretty tedious to construct a vector containing all numbers 1 through 20 EXCEPT 2 and 10.
R accepts negative integer indexes. Whereas x[c(2, 10)] gives us ONLY the 2nd and 10th elements of x, x[c(-2, -10)] gives us all elements of x EXCEPT for the 2nd and 10 elements.
x[c(-2,-10)]
## [1] -1.2731728 0.6789480 NA -0.4966300 NA NA
## [7] NA -0.4829580 -0.4601567 -0.4970408 -0.3120564 -0.2704296
## [13] NA NA NA NA -0.5934239 -0.6427310
A shorthand way of specifying multiple negative numbers is to put the negative sign out in front of the vector of positive numbers. Type x[-c(2, 10)] to get the exact same result.
x[-c(2,10)]
## [1] -1.2731728 0.6789480 NA -0.4966300 NA NA
## [7] NA -0.4829580 -0.4601567 -0.4970408 -0.3120564 -0.2704296
## [13] NA NA NA NA -0.5934239 -0.6427310
So far, weâ€™ve covered three types of index vectors â€“ logical, positive integer, and negative integer. The only remaining type requires us to introduce the concept of â€˜namedâ€™ elements.
Create a numeric vector with three named elements using vect <- c(foo = 11, bar = 2, norf = NA). When we print vect to the console, youâ€™ll see that each element has a name.
vect <- c(foo = 11, bar = 2, norf = NA)
vect
## foo bar norf
## 11 2 NA
We can also get the names of vect by passing vect as an argument to the names() function.
names(vect)
## [1] "foo" "bar" "norf"
Alternatively, we can create an unnamed vector vect2 with c(11, 2, NA). Then, we can add the names
attribute to vect2 after the fact with names(vect2) <- c(â€œfooâ€, â€œbarâ€, â€œnorfâ€).
vect2 <- c(11,2,NA)
names(vect2) <- c("foo","bar","norf")
Now, letâ€™s check that vect and vect2 are the same by passing them as arguments to the identical() function.
identical(vect,vect2)
## [1] TRUE
[1] TRUE
You can see that, vect and vect2 are identical named vectors.
Now, back to the matter of subsetting a vector by named elements. Which of the following commands do you think would give us the second element of vect?
vect["bar"]
## bar
## 2
Likewise, we can specify a vector of names with vect[c(â€œfooâ€, â€œbarâ€)].
vect[c("foo", "bar")]
## foo bar
## 11 2
Now you know all four methods of subsetting data from vectors. Different approaches are best in different scenarios and when in doubt, try it out!
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