1: R Programming
2: Take me to the swirl course repository!
Selection: 1
| Please choose a lesson, or type 0 to return to course menu.
1: Basic Building Blocks 2: Workspace and Files 3: Sequences of Numbers
4: Vectors 5: Missing Values 6: Subsetting Vectors
7: Matrices and Data Frames 8: Logic 9: Functions
10: lapply and sapply 11: vapply and tapply 12: Looking at Data
13: Simulation 14: Dates and Times 15: Base Graphics
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| In this lesson, we’ll see how to extract elements from a vector based on some conditions that we specify.
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|=== | 3%
| 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
| lesson, you’ll know how to handle each of these scenarios.
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|===== | 5%
| I’ve created for you a vector called x that contains a random ordering of 20 numbers (from a standard normal
| distribution) and 20 NAs. Type x now to see what it looks like.
> x
[1] NA NA NA -0.72353476 -0.89198458 NA NA -0.23626102 NA
[10] NA 1.07340403 -0.34040992 1.20108728 -0.67248237 NA NA NA NA
[19] 0.35705287 2.57339120 0.30426816 NA 1.07870859 0.04976609 NA NA 0.93535528
[28] 0.73660119 NA 1.26329875 0.79243764 0.75671912 0.67751338 NA NA NA
[37] NA NA -0.33559365 1.29938969
| You nailed it! Good job!
|======== | 8%
| The way you tell R that you want to select some particular elements (i.e. a ‘subset’) from a vector is by
| placing an ‘index vector’ in square brackets immediately following the name of the vector.
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|=========== | 10%
| For a simple example, try x[1:10] to view the first ten elements of x.
> x[1:10]
[1] NA NA NA -0.7235348 -0.8919846 NA NA -0.2362610 NA
[10] NA
| You got it!
|============= | 13%
| Index vectors come in four different flavors — logical vectors, vectors of positive integers, vectors of
| negative integers, and vectors of character strings — each of which we’ll cover in this lesson.
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|================ | 15%
| Let’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. 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.
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|================== | 18%
| What do you think x[is.na(x)] will give you?
1: A vector with no NAs
2: A vector of length 0
3: A vector of TRUEs and FALSEs
4: A vector of all NAs
Selection: 4
| Perseverance, that’s the answer.
|===================== | 21%
| Prove it to yourself by typing x[is.na(x)].
> x[is.na(x)]
[1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
| Keep working like that and you’ll get there!
|======================== | 23%
| Recall that `!` gives us the negation of a logical expression, so !is.na(x) can be read as ‘is not NA’.
| 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)]. Give it a try.
> y <- x[!is.na(x)]
| Keep up the great work!
|========================== | 26%
| Print y to the console.
> y
[1] -0.72353476 -0.89198458 -0.23626102 1.07340403 -0.34040992 1.20108728 -0.67248237 0.35705287 2.57339120
[10] 0.30426816 1.07870859 0.04976609 0.93535528 0.73660119 1.26329875 0.79243764 0.75671912 0.67751338
[19] -0.33559365 1.29938969
| That’s a job well done!
|============================= | 28%
| Now that we’ve isolated the non-missing values of x and put them in y, we can subset y as we please.
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|================================ | 31%
| 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?
1: A vector of all NAs
2: A vector of length 0
3: A vector of all the positive elements of y
4: A vector of TRUEs and FALSEs
5: A vector of all the negative elements of y
Selection: 3
| You are doing so well!
|================================== | 33%
| 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] 1.07340403 1.20108728 0.35705287 2.57339120 0.30426816 1.07870859 0.04976609 0.93535528 0.73660119
[10] 1.26329875 0.79243764 0.75671912 0.67751338 1.29938969
| Your dedication is inspiring!
|===================================== | 36%
| You might wonder why we didn’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 NA NA NA NA NA NA 1.07340403 1.20108728
[10] NA NA NA NA 0.35705287 2.57339120 0.30426816 NA 1.07870859
[19] 0.04976609 NA NA 0.93535528 0.73660119 NA 1.26329875 0.79243764 0.75671912
[28] 0.67751338 NA NA NA NA NA 1.29938969
| Perseverance, that’s the answer.
|======================================== | 38%
| 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.
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|========================================== | 41%
| Combining our knowledge of logical operators with our new knowledge of subsetting, we could do this —
| x[!is.na(x) & x > 0]. Try it out.
> x[!is.na(x) & x > 0]
[1] 1.07340403 1.20108728 0.35705287 2.57339120 0.30426816 1.07870859 0.04976609 0.93535528 0.73660119
[10] 1.26329875 0.79243764 0.75671912 0.67751338 1.29938969
| You nailed it! Good job!
|============================================= | 44%
| In this case, we request only values of x that are both non-missing AND greater than zero.
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|================================================ | 46%
| I’ve already shown you how to subset just the first ten values of x using x[1:10]. In this case, we’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.
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|================================================== | 49%
| Many programming languages use what’s called ‘zero-based indexing’, which means that the first element of a
| vector is considered element 0. R uses ‘one-based indexing’, which (you guessed it!) means the first element
| of a vector is considered element 1.
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|===================================================== | 51%
| Can you figure out how we’d subset the 3rd, 5th, and 7th elements of x? Hint — Use the c() function to
| specify the element numbers as a numeric vector.
> x[c(3, 5, 7)]
[1] NA -0.8919846 NA
| You’re the best!
|======================================================= | 54%
| It’s important that when using integer vectors to subset our vector x, we stick with the set of indexes {1, 2,
| …, 40} since x only has 40 elements. What happens if we ask for the zeroth element of x (i.e. x[0])? Give it
| a try.
> x[0]
numeric(0)
| You are doing so well!
|========================================================== | 56%
| As you might expect, we get nothing useful. Unfortunately, R doesn’t prevent us from doing this. What if we
| ask for the 3000th element of x? Try it out.
> x[300]
[1] NA
| Not quite, but you’re learning! Try again. Or, type info() for more options.
| Request the 3000th element of x (which does not exist) with x[3000].
> x[3000]
[1] NA
| Excellent job!
|============================================================= | 59%
| Again, nothing useful, but R doesn’t prevent us from asking for it. This should be a cautionary tale. You
| should always make sure that what you are asking for is within the bounds of the vector you’re working with.
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|=============================================================== | 62%
| What if we’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 40 EXCEPT 2 and 10.
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|================================================================== | 64%
| Luckily, 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. Try x[c(-2, -10)] now to see
| this.
> x[c(-2, -10)]
[1] NA NA -0.72353476 -0.89198458 NA NA -0.23626102 NA 1.07340403
[10] -0.34040992 1.20108728 -0.67248237 NA NA NA NA 0.35705287 2.57339120
[19] 0.30426816 NA 1.07870859 0.04976609 NA NA 0.93535528 0.73660119 NA
[28] 1.26329875 0.79243764 0.75671912 0.67751338 NA NA NA NA NA
[37] -0.33559365 1.29938969
| You got it right!
|===================================================================== | 67%
| 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] NA NA -0.72353476 -0.89198458 NA NA -0.23626102 NA 1.07340403
[10] -0.34040992 1.20108728 -0.67248237 NA NA NA NA 0.35705287 2.57339120
[19] 0.30426816 NA 1.07870859 0.04976609 NA NA 0.93535528 0.73660119 NA
[28] 1.26329875 0.79243764 0.75671912 0.67751338 NA NA NA NA NA
[37] -0.33559365 1.29938969
| You are doing so well!
|======================================================================= | 69%
| So far, we’ve covered three types of index vectors — logical, positive integer, and negative integer. The
| only remaining type requires us to introduce the concept of ‘named’ elements.
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|========================================================================== | 72%
| Create a numeric vector with three named elements using vect <- c(foo = 11, bar = 2, norf = NA).
> vect <- c(foo = 11, bar = 2, norf = NA)
| You are quite good my friend!
|============================================================================= | 74%
| When we print vect to the console, you'll see that each element has a name. Try it out.
> vect
foo bar norf
11 2 NA
| You are really on a roll!
|=============================================================================== | 77%
| We can also get the names of vect by passing vect as an argument to the names() function. Give that a try.
> names(vect)
[1] “foo” “bar” “norf”
| Excellent job!
|================================================================================== | 79%
| Alternatively, we can create an unnamed vector vect2 with c(11, 2, NA). Do that now.
> vect <- c(11, 2, NA)
| Not quite! Try again. Or, type info() for more options.
| Create an ordinary (unnamed) vector called vect2 that contains c(11, 2, NA).
> vect2 <- c(11, 2, NA)
| You got it right!
|===================================================================================== | 82%
| Then, we can add the `names` attribute to vect2 after the fact with names(vect2) <- c("foo", "bar", "norf").
| Go ahead.
> names(vect2) <- c("foo", "bar", "norf")
| That's the answer I was looking for.
|======================================================================================= | 85%
| Now, let's check that vect and vect2 are the same by passing them as arguments to the identical() function.
> idenitical(vect, vect2)
Error: could not find function “idenitical”
> identical(vect, vect2)
[1] TRUE
| You’re the best!
|========================================================================================== | 87%
| Indeed, vect and vect2 are identical named vectors.
…
|============================================================================================ | 90%
| 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?
1: vect[“2”]
2: vect[bar]
3: vect[“bar”]
Selection: 1
| Not quite right, but keep trying.
| If we want the element named “bar” (i.e. the second element of vect), which command would get us that?
1: vect[“bar”]
2: vect[bar]
3: vect[“2”]
Selection: 2
| Try again. Getting it right on the first try is boring anyway!
| If we want the element named “bar” (i.e. the second element of vect), which command would get us that?
1: vect[“2”]
2: vect[“bar”]
3: vect[bar]
Selection: 2
| Excellent work!
|=============================================================================================== | 92%
| Now, try it out.
> vect[“bar”]
bar
2
| You are quite good my friend!
|================================================================================================== | 95%
| Likewise, we can specify a vector of names with vect[c(“foo”, “bar”)]. Try it out.
> vect[c(“foo”, “bar”)]
foo bar
11 2
| You are really on a roll!
|==================================================================================================== | 97%
| 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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|=======================================================================================================| 100%
| Would you like to receive credit for completing this course on Coursera.org?
1: Yes
2: No