7.4: Extracting a Subset of a Vector

One very important kind of data handling is being able to extract a particular subset of the data. For instance, you might be interested only in analysing the data from one experimental condition, or you may want to look closely at the data from people over 50 years in age. To do this, the first step is getting R to extract the subset of the data corresponding to the observations that you’re interested in. In this section I’ll talk about subsetting as it applies to vectors, extending the discussion from Chapters 3 and 4. In Section 7.5 I’ll go on to talk about how this discussion extends to data frames.

Refresher

This section returns to the nightgarden.Rdata data set. If you’re reading this whole chapter in one sitting, then you should already have this data set loaded. If not, don’t forget to use the load("nightgarden.Rdata") command. For this section, let’s ignore the itng data frame that we created earlier, and focus instead on the two vectors speaker and utterance (see Section 7.1 if you’ve forgotten what those vectors look like). Suppose that what I want to do is pull out only those utterances that were made by Makka-Pakka. To that end, I could first use the equality operator to have R tell me which cases correspond to Makka-Pakka speaking:

is.MP.speaking <- speaker == "makka-pakka"
is.MP.speaking

##  [1] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE

and then use logical indexing to get R to print out those elements of utterance for which is.MP.speaking is true, like so:

utterance[ is.MP.speaking ]

## [1] "pip" "pip" "onk" "onk"

Or, since I’m lazy, I could collapse it to a single command like so:

utterance[ speaker == "makka-pakka" ]

## [1] "pip" "pip" "onk" "onk"

Using %in%match multiple cases

A second useful trick to be aware of is the %in% operator¹¹⁰. It’s actually very similar to the == operator, except that you can supply a collection of acceptable values. For instance, suppose I wanted to keep only those cases when the utterance is either “pip” or “oo”. One simple way do to this is:

utterance %in% c("pip","oo") 

##  [1]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE

What this does if return TRUE for those elements of utterance that are either "pip" or "oo" and returns FALSE for all the others. What that means is that if I want a list of all those instances of characters speaking either of these two words, I could do this:

speaker[ utterance %in% c("pip","oo") ]

## [1] "upsy-daisy"  "upsy-daisy"  "tombliboo"   "makka-pakka" "makka-pakka"

Using negative indices to drop elements

Before moving onto data frames, there’s a couple of other tricks worth mentioning. The first of these is to use negative values as indices. Recall from Section 3.10 that we can use a vector of numbers to extract a set of elements that we would like to keep. For instance, suppose I want to keep only elements 2 and 3 from utterance. I could do so like this:

utterance[2:3]

## [1] "pip" "onk"

But suppose, on the other hand, that I have discovered that observations 2 and 3 are untrustworthy, and I want to keep everything except those two elements. To that end, R lets you use negative numbers to remove specific values, like so:

utterance [ -(2:3) ]

## [1] "pip" "onk" "ee"  "oo"  "pip" "pip" "onk" "onk"

The output here corresponds to element 1 of the original vector, followed by elements 4, 5, and so on. When all you want to do is remove a few cases, this is a very handy convention.

Splitting a vector by group

One particular example of subsetting that is especially common is the problem of splitting one one variable up into several different variables, one corresponding to each group. For instance, in our In the Night Garden example, I might want to create subsets of the utterance variable for every character. One way to do this would be to just repeat the exercise that I went through earlier separately for each character, but that quickly gets annoying. A faster way do it is to use the split() function. The arguments are:

• x. The variable that needs to be split into groups.
• f. The grouping variable.

What this function does is output a list (Section 4.9), containing one variable for each group. For instance, I could split up the utterance variable by speaker using the following command:

speech.by.char <- split( x = utterance, f = speaker )
speech.by.char

## $`makka-pakka`
## [1] "pip" "pip" "onk" "onk"
## 
## $tombliboo
## [1] "ee" "oo"
## 
## $`upsy-daisy`
## [1] "pip" "pip" "onk" "onk"

Once you’re starting to become comfortable working with lists and data frames, this output is all you need, since you can work with this list in much the same way that you would work with a data frame. For instance, if you want the first utterance made by Makka-Pakka, all you need to do is type this:

speech.by.char$`makka-pakka`[1]

## [1] "pip"

Just remember that R does need you to add the quoting characters (i.e. '). Otherwise, there’s nothing particularly new or difficult here.

However, sometimes – especially when you’re just starting out – it can be convenient to pull these variables out of the list, and into the workspace. This isn’t too difficult to do, though it can be a little daunting to novices. To that end, I’ve included a function called importList() in the lsr package that does this.¹¹¹ First, here’s what you’d have if you had wiped the workspace before the start of this section:

who()

##    -- Name --         -- Class --   -- Size --
##    age                numeric       11        
##    age.breaks         numeric       4         
##    age.group          factor        11        
##    age.group2         factor        11        
##    age.group3         factor        11        
##    age.labels         character     3         
##    df                 data.frame    10 x 4    
##    is.MP.speaking     logical       10        
##    itng               data.frame    10 x 2    
##    itng.table         table         3 x 4     
##    likert.centred     numeric       10        
##    likert.raw         numeric       10        
##    opinion.dir        numeric       10        
##    opinion.strength   numeric       10        
##    some.data          numeric       18        
##    speaker            character     10        
##    speech.by.char     list          3         
##    utterance          character     10

Now we use the importList() function to copy all of the variables within the speech.by.char list:

importList( speech.by.char, ask = FALSE)

Because the importList() function is attempting to create new variables based on the names of the elements of the list, it pauses to check that you’re okay with the variable names. The reason it does this is that, if one of the to-be-created variables has the same name as a variable that you already have in your workspace, that variable will end up being overwritten, so it’s a good idea to check. Assuming that you type y, it will go on to create the variables. Nothing appears to have happened, but if we look at our workspace now:

who()

##    -- Name --         -- Class --   -- Size --
##    age                numeric       11        
##    age.breaks         numeric       4         
##    age.group          factor        11        
##    age.group2         factor        11        
##    age.group3         factor        11        
##    age.labels         character     3         
##    df                 data.frame    10 x 4    
##    is.MP.speaking     logical       10        
##    itng               data.frame    10 x 2    
##    itng.table         table         3 x 4     
##    likert.centred     numeric       10        
##    likert.raw         numeric       10        
##    makka.pakka        character     4         
##    opinion.dir        numeric       10        
##    opinion.strength   numeric       10        
##    some.data          numeric       18        
##    speaker            character     10        
##    speech.by.char     list          3         
##    tombliboo          character     2         
##    upsy.daisy         character     4         
##    utterance          character     10

we see that there are three new variables, called makka.pakka, tombliboo and upsy.daisy. Notice that the importList() function has converted the original character strings into valid R variable names, so the variable corresponding to "makka-pakka" is actually makka.pakka.¹¹² Nevertheless, even though the names can change, note that each of these variables contains the exact same information as the original elements of the list did. For example:

> makka.pakka
[1] "pip" "pip" "onk" "onk"