Lesson 8: More Examples of Extracting Rows, Columns

Often we need to extract rows or columns from a data frame, subject to more than one condition. For instance, say we wish to extract from tg the sub-data frame consisting of OJ and length less than 8.8.

We could do this, using the ampersand symbol &, which means a logical AND operation:

> tg[tg$supp=='OJ' & tg$len < 8.8,]
   len supp dose
37 8.2   OJ  0.5

Ah, it turns out that only one case satisfied both conditions.

If we want all rows that satisfy at least one of the conditions, not necessarily both, then we use the OR operator, |. Say we want to obtain all rows in which either len is greater than 28 or the treatment dose was 1.0 or both:

> tg[tg$len > 28 | tg$dose == 1.0,]
    len supp dose
11 16.5   VC    1
12 16.5   VC    1
13 15.2   VC    1
14 17.3   VC    1
15 22.5   VC    1
16 17.3   VC    1
17 13.6   VC    1
18 14.5   VC    1
19 18.8   VC    1
20 15.5   VC    1
23 33.9   VC    2
26 32.5   VC    2
30 29.5   VC    2
41 19.7   OJ    1
42 23.3   OJ    1
43 23.6   OJ    1
44 26.4   OJ    1
45 20.0   OJ    1
46 25.2   OJ    1
47 25.8   OJ    1
48 21.2   OJ    1
49 14.5   OJ    1
50 27.3   OJ    1
56 30.9   OJ    2
59 29.4   OJ    2

By the way, note that the original row numbers are displayed too. For example, the first case satisfying the conditions was row number 11 in the original data frame tg.

> w <- tg[tg$len > 28 | tg$dose == 1.0,]

Again, I chose the name w arbitrarily. Names must begin with a letter, and consist only of letters, digits and a few special characters such as - or .

Note that w is a new data frame, on which we can perform the usual operations, e.g.

> head(w)
    len supp dose
11 16.5   VC    1
12 16.5   VC    1
13 15.2   VC    1
14 17.3   VC    1
15 22.5   VC    1
16 17.3   VC    1
> nrow(w)
[1] 25

We may only be interested in say, how many cases satisfied the given conditions. As before, we can use nrow for that, as seen here.

As seen early, we can also extract columns. Say our analysis will use only tooth length and dose. We write c(1,3) in the “what columns we want” place, indicating columns 1 and 3:

> lendose <- tg[,c(1,3)]
> head(lendose)
   len dose
1  4.2  0.5
2 11.5  0.5
3  7.3  0.5
4  5.8  0.5
5  6.4  0.5
6 10.0  0.5

From now on, we would work with lendose instead of tg.

It’s a little nicer, though, the specify the columns by name instead of number:

> lendose <- tg[,c('len','dose')]
> head(lendose)
   len dose
1  4.2  0.5
2 11.5  0.5
3  7.3  0.5
4  5.8  0.5
5  6.4  0.5
6 10.0  0.5

The logical operations work on vectors too. For example, say in the Nile data we wish to know how many years had flows in the extremes, say below 500 or above 1200:

> exts <- Nile[Nile < 800 | Nile > 1300]
> head(exts)
[1] 1370  799  774  694  701  692
> length(exts)
[1] 27

By the way, if this count were our only interest, i.e. we have no further use for exts, we can skip assigning to exts, and do things directly:

> length(Nile[Nile < 800 | Nile > 1300])
[1] 27

This is fine for advanced, experienced R users, but really, “one step at a time” is better for beginners.

Recap: What we’ve learned in this lesson

Here we got more practice in manipulating data frames, and were introduced to the logical operators & and |. We also saw another example of using nrow as a means of counting how many rows satisfy given conditions.

Again, these are all “bread and butter” operations that arise quite freqently in real world R usage.

By the way, note how the essence of R is “combining little things in order to do big things,” e.g. combining the subsetting operation, the ’&’ operator, and nrow to get a count of rows satisfying given conditions. This too is the “bread and butter” of R. It’s up to you, the R user, to creatively combine R’s little operations (and later, some big ones) to achieve whatever goals you have for your data. Programming is a creative process. It’s like a grocery store and cooking: The store has lots of different potential ingredients, and you decide which ones to buy and combine into a meal.