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The data frame hsb_small.csv will be used for the following examples.
To manipulate data frames in R we can use the bracket notation to access the indices for the observations and the variables. It is easiest to think of the data frame as a rectangle of data where the rows are the observations and the columns are the variables. Just like in matrix algebra, the indices for a rectangle of data follow the RxC principle; in other words, the first index is for Rows and the second index is for Columns [R, C]. When we only want to subset variables (or columns) we use the second index and leave the first index blank. Leaving an index blank indicates that you want to keep all the elements in that dimension. In the first example we create the data frame hsb3 containing only the variables id, read and write, but all the observations from the original data frame hsb2.small. In order to know which variables correspond to which number in the index we use the names function, which will list the names of the variables in the order in which they appear in the data frame. From this list we see that id is variable 1, read is variable 7 and write is variable 8. Also, please note that you have to use the attach function before you can use the names function.
hsb2.small <- read.table('c:/hsb2_small.csv', header=T, sep=",")
#attaching the data
attach(hsb2.small)
# using the names function to see names of the variables and which column
# of data to which they correspond
names(hsb2.small)
[1] "id" "female" "race" "ses" "schtyp" "prog" "read"
[8] "write" "math" "science" "socst"
hsb3 <- hsb2.small[, c(1, 7, 8)]
hsb3
id read write
1 70 57 52
2 121 68 59
3 86 44 33
4 141 63 44
5 172 47 52
6 113 44 52
7 50 50 59
8 11 34 46
9 84 63 57
10 48 57 55
11 75 60 46
12 60 57 65
13 95 73 60
14 104 54 63
15 38 45 57
16 115 42 49
17 76 47 52
18 195 57 57
19 114 68 65
20 85 55 39
21 167 63 49
22 143 63 63
23 41 50 40
24 20 60 52
25 12 37 44
If the variables we want are in consecutive columns, we can use the colon notation rather than list them using the c function. In the next example we create the data frame hsb4 containing the first four variables of hsb2.small.
hsb4 <- hsb2.small[ , 1:4]
hsb4
id female race ses
1 70 0 4 1
2 121 1 4 2
3 86 0 4 3
4 141 0 4 3
5 172 0 4 2
6 113 0 4 2
7 50 0 3 2
8 11 0 1 2
9 84 0 4 2
10 48 0 3 2
11 75 0 4 2
12 60 0 4 2
13 95 0 4 3
14 104 0 4 3
15 38 0 3 1
16 115 0 4 1
17 76 0 4 3
18 195 0 4 2
19 114 0 4 3
20 85 0 4 2
21 167 0 4 2
22 143 0 4 2
23 41 0 3 2
24 20 0 1 3
25 12 0 1 2
We subset observations by also using the bracket notation but now we use the first index and leave the second index blank. This indicates that we want all the variables for specific observations. In the first example we create the data frame hsb5, which contains the first 10 observations of hsb2.small.
hsb5 <- hsb2.small[1:10, ]
hsb5
id female race ses schtyp prog read write math science socst
1 70 0 4 1 1 1 57 52 41 47 57
2 121 1 4 2 1 3 68 59 53 63 61
3 86 0 4 3 1 1 44 33 54 58 31
4 141 0 4 3 1 3 63 44 47 53 56
5 172 0 4 2 1 2 47 52 57 53 61
6 113 0 4 2 1 2 44 52 51 63 61
7 50 0 3 2 1 1 50 59 42 53 61
8 11 0 1 2 1 2 34 46 45 39 36
9 84 0 4 2 1 1 63 57 54 58 51
10 48 0 3 2 1 2 57 55 52 50 51
We can also subset observations based on logical tests. In the following example we create the data frame hsb6, which contains only the observations for which ses=1. For a logical equality we need to use the double equal sign notation.
hsb6 <- hsb2.small[ses == 1, ]
hsb6
id female race ses schtyp prog read write math science socst
1 70 0 4 1 1 1 57 52 41 47 57
15 38 0 3 1 1 2 45 57 50 31 56
16 115 0 4 1 1 1 42 49 43 50 56
In the previous example we used a logical test to subset the observations, but we only tested for one variable being equal to a single value. We can also subset using a logical test that will test a single variable being equal to the elements in a list, and we do this by using the %in% function. In the following example we create the data frame hsb7, which contains the observations where id is equal to 11, 12, 20, 48, 86 or 195.
hsb7 <- hsb2.small[id %in% c(12, 48, 86, 11, 20, 195), ]
hsb7
id female race ses schtyp prog read write math science socst
3 86 0 4 3 1 1 44 33 54 58 31
8 11 0 1 2 1 2 34 46 45 39 36
10 48 0 3 2 1 2 57 55 52 50 51
18 195 0 4 2 2 1 57 57 60 58 56
24 20 0 1 3 1 2 60 52 57 61 61
25 12 0 1 2 1 3 37 44 45 39 46
It is also possible to combine logical tests. In the following example we create the data frame hsb8, which contains only the observations where ses=3 and female=0.
hsb8 <- hsb2.small[ses==3 & female==0, ]
hsb8
id female race ses schtyp prog read write math science socst
3 86 0 4 3 1 1 44 33 54 58 31
4 141 0 4 3 1 3 63 44 47 53 56
13 95 0 4 3 1 2 73 60 71 61 71
14 104 0 4 3 1 2 54 63 57 55 46
17 76 0 4 3 1 2 47 52 51 50 56
19 114 0 4 3 1 2 68 65 62 55 61
24 20 0 1 3 1 2 60 52 57 61 61
Subsetting rows using the subset function
The subset function with a logical statement will let you subset the data frame by observations. In the following example the write.50 data frame contains only the observations for which the values of the variable write is greater than 50.
write.50 <- subset(hsb2.small, write > 50)
write.50
id female race ses schtyp prog read write math science socst
1 70 0 4 1 1 1 57 52 41 47 57
2 121 1 4 2 1 3 68 59 53 63 61
5 172 0 4 2 1 2 47 52 57 53 61
6 113 0 4 2 1 2 44 52 51 63 61
7 50 0 3 2 1 1 50 59 42 53 61
9 84 0 4 2 1 1 63 57 54 58 51
10 48 0 3 2 1 2 57 55 52 50 51
12 60 0 4 2 1 2 57 65 51 63 61
13 95 0 4 3 1 2 73 60 71 61 71
14 104 0 4 3 1 2 54 63 57 55 46
15 38 0 3 1 1 2 45 57 50 31 56
17 76 0 4 3 1 2 47 52 51 50 56
18 195 0 4 2 2 1 57 57 60 58 56
19 114 0 4 3 1 2 68 65 62 55 61
22 143 0 4 2 1 3 63 63 75 72 66
24 20 0 1 3 1 2 60 52 57 61 61
Subsetting rows using multiple conditional statements
There is no limit to how many logical statements may be combined to achieve the subsetting that is desired. The data frame write.1 contains only the observations for which the values of the variable write is greater than 50 and for which the variable read is greater than 60.
write.1 <- subset(hsb2.small, write > 50 & read > 60)
write.1
id female race ses schtyp prog read write math science socst
2 121 1 4 2 1 3 68 59 53 63 61
9 84 0 4 2 1 1 63 57 54 58 51
13 95 0 4 3 1 2 73 60 71 61 71
19 114 0 4 3 1 2 68 65 62 55 61
22 143 0 4 2 1 3 63 63 75 72 66
Subsetting both rows and columns
It is possible to subset both rows and columns using the subset function. The select argument lets you subset variables (columns). The data frame write.2 contains only the variables write and read and then only the observations of these two variables where the values of variable write are greater than 50 and the values of variable read are greater than 65.
write.2 <- subset(hsb2.small, write > 50 & read > 60, select = c(write, read)) write.2 write read 2 59 68 9 57 63 13 60 73 19 65 68 22 63 63
In the data frame write.3 contains only the observations in variables read through science for which the values in the variable science are less than 55.
write.3 <- subset(hsb2.small, science < 55, select = read:science)
write.3
read write math science
1 57 52 41 47
4 63 44 47 53
5 47 52 57 53
7 50 59 42 53
8 34 46 45 39
10 57 55 52 50
11 60 46 51 53
15 45 57 50 31
16 42 49 43 50
17 47 52 51 50
20 55 39 57 53
25 37 44 45 39
We can subset variables and observations by simply combining the two above methods of subsetting. We accomplish this by subsetting using both indices at the same time. In the following example we create the data frame hsb9 in which we keep only the variables id, female, race, ses and read and only the observations where ses=3.
# using the names function to see names of the variables and which column
# of data to which they correspond
names(hsb2.small)
[1] "id" "female" "race" "ses" "schtyp" "prog" "read"
[8] "write" "math" "science" "socst"
hsb9 <- hsb2.small[ses == 3, c(1:4, 7)]
hsb9
id female race ses read
3 86 0 4 3 44
4 141 0 4 3 63
13 95 0 4 3 73
14 104 0 4 3 54
17 76 0 4 3 47
19 114 0 4 3 68
24 20 0 1 3 60
detach(hsb2.small)
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