R Is Not So Hard! A Tutorial, Part 10: Creating Summary tables with aggregate()

In Part 10, let’s look at the aggregate command for creating summary tables using R.

You may have a complex data set that includes categorical variables of several levels, and you may wish to create summary tables for each level of the categorical variable.

For example, your data set may include the variable Gender, a two-level categorical variable with levels Male and Female. Your data set may include other categorical variables such as Ethnicity, Hair Colour, the Treatments received by patients in a medical study, or the number of cylinders in motor vehicles.

In any case, you may wish to produce summary statistics for each level of the categorical variable. This is where the aggregate command is so helpful.

Here is a data set of patients receiving medical treatment (A, B or C). We have data on their gender, their body mass in kg, whether or not they exercise, whether or not they smoke, and whether or not they recovered after treatment. Cut and paste the following data set into R.

patients <- structure(list(GENDER = c("M","M","F","F","F","M","M","F","M","M","F","F","F","M","M","M","F","F","M","M","M","F","F","F","M","M","F"), TREATMENT = c("A","A","B","A","B","B","C","B","A","A","B","B","B","A","C","B","B","A","C","A","A","B","C","B","B","B","C"),MASS = c(79,87,65,58,72,95,76,56,77,104,67,82,59,68,79,125,83,63,57,84,72,68,65,64,87,92,56),
SMOKE = c("Y","Y","N","Y","N","N","N","N","Y","N","N","Y","N","Y","N","Y","N","Y","N","Y","Y","N","N","N","Y","N","Y"),EXERCISE = c("Y","Y","N","N","N","N","Y","Y","Y","N","Y","Y","N","N","Y","N","N","N","N","N","Y","N","N","N","N","Y","N"),RECOVER = c("Y","Y","Y","N","N","Y","Y","N","N","Y","N","N","N","N","Y","Y","N","N","Y","Y","Y","Y","N","Y","Y","Y","Y")), .Names = c("GENDER", "TREATMENT","MASS","SMOKE","EXERCISE","RECOVER"),
row.names = 1:27,
class = "data.frame")


Now attach and examine the dataset.

attach(patients)
patients

   GENDER TREATMENT MASS SMOKE EXERCISE RECOVER
1    M       A      79    Y      Y       Y
2    M       A      87    Y      Y       Y
3    F       B      65    N      N       Y
4    F       A      58    Y      N       N
5    F       B      72    N      N       N
6    M       B      95    N      N       Y
7    M       C      76    N      Y       Y
8    F       B      56    N      Y       N
9    M       A      77    Y      Y       N
10   M       A      104   N      N       Y
11   F       B      67    N      Y       N
12   F       B      82    Y      Y       N
13   F       B      59    N      N       N
14   M       A      68    Y      N       N
15   M       C      79    N      Y       Y
16   M       B      125   Y      N       Y
17   F       B      83    N      N       N
18   F       A      63    Y      N       N
19   M       C      57    N      N       Y
20   M       A      84    Y      N       Y
21   M       A      72    Y      Y       Y
22   F       B      68    N      N       Y
23   F       C      65    N      N       N
24   F       B      64    N      N       Y
25   M       B      87    Y      N       Y
26   M       B      92    N      Y       Y
27   F       C      56    Y      N       Y

Let’s use the aggregate command to obtain a table of mean body mass across the two levels of gender. Note the syntax. The continuous variable becomes the first argument. Then the categorical variable appears inside the list command. Finally, the function you wish to apply (in this case you want the mean) becomes the third argument.

Table1 <- aggregate(MASS, list(GENDER), FUN=mean)
Table1

 Group.1     x
1     F  66.00000
2     M  84.42857

Note that the aggregate command does not return the variable names. Anyway – now we use the aggregate command to obtain a table of mean body mass across the two levels of smoker (i.e. people who smoke and people who do not).

Table2 <- aggregate(MASS, list(SMOKE), FUN=mean)
Table2

  Group.1     x
1      N   73.46667
2      Y   78.16667

The aggregate command allows us to create more complex tables, across the levels of several categorical variables.

Table3 <- aggregate(MASS, list(GENDER, TREATMENT),FUN=mean)
Table3

  Group.1  Group.2     x
1      F      A     60.50000
2      M      A     81.57143
3      F      B     68.44444
4      M      B     99.75000
5      F      C     60.50000
6      M      C     70.66667

Finally, let’s look at maximum body mass across the levels of gender, smoker and treatment.

Table4 <- aggregate(MASS, list(GENDER, SMOKE, TREATMENT),FUN=max)
Table4

  Group.1 Group.2 Group.3  x
1      M      N      A     104
2      F      Y      A     63
3      M      Y      A     87
4      F      N      B     83
5      M      N      B     95
6      F      Y      B     82
7      M      Y      B     125
8      F      N      C     65
9      M      N      C     79
10     F      Y      C     56

That wasn’t so hard! In our next blog post we will go over creating bar charts in R.

About the Author: David Lillis has taught R to many researchers and statisticians. His company, Sigma Statistics and Research Limited, provides both on-line instruction and face-to-face workshops on R, and coding services in R. David holds a doctorate in applied statistics.

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