R Function of the Day: table

[This article was first published on Blogistic Reflections, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The R Function of the Day series will focus on describing in plain language how certain R functions work, focusing on simple examples that you can apply to gain insight into your own data.

Today, I will discuss the table function.

What situation is table useful in?

The table function is a very basic, but essential, function to master while performing interactive data analyses. It simply creates tabular results of categorical variables. However, when combined with the powers of logical expressions in R, you can gain even more insights into your data, including identifying potential problems.

Example 1

We want to know how many subjects are enrolled at each center in a clinical trial.

Example 2

We want to know how many subjects are under the age of 60 in a clinical trial.

Example 3

Which center has the most subjects with a missing value for age in the clinical trial?

How do I use table?

The table function simply needs an object that can be interpreted as a categorical variable (called a “factor” in R).

> ## generate data for medical example
> clinical.trial <-
    data.frame(patient = 1:100,
               age = rnorm(100, mean = 60, sd = 6),
               treatment = gl(2, 50,
                 labels = c("Treatment", "Control")),
               center = sample(paste("Center", LETTERS[1:5]), 100, replace = TRUE))
> ## set some ages to NA (missing)
> is.na(clinical.trial$age) <- sample(1:100, 20)
> summary(clinical.trial)
    patient            age            treatment       center
 Min.   :  1.00   Min.   :46.61   Treatment:50   Center A:22
 1st Qu.: 25.75   1st Qu.:56.19   Control  :50   Center B:10
 Median : 50.50   Median :60.59                  Center C:28
 Mean   : 50.50   Mean   :60.57                  Center D:23
 3rd Qu.: 75.25   3rd Qu.:64.84                  Center E:17
 Max.   :100.00   Max.   :77.83
                  NA's   :20.00                                

Example 1 is the most trivial. We want to know how many subjects are enrolled at each center, so simply pass in the variable “center” to the table function.

> ## a simple example of a table call
> table(clinical.trial$center)
Center A Center B Center C Center D Center E
      22       10       28       23       17  

For example 2, we need to create a logical vector indicating whether or not a patient is under 60 or not. We can then pass that into the table function. Also, since there are missing ages, we might be interested in seeing those in the table also. It is shown both ways by setting the “useNA” argument to table.

> ## a logical vector is created and passed into table
> table(clinical.trial$age < 60)
   41    39
> ## the useNA argument shows the missing values, too
> table(clinical.trial$age < 60, useNA = "always")
   41    39    20  

Example 3, finding the center that has the most missing values for age, sounds the trickiest, but is once again an extremely simple task with the table function. You just need to know that the is.na function returns a logical vector that indicates whether an observation is missing or not.

> ## the table of missing age by center
> table(clinical.trial$center, is.na(clinical.trial$age))
           FALSE TRUE
  Center A    16    6
  Center B     8    2
  Center C    23    5
  Center D    20    3
  Center E    13    4
> ## centers with most missing ages listed in order
> ## highest to lowest
> sort(table(clinical.trial$center, is.na(clinical.trial$age))[, 2],
       decreasing = TRUE)
Center A Center C Center E Center D Center B
       6        5        4        3        2  

Summary of table

Although table is an extremely simple function, its use should not be avoided when exploring a dataset. These examples have shown you how to use table on variables in a dataset, and on variables created from a logical expression in R. The “useNA” argument was also introduced.

To leave a comment for the author, please follow the link and comment on their blog: Blogistic Reflections.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)