Labelling Vectors

Many statistical software programs, such as SAS and SPSS, provide support for labelling variables. Variable labels provide a mechanism to communicate what a variable represents that is not constrained by the naming conventions of the language.

R does not include native support for labels. Some packages, most notably the Hmisc package, have provided this support. However, design choices have been made in Hmisc such that the methods associated with assigning labels are not exported from the package. This makes the use of these functions impractical when extending label support to other packages.

The labelVector package provides basic support for labelling atomic vectors and making this support available to other package developers.

It should be noted that labels have not been widely adopted in R programming. Many R operations do not preserve variable attributes, which can result in the loss of labels when a vector is passed through some functions. Indeed, this may be appropriate, since performing transformations likely alters the meaning of the label. Thus, it is most appropriate to assign labels to completed variables that are unlikely to undergo further transformations.


When generating summaries for reports to be delivered to a non-technical audience, the variable names used in analytical code may not be adequately descriptive to the audience to provide the full context and meaning of the results. Variable labels are a compromise that may be inserted to clarify meaning to the audience without requiring excessively difficult variable names to be used in code.

In the table below, a linear model estimating gas mileage is given with terms taken from the variable labels.

term estimate se t p
(Intercept) 9.617781 6.9595930 1.381946 0.1779152
qsec 1.225886 0.2886696 4.246676 0.0002162
am 2.935837 1.4109045 2.080819 0.0467155
wt -3.916504 0.7112016 -5.506882 0.0000070

In constrast, the following table replaces these term labels with longer, more human-readable terms that assist in the interpretation of the model.

term estimate se t p
(Intercept) 9.617781 6.9595930 1.381946 0.1779152
Quarter mile time 1.225886 0.2886696 4.246676 0.0002162
Automatic / Manual 2.935837 1.4109045 2.080819 0.0467155
Vehicle weight -3.916504 0.7112016 -5.506882 0.0000070

Setting Labels

Labels are set using the set_label function, which applies a length one character string to the label attribute of the variable. The print method for labelled vectors mimics the print method from the Hmisc package.

x <- 1:10
x <- set_label(x, "some integers")

## some integers
##  [1]  1  2  3  4  5  6  7  8  9 10

Labels may be retrieved from a labelled vector using the get_label function.

## [1] "some integers"

When a vector does not have a label attribute, the object given to get_label is deparsed and returned as a string instead.

y <- letters

attr(y, "label") # y has no label attribute
## [1] "y"

This behavior comes with a caveat that the string returned will match exactly the content given to get_label.

## [1] "Automatic / Manual"

Working with Data Frames

labelVector provides a method to set labels for vectors contained within a data frame without having to use loops, applys, or repetitive code. The data.frame method allows labels to be set with on the pattern of var = "label" within the set_label call. This method is also suitable for use inside of chained operations made popular by the magrittr and dplyr packages.

mtcars2 <- 
            am = "Automatic",
            mpg = "Miles per gallon",
            cyl = "Cylinders",
            qsec = "Quarter mile time")

There is a similar get_label method for data frames that retrieves the labels of each variable in the data frame.

##  [1] "Miles per gallon"  "Cylinders"         "disp"             
##  [4] "hp"                "drat"              "Vehicle weight"   
##  [7] "Quarter mile time" "vs"                "Automatic"        
## [10] "gear"              "carb"

Or if you desire only to retrieve the labels for a subset of variables, you may use the call

get_label(mtcars2, vars = c("am", "mpg", "cyl", "qsec"))
## [1] "Automatic"         "Miles per gallon"  "Cylinders"        
## [4] "Quarter mile time"

Interaction with Hmisc

Whereas labelVector provides a similar functionality as is provided by the Hmisc package, and considering the widespread use of Hmisc, consideration is taken for the possibility that labelVector and Hmisc may need to work in the same environment. This is permissible since set_label and get_label both work on the label attribute of a vector and their names do not conflict with the label generic exported by Hmisc.

Notice below that the variable label created using the Hmisc functions is still retrievable with get_label.


var_with_Hmisc_label <- 1:10
label(var_with_Hmisc_label) <- "This label created with Hmisc"

## [1] "This label created with Hmisc"
## [1] "This label created with Hmisc"
## This label created with Hmisc 
##  [1]  1  2  3  4  5  6  7  8  9 10

In a similar vein, variable labels created with set_label may be retrieved using the Hmisc functions.

var_with_labelVector_label <- 1:10
var_with_labelVector_label <- 
  set_label(var_with_labelVector_label, "This label created with labelVector")

## [1] "This label created with labelVector"
## [1] "This label created with labelVector"

Example in Use


mtcars <- 
            qsec = "Quarter mile time",
            am = "Automatic / Manual",
            wt = "Vehicle weight")

fit <- lm(mpg ~ qsec + am + wt, 
          data = mtcars)

# Create a summary table
res <-, 
                     stringsAsFactors = FALSE)
res <- cbind(rownames(res), res)
rownames(res) <- NULL
names(res) <- c("term", "estimate", "se", "t", "p")
res$term <- as.character(res$term)

res$term[-1] <- get_label(mtcars, vars = res$term[-1])
term estimate se t p
(Intercept) 9.617781 6.9595930 1.381946 0.1779152
Quarter mile time 1.225886 0.2886696 4.246676 0.0002162
Automatic / Manual 2.935837 1.4109045 2.080819 0.0467155
Vehicle weight -3.916504 0.7112016 -5.506882 0.0000070