library(knitr) opts_chunk$set(message = FALSE, warning = FALSE)
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.
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.
library(labelVector) mtcars <- set_label(mtcars, qsec = "Quarter mile time", am = "Automatic / Manual", wt = "Vehicle weight") fit <- lm(mpg ~ qsec + am + wt, data = mtcars) # Create a summary table res <- as.data.frame(coef(summary(fit)), stringsAsFactors = FALSE) res <- cbind(rownames(res), res) rownames(res) <- NULL names(res) <- c("term", "estimate", "se", "t", "p") res$term <- as.character(res$term) kable(res)
In constrast, the following table replaces these term labels with longer, more human-readable terms that assist in the interpretation of the model.
res$term[-1] <- get_label(mtcars, vars = res$term[-1]) kable(res)
Labels are set using the
set_label function, which applies a length one character string to the
label attribute of the variable. The
labelled vectors mimics the print method from the
library(labelVector) x <- 1:10 x <- set_label(x, "some integers") x
Labels may be retrieved from a labelled vector using the
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 get_label(y)
This behavior comes with a caveat that the string returned will match exactly the content given to
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
mtcars2 <- set_label(mtcars, 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.
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"))
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
Hmisc may need to work in the same environment. This is permissible since
get_label both work on the
label attribute of a vector and their names do not conflict with the
label generic exported by
Notice below that the variable label created using the
Hmisc functions is still retrievable with
library(Hmisc) var_with_Hmisc_label <- 1:10 label(var_with_Hmisc_label) <- "This label created with Hmisc" label(var_with_Hmisc_label) get_label(var_with_Hmisc_label) var_with_Hmisc_label
In a similar vein, variable labels created with
set_label may be retrieved using the
var_with_labelVector_label <- 1:10 var_with_labelVector_label <- set_label(var_with_labelVector_label, "This label created with labelVector") get_label(var_with_labelVector_label) label(var_with_labelVector_label)
library(labelVector) mtcars <- set_label(mtcars, qsec = "Quarter mile time", am = "Automatic / Manual", wt = "Vehicle weight") fit <- lm(mpg ~ qsec + am + wt, data = mtcars) # Create a summary table res <- as.data.frame(coef(summary(fit)), 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]) kable(res)
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