# Using quasiquotation to add variable and value labels

#### 2020-09-24

Labelling data is typically a task for end-users and is applied in own scripts or functions rather than in packages. However, sometimes it can be useful for both end-users and package developers to have a flexible way to add variable and value labels to their data. In such cases, quasiquotation is helpful.

This vignette demonstrate how to use quasiquotation in sjlabelled to label your data.

## Adding value labels to variables using quasiquotation

Usually, set_labels() can be used to add value labels to variables. The syntax of this function is easy to use, and set_labels() allows to add value labels to multiple variables at once, if these variables share the same value labels.

In the following examples, we will use the frq() function, that shows an extra label-column containing value labels, if the data is labelled. If the data has no value labels, this column is not shown in the output.

library(sjlabelled)
library(sjmisc) # for frq()-function
library(rlang)

# unlabelled data
dummies <- data.frame(
dummy1 = sample(1:3, 40, replace = TRUE),
dummy2 = sample(1:3, 40, replace = TRUE),
dummy3 = sample(1:3, 40, replace = TRUE)
)

# set labels for all variables in the data frame
test <- set_labels(dummies, labels = c("low", "mid", "hi"))

attr(test$dummy1, "labels") #> low mid hi #> 1 2 3 frq(test, dummy1) #> #> dummy1 <integer> #> # total N=40 valid N=40 mean=2.08 sd=0.80 #> #> Value | Label | N | Raw % | Valid % | Cum. % #> --------------------------------------------- #> 1 | low | 11 | 27.50 | 27.50 | 27.50 #> 2 | mid | 15 | 37.50 | 37.50 | 65.00 #> 3 | hi | 14 | 35.00 | 35.00 | 100.00 #> <NA> | <NA> | 0 | 0.00 | <NA> | <NA> # and set same value labels for two of three variables test <- set_labels( dummies, dummy1, dummy2, labels = c("low", "mid", "hi") ) frq(test) #> #> dummy1 <integer> #> # total N=40 valid N=40 mean=2.08 sd=0.80 #> #> Value | Label | N | Raw % | Valid % | Cum. % #> --------------------------------------------- #> 1 | low | 11 | 27.50 | 27.50 | 27.50 #> 2 | mid | 15 | 37.50 | 37.50 | 65.00 #> 3 | hi | 14 | 35.00 | 35.00 | 100.00 #> <NA> | <NA> | 0 | 0.00 | <NA> | <NA> #> #> #> dummy2 <integer> #> # total N=40 valid N=40 mean=2.02 sd=0.77 #> #> Value | Label | N | Raw % | Valid % | Cum. % #> --------------------------------------------- #> 1 | low | 11 | 27.50 | 27.50 | 27.50 #> 2 | mid | 17 | 42.50 | 42.50 | 70.00 #> 3 | hi | 12 | 30.00 | 30.00 | 100.00 #> <NA> | <NA> | 0 | 0.00 | <NA> | <NA> #> #> #> dummy3 <integer> #> # total N=40 valid N=40 mean=1.85 sd=0.77 #> #> Value | N | Raw % | Valid % | Cum. % #> ------------------------------------- #> 1 | 15 | 37.50 | 37.50 | 37.50 #> 2 | 16 | 40.00 | 40.00 | 77.50 #> 3 | 9 | 22.50 | 22.50 | 100.00 #> <NA> | 0 | 0.00 | <NA> | <NA> val_labels() does the same job as set_labels(), but in a different way. While set_labels() requires variables to be specified in the ...-argument, and labels in the labels-argument, val_labels() requires both to be specified in the .... val_labels() requires named vectors as argument, with the left-hand side being the name of the variable that should be labelled, and the right-hand side containing the labels for the values. test <- val_labels(dummies, dummy1 = c("low", "mid", "hi")) attr(test$dummy1, "labels")
#> low mid  hi
#>   1   2   3

# remaining variables are not labelled
frq(test)
#>
#> dummy1 <integer>
#> # total N=40  valid N=40  mean=2.08  sd=0.80
#>
#> Value | Label |  N | Raw % | Valid % | Cum. %
#> ---------------------------------------------
#>     1 |   low | 11 | 27.50 |   27.50 |  27.50
#>     2 |   mid | 15 | 37.50 |   37.50 |  65.00
#>     3 |    hi | 14 | 35.00 |   35.00 | 100.00
#>  <NA> |  <NA> |  0 |  0.00 |    <NA> |   <NA>
#>
#>
#> dummy2 <integer>
#> # total N=40  valid N=40  mean=2.02  sd=0.77
#>
#> Value |  N | Raw % | Valid % | Cum. %
#> -------------------------------------
#>     1 | 11 | 27.50 |   27.50 |  27.50
#>     2 | 17 | 42.50 |   42.50 |  70.00
#>     3 | 12 | 30.00 |   30.00 | 100.00
#>  <NA> |  0 |  0.00 |    <NA> |   <NA>
#>
#>
#> dummy3 <integer>
#> # total N=40  valid N=40  mean=1.85  sd=0.77
#>
#> Value |  N | Raw % | Valid % | Cum. %
#> -------------------------------------
#>     1 | 15 | 37.50 |   37.50 |  37.50
#>     2 | 16 | 40.00 |   40.00 |  77.50
#>     3 |  9 | 22.50 |   22.50 | 100.00
#>  <NA> |  0 |  0.00 |    <NA> |   <NA>

Unlike set_labels(), val_labels() allows the user to add different value labels to different variables in one function call. Another advantage, or difference, of val_labels() is it’s flexibility in defining variable names and value labels by using quasiquotation.

### Add labels that are stored in a vector

To use quasiquotation, we need the rlang package to be installed and loaded. Now we can have labels in a character vector, and use !! to unquote this vector.

labels <- c("low_quote", "mid_quote", "hi_quote")
test <- val_labels(dummies, dummy1 = !! labels)
#> NULL

# value labels
attr(test$dummy2, "labels") #> lo_var mid_var high_var #> 1 2 3 ### Both variable names and value labels are stored in a vector Finally, we can combine the above approaches to be flexible regarding both variable names and value labels. variable <- "dummy3" labels <- c("low", "mid", "hi") test <- val_labels(dummies, !! variable := !! labels) attr(test$dummy3, "labels")
#> low mid  hi
#>   1   2   3

## Adding variable labels using quasiquotation

set_label() is the equivalent to set_labels() to add variable labels to a variable. The equivalent to val_labels() is var_labels(), which works in the same way as val_labels(). In case of variable labels, a label-attribute is added to a vector or factor (instead of a labels-attribute, which is used for value labels).

The following examples show how to use var_labels() to add variable labels to the data. We demonstrate this function without further explanation, because it is actually very similar to val_labels().

dummy <- data.frame(
a = sample(1:4, 10, replace = TRUE),
b = sample(1:4, 10, replace = TRUE),
c = sample(1:4, 10, replace = TRUE)
)

# simple usage
test <- var_labels(dummy, a = "first variable", c = "third variable")

attr(test$a, "label") #> [1] "first variable" attr(test$b, "label")
#> NULL
attr(test$c, "label") #> [1] "third variable" # quasiquotation for labels v1 <- "First variable" v2 <- "Second variable" test <- var_labels(dummy, a = !! v1, b = !! v2) attr(test$a, "label")
#> [1] "First variable"
attr(test$b, "label") #> [1] "Second variable" attr(test$c, "label")
#> NULL

# quasiquotation for variable names
x1 <- "a"
x2 <- "c"
test <- var_labels(dummy, !! x1 := "First", !! x2 := "Second")

attr(test$a, "label") #> [1] "First" attr(test$b, "label")
#> NULL
attr(test$c, "label") #> [1] "Second" # quasiquotation for both variable names and labels test <- var_labels(dummy, !! x1 := !! v1, !! x2 := !! v2) attr(test$a, "label")
#> [1] "First variable"
attr(test$b, "label") #> NULL attr(test$c, "label")
#> [1] "Second variable"

## Conclusion

As we have demonstrated, var_labels() and val_labels() are one of the most flexible and easy-to-use ways to add value and variable labels to our data. Another advantage is the consistent design of all functions in sjlabelled, which allows seamless integration into pipe-workflows.