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R and labelled data: Using quasiquotation to add variable and value labels #rsta...

 5 years ago
source link: https://www.tuicool.com/articles/hit/NFj63if
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(This article was first published on R – Strenge Jacke! , and kindly contributed toR-bloggers)

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  
#> # total N=40  valid N=40  mean=2.23  sd=0.86
#>  
#>  val label frq raw.prc valid.prc cum.prc
#>    1   low  11    27.5      27.5    27.5
#>    2   mid   9    22.5      22.5    50.0
#>    3    hi  20    50.0      50.0   100.0
#>   NA    NA   0     0.0        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  
#> # total N=40  valid N=40  mean=2.23  sd=0.86
#>  
#>  val label frq raw.prc valid.prc cum.prc
#>    1   low  11    27.5      27.5    27.5
#>    2   mid   9    22.5      22.5    50.0
#>    3    hi  20    50.0      50.0   100.0
#>   NA    NA   0     0.0        NA      NA
#> 
#> # dummy2  
#> # total N=40  valid N=40  mean=2.10  sd=0.74
#>  
#>  val label frq raw.prc valid.prc cum.prc
#>    1   low   9    22.5      22.5    22.5
#>    2   mid  18    45.0      45.0    67.5
#>    3    hi  13    32.5      32.5   100.0
#>   NA    NA   0     0.0        NA      NA
#> 
#> # dummy3  
#> # total N=40  valid N=40  mean=1.98  sd=0.83
#>  
#>   val frq raw.prc valid.prc cum.prc
#>     1  14    35.0      35.0    35.0
#>     2  13    32.5      32.5    67.5
#>     3  13    32.5      32.5   100.0
#>     0     0.0        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  
#> # total N=40  valid N=40  mean=2.23  sd=0.86
#>  
#>  val label frq raw.prc valid.prc cum.prc
#>    1   low  11    27.5      27.5    27.5
#>    2   mid   9    22.5      22.5    50.0
#>    3    hi  20    50.0      50.0   100.0
#>   NA    NA   0     0.0        NA      NA
#> 
#> # dummy2  
#> # total N=40  valid N=40  mean=2.10  sd=0.74
#>  
#>   val frq raw.prc valid.prc cum.prc
#>     1   9    22.5      22.5    22.5
#>     2  18    45.0      45.0    67.5
#>     3  13    32.5      32.5   100.0
#>     0     0.0        NA      NA
#> 
#> # dummy3  
#> # total N=40  valid N=40  mean=1.98  sd=0.83
#>  
#>   val frq raw.prc valid.prc cum.prc
#>     1  14    35.0      35.0    35.0
#>     2  13    32.5      32.5    67.5
#>     3  13    32.5      32.5   100.0
#>     0     0.0        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)
attr(test$dummy1, "labels")
#> low_quote mid_quote  hi_quote 
#>         1         2         3

Define variable names that are stored in a vector

The same can be done with the names of variables that should get new value labels. We then need  !! to unquote the variable name and  := as assignment. 

variable <- "dummy2"
test <- val_labels(dummies, !! variable := c("lo_var", "mid_var", "high_var"))

# no value labels
attr(test$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.


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