Conversion semantics

There are some differences between the way that R, SAS, SPSS, and Stata represented labelled data and missing values. While SAS, SPSS, and Stata share some obvious similarities, R is little different. This vignette explores the differences, and shows you how haven bridges the gap.

Value labels

Base R has one data type that effectively maintains a mapping between integers and character labels: the factor. This however, is not the primary use of factors: they are instead designed to automatically generate useful contrasts for linear models. Factors differ from the labelled values provided by the other tools in important ways:

Value labels in SAS are a little different again. In SAS, labels are just special case of general formats. Formats include currencies and dates, but user-defined just assigns labels to individual values (including special missings value). Formats have names and existing independently of the variables they are associated with. You create a named format with PROC FORMAT and then associated with variables in a DATA step (the names of character formats thealways start with $).

labelled()

To allow you to import labelled vectors into R, haven provdies the S3 labelled class, created with labelled(). This class allows you to associated arbitrary labels with numeric or character vectors:

x1 <- labelled(
  sample(1:5), 
  c(Good = 1, Bad = 5)
)
x1
#> <Labelled integer>
#> [1] 1 2 4 5 3
#> 
#> Labels:
#>  value label
#>      1  Good
#>      5   Bad

x2 <- labelled(
  c("M", "F", "F", "F", "M"), 
  c(Male = "M", Female = "F")
)
x2
#> <Labelled character>
#> [1] M F F F M
#> 
#> Labels:
#>  value  label
#>      M   Male
#>      F Female

The goal of haven is not to provide a labelled vector that you can use everywhere in your analysis. The goal is to provide an intermediate datastructure that you can convert into a regular R data frame. You can do this by either converting to a factor or stripping the labels:

as_factor(x1)
#> [1] Good 2    4    Bad  3   
#> Levels: Good 2 3 4 Bad
zap_labels(x1)
#> [1] 1 2 4 5 3

as_factor(x2)
#> [1] Male   Female Female Female Male  
#> Levels: Female Male
zap_labels(x2)
#> [1] "M" "F" "F" "F" "M"

See the documentation for as_factor() for more options to control exactly what the factor uses for levels.

Both as_factor() and zap_labels() have data frame methods if you want to apply the same strategy to every column in a data frame:

df <- tibble::data_frame(x1, x2, z = 1:5)
df
#> # A tibble: 5 x 3
#>          x1        x2     z
#>   <int+lbl> <chr+lbl> <int>
#> 1         1         M     1
#> 2         2         F     2
#> 3         4         F     3
#> 4         5         F     4
#> 5         3         M     5

zap_labels(df)
#> # A tibble: 5 x 3
#>      x1    x2     z
#>   <int> <chr> <int>
#> 1     1     M     1
#> 2     2     F     2
#> 3     4     F     3
#> 4     5     F     4
#> 5     3     M     5
as_factor(df)
#> # A tibble: 5 x 3
#>       x1     x2     z
#>   <fctr> <fctr> <int>
#> 1   Good   Male     1
#> 2      2 Female     2
#> 3      4 Female     3
#> 4    Bad Female     4
#> 5      3   Male     5

Missing values

All three tools provide a global “system missing value” which is displayed as .. This is roughly equivalent to R’s NA, although neither Stata nor SAS propagate missingness in numeric comparisons: SAS treats the missing value as the smallest possible number (i.e. -inf), and Stata treats it as the largest possible number (i.e. inf).

Each tool also provides a mechanism for recording multiple types of missingness:

Stata and SAS only support tagged missing values for numeric columns. SPSS supports up to three distinct values for character columns. Generally, operations involving a user-missing type return a system missing value.

Haven models these missing values in two different ways:

Tagged missing values

To support Stata’s extended and SAS’s special missing value, haven implements a tagged NA. It does this by taking advantage of the internal structure of a floating point NA. That allows these values to behave identical to NA in regular R operations, while still preserving the value of the tag.

The R interface for creating with tagged NAs is a little clunky because generally they’ll be created by haven for you. But you can create your own with tagged_na():

x <- c(1:3, tagged_na("a", "z"), 3:1)
x
#> [1]  1  2  3 NA NA  3  2  1

Note these tagged NAs behave identically to regular NAs, even when printing. To see their tags, use print_tagged_na():

print_tagged_na(x)
#> [1]     1     2     3 NA(a) NA(z)     3     2     1

To test if a value is a tagged NA, use is_tagged_na(), and to extract the value of the tag, use na_tag():

is_tagged_na(x)
#> [1] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
is_tagged_na(x, "a")
#> [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE

na_tag(x)
#> [1] NA  NA  NA  "a" "z" NA  NA  NA

My expectation is that tagged missings are most often used in conjuction with labels (described below), so labelled vectors print the tags for you, and as_factor() knows how to relabel:

y <- labelled(x, c("Not home" = tagged_na("a"), "Refused" = tagged_na("z")))
y
#> <Labelled double>
#> [1]     1     2     3 NA(a) NA(z)     3     2     1
#> 
#> Labels:
#>  value    label
#>  NA(a) Not home
#>  NA(z)  Refused

as_factor(y)
#> [1] 1        2        3        Not home Refused  3        2        1       
#> Levels: 1 2 3 Not home Refused

User defined missing values

SPSS’s user-defined values work differently to SAS and Stata. Each column can have either up to three distinct values that are considered as missing, or a range. Haven provides labelled_spss() as a subclass of labelled() to model these additional user-defined missings.

x1 <- labelled_spss(c(1:10, 99), c(Missing = 99), na_value = 99)
x2 <- labelled_spss(c(1:10, 99), c(Missing = 99), na_range = c(90, Inf))

x1
#> <Labelled SPSS double>
#>  [1]  1  2  3  4  5  6  7  8  9 10 99
#> Missing values: 99
#> 
#> Labels:
#>  value   label
#>     99 Missing
x2
#> <Labelled SPSS double>
#>  [1]  1  2  3  4  5  6  7  8  9 10 99
#> Missing range:  [90, Inf]
#> 
#> Labels:
#>  value   label
#>     99 Missing

These objects are somewhat dangerous to work with in R because most R functions don’t know those values are missing:

mean(x1)
#> [1] 14

Because of that danger, the default behaviour of read_spss() is to return regular labelled objects where user-defined missing values have been converted to NAs. To get read_spss() to return labelled_spss() objects, you’ll need to set user_na = TRUE.

I’ve defined an is.na() method so you can find them yourself:

is.na(x1)
#>  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE

And the presence of that method does mean many functions with an na.rm argument will work correctly:

mean(x1, na.rm = TRUE)
#> [1] 5.5

But generally you should either convert to a factor, convert to regular missing vaues, or strip the all the labels:

as_factor(x1)
#>  [1] 1       2       3       4       5       6       7       8      
#>  [9] 9       10      Missing
#> Levels: 1 2 3 4 5 6 7 8 9 10 Missing
zap_missing(x1)
#> <Labelled double>
#>  [1]  1  2  3  4  5  6  7  8  9 10 NA
#> 
#> Labels:
#>  value   label
#>     99 Missing
zap_labels(x1)
#>  [1]  1  2  3  4  5  6  7  8  9 10 NA