# Value Labels in IPUMS data

## IPUMS variable metadata

IPUMS data come with three primary types of variable-level metadata:

- Variable labels are succinct labels that serve as human-readable variable names (in contrast to more esoteric column names).

- Variable descriptions are extended text descriptions of the contents of a variable. These provide more information about what a given variable measures.

- Value labels link particular data values to more meaningful text labels. For instance, the `HEALTH` variable may have data values including `1` and `2`, but these are actually stand-ins for “Excellent” and “Very good” health. This mapping would be contained in a value-label pair that includes a value and its associated label.

The rest of this article will focus on value labels; for more about variable labels and descriptions, see `?ipums_var_info`.

## Value labels

ipumsr uses the `labelled` class from the `{haven}` package to handle value labels.

You can see this in the column data types when loading IPUMS data. Note that `<int+lbl>` appears below `MONTH` and `ASECFLAG`:

    library(ipumsr)

    ddi <- read_ipums_ddi(ipums_example("cps_00160.xml"))
    cps <- read_ipums_micro(ddi, verbose = FALSE)

    cps[, 1:5]
    #> # A tibble: 10,883 × 5
    #>     YEAR SERIAL MONTH       CPSID ASECFLAG
    #>    <dbl>  <dbl> <int+lbl>   <dbl> <int+lbl>
    #>  1  2016  24138 3 [March] 2.02e13 1 [ASEC]
    #>  2  2016  24139 3 [March] 2.02e13 1 [ASEC]
    #>  3  2016  24139 3 [March] 2.02e13 1 [ASEC]
    #>  4  2016  24140 3 [March] 2.02e13 1 [ASEC]
    #>  5  2016  24140 3 [March] 2.02e13 1 [ASEC]
    #>  6  2016  24140 3 [March] 2.02e13 1 [ASEC]
    #>  7  2016  24141 3 [March] 2.02e13 1 [ASEC]
    #>  8  2016  24142 3 [March] 2.02e13 1 [ASEC]
    #>  9  2016  24142 3 [March] 2.02e13 1 [ASEC]
    #> 10  2016  24142 3 [March] 2.02e13 1 [ASEC]
    #> # ℹ 10,873 more rows

This indicates that the data contained in these columns are integers but include value labels. You can use the function `is.labelled()` to determine if a variable is indeed labelled:

    is.labelled(cps$STATEFIP)
    #> [1] TRUE

Some of the labels are actually printed inline alongside their data values, but it can be easier to see them by isolating them:

    # Labels print when accessing the column
    head(cps$MONTH)
    #> <labelled<integer>[6]>: Month
    #> [1] 3 3 3 3 3 3
    #>
    #> Labels:
    #>  value     label
    #>      1   January
    #>      2  February
    #>      3     March
    #>      4     April
    #>      5       May
    #>      6      June
    #>      7      July
    #>      8    August
    #>      9 September
    #>     10   October
    #>     11  November
    #>     12  December

    # Get labels alone
    ipums_val_labels(cps$MONTH)
    #> # A tibble: 12 × 2
    #>      val lbl
    #>    <int> <chr>
    #>  1     1 January
    #>  2     2 February
    #>  3     3 March
    #>  4     4 April
    #>  5     5 May
    #>  6     6 June
    #>  7     7 July
    #>  8     8 August
    #>  9     9 September
    #> 10    10 October
    #> 11    11 November
    #> 12    12 December

## `labelled` vs. `factor`

Base R already supports the linking of numeric data to categories using its `factor` data type. While factors may be more familiar, they were designed to support efficient calculations in linear models, not as a human-readable labeling system for interpreting and processing data.

Compared to factors, `labelled` vectors have two main properties that make them more suitable for working with IPUMS data:

- They don’t require that all values be labelled
- They don’t require values to be assigned to increasing integers starting at 1

Consider the case of the `AGE` variable. For many IPUMS products, `AGE` provides a person’s age in years, but certain special values have other interpretations:

    head(cps$AGE)
    #> <labelled<integer>[6]>: Age
    #> [1] 54 54 52 38 15 38
    #>
    #> Labels:
    #>  value               label
    #>      0        Under 1 year
    #>     90 90 (90+, 1988-2002)
    #>     99                 99+

As you can see, the 0 value represents all ages less than 1, and the 90 and 99 values actually represent ranges of ages. Coercing `AGE` to a factor would convert all values of 0 to 1, because factors always assign values starting at 1:

    cps$AGE_FACTOR <- as_factor(cps$AGE)

    age0_factor <- cps[cps$AGE == 0, ]$AGE_FACTOR

    # The levels look the same
    unique(age0_factor)
    #> [1] Under 1 year
    #> 84 Levels: Under 1 year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ... 99+

    # But the values have changed
    unique(as.numeric(age0_factor))
    #> [1] 1

Additionally, because not all values exist in the data, high values, like 85, 90, and 99 have been mapped to lower values:

    age85_factor <- cps[cps$AGE == 85, ]$AGE_FACTOR

    unique(as.numeric(age85_factor))
    #> [1] 82

These different representations lead to inconsistencies in calculated values:

    mean(cps$AGE)
    #> [1] 35.0226

    mean(as.numeric(cps$AGE_FACTOR))
    #> [1] 35.94836

### Cautions regarding `labelled` variables

While `labelled` variables provide the benefits described above, they also present challenges.

For example, you may have noticed that both of the means calculated above are suspect:

- In the case of `AGE_FACTOR`, the values have been remapped during conversion and several are inconsistent with the original data.
- In the case of `AGE`, we have considered all people over 90 to be exactly 90, and all people over 99 to be exactly 99—`labelled` variables don’t ensure that calculations are correct any more than factors do!

Furthermore, many R functions ignore value labels or even actively remove them from the data:

    ipums_val_labels(cps$HEALTH)
    #> # A tibble: 5 × 2
    #>     val lbl
    #>   <int> <chr>
    #> 1     1 Excellent
    #> 2     2 Very good
    #> 3     3 Good
    #> 4     4 Fair
    #> 5     5 Poor

    HEALTH2 <- ifelse(cps$HEALTH > 3, 3, cps$HEALTH)

    ipums_val_labels(HEALTH2)
    #> # A tibble: 0 × 2
    #> # ℹ 2 variables: val <dbl>, lbl <chr>

So, `labelled` vectors are not intended for use throughout the entire analysis process. Instead, they should be used during the initial data preparation process to convert raw data into values that are more meaningful. These can then be converted to other variable types (often factors) for analysis.

Unfortunately, this isn’t a process that can typically be automated, as it depends primarily on the research questions the data will be used to address. However, ipumsr provides several functions to manipulate value labels to make this process easier.

## Prepping data with value labels

### Convert labelled values to other data types1

Use `as_factor()` once labels have the correct categories and need no further manipulation. For instance, `MONTH` already has sensible categories, so we can convert it to a factor right away:

    ipums_val_labels(cps$MONTH)
    #> # A tibble: 12 × 2
    #>      val lbl
    #>    <int> <chr>
    #>  1     1 January
    #>  2     2 February
    #>  3     3 March
    #>  4     4 April
    #>  5     5 May
    #>  6     6 June
    #>  7     7 July
    #>  8     8 August
    #>  9     9 September
    #> 10    10 October
    #> 11    11 November
    #> 12    12 December
    cps$MONTH <- as_factor(cps$MONTH)

`as_factor()` can also convert all `labelled` variables in a data frame to factors at once. If you prefer to work with factors, you can do this conversion immediately after loading data, and then prepare these variables using techniques you would use for factors.

    cps <- as_factor(cps)

    # ... further preparation of variables as factors

If you prefer to handle these variables in `labelled` format, you can use the `lbl_*` helpers first, then call `as_factor()` on the entire data frame.

Some variables may be more appropriate to use as numeric values rather than factors. In these cases, you can simply remove the labels with `zap_labels()`.

`INCTOT`, which measures personal income, fits this description:

    inctot_num <- zap_labels(cps$INCTOT)

    typeof(inctot_num)
    #> [1] "double"

    ipums_val_labels(inctot_num)
    #> # A tibble: 0 × 2
    #> # ℹ 2 variables: val <dbl>, lbl <chr>

Note that labelled values are not generally intended to be interpreted as numeric values, so `zap_labels()` should only be used after labels have been properly handled. For example, in `INCTOT`, labelled values used to identify missing values are encoded with large numbers:

    ipums_val_labels(cps$INCTOT)
    #> # A tibble: 2 × 2
    #>         val lbl
    #>       <dbl> <chr>
    #> 1 999999998 Missing. (1962-1964 only)
    #> 2 999999999 N.I.U.

Treating these as legitimate observations will significantly skew any calculations with this variable if not first converted to `NA`.

### Create missing values based on value labels

Many IPUMS variables use labelled values to identify missing data. This allows for more detail about why certain observations were missing than would be available were values loaded as `NA`.

As we saw with `INCTOT`, value labels were used to identify two types of missing data: those that are legitimately missing and those that are not in the universe of observations.

    ipums_val_labels(cps$INCTOT)
    #> # A tibble: 2 × 2
    #>         val lbl
    #>       <dbl> <chr>
    #> 1 999999998 Missing. (1962-1964 only)
    #> 2 999999999 N.I.U.

To convert one or both of these labelled values to `NA`, use `lbl_na_if()`. To use `lbl_na_if()`, you must supply a function to handle the conversion. The function should take a value-label pair as its input and output `TRUE` for those pairs whose values should be converted to `NA`.

#### Syntax for value label functions

Several `lbl_*` helper functions, including `lbl_na_if()`, require a user-defined function to handle recoding of value-label pairs. ipumsr provides a syntax to easily reference the values and labels in this user-defined function:

- The `.val` argument references the values
- The `.lbl` argument references the labels

For instance, to convert all values equal to `999999999` to `NA`, we can provide a function that uses the `.val` argument:

    # Convert to NA using function that returns TRUE for all labelled values equal to 99999999
    inctot_na <- lbl_na_if(
      cps$INCTOT,
      function(.val, .lbl) .val == 999999999
    )

    # All 99999999 values have been converted to NA
    any(inctot_na == 999999999, na.rm = TRUE)
    #> [1] FALSE

    # And the label has been removed:
    ipums_val_labels(inctot_na)
    #> # A tibble: 1 × 2
    #>         val lbl
    #>       <dbl> <chr>
    #> 1 999999998 Missing. (1962-1964 only)

We could achieve the same result by referencing the labels themselves:

    # Convert to NA for labels that contain "N.I.U."
    inctot_na2 <- lbl_na_if(
      cps$INCTOT,
      function(.val, .lbl) grepl("N.I.U.", .lbl)
    )

    # Same result
    all(inctot_na2 == inctot_na, na.rm = TRUE)
    #> [1] TRUE

You can also specify the function using a one-sided formula:

    lbl_na_if(cps$INCTOT, ~ .val == 999999999)

Note that `.val` only refers to labelled values—unlabelled values are not affected:

    x <- lbl_na_if(cps$INCTOT, ~ .val >= 0)

    # Unlabelled values greater than the cutoff are still present:
    length(which(x > 0))
    #> [1] 7501

To convert unlabelled values to `NA`, use `dplyr::na_if()` instead.

### Relabel values

`lbl_relabel()` can be used to create new value-label pairs, often to recombine existing labels into more general categories. It takes a two-sided formula to handle the relabeling:

- On the left-hand side, use the `lbl()` helper to define a new value-label pair.
- On the right-hand side, provide a function that returns `TRUE` for those value-label pairs that should be relabelled with the new value-label pair from the left-hand side.

The function again uses the `.val` and `.lbl` syntax mentioned above to refer to values and labels, respectively.

For instance, we could reclassify the categories in `MIGRATE1` such that all migration within a state is captured in a single category:

    ipums_val_labels(cps$MIGRATE1)
    #> # A tibble: 8 × 2
    #>     val lbl
    #>   <int> <chr>
    #> 1     0 NIU
    #> 2     1 Same house
    #> 3     2 Different house, place not reported
    #> 4     3 Moved within county
    #> 5     4 Moved within state, different county
    #> 6     5 Moved between states
    #> 7     6 Abroad
    #> 8     9 Unknown

    cps$MIGRATE1 <- lbl_relabel(
      cps$MIGRATE1,
      lbl(0, "NIU / Missing / Unknown") ~ .val %in% c(0, 2, 9),
      lbl(1, "Stayed in state") ~ .val %in% c(1, 3, 4)
    )

    ipums_val_labels(cps$MIGRATE1)
    #> # A tibble: 4 × 2
    #>     val lbl
    #>   <dbl> <chr>
    #> 1     0 NIU / Missing / Unknown
    #> 2     1 Stayed in state
    #> 3     5 Moved between states
    #> 4     6 Abroad

Many IPUMS variables include detailed labels that are grouped together into more general categories. These are often encoded with multi-digit values, where the starting digit refers to the larger category.

For instance, the `EDUC` variable contains categories for individual grades as well as categories for multiple grade groups:

    head(ipums_val_labels(cps$EDUC), 15)
    #> # A tibble: 15 × 2
    #>      val lbl
    #>    <int> <chr>
    #>  1     0 NIU or no schooling
    #>  2     1 NIU or blank
    #>  3     2 None or preschool
    #>  4    10 Grades 1, 2, 3, or 4
    #>  5    11 Grade 1
    #>  6    12 Grade 2
    #>  7    13 Grade 3
    #>  8    14 Grade 4
    #>  9    20 Grades 5 or 6
    #> 10    21 Grade 5
    #> 11    22 Grade 6
    #> 12    30 Grades 7 or 8
    #> 13    31 Grade 7
    #> 14    32 Grade 8
    #> 15    40 Grade 9

You could use `lbl_relabel()` to collapse the detailed categories into the more general ones, but you would have to define new value labels for all the categories. Instead, you could use `lbl_collapse()`.

`lbl_collapse()` uses a function that takes `.val` and `.lbl` arguments and returns the new value each input value should be assigned to. The label of the lowest original value is used for each collapsed group. To group by the tens digit, use the integer division operator `%/%`:

    # %/% refers to integer division, which divides but discards the remainder
    10 %/% 10
    #> [1] 1
    11 %/% 10
    #> [1] 1

    # Convert to groups by tens digit
    cps$EDUC2 <- lbl_collapse(cps$EDUC, ~ .val %/% 10)

    ipums_val_labels(cps$EDUC2)
    #> # A tibble: 14 × 2
    #>      val lbl
    #>    <dbl> <chr>
    #>  1     0 NIU or no schooling
    #>  2     1 Grades 1, 2, 3, or 4
    #>  3     2 Grades 5 or 6
    #>  4     3 Grades 7 or 8
    #>  5     4 Grade 9
    #>  6     5 Grade 10
    #>  7     6 Grade 11
    #>  8     7 Grade 12
    #>  9     8 1 year of college
    #> 10     9 2 years of college
    #> 11    10 3 years of college
    #> 12    11 4 years of college
    #> 13    12 5+ years of college
    #> 14    99 Missing/Unknown

#### Relabeling caveats

It is always worth checking that the new labels make sense based on your research question. For instance, in the above example, both `"12th grade, no diploma"` and `"High school diploma or equivalent"` are collapsed to a single group as they both have values in the 70s. This may be suitable for your purposes, but for more control, it is best to use `lbl_relabel()`.

Note that `lbl_relabel()` and `lbl_collapse()` only operate on labelled values, and are therefore designed for use with fully `labelled` vectors. That is, if you attempt to relabel a vector that has some unlabelled values, they will be converted to `NA`.

To avoid this, you can add labels for all values using `lbl_add_vals()` before relabeling (see below). In general, this shouldn’t be necessary, as most partially-labelled vectors only include labels with ancillary information, like missing value indicators. These can typically be handled by other helpers, like `lbl_na_if()`, without requiring relabeling.

### Remove unused value labels

Some variables may contain labels for values that don’t appear in the data. Unused levels still appear in factor representations of these variables, so it is often beneficial to remove them with `lbl_clean()`:

    ipums_val_labels(cps$STATEFIP)
    #> # A tibble: 75 × 2
    #>      val lbl
    #>    <int> <chr>
    #>  1     1 Alabama
    #>  2     2 Alaska
    #>  3     4 Arizona
    #>  4     5 Arkansas
    #>  5     6 California
    #>  6     8 Colorado
    #>  7     9 Connecticut
    #>  8    10 Delaware
    #>  9    11 District of Columbia
    #> 10    12 Florida
    #> # ℹ 65 more rows
    ipums_val_labels(lbl_clean(cps$STATEFIP))
    #> # A tibble: 5 × 2
    #>     val lbl
    #>   <int> <chr>
    #> 1    19 Iowa
    #> 2    27 Minnesota
    #> 3    38 North Dakota
    #> 4    46 South Dakota
    #> 5    55 Wisconsin

### Add new labels

As mentioned above, value labels are intended to be used as an intermediate data structure for preparing newly-imported data. As such, you’re not likely to need to add new labels, but if you do, use `lbl_add()`, `lbl_add_vals()`, or `lbl_define()`.

`lbl_add()` takes an arbitrary number of `lbl()` placeholders that will be added to a given `labelled` vector:

    x <- haven::labelled(
      c(100, 200, 105, 990, 999, 230),
      c(`Unknown` = 990, NIU = 999)
    )

    lbl_add(
      x,
      lbl(100, "$100"),
      lbl(105, "$105"),
      lbl(200, "$200"),
      lbl(230, "$230")
    )
    #> <labelled<double>[6]>
    #> [1] 100 200 105 990 999 230
    #>
    #> Labels:
    #>  value   label
    #>    100    $100
    #>    105    $105
    #>    200    $200
    #>    230    $230
    #>    990 Unknown
    #>    999     NIU

`lbl_add_vals()` adds labels for all unlabelled values in a `labelled` vector with an optional labeller function. (This can be useful if you wish to operate on a partially labelled vector with a function that requires labelled input, like `lbl_relabel()`.)

    # `.` refers to each label value
    lbl_add_vals(x, ~ paste0("$", .))
    #> <labelled<double>[6]>
    #> [1] 100 200 105 990 999 230
    #>
    #> Labels:
    #>  value   label
    #>    100    $100
    #>    105    $105
    #>    200    $200
    #>    230    $230
    #>    990 Unknown
    #>    999     NIU

`lbl_define()` makes a `labelled` vector out of an unlabelled one. Use the same syntax as is used for `lbl_relabel()` to define new labels based on the unlabelled values:

    age <- c(10, 12, 16, 18, 20, 22, 25, 27)

    # Group age values into two label groups.
    # Values not captured by the right hand side functions remain unlabelled
    lbl_define(
      age,
      lbl(1, "Pre-college age") ~ .val < 18,
      lbl(2, "College age") ~ .val >= 18 & .val <= 22
    )
    #> <labelled<double>[8]>
    #> [1]  1  1  1  2  2  2 25 27
    #>
    #> Labels:
    #>  value           label
    #>      1 Pre-college age
    #>      2     College age

Once all labelled variables have been appropriately converted to factors or numeric values, the data can move forward in the processing pipeline.

## Other resources

The `{haven}` package, which underlies ipumsr’s handling of value labels, provides more details on the `labelled` class. See `vignette("semantics", package = "haven")`.

The `{labelled}` package provides other methods for manipulating value labels, some of which overlap those provided by ipumsr.

The `{questionr}` package includes functions for exploring `labelled` variables. In particular, the functions `describe`, `freq` and `lookfor` all print out to console information about the variable using the value labels.

Finally, the foreign and prettyR packages don’t use the `labelled` class, but provide similar functionality for handling value labels, which could be adapted for use with `labelled` vectors.

1. Note that if you’re reading this article on the ipumsr website, auto-generated links for the function `as_factor()` point to the `{forcats}` package, but the documentation for `haven::as_factor.labelled()` is more informative for working with labelled variables.↩︎