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
HEALTHvariable may have data values including1and2, 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 rowsThis 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$STATEFIP)
#> [1] TRUESome of the labels are actually printed inline alongside their data values, but it can be easier to see them by isolating them:
# Labels print when accessing the column
head(cps$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] 1Additionally, 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] 82These different representations lead to inconsistencies in calculated values:
mean(cps$AGE)
#> [1] 35.0226
mean(as.numeric(cps$AGE_FACTOR))
#> [1] 35.94836Cautions 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—labelledvariables 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 factorsIf 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$INCTOT)
#> # A tibble: 2 × 2
#> val lbl
#> <dbl> <chr>
#> 1 999999998 Missing. (1962-1964 only)
#> 2 999999999 N.I.U.To convert one or both of these labelled values to NA,
use lbl_na_if(). To use lbl_na_if(), you must
supply a function to handle the conversion. The function should take a
value-label pair as its input and output TRUE for those
pairs whose values should be converted to NA.
Syntax for value label functions
Several lbl_* helper functions, including
lbl_na_if(), require a user-defined function to handle
recoding of value-label pairs. ipumsr provides a syntax to easily
reference the values and labels in this user-defined function:
- The
.valargument references the values - The
.lblargument references the labels
For instance, to convert all values equal to 999999999
to NA, we can provide a function that uses the
.val argument:
# Convert to NA using function that returns TRUE for all labelled values equal to 99999999
inctot_na <- lbl_na_if(
cps$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$INCTOT,
function(.val, .lbl) grepl("N.I.U.", .lbl)
)
# Same result
all(inctot_na2 == inctot_na, na.rm = TRUE)
#> [1] TRUEYou can also specify the function using a one-sided formula:
lbl_na_if(cps$INCTOT, ~ .val == 999999999)Note that .val only refers to labelled
values—unlabelled values are not affected:
x <- lbl_na_if(cps$INCTOT, ~ .val >= 0)
# Unlabelled values greater than the cutoff are still present:
length(which(x > 0))
#> [1] 7501To convert unlabelled values to NA, use
dplyr::na_if() instead.
Relabel values
lbl_relabel() can be used to create new value-label
pairs, often to recombine existing labels into more general categories.
It takes a two-sided formula to handle the relabeling:
- On the left-hand side, use the
lbl()helper to define a new value-label pair. - On the right-hand side, provide a function that returns
TRUEfor those value-label pairs that should be relabelled with the new value-label pair from the left-hand side.
The function again uses the .val and .lbl
syntax mentioned above to refer to values and
labels, respectively.
For instance, we could reclassify the categories in
MIGRATE1 such that all migration within a state is captured
in a single category:
ipums_val_labels(cps$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$MIGRATE1)
#> # A tibble: 4 × 2
#> val lbl
#> <dbl> <chr>
#> 1 0 NIU / Missing / Unknown
#> 2 1 Stayed in state
#> 3 5 Moved between states
#> 4 6 AbroadMany IPUMS variables include detailed labels that are grouped together into more general categories. These are often encoded with multi-digit values, where the starting digit refers to the larger category.
For instance, the EDUC variable contains categories for
individual grades as well as categories for multiple grade groups:
head(ipums_val_labels(cps$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 9You 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$EDUC2)
#> # A tibble: 14 × 2
#> val lbl
#> <dbl> <chr>
#> 1 0 NIU or no schooling
#> 2 1 Grades 1, 2, 3, or 4
#> 3 2 Grades 5 or 6
#> 4 3 Grades 7 or 8
#> 5 4 Grade 9
#> 6 5 Grade 10
#> 7 6 Grade 11
#> 8 7 Grade 12
#> 9 8 1 year of college
#> 10 9 2 years of college
#> 11 10 3 years of college
#> 12 11 4 years of college
#> 13 12 5+ years of college
#> 14 99 Missing/UnknownRelabeling caveats
It is always worth checking that the new labels make sense based on
your research question. For instance, in the above example, both
"12th grade, no diploma" and
"High school diploma or equivalent" are collapsed to a
single group as they both have values in the 70s. This may be suitable
for your purposes, but for more control, it is best to use
lbl_relabel().
Note that lbl_relabel() and lbl_collapse()
only operate on labelled values, and are therefore designed for
use with fully labelled vectors. That is, if you attempt to
relabel a vector that has some unlabelled values, they will be converted
to NA.
To avoid this, you can add labels for all values using
lbl_add_vals() before relabeling (see below). In general, this shouldn’t be necessary, as
most partially-labelled vectors only include labels with ancillary
information, like missing value indicators. These can typically be
handled by other helpers, like lbl_na_if(), without
requiring relabeling.
Remove unused value labels
Some variables may contain labels for values that don’t appear in the
data. Unused levels still appear in factor representations of these
variables, so it is often beneficial to remove them with
lbl_clean():
ipums_val_labels(cps$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 WisconsinAdd 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 $230
#> 990 Unknown
#> 999 NIUlbl_add_vals() adds labels for all unlabelled values in
a labelled vector with an optional labeller function. (This
can be useful if you wish to operate on a partially labelled vector with
a function that requires labelled input, like
lbl_relabel().)
# `.` refers to each label value
lbl_add_vals(x, ~ paste0("$", .))
#> <labelled<double>[6]>
#> [1] 100 200 105 990 999 230
#>
#> Labels:
#> value label
#> 100 $100
#> 105 $105
#> 200 $200
#> 230 $230
#> 990 Unknown
#> 999 NIUlbl_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 ageOnce 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.