---
title: "Value Labels"
author: "Minnesota Population Center"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{value-labels}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Value labels in the ipumsr package
Integrated variables in IPUMS data often have value labels, which attach
text labels to the values taken by a variable (for example the HEALTH variable 
has value labels: 1 = "Excellent", 2 = "Very good", etc.). The `ipumsr` package
does import the labels, but not as factors, which may be how you were expecting them.
The values can be hard to spot at first:

```{r}
library(ipumsr)
ddi <- read_ipums_ddi(ipums_example("cps_00015.xml"))
cps <- read_ipums_micro(ddi, verbose = FALSE)

cps
```

The first clue that some of the variables are labelled is the `<dbl+lbl>`
that appear below `STATEFIP`, `ASECFLAG` and other variables. The tibble package 
prints the variable's type information below the variable name, and this "+lbl" indicates
that the variable uses the `labelled` type. You also can use
the `is.labelled()` function to check if a variable is labelled.

```{r}
is.labelled(cps$STATEFIP)
sapply(cps, is.labelled)
```

There are a few options to see what the actual labels are:
```{r}
# Printing the variable directly (or a subset)
head(cps$MONTH)

# Just get the labels
ipums_val_labels(cps$MONTH)

# or if you're working interactively you can use ipums_view
# ipums_view(ddi)
```

# Why use the labelled class instead of base R's factors?
The usual way to connect numeric data to labels in R is in `factor` variables. 
Though this data type is more native to R, and more widely supported by R code,
it was designed for efficient calculations in linear models, not as a general 
purpose value labeling system and so is missing important features that the 
value labels provided by IPUMS require.

Factors only allow for integers to be mapped to a text label, and these integers
have to be a count starting at 1. This doesn't work for IPUMS data because often
our variables have specific meanings for the codes. For example, the variable
`AGE` uses the value to mean the actual age, but does have labels
for age 0 and the top codes.

```{r}
head(cps$AGE)

cps$AGE_FACTOR <- as_factor(cps$AGE)
head(cps$AGE_FACTOR)
```

It may seem like the new `AGE_FACTOR` variable is okay, but it can be very 
confusing!
```{r}
mean(cps$AGE)

# mean(cps$AGE_FACTOR) # error because data is a factor not numeric

mean(as.numeric(cps$AGE_FACTOR)) # A common mistake

# The "more" correct way, but NA because of the text labels
mean(as.numeric(as.character(cps$AGE_FACTOR)))
```

Because the factor variable has to assign values starting at 1, but the
AGE variable started at 0, most values were 1 higher than they should
have been. Not all values are 1 higher though, because not all values
exist in the data, so 85, 90, and 99 are 82, 83 and 84 respectively.

Other variables have special meanings behind certain codes. For example,
often missing or NIU values are indicated in IPUMS by values starting
with the number 9 that are offset from the typical values. R's factors
do not allow for this separation, so the missing codes will be harder
to distinguish.

Factors also require that every value be labelled, which is not always
true in IPUMS data. In the AGE variable, the only values with labels
are 0, 90 and 99. For all other values, there is not additional label
information.

# Is the labelled class a panacea? (Hint: no)
Though the labelled class does express all of the meaning provided by IPUMS value
labels into R, many R functions cannot use them or even actively remove them
from the data.

```{r}
ipums_val_labels(cps$HEALTH)

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

Therefore, your first task when importing an IPUMS data set will usually be 
to convert the labelled values to other data structures. The bad news is 
that there's no good automatic way to do this; a lot depends on how you plan to use
the variables in your analysis and your preferences. 

The good news is that the ipumsr package provides several functions to 
make this process easier.

I think it is easiest to learn them by seeing them in action, so
see below for a workflow for bringing in the CPS example extract.
For your reference, here is a list of the functions:

- `as_factor()` (reexported from the haven package)
- `zap_labels()` (also from haven)
- `lbl_na_if()`
- `lbl_collapse()`
- `lbl_relabel()`
- `lbl_add()`
- `lbl_add_vals()`

# Workflow example using ipumsr functions
## `as_factor()`
The HEALTH variable is structured just like a factor and so can be converted directly.
The `as_factor()` function is the easiest way to do so.
```{r}
ipums_val_labels(cps$HEALTH)
cps$HEALTH <- as_factor(cps$HEALTH)
```

The ASECFLAG and MONTH variables can also be converted directly (these were included by default by 
the IPUMS extract engine, but aren't useful here because this data set only has respondents 
from ASEC in March)
```{r}
cps$ASECFLAG <- as_factor(cps$ASECFLAG)
cps$MONTH <- as_factor(cps$MONTH)
```

`as_factor` works on data.frames by converting every labelled variable to a factor. 
However, this can create confusing variables like AGE_FACTOR from above, so this
isn't the best thing to do right away.

## `zap_labels()`
I may decide that for my analysis, the AGE variable is most useful as the numeric values.
The `zap_labels()` function removes the labels.
```{r}
cps$AGE <- zap_labels(cps$AGE)
```
The top-codes (which are only available on the CPS website) indicate that the value
80 actually indicates 80-84 and 85 indicates 85+, so another option would be to
convert them to a factor with age ranges.

## `lbl_clean()`
This extract only contains data from a few states and I don't want the factor to have
a level for the unused ones. The `lbl_clean()` function keeps only labels for values
in the current data set and returns a labelled variable which we can convert to a factor.
```{r}
ipums_val_labels(cps$STATEFIP)
cps$STATEFIP <- lbl_clean(cps$STATEFIP)

ipums_val_labels(cps$STATEFIP)
cps$STATEFIP <- as_factor(cps$STATEFIP)
```

## `lbl_na_if()`
The INCTOT variable has 2 labelled values that are not actually incomes:
99999998 indicates "Missing" and 99999999 indicate "Not in Universe".
On the CPS website, the Universe tab indicates that the Universe for 2016
is respondents age 15+. Let's say for my analysis, I can treat these values
as missing. 

The `lbl_na_if()` function takes the variable and a function that refers to
.val and .lbl (the values and labels respectively) and returns an indicator
of whether to set those values to NA and remove the label. You can also use the 
`~` notation from the purrr package to create succinct anonymous functions.

The .val and .lbl only refer to values that already have labels, they do not apply 
to unlabeled values. See the `lbl_add()` and `lbl_add_vals()` functions below for
working with unlabeled values.
```{r}
# Caution: R defaults to printing large numbers like 99999999 in rounded 
# exponential format (1e+08) but that's not how they are actually stored
ipums_val_labels(cps$INCTOT)

# All of these are equivalent
INCTOT1 <- lbl_na_if(cps$INCTOT, ~.val >= 99999990)
INCTOT2 <- lbl_na_if(cps$INCTOT, ~.lbl %in% c("Missing.", "N.I.U. (Not in Universe)."))
INCTOT3 <- lbl_na_if(cps$INCTOT, function(.val, .lbl) {
  is_missing <- .val == 99999998
  is_niu <- .lbl == "N.I.U. (Not in Universe)."
  return(is_missing | is_niu)
})

# Change to a factor in the original cps data.frame
cps$INCTOT <- lbl_na_if(cps$INCTOT, ~.val >= 9999990)
cps$INCTOT <- as_factor(cps$INCTOT)
```

## `lbl_collapse()`
The EDUC variable provides an example of a common IPUMS practice of grouping
categories together by the starting digits. For example, the value 10 indicates 
"Grades 1, 2, 3, or 4", and 11 - "Grade 1", 12 - "Grade 2", etc. 

Let's say that I only care about those more general categories provided
by the first 2 digits. The `lbl_collapse()` function allows me to provide a function
that takes .val and .lbl and returns the value to assign it to. If that code is already
used, then the all of the values assigned to it will get that label, otherwise the label
of the smallest value is used. Just like with `lbl_na_if()`, the purrr-style compact 
syntax using `~` functions is supported.

```{r}
ipums_val_labels(cps$EDUC)
# %/% is integer division, which divides by the number but doesn't keep the remainder
cps$EDUC <- lbl_collapse(cps$EDUC, ~.val %/% 10)

ipums_val_labels(cps$EDUC)

cps$EDUC <- as_factor(cps$EDUC)
```

## `lbl_relabel()`
Sometimes you may wish to move the labels into new categories. For example, the categories 
in MIGRATE1 may not quite map what I want to use in my analysis.

The `lbl_relabel()` function provides a more flexible way to group existing labelled
values into new ones. It takes a two-sided formula, where the left-hand side is a 
label (defined with the `lbl()` function) and the right hand side is an expression
that can use .val and .lbl to evaluate to a logical indicating which values should
be assigned to this label.

```{r}
ipums_val_labels(cps$MIGRATE1)

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)

cps$MIGRATE1 <- as_factor(cps$MIGRATE1)
```

## `lbl_add()` and `lbl_add_vals()`
These functions allow you to create labels for values that aren't already labelled. 
It's harder to come up with real world examples of when these functions would be useful, 
but just in case you come across such a situation, here's how they work.

```{r}
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, "$230"))
lbl_add_vals(x, ~paste0("$", .))
```

## Ready for analysis
And now, after converting all those labels to factors, I'm ready for analysis! If you
think of any other helper functions that would be useful for dealing with labels please
let us know by filing an issue on [github](https://github.com/mnpopcenter/ipumsr/issues).

# More detail on how the lbl_* functions work
One implementation detail that may help you understand the lbl_* functions
better is that the value labels are stored separately from the actual data. This
can be important because it allows for values to exist in the data without
labels (such as the non-special codes in the INCTOT variable from the example
above) and also for value labels to exist even if they don't exist in the data
(like the STATEFIPS that we didn't include in our extract).

The `.val` and `.lbl` pronouns that are usable in functions like `lbl_na_if()`,
`lbl_collapse()` and `lbl_relabel()` only include these labelled values, *not*
all values in the dataset. Though this makes many calculations simpler, 
because only the labelled values are considered, it can be confusing when you
want to work with the unlabeled values.

For example, considering the INCTOT variable, if there are unlabeled values
that you want to set to NA, you cannot use `lbl_na_if()` directly. 
```{r}
# Reload cps data so that INCTOT is a labelled class again
cps <- read_ipums_micro(ddi, verbose = FALSE)

# Try to set all values above 1000000 to NA
test1 <- lbl_na_if(cps$INCTOT, ~.val > 1000000)
test1 <- zap_labels(test1)
max(test1, na.rm = TRUE)
# Didn't work
```

Instead you should add the value labels with `lbl_add_vals()` (or you could use
a function that doesn't use the labels, such as `dplyr::na_if()`)
```{r}
test2 <- lbl_add_vals(cps$INCTOT)
test2 <- lbl_na_if(test2, ~.val > 1000000)
test2 <- zap_labels(test2)
max(test2, na.rm = TRUE)
```

# Other resources
The haven package vignette 'semantics' has some more details about the motivation and
implementation of the labelled class. You can view it by running the command:
`vignette("semantics", package = "haven")`

The [labelled](http://larmarange.github.io/labelled/articles/intro_labelled.html) 
package provides other methods for manipulating
value labels. It is not installed by ipumsr, but is available on CRAN via the
following command: `install.packages("labelled")`

The [questionr](https://juba.github.io/questionr/) package includes great
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. It is also not installed by ipumsr, but can be
installed from CRAN using: `install.packages("questionr")`

Finally, the [foreign](https://cran.r-project.org/package=foreign) and 
[prettyR](https://cran.r-project.org/package=prettyR) packages don't use the 
`labelled` class data structure from haven (which ipumsr uses), but do have very
similar concepts for attaching value labels. Code designed for these packages 
could be adapted for use with the haven labelled class without too much 
difficulty.
