---
title: "Introduction to `ipumsr` - IPUMS Data in R"
author: "Minnesota Population Center"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{ipums}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

The `ipumsr` package allows you to read data from your IPUMS extract into R along with 
the associated metadata like variable labels, value labels and more. IPUMS 
is a great source of international census and survey data.

> IPUMS provides census and survey data from around the world integrated across 
> time and space. IPUMS integration and documentation makes it easy to study change, 
> conduct comparative research, merge information across data types, and analyze 
> individuals within family and community context. Data and services are available free 
> of charge. 

Learn more here: <https://ipums.org/what-is-ipums>

## Additional Vignettes
This vignette gives the basic outline of the `ipumsr` package. Additional
vignettes provide guidance on working with IPUMS value labels, IPUMS geographic data,
and data from three specific IPUMS projects -- CPS, NHGIS, and Terra. 

To view any of these additional vignettes, run one the following commands after 
installing the `ipumsr` package:
```{r, eval = FALSE}
vignette("value-labels", package = "ipumsr")
vignette("ipums-geography", package = "ipumsr")
vignette("ipums-cps", package = "ipumsr")
vignette("ipums-nhgis", package = "ipumsr")
vignette("ipums-terra", package = "ipumsr")
```

## Getting Data from the IPUMS website
Registered users can download IPUMS data from our website at <https://www.ipums.org>. 
The website provides an interactive extract system that allows you to select
only the samples and variables that are relevant to your research question.

**For microdata projects (all supported projects except NHGIS and IPUMS Terra)**, once you 
have created your extract, you should choose to download your data in either a
fixed-width text (.dat file extension) or comma delimited (.csv file extension) 
format. 

Once your extract is complete, download the data file and the
DDI. Downloading the DDI is a little bit different depending on your browser.
On most browsers you should right-click the file and select "Save As...".
If this saves a file with a .xml file extension, then you should be ready. However, Safari users
must select "Download Linked File" instead of "Download Linked File **As**". On
Safari, selecting the wrong version of these two will download a file with a .html file
extension instead of a .xml extension.

![Annotated screenshot for downloading microdata (may look different on your computer)](microdata_annotated_screenshot.png){ width=700px }

**For NHGIS**, download the table data in a comma delimited format, and, if you want 
the associated mapping data, the GIS data. NHGIS provides the option to download 
comma-delimited files with an extra header row; it does not matter which option you select.

**For IPUMS Terra**, download the .zip extract "bundles". If you want the associated 
mapping data, select the "include boundary files" option. You do not need to unzip
the data.

## Import Functions
Once your extract is downloaded, the `ipumsr` package functions `read_*()`
help you load the data into R. 

- `read_ipums_micro()` / `read_ipums_micro_list()`: Reads data from microdata projects (USA, CPS, International, DHS, Time Use, Health Surveys and Higher Ed)
- `read_nhgis()` / `read_nhgis_sf()` / `read_nhgis_sp()`: Reads data from the NHGIS project. `read_nhgis` loads only tabular data, whereas `read_nhgis_sf()` and `read_nhgis_sp()` load tabular data and shapefiles.
- `read_terra_micro()`, `read_terra_micro_sf()`, `read_terra_micro_sp()`, `read_terra_area()` and `read_terra_raster()` load
data form the IPUMS Terra project.
- `read_ipums_sf()` and `read_ipums_sp()` load boundary files.
- `read_ipums_ddi()`: Reads DDI files with metadata that are included with some extracts (mainly microdata)
- `read_ipums_codebook()`: Reads the text codebook included with some extracts (mainly NHGIS and some TerraPop extracts)

## Metadata Functions
Once the data is in R, you can view information about the extract using the metadata functions.

- `ipums_view()`: Makes a webpage that displays in the RStudio Viewer which provides information about the extract as a whole (like your extract notes or the citation information) and the specific variables included (like the variable label, description and value labels).
- `ipums_file_info()`: Returns the file-level metadata contained in `ipums_view()` as an R data structure.
- `ipums_var_info()`: Returns the variable-level metadata contained in `ipums_view()` as an R data structure.

## Grab Bag
### Survey Weights
The data from most IPUMS projects contain some form of weighting variable that should be used to 
calculate estimates that are representative of the whole population. Many projects also provide 
specifications to help estimate variance given the complex design of the survey, such as replicate
weights or design variables like STRATUM and PSU. The 
[survey package](https://cran.r-project.org/package=survey) provides functions that allow you to 
estimate variance taking this into account, and the [srvyr package](https://cran.r-project.org/package=srvyr)
implements dplyr-like syntax for survey analysis, using the survey package's functions. 

For more information about what these variables mean and how to use them, see the 
website for the project you are interested in.

### Non-Extract Data
Some projects have data that is not contained within the extract system, such that no DDI is provided
for this data. In this case, either use the comma delimited file (.csv file extension) if available, or use the `haven` package to read one
of the files intended for another statistical software (like Stata, SAS or SPSS). 

### Value Labels
The way that IPUMS treats value labels does not align with factors (the main way that R is able
to store values associated with labels). R's `factor` variables can only store
values as an integer sequence (1, 2, 3, ...), but IPUMS conventions are to store 
missing and not-in-universe codes as large numbers, to distinguish them from the normal values.

Therefore, the `ipumsr` package uses the `labelled` class from the `haven` package to store
labelled values. See the "value-labels" vignette for more information 
(`vignette('value-labels')`).

If you want to use IPUMS value labels attached to a variable, it is generally best to convert from
the `labelled` class to `factor` early on in your data analysis workflow. This is
because many data manipulation functions will drop the labels stored in a variable with class `labelled`. The
function `as_factor()` is the main function to create factors from labels, but 
often you will need to do more manipulation before that.

```{r}
library(ipumsr)
library(dplyr, warn.conflicts = FALSE)

# Note that you can pass in the loaded DDI into the `read_ipums_micro()`
cps_ddi <- read_ipums_ddi(ipums_example("cps_00006.xml"))
cps_data <- read_ipums_micro(cps_ddi, verbose = FALSE)

# Show which variables have labels
cps_data %>%
  select_if(is.labelled)

# Notice how the tibble print function shows the dbl+lbl class on top

# Investigate labels
ipums_val_labels(cps_data$STATEFIP)

# Convert the labels to factors (and drop the unused levels)
cps_data <- cps_data %>%
  mutate(STATE_factor = as_factor(lbl_clean(STATEFIP)))

table(cps_data$STATE_factor, useNA = "always")
```
```{r, error = TRUE}
# Manipulating the labelled value before as_factor 
# often leads to losing the information...
# Say we want to set Iowa (STATEFIP == 19) to missing
cps_data <- cps_data %>%
  mutate(STATE_factor2 = as_factor(ifelse(STATEFIP == 19, NA, STATEFIP)))
```
```{r}
# ipumsr provides helpers for these kinds of tasks, like lbl_na_if().
# See the value-labels vignette for more information
cps_data <- cps_data %>%
  mutate(STATE_factor3 = as_factor(lbl_na_if(STATEFIP, ~.val == 19)))

# The as_factor function also has a "levels" argument that can 
# put both the labels and values into the factor
cps_data <- cps_data %>%
  mutate(STATE_factor4 = droplevels(as_factor(STATEFIP, levels = "both")))

table(cps_data$STATE_factor4, useNA = "always")

```

### Other IPUMS attributes
As with value labels, the other attributes that `ipumsr` stores about the data are often lost
during an analysis. One way to deal with this is to load the DDI or codebook in addition
to the actual data using the functions `read_ipums_ddi()` and `read_ipums_codebook()`. This
way, when you wish to refer to variable labels or other metadata, you can use the 
DDI object, which does not get modified during your analysis.

```{r}
library(ipumsr)
library(dplyr, warn.conflicts = FALSE)

# Note that you can pass in the loaded DDI into the `read_ipums_micro()`
cps_ddi <- read_ipums_ddi(ipums_example("cps_00006.xml"))
cps_data <- read_ipums_micro(cps_ddi, verbose = FALSE)

# Currently variable description is available for year
ipums_var_desc(cps_data$YEAR)

# But after using ifelse it is gone
cps_data <- cps_data %>%
  mutate(YEAR = ifelse(YEAR == 1962, 62, NA))
ipums_var_desc(cps_data$YEAR)

# So you can use the DDI
ipums_var_desc(cps_ddi, "YEAR")

# The DDI also has file level information that is not available from just
# the data.
ipums_file_info(cps_ddi, "extract_notes") %>% cat()
```

### "dplyr select-style" Syntax
Several functions within the `ipumsr` package allow for "dplyr select-style" syntax.
This means that they accept either a character vector of values (e.g. `c("YEAR", "AGE")`), 
bare vectors of values (e.g. `c(YEAR, AGE)`) and the helper functions allowed in 
`dplyr::select()` (e.g. `one_of(c("YEAR", "AGE"))`).

```{r}
library(ipumsr)
library(dplyr, warn.conflicts = FALSE)

# The vars argument for `read_ipums_micro` uses this syntax
# So these are all equivalent
cf <- ipums_example("cps_00006.xml")
read_ipums_micro(cf, vars = c("YEAR", "INCTOT"), verbose = FALSE) %>%
  names()

read_ipums_micro(cf, vars = c(YEAR, INCTOT), verbose = FALSE) %>%
  names()

read_ipums_micro(cf, vars = c(one_of("YEAR"), starts_with("INC")), verbose = FALSE) %>%
  names()

# `data_layer` and `shape_layer` arguments to `read_nhgis()` and terra functions
# also use it.
# (Sometimes extracts have multiple files, though all examples only have one)
nf <- ipums_example("nhgis0008_csv.zip")
ipums_list_files(nf)

ipums_list_files(nf, data_layer = "nhgis0008_csv/nhgis0008_ds135_1990_pmsa.csv")

ipums_list_files(nf, data_layer = contains("ds135"))
```

### Hierarchical data structures
For certain IPUMS projects, the data is hierarchical, multiple people are 
included in a single household, or multiple activities are performed by
a single person. The `ipumsr` package provides two data structures for
storing such data (for users who did not select the "rectangularize" option
on the website). The data can be loaded as a `"list"` or `"long"`. 

**List** data loads each record type into a separate data.frame. The names 
of the recordtype data.frames are the value of the RECTYPE variable (e.g. "H" 
and "P"). Use the function `read_ipums_micro_list()` to load the data
this way.

**Long** data has one row per unit, regardless of what type of record the unit is.
Therefore, datasets loaded this way often contain variables with a large 
number of missings, for the variables that only apply to certain record types. 
Use the function `read_ipums_micro()` to load the data this way.
```{r}
library(ipumsr)
library(dplyr, warn.conflicts = FALSE)

# List data
cps <- read_ipums_micro_list(
  ipums_example("cps_00010.xml"),
  verbose = FALSE
)

cps$PERSON

cps$HOUSEHOLD

# Long data
cps <- read_ipums_micro(
  ipums_example("cps_00010.xml"),
  verbose = FALSE
)

cps
```

### Geospatial Packages: `sf` vs `sp`
The `ipumsr` package allows for loading geospatial data in two formats (sf for
Simple Features and sp for Spatial). The `sf` package is relatively new, and so 
does not have as widespread support as the `sp` package. However, (in my opinion)
it does allow for easier analysis, and so may be a better place to start if you
have not used GIS data in R before.

For more details about how to load geographic data using ipumsr, see the vignette
"ipums-geography" (`vignette("ipums-geography", package = "ipumsr")`)
