IPUMS Data and R

This article provides an overview of how to find, request, download, and read IPUMS data into R. For a general introduction to IPUMS and ipumsr, see the ipumsr home page.

Obtaining IPUMS data

IPUMS data are free, but do require registration. New users can register with a particular IPUMS project by clicking the Register link at the top right of the project website.

Users obtain IPUMS data by creating and submitting an extract request. This specifies which data to include in the resulting extract (or data extract). IPUMS servers process each submitted extract request, and when complete, users can download the extract containing the requested data.

Extracts typically contain both data and metadata files. Data files typically come as fixed-width (.dat) files or comma-delimited (.csv) files. Metadata files contain information about the data file and its contents, including variable descriptions and parsing instructions for fixed-width data files. IPUMS microdata projects provide metadata in DDI (.xml) files. Aggregate data projects provide metadata in either .txt or .csv formats.

Users can submit extract requests and download extracts via either the IPUMS website or the IPUMS API. ipumsr provides a set of client tools to interface with the API. Note that only certain IPUMS projects are currently supported by the IPUMS API.

Obtaining data via an IPUMS project website

To create a new extract request via an IPUMS project website (e.g. IPUMS CPS), navigate to the extract interface for that project by clicking Select Data in the heading of the project website.

Screenshot of the Select Data link at the top of the IPUMS CPS homepage

The project’s extract interface allows you to explore what’s available, find documentation about data concepts and sources, and specify the data you’d like to download. The data selection parameters will differ across projects; see each project’s documentation for more details on the available options.

If you’ve never created an extract for the project you’re interested in, a good way to learn the basics is to watch a project-specific video on creating extracts hosted on the IPUMS Tutorials page.

Downloading from microdata projects

Once your extract is ready, click the green Download button to download the data file. Then, right-click the DDI link in the Codebook column, and select Save Link As… (see below).

Screenshot of the My Data page on IPUMS USA website, with the "Download .DAT" button and the "DDI" codebook link each surrounded by a red box for emphasis. The right-click menu has been called up on the "DDI" codebook link, and the menu option "Save Link As..." is also surrounded by a red box for emphasis.

Note that some browsers may display different text, but there should be an option to download the DDI file as .xml. (For instance, on Safari, select Download Linked File As….) For ipumsr to read the metadata, you must save the file in .xml format, not .html format.

Downloading from aggregate data projects

Aggregate data projects include data and metadata together in a single .zip archive. To download them, simply click on the green Tables button (for tabular data) and/or GIS Files button (for spatial boundary or location data) in the Download Data column.

Obtaining data via the IPUMS API

Users can also create and submit extract requests within R by using ipumsr functions that interface with the IPUMS API. The IPUMS API currently supports access to the extract system for certain IPUMS collections.

Extract support

ipumsr provides an interface to the IPUMS extract system via the IPUMS API for the following collections:

  • IPUMS International
  • IPUMS Health Surveys (NHIS, MEPS)

Metadata support

For IPUMS NHGIS, ipumsr provides access to comprehensive metadata via the IPUMS API. Users can query NHGIS metadata to explore available data when specifying NHGIS extract requests.

Increased access to metadata for microdata projects is in progress. Currently, the IPUMS API only provides a listing of available samples for each microdata collection. At this time, creating extract requests for these projects requires using the corresponding project websites to find samples and variables of interest and obtain their API identifiers for use in R extract definitions.


Once you have identified the data you would like to request, you can use ipumsr functions to define your extract request, submit it, wait for it to process, and download the associated data.

First, define the parameters of your extract. The available extract definition options will differ by IPUMS data collection. See the microdata API request and NHGIS API request vignettes for more details on defining an extract.

# Define a microdata extract request, e.g. for IPUMS CPS
cps_extract_request <- define_extract_micro(
  collection = "cps",
  description = "2018-2019 CPS Data",
  samples = c("cps2018_05s", "cps2019_05s"),
  variables = c("SEX", "AGE", "YEAR")

# Define an NHGIS extract request
nhgis_extract_request <- define_extract_nhgis(
  description = "NHGIS Data via IPUMS API",
  datasets = ds_spec(
    data_tables = c("NP1", "NP2", "NP3"),
    geog_levels = "state"

Next, submit your extract definition. After waiting for it to complete, you can download the files directly to your local machine without leaving your R session:

submitted_extract <- submit_extract(cps_extract_request)
downloadable_extract <- wait_for_extract(submitted_extract)
path_to_data_files <- download_extract(downloadable_extract)

You can also get the specifications of your previous extract requests, even if they weren’t made with the API:

past_extracts <- get_extract_history("nhgis")

See the introduction to the IPUMS API for more details about how to use ipumsr to interact with the IPUMS API.

Reading IPUMS data

Once you have downloaded an extract, you can load the data into R with the family of read_*() functions in ipumsr. These functions expand on those provided in {readr} in two ways:

File loading is covered in depth in the reading IPUMS data vignette.

Microdata files

For microdata files, use the read_ipums_micro_*() family with the DDI (.xml) metadata file for your extract:

cps_file <- ipums_example("cps_00157.xml")
cps_data <- read_ipums_micro(cps_file)
#> Use of data from IPUMS CPS is subject to conditions including that users should cite the data appropriately. Use command `ipums_conditions()` for more details.
#> # A tibble: 6 × 8
#>   <dbl>  <dbl> <int+lbl>   <dbl> <int+lbl>       <dbl>  <dbl> <dbl+lbl>         
#> 1  1962     80 3 [March]   1476. 55 [Wisconsin]      1  1476.      4883         
#> 2  1962     80 3 [March]   1476. 55 [Wisconsin]      2  1471.      5800         
#> 3  1962     80 3 [March]   1476. 55 [Wisconsin]      3  1579. 999999998 [Missin…
#> 4  1962     82 3 [March]   1598. 27 [Minnesota]      1  1598.     14015         
#> 5  1962     83 3 [March]   1707. 27 [Minnesota]      1  1707.     16552         
#> 6  1962     84 3 [March]   1790. 27 [Minnesota]      1  1790.      6375

NHGIS files

For NHGIS files, use read_nhgis():

nhgis_file <- ipums_example("nhgis0972_csv.zip")
nhgis_data <- read_nhgis(nhgis_file, verbose = FALSE)

#> # A tibble: 6 × 25
#>   <chr>   <dbl> <chr>  <chr> <lgl>         <dbl> <chr>      <chr> <lgl>   <lgl> 
#> 1 G0080    1990 OH     28    NA             1692 Akron, OH… 0080  NA      NA    
#> 2 G0360    1990 CA     49    NA             4472 Anaheim--… 0360  NA      NA    
#> 3 G0440    1990 MI     35    NA             2162 Ann Arbor… 0440  NA      NA    
#> 4 G0620    1990 IL     14    NA             1602 Aurora--E… 0620  NA      NA    
#> 5 G0845    1990 PA     78    NA             6282 Beaver Co… 0845  NA      NA    
#> 6 G0875    1990 NJ     70    NA             5602 Bergen--P… 0875  NA      NA    
#> # ℹ 15 more variables: AREALAND <chr>, AREAWAT <chr>, ANPSADPI <chr>,
#> #   FUNCSTAT <chr>, INTPTLAT <dbl>, INTPTLNG <dbl>, PSADC <dbl>, D6Z001 <dbl>,
#> #   D6Z002 <dbl>, D6Z003 <dbl>, D6Z004 <dbl>, D6Z005 <dbl>, D6Z006 <dbl>,
#> #   D6Z007 <dbl>, D6Z008 <dbl>

Spatial boundary files

ipumsr also supports the reading of IPUMS shapefiles (spatial boundary and location files) into the sf format provided by the {sf} package:

shp_file <- ipums_example("nhgis0972_shape_small.zip")
nhgis_shp <- read_ipums_sf(shp_file)

#> Simple feature collection with 6 features and 8 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -129888.4 ymin: -967051.1 xmax: 1948770 ymax: 751282.5
#> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic
#> # A tibble: 6 × 9
#>   <chr> <chr>   <chr>   <chr>   <chr>           <dbl>     <dbl> <chr>    
#> 1 3280  3282    41      G3280   3280      2840869482.   320921. G32823280
#> 2 5760  5602    70      G5760   5760       237428573.   126226. G56025760
#> 3 1145  3362    42      G1145   1145      3730749183.   489789. G33621145
#> 4 1920  1922    31      G1920   1920     12068105590.   543164. G19221920
#> 5 0080  1692    28      G0080   0080      2401347006.   218892. G16920080
#> 6 1640  1642    21      G1640   1640      5608404797.   415671. G16421640
#> # ℹ 1 more variable: geometry <MULTIPOLYGON [m]>

Ancillary files

ipumsr is primarily designed to read data produced by the IPUMS extract system. However, IPUMS does distribute other files, often available via direct download. In many cases, these can be loaded with ipumsr. Otherwise, these files can likely be handled by existing data reading packages like {readr} (for delimited files) or {haven} (for Stata, SPSS, or SAS files).

Exploring file metadata

Load a file’s metadata with read_ipums_ddi() (for microdata projects) and read_nhgis_codebook() (for NHGIS). These provide file- and variable-level metadata for a given data source, which can be used to interpret the data contents.

cps_meta <- read_ipums_ddi(cps_file)
nhgis_meta <- read_nhgis_codebook(nhgis_file)

Summarize the variable metadata for a dataset using ipums_var_info():

#> # A tibble: 8 × 10
#>   var_name var_label        var_desc val_labels code_instr start   end imp_decim
#>   <chr>    <chr>            <chr>    <list>     <chr>      <dbl> <dbl>     <dbl>
#> 1 YEAR     Survey year      "YEAR r… <tibble>   "YEAR is …     1     4         0
#> 2 SERIAL   Household seria… "SERIAL… <tibble>   "SERIAL i…     5     9         0
#> 3 MONTH    Month            "MONTH … <tibble>    <NA>         10    11         0
#> 4 ASECWTH  Annual Social a… "ASECWT… <tibble>   "ASECWTH …    12    22         4
#> 5 STATEFIP State (FIPS cod… "STATEF… <tibble>    <NA>         23    24         0
#> 6 PERNUM   Person number i… "PERNUM… <tibble>   "PERNUM i…    25    26         0
#> 7 ASECWT   Annual Social a… "ASECWT… <tibble>   "ASECWT i…    27    37         4
#> 8 INCTOT   Total personal … "INCTOT… <tibble>   "99999999…    38    46         0
#> # ℹ 2 more variables: var_type <chr>, rectypes <lgl>

You can also get contextual details for specific variables:

#> [1] "INCTOT indicates each respondent's total pre-tax personal income or losses from all sources for the previous calendar year.  Amounts are expressed as they were reported to the interviewer; users must adjust for inflation using Consumer Price Index adjustment factors."
#> # 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

Labelled values

ipumsr also provides a family of lbl_*() functions to assist in accessing and manipulating the value-level metadata included in IPUMS data. This allows for value labels to be incorporated into the data processing pipeline. For instance:

# Remove labels for values that do not appear in the data
cps_data$STATEFIP <- lbl_clean(cps_data$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
# Combine North and South Dakota into a single value/label pair
cps_data$STATEFIP <- lbl_relabel(
  lbl("38_46", "Dakotas") ~ grepl("Dakota", .lbl)

#> # A tibble: 4 × 2
#>   val   lbl      
#>   <chr> <chr>    
#> 1 19    Iowa     
#> 2 27    Minnesota
#> 3 38_46 Dakotas  
#> 4 55    Wisconsin

See the value labels vignette for more details.