The P.L. 94-171 Legacy File

The redistricting data mandated by P.L. 94-171 is composed of six tables, five of population characteristics, and one of housing characteristics. In the legacy data format, these six tables (and dozens of levels of geography) are split into four files: File01, File02, File03, and the Geographic Header Record. Files 01–03 have the actual decennial census data for each table, while the geographic header has geographic identifiers (FIPS code, precinct IDs, etc.) and information (area, water, etc.).

P.L. 94-171 file layout

These four files have the same rows, each of which is identified by a LOGRECNO number. Combining the columns shared across the four files yields the full P.L. 94-171 file

The basic unit of Census geography is the block; all other geographies are constructed from them. But the P.L. 94-171 file is not composed of blocks alone. The Census has already tabulated the six table statistics across every possible level of geographies. To get information for a single geography level—blocks, or counties, or school districts—one needs only to subset to the rows which correspond to this geographic level (coded in the SUMLEV column).

So the basic process for working with legacy P.L. 94-171 data is:

1. Read in the four P.L. files (pl_read(), pl_url())
2. Combine the four files into one, and subset to the desired geography level (pl_subset(); geography level codes listed in pl_geog_levels)
3. Select the desired variables from the six included tables (pl_select_standard())
4. Optionally, combine the processed data with the corresponding tigris shapefile.

The utility function pl_tidy_shp() combines all of these steps into one function for most common use case of tabulating basic redistricting information at the block level. This is demonstrated in the README

Using the PL94171 package

library(PL94171)

The four components of the P.L. 94-171 file should be downloaded into their own directory. Here, we’ll use the example data included in the package, from the 2018 end-to-end Census test in Providence County, Rhode Island, and read it into our R session. In general, you can provide a URL in place of a file path, and the package will read the data from the URL. The pl_url() function will automatically construct the URL to the data for a given state and year.

# `extdata/ri2018_2020Style.pl` is a directory with the four P.L. 94-171 files
path <- system.file("extdata/ri2018_2020Style.pl", package = "PL94171")
pl_raw <- pl_read(path)
# try `pl_read(pl_url("RI", 2010))`

This creates a large list where each individual P.L. 94-171 file component is a separate entry in the list. If we look at the top of one of these entries, we see the same structure as in the schematic above: each redistricting variable is a column, the rows are indexed by LOGRECNO, and various levels of aggregation are all included as different sets of rows in the same table (notice the countywide population counts in the first two rows).

head(pl_raw$`00003`)
#> # A tibble: 6 × 15
#>   FILEID STUSAB CHARITER CIFSN LOGRECNO P0050001 P0050…¹ P0050…² P0050…³ P0050…⁴
#>   <chr>  <chr>  <chr>    <chr>    <int>    <int>   <int>   <int>   <int>   <int>
#> 1 PLST   RI     000      03           1    15766    7311    3266     261    3387
#> 2 PLST   RI     000      03           2    15766    7311    3266     261    3387
#> 3 PLST   RI     000      03          19      732       0       0       0       0
#> 4 PLST   RI     000      03          20        0       0       0       0       0
#> 5 PLST   RI     000      03          21        1       0       0       0       0
#> 6 PLST   RI     000      03          22        1       1       0       0       1
#> # … with 5 more variables: P0050006 <int>, P0050007 <int>, P0050008 <int>,
#> #   P0050009 <int>, P0050010 <int>, and abbreviated variable names ¹​P0050002,
#> #   ²​P0050003, ³​P0050004, ⁴​P0050005

To subset to a desired geography level, we must first identify the corresponding SUMLEV code.

print(pl_geog_levels)
#> # A tibble: 85 × 2
#>    SUMLEV SUMLEV_description                                     
#>    <chr>  <chr>                                                  
#>  1 040    State                                                  
#>  2 050    State-County                                           
#>  3 060    State-County-County Subdivision                        
#>  4 067    State-County-County Subdivision-Subminor Civil Division
#>  5 140    State-County-Census Tract                              
#>  6 150    State-County-Census Tract-Block Group                  
#>  7 155    State-Place-County                                     
#>  8 160    State-Place                                            
#>  9 170    State-Consolidated City                                
#> 10 172    State-Consolidated City-Place within Consolidated City 
#> # … with 75 more rows

Here, we’ll look at Census tracts, which are SUMLEV=140.

pl <- pl_subset(pl_raw, sumlev="140")
print(dim(pl))
#> [1]   7 397

We see that all four components have been combined into one large table, with data for each of the seven Census tracts in the example file recorded in a single row. To extract commonly-used variables from the 397 columns, we can run the following:

pl <- pl_select_standard(pl, clean_names = TRUE)
print(pl)
#> # A tibble: 7 × 24
#>   GEOID  state county row_id summa…¹ vtd     pop pop_h…² pop_w…³ pop_b…⁴ pop_a…⁵
#>   <chr>  <chr> <chr>   <int> <chr>   <chr> <int>   <int>   <int>   <int>   <int>
#> 1 44007… RI    007        19 140     <NA>   3970    1442    1445     623       0
#> 2 44007… RI    007        20 140     <NA>   4735    2555     815     703       0
#> 3 44007… RI    007        21 140     <NA>   5703    3766     570     636       0
#> 4 44007… RI    007        22 140     <NA>   6647    3827     754    1293       3
#> 5 44007… RI    007        23 140     <NA>   3433    2130     142     874       0
#> 6 44007… RI    007        24 140     <NA>   2940    2249      39     593       0
#> 7 44007… RI    007        25 140     <NA>   1797     778     170     483      24
#> # … with 13 more variables: pop_asian <int>, pop_nhpi <int>, pop_other <int>,
#> #   pop_two <int>, vap <int>, vap_hisp <int>, vap_white <int>, vap_black <int>,
#> #   vap_aian <int>, vap_asian <int>, vap_nhpi <int>, vap_other <int>,
#> #   vap_two <int>, and abbreviated variable names ¹​summary_level, ²​pop_hisp,
#> #   ³​pop_white, ⁴​pop_black, ⁵​pop_aian

Above, we set clean_names = TRUE, which is the default. This creates a set of variables familiar to the redist family of packages.

To combine these data with a shapefile, we must use the tinytiger package. The GEOID column is shared between the P.L. 94-171 data and the TIGER shapefiles from tinytiger.

library(tinytiger)
library(sf)
library(dplyr)
library(ggplot2)

ri_tracts = tt_tracts("RI", county="Providence", year=2020)

#> Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

Then we can join the shapes and data

full_join(pl, ri_tracts, by="GEOID") %>%
ggplot(aes(fill=pop, geometry=geometry)) +
    geom_sf(size=0) +
    theme_void()