---
title: "Reading Legacy Census Redistricting Data"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Reading Legacy Census Redistricting Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

The US Census Bureau releases redistricting data (P.L. 94-171) following the decennial census. This article walks through the basics of reading that data and getting it into working form.

# 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](https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_StatesTechDoc_English.pdf).
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](../man/figures/pl94171_layout.png)

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()`)
1. Combine the four files into one, and subset to the desired geography level 
   (`pl_subset()`; geography level codes listed in `pl_geog_levels`)
1. Select the desired variables from the six included tables (`pl_select_standard()`)
1. Optionally, combine the processed data with the corresponding 
   [`tigris` shapefile](https://cran.r-project.org/package=tigris).

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](../index.html)

# Using the `PL94171` package

```{r setup, message=F, warning=F}
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.

```{r}
# `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).

```{r}
head(pl_raw$`00003`)
```

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

```{r}
print(pl_geog_levels)
```

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

```{r}
pl <- pl_subset(pl_raw, sumlev="140")
print(dim(pl))
```

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](../reference/pl_select_standard.html)
from the 397 columns, we can run the following:

```{r}
pl <- pl_select_standard(pl, clean_names = TRUE)
print(pl)
```

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`.

```{r message=F, warning=F, eval = FALSE}
library(tinytiger)
library(sf)
library(dplyr)
library(ggplot2)

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

```{r, echo = FALSE}
library(tinytiger)
library(sf)
library(dplyr)
library(ggplot2)
with_retry <- function(fn, ..., max_iter = 5) {
    out <- NULL
    i <- 1
    try({out <- fn(...)}, silent = TRUE)
    while (i <= max_iter && is.null(out)) {
        Sys.sleep(0.5)
        try({out <- fn(...)}, silent = TRUE)
        i <- i + 1
    } 
    out
}

ri_tracts = with_retry(fn = tt_tracts, state = "RI", county = "Providence", year = 2020)
```

Then we can join the shapes and data
```{r, eval = !is.null(ri_tracts)}
full_join(pl, ri_tracts, by="GEOID") %>%
ggplot(aes(fill=pop, geometry=geometry)) +
    geom_sf(size=0) +
    theme_void()
```
