Normalize Geographic Variables

library(campfin)
packageVersion("campfin")
#> [1] '1.0.8'
library(dplyr)
library(readr)
library(stringr)

Background

This vignette contains an example of the workflow used by the wranglers on The Accountability Project, a tool created by The Investigative Reporting Workshop in Washington, DC. The Accountability Project curates, cleans, and indexes public data to give journalists, researchers, and the public a simple way to search across otherwise siloed records. The data focuses on people, organizations and locations.

Since state-level campaign finance data is typically reported by the campaigns and provided to them by the contributors themselves, there is often great disparity in data quality. The campfin package was created to reduce this disparity in a consistent, confident, and programmatic way.

Messy Data

In this vignette, we will clean some exaggerated fake messy contribution data from a fictitious campaign in Vermont. This data is found in the vt_contribs.csv file included with our package.

ex_file <- system.file("extdata", "vt_contribs.csv", package = "campfin")
id cand date amount name address city state zip
01 Bob Miller 02/09/2019 10 Lisa Miller 4 Sheffield square Road Sheffield VT 05866
02 Bob Miller 03/09/2009 20 Deb Brown Requested Requested RE 00000
03 Chuck White 04/09/2019 25 N/A p.o. box 567 Midlebury Vermont 05753-0567
04 Chuck White 05/09/2019 100 Josh Jones sugarhouse SQU e Corinth VT 5076
05 Bob Miller 02/09/2019 10 Lisa Miller 4 Sheffield square Road Sheffield VT 05866
06 Chuck White 06/09/2019 1000 Bob Taylor 55 thisplace av young america mn 55555
07 Chuck White 07/09/2019 -600 Alex Johnson 11 Liberty AVN Bristol, VT VT 99999
08 Alice Walsh 08/09/2019 0 Ruth Smith 2 Burlington sqre Brulington vt 05401
09 Alice Walsh 09/09/2019 69 Joe Garcia 770 5th-st-nw Washington D.C. 20001-2674
10 Alice Walsh 11/09/2019 222 Dave Wilson XXXXXXXXXXXXXXXXX SA Texas 78202

What are some of the potential problems we can see in this data?

While this data is obviously much smaller and more full of errors than real campaign finance data, these errors are not uncommon and need to be addressed. The campfin package contains many of the tools we need to first find and then fix these common problems.

Read

In most cases, the first step is to download and read the file from a state agency. When reading the data with the popular readr::read_delim() function, the col_date_mdy() function can be used as a quick shortcut for readr::col_date(format = "%m/%d/%Y"), the format most commonly found in U.S. campaign finance data.

vt <- read_csv(
  file = ex_file,
  trim_ws = FALSE,
  na = c("", "NA", "N/A"),
  col_types = cols(
    amount = col_number(),
    date = col_date_mdy()
  )
)

We can see how the new date column is an actual date object, allowing for mathematical manipulation.

min(vt$date)
#> [1] "2009-03-09"

Next, we should try to normalize our data as much as possible. We can use some simple counting functions and built in vectors to check the cleanliness of our raw data.

prop_in(vt$city, str_to_lower(valid_city))
#> [1] 0.1
prop_in(vt$state, valid_state)
#> [1] 0.4
prop_in(vt$zip, valid_zip)
#> [1] 0.5
col_stats(vt, n_distinct)
#> # A tibble: 9 × 4
#>   col     class      n     p
#>   <chr>   <chr>  <int> <dbl>
#> 1 id      <chr>     10   1  
#> 2 cand    <chr>      3   0.3
#> 3 date    <date>     9   0.9
#> 4 amount  <dbl>      9   0.9
#> 5 name    <chr>      9   0.9
#> 6 address <chr>      9   0.9
#> 7 city    <chr>      9   0.9
#> 8 state   <chr>      7   0.7
#> 9 zip     <chr>      9   0.9
col_stats(vt, count_na)
#> # A tibble: 9 × 4
#>   col     class      n     p
#>   <chr>   <chr>  <int> <dbl>
#> 1 id      <chr>      0   0  
#> 2 cand    <chr>      0   0  
#> 3 date    <date>     0   0  
#> 4 amount  <dbl>      0   0  
#> 5 name    <chr>      1   0.1
#> 6 address <chr>      0   0  
#> 7 city    <chr>      0   0  
#> 8 state   <chr>      0   0  
#> 9 zip     <chr>      0   0

A typical benchmark is to reach greater than 95% valid. That is, we want to normalize our data enough that less than 5% of our data can not be easily confirmed as valid using a fairly comprehensive list of cities, states, and ZIP codes.

We will first try to reach this threshold by normalizing our data. This process involves reducing inconsistencies through string manipulation. There are separate normal_*() functions for each of the 4 types of geographic variables. Typically we use dplyr::mutate() to create new, normalized versions of our messy columns, preserving the old data for transparency. Here, we will just overwrite our example data for simplicity.

vt <- vt %>% 
  mutate(
    address = normal_address(
      address = address,
      abbs = usps_street,
      na = invalid_city,
      na_rep = TRUE
    ),
    city = normal_city(
      city = city,
      abbs = usps_city,
      states = "VT",
      na = invalid_city
    ),
    state = normal_state(
      state = state,
      abbreviate = TRUE,
      na_rep = TRUE,
      valid = valid_state
    ),
    zip = normal_zip(
      zip = zip,
      na_rep = TRUE
    )
  )

We can see how these functions and our built in data was used to normalize the geographic contributor data and remove anything that didn’t present real information. This format is much more easily explored and search.

#> # A tibble: 10 × 4
#>    address               city          state zip  
#>    <chr>                 <chr>         <chr> <chr>
#>  1 4 SHEFFIELD SQUARE RD SHEFFIELD     VT    05866
#>  2 <NA>                  <NA>          <NA>  <NA> 
#>  3 PO BOX 567            MIDLEBURY     VT    05753
#>  4 SUGARHOUSE SQ         EAST CORINTH  VT    5076 
#>  5 4 SHEFFIELD SQUARE RD SHEFFIELD     VT    05866
#>  6 55 THISPLACE AVE      YOUNG AMERICA MN    55555
#>  7 11 LIBERTY AVE        BRISTOL       VT    <NA> 
#>  8 2 BURLINGTON SQ       BRULINGTON    VT    05401
#>  9 770 5TH ST NW         WASHINGTON    DC    20001
#> 10 <NA>                  SA            TX    78202

Cities

However, the problem has not been solved. City names are the most troublesome; There are so many city names and such great variety (compared to states and ZIP codes), that it can be difficult to normalize and difficult to assess.

Our valid_city vector contains many city names, but far less than exist in the country, especially when you account for neighborhoods that aren’t really cities, but shouldn’t be changed (some of these are contained in our curated extra_city vector).

length(valid_city)
#> [1] 19083
sample(valid_city, 6)
#> [1] "LEMASTERS"     "COPPER CITY"   "CATOOSA"       "OJIBWA"       
#> [5] "SUMMERFIELD"   "ROBINSONVILLE"
sample(extra_city, 6)
#> [1] "MENOMONIE SAINT"  "DANIEL ISLAND"    "TOWN AND COUNTRY" "PARMA HEIGHTS"   
#> [5] "SANATOGA"         "VINEYARD"
# combine both vectors
many_city <- c(valid_city, extra_city)

Still, checking against this list is a good way to check for values that need additional attention.

#> # A tibble: 3 × 5
#>   id    city       state zip   valid
#>   <chr> <chr>      <chr> <chr> <lgl>
#> 1 03    MIDLEBURY  VT    05753 FALSE
#> 2 08    BRULINGTON VT    05401 FALSE
#> 3 10    SA         TX    78202 FALSE

It might not be clear what’s actually wrong with these values. A common way to check is by comparing them against expected city for a given ZIP code.

bad <- left_join(
  x = bad,
  y = zipcodes,
  by = c("zip", "state"), 
  suffix = c("_raw", "_match")
)
id city_raw state zip city_match
03 MIDLEBURY VT 05753 MIDDLEBURY
08 BRULINGTON VT 05401 BURLINGTON
10 SA TX 78202 SAN ANTONIO

Now the problems become clear. Two city names are misspelled and the third is an obvious abbreviation. When dealing with millions of city names, we need a way to check each raw value against it’s expected ZIP code match.

The str_dist() and is_abbrev() functions can be used to compared the value we have with the value we expect. By only checking against the corresponding city to that record’s ZIP code, we are making extremely confident changes (compared to the incredibly useful clustering algorithms like those provided by the refinr package).

First, we can use str_dist() to check the distance between the two strings; distance is defined as the number of changes we’d need to make to our normalized value to get our expected matched value. If that distance is small (usually 1 or 2), we can pretty confidently use the matched value.

str_dist("example", "xampel")
#> [1] 2

But the string distance does not catch colloquial city abbreviations (e.g., NYC, BOS, LA, CHI, ABQ, BRNX, DFW, OKC). Many residents get so used to writing their city’s name they use abbreviations and assume them to be universally understood. The is_abbrev() function can be used to check to one string might be an abbreviation for another. Every abbreviation generated by the abbreviate() function satisfied the requirements of is_abbrev().

is_abbrev(abb = "NYC", full = "New York City")
#> [1] TRUE
is_abbrev(abb = "DC", full = "Washington")
#> [1] FALSE
bad <- bad %>% 
  mutate(
    match_dist = str_dist(city_raw, city_match),
    match_abb = is_abbrev(city_raw, city_match)
  )
id city_raw state zip city_match match_dist match_abb
03 MIDLEBURY VT 05753 MIDDLEBURY 1 FALSE
08 BRULINGTON VT 05401 BURLINGTON 1 FALSE
10 SA TX 78202 SAN ANTONIO 9 TRUE

Here’s what this process would look like when employed on an entire data frame. It’s important to ensure that the number of rows in our campaign finance data is kept consistent throughout the wrangling process and that original columns are left unchanged.

vt <- vt %>%
  rename(city_raw = city) %>% 
  # match city by ZIP
  left_join(zipcodes) %>% 
  rename(city_match = city) %>%
  mutate(
    # check against match
    match_dist = str_dist(city_raw, city_match),
    match_abb = is_abbrev(city_raw, city_match),
    city = ifelse(match_abb | match_dist == 1, city_match, city_raw)
  ) %>% 
  # remove intermediary columns
  select(-city_raw, -city_match, -match_dist, -match_abb)
#> Joining, by = c("state", "zip")

Now every city, state, and zip value is contained in our list of valid values.

id state zip city all_valid
01 VT 05866 SHEFFIELD TRUE
03 VT 05753 MIDDLEBURY TRUE
05 VT 05866 SHEFFIELD TRUE
06 MN 55555 YOUNG AMERICA TRUE
08 VT 05401 BURLINGTON TRUE
09 DC 20001 WASHINGTON TRUE
10 TX 78202 SAN ANTONIO TRUE

Once our data is as normal as we can confidently make it, we can begin to explore. First, we’ll explore the data for missing values with flag_na(), which takes a tidyselect input of columns (or something like dplyr::everything()).

(vt <- flag_na(vt, name))
#> # A tibble: 10 × 10
#>    id    cand        date       amount name   address  state zip   city  na_flag
#>    <chr> <chr>       <date>      <dbl> <chr>  <chr>    <chr> <chr> <chr> <lgl>  
#>  1 01    Bob Miller  2019-02-09     10 Lisa … 4 SHEFF… VT    05866 SHEF… FALSE  
#>  2 02    Bob Miller  2009-03-09     20 Deb B… <NA>     <NA>  <NA>  <NA>  FALSE  
#>  3 03    Chuck White 2019-04-09     25 <NA>   PO BOX … VT    05753 MIDD… TRUE   
#>  4 04    Chuck White 2019-05-09    100 Josh … SUGARHO… VT    5076  <NA>  FALSE  
#>  5 05    Bob Miller  2019-02-09     10 Lisa … 4 SHEFF… VT    05866 SHEF… FALSE  
#>  6 06    Chuck White 2019-06-09   1000 Bob T… 55 THIS… MN    55555 YOUN… FALSE  
#>  7 07    Chuck White 2019-07-09   -600 Alex … 11 LIBE… VT    <NA>  <NA>  FALSE  
#>  8 08    Alice Walsh 2019-08-09      0 Ruth … 2 BURLI… VT    05401 BURL… FALSE  
#>  9 09    Alice Walsh 2019-09-09     69 Joe G… 770 5TH… DC    20001 WASH… FALSE  
#> 10 10    Alice Walsh 2019-11-09    222 Dave … <NA>     TX    78202 SAN … FALSE

Next, we’ll want to check for duplicate rows using flag_dupes(), which takes the same kind of arguments. Here, we can ignore the supposedly unique id variable. It’s possible for a person to make the same contribution on the same date, but we should flag them nonetheless.

(vt <- flag_dupes(vt, -id, .both = TRUE))
#> # A tibble: 10 × 11
#>    id    cand        date       amount name   address  state zip   city  na_flag
#>    <chr> <chr>       <date>      <dbl> <chr>  <chr>    <chr> <chr> <chr> <lgl>  
#>  1 01    Bob Miller  2019-02-09     10 Lisa … 4 SHEFF… VT    05866 SHEF… FALSE  
#>  2 02    Bob Miller  2009-03-09     20 Deb B… <NA>     <NA>  <NA>  <NA>  FALSE  
#>  3 03    Chuck White 2019-04-09     25 <NA>   PO BOX … VT    05753 MIDD… TRUE   
#>  4 04    Chuck White 2019-05-09    100 Josh … SUGARHO… VT    5076  <NA>  FALSE  
#>  5 05    Bob Miller  2019-02-09     10 Lisa … 4 SHEFF… VT    05866 SHEF… FALSE  
#>  6 06    Chuck White 2019-06-09   1000 Bob T… 55 THIS… MN    55555 YOUN… FALSE  
#>  7 07    Chuck White 2019-07-09   -600 Alex … 11 LIBE… VT    <NA>  <NA>  FALSE  
#>  8 08    Alice Walsh 2019-08-09      0 Ruth … 2 BURLI… VT    05401 BURL… FALSE  
#>  9 09    Alice Walsh 2019-09-09     69 Joe G… 770 5TH… DC    20001 WASH… FALSE  
#> 10 10    Alice Walsh 2019-11-09    222 Dave … <NA>     TX    78202 SAN … FALSE  
#> # … with 1 more variable: dupe_flag <lgl>

Conclude

This normalized data is now ready to be uploaded to the Accountability Project and searched alongside 1 billion other records! These cleaned names and addresses might bring up search results alongside one of our other sets of public data: campaign expenditures, registered voters, nonprofit organizations, stimulus spending, government contracts, lobbyist registrations, etc.

id cand date amount name address state zip city na_flag dupe_flag
01 Bob Miller 2019-02-09 10 Lisa Miller 4 SHEFFIELD SQUARE RD VT 05866 SHEFFIELD FALSE TRUE
02 Bob Miller 2009-03-09 20 Deb Brown NA NA NA NA FALSE FALSE
03 Chuck White 2019-04-09 25 NA PO BOX 567 VT 05753 MIDDLEBURY TRUE FALSE
04 Chuck White 2019-05-09 100 Josh Jones SUGARHOUSE SQ VT 5076 NA FALSE FALSE
05 Bob Miller 2019-02-09 10 Lisa Miller 4 SHEFFIELD SQUARE RD VT 05866 SHEFFIELD FALSE TRUE
06 Chuck White 2019-06-09 1000 Bob Taylor 55 THISPLACE AVE MN 55555 YOUNG AMERICA FALSE FALSE
07 Chuck White 2019-07-09 -600 Alex Johnson 11 LIBERTY AVE VT NA NA FALSE FALSE
08 Alice Walsh 2019-08-09 0 Ruth Smith 2 BURLINGTON SQ VT 05401 BURLINGTON FALSE FALSE
09 Alice Walsh 2019-09-09 69 Joe Garcia 770 5TH ST NW DC 20001 WASHINGTON FALSE FALSE
10 Alice Walsh 2019-11-09 222 Dave Wilson NA TX 78202 SAN ANTONIO FALSE FALSE