Normalize Geographic Variables

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")

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

• The date column is not parsed as an R date, making it impossible to perform mathematical calculations like min().
• There is one negative amount value and another that’s zero. Not necessarily uncommon, but worth noting.
• One record is missing both the contributor’s name.
• One record is duplicated across every column.
• In address we see:
  • Inconsistent capitalization.
  • A mix of full and abbreviated suffixes.
  • Alternative versions of NA.
  • Unnecessary and inconsistent punctuation.
  • Excess trailing and internal white space.
  • Incorrect punctuation and replaced spaces.
  • Repeating character strings used instead of NA.
• In city we see many of the same problems, plus:
  • Geographic abbreviations.
  • Repeated state information.
  • Misspellings.
  • Colloquial city abbreviations.
• In state we see a mix of full and abbreviated state names.
• In zip:
  • Repeated digits used for NA values (NB: 55555 is a valid ZIP code, 99999 is not).
  • Unnecessary and inconsistent ZIP+4 usage.
  • Leading zeroes dropped by Excel or some other program.

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 5THSTNW           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] "SUTHERLAND SPRINGS" "ELON COLLEGE"       "DODGERTOWN"        
#> [4] "JEFFERSONVILLE"     "LARNED"             "MACHIASPORT"
sample(extra_city, 6)
#> [1] "ETIWANDA"        "FORKS TOWNSHIP"  "VANPORT"         "SYMMES TOWNSHIP"
#> [5] "YARDVILLE"       "DEL CITY"
# 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")
)

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)
  )

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 with `by = join_by(state, zip)`

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

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 M… 4 SHEF… VT    05866 SHEF… FALSE  
#>  2 02    Bob Miller  2009-03-09     20 Deb Br… <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 J… SUGARH… VT    5076  <NA>  FALSE  
#>  5 05    Bob Miller  2019-02-09     10 Lisa M… 4 SHEF… VT    05866 SHEF… FALSE  
#>  6 06    Chuck White 2019-06-09   1000 Bob Ta… 55 THI… MN    55555 YOUN… FALSE  
#>  7 07    Chuck White 2019-07-09   -600 Alex J… 11 LIB… VT    <NA>  <NA>  FALSE  
#>  8 08    Alice Walsh 2019-08-09      0 Ruth S… 2 BURL… VT    05401 BURL… FALSE  
#>  9 09    Alice Walsh 2019-09-09     69 Joe Ga… 770 5T… DC    20001 WASH… FALSE  
#> 10 10    Alice Walsh 2019-11-09    222 Dave W… <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 M… 4 SHEF… VT    05866 SHEF… FALSE  
#>  2 02    Bob Miller  2009-03-09     20 Deb Br… <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 J… SUGARH… VT    5076  <NA>  FALSE  
#>  5 05    Bob Miller  2019-02-09     10 Lisa M… 4 SHEF… VT    05866 SHEF… FALSE  
#>  6 06    Chuck White 2019-06-09   1000 Bob Ta… 55 THI… MN    55555 YOUN… FALSE  
#>  7 07    Chuck White 2019-07-09   -600 Alex J… 11 LIB… VT    <NA>  <NA>  FALSE  
#>  8 08    Alice Walsh 2019-08-09      0 Ruth S… 2 BURL… VT    05401 BURL… FALSE  
#>  9 09    Alice Walsh 2019-09-09     69 Joe Ga… 770 5T… DC    20001 WASH… FALSE  
#> 10 10    Alice Walsh 2019-11-09    222 Dave W… <NA>    TX    78202 SAN … FALSE  
#> # ℹ 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.