Jennifer LaFleur/Kiernan Nicholls 2023-01-03 13:14:08
The Accountability Project is an effort to cut across data silos and give journalists, policy professionals, activists, and the public at large a simple way to search across huge volumes of public data about people and organizations.
Our goal is to standardize public data on a few key fields by thinking of each dataset row as a transaction. For each transaction there should be (at least) 3 variables:
- All parties to a transaction.
- The date of the transaction.
- The amount of money involved.
This document describes the process used to complete the following objectives:
- How many records are in the database?
- Check for entirely duplicated records.
- Check ranges of continuous variables.
- Is there anything blank or missing?
- Check for consistency issues.
- Create a five-digit ZIP Code called
zip. - Create a
yearfield from the transaction date. - Make sure there is data on both parties to a transaction.
The following packages are needed to collect, manipulate, visualize,
analyze, and communicate these results. The pacman package will
facilitate their installation and attachment.
The IRW’s campfin package will also have to be installed from GitHub.
This package contains functions custom made to help facilitate the
processing of campaign finance data.
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("irworkshop/campfin")
pacman::p_load(
tidyverse, # data manipulation
lubridate, # datetime strings
gluedown, # printing markdown
magrittr, # pipe operators
janitor, # clean data frames
refinr, # cluster and merge
scales, # format strings
knitr, # knit documents
vroom, # read files fast
rvest, # html scraping
glue, # combine strings
here, # relative paths
httr, # http requests
fs, # local storage
stringi #string functions
)This document should be run as part of the R_campfin project, which
lives as a sub-directory of the more general, language-agnostic
irworkshop/accountability_datacleaning
GitHub repository.
The R_campfin project uses the RStudio
projects
feature and should be run as such. The project also uses the dynamic
here::here() tool for file paths relative to your machine.
# where does this document knit?
here::here()
#> [1] "C:/Users/jla/Documents/jen_transition/AU_CLASSES/AUFALL22/rdata"Kentucky contracts data were obtained via Public Records request by Nami Hijikata.
kyc <- read.csv("KY_Contracts.csv")
head(kyc)
#> # A tibble: 6 × 21
#> Link ContractIde…¹ Class…² Class…³ Cited…⁴ Contr…⁵ DocId Branc…⁶ CabName DeptN…⁷ Reaso…⁸ Start…⁹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 NULL 4A5172AE-F58… ENGINE… 925 Compet… MA 2100… Judici… NULL Judici… NULL 7/1/20…
#> 2 NULL C0FCAC3F-7C4… NULL NULL Memora… PO 1900… Judici… NULL Judici… Accoun… 7/1/20…
#> 3 NULL E6143871-8DC… COMPUT… 209 Small … PO 2200… Execut… Educat… Depart… NULL 10/1/2…
#> 4 NULL 55A241F0-821… NULL NULL Memora… PO 1900… Judici… NULL Judici… Accoun… 7/1/20…
#> 5 NULL 289B18CE-405… MISCEL… 961 Memora… PO 1900… Judici… NULL Judici… NULL 7/1/20…
#> 6 NULL 434BAECA-C3E… NULL NULL Commer… PO 2200… Execut… Touris… Kentuc… NULL 8/17/2…
#> # … with 9 more variables: EndDate <chr>, VendCustId <chr>, VendName <chr>, VendAddress1 <chr>,
#> # VendCity <chr>, VendState <chr>, VendZip <chr>, ProcurementName <chr>, ContractAmount <dbl>,
#> # and abbreviated variable names ¹ContractIdentifier, ²Classification, ³ClassificationCode,
#> # ⁴CitedAuthDescription, ⁵ContractTypeCode, ⁶BranchName, ⁷DeptName, ⁸ReasonModification,
#> # ⁹StartDateThere are two dates in the file. We used the year from start date as a separate year column.
kyc <- mutate(kyc, year = stri_sub(StartDate,-4))prop_na(kyc$StartDate)
#> [1] 0
min(kyc$date, na.rm = TRUE)
#> [1] Inf
sum(kyc$year < 2000, na.rm = TRUE)
#> [1] 0
max(kyc$date, na.rm = TRUE)
#> [1] -Inf
sum(kyc$date > today(), na.rm = TRUE)
#> [1] 0
kyc$year <- na_if(kyc$year, 9999)
Contracts have both StartDate and EndDate; we combine these two
variables into a single date to represent the transaction. We also make
the headers snake case. Replace the word NULL with actually NAs. Combine
cab_name and dept_name.
kyc <- kyc %>%
clean_names("snake") %>%
replace(kyc == "NULL", NA) %>%
mutate(date = paste0(start_date," to ", end_date)) %>%
unite("full_agency", c(cab_name, dept_name), sep=", ", remove = FALSE, na.rm = TRUE) %>%
mutate(vend_city=toupper(vend_city))glimpse(kyc)
#> Rows: 50,771
#> Columns: 24
#> $ link <chr> NA, NA, NA, NA, NA, NA, "https://secure2.kentucky.gov/Transparency…
#> $ contract_identifier <chr> "4A5172AE-F580-4254-B59B-00008DE3E276", "C0FCAC3F-7C48-4439-BCE9-0…
#> $ classification <chr> "ENGINEERING SERVICES, PROFESSIONAL", NA, "COMPUTER SOFTWARE FOR M…
#> $ classification_code <chr> "925", NA, "209", NA, "961", NA, "924", "918", "912", "961", "924"…
#> $ cited_auth_description <chr> "Competitive Sealed Bidding", "Memorandum of Agreement", "Small Pu…
#> $ contract_type_code <chr> "MA", "PO", "PO", "PO", "PO", "PO", "PO", "PO", "PO", "PO", "PO", …
#> $ doc_id <chr> "2100001036", "1900001843", "2200001377", "1900001881", "190000189…
#> $ branch_name <chr> "Judicial", "Judicial", "Executive", "Judicial", "Judicial", "Exec…
#> $ full_agency <chr> "Judicial Department", "Judicial Department", "Education & Workfor…
#> $ cab_name <chr> NA, NA, "Education & Workforce Development Cabinet", NA, NA, "Tour…
#> $ dept_name <chr> "Judicial Department", "Judicial Department", "Department For Work…
#> $ reason_modification <chr> NA, "Accounting lines were not broken out by county.", NA, "Accoun…
#> $ start_date <chr> "7/1/2021", "7/1/2018", "10/1/2019", "7/1/2018", "7/1/2018", "8/17…
#> $ end_date <chr> "6/30/2022", "6/30/2019", "9/30/2020", "6/30/2019", "6/30/2019", "…
#> $ vend_cust_id <chr> "KY0023046", "KY0036004", "KY0028928", "KY0018713", "KY0028291", "…
#> $ vend_name <chr> "BRANDSTETTER CARROLL INC", "BLUEGRASS REGIONAL MH MR", "STATE OF …
#> $ vend_address1 <chr> "2360 CHAUVIN DR", "1351 NEWTOWN PIKE", "EUGENE T MAHONEY STATE PA…
#> $ vend_city <chr> "LEXINGTON", "LEXINGTON", "ASHLAND", "LOUISVILLE", "CORBIN", "HARR…
#> $ vend_state <chr> "KY", "KY", "NE", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", …
#> $ vend_zip <chr> "40517", "40511-1277", "68003", "40223", "40702", "40330", "42633"…
#> $ procurement_name <chr> "Standard Goods and Services", "Memorandum of Agreement", "Standar…
#> $ contract_amount <dbl> 150000.00, 216000.00, 191.67, 67000.00, 10000.00, 75000.00, 8200.0…
#> $ year <chr> "2021", "2018", "2019", "2018", "2018", "2021", "2019", "2021", "2…
#> $ date <chr> "7/1/2021 to 6/30/2022", "7/1/2018 to 6/30/2019", "10/1/2019 to 9/…
tail(kyc)
#> # A tibble: 6 × 24
#> link contr…¹ class…² class…³ cited…⁴ contr…⁵ doc_id branc…⁶ full_…⁷ cab_n…⁸ dept_…⁹ reaso…˟
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 <NA> 318535… <NA> <NA> Memora… PO 19000… Judici… Judici… <NA> Judici… Accoun…
#> 2 <NA> 483E15… MISCEL… 961 Memora… PO 19000… Judici… Judici… <NA> Judici… <NA>
#> 3 <NA> BF8D43… RENTAL… 981 Emerge… PO 23000… Execut… Cabine… Cabine… Depart… <NA>
#> 4 <NA> CC826E… <NA> <NA> Memora… PO 19000… Judici… Judici… <NA> Judici… <NA>
#> 5 https://s… 8EE4E5… <NA> <NA> Memora… PO 19000… Judici… Judici… <NA> Judici… <NA>
#> 6 <NA> 396B1C… <NA> <NA> Memora… PO 19000… Judici… Judici… <NA> Judici… Accoun…
#> # … with 12 more variables: start_date <chr>, end_date <chr>, vend_cust_id <chr>, vend_name <chr>,
#> # vend_address1 <chr>, vend_city <chr>, vend_state <chr>, vend_zip <chr>,
#> # procurement_name <chr>, contract_amount <dbl>, year <chr>, date <chr>, and abbreviated
#> # variable names ¹contract_identifier, ²classification, ³classification_code,
#> # ⁴cited_auth_description, ⁵contract_type_code, ⁶branch_name, ⁷full_agency, ⁸cab_name,
#> # ⁹dept_name, ˟reason_modificationIf we count the number of missing values per column, we can see a lot of the values from the columns found only in one type of file are missing.
col_stats(kyc, count_na)
#> # A tibble: 24 × 4
#> col class n p
#> <chr> <chr> <int> <dbl>
#> 1 link <chr> 38607 0.760
#> 2 contract_identifier <chr> 0 0
#> 3 classification <chr> 17130 0.337
#> 4 classification_code <chr> 17130 0.337
#> 5 cited_auth_description <chr> 0 0
#> 6 contract_type_code <chr> 0 0
#> 7 doc_id <chr> 0 0
#> 8 branch_name <chr> 0 0
#> 9 full_agency <chr> 0 0
#> 10 cab_name <chr> 24918 0.491
#> 11 dept_name <chr> 0 0
#> 12 reason_modification <chr> 30930 0.609
#> 13 start_date <chr> 0 0
#> 14 end_date <chr> 0 0
#> 15 vend_cust_id <chr> 0 0
#> 16 vend_name <chr> 0 0
#> 17 vend_address1 <chr> 0 0
#> 18 vend_city <chr> 0 0
#> 19 vend_state <chr> 10 0.000197
#> 20 vend_zip <chr> 14 0.000276
#> 21 procurement_name <chr> 0 0
#> 22 contract_amount <dbl> 0 0
#> 23 year <chr> 0 0
#> 24 date <chr> 0 0There are no duplicate records in this database.
kyc <- flag_dupes(kyc, everything())
#> Warning in flag_dupes(kyc, everything()): no duplicate rows, column not createdcol_stats(kyc, n_distinct)
#> # A tibble: 24 × 4
#> col class n p
#> <chr> <chr> <int> <dbl>
#> 1 link <chr> 11840 0.233
#> 2 contract_identifier <chr> 49875 0.982
#> 3 classification <chr> 202 0.00398
#> 4 classification_code <chr> 207 0.00408
#> 5 cited_auth_description <chr> 117 0.00230
#> 6 contract_type_code <chr> 2 0.0000394
#> 7 doc_id <chr> 19819 0.390
#> 8 branch_name <chr> 2 0.0000394
#> 9 full_agency <chr> 147 0.00290
#> 10 cab_name <chr> 14 0.000276
#> 11 dept_name <chr> 144 0.00284
#> 12 reason_modification <chr> 4703 0.0926
#> 13 start_date <chr> 1436 0.0283
#> 14 end_date <chr> 1427 0.0281
#> 15 vend_cust_id <chr> 6463 0.127
#> 16 vend_name <chr> 6502 0.128
#> 17 vend_address1 <chr> 6721 0.132
#> 18 vend_city <chr> 1286 0.0253
#> 19 vend_state <chr> 57 0.00112
#> 20 vend_zip <chr> 2460 0.0485
#> 21 procurement_name <chr> 14 0.000276
#> 22 contract_amount <dbl> 12150 0.239
#> 23 year <chr> 5 0.0000985
#> 24 date <chr> 7647 0.151explore_plot(kyc, classification, na.rm = TRUE)
explore_plot(kyc, procurement_name, na.rm = TRUE)
mean(kyc$contract_amount)
#> [1] 264228.9
noquote(map_chr(summary(kyc$contract_amount), dollar))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> $0 $7,863.70 $25,000 $264,229 $113,000 $311,589,050
To improve the searchability of the database, we will perform some
consistent, confident string normalization. For geographic variables
like city names and ZIP codes, the corresponding campfin::normal_*()
functions are tailor made to facilitate this process.
For the street address variable, the campfin::normal_address()
function will force consistence case, remove punctuation, and abbreviate
official USPS suffixes.
addr_norm <- kyc %>%
distinct(vend_address1) %>%
mutate(
address_norm = normal_address(
address = vend_address1,
abbs = usps_street,
na_rep = TRUE
)
)addr_norm
#> # A tibble: 6,721 × 2
#> vend_address1 address_norm
#> <chr> <chr>
#> 1 2360 CHAUVIN DR 2360 CHAUVIN DR
#> 2 1351 NEWTOWN PIKE 1351 NEWTOWN PIKE
#> 3 EUGENE T MAHONEY STATE PARK EUGENE T MAHONEY STATE PARK
#> 4 10101 LINN STATION RD SUITE 600 10101 LINN STATION RD SUITE 600
#> 5 PO BOX 568 PO BOX 568
#> 6 1015 Danville Road 1015 DANVILLE RD
#> 7 134 COLLEGE ST 134 COLLEGE ST
#> 8 3332 NEWBURG RD 3332 NEWBURG RD
#> 9 PO BOX 4157 PO BOX 4157
#> 10 9226 MAIN STREET 9226 MAIN ST
#> # … with 6,711 more rowskyc <- left_join(kyc, addr_norm, by = "vend_address1")For ZIP codes, the campfin::normal_zip() function will attempt to
create valid five digit codes by removing the ZIP+4 suffix and
returning leading zeroes dropped by other programs like Microsoft Excel.
kyc <- kyc %>%
mutate(
zip_norm = normal_zip(
zip = vend_zip,
na_rep = TRUE
)
)progress_table(
kyc$vend_zip,
kyc$zip_norm,
compare = valid_zip
)
#> # A tibble: 2 × 6
#> stage prop_in n_distinct prop_na n_out n_diff
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 kyc$vend_zip 0.798 2460 0.000276 10232 822
#> 2 kyc$zip_norm 0.988 1944 0.000315 588 148Valid two digit state abbreviations can be made using the
campfin::normal_state() function.
kyc <- kyc %>%
mutate(
state_norm = normal_state(
state = vend_state,
abbreviate = TRUE,
na_rep = TRUE,
valid = valid_state
)
)kyc %>%
filter(vend_state != state_norm) %>%
count(vend_state, state_norm, sort = TRUE)
#> # A tibble: 0 × 3
#> # … with 3 variables: vend_state <chr>, state_norm <chr>, n <int>progress_table(
kyc$vend_state,
kyc$state_norm,
compare = valid_state
)
#> # A tibble: 2 × 6
#> stage prop_in n_distinct prop_na n_out n_diff
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 kyc$vend_state 0.999 57 0.000197 37 8
#> 2 kyc$state_norm 1 50 0.000926 0 1Cities are the most difficult geographic variable to normalize, simply due to the wide variety of valid cities and formats.
The campfin::normal_city() function is a good start, again converting
case, removing punctuation, but expanding USPS abbreviations. We can
also remove invalid_city values.
norm_city <- kyc %>%
distinct(vend_city, state_norm, zip_norm) %>%
mutate(
city_norm = normal_city(
city = vend_city,
abbs = usps_city,
states = c("KY", "DC", "KENTUCKY"),
na = invalid_city,
na_rep = TRUE
)
)We can further improve normalization by comparing our normalized value against the expected value for that record’s state abbreviation and ZIP code. If the normalized value is either an abbreviation for or very similar to the expected value, we can confidently swap those two.
norm_city <- norm_city %>%
rename(city_raw = vend_city) %>%
left_join(
y = zipcodes,
by = c(
"state_norm" = "state",
"zip_norm" = "zip"
)
) %>%
rename(city_match = city) %>%
mutate(
match_abb = is_abbrev(city_norm, city_match),
match_dist = str_dist(city_norm, city_match),
city_swap = if_else(
condition = !is.na(match_dist) & (match_abb | match_dist == 1),
true = city_match,
false = city_norm
)
) %>%
select(
-city_match,
-match_dist,
-match_abb
)kyc <- left_join(
x = kyc,
y = norm_city,
by = c(
"vend_city" = "city_raw",
"state_norm",
"zip_norm"
)
)The OpenRefine algorithms can be used to group similar strings and replace the less common versions with their most common counterpart. This can greatly reduce inconsistency, but with low confidence; we will only keep any refined strings that have a valid city/state/zip combination.
good_refine <- kyc %>%
mutate(
city_refine = city_swap %>%
key_collision_merge() %>%
n_gram_merge(numgram = 1)
) %>%
filter(city_refine != city_swap) %>%
inner_join(
y = zipcodes,
by = c(
"city_refine" = "city",
"state_norm" = "state",
"zip_norm" = "zip"
)
)#> # A tibble: 3 × 5
#> state_norm zip_norm city_swap city_refine n
#> <chr> <chr> <chr> <chr> <int>
#> 1 OH 44202 AUOROA AURORA 4
#> 2 IL 60585 PLAINFIELD IL PLAINFIELD 1
#> 3 KY 42754 LEICHTFIELD LEITCHFIELD 1
Then we can join the refined values back to the database.
kyc <- kyc %>%
left_join(good_refine) %>%
mutate(city_refine = coalesce(city_refine, city_swap))Our goal for normalization was to increase the proportion of city values known to be valid and reduce the total distinct values by correcting misspellings.
| stage | prop_in | n_distinct | prop_na | n_out | n_diff |
|---|---|---|---|---|---|
| str_to_upper(kyc$vend_city) | 0.988 | 1286 | 0 | 586 | 143 |
| kyc$city_swap | 0.997 | 1213 | 0 | 127 | 44 |
You can see how the percentage of valid values increased with each stage.
More importantly, the number of distinct values decreased each stage. We were able to confidently change many distinct invalid values to their valid equivalent.
Before exporting, we can remove the intermediary normalization columns
and rename all added variables with the _clean suffix.
kyc <- kyc %>%
select(
-city_norm,
-city_swap,
city_clean = city_refine
) %>%
rename_all(~str_replace(., "_norm", "_clean")) %>%
rename_all(~str_remove(., "_raw")) %>%
relocate(state_clean, zip_clean, .after = city_clean)glimpse(sample_n(kyc, 50))
#> Rows: 50
#> Columns: 28
#> $ link <chr> NA, NA, NA, NA, "https://secure2.kentucky.gov/TransparencyWebApi/v…
#> $ contract_identifier <chr> "1075F0E1-1E50-4D88-BA82-E3739C36FE73", "45C78EA1-63CD-4240-96F9-5…
#> $ classification <chr> NA, "MISCELLANEOUS PROFESSIONAL SERVICES", NA, NA, "CONSULTING SER…
#> $ classification_code <chr> NA, "961", NA, NA, "918", "912", NA, "912", "803", NA, "961", NA, …
#> $ cited_auth_description <chr> "Memorandum of Agreement", "Memorandum of Agreement", "Memorandum …
#> $ contract_type_code <chr> "PO", "PO", "PO", "PO", "PO", "PO", "PO", "PO", "PO", "PO", "PO", …
#> $ doc_id <chr> "1900001860", "1900001899", "1900001848", "1900001862", "200000272…
#> $ branch_name <chr> "Judicial", "Judicial", "Judicial", "Judicial", "Executive", "Exec…
#> $ full_agency <chr> "Judicial Department", "Judicial Department", "Judicial Department…
#> $ cab_name <chr> NA, NA, NA, NA, "Education & Workforce Development Cabinet", "Ener…
#> $ dept_name <chr> "Judicial Department", "Judicial Department", "Judicial Department…
#> $ reason_modification <chr> "Accounting line was not broken out by county.", NA, NA, NA, NA, N…
#> $ start_date <chr> "7/1/2018", "7/1/2018", "7/1/2018", "7/1/2018", "7/1/2020", "12/1/…
#> $ end_date <chr> "6/30/2019", "6/30/2019", "6/30/2019", "6/30/2019", "6/30/2021", "…
#> $ vend_cust_id <chr> "KY0036340", "KY0028291", "KY0035990", "KY0035989", "KY0035868", "…
#> $ vend_name <chr> "Western KY Regional Mental Health & Retardation Advisory Brd", "C…
#> $ vend_address1 <chr> "425 Braodway Street", "PO BOX 568", "PO BOX 790", "P O BOX 2680",…
#> $ vend_city <chr> "PADUCAH", "CORBIN", "ASHLAND", "COVINGTON", "GREENSBURG", "EDMONT…
#> $ vend_state <chr> "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", …
#> $ vend_zip <chr> "42001", "40702", "41105-0790", "41011-2680", "42743", "42129", "4…
#> $ procurement_name <chr> "Memorandum of Agreement", "Memorandum of Agreement", "Memorandum …
#> $ contract_amount <dbl> 118000.00, 10000.00, 40000.00, 3000.00, 140011.00, 1350.00, 113000…
#> $ year <chr> "2018", "2018", "2018", "2018", "2020", "2019", "2018", "2021", "2…
#> $ date <chr> "7/1/2018 to 6/30/2019", "7/1/2018 to 6/30/2019", "7/1/2018 to 6/3…
#> $ address_clean <chr> "425 BRAODWAY ST", "PO BOX 568", "PO BOX 790", "P O BOX 2680", "PO…
#> $ city_clean <chr> "PADUCAH", "CORBIN", "ASHLAND", "COVINGTON", "GREENSBURG", "EDMONT…
#> $ state_clean <chr> "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", …
#> $ zip_clean <chr> "42001", "40702", "41105", "41011", "42743", "42129", "41701", "41…- There are 50,771 records in the database.
- There are 0 duplicate records in the database.
- The range and distribution of
amountanddateseem reasonable. - There are 0 records missing key variables.
- There are no geographic variables to be normalized.
- The 4-digit
yearvariable has been created.
Now the file can be saved on disk for upload to the Accountability server.
clean_dir <- dir_create(here("ky", "contracts", "data", "clean"))
clean_path <- path(clean_dir, "ky_contracts_clean.csv")
write_csv(kyc, clean_path, na = "")
file_size(clean_path)
#> 22.8MThe encoding of the exported file should be UTF-8 or ASCII.
enc <- system2("file", args = paste("-i", clean_path), stdout = TRUE)
str_replace_all(enc, clean_path, basename)
#> [1] "ky_contracts_clean.csv: text/csv; charset=us-ascii"The following table describes the variables in our final exported file:
| Column | Type | Overlaped | Definition |
|---|---|---|---|
link |
character |
TRUE | link |
contract_identifier |
character |
TRUE | contract_identifier |
classification |
character |
TRUE | classification |
classification_code |
character |
FALSE | classification_code |
cited_auth_description |
character |
TRUE | cited_auth_description |
contract_type_code |
character |
TRUE | contract_type_code |
doc_id |
character |
FALSE | doc_id |
branch_name |
character |
TRUE | branch_name |
full_agency |
character |
FALSE | full_agency |
cab_name |
character |
FALSE | cab_name |
dept_name |
character |
FALSE | dept_name |
reason_modification |
character |
FALSE | reason_modification |
start_date |
character |
TRUE | start_date |
end_date |
character |
TRUE | end_date |
vend_cust_id |
character |
FALSE | vend_cust_id |
vend_name |
character |
FALSE | vend_name |
vend_address1 |
character |
FALSE | vend_address1 |
vend_city |
character |
TRUE | vend_city |
vend_state |
character |
TRUE | vend_state |
vend_zip |
character |
TRUE | vend_zip |
procurement_name |
character |
TRUE | procurement_name |
contract_amount |
double |
TRUE | contract_amount |
year |
character |
FALSE | year |
date |
character |
FALSE | date |
address_clean |
character |
FALSE | address_clean |
city_clean |
character |
FALSE | city_clean |
state_clean |
character |
FALSE | state_clean |
zip_clean |
character |
FALSE | zip_clean |
write_lines(
x = c("# Kentucky Contracts Data Dictionary\n", dict_md),
path = here("ky", "contracts", "ky_contracts_dict.md"),
)