DC. The Accountability Project curates, cleans, and indexes public data to give journalists, researchers and others a simple way to search across otherwise siloed records.
The data focuses on people, organizations and locations. This package was created specifically to help with state-level campaign finance data, although the tools included are useful in general database exploration and normalization.
You can install the released version of campfin from CRAN with:
The development version can be installed from GitHub with:
# install.packages("remotes") remotes::install_github("irworkshop/campfin")
The package was originally built to normalize geographic data using the
normal_*() functions, which take the messy self-reported geographic data of a contributor, vendor, candidate, or committee and return normalized text that is more searchable. They are largely wrappers around the stringr package, and can call other sub-functions to streamline normalization.
normal_address()takes a street address and reduces inconsistencies.
normal_zip()takes ZIP Codes and aims to return a valid 5-digit code.
normal_state()takes US states and returns a 2 digit abbreviation.
normal_city()takes cities and reduces inconsistencies.
normal_phone()consistently formats US telephone numbers.
Please see the vignette on normalization for an example of how these functions are used to fix a wide variety of string inconsistencies and make campaign finance data more consistent.
The campfin package contains a number of built in data frames and strings used to help wrangle campaign finance data.
/data-raw directory contains the code used to create the objects.
zipcodes (plural) table is a new version of the
zipcode (singular) table from the archived zipcode R package.
This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources.
valid_zip vectors are sorted, unique columns from the
zipcodes data frame.
sample_frac(zipcodes) #> # A tibble: 44,336 × 3 #> city state zip #> <chr> <chr> <chr> #> 1 SALTER PATH NC 28575 #> 2 PARK FOREST IL 60466 #> 3 LOUISVILLE KY 40250 #> 4 HOYTVILLE OH 43529 #> 5 CALIFORNIA CITY CA 93504 #> 6 SPEEDWELL TN 37870 #> 7 YORKVILLE TN 38389 #> 8 SMITHFIELD VA 23431 #> 9 PENSACOLA FL 32581 #> 10 DES MOINES NM 88418 #> # … with 44,326 more rows
usps_* data frames were scraped from the official United States Postal Service (USPS) Postal Addressing Standards. These data frames are designed to work with the abbreviation functionality of
normal_city() to replace common abbreviations with their full equivalent.
usps_city is a curated subset of
usps_state, whose full version appear at least once in the
valid_city vector from
valid_name vectors contain the columns from
usps_state and include territories not found in R’s build in
sample_n(usps_street, 3) #> # A tibble: 3 × 2 #> full abb #> <chr> <chr> #> 1 ANNEX ANX #> 2 STRAVEN STRA #> 3 PORT PRT sample_n(usps_state, 3) #> # A tibble: 3 × 2 #> full abb #> <chr> <chr> #> 1 TENNESSEE TN #> 2 DELAWARE DE #> 3 NORTH CAROLINA NC setdiff(valid_state, state.abb) #>  "AS" "AA" "AE" "AP" "DC" "FM" "GU" "MH" "MP" "PW" "PR" "VI"
The campfin project is released with a Contributor Code of Conduct. By contributing, you agree to abide by its terms.