campfin package

Description

The campfin package was written to facilitate the work done on The Accountability Project (TAP) by the data journalists at The Investigative Reporting Workshop in Washington, DC.

Details

TAP is an effort to centralize many public databases into a searchable website. To improve the searchability of the database, the campfin package is used to normalize values in a common format. The normalization vignette provides examples on how this process improved a database.

The other functions in this package are written to facilitate the exploration of a database. The Investigative Reporting Workshop writes public data diaries to document the data wrangling process; the open source campfin functions help download, import, explore, and wrangle public database.

Author(s)

Maintainer: Kiernan Nicholls kiernann@protonmail.com [copyright holder]

Authors:

• Yanqi Xu yanqixu@outlook.com

Other contributors:

• Investigative Reporting Workshop [copyright holder]

• Schuyler Erle schuyler@geocoder.us [copyright holder]

See Also

Useful links:

• https://github.com/irworkshop/campfin

• https://irworkshop.github.io/campfin/

• Report bugs at https://github.com/irworkshop/campfin/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Inverted match

Description

⁠%out%⁠ is an inverted version of the infix %in% operator.

Usage

x %out% table

Arguments

Details

⁠%out%⁠ is currently defined as "%out%" <- function(x, table) match(x, table, nomatch = 0) == 0

Value

logical; if x is not present in table

Examples

c("A", "B", "3") %out% LETTERS

Abbreviate full strings

Description

Create or use a named vector (c("full" = "abb")) and pass it to stringr::str_replace_all(). The full argument is surrounded with ⁠\\b⁠ to capture only isolated intended full versions. Note that the built-in usps_street, usps_city, and usps_state dataframes have the columns reversed from what this function needs (to work by default with the counterpart expand_abbrev()).

Usage

abbrev_full(x, full = NULL, rep = NULL, end = FALSE)

Arguments

Value

The vector x with full words replaced with their abbreviations.

See Also

Other geographic normalization functions: abbrev_state(), check_city(), expand_abbrev(), expand_state(), fetch_city(), normal_address(), normal_city(), normal_state(), normal_zip(), str_normal()

Examples

abbrev_full("MOUNT VERNON", full = c("MOUNT" = "MT"))
abbrev_full("123 MOUNTAIN ROAD", full = usps_street)
abbrev_full("123 MOUNTAIN ROAD", full = usps_street, end = TRUE)
abbrev_full("Vermont", full = state.name, rep = state.abb)

Abbreviate US state names

Description

This function is used to first normalize a full state name and then call abbrev_full() using valid_name and valid_state as the full and rep arguments.

Usage

abbrev_state(full)

Arguments

Value

The 2-letter USPS abbreviation of for state names (e.g., "VT").

See Also

Other geographic normalization functions: abbrev_full(), check_city(), expand_abbrev(), expand_state(), fetch_city(), normal_address(), normal_city(), normal_state(), normal_zip(), str_normal()

Examples

abbrev_state(full = state.name)
abbrev_state(full = c("new mexico", "france"))

Add proportions

Description

Use prop.table() to add a proportion column to a dplyr::count() tibble.

Usage

add_prop(.data, n, sum = FALSE)

Arguments

Details

mean(x %in% y)

Value

A data frame with the new column p.

Examples

add_prop(dplyr::count(ggplot2::diamonds, cut))

Check if all files in a directory are new

Description

Tests whether all the files in a given directory have a modification date equal to the system date. Useful when repeatedly running code with a lengthy download stage. Many state databases are updated daily, so new data can be helpful but not always necessary. Set this function in an if statement.

Usage

all_files_new(path, glob = NULL, ...)

Arguments

Value

logical; Whether all() files in the directory have a modification date equal to today.

Examples

tmp <- tempdir()
file.create(tempfile(pattern = as.character(1:5)))
all_files_new(tmp)

Check whether an input is a valid place with Google Maps API

Description

Check whether a place is a valid place or misspelling by matching against the Google Geocoding search result. Use the httr::GET() to send a request to the Google Maps API for geocoding information. The query will concatenate all the geographical information that is passed in into a long string. Then the function pulls the formatted_address endpoint of the API results and then identifies and extracts the long name field from the API locality result and compare it against the input to see if the input and output match up. Note that you will need to pass in your Google Maps Place API key to the key argument.

Usage

check_city(city = NULL, state = NULL, zip = NULL, key = NULL, guess = FALSE)

Arguments

Value

A logical value by default. If the city returned by the API comes back the same as the city input, the function will evaluate to TRUE, in all other circumstances (including API errors) FALSE is returned.

If the the guess argument is set to TRUE, a tibble with 1 row and six columns is returned:

• original_city: The city value sent to the API.

• original_state: The state value sent to the API.

• original_zip: The zip value sent to the API.

• check_city_flag: logical; whether the guessed city matches.

• guess_city: The legal city guessed by the API.

• guess_place: The generic locality guessed by the API.

See Also

https://developers.google.com/maps/documentation/geocoding/overview?csw=1

Other geographic normalization functions: abbrev_full(), abbrev_state(), expand_abbrev(), expand_state(), fetch_city(), normal_address(), normal_city(), normal_state(), normal_zip(), str_normal()

Parse USA date columns in readr functions

Description

Parse dates with format MM/DD/YYYY. This function simply wraps around readr::col_date() with the format argument set to "%m/%d/%Y". Many US campaign finance datasets use this format.

Usage

col_date_mdy()

col_date_usa()

Value

A POSIXct vector.

Examples

readr::read_csv(file = "x\n11/09/2016", col_types = readr::cols(x = col_date_mdy()))

Apply a statistic function to all column vectors

Description

Apply a counting summary function like dplyr::n_distinct() or count_na() to every column of a data frame and return the results along with a percentage of that value.

Usage

col_stats(data, fun, print = TRUE)

glimpse_fun(data, fun, print = TRUE)

Arguments

Value

A tibble with a row for every column with the count and proportion.

Examples

col_stats(dplyr::storms, dplyr::n_distinct)
col_stats(dplyr::storms, campfin::count_na)

Count set difference

Description

Find the length of the set of difference between x and y vectors.

Usage

count_diff(x, y, ignore.case = FALSE)

Arguments

Details

sum(x %out% y)

Value

The number of unique values of x not in y.

See Also

Other counting wrappers: count_in(), count_na(), count_out(), na_in(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_in(), what_out()

Examples

# only unique values are checked
count_diff(c("VT", "NH", "ZZ", "ZZ", "ME"), state.abb)

Count in

Description

Count the total values of x that are %in% the vector y.

Usage

count_in(x, y, na.rm = TRUE, ignore.case = FALSE)

Arguments

Details

sum(x %out% y)

Value

The sum of x present in y.

See Also

Other counting wrappers: count_diff(), count_na(), count_out(), na_in(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_in(), what_out()

Examples

count_in(c("VT", "NH", "ZZ", "ME"), state.abb)

Count missing

Description

Count the total values of x that are NA.

Usage

count_na(x)

Arguments

Details

sum(is.na(x))

Value

The sum of x that are NA

See Also

Other counting wrappers: count_diff(), count_in(), count_out(), na_in(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_in(), what_out()

Examples

count_na(c("VT", "NH", NA, "ME"))

Count out

Description

Count the total values of x that are are ⁠%out%⁠ of the vector y.

Usage

count_out(x, y, na.rm = TRUE, ignore.case = FALSE)

Arguments

Details

sum(x %out% y)

Value

The sum of x absent in y.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), na_in(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_in(), what_out()

Examples

count_out(c("VT", "NH", "ZZ", "ME"), state.abb)

Dark Color Palette

Description

The Dark2 brewer color palette

Usage

dark2

Format

A named character vector of hex color codes (length 8).

Expand Abbreviations

Description

Create or use a named vector (c("abb" = "rep")) and pass it to stringr::str_replace_all(). The abb argument is surrounded with ⁠\\b⁠ to capture only isolated abbreviations. To be used inside normal_address() and normal_city() with usps_street and usps_city, respectively.

Usage

expand_abbrev(x, abb = NULL, rep = NULL)

Arguments

Value

The vector x with abbreviation replaced with their full version.

See Also

Other geographic normalization functions: abbrev_full(), abbrev_state(), check_city(), expand_state(), fetch_city(), normal_address(), normal_city(), normal_state(), normal_zip(), str_normal()

Examples

expand_abbrev(x = "MT VERNON", abb = c("MT" = "MOUNT"))
expand_abbrev(x = "VT", abb = state.abb, rep = state.name)
expand_abbrev(x = "Low FE Level", abb = tibble::tibble(x = "FE", y = "Iron"))

Expand US state names

Description

This function is used to first normalize an abb and then call expand_abbrev() using valid_state and valid_name as the abb and rep arguments.

Usage

expand_state(abb)

Arguments

Value

The 2-letter USPS abbreviation of for state names (e.g., "VT").

See Also

Other geographic normalization functions: abbrev_full(), abbrev_state(), check_city(), expand_abbrev(), fetch_city(), normal_address(), normal_city(), normal_state(), normal_zip(), str_normal()

Examples

expand_state(abb = state.abb)
expand_state(abb = c("nm", "fr"))

Create Basic Barplots

Description

This function simply wraps around ggplot2::geom_col() to take a dataframe and categorical variable to return a custom barplot ggplot object. The bars are arranged in descending order and are limited to the 8 most frequent values.

Usage

explore_plot(data, var, nbar = 8, palette = "Dark2", na.rm = TRUE)

Arguments

Value

A ggplot barplot object. Can then be combined with other ggplot layers with + to customize.

Examples

explore_plot(iris, Species)

Additional US City Names

Description

Cities not contained in valid_city, but are accepted localities (neighborhoods or census designated places). This vector consists of normalized self-reported cities in the public data processed by accountability project that were validated by Google Maps Geocoding API (whose check_city() results evaluate to TRUE). The most recent updated version of the extra_city can be found in this Google Sheet

Usage

extra_city

Format

A sorted vector of unique locality names (length 127).

Return Closest Match Result of Cities from Google Maps API

Description

Use the httr::GET() to send a request to the Google Maps API for geocoding information. The query will concatenate all the geographical information that is passed in into a single string. Then the function pulls the formatted_address endpoint of the API results and extracts the the first field of the result. Note that you will need to pass in your Google Maps Place API key with the key argument.

Usage

fetch_city(address = NULL, key = NULL)

Arguments

Value

A character vector of formatted address endpoints from Google. This will include all the fields from street address, city, state/province, zipcode/postal code to country/regions. NA_character_ is returned for all errored API calls.

See Also

https://developers.google.com/maps/documentation/geocoding/overview?csw=1

Other geographic normalization functions: abbrev_full(), abbrev_state(), check_city(), expand_abbrev(), expand_state(), normal_address(), normal_city(), normal_state(), normal_zip(), str_normal()

File modification date age

Description

The period of time since a system file was modified.

Usage

file_age(...)

Arguments

Value

A Period class object.

Examples

file_age(system.file("README.md", package = "campfin"))

File Encoding

Description

Call the file command line tool with option -i.

Usage

file_encoding(path)

Arguments

Value

A tibble of file encoding.

Flag Duplicate Rows With New Column

Description

This function uses dplyr::mutate() to create a new dupe_flag logical variable with TRUE values for any record duplicated more than once.

Usage

flag_dupes(data, ..., .check = TRUE, .both = TRUE)

Arguments

Value

A data frame with a new dupe_flag logical variable.

Examples

flag_dupes(iris, dplyr::everything())
flag_dupes(iris, dplyr::everything(), .both = FALSE)

Flag Missing Values With New Column

Description

This function uses dplyr::mutate() to create a new na_flag logical variable with TRUE values for any record missing any value in the selected columns.

Usage

flag_na(data, ...)

Arguments

Value

A data frame with a new na_flag logical variable.

Examples

flag_na(dplyr::starwars, hair_color)

Flush Garbage Memory

Description

Run a full gc() a number of times.

Usage

flush_memory(n = 1)

Arguments

Guess the delimiter of a text file

Description

Taken from code used in vroom::vroom() with automatic reading.

Usage

guess_delim(file, delims = c(",", "\t", "|", ";"), string = FALSE)

Arguments

Value

The single character guessed as a delimiter.

Source

https://github.com/tidyverse/vroom/blob/85143f7a417376eaf0e2037ca9575f637e4346c2/R/vroom.R#L288

Examples

guess_delim(system.file("extdata", "vt_contribs.csv", package = "campfin"))
guess_delim("ID;FirstName;MI;LastName;JobTitle", string = TRUE)
guess_delim("
a|b|c
1|2|3
")

Invalid City Names

Description

A custom vector containing common invalid city names.

Usage

invalid_city

Format

A vector of length 54.

Invert a named vector

Description

Invert the names and elements of a vector, useful when using named vectors as the abbreviation arguments both of expand_abbrev() and abbrev_full() (or their parent normalization functions like normal_address())

Usage

invert_named(x)

Arguments

Value

A named vector with names in place of elements and vice versa.

Examples

invert_named(x = c("name" = "element"))

Check if abbreviation

Description

To return a value of TRUE, (1) the first letter of abb must match the first letter of full, (2) all letters of abb must exist in full, and (3) those letters of abb must be in the same order as they appear in full.

Usage

is_abbrev(abb, full)

Arguments

Value

logical; whether abb is potential abbreviation of full

Examples

is_abbrev(abb = "BRX", full = "BRONX")
is_abbrev(abb = state.abb, full = state.name)
is_abbrev(abb = "NOLA", full = "New Orleans")
is_abbrev(abb = "FE", full = "Iron")

Check if Binary

Description

Uses dplyr::n_distinct() to check if there are only two unique values.

Usage

is_binary(x, na.rm = TRUE)

Arguments

Value

TRUE if only 2 unique values.

Examples

if (is_binary(x <- c("Yes", "No"))) x == "Yes"

Check if even

Description

Check if even

Usage

is_even(x)

Arguments

Value

logical; Whether the integer is even or odd.

Examples

is_even(1:10)
is_even(10L)

Convert letters or numbers to their keypad counterpart

Description

This function works best when converting numbers to letters, as each number only has a single possible letter. For each letter, there are 3 or 4 possible letters, resulting in a number of possible conversions. This function was intended to convert phonetic telephone numbers to their valid numeric equivalent; when used in this manner, each letter in a string can be lazily replaced without changing the rest of the string.

Usage

keypad_convert(x, ext = FALSE)

Arguments

Details

When replacing letters, this function relies on the feature of stringr::str_replace_all() to work with named vectors (c("A" = "2")).

Value

If a character vector is supplied, a vector of each elements numeric counterpart is returned. If a numeric vector (or a completely coercible character vector) is supplied, then a list is returned, each element of which contacts a vector of letters for each number.

Examples

keypad_convert("1-800-CASH-NOW ext123")
keypad_convert(c("abc", "123"))
keypad_convert(letters)

Find most common values

Description

From a character vector, which values are most common?

Usage

most_common(x, n = 6)

Arguments

Value

Sorted vector of n most common values.

Examples

most_common(iris$Species, n = 1)

Remove in

Description

Set NA for the values of x that are %in% the vector y.

Usage

na_in(x, y, ignore.case = FALSE)

Arguments

Value

The vector x missing any values in y.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_in(), what_out()

Examples

na_in(c("VT", "NH", "ZZ", "ME"), state.abb)
na_in(1:10, seq(1, 10, 2))

Remove out

Description

Set NA for the values of x that are ⁠%out%⁠ of the vector y.

Usage

na_out(x, y, ignore.case = FALSE)

Arguments

Value

The vector x missing any values not in y.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_in(), na_rep(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_in(), what_out()

Examples

na_out(c("VT", "NH", "ZZ", "ME"), state.abb)
na_out(1:10, seq(1, 10, 2))

Remove repeated character elements

Description

Set NA for the values of x that contain a single repeating character and no other characters.

Usage

na_rep(x, n = 0)

Arguments

Details

Uses the regular expression "^(.)\\1+$".

Value

The vector x with NA replacing repeating character values.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_in(), na_out(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_in(), what_out()

Examples

na_rep(c("VT", "NH", "ZZ", "ME"))

Show non-ASCII lines of file

Description

Show non-ASCII lines of file

Usage

non_ascii(path, highlight = FALSE)

Arguments

Value

Tibble of line locations.

Examples

non_ascii(system.file("README.md", package = "campfin"))

Normalize street addresses

Description

Return consistent version of a US Street Address using ⁠stringr::str_*()⁠ functions. Letters are capitalized, punctuation is removed or replaced, and excess whitespace is trimmed and squished. Optionally, street suffix abbreviations ("AVE") can be replaced with their long form ("AVENUE"). Invalid addresses from a vector can be removed (possibly using invalid_city) as well as single (repeating) character strings ("XXXXXX").

Usage

normal_address(
  address,
  abbs = NULL,
  na = c("", "NA"),
  punct = "",
  na_rep = FALSE,
  abb_end = TRUE
)

Arguments

Value

A vector of normalized street addresses.

See Also

Other geographic normalization functions: abbrev_full(), abbrev_state(), check_city(), expand_abbrev(), expand_state(), fetch_city(), normal_city(), normal_state(), normal_zip(), str_normal()

Examples

normal_address("P.O. #123, C/O John Smith", abbs = usps_street)
normal_address("12east 2nd street, #209", abbs = usps_street, abb_end = FALSE)

Normalize city names

Description

Return consistent version of a city names using ⁠stringr::str_*()⁠ functions. Letters are capitalized, hyphens and underscores are replaced with whitespace, other punctuation is removed, numbers are removed, and excess whitespace is trimmed and squished. Optionally, geographic abbreviations ("MT") can be replaced with their long form ("MOUNT"). Invalid addresses from a vector can be removed (possibly using invalid_city) as well as single (repeating) character strings ("XXXXXX").

Usage

normal_city(city, abbs = NULL, states = NULL, na = c("", "NA"), na_rep = FALSE)

Arguments

Value

A vector of normalized city names.

See Also

Other geographic normalization functions: abbrev_full(), abbrev_state(), check_city(), expand_abbrev(), expand_state(), fetch_city(), normal_address(), normal_state(), normal_zip(), str_normal()

Examples

normal_city(
  city = c("Stowe, VT", "UNKNOWN CITY", "Burlington", "ST JOHNSBURY", "XXX"),
  abbs = c("ST" = "SAINT"),
  states = "VT",
  na = invalid_city,
  na_rep = TRUE
)

Normalize phone number

Description

Take US phone numbers in any number of formats and try to convert them to a standard format.

Usage

normal_phone(
  number,
  format = "(%a) %e-%l",
  na_bad = FALSE,
  convert = FALSE,
  rm_ext = FALSE
)

Arguments

Value

A normalized telephone number.

Examples

normal_phone(number = c("916-225-5887"))

Normalize US State Abbreviations

Description

Return consistent version of a state abbreviations using ⁠stringr::str_*()⁠ functions. Letters are capitalized, all non-letters characters are removed, and excess whitespace is trimmed and squished, and then abbrev_full() is called with usps_state.

Usage

normal_state(
  state,
  abbreviate = TRUE,
  na = c("", "NA"),
  na_rep = FALSE,
  valid = NULL
)

Arguments

Value

A vector of normalized 2-digit state abbreviations.

See Also

Other geographic normalization functions: abbrev_full(), abbrev_state(), check_city(), expand_abbrev(), expand_state(), fetch_city(), normal_address(), normal_city(), normal_zip(), str_normal()

Examples

normal_state(
  state = c("VT", "N/A", "Vermont", "XX", "ZA"),
  abbreviate = TRUE,
  na = c("", "NA"),
  na_rep = TRUE,
  valid = NULL
)

Normalize ZIP codes

Description

Return consistent version US ZIP codes using ⁠stringr::str_*()⁠ functions. Non-number characters are removed, strings are padded with zeroes on the left, and ZIP+4 suffixes are removed. Invalid ZIP codes from a vector can be removed as well as single (repeating) character strings.

Usage

normal_zip(zip, na = c("", "NA"), na_rep = FALSE, pad = FALSE)

Arguments

Value

A character vector of normalized 5-digit ZIP codes.

See Also

Other geographic normalization functions: abbrev_full(), abbrev_state(), check_city(), expand_abbrev(), expand_state(), fetch_city(), normal_address(), normal_city(), normal_state(), str_normal()

Examples

normal_zip(
  zip = c("05672-5563", "N/A", "05401", "5819", "00000"),
  na = c("", "NA"),
  na_rep = TRUE,
  pad = TRUE
)

Abbreviate a file path

Description

This is an inverse of path.expand(), which replaces the home directory or project directory with a tilde.

Usage

path.abbrev(path, dir = fs::path_wd())

Arguments

Value

Abbreviated file paths.

Examples

print(fs::path_wd("test"))
path.abbrev(fs::path_wd("test"))

Create a progress table

Description

Create a tibble with rows for each stage of normalization and columns for the various statistics most useful in assessing the progress of each stage.

Usage

progress_table(..., compare)

Arguments

Value

A table with a row for each vector in ....

Examples

progress_table(state.name, toupper(state.name), compare = valid_name)

Proportion missing

Description

Find the proportion of values of x that are distinct.

Usage

prop_distinct(x)

Arguments

Details

length(unique(x))/length(x)

Value

The ratio of distinct values x to total values of x.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_in(), na_out(), na_rep(), prop_in(), prop_na(), prop_out(), what_in(), what_out()

Examples

prop_distinct(c("VT", "VT", NA, "ME"))

Proportion in

Description

Find the proportion of values of x that are %in% the vector y.

Usage

prop_in(x, y, na.rm = TRUE, ignore.case = FALSE)

Arguments

Details

mean(x %in% y)

Value

The proportion of x present in y.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_in(), na_out(), na_rep(), prop_distinct(), prop_na(), prop_out(), what_in(), what_out()

Examples

prop_in(c("VT", "NH", "ZZ", "ME"), state.abb)

Proportion missing

Description

Find the proportion of values of x that are NA.

Usage

prop_na(x)

Arguments

Details

mean(is.na(x))

Value

The proportion of values of x that are NA.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_in(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_out(), what_in(), what_out()

Examples

prop_na(c("VT", "NH", NA, "ME"))

Proportion out

Description

Find the proportion of values of x that are ⁠%out%⁠ of the vector y.

Usage

prop_out(x, y, na.rm = TRUE, ignore.case = FALSE)

Arguments

Details

mean(x %out% y)

Value

The proportion of x absent in y.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_in(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_na(), what_in(), what_out()

Examples

prop_out(c("VT", "NH", "ZZ", "ME"), state.abb)

Read column names

Description

Read the first line of a delimited file as vector.

Usage

read_names(file, delim = guess_delim(file))

Arguments

Value

Character vector of column names.

Examples

read_names("date,lgl\n11/09/2016,TRUE")

Convert data frame name suffixes to prefixes

Description

When performing a dplyr::left_join(), the suffix argument allows the user to replace the default .x and .y that are appended to column names shared between the two data frames. This function allows a user to convert those suffixes to prefixes.

Usage

rename_prefix(df, suffix = c(".x", ".y"), punct = TRUE)

Arguments

Value

A data frame with new column names.

Examples

a <- data.frame(x = letters[1:3], y = 1:3)
b <- data.frame(x = letters[1:3], y = 4:6)
df <- dplyr::left_join(a, b, by = "x", suffix = c(".a", ".b"))
rename_prefix(df, suffix = c(".a", ".b"), punct = TRUE)

Form a word break regex pattern

Description

Wrap a word in word boundary (⁠\\b⁠) characters. Useful when combined with stringr::str_which() and stringr::str_detect() to match only entire words and not that word inside another word (e.g., "sting" and "testing").

Usage

rx_break(pattern)

Arguments

Value

The a glue vector of pattern wrapped in ⁠\\b⁠.

Examples

rx_break("test")
rx_break(state.abb[1:5])

Phone number regex

Description

The regex string to match US phone numbers in a variety of common formats.

Usage

rx_phone

Format

A character string (length 1).

State regex

Description

The regex string to extract state string preceding ZIP code.

Usage

rx_state

Format

A character string (length 1).

URL regex

Description

The regex string to match valid URLs.

Usage

rx_url

Format

A character string (length 1).

ZIP code regex

Description

The regex string to extract ZIP code from the end of address.

Usage

rx_zip

Format

A character string (length 1).

Truncate and wrap x-axis labels

Description

Truncate the labels of a plot's discrete x-axis labels so that the text does not overflow and collide with other bars.

Usage

scale_x_truncate(n = 15, ...)

scale_x_wrap(width = 15, ...)

Arguments

Calculate string distance

Description

This function wraps around stringdist::stringdist().

Usage

str_dist(a, b, method = "osa", ...)

Arguments

Value

The distance between string a and string b.

Examples

str_dist(a = "BRULINGTN", b = "BURLINGTON")

Normalize a character string

Description

The generic normalization that underpins functions like normal_city() and normal_address(). This function simply chains together three ⁠stringr::str_*()⁠ functions:

1. Convert to uppercase.

2. Replace punctuation with whitespaces.

3. Trim and squish excess whitespace.

Usage

str_normal(x, case = TRUE, punct = "", quote = TRUE, squish = TRUE)

Arguments

Value

A normalized vector of the same length.

See Also

Other geographic normalization functions: abbrev_full(), abbrev_state(), check_city(), expand_abbrev(), expand_state(), fetch_city(), normal_address(), normal_city(), normal_state(), normal_zip()

Examples

str_normal("   TestING 123   example_test.String   ")

Check if a single file is new

Description

This function tests whether a single file has a modification date equal to the system date. Useful when repeatedly running code with a lengthy download stage. Many state databases are updated daily, so new data can be helpful but not always necessary. Set this function in an if statement.

Usage

this_file_new(path)

Arguments

Value

logical; Whether the file has a modification date equal to today.

Examples

tmp <- tempfile()
this_file_new(tmp)

Make a File Path from a URL

Description

Combine the basename() of a file URL with a directory path.

Usage

url2path(url, dir)

Arguments

Details

Useful in the destfile argument to download.file() to save a file with the same name as the URL's file name.

Value

The desired file path to a URL file.

Examples

url2path("https://floridalobbyist.gov/reports/llob.txt", tempdir())

Check a URL file size

Description

Call httr::HEAD() and return the number of bytes in the file to be downloaded.

Usage

url_file_size(url)

Arguments

Value

The size of a file to be downloaded.

Create a new template data diary

Description

Take the arguments supplied and put them into the appropriate places in a new template diary. Write the new template diary in the supplied directory.

Usage

use_diary(
  st,
  type,
  author,
  path = "state/{st}/{type}/docs/{st}_{type}_diary.Rmd",
  auto = FALSE
)

Arguments

Value

The file path of new diary, invisibly.

Examples

use_diary("VT", "contribs", "Kiernan Nicholls", NA, auto = FALSE)
use_diary("DC", "expends", "Kiernan Nicholls", tempfile(), auto = FALSE)

USPS City Abbreviations

Description

A curated and edited subset of usps_street containing the USPS abbreviations found in city names. Useful as the geo_abbs argument of normal_city().

Usage

usps_city

Format

A tibble with 154 rows of 2 variables:

full

Primary Street Suffix

abb

Commonly Used Street Suffix or Abbreviation

...

Source

USPS Appendix C1, Street Abbreviations

USPS State Abbreviations

Description

A tibble containing the USPS.

Usage

usps_state

Format

A tibble with 62 rows of 2 variables:

full

Primary Street Suffix

abb

Commonly Used Street Suffix or Abbreviation

...

Source

USPS Appendix B, Two–Letter State Abbreviations

USPS Street Abbreviations

Description

A tibble containing common street suffixes or suffix abbreviations and their full equivalent. Useful as the add_abbs argument of normal_address().

Usage

usps_street

Format

A tibble with 325 rows of 3 variables:

full

Primary Street Suffix.

abb

Commonly Used Street Suffix or Abbreviation.

...

Source

USPS Appendix C1 Street Abbreviations.

US State Abbreviations

Description

The abb column of the usps_state tibble.

Usage

valid_abb

Format

A vector of 2-digit abbreviations (length 62).

US City Names

Description

The city column of the zipcodes tibble.

Usage

valid_city

Format

A sorted vector of unique city names (length 19,083).

US State Names

Description

The state column of the usps_state tibble.

Usage

valid_name

Format

A vector of state names (length 62).

Details

Contains 12 more names than datasets::state.name.

US State Abbreviations

Description

The abb column of the usps_state tibble.

Usage

valid_state

Format

A vector of 2-digit abbreviations (length 62).

Almost all of the valid USA ZIP Codes

Description

The zip column of the geo tibble.

Usage

valid_zip

Format

A sorted vector of 5-digit ZIP codes (length 44334).

Which in

Description

Return the values of x that are %in% of the vector y.

Usage

what_in(x, y, ignore.case = FALSE)

Arguments

Details

x[which(x %in% y)]

Value

The elements of x that are %in% y.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_in(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_out()

Examples

what_in(c("VT", "DC", NA), state.abb)

Which out

Description

Return the values of x that are ⁠%out%⁠ of the vector y.

Usage

what_out(x, y, na.rm = TRUE, ignore.case = FALSE)

Arguments

Details

x[which(x %out% y)]

Value

The elements of x that are ⁠%out%⁠ y.

See Also

Other counting wrappers: count_diff(), count_in(), count_na(), count_out(), na_in(), na_out(), na_rep(), prop_distinct(), prop_in(), prop_na(), prop_out(), what_in()

Examples

what_out(c("VT", "DC", NA), state.abb)

US City, state, and ZIP

Description

This tibble is the third version of a popular zipcodes database. The original CivicSpace US ZIP Code Database was created by Schuyler Erle using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information from the Census Bureau’s TIGER/Line 2003 data set. The second version was published as the zipcode::zipcode dataframe object. This version has dropped the latitude and longitude, reorganized columns, and normalize the city values with normal_city().

Usage

zipcodes

Format

A tibble with 44,336 rows of 3 variables:

city

Normalized city name.

state

Two letter state abbreviation.

zip

Five-digit ZIP Code.

...

Source

Daniel Coven's federalgovernmentzipcodes.us web site and the CivicSpace US ZIP Code Database written by Schuyler Erle schuyler@geocoder.us, 5 August 2004. Original CSV files available from https://web.archive.org/web/20221005220101/http://federalgovernmentzipcodes.us/free-zipcode-database-Primary.csv