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Prepare npi 0.3.0 CRAN release
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‎DESCRIPTION‎

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Package: npi
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Title: Access the U.S. National Provider Identifier Registry API
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Version: 0.2.0.9000
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Version: 0.3.0
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Authors@R: c(
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person("Frank", "Farach", , "frank.farach@gmail.com",
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role = c("cre", "aut", "cph"),
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person(c("Emily", "C."), "Zabor", , "zabore2@ccf.org", role = "rev",
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comment = c(ORCID = "0000-0002-1402-4498"))
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)
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Author: Frank Farach [aut, cre, cph],
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Sam Parmar [ctb],
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Matthias Grenié [rev],
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Emily C. Zabor [rev]
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Maintainer: Frank Farach <frank.farach@gmail.com>
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Description: Access the United States National Provider Identifier
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Registry API <https://npiregistry.cms.hhs.gov/api/>. Obtain and transform

‎NEWS.md‎

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# npi (development version)
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# npi 0.3.0 (2026-07-01)
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## MINOR IMPROVEMENTS
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* `npi_search()` now normalizes `address_purpose` values to API constants and accepts case-insensitive input.
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* `npi_is_valid()` now supports vector inputs and returns logical vectors.
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* `npi_search()` returns a typed empty `npi_results` object when no records are found.
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## BUG FIXES
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* `npi_summarize()` now preserves input rows when NPI records have empty or missing nested address or taxonomy data.
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* Updated the package CITATION file to use `bibentry()` instead of the deprecated `citEntry()` format.
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## DOCUMENTATION FIXES
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* Clarified `npi_summarize()` taxonomy fallback behavior when no primary taxonomy is marked.

‎R/npi_results_s3.R‎

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@@ -83,6 +83,29 @@ validate_npi_results <- function(x, ...) {
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}
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empty_npi_summary <- function() {
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tibble::tibble(
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npi = integer(),
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name = character(),
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enumeration_type = character(),
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primary_practice_address = character(),
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phone = character(),
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primary_taxonomy = character()
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)
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}
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add_missing_columns <- function(df, columns, default = NA_character_) {
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for (column in columns) {
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if (!column %in% names(df)) {
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df[[column]] <- rep(default, nrow(df))
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}
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}
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df
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}
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#' Summary method for \code{npi_results} S3 object
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#'
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#' @importFrom rlang .data
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#' @export
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npi_summarize.npi_results <- function(object, ...) {
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basic <- get_list_col(object, "basic")
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validate_npi_results(object)
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if (nrow(object) == 0L) {
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return(empty_npi_summary())
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}
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basic <- get_list_col(object, "basic") %>%
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add_missing_columns(
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c(
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"basic_first_name", "basic_last_name",
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"basic_organization_name"
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)
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) %>%
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dplyr::group_by(.data$npi) %>%
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dplyr::slice_head(n = 1L) %>%
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dplyr::ungroup() %>%
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dplyr::select(
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.data$npi, .data$basic_first_name, .data$basic_last_name,
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.data$basic_organization_name
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)
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address_loc <- get_list_col(object, "addresses") %>%
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add_missing_columns(
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c(
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"addresses_address_purpose", "addresses_address_1",
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"addresses_address_2", "addresses_city", "addresses_state",
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"addresses_postal_code", "addresses_telephone_number"
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)
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) %>%
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dplyr::filter(.data$addresses_address_purpose == "LOCATION") %>%
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dplyr::mutate(
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postal_code = hyphenate_full_zip(.data$addresses_postal_code)
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addresses_postal_code = hyphenate_full_zip(.data$addresses_postal_code)
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)
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address_loc$primary_practice_address <- make_full_address(
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address_loc,
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"addresses_address_1",
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"addresses_address_2",
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"addresses_city",
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"addresses_state",
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"addresses_postal_code"
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)
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address_loc$phone <- address_loc$addresses_telephone_number
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address_loc <- address_loc %>%
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dplyr::group_by(.data$npi) %>%
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dplyr::slice_head(n = 1L) %>%
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dplyr::ungroup() %>%
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dplyr::select(.data$npi, .data$primary_practice_address, .data$phone)
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# Some NPI records have only one taxonomy row with primary == FALSE;
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# include these along with those where primary == TRUE
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tax_primary <- get_list_col(object, "taxonomies") %>%
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add_missing_columns("taxonomies_primary", default = FALSE) %>%
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add_missing_columns("taxonomies_desc") %>%
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dplyr::group_by(.data$npi) %>%
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dplyr::mutate(n_primary = sum(.data$taxonomies_primary == TRUE)) %>%
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dplyr::filter(.data$taxonomies_primary == TRUE | .data$n_primary == 0) %>%
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dplyr::slice_head()
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dplyr::mutate(n_primary = sum(.data$taxonomies_primary %in% TRUE)) %>%
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dplyr::filter(.data$taxonomies_primary %in% TRUE | .data$n_primary == 0L) %>%
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dplyr::slice_head(n = 1L) %>%
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dplyr::ungroup() %>%
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dplyr::transmute(
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npi = .data$npi,
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primary_taxonomy = .data$taxonomies_desc
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)
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tibble::tibble(
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npi = object$npi,
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name = ifelse(object$enumeration_type == "Individual",
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paste(basic$basic_first_name, basic$basic_last_name),
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basic$basic_organization_name
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),
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enumeration_type = object$enumeration_type,
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primary_practice_address = address_loc %>%
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make_full_address(
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"addresses_address_1",
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"addresses_address_2",
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"addresses_city",
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"addresses_state",
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"addresses_postal_code"
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),
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phone = address_loc$addresses_telephone_number,
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primary_taxonomy = tax_primary$taxonomies_desc
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)
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object %>%
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dplyr::select(.data$npi, .data$enumeration_type) %>%
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dplyr::left_join(basic, by = "npi") %>%
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dplyr::mutate(
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name = ifelse(
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.data$enumeration_type == "Individual",
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stringr::str_c(.data$basic_first_name, " ", .data$basic_last_name),
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.data$basic_organization_name
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)
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) %>%
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dplyr::left_join(address_loc, by = "npi") %>%
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dplyr::left_join(tax_primary, by = "npi") %>%
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dplyr::select(
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.data$npi, .data$name, .data$enumeration_type,
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.data$primary_practice_address, .data$phone, .data$primary_taxonomy
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)
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}
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‎cran-comments.md‎

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## Resubmission
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# npi 0.3.0
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This is a resubmission. In this version I have:
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This update fixes current CRAN check failures for npi 0.2.0:
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* Added a web reference for the API in the description field of the DESCRIPTION
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file.
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* Fixed vignette rebuild errors caused by NPI records whose nested address data
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did not contain the expected location-address fields.
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* Added \value and an explanation of the structure and meaning of the output to
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pipe.Rd.
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* Updated the package CITATION file to use `bibentry()` instead of the
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deprecated `citEntry()` format.
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## R CMD check results
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0 errors | 0 warnings | 1 note
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Standard local check:
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* This is a new release.
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0 errors | 0 warnings | 0 notes
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`R CMD check --as-cran npi_0.3.0.tar.gz` reported 0 errors, 0 warnings, and
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2 notes in this local macOS environment:
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* `unable to verify current time`
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* HTML validation problems caused by `/usr/bin/tidy`, Apple Inc. build 4474
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from 2006, not recognizing HTML5 tags generated by R's help system.
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## Reverse dependencies
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There are no reverse dependencies on CRAN.

‎inst/CITATION‎

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citHeader("To cite npi in publications, please use:")
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citEntry(
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entry = "Manual",
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title = "npi: Access the U.S. National Provider Identifier Registry API (Version 0.2.0)",
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author = as.person("Frank J Farach"),
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year = 2022,
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url = "https://github.com/ropensci/npi",
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doi = "10.5281/zenodo.6326729",
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textVersion = paste("Farach, F. J. (2022). npi: Access the U.S. National Provider Identifier Registry API (Version 0.2.0). https://github.com/ropensci/npi"
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)
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)
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bibentry(
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bibtype = "Manual",
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title = "{npi}: Access the U.S. National Provider Identifier Registry API",
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author = person("Frank", "Farach", role = c("cre", "aut")),
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url = "https://github.com/ropensci/npi",
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year = 2022,
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doi = "10.5281/zenodo.6326729",
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note = "R package version 0.2.0"
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note = paste("R package version", meta$Version)
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)

‎tests/testthat/test-api.R‎

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expect_identical(names(npi_summarize(atl)), expect_names)
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})
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})
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test_that("npi_summarize() handles empty results", {
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res <- npi_summarize(new_empty_npi_results())
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expect_types <- c("integer", rep("character", 5))
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expect_names <- c(
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"npi", "name", "enumeration_type",
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"primary_practice_address", "phone",
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"primary_taxonomy"
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)
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checkmate::expect_tibble(res, types = expect_types, nrows = 0L)
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expect_identical(names(res), expect_names)
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})
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test_that("npi_summarize() keeps rows aligned when nested data is missing", {
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data(npis)
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res <- npis[1:2, ]
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res$addresses[[1]] <- tibble::tibble()
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res$taxonomies[[1]] <- tibble::tibble()
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summarized <- npi_summarize(res)
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expect_identical(summarized$npi, res$npi)
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expect_true(is.na(summarized$primary_practice_address[1]))
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expect_true(is.na(summarized$phone[1]))
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expect_true(is.na(summarized$primary_taxonomy[1]))
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expect_equal(
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summarized$primary_practice_address[2],
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npi_summarize(npis[2, ])$primary_practice_address
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)
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})

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