- Fix broken CRAN check due to stale URL.

- Add new `nato` dataset.
- Take out `how = "dstk"` functionality because the underlying API seems definitely dead now.

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: passport
 Type: Package
 Title: Travel Smoothly Between Country Name and Code Formats
-Version: 0.2.0.9000
+Version: 0.2.0.9001
 Authors@R:
     c(
         person(
@@ -39,6 +39,6 @@ URL: https://github.com/alistaire47/passport, https://alistaire47.github.io/pass
 BugReports: https://github.com/alistaire47/passport/issues
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.1.0
+RoxygenNote: 7.1.1
 Roxygen: list(markdown = TRUE)
 VignetteBuilder: knitr

NEWS.md

@@ -1,24 +1,22 @@
-# passport 0.2.0.9000
+# passport 0.2.0.9001
 
+* Add `nato` dataset of NATO/OTAN member defense expenditures.
+* Remove `"dstk"` option for `parse_country()` as the Data Science Tookit API 
+    appears dead.
 * Refactor README and vigette to use explicit packages instead of tidyverse.
 
 # passport 0.2.0
 
 * Add `order_countries()` to make sorting vectors of countries simpler, 
     particularly when passed to the `limits` parameter of a ggplot2 discrete 
     scale.
-
 * Change `country_format()` to accept a `factor` parameter along to 
     `as_country_name()`.
-
 * Update documentation and tests to account for DSTK downtime.
 
 # passport 0.1.1
 
 * Added a `NEWS.md` file to track changes to the package.
-
 * Added option to use geocoding web APIs to supercharge `parse_country()`.
-
 * Added `convert_country()` constructor for ggplot2 convenience.
-
 * Added essential API for converting between country names and codes.

R/codes.R

@@ -13,21 +13,24 @@
 #'
 #' @format A data.frame of 427 rows and 9 variables.
 #'
-#' Structure:
+#' Variables:
 #'
-#' - `column`: The column name in the internal `passport:::countries`
-#'     data.frame. Valid for use in `from` and `to` parameters.
-#' - `code`: `column` with hyphens for underscores, which is a valid IANA
+#' \describe{
+#'     \item{`column`}{The column name in the internal `passport:::countries`
+#'     data.frame. Valid for use in `from` and `to` parameters.}
+#'     \item{`code`}{`column` with hyphens for underscores, which is a valid IANA
 #'     language tag for Unicode CLDR country names. Valid for use in `from` and
-#'     `to` parameters.
-#' - `name`: Full name or code name for non-CLDR options.
-#' - `notes`: Things to note, including deprecations, oddities, etc.
-#' - `language`: Full language name parsed from `code`.
-#' - `region`: Full country or region name parsed from `code`.
-#' - `script`: Full language script name parsed from `code`.
-#' - `variant`: Full variant parsed from `code`. Also used for
-#'     organization-standardized names.
-#' - `extension`: Further specification of name type.
+#'     `to` parameters.}
+#'     \item{`name`}{Full name or code name for non-CLDR options.}
+#'     \item{`notes`}{Things to note, including deprecations, oddities, etc.}
+#'     \item{`language`}{Full language name parsed from `code`.}
+#'     \item{`region`}{Full country or region name parsed from `code`.}
+#'     \item{`script`}{Full language script name parsed from `code`.}
+#'     \item{`variant`}{Full variant parsed from `code`. Also used for
+#'     organization-standardized names.}
+#'     \item{`extension`}{Further specification of name type.}
+#' }
+#'
 #'
 #' @examples
 #' # A searchable widget to find a code or name

R/nato.R

@@ -0,0 +1,62 @@
+#' NATO Member Defense Expenditures
+#'
+#' A sample dataset of NATO/OTAN member defense expenditures.
+#'
+#' @source <https://www.nato.int/cps/en/natohq/news_167080.htm>
+#'
+#' @format A data.frame of 232 rows and 14 variables.
+#'
+#' Variables:
+#'
+#' \describe{
+#'     \item{`country_stanag`}{Country code in NATO STANAG format}
+#'     \item{`year`}{Year, from 2012 to 2019. 2018-2019 numbers may be estimates.}
+#'     \item{`Defense expenditure (USD, current prices)`}{
+#'         Defense expenditures in US dollars, using current prices and exchange rates.
+#'     }
+#'     \item{`Defense expenditure (USD, 2015 prices)`}{
+#'         Defense expenditures in US dollars, using 2015 prices and exchange rates.
+#'     }
+#'     \item{`Defense expenditure (% real GDP)`}{
+#'         Defense expenditure as a percentage of real gross domestic product.
+#'         Based on 2015 prices.
+#'     }
+#'     \item{`Defense expenditure annual real change (% GDP)`}{
+#'         Annual change in defense expenditure as a percentage of real gross
+#'         domestic product. Based on 2015 prices.
+#'     }
+#'     \item{`Real GDP (2015 prices)`}{
+#'         Real gross domestic product in 2015 US dollars and at 2015 exchange
+#'         rates.
+#'     }
+#'     \item{`GDP per capita (USD)`}{
+#'         Gross demestic product per capita in 2015 US dollars and at 2015
+#'         exchange rates.
+#'     }
+#'     \item{`Defense expenditure per capita (USD)`}{
+#'         Defense expenditure per capita in 2015 US dollars.
+#'     }
+#'     \item{`Military personnel`}{Number of military personnel}
+#'     \item{`Equipment expenditure (%)`}{
+#'         Percent of defense expenditure spent on equipment. Includes major
+#'         equipment expenditure and R&D devoted to major equipment.
+#'     }
+#'     \item{`Personnel expenditure (%)`}{
+#'         Percentage of defense expenditure spent on personnel. Includes both
+#'         military and civilian expenditure and pensions.
+#'     }
+#'     \item{`Infrastructure expenditure (%)`}{
+#'         Percentage of defense expenditure spent on infrastructure. Includes
+#'         NATO common infrastructure and national military construction.
+#'     }
+#'     \item{`Other expenditure (%)`}{
+#'         Percentage of defense expenditure spent on other categories besides
+#'         equipment, personnel, and infrastructure. Includes operations and
+#'         maintenance expenditure, other R&D expenditure, and other expenditure
+#'         not otherwise captured.
+#'     }
+#' }
+#'
+#' @examples
+#' as_country_name(nato$country_stanag, from = 'stanag')
+"nato"

R/parse_country.R

@@ -28,20 +28,18 @@ fromJSON <- function(...) {
     jsonlite::fromJSON(...)
 }
 
-parse_by_geocoding <- function(location, source = c("google", "dstk")) {
+parse_by_geocoding <- function(location, source = c("google")) {
     query <- vapply(location, utils::URLencode, character(1), reserved = TRUE)
     source <- match.arg(source)
 
-    base_url <- c("google" = "https://maps.googleapis.com",
-                  "dstk" = "http://www.datasciencetoolkit.org")[source]
+    base_url <- c("google" = "https://maps.googleapis.com")[source]
     urls <- paste0(base_url, "/maps/api/geocode/json?&address=", query)
     vapply(
         urls,
         function(url) {
             response <- fromJSON(url)
             if (response$status != "OK") {
-                message <- c("google" = "Google Maps geocoding API call failed; status = %s. Free usage tier is limited to 2500 queries per day.",
-                             "dstk" = "Data Science Toolkit geocoding API call failed; status = %s")[source]
+                message <- c("google" = "Google Maps geocoding API call failed; status = %s. Free usage tier is limited to 2500 queries per day.")[source]
                 stop(sprintf(message, response$status))
             }
             components <- response$results$address_components[[1]]
@@ -76,12 +74,8 @@ parse_by_geocoding <- function(location, source = c("google", "dstk")) {
 #' input. For more calls, see options that allow passing an API key like
 #' `ggmap::geocode()` with `output = "all"` or `googleway::google_geocode()`.
 #'
-#' If `how = "dstk"`, `parse_country` will use the Data Science Toolkit
-#' geocoding API.
-#'
 #' Note that due to their flexibility, the APIs may fail unpredictably, e.g.
-#' `parse_country("foo", how = "google")` returns `"CH"` and
-#' `parse_country("foo", how = "dstk")` returns `"DJ"` whereas `how = "regex"`
+#' `parse_country("foo", how = "google")` returns `"CH"` whereas `how = "regex"`
 #' fails with a graceful `NA` and warning.
 #'
 #'
@@ -90,11 +84,10 @@ parse_by_geocoding <- function(location, source = c("google", "dstk")) {
 #' @param to Format to which to convert. Defaults to `"iso2c"`; see [`codes`]
 #'     for more options.
 #' @param  how How to parse; defaults to `"regex"`. `"google"`` uses the Google
-#'     Maps geocoding API; `"dstk"`` uses the Data Science Toolkit geocoding
-#'     API. See "Details" for more information.
+#'     Maps geocoding API. See "Details" for more information.
 #' @param language If `how = "regex"`, the language from which to parse country
 #'     names. Currently accepts `"en"` (default) and `"de"`. Ignored if
-#'     `how = "google"` or `how = "dstk".`
+#'     `how = "google"`.
 #'
 #' @return A character vector or factor of ISO 2-character country codes or
 #' other specified codes or names. Warns of any parsing failure.
@@ -115,7 +108,7 @@ parse_by_geocoding <- function(location, source = c("google", "dstk")) {
 #' @export
 parse_country <- function(x,
                           to = "iso2c",
-                          how = c("regex", "google", "dstk"),
+                          how = c("regex", "google"),
                           language = c("en", "de"),
                           factor = is.factor(x)) {
     # parameter checking

README.Rmd

@@ -93,35 +93,24 @@ parse_country(c("1600 Pennsylvania Ave, DC", "Eiffel Tower"), how = "google")
 
 ### II. Convert
 
-If data comes with countries already coded, convert them with 
-`as_country_code()`:
+If data comes with countries already coded, 
 
-```{r convert-1, message = FALSE}
-# 2016 Olympic gold medal data
-olympics <- read.table("https://raw.githubusercontent.com/nbremer/olympicfeathers/gh-pages/data/raw%20medal%20data/Rio%202016%20gold%20medal%20winners.txt", 
-                       sep = "\t", header = TRUE, na.strings = "", 
-                       stringsAsFactors = FALSE)
-
-olympics %>% count(country = as_country_code(NOC, from = "ioc"), sort = TRUE)
-```
-
-or to convert to country names, use `as_country_name()`:
+- convert them to ISO or other codes with `as_country_code()`
+- convert them to country names with `as_country_name()`
+- convert them to other languages with `as_country_name()`
 
-```{r convert-2}
-olympics %>% 
-    count(country = as_country_name(NOC, from = "ioc"), 
-          Event_gender) %>% 
-    spread(Event_gender, n) %>% 
-    arrange(desc(W))
-```
-
-or translate to another language:
-
-```{r convert-3}
-olympics$NOC %>% 
-    unique() %>% 
-    as_country_name(from = "ioc", to = "ta-my") %>% 
-    head(10)
+```{r convert-1, message = FALSE}
+# NATO member defense expenditure data; see `?nato`
+data("nato", package = "passport")
+
+nato %>% 
+    select(country_stanag) %>% 
+    distinct() %>%
+    mutate(
+        country_iso = as_country_code(country_stanag, from = "stanag"),
+        country_name = as_country_name(country_stanag, from = "stanag", short = FALSE),
+        country_name_thai = as_country_name(country_stanag, from = "stanag", to = "ta-my")
+    )
 ```
 
 Language formats largely follow [IETF language tag BCP