much saner return types for all the functions; added the ability to specify extra fields for return values.

DESCRIPTION

@@ -17,5 +17,6 @@ Depends:
 Imports:
     purrr,
     httr,
-    jsonlite
+    jsonlite,
+    tibble
 RoxygenNote: 6.0.1

NAMESPACE

@@ -9,3 +9,4 @@ export(gio_reverse)
 import(httr)
 import(purrr)
 importFrom(jsonlite,fromJSON)
+importFrom(tibble,as_tibble)

R/geocod-batch.R

@@ -2,29 +2,52 @@
 #'
 #' @md
 #' @param addresses character vector of addresses (10K max)
+#' @param fields vector of additional fields to return with query results. Note that these
+#'        count as extra lookups and impact your dailu quota/costs. See [the official documentation](https://geocod.io/docs/#fields)
+#'        for more information on costs/pricing. Can be `cd`, `cd113`, `cd114`, or `cd115` for
+#'        current or historical Congressional districts (U.S.); `stateleg` for State Legislative District (House & Senate, U.S.);
+#'        `school` forSchool District (elementary/secondary or unified, U.S.); `census` for
+#'        Census Block/Tract & FIPS codes (U.S.) or `timezone` for timezone.
 #' @param api_key `geocod.io` API key
 #' @export
 #' @examples
-#' gio_batch_geocode(c(
+#' addresses <- c(
 #'   "1109 N Highland St, Arlington VA",
 #'   "525 University Ave, Toronto, ON, Canada",
 #'   "4410 S Highway 17 92, Casselberry FL",
 #'   "15000 NE 24th Street, Redmond WA",
 #'   "17015 Walnut Grove Drive, Morgan Hill CA"
-#' ))
-gio_batch_geocode <- function(addresses, api_key=gio_auth()) {
+#' )
+#'
+#' gio_batch_geocode(addresses)
+#' gio_batch_geocode(addresses, fields=c("cd", "stateleg"))
+gio_batch_geocode <- function(addresses, fields, api_key=gio_auth()) {
 
   if (length(addresses) > 10000) {
     message("Too many addresses. Submitting first 10,000")
     addresses <- addresses[1:10000]
   }
 
+  params <- list(api_key=api_key)
+  if (!missing(fields)) params$fields <- paste0(trimws(fields), collapse=",")
+
   res <- httr::POST("https://api.geocod.io/v1/geocode",
-                    query=list(api_key=api_key),
-                    body=as.list(addresses), encode="json")
+                    query=params, body=as.list(addresses), encode="json")
 
   httr::stop_for_status(res)
 
-  jsonlite::fromJSON(httr::content(res, as="text", encoding="UTF-8"))
+  res <- jsonlite::fromJSON(httr::content(res, as="text", encoding = "UTF-8"),
+                            flatten = TRUE)
+
+  res <- res$results
+
+  new_names <- gsub("\\.", "_", colnames(res))
+  new_names <- gsub("response_input|address_components|fields", "", new_names)
+  new_names <- gsub("[_]+", "_", new_names)
+  new_names <- sub("^_", "", new_names)
+
+  res <- set_names(res, new_names)
+
+  as_tibble(res)
 
 }

R/geocod-one.R

@@ -2,18 +2,40 @@
 #'
 #' @md
 #' @param address address to geocode
+#' @param fields vector of additional fields to return with query results. Note that these
+#'        count as extra lookups and impact your dailu quota/costs. See [the official documentation](https://geocod.io/docs/#fields)
+#'        for more information on costs/pricing. Can be `cd`, `cd113`, `cd114`, or `cd115` for
+#'        current or historical Congressional districts (U.S.); `stateleg` for State Legislative District (House & Senate, U.S.);
+#'        `school` forSchool District (elementary/secondary or unified, U.S.); `census` for
+#'        Census Block/Tract & FIPS codes (U.S.) or `timezone` for timezone.
 #' @param api_key `geocod.io` API key
 #' @export
-#' @examples
-#' gio_geocode("1109 N Highland St, Arlington, VA")
-gio_geocode <- function(address, api_key=gio_auth()) {
+#' @examples \dontrun{
+#' gio_geocode("1109 N Highland St, Arlcdceington, VA")
+#' gio_geocode("1109 N Highland St, Arlington, VA",
+#'             fields=c("cd", "stateleg"))
+#' }
+gio_geocode <- function(address, fields, api_key=gio_auth()) {
+
+  params <- list(q=address, api_key=api_key)
+  if (!missing(fields)) params$fields <- paste0(trimws(fields), collapse=",")
 
-  res <- httr::GET("https://api.geocod.io/v1/geocode",
-                   query=list(q=address, api_key=api_key))
+  res <- httr::GET("https://api.geocod.io/v1/geocode", query=params)
 
   httr::stop_for_status(res)
 
-  jsonlite::fromJSON(httr::content(res, as="text", encoding="UTF-8"))
+  res <- jsonlite::fromJSON(httr::content(res, as="text", encoding = "UTF-8"),
+                            flatten = TRUE)
+
+  res <- res$results
+
+  new_names <- gsub("\\.", "_", colnames(res))
+  new_names <- gsub("address_components|fields", "", new_names)
+  new_names <- sub("^_", "", new_names)
+
+  res <- set_names(res, new_names)
+
+  as_tibble(res)
 
 }
 
@@ -21,14 +43,23 @@ gio_geocode <- function(address, api_key=gio_auth()) {
 #'
 #' @md
 #' @param street,city,state,postal_code,country address components
+#' @param fields vector of additional fields to return with query results. Note that these
+#'        count as extra lookups and impact your dailu quota/costs. See [the official documentation](https://geocod.io/docs/#fields)
+#'        for more information on costs/pricing. Can be `cd`, `cd113`, `cd114`, or `cd115` for
+#'        current or historical Congressional districts (U.S.); `stateleg` for State Legislative District (House & Senate, U.S.);
+#'        `school` forSchool District (elementary/secondary or unified, U.S.); `census` for
+#'        Census Block/Tract & FIPS codes (U.S.) or `timezone` for timezone.
 #' @param api_key `geocod.io` API key
 #' @export
 #' @examples
 #' gio_geocode_components("1109 N Highland St", "Arlington", "VA")
+#' gio_geocode_components("1109 N Highland St", "Arlington", "VA",
+#'                        fields=c("census", "stateleg"))
 gio_geocode_components <- function(street, city, state, postal_code, country,
-                                   api_key=gio_auth()) {
+                                   fields, api_key=gio_auth()) {
 
   params <- list(api_key=api_key)
+  if (!missing(fields)) params$fields <- paste0(trimws(fields), collapse=",")
 
   if (!missing(street)) params$street <- street
   if (!missing(city)) params$city <- city
@@ -40,6 +71,17 @@ gio_geocode_components <- function(street, city, state, postal_code, country,
 
   httr::stop_for_status(res)
 
-  jsonlite::fromJSON(httr::content(res, as="text", encoding="UTF-8"))
+  res <- jsonlite::fromJSON(httr::content(res, as="text", encoding = "UTF-8"),
+                            flatten = TRUE)
+
+  res <- res$results
+
+  new_names <- gsub("\\.", "_", colnames(res))
+  new_names <- gsub("address_components|fields", "", new_names)
+  new_names <- sub("^_", "", new_names)
+
+  res <- set_names(res, new_names)
+
+  as_tibble(res)
 
 }

R/geocod-reverse-batch.R

@@ -2,6 +2,12 @@
 #'
 #' @md
 #' @param coordinates data frame of coordinates with `lat` and `lon` columns
+#' @param fields vector of additional fields to return with query results. Note that these
+#'        count as extra lookups and impact your dailu quota/costs. See [the official documentation](https://geocod.io/docs/#fields)
+#'        for more information on costs/pricing. Can be `cd`, `cd113`, `cd114`, or `cd115` for
+#'        current or historical Congressional districts (U.S.); `stateleg` for State Legislative District (House & Senate, U.S.);
+#'        `school` forSchool District (elementary/secondary or unified, U.S.); `census` for
+#'        Census Block/Tract & FIPS codes (U.S.) or `timezone` for timezone.
 #' @param api_key `geocod.io` API key
 #' @export
 #' @examples
@@ -10,7 +16,8 @@
 #'   lon = c(-77.9658000, -96.6303900, -117.8362320, -80.6784760)
 #' ) -> to_code
 #' gio_batch_reverse(to_code)
-gio_batch_reverse <- function(coordinates, api_key=gio_auth()) {
+#' gio_batch_reverse(to_code, fields=c("census", "stateleg"))
+gio_batch_reverse <- function(coordinates, fields, api_key=gio_auth()) {
 
   if (nrow(coordinates) > 10000) {
     message("Too many addresses. Submitting first 10,000")
@@ -27,13 +34,28 @@ gio_batch_reverse <- function(coordinates, api_key=gio_auth()) {
 
   pairs <- sprintf("%s,%s", coordinates$lat, coordinates$lon)
 
+  params <- list(api_key=api_key)
+  if (!missing(fields)) params$fields <- paste0(trimws(fields), collapse=",")
+
   res <- httr::POST("https://api.geocod.io/v1/reverse",
-                    query=list(api_key=api_key),
-                    body=as.list(pairs), encode="json")
+                    query=params, body=as.list(pairs), encode="json")
 
   httr::stop_for_status(res)
 
-  jsonlite::fromJSON(httr::content(res, as="text", encoding="UTF-8"))
+  res <- jsonlite::fromJSON(httr::content(res, as="text", encoding = "UTF-8"),
+                            flatten = TRUE)
+
+  res <- res$results$response.results
+
+  res <- map_df(res, ~.)
+
+  new_names <- gsub("\\.", "_", colnames(res))
+  new_names <- gsub("response_input|address_components|fields", "", new_names)
+  new_names <- gsub("[_]+", "_", new_names)
+  new_names <- sub("^_", "", new_names)
+
+  res <- set_names(res, new_names)
 
-}
+  as_tibble(res)
 
+}

R/geocod-reverse.R

@@ -3,16 +3,37 @@
 #' @md
 #' @param lat,lon lat/lon pair to reverse geocode
 #' @param api_key `geocod.io` API key
+#' @param fields vector of additional fields to return with query results. Note that these
+#'        count as extra lookups and impact your dailu quota/costs. See [the official documentation](https://geocod.io/docs/#fields)
+#'        for more information on costs/pricing. Can be `cd`, `cd113`, `cd114`, or `cd115` for
+#'        current or historical Congressional districts (U.S.); `stateleg` for State Legislative District (House & Senate, U.S.);
+#'        `school` forSchool District (elementary/secondary or unified, U.S.); `census` for
+#'        Census Block/Tract & FIPS codes (U.S.) or `timezone` for timezone.
 #' @export
+#' @returh `tibble`
 #' @examples
 #' gio_reverse(38.9002898, -76.9990361)
-gio_reverse <- function(lat, lon, api_key=gio_auth()) {
+#' gio_reverse(38.9002898, -76.9990361, fields=c("census", "stateleg"))
+gio_reverse <- function(lat, lon, fields, api_key=gio_auth()) {
 
-  res <- httr::GET("https://api.geocod.io/v1/reverse",
-                   query=list(q=sprintf("%s,%s", lat, lon), api_key=api_key))
+  params <- list(q=sprintf("%s,%s", lat, lon), api_key=api_key)
+  if (!missing(fields)) params$fields <- paste0(trimws(fields), collapse=",")
+
+  res <- httr::GET("https://api.geocod.io/v1/reverse", query=params)
 
   httr::stop_for_status(res)
 
-  jsonlite::fromJSON(httr::content(res, as="text", encoding="UTF-8"))
+  res <- jsonlite::fromJSON(httr::content(res, as="text", encoding = "UTF-8"),
+                            flatten = TRUE)
+
+  res <- res$results
+
+  new_names <- gsub("\\.", "_", colnames(res))
+  new_names <- gsub("address_components|fields", "", new_names)
+  new_names <- sub("^_", "", new_names)
+
+  res <- set_names(res, new_names)
+
+  as_tibble(res)
 
 }

R/rgeocodio-package.R

@@ -8,4 +8,5 @@
 #' @author Bob Rudis (bob@@rud.is)
 #' @import purrr httr
 #' @importFrom jsonlite fromJSON
+#' @importFrom tibble as_tibble
 NULL

README.Rmd

@@ -15,10 +15,12 @@ The following functions are implemented:
 - `gio_geocode_components`:	Geocode a single address
 - `gio_reverse`:	Reverse geocode a single lat/lon pair
 
+All functions return tidy `tibble`s with sane column names.
+
 ### TODO
 
-- enable selection of additional fields
-- better return types
+- <strike>enable selection of additional fields</strike>
+- <strike>better return types</strike>
 - more error checking
 - tests
 
@@ -46,13 +48,15 @@ gio_geocode_components("1109 N Highland St", "Arlington", "VA")
 
 gio_reverse(38.9002898, -76.9990361)
 
-gio_batch_geocode(c(
+addresses <- c(
   "1109 N Highland St, Arlington VA",
   "525 University Ave, Toronto, ON, Canada",
   "4410 S Highway 17 92, Casselberry FL",
   "15000 NE 24th Street, Redmond WA",
   "17015 Walnut Grove Drive, Morgan Hill CA"
-))
+)
+
+gio_batch_geocode(addresses)
 
 data.frame(
   lat = c(35.9746000, 32.8793700, 33.8337100, 35.4171240),
@@ -62,6 +66,21 @@ data.frame(
 gio_batch_reverse(to_code)
 ```
 
+### Extra Fields
+
+```{r message=FALSE, warning=FALSE, error=FALSE}
+gio_geocode("1109 N Highland St, Arlington, VA", fields=c("cd", "stateleg"))
+
+gio_geocode_components("1109 N Highland St", "Arlington", "VA",
+                      fields=c("census", "stateleg"))
+
+gio_reverse(38.9002898, -76.9990361, fields=c("census", "stateleg"))
+
+gio_batch_geocode(addresses, fields=c("cd", "stateleg"))
+
+gio_batch_reverse(to_code, fields=c("census", "stateleg"))
+```
+
 ### Test Results
 
 ```{r message=FALSE, warning=FALSE, error=FALSE}

README.md

@@ -12,10 +12,12 @@ The following functions are implemented:
 -   `gio_geocode_components`: Geocode a single address
 -   `gio_reverse`: Reverse geocode a single lat/lon pair
 
+All functions return tidy `tibble`s with sane column names.
+
 ### TODO
 
--   enable selection of additional fields
--   better return types
+-   <strike>enable selection of additional fields</strike>
+-   <strike>better return types</strike>
 -   more error checking
 -   tests
 
@@ -40,167 +42,59 @@ packageVersion("rgeocodio")
 gio_geocode("1109 N Highland St, Arlington, VA")
 ```
 
-    ## $input
-    ## $input$address_components
-    ## $input$address_components$number
-    ## [1] "1109"
-    ## 
-    ## $input$address_components$predirectional
-    ## [1] "N"
-    ## 
-    ## $input$address_components$street
-    ## [1] "Highland"
-    ## 
-    ## $input$address_components$suffix
-    ## [1] "St"
-    ## 
-    ## $input$address_components$formatted_street
-    ## [1] "N Highland St"
-    ## 
-    ## $input$address_components$city
-    ## [1] "Arlington"
-    ## 
-    ## $input$address_components$state
-    ## [1] "VA"
-    ## 
-    ## $input$address_components$zip
-    ## [1] "22201"
-    ## 
-    ## $input$address_components$country
-    ## [1] "US"
-    ## 
-    ## 
-    ## $input$formatted_address
-    ## [1] "1109 N Highland St, Arlington, VA 22201"
-    ## 
-    ## 
-    ## $results
-    ##   address_components.number address_components.predirectional address_components.street address_components.suffix
-    ## 1                      1109                                 N                  Highland                        St
-    ##   address_components.formatted_street address_components.city address_components.county address_components.state
-    ## 1                       N Highland St               Arlington          Arlington County                       VA
-    ##   address_components.zip address_components.country                       formatted_address location.lat location.lng
-    ## 1                  22201                         US 1109 N Highland St, Arlington, VA 22201     38.88667    -77.09473
-    ##   accuracy accuracy_type                     source
-    ## 1        1       rooftop Virginia GIS Clearinghouse
+    ## # A tibble: 1 × 16
+    ##                         formatted_address accuracy accuracy_type                     source number predirectional
+    ## *                                   <chr>    <int>         <chr>                      <chr>  <chr>          <chr>
+    ## 1 1109 N Highland St, Arlington, VA 22201        1       rooftop Virginia GIS Clearinghouse   1109              N
+    ## # ... with 10 more variables: street <chr>, suffix <chr>, formatted_street <chr>, city <chr>, county <chr>,
+    ## #   state <chr>, zip <chr>, country <chr>, location_lat <dbl>, location_lng <dbl>
 
 ``` r
 gio_geocode_components("1109 N Highland St", "Arlington", "VA")
 ```
 
-    ## $input
-    ## $input$address_components
-    ## $input$address_components$number
-    ## [1] "1109"
-    ## 
-    ## $input$address_components$predirectional
-    ## [1] "N"
-    ## 
-    ## $input$address_components$street
-    ## [1] "Highland"
-    ## 
-    ## $input$address_components$suffix
-    ## [1] "St"
-    ## 
-    ## $input$address_components$formatted_street
-    ## [1] "N Highland St"
-    ## 
-    ## $input$address_components$city
-    ## [1] "Arlington"
-    ## 
-    ## $input$address_components$state
-    ## [1] "VA"
-    ## 
-    ## $input$address_components$zip
-    ## [1] "22201"
-    ## 
-    ## $input$address_components$country
-    ## [1] "US"
-    ## 
-    ## 
-    ## $input$formatted_address
-    ## [1] "1109 N Highland St, Arlington, VA 22201"
-    ## 
-    ## 
-    ## $results
-    ##   address_components.number address_components.predirectional address_components.street address_components.suffix
-    ## 1                      1109                                 N                  Highland                        St
-    ##   address_components.formatted_street address_components.city address_components.county address_components.state
-    ## 1                       N Highland St               Arlington          Arlington County                       VA
-    ##   address_components.zip address_components.country                       formatted_address location.lat location.lng
-    ## 1                  22201                         US 1109 N Highland St, Arlington, VA 22201     38.88667    -77.09473
-    ##   accuracy accuracy_type                     source
-    ## 1        1       rooftop Virginia GIS Clearinghouse
+    ## # A tibble: 1 × 16
+    ##                         formatted_address accuracy accuracy_type                     source number predirectional
+    ## *                                   <chr>    <int>         <chr>                      <chr>  <chr>          <chr>
+    ## 1 1109 N Highland St, Arlington, VA 22201        1       rooftop Virginia GIS Clearinghouse   1109              N
+    ## # ... with 10 more variables: street <chr>, suffix <chr>, formatted_street <chr>, city <chr>, county <chr>,
+    ## #   state <chr>, zip <chr>, country <chr>, location_lat <dbl>, location_lng <dbl>
 
 ``` r
 gio_reverse(38.9002898, -76.9990361)
 ```
 
-    ## $results
-    ##   address_components.number address_components.street address_components.suffix address_components.postdirectional
-    ## 1                       500                         H                        St                                 NE
-    ## 2                       800                       5th                        St                                 NE
-    ## 3                       474                         H                        St                                 NE
-    ##   address_components.formatted_street address_components.city address_components.county address_components.state
-    ## 1                             H St NE              Washington      District of Columbia                       DC
-    ## 2                           5th St NE              Washington      District of Columbia                       DC
-    ## 3                             H St NE              Washington      District of Columbia                       DC
-    ##   address_components.zip address_components.country                   formatted_address location.lat location.lng
-    ## 1                  20002                         US   500 H St NE, Washington, DC 20002      38.9002    -76.99951
-    ## 2                  20002                         US 800 5th St NE, Washington, DC 20002      38.9002    -76.99951
-    ## 3                  20002                         US   474 H St NE, Washington, DC 20002      38.9002    -76.99994
-    ##   accuracy  accuracy_type                                        source
-    ## 1     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau
-    ## 2     0.18 nearest_street TIGER/Line® dataset from the US Census Bureau
-    ## 3     0.18 nearest_street TIGER/Line® dataset from the US Census Bureau
+    ## # A tibble: 3 × 16
+    ##                     formatted_address accuracy  accuracy_type                                        source number
+    ## *                               <chr>    <dbl>          <chr>                                         <chr>  <chr>
+    ## 1   500 H St NE, Washington, DC 20002     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau    500
+    ## 2 800 5th St NE, Washington, DC 20002     0.18 nearest_street TIGER/Line® dataset from the US Census Bureau    800
+    ## 3   474 H St NE, Washington, DC 20002     0.18 nearest_street TIGER/Line® dataset from the US Census Bureau    474
+    ## # ... with 11 more variables: street <chr>, suffix <chr>, postdirectional <chr>, formatted_street <chr>, city <chr>,
+    ## #   county <chr>, state <chr>, zip <chr>, country <chr>, location_lat <dbl>, location_lng <dbl>
 
 ``` r
-gio_batch_geocode(c(
+addresses <- c(
   "1109 N Highland St, Arlington VA",
   "525 University Ave, Toronto, ON, Canada",
   "4410 S Highway 17 92, Casselberry FL",
   "15000 NE 24th Street, Redmond WA",
   "17015 Walnut Grove Drive, Morgan Hill CA"
-))
+)
+
+gio_batch_geocode(addresses)
 ```
 
-    ## $results
-    ##                                      query response.input.address_components.number
-    ## 1         1109 N Highland St, Arlington VA                                     1109
-    ## 2  525 University Ave, Toronto, ON, Canada                                      525
-    ## 3     4410 S Highway 17 92, Casselberry FL                                     4410
-    ## 4         15000 NE 24th Street, Redmond WA                                    15000
-    ## 5 17015 Walnut Grove Drive, Morgan Hill CA                                    17015
-    ##   response.input.address_components.predirectional response.input.address_components.street
-    ## 1                                                N                                 Highland
-    ## 2                                             <NA>                               University
-    ## 3                                             <NA>                             State Rte 17
-    ## 4                                               NE                                     24th
-    ## 5                                             <NA>                             Walnut Grove
-    ##   response.input.address_components.suffix response.input.address_components.formatted_street
-    ## 1                                       St                                      N Highland St
-    ## 2                                      Ave                                     University Ave
-    ## 3                                     <NA>                                       State Rte 17
-    ## 4                                       St                                         NE 24th St
-    ## 5                                       Dr                                    Walnut Grove Dr
-    ##   response.input.address_components.city response.input.address_components.state response.input.address_components.zip
-    ## 1                              Arlington                                      VA                                 22201
-    ## 2                                Toronto                                      ON                                  <NA>
-    ## 3                            Casselberry                                      FL                                 32707
-    ## 4                                Redmond                                      WA                                 98052
-    ## 5                            Morgan Hill                                      CA                                 95037
-    ##   response.input.address_components.country             response.input.formatted_address
-    ## 1                                        US      1109 N Highland St, Arlington, VA 22201
-    ## 2                                        CA              525 University Ave, Toronto, ON
-    ## 3                                        US     4410 State Rte 17, Casselberry, FL 32707
-    ## 4                                        US          15000 NE 24th St, Redmond, WA 98052
-    ## 5                                        US 17015 Walnut Grove Dr, Morgan Hill, CA 95037
-    ##                                                                                                                                                                                                                                                                                                                                                                                                              response.results
-    ## 1                                                                                                                                                                                                                                    1109, N, Highland, St, N Highland St, Arlington, Arlington County, VA, 22201, US, 1109 N Highland St, Arlington, VA 22201, 38.886665, -77.094733, 1, rooftop, Virginia GIS Clearinghouse
-    ## 2                                                                                                                                                                                                                                                                        525, University, Ave, University Ave, Toronto, ON, CA, 525 University Ave, Toronto, ON, 43.656258, -79.388223, 1, rooftop, City of Toronto Open Data
-    ## 3 4410, 4410, Us Hwy 17, Us Hwy 17, Us Hwy 17, Us Hwy 17, Casselberry, Casselberry, Seminole County, Seminole County, FL, FL, 32707, 32707, US, US, 4410 Us Hwy 17, Casselberry, FL 32707, 4410 Us Hwy 17, Casselberry, FL 32707, 28.671168, 28.670668, -81.33834, -81.338163, 1, 0.8, range_interpolation, range_interpolation, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau
-    ## 4        15000, 15000, NE, NE, 24th, 24th, St, St, NE 24th St, NE 24th St, Redmond, Redmond, King County, King County, WA, WA, 98052, 98052, US, US, 15000 NE 24th St, Redmond, WA 98052, 15000 NE 24th St, Redmond, WA 98052, 47.631663, 47.631363, -122.141532, -122.141535, 1, 0.8, range_interpolation, range_interpolation, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau
-    ## 5                                                                                                                                                                                                         Walnut Grove, Dr, Walnut Grove Dr, Morgan Hill, Santa Clara County, CA, 95037, US, Walnut Grove Dr, Morgan Hill, CA 95037, 37.138619, -121.64061, 0.8, street_center, TIGER/Line® dataset from the US Census Bureau
+    ## # A tibble: 5 × 12
+    ##                                      query      response_results                            formatted_address number
+    ## *                                    <chr>                <list>                                        <chr>  <chr>
+    ## 1         1109 N Highland St, Arlington VA <data.frame [1 × 16]>      1109 N Highland St, Arlington, VA 22201   1109
+    ## 2  525 University Ave, Toronto, ON, Canada <data.frame [1 × 13]>              525 University Ave, Toronto, ON    525
+    ## 3     4410 S Highway 17 92, Casselberry FL <data.frame [2 × 14]>     4410 State Rte 17, Casselberry, FL 32707   4410
+    ## 4         15000 NE 24th Street, Redmond WA <data.frame [2 × 16]>          15000 NE 24th St, Redmond, WA 98052  15000
+    ## 5 17015 Walnut Grove Drive, Morgan Hill CA <data.frame [1 × 14]> 17015 Walnut Grove Dr, Morgan Hill, CA 95037  17015
+    ## # ... with 8 more variables: predirectional <chr>, street <chr>, suffix <chr>, formatted_street <chr>, city <chr>,
+    ## #   state <chr>, zip <chr>, country <chr>
 
 ``` r
 data.frame(
@@ -211,17 +105,113 @@ data.frame(
 gio_batch_reverse(to_code)
 ```
 
-    ## $results
-    ##                  query
-    ## 1     35.9746,-77.9658
-    ## 2   32.87937,-96.63039
-    ## 3 33.83371,-117.836232
-    ## 4 35.417124,-80.678476
-    ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                results
-    ## 1   101, 100, 125, State Hwy 58, Alston, Alston, State Hwy 58, N Alston St, S Alston St, Nashville, Nashville, Nashville, Nash County, Nash County, Nash County, NC, NC, NC, 27856, 27856, 27856, US, US, US, NA, N, S, NA, St, St, 101 State Hwy 58, Nashville, NC 27856, 100 N Alston St, Nashville, NC 27856, 125 S Alston St, Nashville, NC 27856, 35.974536, 35.974536, 35.974263, -77.965716, -77.965716, -77.965823, 1, 0.37, 0.36, nearest_street, nearest_street, nearest_street, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau
-    ## 2                          100, 2961, 3084, E, S, S, Kingsley, 1st, 1st, Rd, St, St, E Kingsley Rd, S 1st St, S 1st St, Garland, Garland, Garland, Dallas County, Dallas County, Dallas County, TX, TX, TX, 75041, 75041, 75041, US, US, US, 100 E Kingsley Rd, Garland, TX 75041, 2961 S 1st St, Garland, TX 75041, 3084 S 1st St, Garland, TX 75041, 32.878693, 32.881541, 32.878897, -96.630918, -96.630962, -96.630992, 1, 0.92, 0.87, nearest_street, nearest_street, nearest_street, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau
-    ## 3                     2700, 2566, 2790, N, N, N, Tustin, Tustin, Tustin, St, St, St, N Tustin St, N Tustin St, N Tustin St, Orange, Orange, Orange, Orange County, Orange County, Orange County, CA, CA, CA, 92865, 92865, 92865, US, US, US, 2700 N Tustin St, Orange, CA 92865, 2566 N Tustin St, Orange, CA 92865, 2790 N Tustin St, Orange, CA 92865, 33.832923, 33.830224, 33.834775, -117.836211, -117.836152, -117.836249, 1, 0, 0, nearest_street, nearest_street, nearest_street, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau
-    ## 4 5968, 5500, 450, Village, York, Sportsman, Dr, St, Dr, NW, NW, NA, Village Dr NW, York St NW, Sportsman Dr, Concord, Concord, Concord, Cabarrus County, Cabarrus County, Cabarrus County, NC, NC, NC, 28027, 28027, 28027, US, US, US, 5968 Village Dr NW, Concord, NC 28027, 5500 York St NW, Concord, NC 28027, 450 Sportsman Dr, Concord, NC 28027, 35.413592, 35.41373, 35.420958, -80.675786, -80.67508, -80.675289, 1, 0.92, 0.89, nearest_street, nearest_street, nearest_street, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau, TIGER/Line® dataset from the US Census Bureau
+    ## # A tibble: 12 × 17
+    ##                        formatted_address accuracy  accuracy_type                                        source number
+    ##                                    <chr>    <dbl>          <chr>                                         <chr>  <chr>
+    ## 1  101 State Hwy 58, Nashville, NC 27856     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau    101
+    ## 2   100 N Alston St, Nashville, NC 27856     0.37 nearest_street TIGER/Line® dataset from the US Census Bureau    100
+    ## 3   125 S Alston St, Nashville, NC 27856     0.36 nearest_street TIGER/Line® dataset from the US Census Bureau    125
+    ## 4   100 E Kingsley Rd, Garland, TX 75041     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau    100
+    ## 5       2961 S 1st St, Garland, TX 75041     0.92 nearest_street TIGER/Line® dataset from the US Census Bureau   2961
+    ## 6       3084 S 1st St, Garland, TX 75041     0.87 nearest_street TIGER/Line® dataset from the US Census Bureau   3084
+    ## 7     2700 N Tustin St, Orange, CA 92865     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau   2700
+    ## 8     2566 N Tustin St, Orange, CA 92865     0.00 nearest_street TIGER/Line® dataset from the US Census Bureau   2566
+    ## 9     2790 N Tustin St, Orange, CA 92865     0.00 nearest_street TIGER/Line® dataset from the US Census Bureau   2790
+    ## 10 5968 Village Dr NW, Concord, NC 28027     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau   5968
+    ## 11    5500 York St NW, Concord, NC 28027     0.92 nearest_street TIGER/Line® dataset from the US Census Bureau   5500
+    ## 12   450 Sportsman Dr, Concord, NC 28027     0.89 nearest_street TIGER/Line® dataset from the US Census Bureau    450
+    ## # ... with 12 more variables: street <chr>, formatted_street <chr>, city <chr>, county <chr>, state <chr>, zip <chr>,
+    ## #   country <chr>, predirectional <chr>, suffix <chr>, location_lat <dbl>, location_lng <dbl>, postdirectional <chr>
+
+### Extra Fields
+
+``` r
+gio_geocode("1109 N Highland St, Arlington, VA", fields=c("cd", "stateleg"))
+```
+
+    ## # A tibble: 1 × 24
+    ##                         formatted_address accuracy accuracy_type                     source number predirectional
+    ## *                                   <chr>    <int>         <chr>                      <chr>  <chr>          <chr>
+    ## 1 1109 N Highland St, Arlington, VA 22201        1       rooftop Virginia GIS Clearinghouse   1109              N
+    ## # ... with 18 more variables: street <chr>, suffix <chr>, formatted_street <chr>, city <chr>, county <chr>,
+    ## #   state <chr>, zip <chr>, country <chr>, location_lat <dbl>, location_lng <dbl>, congressional_district_name <chr>,
+    ## #   congressional_district_district_number <int>, congressional_district_congress_number <chr>,
+    ## #   congressional_district_congress_years <chr>, state_legislative_districts_senate_name <chr>,
+    ## #   state_legislative_districts_senate_district_number <chr>, state_legislative_districts_house_name <chr>,
+    ## #   state_legislative_districts_house_district_number <chr>
+
+``` r
+gio_geocode_components("1109 N Highland St", "Arlington", "VA",
+                      fields=c("census", "stateleg"))
+```
+
+    ## # A tibble: 1 × 27
+    ##                         formatted_address accuracy accuracy_type                     source number predirectional
+    ## *                                   <chr>    <int>         <chr>                      <chr>  <chr>          <chr>
+    ## 1 1109 N Highland St, Arlington, VA 22201        1       rooftop Virginia GIS Clearinghouse   1109              N
+    ## # ... with 21 more variables: street <chr>, suffix <chr>, formatted_street <chr>, city <chr>, county <chr>,
+    ## #   state <chr>, zip <chr>, country <chr>, location_lat <dbl>, location_lng <dbl>,
+    ## #   state_legislative_districts_senate_name <chr>, state_legislative_districts_senate_district_number <chr>,
+    ## #   state_legislative_districts_house_name <chr>, state_legislative_districts_house_district_number <chr>,
+    ## #   census_census_year <int>, census_state_fips <chr>, census_county_fips <chr>, census_place_fips <chr>,
+    ## #   census_tract_code <chr>, census_block_code <chr>, census_block_group <chr>
+
+``` r
+gio_reverse(38.9002898, -76.9990361, fields=c("census", "stateleg"))
+```
+
+    ## # A tibble: 3 × 25
+    ##                     formatted_address accuracy  accuracy_type                                        source number
+    ## *                               <chr>    <dbl>          <chr>                                         <chr>  <chr>
+    ## 1   500 H St NE, Washington, DC 20002     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau    500
+    ## 2 800 5th St NE, Washington, DC 20002     0.18 nearest_street TIGER/Line® dataset from the US Census Bureau    800
+    ## 3   474 H St NE, Washington, DC 20002     0.18 nearest_street TIGER/Line® dataset from the US Census Bureau    474
+    ## # ... with 20 more variables: street <chr>, suffix <chr>, postdirectional <chr>, formatted_street <chr>, city <chr>,
+    ## #   county <chr>, state <chr>, zip <chr>, country <chr>, location_lat <dbl>, location_lng <dbl>,
+    ## #   state_legislative_districts_senate_name <chr>, state_legislative_districts_senate_district_number <chr>,
+    ## #   census_census_year <int>, census_state_fips <chr>, census_county_fips <chr>, census_place_fips <chr>,
+    ## #   census_tract_code <chr>, census_block_code <chr>, census_block_group <chr>
+
+``` r
+gio_batch_geocode(addresses, fields=c("cd", "stateleg"))
+```
+
+    ## # A tibble: 5 × 12
+    ##                                      query      response_results                            formatted_address number
+    ## *                                    <chr>                <list>                                        <chr>  <chr>
+    ## 1         1109 N Highland St, Arlington VA <data.frame [1 × 24]>      1109 N Highland St, Arlington, VA 22201   1109
+    ## 2  525 University Ave, Toronto, ON, Canada <data.frame [1 × 13]>              525 University Ave, Toronto, ON    525
+    ## 3     4410 S Highway 17 92, Casselberry FL <data.frame [2 × 22]>     4410 State Rte 17, Casselberry, FL 32707   4410
+    ## 4         15000 NE 24th Street, Redmond WA <data.frame [2 × 24]>          15000 NE 24th St, Redmond, WA 98052  15000
+    ## 5 17015 Walnut Grove Drive, Morgan Hill CA <data.frame [1 × 22]> 17015 Walnut Grove Dr, Morgan Hill, CA 95037  17015
+    ## # ... with 8 more variables: predirectional <chr>, street <chr>, suffix <chr>, formatted_street <chr>, city <chr>,
+    ## #   state <chr>, zip <chr>, country <chr>
+
+``` r
+gio_batch_reverse(to_code, fields=c("census", "stateleg"))
+```
+
+    ## # A tibble: 12 × 28
+    ##                        formatted_address accuracy  accuracy_type                                        source number
+    ##                                    <chr>    <dbl>          <chr>                                         <chr>  <chr>
+    ## 1  101 State Hwy 58, Nashville, NC 27856     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau    101
+    ## 2   100 N Alston St, Nashville, NC 27856     0.37 nearest_street TIGER/Line® dataset from the US Census Bureau    100
+    ## 3   125 S Alston St, Nashville, NC 27856     0.36 nearest_street TIGER/Line® dataset from the US Census Bureau    125
+    ## 4   100 E Kingsley Rd, Garland, TX 75041     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau    100
+    ## 5       2961 S 1st St, Garland, TX 75041     0.92 nearest_street TIGER/Line® dataset from the US Census Bureau   2961
+    ## 6       3084 S 1st St, Garland, TX 75041     0.87 nearest_street TIGER/Line® dataset from the US Census Bureau   3084
+    ## 7     2700 N Tustin St, Orange, CA 92865     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau   2700
+    ## 8     2566 N Tustin St, Orange, CA 92865     0.00 nearest_street TIGER/Line® dataset from the US Census Bureau   2566
+    ## 9     2790 N Tustin St, Orange, CA 92865     0.00 nearest_street TIGER/Line® dataset from the US Census Bureau   2790
+    ## 10 5968 Village Dr NW, Concord, NC 28027     1.00 nearest_street TIGER/Line® dataset from the US Census Bureau   5968
+    ## 11    5500 York St NW, Concord, NC 28027     0.92 nearest_street TIGER/Line® dataset from the US Census Bureau   5500
+    ## 12   450 Sportsman Dr, Concord, NC 28027     0.89 nearest_street TIGER/Line® dataset from the US Census Bureau    450
+    ## # ... with 23 more variables: street <chr>, formatted_street <chr>, city <chr>, county <chr>, state <chr>, zip <chr>,
+    ## #   country <chr>, predirectional <chr>, suffix <chr>, location_lat <dbl>, location_lng <dbl>,
+    ## #   state_legislative_districts_senate_name <chr>, state_legislative_districts_senate_district_number <chr>,
+    ## #   state_legislative_districts_house_name <chr>, state_legislative_districts_house_district_number <chr>,
+    ## #   census_census_year <int>, census_state_fips <chr>, census_county_fips <chr>, census_place_fips <chr>,
+    ## #   census_tract_code <chr>, census_block_code <chr>, census_block_group <chr>, postdirectional <chr>
 
 ### Test Results
 
@@ -232,7 +222,7 @@ library(testthat)
 date()
 ```
 
-    ## [1] "Sun Mar  5 14:40:22 2017"
+    ## [1] "Mon Mar  6 07:21:16 2017"
 
 ``` r
 test_dir("tests/")

man/gio_batch_geocode.Rd

@@ -4,22 +4,32 @@
 \alias{gio_batch_geocode}
 \title{Geocode up to 10,000 addresses}
 \usage{
-gio_batch_geocode(addresses, api_key = gio_auth())
+gio_batch_geocode(addresses, fields, api_key = gio_auth())
 }
 \arguments{
 \item{addresses}{character vector of addresses (10K max)}
 
+\item{fields}{vector of additional fields to return with query results. Note that these
+count as extra lookups and impact your dailu quota/costs. See \href{https://geocod.io/docs/#fields}{the official documentation}
+for more information on costs/pricing. Can be \code{cd}, \code{cd113}, \code{cd114}, or \code{cd115} for
+current or historical Congressional districts (U.S.); \code{stateleg} for State Legislative District (House & Senate, U.S.);
+\code{school} forSchool District (elementary/secondary or unified, U.S.); \code{census} for
+Census Block/Tract & FIPS codes (U.S.) or \code{timezone} for timezone.}
+
 \item{api_key}{\code{geocod.io} API key}
 }
 \description{
 Geocode up to 10,000 addresses
 }
 \examples{
-gio_batch_geocode(c(
+addresses <- c(
   "1109 N Highland St, Arlington VA",
   "525 University Ave, Toronto, ON, Canada",
   "4410 S Highway 17 92, Casselberry FL",
   "15000 NE 24th Street, Redmond WA",
   "17015 Walnut Grove Drive, Morgan Hill CA"
-))
+)
+
+gio_batch_geocode(addresses)
+gio_batch_geocode(addresses, fields=c("cd", "stateleg"))
 }

man/gio_batch_reverse.Rd

@@ -4,11 +4,18 @@
 \alias{gio_batch_reverse}
 \title{Reverse geocode up to 10,000 coordinates}
 \usage{
-gio_batch_reverse(coordinates, api_key = gio_auth())
+gio_batch_reverse(coordinates, fields, api_key = gio_auth())
 }
 \arguments{
 \item{coordinates}{data frame of coordinates with \code{lat} and \code{lon} columns}
 
+\item{fields}{vector of additional fields to return with query results. Note that these
+count as extra lookups and impact your dailu quota/costs. See \href{https://geocod.io/docs/#fields}{the official documentation}
+for more information on costs/pricing. Can be \code{cd}, \code{cd113}, \code{cd114}, or \code{cd115} for
+current or historical Congressional districts (U.S.); \code{stateleg} for State Legislative District (House & Senate, U.S.);
+\code{school} forSchool District (elementary/secondary or unified, U.S.); \code{census} for
+Census Block/Tract & FIPS codes (U.S.) or \code{timezone} for timezone.}
+
 \item{api_key}{\code{geocod.io} API key}
 }
 \description{
@@ -20,4 +27,5 @@ data.frame(
   lon = c(-77.9658000, -96.6303900, -117.8362320, -80.6784760)
 ) -> to_code
 gio_batch_reverse(to_code)
+gio_batch_reverse(to_code, fields=c("census", "stateleg"))
 }

man/gio_geocode.Rd

@@ -4,16 +4,26 @@
 \alias{gio_geocode}
 \title{Geocode a single address}
 \usage{
-gio_geocode(address, api_key = gio_auth())
+gio_geocode(address, fields, api_key = gio_auth())
 }
 \arguments{
 \item{address}{address to geocode}
 
+\item{fields}{vector of additional fields to return with query results. Note that these
+count as extra lookups and impact your dailu quota/costs. See \href{https://geocod.io/docs/#fields}{the official documentation}
+for more information on costs/pricing. Can be \code{cd}, \code{cd113}, \code{cd114}, or \code{cd115} for
+current or historical Congressional districts (U.S.); \code{stateleg} for State Legislative District (House & Senate, U.S.);
+\code{school} forSchool District (elementary/secondary or unified, U.S.); \code{census} for
+Census Block/Tract & FIPS codes (U.S.) or \code{timezone} for timezone.}
+
 \item{api_key}{\code{geocod.io} API key}
 }
 \description{
 Geocode a single address
 }
 \examples{
-gio_geocode("1109 N Highland St, Arlington, VA")
+\dontrun{
+gio_geocode("1109 N Highland St, Arlcdceington, VA")
+gio_geocode("1109 N Highland St, Arlington, VA", fields=c("cd", "stateleg"))
+}
 }

man/gio_geocode_components.Rd

@@ -4,12 +4,19 @@
 \alias{gio_geocode_components}
 \title{Geocode a single address}
 \usage{
-gio_geocode_components(street, city, state, postal_code, country,
+gio_geocode_components(street, city, state, postal_code, country, fields,
   api_key = gio_auth())
 }
 \arguments{
 \item{street, city, state, postal_code, country}{address components}
 
+\item{fields}{vector of additional fields to return with query results. Note that these
+count as extra lookups and impact your dailu quota/costs. See \href{https://geocod.io/docs/#fields}{the official documentation}
+for more information on costs/pricing. Can be \code{cd}, \code{cd113}, \code{cd114}, or \code{cd115} for
+current or historical Congressional districts (U.S.); \code{stateleg} for State Legislative District (House & Senate, U.S.);
+\code{school} forSchool District (elementary/secondary or unified, U.S.); \code{census} for
+Census Block/Tract & FIPS codes (U.S.) or \code{timezone} for timezone.}
+
 \item{api_key}{\code{geocod.io} API key}
 }
 \description{

man/gio_reverse.Rd

@@ -4,11 +4,18 @@
 \alias{gio_reverse}
 \title{Reverse geocode a single lat/lon pair}
 \usage{
-gio_reverse(lat, lon, api_key = gio_auth())
+gio_reverse(lat, lon, fields, api_key = gio_auth())
 }
 \arguments{
 \item{lat, lon}{lat/lon pair to reverse geocode}
 
+\item{fields}{vector of additional fields to return with query results. Note that these
+count as extra lookups and impact your dailu quota/costs. See \href{https://geocod.io/docs/#fields}{the official documentation}
+for more information on costs/pricing. Can be \code{cd}, \code{cd113}, \code{cd114}, or \code{cd115} for
+current or historical Congressional districts (U.S.); \code{stateleg} for State Legislative District (House & Senate, U.S.);
+\code{school} forSchool District (elementary/secondary or unified, U.S.); \code{census} for
+Census Block/Tract & FIPS codes (U.S.) or \code{timezone} for timezone.}
+
 \item{api_key}{\code{geocod.io} API key}
 }
 \description{
@@ -16,4 +23,5 @@ Reverse geocode a single lat/lon pair
 }
 \examples{
 gio_reverse(38.9002898, -76.9990361)
+gio_reverse(38.9002898, -76.9990361, fields=c("census", "stateleg"))
 }