Added note on memoise and other edits to prep for v1.0.5 release

Author: jessecambon

NEWS.md

@@ -1,8 +1,8 @@
 # tidygeocoder (development version)
 
 - Corrected documentation for `quiet` parameter in `geo()` and `reverse_geo()`
-- Reconfigured the vignette so that it is precomputed and is not executed during
-`R CMD check` (ie. `devtools::check()`). Also added a check at the beginning of the vignette to check for the availability of APIs. This fixes an issue that occurred on Mac CRAN checks ([#152](https://github.com/jessecambon/tidygeocoder/issues/152)).
+- To fix an issue that occurred on Mac CRAN checks ([#152](https://github.com/jessecambon/tidygeocoder/issues/152)) the vignette is now precomputed so that it does not run during `R CMD check` (ie. `devtools::check()`).
+- Changed some default parameter values to facilitate the use of the [memoise package](https://memoise.r-lib.org/) ([@dpprdan](https://github.com/dpprdan), [#154](https://github.com/jessecambon/tidygeocoder/pull/154)).
 
 # tidygeocoder 1.0.4
 

README.Rmd

@@ -26,7 +26,7 @@ options(tibble.print_min = 5, tibble.print_max = 5)
 [![CRAN Downloads Per Month](http://cranlogs.r-pkg.org/badges/tidygeocoder)](https://cran.r-project.org/package=tidygeocoder)
 [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
 [![R Build Status](https://github.com/jessecambon/tidygeocoder/workflows/R-CMD-check/badge.svg)](https://github.com/jessecambon/tidygeocoder/actions?workflow=R-CMD-check)
-[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.5574263.svg)](https://doi.org/10.5281/zenodo.5574263)
+[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.5627341.svg)](https://doi.org/10.5281/zenodo.5627341)
 <!-- badges: end -->
 
 Tidygeocoder makes getting data from geocoding services easy. A unified high-level interface is provided for a selection of [supported geocoding services](https://jessecambon.github.io/tidygeocoder/articles/geocoder_services.html) and results are returned in [tibble](https://tibble.tidyverse.org/) (dataframe) format.
@@ -45,13 +45,13 @@ In addition to the usage examples below, see the [Getting Started Vignette](http
 
 To install the stable version from CRAN (the official R package servers):
 
-```{r, eval=F}
+```{r, eval = FALSE}
 install.packages('tidygeocoder')
 ```
 
 Alternatively, you can install the latest development version from GitHub:
 
-```{r, eval=F}
+```{r, eval = FALSE}
 devtools::install_github("jessecambon/tidygeocoder")
 ```
 
@@ -61,11 +61,10 @@ In this first example we will geocode a few addresses using the `geocode()` func
 
 ```{r}
 library(dplyr, warn.conflicts = FALSE)
-library(tibble)
 library(tidygeocoder)
 
 # create a dataframe with addresses
-some_addresses <- tribble(
+some_addresses <- tibble::tribble(
 ~name,                  ~addr,
 "White House",          "1600 Pennsylvania Ave NW, Washington, DC",
 "Transamerica Pyramid", "600 Montgomery St, San Francisco, CA 94111",     
@@ -87,16 +86,16 @@ Now that we have the longitude and latitude coordinates, we can use ggplot to pl
 
 ```{r usamap}
 library(ggplot2)
-library(maps)
-library(ggrepel)
 
 ggplot(lat_longs, aes(longitude, latitude), color = "grey99") +
   borders("state") + geom_point() +
-  geom_label_repel(aes(label = name)) +
+  ggrepel::geom_label_repel(aes(label = name)) +
   theme_void()
 ```
 
-To perform reverse geocoding (obtaining addresses from geographic coordinates), we can use the `reverse_geocode()` function. The arguments are similar to the `geocode()` function, but now we specify the input data columns with the `lat` and `long` arguments. The dataset used here is from the geocoder query above. The single line address is returned in a column named by the `address` argument and all columns from the geocoding service are returned because `full_results = TRUE`. See the `reverse_geo()` function documentation for more details.
+To perform reverse geocoding (obtaining addresses from geographic coordinates), we can use the `reverse_geocode()` function. The arguments are similar to the `geocode()` function, but now we specify the input data columns with the `lat` and `long` arguments. The  input dataset used here is from the geocoder query above. 
+
+The single line address is returned in a column named by the `address` argument and all columns from the geocoding service results are returned because `full_results = TRUE`. See the `reverse_geo()` function documentation for more details.
 
 <!-- 
 Removing the licence column is done just to prevent a note from 
@@ -117,11 +116,12 @@ knitr::kable(reverse)
 
 For inspiration, here are a few articles (with code) that leverage tidygeocoder:
 
-- [Exercises: Spatial Data Wrangling with sf](http://www2.stat.duke.edu/courses/Spring21/sta323.001/exercises/lec_12.html) - part of a [statistical computing course](http://www2.stat.duke.edu/courses/Spring21/sta323.001/) at Duke.
-- [Geocoding the Minard Map](https://www.jla-data.net/eng/minard-map-tidygeocoder/) - recreating a famous infographic with geocoding.
-- [Mapping a network of women in demography](https://www.monicaalexander.com/posts/2021-21-02-mapping/) - using rvest and tidygeocoder to map Google Scholar data.
-- [Road Routing in R](https://www.jla-data.net/eng/routing-in-r-context/) - demonstration of three different routing APIs.
-- [Mapping Texas Ports With R](https://www.sharpsightlabs.com/blog/mapping-texas-ports-with-r-part1/) - mapping the Texas coast with rnaturalearth and sf.
+- [Exercises: Spatial Data Wrangling with sf](http://www2.stat.duke.edu/courses/Spring21/sta323.001/exercises/lec_12.html) - part of a [statistical computing course](http://www2.stat.duke.edu/courses/Spring21/sta323.001/) at Duke
+- [Geocoding the Minard Map](https://www.jla-data.net/eng/minard-map-tidygeocoder/) - recreating a famous infographic with geocoding
+- [Mapping a network of women in demography](https://www.monicaalexander.com/posts/2021-21-02-mapping/) - using rvest and tidygeocoder to map Google Scholar data
+- [Mapping Routes](https://bensstats.wordpress.com/2021/10/21/robservations-15-i-reverse-engineered-atlas-co-well-some-of-it/) - mapping routes with tidygeocoder and osrm
+- [Road Routing in R](https://www.jla-data.net/eng/routing-in-r-context/) - demonstration of three different routing APIs
+- [Mapping Texas Ports With R](https://www.sharpsightlabs.com/blog/mapping-texas-ports-with-r-part1/) - mapping the Texas coast with rnaturalearth and sf
 
 ## Contributing
 

README.md

@@ -17,7 +17,7 @@ Month](http://cranlogs.r-pkg.org/badges/tidygeocoder)](https://cran.r-project.or
 stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
 [![R Build
 Status](https://github.com/jessecambon/tidygeocoder/workflows/R-CMD-check/badge.svg)](https://github.com/jessecambon/tidygeocoder/actions?workflow=R-CMD-check)
-[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.5574263.svg)](https://doi.org/10.5281/zenodo.5574263)
+[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.5627341.svg)](https://doi.org/10.5281/zenodo.5627341)
 <!-- badges: end -->
 
 Tidygeocoder makes getting data from geocoding services easy. A unified
@@ -66,11 +66,10 @@ In this first example we will geocode a few addresses using the
 
 ``` r
 library(dplyr, warn.conflicts = FALSE)
-library(tibble)
 library(tidygeocoder)
 
 # create a dataframe with addresses
-some_addresses <- tribble(
+some_addresses <- tibble::tribble(
 ~name,                  ~addr,
 "White House",          "1600 Pennsylvania Ave NW, Washington, DC",
 "Transamerica Pyramid", "600 Montgomery St, San Francisco, CA 94111",     
@@ -81,7 +80,7 @@ some_addresses <- tribble(
 lat_longs <- some_addresses %>%
   geocode(addr, method = 'osm', lat = latitude , long = longitude)
 #> Passing 3 addresses to the Nominatim single address geocoder
-#> Query completed in: 3.6 seconds
+#> Query completed in: 3 seconds
 ```
 
 The `geocode()` function geocodes addresses contained in a dataframe.
@@ -103,12 +102,10 @@ ggplot to plot our addresses on a map.
 
 ``` r
 library(ggplot2)
-library(maps)
-library(ggrepel)
 
 ggplot(lat_longs, aes(longitude, latitude), color = "grey99") +
   borders("state") + geom_point() +
-  geom_label_repel(aes(label = name)) +
+  ggrepel::geom_label_repel(aes(label = name)) +
   theme_void()
 ```
 
@@ -117,11 +114,13 @@ ggplot(lat_longs, aes(longitude, latitude), color = "grey99") +
 To perform reverse geocoding (obtaining addresses from geographic
 coordinates), we can use the `reverse_geocode()` function. The arguments
 are similar to the `geocode()` function, but now we specify the input
-data columns with the `lat` and `long` arguments. The dataset used here
-is from the geocoder query above. The single line address is returned in
-a column named by the `address` argument and all columns from the
-geocoding service are returned because `full_results = TRUE`. See the
-`reverse_geo()` function documentation for more details.
+data columns with the `lat` and `long` arguments. The input dataset used
+here is from the geocoder query above.
+
+The single line address is returned in a column named by the `address`
+argument and all columns from the geocoding service results are returned
+because `full_results = TRUE`. See the `reverse_geo()` function
+documentation for more details.
 
 <!-- 
 Removing the licence column is done just to prevent a note from 
@@ -139,9 +138,9 @@ reverse <- lat_longs %>%
 
 | name                 | latitude |  longitude | address\_found                                                                                                                                         | place\_id | osm\_type |   osm\_id | osm\_lat           | osm\_lon            | office      | house\_number | road                          | city          | state                | postcode | country       | country\_code | boundingbox                                          | tourism              | neighbourhood | county        | suburb |
 |:---------------------|---------:|-----------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------|----------:|:----------|----------:|:-------------------|:--------------------|:------------|:--------------|:------------------------------|:--------------|:---------------------|:---------|:--------------|:--------------|:-----------------------------------------------------|:---------------------|:--------------|:--------------|:-------|
-| White House          | 38.89770 |  -77.03655 | White House, 1600, Pennsylvania Avenue Northwest, Washington, District of Columbia, 20500, United States                                               | 159859857 | way       | 238241022 | 38.897699700000004 | -77.03655315        | White House | 1600          | Pennsylvania Avenue Northwest | Washington    | District of Columbia | 20500    | United States | us            | 38.8974908 , 38.897911 , -77.0368537, -77.0362519    | NA                   | NA            | NA            | NA     |
-| Transamerica Pyramid | 37.79520 | -122.40279 | Transamerica Pyramid, 600, Montgomery Street, Chinatown, San Francisco, San Francisco City and County, San Francisco, California, 94111, United States | 106590650 | way       |  24222973 | 37.795200550000004 | -122.40279267840137 | NA          | 600           | Montgomery Street             | San Francisco | California           | 94111    | United States | us            | 37.7948854 , 37.7954472 , -122.4031399, -122.4024317 | Transamerica Pyramid | Chinatown     | San Francisco | NA     |
-| Willis Tower         | 41.87535 |  -87.63576 | South Wacker Drive, Printer’s Row, Loop, Chicago, Cook County, Illinois, 60606, United States                                                          | 182316972 | way       | 337681342 | 41.8753503         | -87.6357587         | NA          | NA            | South Wacker Drive            | Chicago       | Illinois             | 60606    | United States | us            | 41.8749718 , 41.8757997 , -87.6361005, -87.6354602   | NA                   | Printer’s Row | Cook County   | Loop   |
+| White House          | 38.89770 |  -77.03655 | White House, 1600, Pennsylvania Avenue Northwest, Washington, District of Columbia, 20500, United States                                               | 159983331 | way       | 238241022 | 38.897699700000004 | -77.03655315        | White House | 1600          | Pennsylvania Avenue Northwest | Washington    | District of Columbia | 20500    | United States | us            | 38.8974908 , 38.897911 , -77.0368537, -77.0362519    | NA                   | NA            | NA            | NA     |
+| Transamerica Pyramid | 37.79520 | -122.40279 | Transamerica Pyramid, 600, Montgomery Street, Chinatown, San Francisco, San Francisco City and County, San Francisco, California, 94111, United States | 106008002 | way       |  24222973 | 37.795200550000004 | -122.40279267840137 | NA          | 600           | Montgomery Street             | San Francisco | California           | 94111    | United States | us            | 37.7948854 , 37.7954472 , -122.4031399, -122.4024317 | Transamerica Pyramid | Chinatown     | San Francisco | NA     |
+| Willis Tower         | 41.87535 |  -87.63576 | South Wacker Drive, Printer’s Row, Loop, Chicago, Cook County, Illinois, 60606, United States                                                          | 182238096 | way       | 337681342 | 41.8753503         | -87.6357587         | NA          | NA            | South Wacker Drive            | Chicago       | Illinois             | 60606    | United States | us            | 41.8749718 , 41.8757997 , -87.6361005, -87.6354602   | NA                   | Printer’s Row | Cook County   | Loop   |
 
 ## In the Wild
 
@@ -152,19 +151,22 @@ tidygeocoder:
     sf](http://www2.stat.duke.edu/courses/Spring21/sta323.001/exercises/lec_12.html) -
     part of a [statistical computing
     course](http://www2.stat.duke.edu/courses/Spring21/sta323.001/) at
-    Duke.
+    Duke
 -   [Geocoding the Minard
     Map](https://www.jla-data.net/eng/minard-map-tidygeocoder/) -
-    recreating a famous infographic with geocoding.
+    recreating a famous infographic with geocoding
 -   [Mapping a network of women in
     demography](https://www.monicaalexander.com/posts/2021-21-02-mapping/) -
-    using rvest and tidygeocoder to map Google Scholar data.
+    using rvest and tidygeocoder to map Google Scholar data
+-   [Mapping
+    Routes](https://bensstats.wordpress.com/2021/10/21/robservations-15-i-reverse-engineered-atlas-co-well-some-of-it/) -
+    mapping routes with tidygeocoder and osrm
 -   [Road Routing in
     R](https://www.jla-data.net/eng/routing-in-r-context/) -
-    demonstration of three different routing APIs.
+    demonstration of three different routing APIs
 -   [Mapping Texas Ports With
     R](https://www.sharpsightlabs.com/blog/mapping-texas-ports-with-r-part1/) -
-    mapping the Texas coast with rnaturalearth and sf.
+    mapping the Texas coast with rnaturalearth and sf
 
 ## Contributing