diff --git a/R/stl_homicides.R b/R/stl_homicides.R index ff4ccc7..ba9c8dd 100644 --- a/R/stl_homicides.R +++ b/R/stl_homicides.R @@ -1,6 +1,6 @@ #' Homicides in the City of St. Louis, 2008 - 2018 #' -#' An example data set containing the addresses for Homicides reported by the Saint Louis Metropolitan Police Department +#' An example data set containing the addresses for homicides reported by the Saint Louis Metropolitan Police Department #' #' @docType data #' @@ -9,8 +9,8 @@ #' @format A tibble with 1822 rows and 6 variables: #' \describe{ #' \item{street_address}{number, street and street suffix where homicide occured} -#' \item{year}{year homicide occured} -#' \item{date}{data homicide occured} +#' \item{year}{year homicide occurred} +#' \item{date}{data homicide occurred} #' \item{state}{state abbreviation of location, in these data, all "MO"} #' \item{postal_code}{zipcode/postal code of location, in these data all NA} #' \item{city}{city of location, in these data all "St. Louis"} @@ -26,7 +26,7 @@ #' Homicides in the City of St. Louis July, 2018 #' -#' An example data set containing the addresses for Homicides reported by the Saint Louis Metropolitan Police Department +#' An example data set containing the addresses for homicides reported by the Saint Louis Metropolitan Police Department #' #' @docType data #' @@ -35,8 +35,8 @@ #' @format A tibble with 24 rows and 6 variables: #' \describe{ #' \item{street_address}{number, street and street suffix where homicide occured} -#' \item{year}{year homicide occured} -#' \item{date}{data homicide occured} +#' \item{year}{year homicide occurred} +#' \item{date}{data homicide occurred} #' \item{state}{state abbreviation of location, in these data, all "MO"} #' \item{postal_code}{zipcode/postal code of location, in these data all NA} #' \item{city}{city of location, in these data all "St. Louis"} diff --git a/cran-comments.md b/cran-comments.md index c031834..18d2728 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -14,5 +14,20 @@ This is the initial version of the `censusxy` package. ## R CMD check results There were no ERRORs, WARNINGs, or NOTEs with local or CI checks. +There was one NOTE on winbuilder checks: + +* checking CRAN incoming feasibility ... NOTE +Maintainer: 'Christopher Prener ' + +New submission + +Possibly mis-spelled words in DESCRIPTION: + Geocoder (2:38) + geocoded (15:73) + geocoding (10:72, 12:58, 14:31) + vectorized (12:26) + +The four words identified are all spelled correctly. + ## Reverse dependencies Not applicable. diff --git a/docs/articles/censusxy.html b/docs/articles/censusxy.html index 7cb30b2..8ed3286 100644 --- a/docs/articles/censusxy.html +++ b/docs/articles/censusxy.html @@ -126,12 +126,12 @@

Usage

-

This package contains a single exported function, cxy_geocode(). The only required arguments are .data for the data.frame or tibble containing address data, and address specifying the column name containing street addresses. The function supports non-standard evaluation, meaning you do not need to quote arguments for column names.

-
results <- cxy_geocode(stl_homicides, address = street_address)
-

However, it is highly recommended that you include city, state and zip code as well. Doing so will increase speed and accuracy significantly. The homicide deta contain city and state data as well, so the preferred call for these data would be:

-
results <- cxy_geocode(stl_homicides, address = street_address, city = city, state = state)
+

This package contains a single exported function, cxy_geocode(). The only required arguments are .data for the data.frame or tibble containing address data, and address specifying the column name containing street addresses. The function supports non-standard evaluation, meaning you do not need to quote arguments for column names.

+
results <- cxy_geocode(stl_homicides, address = street_address)
+

However, it is highly recommended that you include city, state and zip code as well. Doing so will increase speed and accuracy significantly. The homicide data contain city and state data as well, so the preferred call for these data would be:

+
results <- cxy_geocode(stl_homicides, address = street_address, city = city, state = state)

Finally, two output types are supported. By default, a tibble is returned (output = "tibble") with a minimal set of variables that describe the accuracy of a given observation’s geocoding (style = "minimal"). A complete set of values returned by the API for each observation can be obtained by using style = "full". Alternatively, an sf object can be returned with the geocoded data projected using the WGS 1984 geographic coordinate system:

-
homicide_sf <- cxy_geocode(stl_homicides, id, street_address, city, state, postal_code, output = "sf")
+
homicide_sf <- cxy_geocode(stl_homicides, id, street_address, city, state, postal_code, output = "sf")

Note, however, that it returns only matched addresses, including those approximated by street length. If there are unmatched addresses, they will be dropped from the output. Use output = "tibble" to return all addresses, including those that are unmatched.

Output returned as an sf object can be previewed with a package like mapview:

> mapview::mapview(homicide_sf)
diff --git a/man/stl_homicides.Rd b/man/stl_homicides.Rd index 1dd68b4..6c1e146 100644 --- a/man/stl_homicides.Rd +++ b/man/stl_homicides.Rd @@ -7,8 +7,8 @@ \format{A tibble with 1822 rows and 6 variables: \describe{ \item{street_address}{number, street and street suffix where homicide occured} - \item{year}{year homicide occured} - \item{date}{data homicide occured} + \item{year}{year homicide occurred} + \item{date}{data homicide occurred} \item{state}{state abbreviation of location, in these data, all "MO"} \item{postal_code}{zipcode/postal code of location, in these data all NA} \item{city}{city of location, in these data all "St. Louis"} @@ -20,7 +20,7 @@ data(stl_homicides) } \description{ -An example data set containing the addresses for Homicides reported by the Saint Louis Metropolitan Police Department +An example data set containing the addresses for homicides reported by the Saint Louis Metropolitan Police Department } \examples{ str(stl_homicides) diff --git a/man/stl_homicides_small.Rd b/man/stl_homicides_small.Rd index a057825..f33d3e3 100644 --- a/man/stl_homicides_small.Rd +++ b/man/stl_homicides_small.Rd @@ -7,8 +7,8 @@ \format{A tibble with 24 rows and 6 variables: \describe{ \item{street_address}{number, street and street suffix where homicide occured} - \item{year}{year homicide occured} - \item{date}{data homicide occured} + \item{year}{year homicide occurred} + \item{date}{data homicide occurred} \item{state}{state abbreviation of location, in these data, all "MO"} \item{postal_code}{zipcode/postal code of location, in these data all NA} \item{city}{city of location, in these data all "St. Louis"} @@ -20,7 +20,7 @@ data(stl_homicides_small) } \description{ -An example data set containing the addresses for Homicides reported by the Saint Louis Metropolitan Police Department +An example data set containing the addresses for homicides reported by the Saint Louis Metropolitan Police Department } \examples{ str(stl_homicides_small) diff --git a/vignettes/censusxy.Rmd b/vignettes/censusxy.Rmd index 4821b2f..62c661a 100644 --- a/vignettes/censusxy.Rmd +++ b/vignettes/censusxy.Rmd @@ -50,7 +50,7 @@ This package contains a single exported function, `cxy_geocode()`. The only requ results <- cxy_geocode(stl_homicides, address = street_address) ``` -However, it is highly recommended that you include city, state and zip code as well. Doing so will increase speed and accuracy significantly. The homicide deta contain city and state data as well, so the preferred call for these data would be: +However, it is highly recommended that you include city, state and zip code as well. Doing so will increase speed and accuracy significantly. The homicide data contain city and state data as well, so the preferred call for these data would be: ```r results <- cxy_geocode(stl_homicides, address = street_address, city = city, state = state)