Updates to man for try() in CRAN failures

R/get_census_api_2.R

@@ -22,8 +22,8 @@
 #'  If unsuccessful, function prints the URL query that was constructed.
 #'
 #' @examples
-#' \dontrun{get_census_api_2(data_url = "https://api.census.gov/data/2010/dec/sf1?", key = "...", 
-#' get = c("P005003","P005004","P005005", "P005006"), region = "for=county:*&in=state:34")}
+#' \dontrun{try(get_census_api_2(data_url = "https://api.census.gov/data/2010/dec/sf1?", key = "...", 
+#' get = c("P005003","P005004","P005005", "P005006"), region = "for=county:*&in=state:34"))}
 #' 
 #' @references
 #' Based on code authored by Nicholas Nagle, which is available 

R/merge_names.R

@@ -64,7 +64,7 @@
 #' @importFrom dplyr coalesce
 #' @examples
 #' data(voters)
-#' \dontrun{merge_names(voters, namesToUse = "surname", census.surname = TRUE)}
+#' \dontrun{try(merge_names(voters, namesToUse = "surname", census.surname = TRUE))}
 #' @keywords internal
 merge_names <- function(voter.file, namesToUse, census.surname, table.surnames = NULL, table.first = NULL, table.middle = NULL, clean.names = TRUE, impute.missing = FALSE, model = "BISG") {
 

R/merge_surnames.R

@@ -50,7 +50,7 @@
 #'#'
 #' @examples
 #' data(voters)
-#' \dontrun{merge_surnames(voters)}
+#' \dontrun{try(merge_surnames(voters))}
 #'
 #' @keywords internal
 

R/predict_race.R

@@ -107,24 +107,28 @@
 #'
 #' @examples
 #' data(voters)
-#' predict_race(voter.file = voters, surname.only = TRUE)
+#' try(predict_race(voter.file = voters, surname.only = TRUE))
 #' \dontrun{
-#' predict_race(voter.file = voters, census.geo = "tract", census.key = "...")
+#' try(predict_race(voter.file = voters, census.geo = "tract", census.key = "..."))
 #' }
 #' \dontrun{
-#' predict_race(voter.file = voters, census.geo = "place", census.key = "...", year = "2020")
+#' try(predict_race(
+#'   voter.file = voters, census.geo = "place", census.key = "...", year = "2020"))
 #' }
 #' \dontrun{
-#' CensusObj <- get_census_data("...", state = c("NY", "DC", "NJ"))
-#' predict_race(voter.file = voters, census.geo = "tract", census.data = CensusObj, party = "PID")
+#' CensusObj <- try(get_census_data("...", state = c("NY", "DC", "NJ")))
+#' try(predict_race(
+#'   voter.file = voters, census.geo = "tract", census.data = CensusObj, party = "PID")
+#'   )
 #' }
 #' \dontrun{
-#' CensusObj2 <- get_census_data(key = "...", state = c("NY", "DC", "NJ"), age = T, sex = T)
-#' predict_race(voter.file = voters, census.geo = "tract", census.data = CensusObj2, age = T, sex = T)
+#' CensusObj2 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), age = T, sex = T))
+#' try(predict_race(
+#'   voter.file = voters, census.geo = "tract", census.data = CensusObj2, age = T, sex = T))
 #' }
 #' \dontrun{
-#' CensusObj3 <- get_census_data(key = "...", state = c("NY", "DC", "NJ"), census.geo = "place")
-#' predict_race(voter.file = voters, census.geo = "place", census.data = CensusObj3)
+#' CensusObj3 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), census.geo = "place"))
+#' try(predict_race(voter.file = voters, census.geo = "place", census.data = CensusObj3))
 #' }
 #' @export
 

README.md

@@ -105,7 +105,7 @@ predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = cen
 predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE)  # Pr(Race | Surname, Tract, Party)
 ```
 
-Or you can also use the census_geo_api() to maually construct a census object. The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. Note that this function has the input parameter 'state' that requires a two-letter state abbreviation to proceed.
+Or you can also use the census_geo_api() to manually construct a census object. The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. Note that this function has the input parameter 'state' that requires a two-letter state abbreviation to proceed.
 
 ``` r
 censusObj2  = list()