examples running out of CRAN

Author: macataci

R/aggregate_climate.R

@@ -12,7 +12,7 @@
 #' @param frequency character with the aggregation frequency: (\code{"day"},
 #'  \code{"month"} or \code{"year"}).
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' \donttest{
 #' lat <- c(4.172817, 4.172817, 4.136050, 4.136050, 4.172817)
 #' lon <- c(-74.749121, -74.686169, -74.686169, -74.749121, -74.749121)

R/divipola.R

@@ -5,7 +5,7 @@
 #' codification includes individual codes for each department and municipality
 #' following the political and administrative division.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' divipola <- divipola_table()
 #'
 #' @return \code{data.frame} object with DIVIPOLA table.
@@ -24,7 +24,7 @@ divipola_table <- function() {
 #'
 #' @param department_name character vector with the names of the departments.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' dptos <- c("Tolima", "Huila", "Amazonas")
 #' name_to_code_dep(dptos)
 #'
@@ -78,7 +78,7 @@ name_to_code_dep <- function(department_name) {
 #' @param municipality_name character vector with the names of the
 #' municipalities.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' dptos <- c("Huila", "Antioquia")
 #' mpios <- c("Pitalito", "Turbo")
 #' name_to_code_mun(dptos, mpios)
@@ -179,7 +179,7 @@ retrieve_code <- function(input_token, fixed_tokens, codes_list) {
 #' @param department_code character vector with the DIVIPOLA codes of the
 #' departments.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' dptos <- c("73", "05", "11")
 #' code_to_name_dep(dptos)
 #'
@@ -213,7 +213,7 @@ code_to_name_dep <- function(department_code) {
 #' @param municipality_code character vector with the DIVIPOLA codes of the
 #' municipalities.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' mpios <- c("73001", "11001", "05615")
 #' code_to_name_mun(mpios)
 #'
@@ -244,7 +244,7 @@ code_to_name_mun <- function(municipality_code) {
 #'
 #' @param department_name character vector with the names to be translated.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' dptos <- c("Bogota DC", "San Andres")
 #' name_to_standard_dep(dptos)
 #'
@@ -268,7 +268,7 @@ name_to_standard_dep <- function(department_name) {
 #' departments containing the municipalities.
 #' @param municipality_name character vector with the names to be translated.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' dptos <- c("Bogota", "Tolima")
 #' mpios <- c("Bogota DC", "CarmendeApicala")
 #' name_to_standard_mun(dptos, mpios)

R/download_climate.R

@@ -14,7 +14,7 @@
 #' @param tag character containing climate tag to consult. Please use
 #' \code{cliamte_tags()} to check IDEAM tags.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' \donttest{
 #' ptpm <- download_climate("73148", "2021-11-14", "2021-11-20", "PTPM_CON")
 #' head(ptpm)
@@ -72,7 +72,7 @@ download_climate <- function(code, start_date, end_date, tag) {
 #' \code{"YYYY-MM-DD"}. (Last available date is \code{"2023-05-31"}).
 #' @param tag character containing climate tag to consult.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' \donttest{
 #' lat <- c(4.172817, 4.172817, 4.136050, 4.136050, 4.172817)
 #' lon <- c(-74.749121, -74.686169, -74.686169, -74.749121, -74.749121)
@@ -119,7 +119,7 @@ download_climate_geom <- function(geometry, start_date, end_date, tag) {
 #' @importFrom magrittr %>%
 #' @importFrom rlang .data
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' \donttest{
 #' lat <- c(4.172817, 4.172817, 4.136050, 4.136050, 4.172817)
 #' lon <- c(-74.749121, -74.686169, -74.686169, -74.749121, -74.749121)
@@ -213,7 +213,7 @@ download_climate_stations <- function(stations, start_date, end_date, tag) {
 #' @param geometry \code{sf} object containing the geometry for a given ROI.
 #' The geometry can be either a \code{POLYGON} or \code{MULTIPOLYGON}.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' \donttest{
 #' lat <- c(5.166278, 5.166278, 4.982247, 4.982247, 5.166278)
 #' lon <- c(-75.678072, -75.327859, -75.327859, -75.678072, -75.678072)

R/download_demographic.R

@@ -8,7 +8,7 @@
 #' \code{list_datasets("demographic", "EN")} or
 #' \code{list_datasets("demographic", "ES")} to check available datasets.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' house_under_15 <- download_demographic("DANE_CNPVH_2018_1HD")
 #' head(house_under_15)
 #'

R/download_geospatial.R

@@ -29,7 +29,7 @@
 #' @param include_cnpv logical for including (or not) CNPV demographic and
 #' socioeconomic information. Default is \code{TRUE}.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' \donttest{
 #' departments <- download_geospatial("department")
 #' head(departments)

R/download_population_projections.R

@@ -23,7 +23,7 @@
 #' @param include_ethnic logical for including (or not) division by ethnic
 #' group (only available for \code{"municipality"}). Default is \code{FALSE}.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' pop_proj <- download_pop_projections("national", 2020, 2030)
 #' head(pop_proj)
 #'

R/merge_geo_demographic.R

@@ -15,7 +15,7 @@
 #' lighter but less precise, and are recommended for easier applications like
 #' plots. Default is \code{TRUE}.
 #'
-#' @examples
+#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
 #' \donttest{
 #' merged <- merge_geo_demographic("DANE_CNPVV_2018_9VD", TRUE)
 #' head(merged)