#378

Author: rafapereirabr

r-package/NEWS.md

@@ -1,7 +1,7 @@
 # geobr v1.9.1999999999 dev
 
-- fix url redundancy 
-
+- Now all functions download data from the latest year available, by default.
+- Fix url redundancy to avoid internet connection problems.
 
 # geobr v1.9.1
 

r-package/R/read_amazon.R

@@ -5,7 +5,7 @@
 #' federal law n. 12.651/2012). The original data comes from the Brazilian
 #' Ministry of Environment (MMA) and can be found at "http://mapas.mma.gov.br/i3geo/datadownload.htm".
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2012`.
+#' @template year
 #' @template simplified
 #' @template showProgress
 #' @template cache
@@ -19,7 +19,7 @@
 #' # Read Brazilian Legal Amazon
 #' a <- read_amazon(year = 2012)
 #'
-read_amazon <- function(year = 2012,
+read_amazon <- function(year = NULL,
                         simplified = TRUE,
                         showProgress = TRUE,
                         cache = TRUE){

r-package/R/read_biomes.R

@@ -6,7 +6,7 @@
 #' scale 1:250.000. The 2004 data set is at the scale 1:5.000.000. The original
 #' data comes from IBGE. More information at \url{https://www.ibge.gov.br/apps/biomas/}
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2019`.
+#' @template year
 #' @template simplified
 #' @template showProgress
 #' @template cache
@@ -20,7 +20,7 @@
 #' # Read biomes
 #' b <- read_biomes(year = 2019)
 #'
-read_biomes <- function(year = 2019,
+read_biomes <- function(year = NULL,
                         simplified = TRUE,
                         showProgress = TRUE,
                         cache = TRUE){

r-package/R/read_census_tract.R

@@ -3,12 +3,12 @@
 #' @description
 #' Download spatial data of census tracts of the Brazilian Population Census
 #'
+#' @template year
 #' @param code_tract The 7-digit code of a Municipality. If the two-digit code
 #'         or a two-letter uppercase abbreviation of a state is passed, (e.g. 33
 #'         or "RJ") the function will load all census tracts of that state. If
 #'         `code_tract="all"`, the function downloads all census tracts of the
 #'         country.
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2010`.
 #' @param zone For census tracts before 2010, 'urban' and 'rural' census tracts
 #'             are separate data sets.
 #' @template simplified
@@ -36,8 +36,8 @@
 #' # Read all census tracts of the country at a given year
 #'   c <- read_census_tract(code_tract="all", year=2010)
 #'
-read_census_tract <- function(code_tract,
-                              year = 2010,
+read_census_tract <- function(year = NULL,
+                              code_tract,
                               zone = "urban",
                               simplified = TRUE,
                               showProgress = TRUE,
@@ -50,7 +50,7 @@ read_census_tract <- function(code_tract,
   if (is.null(temp_meta)) { return(invisible(NULL)) }
 
   # Check zone input urban and rural inputs if year <=2007
-  if (year<=2007){
+  if ( temp_meta$year[1] <= 2007){
 
     if (zone == "urban") {message("Using data of Urban census tracts\n")
                           temp_meta <- temp_meta[substr(temp_meta[,3],1,1)== "U", ] }
@@ -96,12 +96,12 @@ read_census_tract <- function(code_tract,
     } else{
 
       # list paths of files to download
-      if (year<=2007 & zone == "urban") {
+      if (temp_meta$year[1] <= 2007 & zone == "urban") {
 
         if (is.numeric(code_tract)){ file_url <- as.character(subset(temp_meta, code==paste0("U",substr(code_tract, 1, 2)))$download_path) }
         if (is.character(code_tract)){ file_url <- as.character(subset(temp_meta, code_abbrev==toupper(substr(code_tract, 1, 2)))$download_path) }
 
-      } else if (year<=2007 & zone == "rural") {
+      } else if (temp_meta$year[1] <= 2007 & zone == "rural") {
 
         if (is.numeric(code_tract)){ file_url <- as.character(subset(temp_meta, code==paste0("R",substr(code_tract, 1, 2)))$download_path) }
         if (is.character(code_tract)){ file_url <- as.character(subset(temp_meta, code_abbrev==toupper(substr(code_tract, 1, 2)))$download_path) }

r-package/R/read_conservation_units.R

@@ -5,7 +5,7 @@
 #' conservation units present in Brazilian territory. The last update of the data
 #' was 09-2019. The original data comes from MMA and can be found at "http://mapas.mma.gov.br/i3geo/datadownload.htm".
 #'
-#' @param date Numeric. Date of the data in YYYYMM format. Defaults to `201909`.
+#' @template date
 #' @template simplified
 #' @template showProgress
 #' @template cache
@@ -19,7 +19,7 @@
 #' # Read conservation_units
 #' b <- read_conservation_units(date = 201909)
 #'
-read_conservation_units <- function(date = 201909,
+read_conservation_units <- function(date = NULL,
                                     simplified = TRUE,
                                     showProgress = TRUE,
                                     cache = TRUE){

r-package/R/read_country.R

@@ -3,7 +3,7 @@
 #' @description
 #' Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674).
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2010`.
+#' @template year
 #' @template simplified
 #' @template showProgress
 #' @template cache
@@ -17,7 +17,7 @@
 #' # Read specific year
 #' br <- read_country(year = 2018)
 #'
-read_country <- function(year = 2010,
+read_country <- function(year = NULL,
                          simplified = TRUE,
                          showProgress = TRUE,
                          cache = TRUE){

r-package/R/read_disaster_risk_area.R

@@ -11,7 +11,7 @@
 #' 'num'). Original data were generated by IBGE and CEMADEN. For more information
 #' about the methodology, see deails at \url{https://www.ibge.gov.br/geociencias/organizacao-do-territorio/tipologias-do-territorio/21538-populacao-em-areas-de-risco-no-brasil.html}
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2010`.
+#' @template year
 #' @template simplified
 #' @template showProgress
 #' @template cache
@@ -25,7 +25,7 @@
 #' # Read all disaster risk areas in an specific year
 #' d <- read_disaster_risk_area(year=2010)
 #'
-read_disaster_risk_area <- function(year = 2010,
+read_disaster_risk_area <- function(year = NULL,
                                     simplified = TRUE,
                                     showProgress = TRUE,
                                     cache = TRUE){

r-package/R/read_health_facilities.R

@@ -17,8 +17,7 @@
 #' `year_update`. More information in the CNES data set available at \url{https://dados.gov.br/}.
 #' These data use Geodetic reference system "SIRGAS2000" and CRS(4674).
 #'
-#' @param date Numeric. Date of the data in YYYYMM format. Defaults to `202303`,
-#'        which was the latest data available by the time of this update.
+#' @template date
 #' @template showProgress
 #' @template cache
 #'
@@ -31,7 +30,7 @@
 #' # Read all health facilities of the whole country
 #' h <- read_health_facilities( date = 202303)
 #'
-read_health_facilities <- function(date = 202303,
+read_health_facilities <- function(date = NULL,
                                    showProgress = TRUE,
                                    cache = TRUE){
 

r-package/R/read_health_region.R

@@ -7,8 +7,7 @@
 #' few municipalities because they are generally more technology intensive, costly and face
 #' shortages of specialized professionals. A macro region comprises one or more health regions.
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2013`, the
-#'        latest available.
+#' @template year
 #' @param macro Logic. If `FALSE` (default), the function downloads health
 #'        regions data. If `TRUE`, the function downloads macro regions data.
 #' @template simplified
@@ -27,7 +26,7 @@
 #' # Read all macro health regions
 #' mhr <- read_health_region( year=2013, macro =TRUE)
 #'
-read_health_region <- function(year = 2013,
+read_health_region <- function(year = NULL,
                                macro = FALSE,
                                simplified = TRUE,
                                showProgress = TRUE,

r-package/R/read_immediate_region.R

@@ -4,7 +4,7 @@
 #' The Immediate Geographic Areas are part of the geographic division of Brazil created in 2017 by IBGE. These regions
 #' were created to replace the "Micro Regions" division. Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2019`.
+#' @template year
 #' @param code_immediate 6-digit code of an immediate region. If the two-digit
 #'        code or a two-letter uppercase abbreviation of a state is passed, (e.g.
 #'        33 or "RJ") the function will load all immediate regions of that state.
@@ -31,8 +31,8 @@
 #'   im <- read_immediate_region()
 #'   im <- read_immediate_region(code_immediate="all")
 #'
-read_immediate_region <- function(code_immediate = "all",
-                                  year = 2019,
+read_immediate_region <- function(year = NULL,
+                                  code_immediate = "all",
                                   simplified = TRUE,
                                   showProgress = TRUE,
                                   cache = TRUE){

r-package/R/read_indigenous_land.R

@@ -8,7 +8,7 @@
 #' updated monthly, the geobr package will only keep the data for a few months
 #' per year.
 #'
-#' @param date Numeric. Date of the data in YYYYMM format. Defaults to `201907`.
+#' @template date
 #' @template simplified
 #' @template showProgress
 #' @template cache
@@ -22,7 +22,7 @@
 #' # Read all indigenous land in an specific date
 #' i <- read_indigenous_land(date=201907)
 #'
-read_indigenous_land <- function(date = 201907,
+read_indigenous_land <- function(date = NULL,
                                  simplified = TRUE,
                                  showProgress = TRUE,
                                  cache = TRUE){

r-package/R/read_intermediate_region.R

@@ -6,7 +6,7 @@
 #' "Meso Regions" division. Data at scale 1:250,000, using Geodetic reference
 #' system "SIRGAS2000" and CRS(4674)
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2019`.
+#' @template year
 #' @param code_intermediate 4-digit code of an intermediate region. If the
 #'        two-digit code or a two-letter uppercase abbreviation of a state is
 #'        passed, (e.g. 33 or "RJ") the function will load all intermediate
@@ -33,8 +33,8 @@
 #'   im <- read_intermediate_region()
 #'   im <- read_intermediate_region(code_intermediate="all")
 #'
-read_intermediate_region <- function(code_intermediate = "all",
-                                     year = 2019,
+read_intermediate_region <- function(year = NULL,
+                                     code_intermediate = "all",
                                      simplified = TRUE,
                                      showProgress = TRUE,
                                      cache = TRUE){

r-package/R/read_meso_region.R

@@ -3,7 +3,7 @@
 #' @description
 #' Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2010`.
+#' @template year
 #' @param code_meso The 4-digit code of a meso region. If the two-digit code or
 #'        a two-letter uppercase abbreviation of a state is passed, (e.g. 33 or
 #'        "RJ") the function will load all meso regions of that state. If
@@ -29,8 +29,8 @@
 #' # Read all meso regions of the country at a given year
 #'   meso <- read_meso_region(code_meso="all", year=2010)
 #'
-read_meso_region <- function(code_meso = "all",
-                             year = 2010,
+read_meso_region <- function(year = NULL,
+                             code_meso = "all",
                              simplified = TRUE,
                              showProgress = TRUE,
                              cache = TRUE){

r-package/R/read_metro_area.R

@@ -8,7 +8,7 @@
 #' generated by Institute of Geography. Data at scale 1:250,000, using Geodetic
 #' reference system "SIRGAS2000" and CRS(4674).
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2018`.
+#' @template year
 #' @param code_state The two-digit code of a state or a two-letter uppercase
 #'                   abbreviation (e.g. 33 or "RJ"). If `code_state="all"` (the
 #'                   default), the function downloads all states.
@@ -26,7 +26,7 @@
 #'   m <- read_metro_area(2005)
 #'
 #'   m <- read_metro_area(2018)
-read_metro_area <- function(year = 2018,
+read_metro_area <- function(year = NULL,
                             code_state = "all",
                             simplified = TRUE,
                             showProgress = TRUE,

r-package/R/read_micro_region.R

@@ -3,7 +3,7 @@
 #' @description
 #' Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2010`.
+#' @template year
 #' @param code_micro 5-digit code of a micro region. If the two-digit code or a
 #'        two-letter uppercase abbreviation of a state is passed, (e.g. 33 or
 #'        "RJ") the function will load all micro regions of that state. If
@@ -29,8 +29,8 @@
 #' # Read all micro regions at a given year
 #'   micro <- read_micro_region(code_micro="all", year=2010)
 #'
-read_micro_region <- function(code_micro = "all",
-                              year = 2010,
+read_micro_region <- function(year = NULL,
+                              code_micro = "all",
                               simplified = TRUE,
                               showProgress = TRUE,
                               cache = TRUE){

r-package/R/read_municipal_seat.R

@@ -6,7 +6,7 @@
 #' seats for various years between 1872 and 2010. Original data were generated by
 #' Brazilian Institute of Geography and Statistics (IBGE).
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2010`.
+#' @template year
 #' @template showProgress
 #' @template cache
 #'
@@ -19,7 +19,7 @@
 #' # Read municipal seats in an specific year
 #' m <- read_municipal_seat(year = 1991)
 #'
-read_municipal_seat <- function(year = 2010,
+read_municipal_seat <- function(year = NULL,
                                 showProgress = TRUE,
                                 cache = TRUE){
 

r-package/R/read_municipality.R

@@ -3,7 +3,7 @@
 #' @description
 #' Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674).
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2010`.
+#' @template year
 #' @param code_muni The 7-digit identification code of a municipality. If
 #'        `code_muni = "all"` (Default), the function downloads all
 #'        municipalities of the country. Alternatively, if a two-digit
@@ -34,8 +34,8 @@
 #' # Read all municipalities of the country at a given year
 #' mun <- read_municipality(code_muni = "all", year = 2018)
 #'
-read_municipality <- function(code_muni = "all",
-                              year = 2010,
+read_municipality <- function(year = NULL,
+                              code_muni = "all",
                               simplified = TRUE,
                               showProgress = TRUE,
                               cache = TRUE,
@@ -56,13 +56,13 @@ read_municipality <- function(code_muni = "all",
            code_muni %in% temp_meta$code |
            substring(code_muni, 1, 2) %in% temp_meta$code |
            code_muni %in% temp_meta$code_abbrev |
-           (year < 1992 & temp_meta$code %in% "mu")
+           (temp_meta$year[1] < 1992 & temp_meta$code %in% "mu")
   )) {
     stop("Error: Invalid Value to argument code_muni.")
   }
 
   # get file url
-  if (code_muni=="all" | year < 1992) {
+  if (code_muni=="all" | temp_meta$year[1] < 1992) {
     file_url <- as.character(temp_meta$download_path)
 
   } else if (is.numeric(code_muni)) { # if using numeric code_muni

r-package/R/read_neighborhood.R

@@ -5,7 +5,7 @@
 #' It is based on aggregations of the census tracts from the Brazilian
 #' census. Only 2010 data is currently available.
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2010`.
+#' @template year
 #' @template simplified
 #' @template showProgress
 #' @template cache
@@ -19,7 +19,7 @@
 #' # Read neighborhoods of Brazilian municipalities
 #' n <- read_neighborhood(year=2010)
 #'
-read_neighborhood <- function(year = 2010,
+read_neighborhood <- function(year = NULL,
                               simplified = TRUE,
                               showProgress = TRUE,
                               cache = TRUE){

r-package/R/read_pop_arrangements.R

@@ -6,7 +6,7 @@
 #' Geography and Statistics (IBGE)  For more information about the methodology,
 #' see details at \url{https://www.ibge.gov.br/apps/arranjos_populacionais/2015/pdf/publicacao.pdf}
 #'
-#' @param year Numeric. Year of the data in YYYY format. Defaults to `2015`.
+#' @template year
 #' @template simplified
 #' @template showProgress
 #' @template cache
@@ -20,7 +20,7 @@
 #' # Read urban footprint of Brazilian cities in an specific year
 #' uc <- read_pop_arrangements(year=2015)
 #'
-read_pop_arrangements <- function(year = 2015,
+read_pop_arrangements <- function(year = NULL,
                                   simplified = TRUE,
                                   showProgress = TRUE,
                                   cache = TRUE){