Nothing
R/download.R
In datazoom.amazonia: Simplify Access to Data from the Amazon Region
Defines functions
external_download
sidra_download
sidra_download <- function(sidra_code = NULL, year, geo_level = "municipality",
classific = "all", category = "all") {
## Bind Global Variables
code_state <- NULL
code_meso <- NULL
code_micro <- NULL
# Obs: Sometimes there are non-catched municipalieis - user should check on IBGE SIDRA
# f = geo %>% filter(!(code_muni %in% unique(dat_uf$`Município (Código)`)))
# ----------------------------------------------------
## Download from Sidra IBGE
## Include Progress Bar
## Omit Warnings
## We should include support for microregion/mesoregion
# ------------------------------------------------------
##############################
## Setting Basic Parameters ##
##############################
param <- list()
param$sidra_code <- sidra_code
param$year <- year
param$classific <- classific
param$category <- category
if (geo_level == "country") {
param$geo_reg <- "Brazil"
}
if (geo_level == "region") {
param$geo_reg <- "Region"
}
if (geo_level == "state") {
param$geo_reg <- "State"
}
if (geo_level == "municipality") {
param$geo_reg <- "City"
}
### SPECIAL CASES: POPULATION FOR YEARS 2007 AND 2010
### CONTAGEM DA POPULACAO AND CENSO DEMOGRAFICO
if (param$sidra_code == 6579) {
if (year == 2007) param$sidra_code <- 793 # https://sidra.ibge.gov.br/tabela/793
if (year == 2010) {
param$sidra_code <- 1378 # https://sidra.ibge.gov.br/tabela/1378
param$classific <- "c1"
param$category <- list(0)
}
}
if (param$sidra_code == 6907) {
param$classific <- c("c12443")
param$category <- list(110056)
}
##################################
## Get Geographical Information ##
##################################
geo <- datazoom.amazonia::municipalities %>%
tidyr::drop_na() # 5 municipalities have no micro code
# uf = list('RO' = 11,'AC' = 12,'AM' = 13,'RR' = 14,'PA' = 15,'AP' = 16,'TO' = 17,
# 'MA' = 21,'PI' = 22,'CE' = 23,'RN' = 24,'PB' = 25,'PE' = 26,'AL' = 27,'SE' = 28,'BA' = 29,
# 'MG' = 31,'ES' = 32,'RJ' = 33,'SP' = 35,
# 'PR' = 41,'SC' = 42,'RS' = 43,
# 'MS' = 50,'MT' = 51,'GO' = 52,'DF' = 53)
###############
## Load Data ##
###############
get_sidra_safe <- purrr::safely(sidrar::get_sidra)
if (param$geo_reg %in% c("Brazil", "Region", "State")) {
base::message(base::cat("Downloading Data at the", param$geo_reg, "level")) ## Show Message
## Download
dat <- suppressMessages(
get_sidra_safe(
x = param$sidra_code,
geo = param$geo_reg,
period = as.character(param$year),
classific = param$classific,
category = param$category
)
)
dat <- dat$result
dat <- dat %>%
tibble::as_tibble() %>%
janitor::clean_names()
if (nrow(dat) == 0) {
stop("Error in Download.")
}
base::message(base::cat("Download Succesfully Completed!"))
return(dat)
}
if (param$geo_reg == "City") {
##############################
## Download at the UF Level ##
##############################
uf_list <- geo %>%
dplyr::select(code_state) %>%
unlist() %>%
unique() %>%
as.list()
names(uf_list) <- uf_list
dat_raw_uf <- purrr::map(uf_list, function(uf) {
base::message(base::cat(which(uf == uf_list), "in", length(uf_list), "states...\n"))
suppressMessages(
get_sidra_safe(
x = param$sidra_code,
geo = param$geo_reg,
period = as.character(param$year),
geo.filter = list("State" = uf),
classific = param$classific,
category = param$category
)
)
})
dat_mod_uf <- base::lapply(dat_raw_uf, "[[", 1)
dat_uf <- dat_mod_uf[unlist(lapply(dat_mod_uf, is.data.frame))] %>% ## Filter for only found dataframes
dplyr::bind_rows() %>%
tibble::as_tibble() %>%
janitor::clean_names()
###########################################
## Checking for Completeness of Download ##
###########################################
missed_uf <- dat_mod_uf[!unlist(lapply(dat_mod_uf, is.data.frame))] %>% names()
rm(dat_mod_uf)
if (length(missed_uf) > 0) {
base::message(base::cat(
"Download at the State Level Completed!", length(missed_uf), "failed.\n",
"Attempting to Download at the Mesoregion Level..."
))
} else if (length(missed_uf) == 0) {
base::message(base::cat("Download Succesfully Completed!"))
return(dat_uf)
}
#################
## Meso Region ##
#################
if (length(missed_uf) > 0) {
geo_meso <- geo %>%
dplyr::filter(code_state %in% missed_uf)
meso_reg_list <- geo_meso %>%
dplyr::select(code_meso) %>%
unlist() %>%
unique() %>%
as.list()
names(meso_reg_list) <- meso_reg_list
dat_raw_meso <- purrr::map(meso_reg_list, function(meso_reg) {
base::message(base::cat(which(meso_reg == meso_reg_list), "in", length(meso_reg_list), "mesoregions...\n"))
base::suppressMessages(
get_sidra_safe(
x = param$sidra_code,
geo = param$geo_reg,
period = as.character(param$year),
geo.filter = list("MesoRegion" = meso_reg),
classific = param$classific,
category = param$category
)
)
})
dat_mod_meso <- base::lapply(dat_raw_meso, "[[", 1)
dat_meso <- dat_mod_meso[unlist(lapply(dat_mod_meso, is.data.frame))] %>% ## Filter for only found dataframes
dplyr::bind_rows() %>%
tibble::as_tibble() %>%
janitor::clean_names()
dat_uf <- dat_uf %>%
dplyr::bind_rows(dat_meso)
###########################################
## Checking for Completeness of Download ##
###########################################
missed_meso <- dat_mod_meso[!unlist(lapply(dat_mod_meso, is.data.frame))] %>% names()
rm(dat_mod_meso)
if (length(missed_meso) > 0) {
base::message(base::cat(
"Download at the Mesoregion Level Completed!", length(missed_meso), "failed.\n",
"Attempting to Download at the Microregion Level...\n"
))
} else if (length(missed_meso) == 0) {
base::message(base::cat("Download Succesfully Completed!"))
return(dat_uf)
}
##################
## Micro Region ##
##################
if (length(missed_meso) > 0) {
geo_micro <- geo %>%
dplyr::filter(code_meso %in% missed_meso)
micro_reg_list <- geo_micro %>%
dplyr::select(code_micro) %>%
unlist() %>%
unique() %>%
as.list()
names(micro_reg_list) <- micro_reg_list
dat_raw_micro <- purrr::map(micro_reg_list, function(micro_reg) {
base::message(base::cat(which(micro_reg == micro_reg_list), "in", length(micro_reg_list), "microregions...\n"))
base::suppressMessages(
get_sidra_safe(
x = param$sidra_code,
geo = param$geo_reg,
period = as.character(param$year),
geo.filter = list("MicroRegion" = micro_reg),
classific = param$classific,
category = param$category
)
)
})
dat_mod_micro <- base::lapply(dat_raw_micro, "[[", 1)
dat_micro <- dat_mod_micro[unlist(lapply(dat_mod_micro, is.data.frame))] %>% ## Filter for only found dataframes
dplyr::bind_rows() %>%
tibble::as_tibble() %>%
janitor::clean_names()
dat_uf <- dat_uf %>%
dplyr::bind_rows(dat_micro)
###########################################
## Checking for Completeness of Download ##
###########################################
missed_micro <- dat_mod_micro[!unlist(lapply(dat_mod_micro, is.data.frame))] %>% names()
rm(dat_mod_micro)
if (length(missed_micro) > 0) {
base::message(base::cat(
length(missed_micro),
"missed API requests at the Microregion level.
Please report this problem to package developers..."
))
}
if (length(missed_micro) == 0) {
base::message(base::cat("Download Succesfully Completed!"))
return(dat_uf)
}
} # End of if Meso
} # End of if Uf
} # End of If - Download at the Municipality Level
}
external_download <- function(dataset = NULL, source = NULL, year = NULL,
geo_level = NULL, coords = NULL, dataset_code = NULL,
sheet = NULL, skip_rows = NULL, file_name = NULL,
state = NULL) {
## Bind Global Variables
link <- NULL
## Define Basic Parameters
param <- list()
param$dataset <- dataset
param$source <- source
# Optional parameters for functions that need them:
param$year <- year # if download is perform separately by year
param$geo_level <- geo_level # if some geo_levels have a different download link
param$coords <- coords
param$dataset_code <- dataset_code
param$skip_rows <- skip_rows # number of rows to skip atop a spreadsheet
param$file_name <- file_name
param$sheet <- sheet # which sheet of a .xlsx to read
param$state <- state
if (is.null(param$skip_rows)) param$skip_rows <- 0 # makes it more error-proof
#####################
## Construct Links ##
#####################
## Pull URL from datasets_link
param$url <- datasets_link(
source = param$source,
dataset = param$dataset,
url = TRUE
)
# For most sources, the URL in datasets_link is already the URL needed for the download
path <- param$url
## Filling in URLs
# Some URLs are in the form www.data/$year$_$dataset$.csv, where expression
# surrounded by $ are placeholders. The code below subs them in for actual parameters
if (!is.null(param$year)) {
path <- path %>%
stringr::str_replace("\\$year\\$", as.character(param$year))
}
if (!is.null(param$state)) {
path <- path %>%
stringr::str_replace("\\$state\\$", param$state)
}
if (!is.null(param$file_name)) {
path <- path %>%
stringr::str_replace("\\$file_name\\$", param$file_name)
}
# Below are the exceptions, for which manipulation is needed
##### Exceptions only #####
# If the datasets_link URL is the download path you need,
# do not change this section for a new function
## MapBiomas
# Download path depends on dataset and geo_level
if (source == "mapbiomas") {
if (dataset %in% c("mapbiomas_cover", "mapbiomas_transition")) {
if (param$geo_level == "state") {
path <- "https://brasil.mapbiomas.org/wp-content/uploads/sites/4/2023/08/TABELA-GERAL-MAPBIOMAS-COL8.0-BIOMASxESTADOS-1.xlsx"
}
if (param$geo_level == "municipality") {
path <- "https://storage.googleapis.com/mapbiomas-public/initiatives/brasil/collection_8/downloads/statistics/tabela_geral_mapbiomas_col8_biomas_municipios.xlsx"
}
}
}
## SEEG
# Download path depends on geo_level
if (source == "seeg") {
if (geo_level == "municipality") {
path <- "https://drive.google.com/u/0/uc?confirm=bhfS&id=1rUc6H8BVKT9TH-ri6obzHVt7WI1eGUzd"
}
}
## TerraClimate
# Download path depends on geographical parameters
if (source == "terraclimate") {
filename <- paste0(
"agg_terraclimate_",
param$dataset_code,
"_1958_CurrentYear_GLOBE.nc"
)
path <- paste0(
param$url,
"/",
filename,
"?",
"&var=",
param$dataset_code,
"&south=",
param$coords$lat_min,
"&north=",
param$coords$lat_max,
"&west=",
param$coords$lon_min,
"&east=",
param$coords$lon_max,
"&horizStride=1",
"&time_start=",
param$year$initial_time,
"&time_end=",
param$year$final_time,
"&timeStride=1",
"&disableProjSubset=on&addLatLon=true&accept=netcdf"
)
}
#######################
## Initiate Download ##
#######################
## Specify file extension to be passed to tempfile()
# For most functions, the file extension is automatically detected
file_extension <- sub(".*\\.", ".", path) %>%
tolower()
##### Exceptions only #####
# Only manually input the file_extension if the download_path does
# not end in ".ext", where .ext is any file extension
# googledrive links do not contain the file extension, for example
if (source %in% c("seeg", "iema", "ips")) {
file_extension <- ".xlsx"
}
if (source == "prodes") {
file_extension <- ".txt"
}
if (source == "terraclimate") {
file_extension <- ".nc"
}
if (source == "deter") {
file_extension <- ".zip"
}
if (source == "ibama") {
if (dataset == "embargoed_areas") {
file_extension <- ".zip"
} else {
file_extension <- ".csv"
}
}
if (source == "imazon") {
file_extension <- ".rds"
}
if (source == "epe") {
if (param$dataset == "national_energy_balance") {
file_extension <- ".csv"
}
}
if (source == "aneel") {
if (dataset == "energy_development_budget") {
file_extension <- ".rds"
}
if (dataset == "energy_generation") {
file_extension <- ".xlsx"
}
if (dataset == "energy_enterprises_distributed") {
file_extension <- ".csv"
}
}
## Define Empty Directory and Files For Download
dir <- tempdir()
temp <- tempfile(fileext = file_extension, tmpdir = dir)
## Picking the way to download the file
download_method <- "standard" # works for most functions
if (source %in% c("iema", "imazon")) {
download_method <- "googledrive"
}
if (source == "aneel") {
if (dataset == "energy_development_budget") {
download_method <- "googledrive"
}
if (dataset == "energy_enterprises_distributed") {
message("This may take a while.\n")
options(timeout = 1000) # increase timeout limit
}
}
if (source == "epe") {
if (dataset == "national_energy_balance") {
download_method <- "googledrive"
}
}
if (source %in% c("deter", "terraclimate", "baci", "sigmine", "mapbiomas")) {
download_method <- "curl"
quiet <- FALSE
}
if (source == "datasus") {
download_method <- "curl"
quiet <- TRUE
}
if (source == "ibama") {
download_method <- "curl"
options(download.file.method = "curl", download.file.extra = "-k -L") # https://stackoverflow.com/questions/69716835/turning-ssl-verification-off-inside-download-file
quiet <- TRUE
}
if (source == "seeg") {
if (geo_level == "municipality") {
download_method <- "googledrive"
}
}
## Downloading file by the selected method
if (download_method == "standard") {
utils::download.file(url = path, destfile = temp, mode = "wb")
}
if (download_method == "curl") {
utils::download.file(url = path, destfile = temp, method = "curl", quiet = quiet)
}
if (download_method == "googledrive") {
message("Please follow the steps from `googledrive` package to download the data. This may take a while.\nIn case of authentication errors, run vignette(\"GOOGLEDRIVE\").")
googledrive::drive_download(path, path = temp, overwrite = TRUE)
}
## Unzipping if the file is zipped
if (file_extension == ".zip") {
utils::unzip(temp, exdir = dir)
}
###############
## Load Data ##
###############
##### Exceptions only #####
if (file_extension == ".zip") {
if (param$dataset == "degrad") {
dat <- sf::read_sf(file.path(dir, param$file_name))
dat$year <- param$year
}
if (param$source == "deter") {
if (param$dataset == "deter_amz") {
dat <- sf::read_sf(file.path(dir, "deter-amz-deter-public.shp"))
}
if (param$dataset == "deter_cerrado") {
dat <- sf::read_sf(file.path(dir, "deter_public.shp"))
}
}
if (param$source == "sigmine") {
dat <- sf::read_sf(file.path(dir, "BRASIL.shp"))
}
if (param$source == "ibama") {
# get latest downloaded file (the name changes daily)
file <- file.info(list.files(dir, pattern = "rel_areas_embargadas_.*.xls"))
file <- file[with(file, order(as.POSIXct(mtime))), ]
file <- rownames(file)
doc <- XML::htmlParse(file.path(dir, file), encoding = "UTF-8")
tableNode <- XML::getNodeSet(doc, "//table")
dataset <- XML::readHTMLTable(tableNode[[1]])
colnames(dataset) <- dataset[5, ]
dat <- dataset[-c(1:5), ]
}
if (param$source == "baci") {
# as year can be a vector, sets up expressions of the form "*YYYY_V202201.csv" for each year to match file names
file_expression <- paste0("*", param$year, "_V202201.csv")
# now turning into *XXXX_V202201.csv|YYYY_V202201.csv|ZZZZ_V202201.csv" to match as regex
file_expression <- paste0(file_expression, collapse = "|")
file <- list.files(dir, pattern = file_expression, full.names = TRUE) %>%
as.list()
# now reads each file
dat <- lapply(file, data.table::fread, header = TRUE, sep = ",")
# each data frame in the list is named after the corresponding year
names(dat) <- param$year
}
}
if (param$source == "epe" & param$dataset == "energy_consumption_per_class") {
# param$sheet contains the selected sheets
# Making a list with all the sheets
dat <- purrr::imap(
param$sheet,
function(sheets, number) {
base::message(
paste0("Reading sheet ", number, " out of ", length(param$sheet), " (", sheets, ")")
)
base::suppressMessages(
readxl::read_xls(temp, sheet = sheets)
)
}
)
names(dat) <- param$sheet
}
if (param$source == "aneel") {
if (param$dataset == "energy_enterprises_distributed") {
dat <- data.table::fread(temp, encoding = "Latin-1")
} else if (dataset == "energy_generation") {
dat <- readxl::read_xlsx(temp, sheet = param$sheet, skip = param$skip_rows, na = c("-", ""))
}
}
if (param$source == "ips") {
dat <- param$sheet %>%
purrr::map(
~ readxl::read_xlsx(temp, sheet = .)
)
}
## Now the rest of the functions
# This Depends on Data Type (.csv, .shp, ...) and on the data source
else {
if (file_extension == ".csv") {
dat <- data.table::fread(temp)
}
if (file_extension == ".txt") {
dat <- readr::read_csv(temp)
}
if (file_extension == ".nc") {
dat <- terra::rast(temp)
}
if (file_extension == ".rds") {
dat <- readr::read_rds(temp)
}
if (file_extension == ".xlsx") {
dat <- readxl::read_xlsx(temp, sheet = param$sheet, skip = param$skip_rows)
}
if (file_extension == ".dbc") {
dat <- read.dbc(temp)
}
}
##############################
## Excluding Temporary File ##
##############################
# Folder is kept
if (file_extension != ".nc") {
unlink(temp)
}
#################
## Return Data ##
#################
return(dat)
}
datasets_link <- function(source = NULL, dataset = NULL, url = FALSE) {
survey <- NULL
link <- tibble::tribble(
~survey, ~dataset, ~sidra_code, ~available_time, ~available_geo, ~link,
########################
## Environmental data ##
########################
## PRODES
"prodes", "deforestation", NA, "2000-2022", "Municipality", "http://www.dpi.inpe.br/prodesdigital/tabelatxt.php?ano=2022&estado=&ordem=MUNICIPIO&type=tabela&output=txt&",
## DETER
"deter", "deter_amz", NA, "2016-2022", "Municipality", "http://terrabrasilis.dpi.inpe.br/file-delivery/download/deter-amz/shape",
"deter", "deter_cerrado", NA, "2018-2022", "Municipality", "http://terrabrasilis.dpi.inpe.br/file-delivery/download/deter-cerrado/shape",
## DEGRAD
"degrad", "degrad", NA, "2007-2016", NA, "http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/degrad/arquivos/degrad$year$_final_shp.zip",
## Imazon
"imazon", "imazon_shp", NA, "2020", "Municipality", "https://docs.google.com/uc?export=download&id=1JHc2J_U8VXHVuWVsi8wVBnNzZ37y1ehv",
## IBAMA
"ibama", "embargoed_areas", NA, NA, "Municipality", "https://servicos.ibama.gov.br/ctf/publico/areasembargadas/downloadListaAreasEmbargadas.php",
"ibama", "distributed_fines", NA, NA, "Municipality", "https://dadosabertos.ibama.gov.br/dados/SICAFI/$state$/Quantidade/multasDistribuidasBensTutelados.csv",
"ibama", "collected_fines", NA, NA, "Municipality", "https://dadosabertos.ibama.gov.br/dados/SICAFI/$state$/Arrecadacao/arrecadacaobenstutelados.csv",
## MapBiomas
"mapbiomas", "mapbiomas_cover", NA, "1985-2019", "Municipality, State", "https://brasil.mapbiomas.org/estatisticas/",
"mapbiomas", "mapbiomas_transition", NA, "1985-2019", "Municipality, State", "https://brasil.mapbiomas.org/estatisticas/",
"mapbiomas", "mapbiomas_deforestation_regeneration", NA, "1988-2017", "Municipality", "https://storage.googleapis.com/mapbiomas-public/initiatives/brasil/collection_8/downloads/tabela_desmatamento_vegetacao_secundaria_mapbiomas_col8.xlsx",
"mapbiomas", "mapbiomas_irrigation", NA, "2000-2019", "State, Biome", "https://mapbiomas-br-site.s3.amazonaws.com/downloads/Estatisticas%20/Colecao_7_Irrigacao_Biomes_UF.xlsx",
"mapbiomas", "mapbiomas_mining", NA, "1985-2020", "Municipality, Indigenous_Land", "https://brasil.mapbiomas.org/wp-content/uploads/sites/4/2023/09/TABELA-MINERACAO-MAPBIOMAS-COL8.0.xlsx",
"mapbiomas", "mapbiomas_fire", NA, "1985-2020", "State", "https://mapbiomas-br-site.s3.amazonaws.com/downloads/MB-Fogo-2-Biome-State.xlsx",
"mapbiomas", "mapbiomas_water", NA, "1985-2022", "State, Municipality, Biome", "https://mapbiomas-br-site.s3.amazonaws.com/Estat%C3%ADsticas/Estatisticas_Superficie%C3%81gua_Col2_SITE.xlsx",
## TerraClimate
"terraclimate", "max_temperature", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "min_temperature", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "wind_speed", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "vapor_pressure_deficit", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "vapor_pressure", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "snow_water_equivalent", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "shortwave_radiation_flux", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "soil_moisture", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "runoff", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "precipitation", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "potential_evaporation", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "climatic_water_deficit", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "water_evaporation", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "palmer_drought_severity_index", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
## SEEG
"seeg", "seeg", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_farming", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_industry", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_energy", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_land", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_residuals", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
## Censo Agro
"censoagro", "agricultural_land_area", "263", "1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "agricultural_area_use", "264", "1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "agricultural_employees_tractors", "265", "1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "agricultural_producer_condition", "280", "1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "animal_production", "281", "1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "animal_products", "282", "1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "vegetable_production_area", "283", "1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "vegetable_production_permanent", "1730", "1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "vegetable_production_temporary", "1731", "1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "livestock_production", "6907", "2017", "Municipality", "https://sidra.ibge.gov.br/tabela/6907",
#################
## Social data ##
#################
## IPS
"ips", "all", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "life_quality", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "sanit_habit", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "violence", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "educ", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "communic", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "mortality", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "deforest", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
## DATASUS
"datasus", "datasus_sim_do", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DORES/$file_name$",
"datasus", "datasus_sim_dofet", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DOFET/$file_name$",
"datasus", "datasus_sim_doext", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DOFET/$file_name$",
"datasus", "datasus_sim_doinf", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DOFET/$file_name$",
"datasus", "datasus_sim_domat", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DOFET/$file_name$",
"datasus", "datasus_cnes_lt", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/LT/$file_name$",
"datasus", "datasus_cnes_st", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/ST/$file_name$",
"datasus", "datasus_cnes_dc", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/DC/$file_name$",
"datasus", "datasus_cnes_eq", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/EQ/$file_name$",
"datasus", "datasus_cnes_sr", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/SR/$file_name$",
"datasus", "datasus_cnes_hb", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/HB/$file_name$",
"datasus", "datasus_cnes_pf", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/PF/$file_name$",
"datasus", "datasus_cnes_ep", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/EP/$file_name$",
"datasus", "datasus_cnes_rc", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/RC/$file_name$",
"datasus", "datasus_cnes_in", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/IN/$file_name$",
"datasus", "datasus_cnes_ee", NA, "2007-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/EE/$file_name$",
"datasus", "datasus_cnes_ef", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/EF/$file_name$",
"datasus", "datasus_cnes_gm", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/GM/$file_name$",
"datasus", "datasus_sih", NA, "2008-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIHSUS/200801_/dados/$file_name$",
## IEMA
"iema", "iema", NA, "2018", "Municipality", "https://drive.google.com/uc?export=download&id=10JMRtzu3k95vl8cQmHkVMQ9nJovvIeNl",
## Population
"population", "population", "6579", "2001-2021", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/estimapop/tabelas",
###################
## Economic data ##
###################
## Comex
"comex", "comex_export_prod", NA, "1997-2023", NA, "https://balanca.economia.gov.br/balanca/bd/comexstat-bd/ncm/EXP_$year$.csv",
"comex", "comex_import_prod", NA, "1997-2023", NA, "https://balanca.economia.gov.br/balanca/bd/comexstat-bd/ncm/IMP_$year$.csv",
"comex", "comex_export_mun", NA, "1997-2023", NA, "https://balanca.economia.gov.br/balanca/bd/comexstat-bd/mun/EXP_$year$_MUN.csv",
"comex", "comex_import_mun", NA, "1997-2023", NA, "https://balanca.economia.gov.br/balanca/bd/comexstat-bd/mun/IMP_$year$_MUN.csv",
## BACI
"baci", "HS92", NA, "1995-2020", "Country", "http://www.cepii.fr/DATA_DOWNLOAD/baci/data/baci_HS92_V202201.zip",
## PIB-Munic
"pibmunic", "pibmunic", "5938", "2002-2020", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pib-munic/tabelas",
## CEMPRE
"cempre", "cempre", "6449", "2006-2020", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/cempre/tabelas",
## PAM
"pam", "all_crops", "5457/all/all", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "permanent_crops", "1613/all/all", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "temporary_crops", "1612/all/all", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "corn", "839/all/all", "2003-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "potato", "1001/all/all", "2003-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "peanut", "1000/all/all", "2003-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "beans", "1002/all/all", "2003-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
# Categories within temporary crops
"pam", "temporary_total", "1612/c81/0", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "pineapple", "1612/c81/2688", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "alfafa", "1612/c81/40471", "1974-1987", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cotton_herbaceous", "1612/c81/2689", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "garlic", "1612/c81/2690", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "peanut_temporary", "1612/c81/2691", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "rice", "1612/c81/2692", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "oats", "1612/c81/2693", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sweet_potato", "1612/c81/2694", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "potato_temporary", "1612/c81/2695", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sugar_cane", "1612/c81/2696", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "forage_cane", "1612/c81/40470", "1974-1987", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "onion", "1612/c81/2697", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "rye", "1612/c81/2698", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "barley", "1612/c81/2699", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "pea", "1612/c81/2700", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "broad_bean", "1612/c81/2701", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "beans_temporary", "1612/c81/2702", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "tobacco", "1612/c81/2703", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sunflower_seeds", "1612/c81/109179", "2005-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "jute_fiber", "1612/c81/2704", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "linen_seeds", "1612/c81/2705", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "malva_fiber", "1612/c81/2706", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "castor_bean", "1612/c81/2707", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cassava", "1612/c81/2708", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "watermelon", "1612/c81/2709", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "melon", "1612/c81/2710", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "corn_temporary", "1612/c81/2711", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "ramie_fiber", "1612/c81/2712", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "soybean", "1612/c81/2713", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sorghum", "1612/c81/2714", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "tomato", "1612/c81/2715", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "wheat", "1612/c81/2716", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "triticale", "1612/c81/109180", "2005-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
# Categories within permanent crops
"pam", "permanent_total", "1613/c82/0", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "avocado", "1613/c82/2717", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cotton_arboreo", "1613/c82/2718", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "acai", "1613/c82/45981", "2015-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "olive", "1613/c82/2719", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "banana", "1613/c82/2720", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "rubber_coagulated_latex", "1613/c82/2721", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "rubber_liquid_latex", "1613/c82/40472", "1981-1987", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cocoa_beans", "1613/c82/2722", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coffee_total", "1613/c82/2723", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coffee_arabica", "1613/c82/31619", "2012-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coffee_canephora", "1613/c82/31620", "2012-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cashew", "1613/c82/40473", "1974-1987", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "khaki", "1613/c82/2724", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cashew_nut", "1613/c82/2725", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "india_tea", "1613/c82/2726", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coconut", "1613/c82/2727", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coconut_bunch", "1613/c82/2728", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "yerba_mate", "1613/c82/2729", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "fig", "1613/c82/2730", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "guava", "1613/c82/2731", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "guarana_seeds", "1613/c82/2732", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "orange", "1613/c82/2733", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "lemon", "1613/c82/2734", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "apple", "1613/c82/2735", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "papaya", "1613/c82/2736", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "mango", "1613/c82/2737", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "passion_fruit", "1613/c82/2738", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "quince", "1613/c82/2739", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "walnut", "1613/c82/2740", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "heart_of_palm", "1613/c82/90001", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "pear", "1613/c82/2741", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "peach", "1613/c82/2742", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "black_pepper", "1613/c82/2743", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sisal_or_agave", "1613/c82/2744", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "tangerine", "1613/c82/2745", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "tung", "1613/c82/2746", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "annatto_seeds", "1613/c82/2747", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "grape", "1613/c82/2748", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
## PEVS
"pevs", "pevs_forest_crops", "289", "1986-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pevs/tabelas/brasil/2019",
"pevs", "pevs_silviculture", "291", "1986-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pevs/tabelas/brasil/2019",
"pevs", "pevs_silviculture_area", "5930", "2013-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pevs/tabelas/brasil/2019",
## PPM
"ppm", "ppm_livestock_inventory", "3939", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
"ppm", "ppm_sheep_farming", "95", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
"ppm", "ppm_animal_origin_production", "74", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
"ppm", "ppm_cow_farming", "94", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
"ppm", "ppm_aquaculture", "3940", "2013-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
## SIGMINE
"sigmine", "sigmine_active", NA, NA, NA, "https://app.anm.gov.br/dadosabertos/SIGMINE/PROCESSOS_MINERARIOS/BRASIL.zip",
## ANEEL
"aneel", "energy_development_budget", NA, "2013-2022", NA, "https://drive.google.com/file/d/1h7mu-9qbKfISk1-k4JSrBhXKBMQHTOH9/view?usp=share_link",
"aneel", "energy_generation", NA, "1908-2021", "Municipality", "https://git.aneel.gov.br/publico/centralconteudo/-/raw/main/relatorioseindicadores/geracao/BD_SIGA.xlsx?inline=false",
"aneel", "energy_enterprises_distributed", NA, NA, NA, "https://dadosabertos.aneel.gov.br/dataset/5e0fafd2-21b9-4d5b-b622-40438d40aba2/resource/b1bd71e7-d0ad-4214-9053-cbd58e9564a7/download/empreendimento-geracao-distribuida.csv",
## EPE
"epe", "energy_consumption_per_class", NA, "2004-2021", "Region, Subsystem, State", "https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/Documents/CONSUMO%20MENSAL%20DE%20ENERGIA%20EL%c3%89TRICA%20POR%20CLASSE.xls",
"epe", "national_energy_balance", NA, "2011-2022", NA, "https://drive.google.com/file/d/1_JTYyAPdbQayR-nrURts6OmbKcm2cLix/view?usp=share_link",
## Shapefile from github repository
"internal", "geo_municipalities", NA, "2020", "Municipality", "https://raw.github.com/datazoompuc/datazoom.amazonia/master/data-raw/geo_municipalities.rds",
)
# returns only the desired rows
if (!is.null(source)) {
link <- link %>%
dplyr::filter(survey == source)
}
if (!is.null(dataset)) {
link <- link %>%
dplyr::filter(dataset == !!dataset)
}
if (url) {
link <- link %>%
purrr::pluck("link")
}
return(link)
}
Try the datazoom.amazonia package in your browser
Any scripts or data that you put into this service are public.
datazoom.amazonia documentation built on May 29, 2024, 11:12 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions external_download sidra_download
sidra_download <- function(sidra_code = NULL, year, geo_level = "municipality",
classific = "all", category = "all") {
## Bind Global Variables
code_state <- NULL
code_meso <- NULL
code_micro <- NULL
# Obs: Sometimes there are non-catched municipalieis - user should check on IBGE SIDRA
# f = geo %>% filter(!(code_muni %in% unique(dat_uf$`Município (Código)`)))
# ----------------------------------------------------
## Download from Sidra IBGE
## Include Progress Bar
## Omit Warnings
## We should include support for microregion/mesoregion
# ------------------------------------------------------
##############################
## Setting Basic Parameters ##
##############################
param <- list()
param$sidra_code <- sidra_code
param$year <- year
param$classific <- classific
param$category <- category
if (geo_level == "country") {
param$geo_reg <- "Brazil"
}
if (geo_level == "region") {
param$geo_reg <- "Region"
}
if (geo_level == "state") {
param$geo_reg <- "State"
}
if (geo_level == "municipality") {
param$geo_reg <- "City"
}
### SPECIAL CASES: POPULATION FOR YEARS 2007 AND 2010
### CONTAGEM DA POPULACAO AND CENSO DEMOGRAFICO
if (param$sidra_code == 6579) {
if (year == 2007) param$sidra_code <- 793 # https://sidra.ibge.gov.br/tabela/793
if (year == 2010) {
param$sidra_code <- 1378 # https://sidra.ibge.gov.br/tabela/1378
param$classific <- "c1"
param$category <- list(0)
}
}
if (param$sidra_code == 6907) {
param$classific <- c("c12443")
param$category <- list(110056)
}
##################################
## Get Geographical Information ##
##################################
geo <- datazoom.amazonia::municipalities %>%
tidyr::drop_na() # 5 municipalities have no micro code
# uf = list('RO' = 11,'AC' = 12,'AM' = 13,'RR' = 14,'PA' = 15,'AP' = 16,'TO' = 17,
# 'MA' = 21,'PI' = 22,'CE' = 23,'RN' = 24,'PB' = 25,'PE' = 26,'AL' = 27,'SE' = 28,'BA' = 29,
# 'MG' = 31,'ES' = 32,'RJ' = 33,'SP' = 35,
# 'PR' = 41,'SC' = 42,'RS' = 43,
# 'MS' = 50,'MT' = 51,'GO' = 52,'DF' = 53)
###############
## Load Data ##
###############
get_sidra_safe <- purrr::safely(sidrar::get_sidra)
if (param$geo_reg %in% c("Brazil", "Region", "State")) {
base::message(base::cat("Downloading Data at the", param$geo_reg, "level")) ## Show Message
## Download
dat <- suppressMessages(
get_sidra_safe(
x = param$sidra_code,
geo = param$geo_reg,
period = as.character(param$year),
classific = param$classific,
category = param$category
)
)
dat <- dat$result
dat <- dat %>%
tibble::as_tibble() %>%
janitor::clean_names()
if (nrow(dat) == 0) {
stop("Error in Download.")
}
base::message(base::cat("Download Succesfully Completed!"))
return(dat)
}
if (param$geo_reg == "City") {
##############################
## Download at the UF Level ##
##############################
uf_list <- geo %>%
dplyr::select(code_state) %>%
unlist() %>%
unique() %>%
as.list()
names(uf_list) <- uf_list
dat_raw_uf <- purrr::map(uf_list, function(uf) {
base::message(base::cat(which(uf == uf_list), "in", length(uf_list), "states...\n"))
suppressMessages(
get_sidra_safe(
x = param$sidra_code,
geo = param$geo_reg,
period = as.character(param$year),
geo.filter = list("State" = uf),
classific = param$classific,
category = param$category
)
)
})
dat_mod_uf <- base::lapply(dat_raw_uf, "[[", 1)
dat_uf <- dat_mod_uf[unlist(lapply(dat_mod_uf, is.data.frame))] %>% ## Filter for only found dataframes
dplyr::bind_rows() %>%
tibble::as_tibble() %>%
janitor::clean_names()
###########################################
## Checking for Completeness of Download ##
###########################################
missed_uf <- dat_mod_uf[!unlist(lapply(dat_mod_uf, is.data.frame))] %>% names()
rm(dat_mod_uf)
if (length(missed_uf) > 0) {
base::message(base::cat(
"Download at the State Level Completed!", length(missed_uf), "failed.\n",
"Attempting to Download at the Mesoregion Level..."
))
} else if (length(missed_uf) == 0) {
base::message(base::cat("Download Succesfully Completed!"))
return(dat_uf)
}
#################
## Meso Region ##
#################
if (length(missed_uf) > 0) {
geo_meso <- geo %>%
dplyr::filter(code_state %in% missed_uf)
meso_reg_list <- geo_meso %>%
dplyr::select(code_meso) %>%
unlist() %>%
unique() %>%
as.list()
names(meso_reg_list) <- meso_reg_list
dat_raw_meso <- purrr::map(meso_reg_list, function(meso_reg) {
base::message(base::cat(which(meso_reg == meso_reg_list), "in", length(meso_reg_list), "mesoregions...\n"))
base::suppressMessages(
get_sidra_safe(
x = param$sidra_code,
geo = param$geo_reg,
period = as.character(param$year),
geo.filter = list("MesoRegion" = meso_reg),
classific = param$classific,
category = param$category
)
)
})
dat_mod_meso <- base::lapply(dat_raw_meso, "[[", 1)
dat_meso <- dat_mod_meso[unlist(lapply(dat_mod_meso, is.data.frame))] %>% ## Filter for only found dataframes
dplyr::bind_rows() %>%
tibble::as_tibble() %>%
janitor::clean_names()
dat_uf <- dat_uf %>%
dplyr::bind_rows(dat_meso)
###########################################
## Checking for Completeness of Download ##
###########################################
missed_meso <- dat_mod_meso[!unlist(lapply(dat_mod_meso, is.data.frame))] %>% names()
rm(dat_mod_meso)
if (length(missed_meso) > 0) {
base::message(base::cat(
"Download at the Mesoregion Level Completed!", length(missed_meso), "failed.\n",
"Attempting to Download at the Microregion Level...\n"
))
} else if (length(missed_meso) == 0) {
base::message(base::cat("Download Succesfully Completed!"))
return(dat_uf)
}
##################
## Micro Region ##
##################
if (length(missed_meso) > 0) {
geo_micro <- geo %>%
dplyr::filter(code_meso %in% missed_meso)
micro_reg_list <- geo_micro %>%
dplyr::select(code_micro) %>%
unlist() %>%
unique() %>%
as.list()
names(micro_reg_list) <- micro_reg_list
dat_raw_micro <- purrr::map(micro_reg_list, function(micro_reg) {
base::message(base::cat(which(micro_reg == micro_reg_list), "in", length(micro_reg_list), "microregions...\n"))
base::suppressMessages(
get_sidra_safe(
x = param$sidra_code,
geo = param$geo_reg,
period = as.character(param$year),
geo.filter = list("MicroRegion" = micro_reg),
classific = param$classific,
category = param$category
)
)
})
dat_mod_micro <- base::lapply(dat_raw_micro, "[[", 1)
dat_micro <- dat_mod_micro[unlist(lapply(dat_mod_micro, is.data.frame))] %>% ## Filter for only found dataframes
dplyr::bind_rows() %>%
tibble::as_tibble() %>%
janitor::clean_names()
dat_uf <- dat_uf %>%
dplyr::bind_rows(dat_micro)
###########################################
## Checking for Completeness of Download ##
###########################################
missed_micro <- dat_mod_micro[!unlist(lapply(dat_mod_micro, is.data.frame))] %>% names()
rm(dat_mod_micro)
if (length(missed_micro) > 0) {
base::message(base::cat(
length(missed_micro),
"missed API requests at the Microregion level.
Please report this problem to package developers..."
))
}
if (length(missed_micro) == 0) {
base::message(base::cat("Download Succesfully Completed!"))
return(dat_uf)
}
} # End of if Meso
} # End of if Uf
} # End of If - Download at the Municipality Level
}
external_download <- function(dataset = NULL, source = NULL, year = NULL,
geo_level = NULL, coords = NULL, dataset_code = NULL,
sheet = NULL, skip_rows = NULL, file_name = NULL,
state = NULL) {
## Bind Global Variables
link <- NULL
## Define Basic Parameters
param <- list()
param$dataset <- dataset
param$source <- source
# Optional parameters for functions that need them:
param$year <- year # if download is perform separately by year
param$geo_level <- geo_level # if some geo_levels have a different download link
param$coords <- coords
param$dataset_code <- dataset_code
param$skip_rows <- skip_rows # number of rows to skip atop a spreadsheet
param$file_name <- file_name
param$sheet <- sheet # which sheet of a .xlsx to read
param$state <- state
if (is.null(param$skip_rows)) param$skip_rows <- 0 # makes it more error-proof
#####################
## Construct Links ##
#####################
## Pull URL from datasets_link
param$url <- datasets_link(
source = param$source,
dataset = param$dataset,
url = TRUE
)
# For most sources, the URL in datasets_link is already the URL needed for the download
path <- param$url
## Filling in URLs
# Some URLs are in the form www.data/$year$_$dataset$.csv, where expression
# surrounded by $ are placeholders. The code below subs them in for actual parameters
if (!is.null(param$year)) {
path <- path %>%
stringr::str_replace("\\$year\\$", as.character(param$year))
}
if (!is.null(param$state)) {
path <- path %>%
stringr::str_replace("\\$state\\$", param$state)
}
if (!is.null(param$file_name)) {
path <- path %>%
stringr::str_replace("\\$file_name\\$", param$file_name)
}
# Below are the exceptions, for which manipulation is needed
##### Exceptions only #####
# If the datasets_link URL is the download path you need,
# do not change this section for a new function
## MapBiomas
# Download path depends on dataset and geo_level
if (source == "mapbiomas") {
if (dataset %in% c("mapbiomas_cover", "mapbiomas_transition")) {
if (param$geo_level == "state") {
path <- "https://brasil.mapbiomas.org/wp-content/uploads/sites/4/2023/08/TABELA-GERAL-MAPBIOMAS-COL8.0-BIOMASxESTADOS-1.xlsx"
}
if (param$geo_level == "municipality") {
path <- "https://storage.googleapis.com/mapbiomas-public/initiatives/brasil/collection_8/downloads/statistics/tabela_geral_mapbiomas_col8_biomas_municipios.xlsx"
}
}
}
## SEEG
# Download path depends on geo_level
if (source == "seeg") {
if (geo_level == "municipality") {
path <- "https://drive.google.com/u/0/uc?confirm=bhfS&id=1rUc6H8BVKT9TH-ri6obzHVt7WI1eGUzd"
}
}
## TerraClimate
# Download path depends on geographical parameters
if (source == "terraclimate") {
filename <- paste0(
"agg_terraclimate_",
param$dataset_code,
"_1958_CurrentYear_GLOBE.nc"
)
path <- paste0(
param$url,
"/",
filename,
"?",
"&var=",
param$dataset_code,
"&south=",
param$coords$lat_min,
"&north=",
param$coords$lat_max,
"&west=",
param$coords$lon_min,
"&east=",
param$coords$lon_max,
"&horizStride=1",
"&time_start=",
param$year$initial_time,
"&time_end=",
param$year$final_time,
"&timeStride=1",
"&disableProjSubset=on&addLatLon=true&accept=netcdf"
)
}
#######################
## Initiate Download ##
#######################
## Specify file extension to be passed to tempfile()
# For most functions, the file extension is automatically detected
file_extension <- sub(".*\\.", ".", path) %>%
tolower()
##### Exceptions only #####
# Only manually input the file_extension if the download_path does
# not end in ".ext", where .ext is any file extension
# googledrive links do not contain the file extension, for example
if (source %in% c("seeg", "iema", "ips")) {
file_extension <- ".xlsx"
}
if (source == "prodes") {
file_extension <- ".txt"
}
if (source == "terraclimate") {
file_extension <- ".nc"
}
if (source == "deter") {
file_extension <- ".zip"
}
if (source == "ibama") {
if (dataset == "embargoed_areas") {
file_extension <- ".zip"
} else {
file_extension <- ".csv"
}
}
if (source == "imazon") {
file_extension <- ".rds"
}
if (source == "epe") {
if (param$dataset == "national_energy_balance") {
file_extension <- ".csv"
}
}
if (source == "aneel") {
if (dataset == "energy_development_budget") {
file_extension <- ".rds"
}
if (dataset == "energy_generation") {
file_extension <- ".xlsx"
}
if (dataset == "energy_enterprises_distributed") {
file_extension <- ".csv"
}
}
## Define Empty Directory and Files For Download
dir <- tempdir()
temp <- tempfile(fileext = file_extension, tmpdir = dir)
## Picking the way to download the file
download_method <- "standard" # works for most functions
if (source %in% c("iema", "imazon")) {
download_method <- "googledrive"
}
if (source == "aneel") {
if (dataset == "energy_development_budget") {
download_method <- "googledrive"
}
if (dataset == "energy_enterprises_distributed") {
message("This may take a while.\n")
options(timeout = 1000) # increase timeout limit
}
}
if (source == "epe") {
if (dataset == "national_energy_balance") {
download_method <- "googledrive"
}
}
if (source %in% c("deter", "terraclimate", "baci", "sigmine", "mapbiomas")) {
download_method <- "curl"
quiet <- FALSE
}
if (source == "datasus") {
download_method <- "curl"
quiet <- TRUE
}
if (source == "ibama") {
download_method <- "curl"
options(download.file.method = "curl", download.file.extra = "-k -L") # https://stackoverflow.com/questions/69716835/turning-ssl-verification-off-inside-download-file
quiet <- TRUE
}
if (source == "seeg") {
if (geo_level == "municipality") {
download_method <- "googledrive"
}
}
## Downloading file by the selected method
if (download_method == "standard") {
utils::download.file(url = path, destfile = temp, mode = "wb")
}
if (download_method == "curl") {
utils::download.file(url = path, destfile = temp, method = "curl", quiet = quiet)
}
if (download_method == "googledrive") {
message("Please follow the steps from `googledrive` package to download the data. This may take a while.\nIn case of authentication errors, run vignette(\"GOOGLEDRIVE\").")
googledrive::drive_download(path, path = temp, overwrite = TRUE)
}
## Unzipping if the file is zipped
if (file_extension == ".zip") {
utils::unzip(temp, exdir = dir)
}
###############
## Load Data ##
###############
##### Exceptions only #####
if (file_extension == ".zip") {
if (param$dataset == "degrad") {
dat <- sf::read_sf(file.path(dir, param$file_name))
dat$year <- param$year
}
if (param$source == "deter") {
if (param$dataset == "deter_amz") {
dat <- sf::read_sf(file.path(dir, "deter-amz-deter-public.shp"))
}
if (param$dataset == "deter_cerrado") {
dat <- sf::read_sf(file.path(dir, "deter_public.shp"))
}
}
if (param$source == "sigmine") {
dat <- sf::read_sf(file.path(dir, "BRASIL.shp"))
}
if (param$source == "ibama") {
# get latest downloaded file (the name changes daily)
file <- file.info(list.files(dir, pattern = "rel_areas_embargadas_.*.xls"))
file <- file[with(file, order(as.POSIXct(mtime))), ]
file <- rownames(file)
doc <- XML::htmlParse(file.path(dir, file), encoding = "UTF-8")
tableNode <- XML::getNodeSet(doc, "//table")
dataset <- XML::readHTMLTable(tableNode[[1]])
colnames(dataset) <- dataset[5, ]
dat <- dataset[-c(1:5), ]
}
if (param$source == "baci") {
# as year can be a vector, sets up expressions of the form "*YYYY_V202201.csv" for each year to match file names
file_expression <- paste0("*", param$year, "_V202201.csv")
# now turning into *XXXX_V202201.csv|YYYY_V202201.csv|ZZZZ_V202201.csv" to match as regex
file_expression <- paste0(file_expression, collapse = "|")
file <- list.files(dir, pattern = file_expression, full.names = TRUE) %>%
as.list()
# now reads each file
dat <- lapply(file, data.table::fread, header = TRUE, sep = ",")
# each data frame in the list is named after the corresponding year
names(dat) <- param$year
}
}
if (param$source == "epe" & param$dataset == "energy_consumption_per_class") {
# param$sheet contains the selected sheets
# Making a list with all the sheets
dat <- purrr::imap(
param$sheet,
function(sheets, number) {
base::message(
paste0("Reading sheet ", number, " out of ", length(param$sheet), " (", sheets, ")")
)
base::suppressMessages(
readxl::read_xls(temp, sheet = sheets)
)
}
)
names(dat) <- param$sheet
}
if (param$source == "aneel") {
if (param$dataset == "energy_enterprises_distributed") {
dat <- data.table::fread(temp, encoding = "Latin-1")
} else if (dataset == "energy_generation") {
dat <- readxl::read_xlsx(temp, sheet = param$sheet, skip = param$skip_rows, na = c("-", ""))
}
}
if (param$source == "ips") {
dat <- param$sheet %>%
purrr::map(
~ readxl::read_xlsx(temp, sheet = .)
)
}
## Now the rest of the functions
# This Depends on Data Type (.csv, .shp, ...) and on the data source
else {
if (file_extension == ".csv") {
dat <- data.table::fread(temp)
}
if (file_extension == ".txt") {
dat <- readr::read_csv(temp)
}
if (file_extension == ".nc") {
dat <- terra::rast(temp)
}
if (file_extension == ".rds") {
dat <- readr::read_rds(temp)
}
if (file_extension == ".xlsx") {
dat <- readxl::read_xlsx(temp, sheet = param$sheet, skip = param$skip_rows)
}
if (file_extension == ".dbc") {
dat <- read.dbc(temp)
}
}
##############################
## Excluding Temporary File ##
##############################
# Folder is kept
if (file_extension != ".nc") {
unlink(temp)
}
#################
## Return Data ##
#################
return(dat)
}
datasets_link <- function(source = NULL, dataset = NULL, url = FALSE) {
survey <- NULL
link <- tibble::tribble(
~survey, ~dataset, ~sidra_code, ~available_time, ~available_geo, ~link,
########################
## Environmental data ##
########################
## PRODES
"prodes", "deforestation", NA, "2000-2022", "Municipality", "http://www.dpi.inpe.br/prodesdigital/tabelatxt.php?ano=2022&estado=&ordem=MUNICIPIO&type=tabela&output=txt&",
## DETER
"deter", "deter_amz", NA, "2016-2022", "Municipality", "http://terrabrasilis.dpi.inpe.br/file-delivery/download/deter-amz/shape",
"deter", "deter_cerrado", NA, "2018-2022", "Municipality", "http://terrabrasilis.dpi.inpe.br/file-delivery/download/deter-cerrado/shape",
## DEGRAD
"degrad", "degrad", NA, "2007-2016", NA, "http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/degrad/arquivos/degrad$year$_final_shp.zip",
## Imazon
"imazon", "imazon_shp", NA, "2020", "Municipality", "https://docs.google.com/uc?export=download&id=1JHc2J_U8VXHVuWVsi8wVBnNzZ37y1ehv",
## IBAMA
"ibama", "embargoed_areas", NA, NA, "Municipality", "https://servicos.ibama.gov.br/ctf/publico/areasembargadas/downloadListaAreasEmbargadas.php",
"ibama", "distributed_fines", NA, NA, "Municipality", "https://dadosabertos.ibama.gov.br/dados/SICAFI/$state$/Quantidade/multasDistribuidasBensTutelados.csv",
"ibama", "collected_fines", NA, NA, "Municipality", "https://dadosabertos.ibama.gov.br/dados/SICAFI/$state$/Arrecadacao/arrecadacaobenstutelados.csv",
## MapBiomas
"mapbiomas", "mapbiomas_cover", NA, "1985-2019", "Municipality, State", "https://brasil.mapbiomas.org/estatisticas/",
"mapbiomas", "mapbiomas_transition", NA, "1985-2019", "Municipality, State", "https://brasil.mapbiomas.org/estatisticas/",
"mapbiomas", "mapbiomas_deforestation_regeneration", NA, "1988-2017", "Municipality", "https://storage.googleapis.com/mapbiomas-public/initiatives/brasil/collection_8/downloads/tabela_desmatamento_vegetacao_secundaria_mapbiomas_col8.xlsx",
"mapbiomas", "mapbiomas_irrigation", NA, "2000-2019", "State, Biome", "https://mapbiomas-br-site.s3.amazonaws.com/downloads/Estatisticas%20/Colecao_7_Irrigacao_Biomes_UF.xlsx",
"mapbiomas", "mapbiomas_mining", NA, "1985-2020", "Municipality, Indigenous_Land", "https://brasil.mapbiomas.org/wp-content/uploads/sites/4/2023/09/TABELA-MINERACAO-MAPBIOMAS-COL8.0.xlsx",
"mapbiomas", "mapbiomas_fire", NA, "1985-2020", "State", "https://mapbiomas-br-site.s3.amazonaws.com/downloads/MB-Fogo-2-Biome-State.xlsx",
"mapbiomas", "mapbiomas_water", NA, "1985-2022", "State, Municipality, Biome", "https://mapbiomas-br-site.s3.amazonaws.com/Estat%C3%ADsticas/Estatisticas_Superficie%C3%81gua_Col2_SITE.xlsx",
## TerraClimate
"terraclimate", "max_temperature", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "min_temperature", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "wind_speed", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "vapor_pressure_deficit", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "vapor_pressure", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "snow_water_equivalent", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "shortwave_radiation_flux", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "soil_moisture", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "runoff", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "precipitation", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "potential_evaporation", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "climatic_water_deficit", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "water_evaporation", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
"terraclimate", "palmer_drought_severity_index", NA, "1958-2022", "Municipality", "http://thredds.northwestknowledge.net:8080/thredds/ncss",
## SEEG
"seeg", "seeg", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_farming", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_industry", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_energy", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_land", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
"seeg", "seeg_residuals", NA, "1970-2021", "Country, State, Municipality", "https://seeg-br.s3.amazonaws.com/Estat%C3%ADsticas/SEEG10/1-SEEG10_GERAL-BR_UF_2022.10.27-FINAL-SITE.xlsx",
## Censo Agro
"censoagro", "agricultural_land_area", "263", "1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "agricultural_area_use", "264", "1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "agricultural_employees_tractors", "265", "1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "agricultural_producer_condition", "280", "1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "animal_production", "281", "1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "animal_products", "282", "1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "vegetable_production_area", "283", "1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "vegetable_production_permanent", "1730", "1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "vegetable_production_temporary", "1731", "1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006", "Country, State", "https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/series-temporais",
"censoagro", "livestock_production", "6907", "2017", "Municipality", "https://sidra.ibge.gov.br/tabela/6907",
#################
## Social data ##
#################
## IPS
"ips", "all", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "life_quality", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "sanit_habit", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "violence", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "educ", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "communic", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "mortality", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
"ips", "deforest", NA, "2014, 2018, 2021, 2023", NA, "https://docs.google.com/uc?export=download&id=1ABcLZFraSd6kELHW-pZgpy7ITzs1JagN&format=xlsx",
## DATASUS
"datasus", "datasus_sim_do", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DORES/$file_name$",
"datasus", "datasus_sim_dofet", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DOFET/$file_name$",
"datasus", "datasus_sim_doext", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DOFET/$file_name$",
"datasus", "datasus_sim_doinf", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DOFET/$file_name$",
"datasus", "datasus_sim_domat", NA, "1996-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIM/CID10/DOFET/$file_name$",
"datasus", "datasus_cnes_lt", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/LT/$file_name$",
"datasus", "datasus_cnes_st", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/ST/$file_name$",
"datasus", "datasus_cnes_dc", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/DC/$file_name$",
"datasus", "datasus_cnes_eq", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/EQ/$file_name$",
"datasus", "datasus_cnes_sr", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/SR/$file_name$",
"datasus", "datasus_cnes_hb", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/HB/$file_name$",
"datasus", "datasus_cnes_pf", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/PF/$file_name$",
"datasus", "datasus_cnes_ep", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/EP/$file_name$",
"datasus", "datasus_cnes_rc", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/RC/$file_name$",
"datasus", "datasus_cnes_in", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/IN/$file_name$",
"datasus", "datasus_cnes_ee", NA, "2007-2021", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/EE/$file_name$",
"datasus", "datasus_cnes_ef", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/EF/$file_name$",
"datasus", "datasus_cnes_gm", NA, "2005-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/CNES/200508_/Dados/GM/$file_name$",
"datasus", "datasus_sih", NA, "2008-2023", "State", "ftp://ftp.datasus.gov.br/dissemin/publicos/SIHSUS/200801_/dados/$file_name$",
## IEMA
"iema", "iema", NA, "2018", "Municipality", "https://drive.google.com/uc?export=download&id=10JMRtzu3k95vl8cQmHkVMQ9nJovvIeNl",
## Population
"population", "population", "6579", "2001-2021", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/estimapop/tabelas",
###################
## Economic data ##
###################
## Comex
"comex", "comex_export_prod", NA, "1997-2023", NA, "https://balanca.economia.gov.br/balanca/bd/comexstat-bd/ncm/EXP_$year$.csv",
"comex", "comex_import_prod", NA, "1997-2023", NA, "https://balanca.economia.gov.br/balanca/bd/comexstat-bd/ncm/IMP_$year$.csv",
"comex", "comex_export_mun", NA, "1997-2023", NA, "https://balanca.economia.gov.br/balanca/bd/comexstat-bd/mun/EXP_$year$_MUN.csv",
"comex", "comex_import_mun", NA, "1997-2023", NA, "https://balanca.economia.gov.br/balanca/bd/comexstat-bd/mun/IMP_$year$_MUN.csv",
## BACI
"baci", "HS92", NA, "1995-2020", "Country", "http://www.cepii.fr/DATA_DOWNLOAD/baci/data/baci_HS92_V202201.zip",
## PIB-Munic
"pibmunic", "pibmunic", "5938", "2002-2020", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pib-munic/tabelas",
## CEMPRE
"cempre", "cempre", "6449", "2006-2020", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/cempre/tabelas",
## PAM
"pam", "all_crops", "5457/all/all", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "permanent_crops", "1613/all/all", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "temporary_crops", "1612/all/all", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "corn", "839/all/all", "2003-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "potato", "1001/all/all", "2003-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "peanut", "1000/all/all", "2003-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "beans", "1002/all/all", "2003-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
# Categories within temporary crops
"pam", "temporary_total", "1612/c81/0", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "pineapple", "1612/c81/2688", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "alfafa", "1612/c81/40471", "1974-1987", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cotton_herbaceous", "1612/c81/2689", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "garlic", "1612/c81/2690", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "peanut_temporary", "1612/c81/2691", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "rice", "1612/c81/2692", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "oats", "1612/c81/2693", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sweet_potato", "1612/c81/2694", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "potato_temporary", "1612/c81/2695", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sugar_cane", "1612/c81/2696", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "forage_cane", "1612/c81/40470", "1974-1987", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "onion", "1612/c81/2697", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "rye", "1612/c81/2698", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "barley", "1612/c81/2699", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "pea", "1612/c81/2700", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "broad_bean", "1612/c81/2701", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "beans_temporary", "1612/c81/2702", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "tobacco", "1612/c81/2703", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sunflower_seeds", "1612/c81/109179", "2005-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "jute_fiber", "1612/c81/2704", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "linen_seeds", "1612/c81/2705", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "malva_fiber", "1612/c81/2706", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "castor_bean", "1612/c81/2707", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cassava", "1612/c81/2708", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "watermelon", "1612/c81/2709", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "melon", "1612/c81/2710", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "corn_temporary", "1612/c81/2711", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "ramie_fiber", "1612/c81/2712", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "soybean", "1612/c81/2713", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sorghum", "1612/c81/2714", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "tomato", "1612/c81/2715", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "wheat", "1612/c81/2716", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "triticale", "1612/c81/109180", "2005-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
# Categories within permanent crops
"pam", "permanent_total", "1613/c82/0", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "avocado", "1613/c82/2717", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cotton_arboreo", "1613/c82/2718", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "acai", "1613/c82/45981", "2015-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "olive", "1613/c82/2719", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "banana", "1613/c82/2720", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "rubber_coagulated_latex", "1613/c82/2721", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "rubber_liquid_latex", "1613/c82/40472", "1981-1987", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cocoa_beans", "1613/c82/2722", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coffee_total", "1613/c82/2723", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coffee_arabica", "1613/c82/31619", "2012-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coffee_canephora", "1613/c82/31620", "2012-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cashew", "1613/c82/40473", "1974-1987", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "khaki", "1613/c82/2724", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "cashew_nut", "1613/c82/2725", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "india_tea", "1613/c82/2726", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coconut", "1613/c82/2727", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "coconut_bunch", "1613/c82/2728", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "yerba_mate", "1613/c82/2729", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "fig", "1613/c82/2730", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "guava", "1613/c82/2731", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "guarana_seeds", "1613/c82/2732", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "orange", "1613/c82/2733", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "lemon", "1613/c82/2734", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "apple", "1613/c82/2735", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "papaya", "1613/c82/2736", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "mango", "1613/c82/2737", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "passion_fruit", "1613/c82/2738", "1988-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "quince", "1613/c82/2739", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "walnut", "1613/c82/2740", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "heart_of_palm", "1613/c82/90001", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "pear", "1613/c82/2741", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "peach", "1613/c82/2742", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "black_pepper", "1613/c82/2743", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "sisal_or_agave", "1613/c82/2744", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "tangerine", "1613/c82/2745", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "tung", "1613/c82/2746", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "annatto_seeds", "1613/c82/2747", "1981-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
"pam", "grape", "1613/c82/2748", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pam/tabelas",
## PEVS
"pevs", "pevs_forest_crops", "289", "1986-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pevs/tabelas/brasil/2019",
"pevs", "pevs_silviculture", "291", "1986-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pevs/tabelas/brasil/2019",
"pevs", "pevs_silviculture_area", "5930", "2013-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/pevs/tabelas/brasil/2019",
## PPM
"ppm", "ppm_livestock_inventory", "3939", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
"ppm", "ppm_sheep_farming", "95", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
"ppm", "ppm_animal_origin_production", "74", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
"ppm", "ppm_cow_farming", "94", "1974-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
"ppm", "ppm_aquaculture", "3940", "2013-2022", "Country, State, Municipality", "https://sidra.ibge.gov.br/pesquisa/ppm/tabelas/brasil/2021",
## SIGMINE
"sigmine", "sigmine_active", NA, NA, NA, "https://app.anm.gov.br/dadosabertos/SIGMINE/PROCESSOS_MINERARIOS/BRASIL.zip",
## ANEEL
"aneel", "energy_development_budget", NA, "2013-2022", NA, "https://drive.google.com/file/d/1h7mu-9qbKfISk1-k4JSrBhXKBMQHTOH9/view?usp=share_link",
"aneel", "energy_generation", NA, "1908-2021", "Municipality", "https://git.aneel.gov.br/publico/centralconteudo/-/raw/main/relatorioseindicadores/geracao/BD_SIGA.xlsx?inline=false",
"aneel", "energy_enterprises_distributed", NA, NA, NA, "https://dadosabertos.aneel.gov.br/dataset/5e0fafd2-21b9-4d5b-b622-40438d40aba2/resource/b1bd71e7-d0ad-4214-9053-cbd58e9564a7/download/empreendimento-geracao-distribuida.csv",
## EPE
"epe", "energy_consumption_per_class", NA, "2004-2021", "Region, Subsystem, State", "https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/Documents/CONSUMO%20MENSAL%20DE%20ENERGIA%20EL%c3%89TRICA%20POR%20CLASSE.xls",
"epe", "national_energy_balance", NA, "2011-2022", NA, "https://drive.google.com/file/d/1_JTYyAPdbQayR-nrURts6OmbKcm2cLix/view?usp=share_link",
## Shapefile from github repository
"internal", "geo_municipalities", NA, "2020", "Municipality", "https://raw.github.com/datazoompuc/datazoom.amazonia/master/data-raw/geo_municipalities.rds",
)
# returns only the desired rows
if (!is.null(source)) {
link <- link %>%
dplyr::filter(survey == source)
}
if (!is.null(dataset)) {
link <- link %>%
dplyr::filter(dataset == !!dataset)
}
if (url) {
link <- link %>%
purrr::pluck("link")
}
return(link)
}
Try the datazoom.amazonia package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.