The datazoom.amazonia package facilitates access to official Brazilian Amazon data, including agriculture, deforestation, production. The package provides functions that download and pre-process selected datasets.
You can install the released version of datazoom.amazonia from
CRAN with:
install.packages("datazoom.amazonia")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("datazoompuc/datazoom.amazonia")
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The PRODES project uses satellites to monitor deforestation in Brazil’s Legal Amazon. The raw data reports total and incremental (year-by-year) low-cut deforested area at the municipality level, going back to the year 2007.
INPE’s most recent data is now published at TerraBrasilis. We read their full raster data for the Legal Amazon region and extract values onto the map of Brazilian municipalities.
Options:
-
dataset:
"deforestation","residual_deforestation","native_vegetation","hydrography","non_forest", or"clouds" -
raw_data: there are two options:
TRUE: if you want the data as it is originally, read as a SpatRaster.FALSE: if you want the treated version of the data, measuring affected areas per municipality.
-
time_period: picks the years for which the data will be downloaded, under the following constraints:
- For dataset
"deforestation", it can be between 2007 and 2023. Deforestation for 2007 includes all cumulative deforestation up to 2007. For other years, deforestation is incremental; - For dataset
"residual_deforestation", it can be between 2010 and 2023; - For all other datasets, only the year 2023 is available.
- For dataset
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download treated data (raw_data = FALSE)
# in portuguese (language = 'pt').
data <- load_prodes(
dataset = "deforestation",
raw_data = FALSE,
time_period = 2020:2023,
language = "pt"
)DETER uses satellite surveillance to detect and report changes in forest cover across the Legal Amazon and the Cerrado biome. Each data point consists of a warning, describing which type of change has affected a certain area of forest at a given date. Broadly speaking, it makes a distinction between events of deforestation, degradation and logging. The data extracted here spans from 2016 onward in the Amazon, and from 2018 onward in the Cerrado.
The raw DETER data shows one warning per row, with each row also
containing a municipality. However, many warnings actually overlap with
2 or up to 4 municipalities, which are not shown in the original data.
Therefore, when the option raw_data = FALSE is selected, the original
spatial information is intersected with a municipalities map of Brazil,
and each warning can be split into more than one row, with each row
corresponding to a municipality.
Options:
- dataset: there are two options:
"deter_amz"for data from the Amazon"deter_cerrado"for data from the Cerrado
- raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
- language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download treated data (raw_data = FALSE) from Amazonia (dataset = "deter_amz")
deter_amz <- load_deter(
dataset = "deter_amz",
raw_data = FALSE
)The DEGRAD project uses satellites to monitor degradation of forest areas. Raw data is available as simple features (sf) objects, read from shapefiles. The project was substituted in 2016 by DETER-B. Accordingly, data is available from 2007 up to 2016.
Original documentation for this data is very scarce, users beware. Some things to keep in mind are:
Event data is organized through yearly editions (DEGRAD 2007-2016). Inside a given edition however, there may be data from different years (events that happened in 2015 inside DEGRAD 2016 for example).
This package provides degradation data with municipality identification.
It does this by intersecting DEGRAD geometries with IBGE’s municipality
geometries from the year 2019. CRS metadata however is missing from the
original data source. A best effort approach is used and a CRS is
assumed
(proj4string: "+proj=longlat +ellps=aust_SA +towgs84=-66.8700,4.3700,-38.5200,0.0,0.0,0.0,0.0 +no_defs").
Options:
-
dataset:
"degrad" -
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# download treated data (raw_data = TRUE) related to forest degradation
# from 2010 to 2012 (time_period = 2010:2012).
data <- load_degrad(
dataset = "degrad",
raw_data = FALSE,
time_period = 2010:2012
)Loads data categorizing each municipality by the level of deforestation pressure it faces. The categories used by Imazon have three levels, ranging from 0 to 3.
Options:
-
dataset:
"imazon_shp" -
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download treated data
data <- load_imazon(raw_data = FALSE)🔴 This function uses the googledrive package to download data. In
case of authentication errors, see googledrive.
The dataset is originally from the Brazilian Institute of Environment and Renewable Natural Resources (Ibama), documenting environmental embargoes and fines at the individual level from 2005 to the present day. In addition, it is possible to download distributed and collected fines from 1994 until the present day.
The function returns either the raw data or a data frame with aggregates considering, for each time-location period, counts for total the number of infractions, infractions that already went to trial, and number of unique perpetrators of infractions. There are also two data frames regarding distributed and collected fines across municipalities
Options:
-
dataset: there are three possible choices.
"embargoed_areas": embargoed areas"distributed_fines": fines that have not been paid by individuals or corporations"collected_fines": fines that have been paid by individuals or corporations
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
states: specifies for which states to download the data. It is “all” by default, but can be a single state such as
"AC"or any vector such asc("AC", "AM"). Does not apply to the"embargoed_areas"dataset. -
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
library(datazoom.amazonia)
# Download treated embargoes data (raw_data = FALSE) in english (language = "eng")
data <- load_ibama(
dataset = "embargoed_areas", raw_data = FALSE,
language = "eng"
)
# Download treated collected fines data from "BA"
data <- load_ibama(
dataset = "collected_fines", raw_data = FALSE,
states = "BA", language = "pt"
)The MapBiomas project gathers data reporting the type of land covering each year by area, that is, for example, the area used for a temporary crop of soybeans. It also reports the transitions in land coverage between years.
The data has yearly frequency and is available starting from the year 1985.
For all datasets, areas are measured in hectares (ha).
Options:
-
dataset: there are 7 possible choices:
"mapbiomas_cover": types of land cover."mapbiomas_transition": changes in land cover."mapbiomas_deforestation_regeneration": deforestation and forest regeneration."mapbiomas_irrigation"(temporarily unavailable, a new collection will be soon delivered): irrigated areas."mapbiomas_mining": areas used for mining."mapbiomas_water"(temporarily unavailable, a new collection will be soon delivered): areas of water surface."mapbiomas_fire": areas of wildfire burn scars.
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
- For dataset
"mapbiomas_cover", can be"municipality"or"indigenous_land" - For dataset
"mapbiomas_transition", can be"municipality"or"biome"(faster download) - For dataset
"mapbiomas_deforestation_regeneration", can only be"municipality" - For dataset
"mapbiomas_mining", can be"indigenous_land"or"municipality" - For dataset
"mapbiomas_irrigation", can be"state"or"biome" - For dataset
"mapbiomas_water", can be"municipality","state"or"biome" - For dataset
"mapbiomas_fire", can only be"state"
- For dataset
-
language: you can choose between Portuguese
("pt")and English("eng").
Examples:
# download treated Mapbiomas Cover data in English
data <- load_mapbiomas(
dataset = "mapbiomas_cover",
raw_data = FALSE,
geo_level = "municipality",
language = "eng"
)
# download treated data on mining on indigenous lands
data <- load_mapbiomas("mapbiomas_mining",
raw_data = FALSE,
geo_level = "indigenous_land"
)Spatial data on several climate variables, extracted from Climatology Lab’s TerraClimate. The table below shows all possible variables to be extracted, which are chosen through the “dataset” parameter. Data ranges from 1958 to 2020.
Netcdf files are downloaded from the THREDDS web server, as recommended for rectangular subsets of the global data.
Click to see all dataset options
| Dataset | Code | Description | Units |
|---|---|---|---|
| max_temperature | tmax | Maximum 2-m Temperature | degC |
| min_temperature | tmin | Minimum 2-m Temperature | degC |
| wind_speed | ws | Wind Speed at 10-m | m/s |
| vapor_pressure_deficit | vpd | Vapor Pressure Deficit | kPa |
| vapor_pressure | vap | 2-m Vapor Pressure | kPa |
| snow_water_equivalent | swe | Snow Water Equivalent at End of Month | mm |
| shortwave_radiation_flux | srad | Downward Shortwave Radiation Flux at the Surface | W/m^2 |
| soil_moisture | soil | Soil Moisture at End of Month | mm |
| runoff | q | Runoff | mm |
| precipitation | ppt | Accumulated Precipitation | mm |
| potential_evaporation | pet | Reference Evapotranspiration | mm |
| climatic_water_deficit | def | Climatic Water Deficit | mm |
| water_evaporation | aet | Actual Evapotranspiration | mm |
| palmer_drought_severity_index | PDSI | Palmer Drought Severity Index | unitless |
Options:
-
dataset: picks the variable to be read. Possible options are shown in the table above.
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng") -
legal_amazon_only: if set to
TRUE, only downloads data from the Legal Amazon region
OBS: A good internet connection is needed, because the data is heavy.
Examples:
# Downloading maximum temperature data from 2000 to 2001
max_temp <- load_climate(dataset = "max_temperature", time_period = 2000:2001)
# Downloading precipitation data only for the legal Amazon in 2010
amz_precipitation <- load_climate(
dataset = "precipitation",
time_period = 2010,
legal_amazon_only = TRUE
)Loads estimates of emission of greenhouse gases of Brazilian cities and states from SEEG. SEEG is the System of Estimates of Emissions and Removals of Greenhouse Gases (SEEG), an initiative of the Observatório do Clima, a network of institutions focused on climate change research in Brazil.
The data provided in SEEG’s Collection 9 is a series covering the period from 2000 to 2018.
Using data collected from government entities, institutes, research centers, NGOs and other institutions, the estimates are created using the methodology of the Brazilian Inventory of Anthropic Emissions and Removals of Greenhouse Gases, assembled by the Ministry of Science, Technology and Innovation (MCTI), and the directives of Intergovernmental Panel on Climate Change (IPCC)
Emissions are divided in five main sources: Agricultural and Cattle Raising, Energy, Changes in Use of Land, Industrial Processes and Residues. All greenhouse gases contained in the national inventory are considered, encompassing CO2, CH4, N2O and the HFCs, with the conversion to carbon equivalence (CO2e) also included, both in the metric of GWP (Global Warming Potential) and GTP (Global Temperature Potential).
The data is downloaded from the SEEG website in the form of one single file, so the option to select a certain range of years is not available. Also, due to the size of the file, a stable internet connection is necessary, and the function may take time to run.
Options:
-
dataset: there are six choices:
"seeg": provides all sectors in a same dataframe. Only works withraw_data = TRUE"seeg_farming""seeg_industry""seeg_energy""seeg_land""seeg_residuals"
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
"country","state", or"municipality" -
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download raw data (raw_data = TRUE) of greenhouse gases (dataset = "seeg")
# by state (geo_level = "state")
data <- load_seeg(
dataset = "seeg",
raw_data = TRUE,
geo_level = "state"
)
# Download treated data (raw_data = FALSE) of industry greenhouse gases (dataset = "seeg_industry")
data <- load_seeg(
dataset = "seeg_industry",
raw_data = FALSE,
geo_level = "state"
)🔴 This function uses the googledrive package to download data at the
municipality level. In case of authentication errors, see
googledrive.
The census of agriculture collects information about agricultural establishments and the agricultural activities carried out there, covering characteristics of the producer and establishment, economy and employment in rural areas, livestock, farming and agroindustry.
Data is collected by IBGE and is available at country, state and municipality level.
Options:
-
dataset:there are 10 possible choices:
"agricultural_land_area": area and number of agricultural properties"agricultural_area_use": area of agricultural properties by use"agricultural_employees_tractors": number of employees and tractors in agricultural properties"agricultural_producer_condition": condition of agricultural producer, whether they own the land"animal_production": number of animals farmed, by species"animal_products": amount of animal products, by product type"vegetable_production_area": area and amount produced, by vegetable product"vegetable_production_temporary": amount produced, by temporary crop"vegetable_production_permanent": amount produced, by permanent crop"livestock_production": amount of bovine cattle, and number of agricultural properties
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
"country"or"state". For dataset"livestock_production", it can also be"municipality" -
time_period: picks the years for which the data will be downloaded:
- For datasets
"agricultural_land_area","agricultural_producer_condition","animal_products", and"vegetable_production_area", it can be one of 1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, or 2006. - For datasets
"vegetable_production_permanent"and"vegetable_production_permanent", it can only be from 1940 onwards - For datasets
"agricultural_area_use","agricultural_employees_tractors","animal_production", it can only be from 1970 onwards - For dataset
"livestock_production", it can only be 2017
- For datasets
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download total land area data at the country level in year 2006
data <- load_censoagro(
dataset = "agricultural_land_area",
raw_data = TRUE,
geo_level = "country",
time_period = 2006
)
# Download temporary production crops data by state (geo_level = "state") in year 2006
# in portuguese (language = "pt")
data <- load_censoagro(
dataset = "vegetable_production_temporary",
raw_data = FALSE,
geo_level = "state",
time_period = 1995,
language = "pt"
)Loads information on the social and environmental performance of the Legal Amazon.
Data from the Amazon Social Progress Index, an initiative from Imazon with support from the Social Progress Imperative that measures the social and environmental progress of its locations. Namely, the 772 municipalities in the Amazon region. Survey is done at the municipal level.
Options:
-
dataset:
"all","life_quality","sanit_habit","violence","educ","communic","mortality", or"deforest"
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
time_period: can be 2014, 2018, 2021, 2023 or a vector with some combination thereof
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download raw data from 2014
data <- load_ips(dataset = "all", raw_data = TRUE, time_period = 2014)
# Download treated deforest data from 2018 in portuguese
data <- load_ips(
dataset = "deforest", raw_data = FALSE,
time_period = 2018, language = "pt"
)Data from the Institute of Environment and Water Resources (IEMA), documenting the number of people without access to eletric energy throughout the Amazon region in the year 2018.
Options:
-
dataset:
"iema" -
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download treated data
data <- load_iema(raw_data = FALSE)🔴 This function uses the googledrive package to download data. In
case of authentication errors, see googledrive.
Loads IBGE information on estimated population (2001-2006, 2008-2009, 2011-2021) or population (2007 and 2010) data. Data is available at country, state and municipality level and from 2001 to 2021.
Options:
-
dataset:
"population" -
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
"country","state", or"municipality" -
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# download treated population data at the state level for 2010 to 2012
data <- load_population(
raw_data = FALSE,
geo_level = "state",
time_period = 2010:2012
)The Comex dataset gathers data extracted from Siscomex (Integrated System of Foreign Trade), which is a database containing information from all products that are imported to or exported from Brazil. Using data reported from the companies which are responsible for the process of transporting the products, the system adheres to internationally standardized nomenclatures, such as the Harmonized System and the Mercosul Common Nomenclature (which pertains to members of the Mercosul organization).
The data has a monthly frequency and is available starting from the year 1989. From 1989 to 1996, a different system of nomenclatures was adopted, but all conversions are available on a dictionary in the Comex website (https://www.gov.br/produtividade-e-comercio-exterior/pt-br/assuntos/comercio-exterior/estatisticas/base-de-dados-bruta/). Systems of nomenclature vary in the degree of detail in terms of the product involved, as well as other characteristics, such as unit and granularity of location.
Options:
-
dataset: there are four choices:
"export_mun": selects exports data by municipality"import_mun": selects imports data by municipality"export_prod": selects exports data by producer"import_prod": selects imports data by producer
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# download treated (raw_data = FALSE) exports data by municipality (dataset = "export_mun")
# from 2020 to 2021 (time_period = 2020:2021)
data <- load_br_trade(
dataset = "export_mun",
raw_data = FALSE,
time_period = 2020:2021
)
# download treated(raw_data = FALSE) imports data by municipality (dataset = "import_mun")
# from 2020 to 2021 (time_period = 2020:2021)
data <- load_br_trade(
dataset = "import_mun",
raw_data = FALSE,
time_period = 2020:2021
)Loads disaggregated data on bilateral trade flows for more than 5000 products and 200 countries. The data is from the CEPII and is built from data directly reported by each country to the United Nations Statistical Division (Comtrade).
As all of the data is packed into one single .zip file in the website, data on all years must be downloaded, even if not all of it is used. Therefore, downloading the data can take a long time.
Options:
-
dataset: there is one choice:
"HS92"which follows the Harmonized System method
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# download treated data for 2016 (takes a long time to download)
clean_baci <- load_baci(
raw_data = FALSE,
time_period = 2016
)Loads IBGE information on gross domestic product at current prices, taxes, net of subsidies, on products at current prices and gross value added at current prices, total and by economic activity, and respective shares. Data is available at country, state and municipality level and from 2002 to 2018.
Options:
-
dataset:
"pibmunic" -
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
"country","state", or"municipality" -
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# download treated municipal GDP data at the state level for 2010 to 2012
data <- load_pibmunic(
raw_data = FALSE,
geo_level = "state",
time_period = 2010:2012
)Employment, salary and firm data from IBGE’s Cadastro Central de Empresas (CEMPRE). Loads information on companies and other organizations and their respective formally constituted local units, registered with the CNPJ - National Register of Legal Entities. Data is available between 2006 and 2019.
Options:
-
dataset:
"cempre" -
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
"country","state"or"municipality" -
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng") -
sectors: defines if the data will be return separated by sectors (
sectors = TRUE) or not (sectors = FALSE)
Examples:
# Download raw data (raw_data = TRUE) at the country level
# from 2008 to 2010 (time_period = 2008:2010).
data <- load_cempre(
raw_data = TRUE,
geo_level = "country",
time_period = 2008:2010
)
# Download treted data (raw_data = FALSE) by state (geo_level = "state")
# from 2008 to 2010 (time_period = 2008:2010) in portuguese (language = "pt").
# In this example, data is split by sector (sectors = TRUE)
data <- load_cempre(
raw_data = FALSE,
geo_level = "state",
time_period = 2008:2010,
language = "pt",
sectors = TRUE
)Municipal Agricultural Production (PAM, in Portuguese) is a nationwide annual survey conducted by IBGE (Brazilian Institute of Geography and Statistics) which provides information on agricultural products, such as quantity produced, area planted and harvested, average quantity of output and monetary value of such output. The products are divided in permanent and temporary farmed land, as well as dedicated surveys to the four products that yield multiple harvests a year (beans, potato, peanut and corn), which all sum to a total survey of 64 agricultural products (31 of temporary tillage and 33 of permanent tillage). Output, however, is only included in the dataset if the planted area occupies over 1 acre or if output exceeds one tonne.
Permanent farming is characterized by a cycle of long duration, whose harvests may be done multiple times across the years without the need of planting seeds again. Temporary farming, on the other hand, consists of cycles of short and medium duration, which after harvesting require planting seeds again.
The data also has multiple aggregation levels, such as nationwide, by region, mesoregion and microregion, as well as state and municipality.
The data available has a yearly frequency and is available from 1974 to the present, with the exception of the four multiple-harvest products, which are only available from 2003. More information can be found on this link (only in Portuguese).
Options:
-
dataset: See tables below
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
"country","region","state", or"municipality" -
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng")
The datasets supported are shown in the tables below, made up of both the original databases and their narrower subsets. Note that downloading only specific crops is considerably faster.
Full datasets provided by IBGE:
| dataset |
|---|
| all_crops |
| temporary_crops |
| permanent_crops |
| corn |
| potato |
| peanut |
| beans |
Datasets generated from Temporary Crops:
| dataset | Name (pt) | Name (eng) |
|---|---|---|
| total_temporary | Total | Total |
| abacaxi | Abacaxi | Pineapple |
| alfafa | Alfafa Fenada | Alfafa Fenada |
| alho | Alho | Garlic |
| algodao_herbaceo | Algodao Herbaceo (em Caroco) | Herbaceous Cotton (in Caroco) |
| amendoim_temporary | Amendoim (em Casca) | Peanuts (in Shell) |
| arroz | Arroz (em Casca) | Rice (in husk) |
| aveia | Aveia (em Grao) | Oats (in grain) |
| batata_doce | Batata Doce | Sweet potato |
| batata_inglesa | Batata Inglesa | English potato |
| cana_de_acucar | Cana de Acucar | Sugar cane |
| cana_para_forragem | Cana para Forragem | Forage cane |
| castor_bean | Mamona (Baga) | Castor bean (Berry) |
| cebola | Cebola | Onion |
| cevada | Cevada (em Grao) | Barley (in Grain) |
| ervilha | Ervilha (em Grao) | Pea (in Grain) |
| fava | Fava (em Grao) | Broad Bean (in Grain) |
| feijao_temporary | Feijao (em Grao) | Beans (in Grain) |
| fumo | Fumo (em Folha) | Smoke (in Sheet) |
| girassol_sementes | Girassol (em Grao) | Sunflower (in Grain) |
| juta_fibra | Juta (Fibra) | Jute (Fiber) |
| linho_sementes | Linho (Semente) | Linen (Seed) |
| malva_fibra | Malva (Fibra) | Malva (Fiber) |
| mandioca | Mandioca | Cassava |
| melancia | Melancia | watermelon |
| melao | Melao | Melon |
| milho_temporary | Milho (em Grao) | corn (in grain) |
| rami_fibra | Rami (Fibra) | Ramie (Fiber) |
| rye | Centeio (em Grao) | Rye (in grain) |
| soja | Soja (em Grao) | Soybean (in grain) |
| sorgo | Sorgo (em Grao) | Sorghum (in Grain) |
| tomate | Tomate | Tomato |
| trigo | Trigo (em Grao) | Wheat in grain) |
| triticale | Triticale (em Grao) | Triticale (in grain) |
Datasets generated from Permanent Crops:
| dataset | Name (pt) | Name (eng) |
|---|---|---|
| acai | Acai | Acai |
| annatto_seeds | Urucum (Semente) | Annatto (Seed) |
| apple | Maca | Apple |
| avocado | Abacate | Avocado |
| banana | Banana (Cacho) | Banana (Bunch) |
| black_pepper | Pimenta do Reino | Black pepper |
| cashew | Caju | Cashew |
| cashew_nut | Castanha de Caju | Cashew Nuts |
| cocoa_beans | Cacau (em Amendoa) | Cocoa (in Almonds) |
| coffee_arabica | Cafe (em Grao) Arabica | Cafe (in Grao) Arabica |
| coffee_canephora | Cafe (em Grao) Canephora | Cafe (in Grain) Canephora |
| coffee_total | Cafe (em Grao) Total | Coffee (in Grain) Total |
| coconut | Coco da Baia | Coconut |
| coconut_bunch | Dende (Cacho de Coco) | Coconut Bunch |
| cotton_arboreo | Algodao Arboreo (em Caroco) | Arboreo cotton (in Caroco) |
| fig | Figo | Fig |
| grape | Uva | Grape |
| guarana_seeds | Guarana (Semente) | Guarana (Seed) |
| guava | Goiaba | Guava |
| heart_of_palm | Palmito | Palm heart |
| india_tea | Cha da India (Folha Verde) | India Tea (Leaf) |
| khaki | Caqui | Khaki |
| lemon | Limao | Lemon |
| mango | Manga | Mango |
| papaya | Mamao | Papaya |
| passion_fruit | Maracuja | Passion fruit |
| peach | Pessego | Peach |
| pear | Pera | Pear |
| permanent_total | Total | Total |
| quince | Marmelo | Quince |
| rubber_coagulated_latex | Borracha (Latex Coagulado) | Rubber (Coagulated Latex) |
| rubber_liquid_latex | Borracha (Latex Liquido) | Rubber (Liquid Latex) |
| sisal_or_agave | Sisal ou Agave (Fibra) | Sisal or Agave (Fiber) |
| tangerine | Tangerina | Tangerine |
| tung | Tungue (Fruto Seco) | Tung (Dry Fruit) |
| walnut | Noz (Fruto Seco) | Walnut (Dry Fruit) |
| yerba_mate | Erva Mate (Folha Verde) | Mate Herb (Leaf) |
Examples:
# download treated data at the state level from 2010 to 2011 for all crops
data <- load_pam(
dataset = "all_crops",
raw_data = FALSE,
geo_level = "state",
time_period = 2010:2011,
language = "eng"
)Loads information on the amount and value of the production of the exploitation of native plant resources and planted forest massifs, as well as existing total and harvested areas of forest crops.
Data is from the Silviculture and Forestry Extraction Production (PEVS, in Portuguese), a nationwide annual survey conducted by IBGE (Brazilian Institute of Geography and Statistics). The data also has multiple aggregation levels, such as nationwide, by region, mesoregion and microregion, as well as state and municipality.
The data available has a yearly frequency and is available from 1986 to the present, with the exception of the data on total area for production, which are only available from 2013 onwards. More information can be found in this link.
Options:
-
dataset: there are three choices:
"pevs_forest_crops": provides data related to both quantity and value of the forestry activities. The data goes from 1986 to 2019 and it is divided by type of product."pevs_silviculture": provides data related to both quantity and value of the silviculture. The data goes from 1986 to 2019 and it is divided by type of product."pevs_silviculture_area": total existing area used for silviculture in 12/31.The data goes from 2013 to 2019 and it is divided by forestry species.
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
"country","region","state", or"municipality" -
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download treated (raw_data = FALSE) silviculture data (dataset = 'pevs_silviculture')
# by state (geo_level = 'state') from 2012 (time_period = 2012)
# in portuguese (language = "pt")
data <- load_pevs(
dataset = "pevs_silviculture",
raw_data = FALSE,
geo_level = "state",
time_period = 2012,
language = "pt"
)
# Download raw (raw_data = TRUE) forest crops data by region
# from 2012 to 2013 in english
data <- load_pevs(
dataset = "pevs_forest_crops",
raw_data = TRUE,
geo_level = "region",
time_period = 2012:2013
)Data on livestock inventories (e.g:cattle, pigs and hogs) in Brazilian Municipalities, as well as amount and value of animal products (e.g:output of milk, hen eggs, quail eggs, honey).
The periodicity of the survey is annual. The geographic coverage is national, with results released for Brazil, Major Regions, Federation Units, Mesoregions, Microregions and Municipalities.
The data available has a yearly frequency and is available from 1974 to the present. More information can be found in this link.
Options:
-
dataset: there are five possible choices:
-
"ppm_livestock_inventory": livestock herds, with the number of animals by species. -
"ppm_sheep_farming": total sheep reared and specific shearing data. -
"ppm_animal_origin_production": animal-origin products like milk, eggs, and honey, as well as wool and other derivatives. -
"ppm_cow_farming": dairy cow farming, with the number of milked cows, their geographic distribution, and productivity rates. -
"ppm_aquaculture": aquaculture activities, including fish farming, shrimp farming, and mollusk farming.
-
-
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
geo_level:
"country","region","state", or"municipality" -
time_period: picks the years for which the data will be downloaded
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download treated data (raw_data = FALSE) about aquaculture (dataset = "ppm_aquaculture")
# from 2013 to 2015 (time_period = 2013:2015) in english
# with the level of aggregation being the country (geo_level = "country").
data <- load_ppm(
dataset = "ppm_aquaculture",
raw_data = FALSE,
geo_level = "country",
time_period = 2013:2015
)
# Download raw data about sheep farming by state from 1980 to 1995 in portuguese (language = "pt")
data <- load_ppm(
dataset = "ppm_sheep_farming",
raw_data = TRUE,
geo_level = "state",
time_period = 1980:1995,
language = "pt"
)Loads information the mines being explored legally in Brazil, including their location, status, product being mined and area in square meters etc. Survey is done at municipal and state level. The National Mining Agency (ANM) is responsible for this survey.
Options:
-
dataset:
"sigmine_active" -
raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
-
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# Download treated data (raw_data = FALSE) in portuguese (language = "pt").
data <- load_sigmine(
dataset = "sigmine_active",
raw_data = FALSE,
language = "pt"
)Loads data from the National Electrical Energy Agency (ANEEL), a Brazilian independent federal agency linked to the Ministry of Mines and Energy (MME). ANEEL works to provide favorable conditions for the Electrical Energy Market to develop with balance and for the benefit of society.
As for now, there are three different datasets available for download: the Energy Development Budget and the Energy Generation.
The Energy Development Budget dataset showcases the Energy Development Account’s (CDE) anual budget expenses. The CDE is designed to promote the Brazilian energy development and is managed by the Electrical Energy Commercialization Chamber (CCEE).
The dataset makes available the year of the observation – from 2013 to 2022 –, the type of expense, its value in R$ (Reais) and its share over the total amount of CDE budget expenses on the year*.
*Note that ‘share_of_total’ values sum to 1 for each year available.
The Energy Generation dataset showcases information about ANEEL’s Generation Informations System (SIGA). SIGA provides information about the Brazilian electrical energy generation installed capacity.
The dataset provides information at the individual venture/entity level. It contains information about the power, source, stage, type of permission, origin and final fuel with which each venture/entity operates, as well as other legal, technical and geographical information.* Operation start dates contained in the dataset go as far back as 1924 up to 2022.
* For more details on each variable, access This link and select “Manual do Usuario”.
The Energy Enterprises dataset showcases information about distributed micro and mini generators, covered by the Regulatory Resolution nº 482/2012. The list of projects is classified by variables that make up their identification, namely: connected distributor, project code, numerical nucleus of the project code, owner name, production class, subgroup, name of the owner, number of consumer units that receive credits, connection date, type of generating unit, source, installed power, municipality, and federative unit where it is located.
The data is expressed in quantities and installed power in kW (kilowatt). The quantity corresponds to the number of distributed micro or mini generators installed in the specified period. The installed power is defined by the sum of the nominal active electric power of the generating units.
* For more details on each variable, access This link and select “Dicionário de dados”.
Options:
- dataset: there are three choices:
"energy_development_budget": government spending towards energy sources"energy_generation": energy generation by entity/corporation"energy_enterprises_distributed": distributed micro and mini generators
- raw_data: there are two options:
TRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.
- language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# download treated data about energy generation
clean_aneel <- load_aneel(
dataset = "energy generation",
raw_data = FALSE
)Loads data from the Energy Research Company (EPE), a Brazilian public company that works closely with the Brazilian Ministry of Mines and Energy (MME) and other agencies to ensure the sustainable development of Brazil’s energy infrastructure. EPE’s duty on that mission is to support MME with quality research and studies in order to aid Brazil’s energy infrastructure planning.
As for now, there are three different datasets available for download: Consumer Energy Consumption, Industrial Energy Consumption, and the National Energy Balance. All of them were obtained from the EPE website.
The Consumer Energy Consumption dataset provides monthly data from 2004 to 2025 about energy consumption and number of consumers. The data is organized by State, Region, or Electric Subsystem, and is broken down by class of service and type of consumer.
The available classes are: Residential, Commercial, Industrial, Rural, and Others. For each observation, the dataset reports the type of consumer (Captive or Free), total consumption in megawatt-hours (MWh), and the number of consumers.
When using the Subsystem or Region level, consumer totals are provided but are not disaggregated for all classes and consumer types.
The Industrial Energy Consumption dataset provides monthly data from 2004 to 2025 on energy consumption by industrial sector. Data is available at the State or Subsystem level. Each observation identifies the industrial sector responsible for the consumption and the amount consumed in megawatt-hours (MWh).
The National Energy Balance is a thorough and extensive research developed and published by EPE that contains useful data about energy production, consumption, imports, exports, transformation, and final use.
The processed dataset provides yearly data from 2003 to 2023. It covers all Brazilian energy sources (such as petróleo, gás natural, carvão, eletricidade, lenha, solar and others) and distinguishes between different types of energy flow: production, transformation, final consumption, losses, and adjustments.
Each energy source appears as a separate column in the original spreadsheets. The cleaned data is returned in long format, with one row per combination of year, energy source, and account type. The account type is labeled to indicate whether it refers to production, transformation (for example, “TRANSFORMAÇÃO – REFINARIAS DE PETRÓLEO”), or consumption (for example, “CONSUMO – RESIDENCIAL”).
Options:
-
dataset: there are three choices:
"consumer_energy_consumption": monthly energy consumption and consumers by State, Region or Electric Subsystem
"industrial_energy_consumption": monthly industrial energy consumption by State or Subsystem
"national_energy_balance": yearly energy flow by account and energy source -
raw_data: there are two options:
TRUE: if you want the data as it is originally.
FALSE: if you want the treated version of the data. -
geo_level: only applies to
"consumer_energy_consumption"and"industrial_energy_consumption"datasets.
"state"
"subsystem" -
language: you can choose between Portuguese
("pt")and English("eng")
Examples:
# download treated data about consumer energy consumption at the state level
clean_epe <- load_epe(
dataset = "consumer_energy_consumption",
geo_level = "state",
raw_data = FALSE
)
# download treated data from the National Energy Balance
balance <- load_epe(
dataset = "national_energy_balance",
raw_data = FALSE
)Many of our functions use a dataset with Brazilian municipalities, their
municipality codes, whether they belong to the Legal Amazon, their
state, and some more variables. It was constructed from the IBGE
spreadsheet
with Legal Amazon municipalities, along with a data frame from the
‘geobr’ package. For more information on the columns, run
??datazoom.amazonia::municipalities.
Regarding the statistics in this package reported at the municipality level, for municipalities that are only partially included in the Legal Amazon (according to IBGE’s latest release), the data refer to the entire municipality, not just to the portion that lies within the Legal Amazon.
# load Brazilian municipalities dataset
data <- datazoom.amazonia::municipalitiesFor some of our functions, the original data is stored in Google Drive
and exceeds the file size limit for which direct downloads are possible.
As a result, the googledrive package is required to download the data
though the Google Drive API and run the function.
The first time the package is called, it requires you to link your Google account and grant permissions to be able to download data through the Google Drive API.
You must tick all boxes when the permissions page opens, or else the following error will occur:
# Error in `gargle_abort_request_failed()`:
# ! Client error: (403) Forbidden
# Insufficient Permission: Request had insufficient authentication scopes.
# • domain: global
# • reason: insufficientPermissions
# • message: Insufficient Permission: Request had insufficient authentication
# scopes.
# Run `rlang::last_error()` to see where the error occurred.For further information, click here to access the official package page.
DataZoom is developed by a team at Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Department of Economics. Our official website is at: https://www.econ.puc-rio.br/datazoom/.
To cite package datazoom.amazonia in publications use:
Data Zoom (2023). Data Zoom: Simplifying Access To Brazilian Microdata.
https://www.econ.puc-rio.br/datazoom/english/index.html
A BibTeX entry for LaTeX users is:
@Unpublished{DataZoom2023,
author = {Data Zoom},
title = {Data Zoom: Simplifying Access To Brazilian Microdata},
url = {https://www.econ.puc-rio.br/datazoom/english/index.html},
year = {2023},
}