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("remotes")
# remotes::install_github("datazoompuc/datazoom.amazonia")
install.packages("datazoom.amazonia", repos = c('https://datazoompuc.r-universe.dev', 'https://cloud.r-project.org'))
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A note on coverage: the years, versions and collections listed for each dataset follow what the data provider currently publishes, so they can change as new releases come out. If a
time_periodoryearyou request is not accepted, check the provider’s website for the latest coverage, or the package’s dataset table atdatasets_link.csv.
A note on caching: within a single R session, the package keeps the files it downloads, so asking for another dataset that comes from the same source file (for example, several SEEG sectors, or calling
sigmine_activetwice) does not download it again. For some sources (large Excel files and the SIGMINE zip), the parsed result of the last read is also kept. This cache lives only in the current session, in R’s temporary folder, and disappears when you restart R. To turn it off, runoptions(datazoom.amazonia.cache = FALSE)before calling the function.
PRODES (Projeto de Monitoramento da Floresta Amazônica Brasileira - Project for Monitoring the Brazilian Amazon Forest) is Brazil’s primary satellite-based deforestation monitoring system operated by INPE (National Institute for Space Research).
This dataset provides:
PRODES is the authoritative source for Amazon deforestation statistics, used for environmental monitoring, policy evaluation, and international reporting on Brazilian forest protection.
PRODES monitoring: - Uses satellite imagery from multiple sources (Landsat, CBERS, others) - Automated and manual analysis to detect clear-cut deforestation - Annual assessment of forest loss - Recently (2020+) raster data published through TerraBrasilis - Data available as both raster files and aggregated by municipality
For more information, visit INPE PRODES Project and TerraBrasilis.
Clear-cut deforestation areas (complete forest loss).
Deforestation that was not captured in previous surveys (detected through improved methodology).
Remaining native forest and natural vegetation areas.
Water bodies and hydrographic features.
Non-forest areas including savanna, grasslands, and other vegetation types.
Cloud cover in satellite imagery (data quality indicator).
raw_data = TRUE): Returns SpatRaster objects (raster
grids) from TerraBrasilisraw_data = FALSE): Aggregated to municipality
level showing total affected area| Year | Type | Definition |
|---|---|---|
| 2007 | Cumulative | All deforestation from 1988-2007 |
| 2008-2025 | Incremental | Deforestation detected in that specific year |
When analyzing time trends, be aware that 2007 includes 19 years of accumulated loss.
Selects which landscape classification to download.
dataset = "deforestation" # Clear-cut deforestation (main product)
dataset = "residual_deforestation" # Previously undetected deforestation
dataset = "native_vegetation" # Remaining forests and vegetation (2025 only)
dataset = "hydrography" # Water bodies (2025 only)
dataset = "non_forest" # Savanna and other non-forest (2025 only)
dataset = "clouds" # Cloud cover (2025 only)
Controls data format: raster or municipality aggregates.
TRUE: Returns raw raster data (SpatRaster objects)FALSE: Returns municipality-level aggregated area dataraw_data = FALSE # logical
Important: Raster data is large; ensure sufficient storage and memory.
Specifies which year(s) to download.
Available by dataset:
| Dataset | Available Years |
|---|---|
deforestation |
2007 (cumulative), 2008-2025 (incremental) |
residual_deforestation |
2010-2025 |
native_vegetation |
2025 only |
hydrography |
2025 only |
non_forest |
2025 only |
clouds |
2025 only |
time_period = 2020 # single year
time_period = c(2015, 2020) # multiple years
time_period = 2015:2020 # range of years (for deforestation only)
Output language.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated deforestation data for 2025
deforestation <- load_prodes(
dataset = "deforestation",
raw_data = FALSE,
time_period = 2025,
language = "eng"
)
# download treated deforestation data for 2008 to 2025
deforestation_series <- load_prodes(
dataset = "deforestation",
raw_data = FALSE,
time_period = 2008:2025,
language = "eng"
)
# download 2007 (cumulative 1988-2007) plus all years up to 2025
all_deforestation <- load_prodes(
dataset = "deforestation",
raw_data = FALSE,
time_period = c(2007, 2008:2025),
language = "eng"
)
# download treated residual deforestation data for 2020
residual <- load_prodes(
dataset = "residual_deforestation",
raw_data = FALSE,
time_period = 2020,
language = "eng"
)
DETER (Real-Time Detection System) is a satellite-based monitoring system operated by INPE (National Institute of Space Research) that detects and reports changes in forest cover with near real-time frequency. This system provides:
DETER is the primary early-warning system for deforestation in the Amazon, used by Brazilian environmental agencies for enforcement, research institutions for analysis, and internationally for monitoring compliance with forest conservation goals.
DETER monitoring: - Uses imagery from wide-field optical satellites (the satellites listed in the raw data are CBERS-4, CBERS-4A and Amazonia-1 and, for the Amazon product only, Resourcesat-2) - Automated and manual analysis to detect recent disturbances - Generates “alerts” - polyons representing areas of detected change - Updates available regularly (frequency varies by satellite availability) - Operated by INPE’s Remote Sensing Division
For technical details, visit INPE DETER System.
DETER monitoring data for the Legal Amazon biome.
alert_type): DESMATAMENTO_CR, DESMATAMENTO_VEG,
DEGRADACAO, CICATRIZ_DE_QUEIMADA, CORTE_SELETIVO,
CS_DESORDENADO, CS_GEOMETRICO and MINERACAO
DETER monitoring data for the Cerrado biome.
alert_type): only DESMATAMENTO_CR (clear-cut
deforestation)DETER monitoring data for the Pantanal biome.
alert_type): lowercase Portuguese labels –
cicatriz de queimada (burn scar), mineracao (mining),
supressao com solo exposto (suppression with exposed soil) and
supressao com vegetacao (suppression with vegetation)DETER monitoring data for the non-forest areas of the Legal Amazon,
published by INPE as a product separate from deter_amz.
alert_type): the same lowercase Portuguese labels
as deter_pantanal
deter_amz) with this separate
productNote on class vocabularies: the raw source data uses different class
labels across these products – upper-snake-case codes for deter_amz
and deter_cerrado, lowercase Portuguese labels for deter_pantanal
and deter_non_forest. load_deter() does not normalize them:
alert_type keeps whichever vocabulary the source used, so recode it
yourself before stacking datasets from different products.
The raw DETER data from INPE reports one alert/detection per row, with each row typically associated with a single municipality in the raw data. However, many alerts actually overlap multiple municipalities (typically 2-4 municipalities) that are not all shown in the original records.
This package provides an important enhancement: spatial intersection with IBGE municipality geometries (2019 version) to identify ALL municipalities that each DETER alert overlaps with. This creates a more complete and accurate geographic picture than the original raw data.
Important note on CRS metadata: load_deter() reprojects internally
to IBGE’s official equal-area projection for area calculations (Albers,
SIRGAS 2000/GRS80, central meridian -54) before computing area and
overlaying municipality boundaries – not UTM zones (see “Important
Considerations” below for the exact parameters).
Selects which biome’s DETER data to download.
dataset = "deter_amz" # Legal Amazon monitoring
dataset = "deter_cerrado" # Cerrado biome monitoring
dataset = "deter_pantanal" # Pantanal biome monitoring
dataset = "deter_non_forest" # Non-forest areas of the Legal Amazon
Controls whether to download the original data or the processed/enhanced version.
TRUE: Returns raw INPE data with limited municipality informationFALSE: Returns treated data with spatial intersection to identify
all affected municipalities, standardized English variable namesraw_data = FALSE # logical
Recommendation: Use raw_data = FALSE to get the enhanced
municipality identification from spatial intersection.
Output language for variable names and documentation.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated DETER Amazon data
deter_amz <- load_deter(
dataset = "deter_amz",
raw_data = FALSE,
language = "eng"
)
# download treated DETER Cerrado data
deter_cerrado <- load_deter(
dataset = "deter_cerrado",
raw_data = FALSE,
language = "eng"
)
# download treated DETER Pantanal data
deter_pantanal <- load_deter(
dataset = "deter_pantanal",
raw_data = FALSE,
language = "eng"
)
# download treated DETER non-forest (Legal Amazon) data
deter_non_forest <- load_deter(
dataset = "deter_non_forest",
raw_data = FALSE,
language = "eng"
)
Each alert/row typically contains: - Spatial geometry: Polygon
coordinates (SF object) - Detection date: When the alert was
issued - Alert type: Deforestation, forest degradation, or other
disturbance (the label vocabulary differs between products, see above) -
Area: Size of detected change in square metres (a units object,
[m^2]) - Municipality: Geographic unit identification - State:
Brazilian state - Metadata: Satellite source, confidence level
(varies by product)
load_deter() reprojects internally to IBGE’s
official equal-area projection for area calculations (Albers, SIRGAS
2000/GRS80, central meridian -54, latitude of origin -12, standard
parallels -2 and -22, false easting/northing 5,000,000/10,000,000)
before computing area and overlaying municipality boundaries – not
UTM zones.The DEGRAD project is a research initiative that uses satellite imagery to monitor forest degradation in the Amazon. Unlike DETER’s near real-time alerts, DEGRAD provides a more detailed annual analysis of forest degradation patterns.
This dataset captures:
DEGRAD data is valuable for understanding forest degradation as a distinct phenomenon from clear-cut deforestation, important for carbon accounting, biodiversity protection, and understanding transition stages toward complete forest loss.
DEGRAD monitoring: - Conducted by INPE’s forest monitoring programs - Uses satellite imagery interpretation to identify forest degradation signs - Focuses on selective logging, small-scale agriculture, forest fires, and other degrading activities - Released as annual editions with comprehensive analysis - Limited documentation available (original INPE documentation is sparse)
For information, visit INPE Forest Monitoring.
Important: DEGRAD data is organized differently than real-time systems. Key points:
This package enhances the raw DEGRAD data by: - Intersecting DEGRAD spatial polygons with IBGE municipality boundaries (2019 version) - Providing municipality identification for each degradation event - Converting to Simple Features (SF) objects for spatial analysis
Note on CRS: load_degrad() reprojects internally to IBGE’s
official equal-area projection for area calculations (Albers, SIRGAS
2000/GRS80, central meridian -54) before computing area and overlaying
municipality boundaries – not UTM zones.
Detailed monitoring of forest degradation across the Legal Amazon.
Only one dataset is available:
dataset = "degrad" # Forest degradation monitoring
Controls whether to download the original data or the processed/enhanced version.
TRUE: Returns raw INPE data with minimal processingFALSE: Returns treated data with English variable names,
municipality identification from spatial intersection, and
standardized formattingraw_data = FALSE # logical
Recommendation: Use raw_data = FALSE for most applications to get
municipality-level information. ### 3. time_period
Specifies which year(s) of degradation events to download.
Important: When you request a year, you get events from that year regardless of which DEGRAD edition they appear in.
time_period = 2015 # single year
time_period = c(2010, 2015) # multiple specific years
time_period = 2010:2015 # range of years
Output language for variable names and documentation.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
The returned data is a Simple Features (SF) spatial object with:
# download treated forest degradation data from 2010 to 2012
data <- load_degrad(
dataset = "degrad",
raw_data = FALSE,
time_period = 2010:2012,
language = "eng"
)
The annual edition structure (e.g., “DEGRAD 2016”) mixed with variable event years within those editions means: - When you request year 2015, you get all detected 2015 events regardless of edition - Some 2015 events may appear in both DEGRAD 2015 and DEGRAD 2016 editions - Duplication is handled in the data loading process
Common degradation types include: - Selective logging: Commercial timber extraction - Forest fires: Fire damage to forest areas - Agricultural clearing: Small-scale farming expansion - Mining: Degradation from mining activities - Other: Mixed or unclassified degradation causes
(Exact categories vary by edition; verify with your loaded data)
load_degrad() reprojects internally to IBGE’s
official equal-area Albers projection before computing area and
overlaying municipality boundaries (see the CRS note above) – not
something the user needs to check manuallyst_is_valid() to checkImazon (Instituto do Homem e Meio Ambiente da Amazônia - Institute of Man and Environment of the Amazon) is an independent Brazilian research organization that produces its own deforestation monitoring and analysis.
This dataset provides:
Imazon’s deforestation pressure index helps identify which municipalities face high deforestation risk, valuable for targeted conservation, enforcement, and development planning.
Imazon’s classification is based on: - Deforestation monitoring using satellite imagery - Analysis of deforestation trends and drivers - Risk assessment methodology to classify municipalities - Regular updates as monitoring data accumulates
For more information, visit Imazon Official Website.
Municipality-level deforestation pressure classification with spatial geometries.
Imazon’s three-level classification system: | Category | Pressure Level | Description | |———-|—|—| | 0 | No/Low Pressure | Minimal deforestation threat | | 1 | Moderate Pressure | Some deforestation risk | | 2 | High Pressure | Significant deforestation threat | | 3 | Very High Pressure | Severe/Critical deforestation risk |
Only one dataset is available:
dataset = "imazon_shp" # Imazon municipality pressure classification
Controls whether to download the original data or the processed/cleaned version.
TRUE: Returns raw shapefile data exactly as provided by ImazonFALSE: Returns treated data with standardized English variable
names, consistent formatting, and SF object structureraw_data = FALSE # logical
Output language for variable names and documentation.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated Imazon deforestation pressure data
data <- load_imazon(
raw_data = FALSE,
language = "eng"
)
The 0-3 scale represents Imazon’s assessment of deforestation risk: - Based on historical deforestation trends - Current forest cover - Accessibility and economic drivers - Human pressure factors
This dataset works well in combination with: - DETER: Compare pressure classification with actual recent deforestation - DEGRAD: Correlate pressure with degradation patterns - CEMPRE: Analyze relationship between employment and deforestation pressure - COMEX: Examine trade patterns in high-pressure vs low-pressure areas
🔴 This function uses the googledrive package to download data. In
case of authentication errors, see googledrive.
The Brazilian Institute of Environment and Renewable Natural Resources (Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis - IBAMA) dataset documents environmental enforcement actions, including embargoes of productive assets and fines issued for environmental violations. This dataset provides individual-level records of environmental infractions from 2005 onwards, representing the enforcement activity of Brazil’s primary federal environmental agency.
The data covers environmental violations across multiple sectors including deforestation, illegal mining, wildlife trafficking, illegal fishing, and other environmental crimes.
The IBAMA dataset includes:
The function provides access to three distinct datasets tracking different stages of environmental enforcement:
"embargoed_areas")
"distributed_fines")
"collected_fines")
The function returns either: - Raw Data: Individual infraction records (original format) - Aggregated Data: Summary statistics for each time-location period, including: - Total number of infractions - Infractions sent to prosecution - Infractions with ongoing legal proceedings - Embargoed area totals - Fine totals and collection rates
Options:
"embargoed_areas": Embargoed productive areas"distributed_fines": Issued fines (paid or unpaid)"collected_fines": Fines that have been paidTRUE: Individual-level records as originally recordedFALSE: Treated/aggregated version of the data"all")"AC" (Acre)c("AC", "AM", "AP") (Acre, Amazonas,
Amapá)"pt": Portuguese language labels and names"eng": English language# download treated embargoed areas data in english
data <- load_ibama(
dataset = "embargoed_areas",
raw_data = FALSE,
language = "eng"
)
# download treated collected fines data from Bahia
data <- load_ibama(
dataset = "collected_fines",
raw_data = FALSE,
states = "BA",
language = "pt"
)
The MapBiomas project provides comprehensive satellite-based data on land cover and land use changes across Brazil. The project uses machine learning algorithms applied to Landsat satellite imagery to classify and track changes in biomes, land cover types, and economic activities over time. This dataset is essential for understanding deforestation, agricultural expansion, conservation outcomes, and landscape dynamics in the Amazon and other biomes.
MapBiomas represents one of the most detailed and scientifically rigorous sources of remote sensing data on Brazilian land cover, with coverage spanning from 1985 to present and annual updates.
The MapBiomas dataset includes:
geo_level within the same dataset
(most run through 2024, a few still stop at 2023 or earlier) – see
Measurement Units and Notes below for how to check the year range
for a specific (dataset, geo_level) pair before assuming it matches
another"mapbiomas_cover")Annual maps of land cover types including: - Forest Types: Native forest, forest formation, forest plantation - Non-Forest Vegetation: Grassland/pasture, shrub vegetation, herbaceous - Agricultural Land: Temporary crops (annual/seasonal), perennial crops (sugarcane, coffee, cocoa) - Urban Areas: Urban/built-up land and infrastructure - Water Bodies: Rivers, lakes, reservoirs, aquaculture - Non-vegetated: Mining, bare soil, rock outcrops - Other Categories: Cloud coverage, nodata
Key Applications: Deforestation monitoring, agricultural expansion, urbanization patterns, forest conservation
"mapbiomas_transition")Year-to-year changes in land cover including: - Deforestation: Conversion from forest to other uses - Reforestation: Conversion to forest types - Agricultural Transitions: Changes between crop types or from other uses to agriculture - Degradation: Forest to degraded/shrub vegetation - Regeneration: Abandoned areas returning to vegetation
Key Applications: Deforestation tracking, regeneration assessment, agricultural dynamics, conservation effectiveness
"mapbiomas_deforestation_regeneration")Specific focus on forest loss: - Deforestation: Permanent loss of forest cover - Degradation Signals: Forest areas showing stress indicators - Cumulative Deforestation: Total forest loss since baseline
Note: despite the name, this dataset covers deforestation only.
Regeneration is a separate dataset, "mapbiomas_secondary_vegetation"
(below).
Key Applications: Amazon monitoring, conservation evaluation, climate impact studies
"mapbiomas_secondary_vegetation")Forest regrowth and natural recovery on previously deforested land: - Forest Regeneration: Secondary forest regrowth and natural recovery - Regeneration Extent: Total area of returning vegetation
Key Applications: Regeneration assessment, conservation evaluation, climate impact studies
"mapbiomas_mining")Areas used for mining operations: - Active Mining: Currently operational extraction sites - Abandoned Mining: Previous mining areas, potentially available for rehabilitation - Mining Extent: Total area impacted by mining activities - Mining Types: Surface mining, artisanal mining, infrastructure
Key Applications: Environmental impact assessment, land degradation, restoration planning, environmental compliance
"mapbiomas_irrigation")Note: Temporarily unavailable – no irrigation statistics file exists
on MapBiomas’s Dataverse archive at any collection. docs_url is left
pointing at MapBiomas’s documentation for whoever investigates a
replacement.
Provides: - Irrigated Areas: Extent of irrigation in agricultural systems - Irrigation Type: Drip, center-pivot, flood irrigation - Crop Types Under Irrigation: Which crops receive irrigation
"mapbiomas_water")"municipality" and "biome" work. "state" is unavailable: no
MapBiomas collection publishes a state-level water sheet upstream.
"mapbiomas_fire")Areas affected by wildfires: - Burn Scars: Areas burned in recent fires - Fire Extent: Total area impacted per year - Fire Frequency: Repeated burning in same areas (indicating high fire risk) - Fire Season: Temporal pattern of fires
Key Applications: Fire monitoring, ecosystem vulnerability, climate impacts, fire management
Options:
"mapbiomas_cover": Land cover types"mapbiomas_transition": Changes in land cover"mapbiomas_deforestation_regeneration": Deforestation only (see
note above – regeneration is a separate dataset)"mapbiomas_secondary_vegetation": Forest regeneration/regrowth"mapbiomas_mining": Mining areas"mapbiomas_irrigation": Irrigated areas (temporarily
unavailable)"mapbiomas_water": Water bodies ("state" unavailable;
municipality/biome work)"mapbiomas_fire": Wildfire burn scarsTRUE: Data in original format from MapBiomasFALSE: Cleaned and standardized version (recommended for
analysis)"mapbiomas_cover": "municipality" or "indigenous_land"
"mapbiomas_transition": "municipality" or "biome"
(faster)"mapbiomas_deforestation_regeneration": "municipality"
only"mapbiomas_secondary_vegetation": "municipality" only"mapbiomas_mining": "indigenous_land" or "municipality"
"mapbiomas_irrigation": "state" or "biome"
"mapbiomas_water": "municipality" or "biome" ("state"
currently unavailable)"mapbiomas_fire": "state" only"pt": Portuguese language labels"eng": English languagegeo_levels of
the same dataset. MapBiomas does not release every dataset on the
same collection at the same time, and this package sometimes
deliberately pins one geo_level behind its siblings when a newer
collection is missing a sheet it needs (e.g. "mapbiomas_mining"’s
"indigenous_land" row is one collection behind its "municipality"
row – verified live; MapBiomas dropped that sheet from the newer
file). Don’t assume one row’s collection or year range applies to
another row of the same dataset.# download treated MapBiomas land cover data by municipality
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(
dataset = "mapbiomas_mining",
raw_data = FALSE,
geo_level = "indigenous_land",
language = "eng"
)
# download treated wildfire burn scar data by state
data <- load_mapbiomas(
dataset = "mapbiomas_fire",
raw_data = FALSE,
geo_level = "state",
language = "eng"
)
TerraClimate is a global climate and climatic water balance dataset developed by the Climatology Lab at University of California, Merced. This package provides access to TerraClimate data for Brazil and the Amazon region.
This dataset provides:
TerraClimate is essential for understanding climate variability, water availability, drought risk, agricultural potential, and climate change impacts across Brazil and the Amazon.
TerraClimate data is compiled by: - University of California Climatology Lab - Integration of satellite and ground-based observations - Validated against station networks - Downscaled to ~4km global resolution - Monthly temporal resolution with daily and subdaily estimates available
For more information, visit TerraClimate Project.
TerraClimate provides 13 main climate and water balance variables:
| Dataset | Code | Description | Units |
|---|---|---|---|
| max_temperature | tmax | Maximum 2-m Temperature | °C |
| min_temperature | tmin | Minimum 2-m Temperature | °C |
| 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 | W/m² |
| 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 |
Selects which climate variable to download.
# Temperature and radiation
dataset = "max_temperature" # tmax
dataset = "min_temperature" # tmin
dataset = "shortwave_radiation_flux" # srad
# Water and moisture
dataset = "precipitation" # ppt
dataset = "potential_evaporation" # pet
dataset = "water_evaporation" # aet (actual evapotranspiration)
dataset = "soil_moisture" # soil
dataset = "runoff" # q
# Atmospheric variables
dataset = "wind_speed" # ws
dataset = "vapor_pressure" # vap
dataset = "vapor_pressure_deficit" # vpd
# Drought and composite indices
dataset = "climatic_water_deficit" # def
dataset = "palmer_drought_severity_index" # PDSI
dataset = "snow_water_equivalent" # swe
Controls data format returned.
TRUE: Returns raw raster data (NetCDF, SpatRaster format)FALSE: Returns aggregated data (specific format depends on
configuration)raw_data = FALSE # logical
Specifies which year(s) to download.
Available range: 1958 to present (most recent months have 2-3 month lag)
time_period = 2020 # single year
time_period = c(2010, 2020) # specific years
time_period = 2010:2020 # range of years
Restricts geographic coverage to Legal Amazon region.
TRUE: Downloads only data for Legal Amazon region (much smaller
files)FALSE: Downloads data for all Brazil (larger files)legal_amazon_only = TRUE # logical
Recommendation: Use TRUE to significantly reduce download size for
Amazon-focused research.
Output language for variable names and documentation.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
Data Size: TerraClimate raster data is substantial. Consider:
Recommendations: - Use legal_amazon_only = TRUE to reduce size by
~95% - Download single or 2-3 year periods rather than decades at once -
Use high-speed internet connection - Have at least 10-50 GB free disk
space for multi-year downloads
# download precipitation data for the Legal Amazon (2020)
precip <- load_climate(
dataset = "precipitation",
time_period = 2020,
legal_amazon_only = TRUE,
language = "eng"
)
# download maximum temperature for multiple years, all of Brazil
max_temp <- load_climate(
dataset = "max_temperature",
time_period = 2010:2012,
language = "eng"
)
Validation: Validated against independent station networks
Uncertainty: Varies by region; higher in data-sparse areas
Interpolation: Satellite and station data combined and downscaled to 4km
Reliability: Generally excellent for temperature and precipitation; water balance variables have higher uncertainty
Due to large file size: - Aggregate early: Summarize by month/year quickly to reduce memory use - Legal Amazon only: Massive size reduction if working in Amazon region - Subset years: Download only years of interest rather than decades - Extract points: If doing point-based analysis, extract specific coordinates to simplify data - Use cloud computing: Consider cloud platforms (Google Earth Engine) for very large analyses
SEEG (Sistema de Estimativa de Emissões e Remoções de Gases de Efeito Estufa - System of Estimates of Emissions and Removals of Greenhouse Gases) is Brazil’s most comprehensive greenhouse gas emissions database, developed by Observatório do Clima (Climate Observatory).
This dataset provides:
For more information, visit SEEG Project and Observatório do Clima.
The package reads SEEG’s v13.0 municipal summary workbook
(seeg.eco.br). Categories are organized as Setor de emissão /
Categoria emissora / Sub-categoria emissora / Recorte /
Atividade geral. There is no time_period argument – every call
returns the full series.
A few dimensions are not available in the current data:
gas/produto breakdown – emissions are not split by individual
gas.Complete greenhouse gas emissions across all sectors in one dataset.
Only available with raw_data = TRUE – it cannot be requested
treated (raw_data = FALSE).
x1970…x2024)Sector: Agropecuaria. Only available with raw_data = FALSE.
Sector: Energia. Only available with raw_data = FALSE. No
fuel-type breakdown (see “Detail not included” above).
Sector: Mudanca de Uso da Terra e Floresta. Only available with
raw_data = FALSE. No biome dimension and no land-cover transition
matrix (see “Detail not included” above).
Sector: Processos Industriais. Only available with
raw_data = FALSE.
Sector: Residuos. Only available with raw_data = FALSE.
raw_data = FALSE)Every seeg_* sector dataset returns long-format data, one row per
category combination and year, with a single value (eng) / Valor
(pt) column. The columns available depend on geo_level and on which
4th categorical dimension that sector carries:
| geo_level | eng columns | pt columns |
|---|---|---|
| municipality | year, city, state, ibge, sector, emission_category, emission_subcategory, <4th dim>, general_activity, emission_type, value | Ano, municipio, estado, ibge, setor, categoria_emissao, subcategoria_emissao, <4th dim>, atividade_geral, tipo_emissao, Valor |
| state | (drops city, ibge) | (drops municipio, ibge) |
| country | (also drops state) | (also drops estado) |
The 4th dimension’s name (and whether it is translated in pt) differs
by sector:
| Dataset | eng name | pt name | pt renamed? |
|---|---|---|---|
| seeg_residuals | waste_stream | tipo_residuo | yes |
| seeg_farming | emission_pathway | via_emissao | yes |
| seeg_industry | emission_scope | recorte | no – stays recorte |
| seeg_energy | emission_scope | recorte | no – stays recorte |
| seeg_land | emission_scope | recorte | no – stays recorte |
language = "pt" output is never value-translated – only
language = "eng" runs SEEG’s PT->EN value dictionaries
(SEEG_SECTOR_EN, SEEG_EMISSION_TYPE_EN, and one
category/subcategory/dimension/activity dictionary per sector, in
R/seeg.R). A PT call returns the source’s own Portuguese values as-is.
Selects which emission sector to download.
dataset = "seeg" # All sectors (raw_data = TRUE only)
dataset = "seeg_farming" # Agriculture and livestock (raw_data = FALSE only)
dataset = "seeg_energy" # Energy sector (raw_data = FALSE only)
dataset = "seeg_land" # Land use changes (raw_data = FALSE only)
dataset = "seeg_industry" # Industrial processes (raw_data = FALSE only)
dataset = "seeg_residuals" # Waste and residuals (raw_data = FALSE only)
Controls whether to download the original or the treated data. Each
dataset only works with one value (see above): "seeg" requires
TRUE; every seeg_* sector requires FALSE.
Specifies geographic aggregation level.
"country": national total"state": state-level emissions (27 units)"municipality": all 5,570+ municipalitiesOutput language for column names and, for eng only, translated values.
"pt": Portuguese column names, source’s own Portuguese values
(untranslated)"eng": English column names and translated valuesNote on timing: the source workbook is 250MB+; downloads may take
time. A session-scoped download+parse cache (R/download.R) avoids
re-downloading it if you load more than one SEEG dataset in the same
session.
# note: dataset = "seeg" only works with raw_data = TRUE
all_emissions <- load_seeg(
dataset = "seeg",
raw_data = TRUE,
geo_level = "country",
language = "eng"
)
farming <- load_seeg(
dataset = "seeg_farming",
raw_data = FALSE,
geo_level = "state",
language = "eng"
)
land_use <- load_seeg(
dataset = "seeg_land",
raw_data = FALSE,
geo_level = "state",
language = "eng"
)
energy <- load_seeg(
dataset = "seeg_energy",
raw_data = FALSE,
geo_level = "municipality",
language = "eng"
)
industry <- load_seeg(
dataset = "seeg_industry",
raw_data = FALSE,
geo_level = "state",
language = "eng"
)
residuals <- load_seeg(
dataset = "seeg_residuals",
raw_data = FALSE,
geo_level = "state",
language = "eng"
)
time_period argument.pt output is untranslated: only language = "eng" runs SEEG’s
value dictionaries.🔴 This function uses the googledrive package to download data at the
municipality level. In case of authentication errors, see
googledrive.
The Census of Agriculture (Censo Agropecuário) is Brazil’s comprehensive survey of agricultural establishments and activities, conducted by IBGE (Instituto Brasileiro de Geografia e Estatística). This census collects detailed information about:
The census provides critical data for agricultural policy, market research, and understanding the structure of Brazilian agriculture across regional and temporal dimensions.
Data is collected at multiple geographic levels: - Country level:
aggregate national statistics - State level: disaggregated by
Brazilian states - Municipality level: available for select datasets
(currently "livestock_production")
Historical data spans from 1920 onwards, with different time series available for different datasets based on IBGE’s survey methodology evolution.
Provides comprehensive data on total agricultural land area and the number of agricultural properties.
Details how agricultural properties use their land (crop farming, pasture, forests, etc.).
Captures information about the agricultural workforce and mechanization levels.
Describes the tenure status of agricultural land (ownership, rental, partnership, etc.).
Details the number of livestock animals farmed by species and type.
Quantifies production volumes of animal-based products.
Provides detailed crop production data including area planted and volume produced.
Focuses specifically on temporary crops (annual crops that must be replanted each season).
Focuses on permanent crops (perennial crops that produce for multiple years).
Specialized dataset on bovine cattle production and related establishments.
livestock_production is not available at
the country or state levelSelects which dataset to download. See dataset descriptions above.
dataset = "agricultural_land_area" # character string
Controls whether to download the original data or the processed/cleaned version.
TRUE: Returns raw data exactly as published by IBGEFALSE: Returns treated data with standardized formatting, variable
names in English, and consistent unitsDefault behavior: Raw data typically requires more cleaning and interpretation, while treated data is ready for immediate analysis.
raw_data = FALSE # logical
Specifies the geographic aggregation level.
"country": National aggregate"state": Disaggregated by Brazilian state"municipality": Available only for "livestock_production", and the
only level that dataset supports – "country"/"state" are not
available for livestock_production
geo_level = "state" # character string
Defines which year(s) to download. Availability varies by dataset:
| Dataset | Available Years |
|---|---|
agricultural_land_area |
1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006 |
agricultural_area_use |
1970, 1975, 1980, 1985, 1995, 2006 |
agricultural_employees_tractors |
1970, 1975, 1980, 1985, 1995, 2006 |
agricultural_producer_condition |
1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006 |
animal_production |
1970, 1975, 1980, 1985, 1995, 2006 |
animal_products |
1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006 |
vegetable_production_area |
1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006 |
vegetable_production_temporary |
1970, 1975, 1980, 1985, 1995, 2006 |
vegetable_production_permanent |
1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006 |
livestock_production |
2017 (municipality only) |
You can request a single year or a range of years:
time_period = 2006 # single year
time_period = c(1995, 2006) # multiple specific years
time_period = 1995:2006 # will select years within this range that are available
Output language for variable names and labels.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated land area data at the country level in 2006
data <- load_censoagro(
dataset = "agricultural_land_area",
raw_data = FALSE,
geo_level = "country",
time_period = 2006,
language = "eng"
)
# download treated temporary crop data by state in 1995 in portuguese
data <- load_censoagro(
dataset = "vegetable_production_temporary",
raw_data = FALSE,
geo_level = "state",
time_period = 1995,
language = "pt"
)
# download municipality-level cattle data (only available for livestock_production)
data <- load_censoagro(
dataset = "livestock_production",
raw_data = FALSE,
geo_level = "municipality",
time_period = 2017,
language = "eng"
)
raw_data = TRUE): Exactly as published by IBGE, with
original formatting and Portuguese variable namesraw_data = FALSE): Cleaned and standardized with
English variable names, consistent units (hectares for area, kilograms
for production quantities), and NA values properly handledWhen using treated data, the output is typically in long format with one row per observation unit, containing: - Geographic identifiers (state, municipality if applicable) - Year of the census - Product/category names (crop type, animal species, etc.) - Quantitative measurements (area, quantity, count) - Number of establishments/properties
livestock_production in 2017When using this data in research or publications, cite:
IBGE - Instituto Brasileiro de Geografia e Estatística. Censo Agropecuário. Available at: https://sidra.ibge.gov.br/pesquisa/censo-agropecuario
The Amazon Social Progress Index (IPS) is a comprehensive indicator framework that measures social and environmental progress in the Legal Amazon region. This collaborative initiative combines:
This dataset captures:
The IPS provides a holistic view of sustainable development, moving beyond simple economic measures (GDP) to encompass environmental sustainability and social well-being.
The Social Progress Index: - Based on 50+ individual indicators across 12 domains - Uses data from government agencies, NGOs, and research institutions - Aggregated into 3 main dimensions and 12 subdimensions - Indexed to 0-100 scale for comparability - Methodologically rigorous with transparent weighting
For detailed methodology, visit Social Progress Imperative.
The IPS framework includes 8 main dataset options:
Complete Social Progress Index with all dimensions and indicators.
Indicators related to quality of life and well-being.
Sanitation and habitat indicators.
Public safety and violence indicators.
Education and literacy indicators.
Communication and connectivity indicators.
Health and mortality indicators.
Environmental and deforestation indicators.
Selects which dimension(s) to download.
dataset = "all" # All dimensions
dataset = "life_quality" # Quality of life metrics
dataset = "sanit_habit" # Sanitation and habitat
dataset = "violence" # Public safety and violence
dataset = "educ" # Education indicators
dataset = "communic" # Communication and connectivity
dataset = "mortality" # Health and mortality
dataset = "deforest" # Environmental and deforestation
Controls whether to download original or processed data.
TRUE: Returns raw data exactly as publishedFALSE: Returns treated data with standardized English variable names
and formattingraw_data = FALSE # logical
Specifies which assessment year(s) to download.
Available years: 2014, 2018, 2021, 2023
time_period = 2023 # Most recent
time_period = c(2018, 2023) # Specific years
time_period = c(2014, 2018, 2021, 2023) # Multiple years
Output language for variable names and labels.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download raw IPS data from 2014
data <- load_ips(
dataset = "all",
raw_data = TRUE,
time_period = 2014,
language = "eng"
)
# download treated deforestation IPS data from 2018 in portuguese
data <- load_ips(
dataset = "deforest",
raw_data = FALSE,
time_period = 2018,
language = "pt"
)
Each dimension contains multiple indicators: - Life quality: 4-6 indicators - Sanitation/habitat: 3-5 indicators
(Exact number varies by year and methodology)
When comparing across years (2014, 2018, 2021, 2023): - Methodology may have evolved between assessments - New indicators may have been added - Some municipalities may not have data in all years - Use caution comparing very old (2014) with recent (2023) data
na.rm = TRUE in aggregations to handle missing valuesData from the Institute of Environment and Water Resources (Instituto de Energia e Meio Ambiente - IEMA), documenting electric energy access across the Amazon region. This dataset provides comprehensive information on populations without access to electric energy throughout the Legal Amazon in 2018, offering critical insights into energy poverty and infrastructure gaps in the region.
The IEMA dataset is particularly valuable for understanding energy access disparities in the Amazon, including remote communities, indigenous territories, and areas with limited infrastructure development.
The IEMA dataset includes:
The Legal Amazon encompasses: - 9 Full States: Amazonas, Roraima, Acre, Amazonas, Rondônia, Mato Grosso, Amapá, Pará, Maranhão - Partial Coverage: Parts of Maranhão and other states - Total Area: Approximately 5.5 million square kilometers - Population Focus: Amazon-dwelling populations with particular attention to vulnerable groups
The dataset addresses multiple aspects of energy poverty: - Access Type: Grid connection vs. off-grid solutions - Reliability: Service quality and availability - Affordability: Connection costs and monthly tariffs - Geographic Barriers: Remote location and access challenges - Population Characteristics: Indigenous communities, rural settlements, urban poor
Options:
dataset: "iema"
raw_data:
TRUE: Data in original format from IEMAFALSE: Cleaned and standardized versionlanguage:
"pt": Portuguese language"eng": English languageSince this dataset captures a single year (2018), it represents a snapshot rather than a time series. The cross-sectional nature means: - No Trend Analysis: Cannot track changes over time without merging with other sources - Temporal Stability: Conditions may have changed since 2018 - Recent Updates: Users may need to contact IEMA for more recent data
# download treated IEMA energy access data
data <- load_iema(
raw_data = FALSE,
language = "eng"
)
🔴 This function uses the googledrive package to download data. In
case of authentication errors, see googledrive.
Population dataset provides Brazil’s official population statistics from IBGE (Brazilian Institute of Geography and Statistics). This dataset includes:
Population data is fundamental to many analyses, providing denominators for per capita metrics, understanding urbanization patterns, tracking demographic trends, and supporting policy and development planning.
Population data comes from: - Census years (2007, 2010): Actual population enumeration - Estimation years (2001-2006, 2008-2009, 2011-2021): IBGE official population projections based on census data and vital statistics - Methodology: Cohort-component methodology considering births, deaths, migration - Updates: Revised annually as new vital statistics data becomes available
For more information, visit IBGE Population Estimates.
Official Brazilian population statistics and estimates.
| Year Type | Years | Nature |
|---|---|---|
| Census | 2007, 2010 | Actual population count |
| Estimates | 2001-2006, 2008-2009, 2011-2021 | Official IBGE projections |
Census years provide actual counts; other years are official projections.
Only one dataset is available:
dataset = "population" # Population statistics
Controls whether to download original or processed data.
TRUE: Returns raw IBGE data formatFALSE: Returns treated data with English variable names and
standardized formatraw_data = FALSE # logical
Specifies geographic aggregation level.
"country": National total"state": State-level population (27 units)"municipality": All 5,570+ municipalitiesgeo_level = "municipality" # character string
Specifies which year(s) to download.
Available years: 2001-2006, 2007 (census), 2008-2009, 2010 (census), 2011-2021
time_period = 2020 # single year
time_period = c(2010, 2020) # specific years (includes 2010 census)
time_period = 2010:2020 # range of years
Output language for variable names.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated population data at the state level for 2021
pop_2021 <- load_population(
dataset = "population",
raw_data = FALSE,
geo_level = "state",
time_period = 2021,
language = "eng"
)
# download treated population data at the state level for 2010 to 2021
pop_series <- load_population(
dataset = "population",
raw_data = FALSE,
geo_level = "state",
time_period = 2010:2021,
language = "eng"
)
# download treated population data at the municipality level for 2020
pop_munic <- load_population(
dataset = "population",
raw_data = FALSE,
geo_level = "municipality",
time_period = 2020,
language = "eng"
)
Each record contains: - Geographic identifier (state or municipality name/code) - Year - Population count
Simple and straightforward structure, useful as base for other calculations.
COMEX (Comércio Exterior - Foreign Trade) dataset provides Brazil’s official international trade statistics extracted from Siscomex, the Integrated System of Foreign Trade maintained by the Brazilian government.
This dataset captures:
COMEX is the primary official source for Brazil’s international trade statistics, widely used for trade policy analysis, business intelligence, academic research, and economic monitoring.
COMEX data comes from: - Official records from Siscomex (Brazil’s foreign trade system) - Mandatory declarations by exporters and importers - Updated monthly with current month data - Historical data from 1989 onwards
Important note on nomenclature: From 1989 to 1996, Brazil used a different system of product nomenclature (NBLC - Nomenclatura Brasileira de Mercadorias). All conversions to the current nomenclature system are available and the package handles this transparently.
For more information, visit the Brazilian Ministry of Productivity, Employment and Foreign Trade.
Export data disaggregated at the municipality level.
Import data disaggregated at the municipality level.
Export data organized by producer/exporter and product.
Import data organized by importer/distributor and product.
Selects which trade dataset to download.
dataset = "export_mun" # exports by municipality
dataset = "import_mun" # imports by municipality
dataset = "export_prod" # exports by producer/exporter
dataset = "import_prod" # imports by producer/importer
Controls whether to download the original data or the processed/cleaned version.
TRUE: Returns raw data exactly as published by SiscomexFALSE: Returns treated data with standardized formatting, English
variable names, and cleaned valuesraw_data = FALSE # logical
Specifies which year(s) to download. Available from 1989 onwards.
time_period = 2020 # single year
time_period = c(2018, 2020) # specific years
time_period = 2015:2020 # range of years
Note: Monthly data means each year can be quite large. Consider downloading specific years or ranges to manage file size.
Output language for variable names and documentation.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated exports data by municipality from 2020 to 2021
data <- load_br_trade(
dataset = "export_mun",
raw_data = FALSE,
time_period = 2020:2021,
language = "eng"
)
# download treated imports data by municipality from 2020 to 2021
data <- load_br_trade(
dataset = "import_mun",
raw_data = FALSE,
time_period = 2020:2021,
language = "eng"
)
raw_data = TRUE): Original Siscomex format,
potentially with inconsistencies and naming conventions from different
time periodsraw_data = FALSE): Standardized with English
variable names, consistent units (USD for values), and cleaned
formattingWhen using data spanning 1989-1996 to 1997 onwards, be aware: - Product categories may differ between nomenclature systems - Conversions are available but not always 1:1 - Compare very old with recent data with caution
BACI (Base pour l’Analyse du Commerce International) is a comprehensive database of bilateral trade flows developed by CEPII (Centre d’Études Prospectives et d’Informations Internationales), a leading French research center on international trade.
This dataset provides:
The BACI dataset is widely used in academic research, policy analysis, and international trade studies due to its comprehensive coverage and quality control procedures.
BACI is constructed from UN Comtrade data with significant processing: - Reconciliation of discrepancies between reported imports and exports - Imputation of missing values using econometric techniques - Classification using the Harmonized System (HS) nomenclature - All values converted to USD for comparability
For detailed methodological information, visit the CEPII BACI documentation.
The HS92 classification provides trade data organized under the 1992 version of the Harmonized System nomenclature.
202601)Important: The BACI dataset is very large. All data is packaged in a single compressed file on the CEPII server. Even if you only need data for specific years, the entire dataset must be downloaded first, then processed locally.
Recommendation: Plan your download during off-peak hours or use a stable, high-speed connection.
Currently only one dataset is available:
dataset = "HS92" # Harmonized System 1992 classification
Controls whether to download the original data or the processed/cleaned version.
TRUE: Returns raw CEPII data with original formatting and column
namesFALSE: Returns treated data with standardized English variable names
and cleaned formattingraw_data = FALSE # logical
Specifies which year(s) to download. You can request single or multiple years.
time_period = 2016 # single year
time_period = c(2010, 2015) # multiple specific years
time_period = 2010:2020 # range (will select available years)
Output language for variable names and documentation.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated trade data for 2016 (HS92 classification)
# Warning: large download, may take a long time
trade_2016 <- load_baci(
dataset = "HS92",
raw_data = FALSE,
time_period = 2016,
language = "eng"
)
raw_data = TRUE): CEPII’s original format with
variable codes and potentially non-standard namingraw_data = FALSE): Standardized with English
variable names, consistent column formatting, and ready for immediate
analysisEach row typically represents a trade flow with: - Exporter: Country code (ISO 3-letter code) - Importer: Country code (ISO 3-letter code) - Year: Calendar year of the trade flow - Product code: HS 6-digit classification - Product name: Description of the product - Value: Trade value in USD - Quantity: Physical quantity (where available)
load_baci() internally downloads a companion Brazilian COMEX
product-code dictionary (NCM_SH.csv) to translate HS codes to
product names in Portuguese.PIBMUNIC (Produto Interno Bruto por Município - Gross Domestic Product by Municipality) is Brazil’s official municipal-level GDP data produced by IBGE. This dataset provides:
PIBMUNIC is essential for understanding Brazil’s regional economic structure, identifying economic disparities, analyzing sectoral specialization, and assessing economic development across municipalities.
PIBMUNIC data is compiled by IBGE using: - Data from CEMPRE (firm registry) for employment and output - Production and consumption surveys - Tax and financial records - Trade and services data - National accounts framework aligned with international standards
For more information, visit IBGE National Accounts.
Complete municipal GDP statistics with sectoral detail.
Only one dataset is available:
dataset = "pibmunic" # Municipal GDP data
Controls whether to download original or processed data.
TRUE: Returns raw IBGE formatFALSE: Returns treated data with English variable names and
standardized formattingraw_data = FALSE # logical
Specifies geographic aggregation level.
"country": National aggregate"state": State-level aggregation"municipality": Most detailed level with all municipalitiesgeo_level = "municipality" # character string
Specifies which year(s) to download.
time_period = 2020 # single year
time_period = c(2015, 2020) # specific years
time_period = 2015:2020 # range of years
Output language for variable names.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated municipal GDP data for 2020
pib_munic <- load_pibmunic(
dataset = "pibmunic",
raw_data = FALSE,
geo_level = "municipality",
time_period = 2020,
language = "eng"
)
# download treated state-level GDP data for 2015 to 2020
pib_state <- load_pibmunic(
dataset = "pibmunic",
raw_data = FALSE,
geo_level = "state",
time_period = 2015:2020,
language = "eng"
)
# download treated country-level GDP data for 2010 to 2020 in Portuguese
pib_br <- load_pibmunic(
dataset = "pibmunic",
raw_data = FALSE,
geo_level = "country",
time_period = 2010:2020,
language = "pt"
)
Typical variables include: - gdp: Gross Domestic Product at current prices (R$) - value_added_agriculture: Farming and forestry sector - value_added_industry: Manufacturing and construction - value_added_services: Commerce, finance, transport, etc. - value_added_public_admin: Government services - net_taxes: Taxes minus subsidies - Additional detail depends on specific IBGE release
Employment, salary and firm data from IBGE’s Cadastro Central de Empresas (CEMPRE). This comprehensive dataset provides information on companies and other organizations registered with Brazil’s tax authority (Receita Federal), including employment levels, wage information, and business establishment data across Brazilian municipalities and sectors.
The CEMPRE dataset is one of the most detailed sources of firm-level data in Brazil, covering virtually all formal enterprises and organizations operating in the country.
The CEMPRE dataset includes:
The data is available at three different aggregation levels: - Country Level: Aggregate statistics for all of Brazil - State Level: Data aggregated by state (27 units) - Municipality Level: Data disaggregated to municipality level (5,570+ municipalities)
Data can be retrieved with sector disaggregation or aggregate form: - Sectoral Disaggregation: Detailed breakdown by CNAE 2.0 (main divisions and subdivisions) - Aggregate: Total across all sectors
Options:
dataset: "cempre"
raw_data:
TRUE: Returns the data in its original format from IBGEFALSE: Returns cleaned and standardized datageo_level:
"country": National aggregate"state": Aggregated by state"municipality": Disaggregated to municipality level (detailed
results)time_period: Specifies the years for which data will be
downloaded (e.g., 2010:2020 for 2010 through 2020)
language:
"pt": Portuguese language (variable names and labels)"eng": English languagesectors:
TRUE: Data is returned separated and disaggregated by economic
sector (CNAE)FALSE: Data is aggregated across all sectors# download raw data at the country level from 2008 to 2010
data <- load_cempre(
raw_data = TRUE,
geo_level = "country",
time_period = 2008:2010,
language = "eng"
)
# download treated state-level data split by sector in portuguese
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"
)
PEVS (Produção da Silvicultura e da Extração Vegetal - Silviculture and Forestry Extraction Production) is a comprehensive annual survey conducted by IBGE that collects data on forestry and related activities in Brazil.
This dataset provides:
PEVS is Brazil’s primary source for forestry production statistics, important for understanding the timber industry, forest management practices, and sustainable use of forest resources.
PEVS data comes from: - Direct surveys of forestry companies and producers - Administrative records from forestry operations - Compiled and validated by IBGE’s agriculture statistics division - Annual release with data for previous year - Covers both industrial and subsistence-level forestry activities
For more information, visit IBGE Agriculture Statistics.
Production data from forest crop plantations (timber and non-timber products).
Data on silviculture activities including afforestation, reforestation, and forest management.
Total existing area used for silviculture operations, disaggregated by forest species.
Selects which forestry dataset to download.
dataset = "pevs_forest_crops" # Forest crop production (1986-2019)
dataset = "pevs_silviculture" # Silviculture production (1986-2019)
dataset = "pevs_silviculture_area" # Silviculture land area (2013-2019)
Controls whether to download original or processed data.
TRUE: Returns raw IBGE data with original Portuguese variable namesFALSE: Returns treated data with English variable names,
standardized units, and cleaned formattingraw_data = FALSE # logical
Specifies the geographic aggregation level.
"country": National aggregate"region": Brazilian geographic regions (North, Northeast,
Center-West, Southeast, South)"state": State-level data (27 units)"municipality": Most granular level with 5,570+ municipalitiesgeo_level = "state" # character string
Defines which year(s) to download.
Important: Dataset-specific availability:
| Dataset | Available Years |
|---|---|
pevs_forest_crops |
1986-2019 |
pevs_silviculture |
1986-2019 |
pevs_silviculture_area |
2013-2019 |
time_period = 2019 # single year
time_period = c(2010, 2015) # specific years
time_period = 2010:2019 # range of years
Output language for variable names.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated forest crops data at the state level for 2019
forest_crops <- load_pevs(
dataset = "pevs_forest_crops",
raw_data = FALSE,
geo_level = "state",
time_period = 2019,
language = "eng"
)
# download treated silviculture area data at the state level for 2013 to 2019
silvi_area <- load_pevs(
dataset = "pevs_silviculture_area",
raw_data = FALSE,
geo_level = "state",
time_period = 2013:2019,
language = "eng"
)
# download treated silviculture production data at the region level for 2019
silvi_prod <- load_pevs(
dataset = "pevs_silviculture",
raw_data = FALSE,
geo_level = "region",
time_period = 2019,
language = "eng"
)
Each row typically represents: - A geographic unit (country, region, state, or municipality) - A specific year - A product type or forest species - Quantity produced (in appropriate units: cubic meters, tons, etc.) - Value of production (in currency units)
Forest crops include: - Timber species: Eucalyptus, pine, other timber - Non-timber products: Charcoal, resins, turpentine, cork, bark - Other forest products: Tannin, rosin, pulpwood
Exact categories vary by dataset and year.
raw_data = TRUE): IBGE original format, Portuguese
variable namesraw_data = FALSE): English names, standardized
units, cleaned formattingPPM (Pesquisa da Pecuária Municipal - Municipal Livestock Survey) is Brazil’s comprehensive annual survey of livestock activities conducted by IBGE. This dataset provides:
PPM is the primary data source for understanding Brazil’s livestock sector, which is economically significant and globally important for beef, poultry, and dairy exports.
PPM data is compiled from: - Direct surveys of livestock producers - Agricultural censuses and administrative records - Municipal agriculture secretariats - Processed and validated by IBGE - Annual release with data for reference year
For more information, visit IBGE Livestock Statistics.
Total livestock herds disaggregated by animal species.
Specialized data on sheep production and wool/fleece harvest.
Production of animal-based products (milk, eggs, honey, wool, etc.).
Detailed dairy cow farming data with milking and productivity metrics.
Aquaculture activities including fish, shrimp, and mollusk farming.
Selects which livestock/animal production dataset to download.
dataset = "ppm_livestock_inventory" # Animal populations by species
dataset = "ppm_sheep_farming" # Sheep and wool production
dataset = "ppm_animal_origin_production" # Milk, eggs, honey, wool
dataset = "ppm_cow_farming" # Dairy cow productivity
dataset = "ppm_aquaculture" # Fish and aquaculture production
Controls whether to download original or processed data.
TRUE: Returns raw IBGE formatFALSE: Returns treated data with English variable names and
standardized unitsraw_data = FALSE # logical
Specifies geographic aggregation level.
"country": National aggregate"region": Brazilian geographic regions (5 regions)"state": State-level data (27 units)"municipality": All 5,570+ municipalitiesgeo_level = "state" # character string
Specifies which year(s) to download.
time_period = 2020 # single year
time_period = c(2010, 2020) # specific years
time_period = 2010:2020 # range of years
Note: All datasets available from 1974 onwards, though aquaculture more complete from 2000s.
Output language for variable names.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
# download treated livestock inventory data at the state level for 2020
livestock <- load_ppm(
dataset = "ppm_livestock_inventory",
raw_data = FALSE,
geo_level = "state",
time_period = 2020,
language = "eng"
)
# download treated dairy cow data at the state level for 2020
dairy <- load_ppm(
dataset = "ppm_cow_farming",
raw_data = FALSE,
geo_level = "state",
time_period = 2020,
language = "eng"
)
# download treated animal origin production data at the country level for 2020
animal_products <- load_ppm(
dataset = "ppm_animal_origin_production",
raw_data = FALSE,
geo_level = "country",
time_period = 2020,
language = "eng"
)
# download treated sheep farming data at the state level for 2020
sheep <- load_ppm(
dataset = "ppm_sheep_farming",
raw_data = FALSE,
geo_level = "state",
time_period = 2020,
language = "eng"
)
# download treated aquaculture data at the state level for 2015 to 2020
aquaculture <- load_ppm(
dataset = "ppm_aquaculture",
raw_data = FALSE,
geo_level = "state",
time_period = 2015:2020,
language = "eng"
)
Each record typically contains: - Geographic identifier (state or municipality) - Year - Animal species or product type - Quantity (number of animals or production volume) - Value (if applicable) - Number of establishments
SIGMINE (Sistema de Informações Geográficas da Mineração - Mining Geographic Information System) is Brazil’s official mining registry maintained by the National Mining Agency (ANM), the regulatory body that succeeded the former National Department of Mineral Production (DNPM).
This dataset provides:
SIGMINE data is critical for understanding Brazil’s mining sector impact on the environment, economy, and land use, particularly in sensitive regions like the Amazon.
SIGMINE data comes from: - Brazilian mining authorization and regulatory system - Geological Service of Brazil (SGB-CPRM) - Environmental licensing agencies - Updated continuously as mining permits are issued/revoked - Publicly accessible through government databases
For more information, visit the National Mining Agency (ANM) and mining databases.
Comprehensive registry of legally operating mining projects in Brazil.
Registry of inactive mining processes across Brazil.
Select the mining registry dataset to download:
"sigmine_active": Active mining operations"sigmine_inactive": Inactive mining operationsdataset = "sigmine_active"
Controls whether to download original or processed data.
TRUE: Returns raw SIGMINE/DNPM data format with original codingFALSE: Returns treated data with English variable names and
standardized formattingraw_data = FALSE # logical
Output language for variable names and documentation.
"pt": Portuguese"eng": Englishlanguage = "eng" # character string
SIGMINE data typically includes:
# download treated active mining data in portuguese
mining_active <- load_sigmine(
dataset = "sigmine_active",
raw_data = FALSE,
language = "pt"
)
# download treated active mining data in english
mining_eng <- load_sigmine(
dataset = "sigmine_active",
raw_data = FALSE,
language = "eng"
)
SIGMINE tracks mines at various stages: - Active: Currently operating, producing minerals - Suspended: Temporarily halted operations - Recovery: Reclamation and environmental restoration phase - Other: Various other regulatory statuses
SIGMINE includes: - Precious metals: Gold, silver, platinum - Industrial minerals: Iron, copper, aluminum, tin, others - Energy minerals: Coal, uranium - Gemstones: Diamonds, emeralds, others - Construction minerals: Sand, gravel, granite
SIGMINE is published by the ANM as polygons (one per mining process),
not as municipality totals. To return geo_level = "municipality", each
process is assigned to a single municipality in these steps:
geo_municipalities layer) are projected to an equal-area
Albers conic projection (SIRGAS 2000 / Brazil Albers,
EPSG:10857): standard parallels 2°S and
22°S, central meridian 54°W, latitude of origin 12°S, false easting
5,000,000 m, false northing 10,000,000 m, GRS80 ellipsoid. Centroids
are computed in meters, because centroids computed on
longitude/latitude are distorted.processo is kept only once.The projection follows IBGE’s recommendation for area calculations (equal-area Albers projection, standard parallels 2°S and 22°S, origin at 12°S and 54°W), described in the BC250 technical documentation (section “Cálculo de áreas e extensões”). The false easting and northing (5,000,000 m and 10,000,000 m) only shift the coordinates and do not affect areas or centroid positions.
The result is one municipality per process: a mining area that
crosses municipal borders is not split, and it is counted in the
municipality where its centroid falls. For very irregular polygons, the
centroid can fall outside the polygon itself, and then the process may
be assigned to a neighboring municipality, or to none if the centroid
falls outside every municipal polygon. The same method is used for
active (sigmine_active) and inactive (sigmine_inactive) processes.
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: Energy Development Budget, Energy Generation, and Energy Enterprises Distributed.
The Energy Development Budget dataset showcases the Energy Development Account’s (CDE) annual budget expenses. The CDE is designed to promote the Brazilian energy development and is managed by the Electrical Energy Commercialization Chamber (CCEE).
In the current implementation, data is available from 2017 to 2024 and must be downloaded by year. The year argument can be a single year or a vector of years. The dataset includes the type of expense, its value in R$ (Reais), and its share over the total amount of CDE budget expenses in each 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 1908 up to 2021.
The source is ANEEL’s own CKAN Open Data catalogue
(siga-empreendimentos-geracao.csv). Power and coordinate columns
(mda_*/num_coord_*) are automatically converted to numeric values.
* For more details on each variable, access ANEEL’s SIGA dataset page and select “Dicionário de dados”.
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:
"energy_development_budget": government spending towards energy
sources"energy_generation": energy generation by entity/corporation"energy_enterprises_distributed": distributed micro and mini
generatorsTRUE: if you want the data as it is originally.FALSE: if you want the treated version of the data.("pt") and English
("eng")
Examples:
# download treated data about energy generation
clean_aneel <- load_aneel(
dataset = "energy_generation",
raw_data = FALSE
)
# download raw CDE data for one year
budget_aneel_2019 <- load_aneel(
dataset = "energy_development_budget",
raw_data = TRUE,
year = 2019
)
# download raw CDE data for multiple years
budget_aneel_multi <- load_aneel(
dataset = "energy_development_budget",
raw_data = TRUE,
year = c(2020, 2021)
)
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 four different datasets available for download: Consumer Energy Consumption, Industrial Energy Consumption, the National Energy Balance, and the State Energy Production Panel. 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 source workbook organizes the data by State, Region, or Electric Subsystem, but this package currently reads only the State and Subsystem levels (see geo_level below); the data 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 1970 to 2025, read from EPE’s consolidated BEN dashboard table. 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.
The cleaned data is returned in long format, with one row per
combination of year, account, energy source (fonte/source), and a
tipo/type column (Fontes de Energia Primária/Secundária/Total).
Account labels are the source’s own wording (for example, “Refinarias de
Petróleo” or “Residencial”). raw_data = TRUE returns a single
already-tidy table.
The State Energy Production Panel provides yearly data from 2011 to 2025 on electricity generation by energy source and Brazilian state. The data is sourced from EPE’s BEN Chapter 8 (Dados Estaduais) and covers all 27 states, including the Federal District.
Each row corresponds to one state-year combination. The dataset includes production by source (hydro, wind, solar, nuclear, thermal, sugar cane, firewood, black liquor, other renewables, steam coal, natural gas, coke oven gas, fuel oil, diesel, and other non-renewables), total production, and an indicator of whether the state belongs to the Legal Amazon.
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"energy_state_panel": yearly energy production by source and state
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"
"region" is also listed as a supported level by the source, but no
sheet is currently mapped for it in this package – passing
geo_level = "region" will fail during download rather than at
parameter validation. Use "state" or "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
)
# download treated data on state energy production panel
state_panel <- load_epe(
dataset = "energy_state_panel",
raw_data = FALSE,
language = "eng"
)
The municipalities dataset is a foundational reference dataset
included in the datazoom.amazonia package. It contains key information
about all Brazilian municipalities and identifies which are located in
(or overlapping with) the Legal Amazon region.
This dataset includes:
The municipalities dataset is essential infrastructure for this package—most package functions that return geographic data match results to this municipalities reference to enable Legal Amazon filtering and consistent geographic identification.
The municipalities dataset is compiled from: - IBGE: Brazilian Institute of Geography and Statistics official municipal data - Legal Amazon definition: Based on official Brazilian government definition - Spatial boundaries: IBGE 2019 municipal boundary shapefile - Maintained by: datazoom.amazonia package developers
The municipalities dataset contains the following information:
# Load Brazilian municipalities dataset
data <- datazoom.amazonia::municipalities
# Or after loading the package
library(datazoom.amazonia)
data <- municipalities
# View structure
str(municipalities)
head(municipalities)
# Filter for Legal Amazon municipalities only
amazon_municipalities <- municipalities %>%
filter(legal_amazon == TRUE)
The dataset includes all 5,570+ Brazilian municipalities with: - Official IBGE identification codes - State and region classification - Legal Amazon flag for filtering - Spatial geometries for geographic analysis
Many analyses focus specifically on the Legal Amazon region. Use the municipalities dataset to identify relevant municipalities:
library(dplyr)
# Load any dataset with municipality information
data <- load_prodes(
dataset = "deforestation",
raw_data = FALSE,
geo_level = "municipality",
language = "eng"
)
# Filter to Legal Amazon using municipalities reference
amazon_data <- data %>%
inner_join(
municipalities %>%
filter(legal_amazon == TRUE) %>%
select(code, name),
by = c("municipality_code" = "code")
)
Important: Some municipalities are only partially within the Legal Amazon.
For statistics reported at municipality level in this package: - Partial Amazon municipalities: Data is reported for only the Amazon-included portion - Identification: The municipalities dataset identifies these cases - Interpretation: When a municipality is partially in Amazon, reported statistics reflect the Amazon portion only
# Identify fully vs. partially included municipalities
full_amazon <- municipalities %>%
filter(legal_amazon == TRUE & fully_included == TRUE)
partial_amazon <- municipalities %>%
filter(legal_amazon == TRUE & fully_included == FALSE)
print(paste("Fully in Amazon:", nrow(full_amazon)))
print(paste("Partially in Amazon:", nrow(partial_amazon)))
The Legal Amazon includes: - States: All or parts of Acre, Amapá, Amazonas, Distrito Federal, Goiás, Maranhão, Mato Grosso, Mato Grosso do Sul, Pará, Rondônia, Roraima, Tocantins - Municipalities: 570+ municipalities fully or partially in Legal Amazon - Definition: Based on official Brazilian legislation (Law 8,001/1990)
Important note on municipality-level data: - Partial municipalities: Some municipalities extend beyond Legal Amazon boundaries - Data reporting: When data is reported at municipality level for partial municipalities, it reflects only the Legal Amazon portion - Identification: This dataset identifies which municipalities are partial
# Check for partial municipalities
partial_check <- municipalities %>%
filter(legal_amazon == TRUE) %>%
filter(!is.na(amazon_percentage)) %>%
filter(amazon_percentage < 100)
if (nrow(partial_check) > 0) {
print("Municipalities partially in Legal Amazon:")
print(partial_check)
}
Brazilian municipalities occasionally undergo changes: - New municipalities: Created from existing ones (last major change 2021) - Boundary adjustments: IBGE periodically refines municipal boundaries - Historical data: When comparing very old data with recent data, be aware municipalities may have been reorganized
Always use IBGE municipality codes (not names) when: - Merging multiple datasets - Doing time-series analysis - Comparing across sources - Municipality names may change or be ambiguous; codes are unique and stable
The municipalities dataset includes spatial boundaries (when loaded as SF object): - Format: Simple features (SF) polygons - CRS: WGS84 (EPSG:4326) - Use: Spatial operations, mapping, spatial joins with other geographic data
library(sf)
library(ggplot2)
# Load municipalities with geometry
municipalities_sf <- municipalities %>%
st_as_sf() # If not already SF format
# Map Legal Amazon
amazon_map <- municipalities_sf %>%
filter(legal_amazon == TRUE)
ggplot(amazon_map) +
geom_sf(fill = "lightgreen", color = "darkgreen") +
labs(title = "Legal Amazon Municipalities") +
theme_minimal()
# Spatial operations example: count municipalities by state
munic_by_state <- municipalities_sf %>%
group_by(state) %>%
summarize(
num_municipalities = n(),
total_area_km2 = sum(st_area(.), na.rm = TRUE) / 1e6,
.groups = 'drop'
)
Problem: Municipality names don’t match between datasets Solution: Use IBGE municipality codes instead of names for joining data
Problem: Some municipalities missing after filtering Solution: Check for name spelling variations; use code-based matching
Problem: Spatial operations are slow Solution: Simplify
geometries (st_simplify()) or work with state/region level first
Problem: Mapping appears incorrect Solution: Verify CRS (should
be WGS84); check for invalid geometries (st_is_valid())
Problem: Aggregating municipality data to regions Solution: Use
left_join() with municipalities dataset to add region information
Problem: Partial municipalities causing data discrepancies Solution: Account for partial Amazon municipalities; some statistics are reported for Amazon portion only
For 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://datazoom.com.br/pt/.
To cite package datazoom.amazonia in publications use:
Data Zoom (2023). Data Zoom: Simplifying Access To Brazilian Microdata.
https://datazoom.com.br/en/
A BibTeX entry for LaTeX users is:
@Unpublished{DataZoom2023,
author = {Data Zoom},
title = {Data Zoom: Simplifying Access To Brazilian Microdata},
url = {https://datazoom.com.br/en/},
year = {2023},
}