Package index • climasus4r

Import & Export

Core functions to retrieve, read, and save DATASUS data

sus_data_import(): Import and preprocess data from DATASUS with intelligent caching
sus_data_read(): Read Processed Health Data with Batch and Parallel Support
sus_data_export(): Export Processed Health Data with Metadata

Cleaning & Standardization

Encoding fixes, column standardization, and feature engineering

sus_data_clean_encoding(): Detect and correct character encoding issues
sus_data_standardize(): Standardize SUS data column names and values
sus_data_create_variables(): Create Derived Variables for Epidemiological Analysis

Filtering & Selection

Filter by ICD-10 codes, disease groups, and demographics

sus_data_filter_cid(): Filter SUS health data by ICD-10 codes or disease groups with multilingual support
sus_data_filter_demographics(): Filter Health Data by Demographic Variables
sus_data_plot_demographics(): Visualise Demographic Profiles from DATASUS Systems
sus_data_plot_aggregate_ts(): Time-Series Plot of Aggregated DATASUS Health Outcomes
sus_data_plot_aggregate_map(): Plot Municipal Map of Aggregated Health Data

Analysis & Spatial Data

Time series aggregation and spatial joins with Brazilian geography and demographic

sus_data_aggregate(): Aggregate Health Data into Time Series
sus_spatial_join(): Spatially Link SUS Data to Brazilian Geographic Units
sus_census_join(): Add Census Socioeconomic variables to Health Data
sus_socio_compute_indicators(): Compute Socioeconomic and Epidemiological Indicators
sus_socio_list_indicators(): List Available Indicators in the Catalogue
sus_climate_inmet(): Import and Process INMET Meteorological Data
sus_climate_uniplu(): Import UNIPLU-BR: Unified Brazilian Rainfall Dataset
sus_climate_fill_inmet(): Fill gaps in INMET climate time series using XGBoost
sus_climate_aggregate(): Integration of Climate and Health Data
sus_climate_compute_indicators(): Compute Bioclimatic and Thermal Stress Indicators
sus_climate_plot_fill(): Visualise time-series gap-filling from sus_climate_fill()
sus_climate_plot_aggregate(): Visualise Climate-Health Aggregate Data
sus_climate_compute_heatwaves(): Detect heatwaves using multiple standard methodologies
sus_climate_plot_heatwaves(): Plot and Analyze Heatwave Events
sus_climate_compute_coldwaves(): Detect coldwaves using multiple standard methodologies
sus_climate_plot_coldwaves(): Plot and Analyze Coldwave Events
hw_active_days(): Return daily rows where at least one heatwave method is active
hw_count_by_year(): Count heatwave events by year, station, and method
hw_get_events(): Extract heatwave events table from a climasus_hw result
cw_active_days(): Return daily rows where at least one coldwave method is active
cw_count_by_year(): Count coldwave events by year, station, and method
cw_get_events(): Extract coldwave events table from a climasus_cw result
sus_climate_normals(): Download and Process INMET Climate Normals
sus_climate_normals_meta(): Browse the INMET Climate Normals Catalogue
sus_climate_anomaly(): Climate Anomaly Computation vs. INMET Climatological Normals
sus_climate_compute_spi(): Compute Standardized Precipitation Index (SPI)
sus_climate_compute_spei(): Compute Standardized Precipitation-Evapotranspiration Index (SPEI)
sus_grid_pdsi(): Import Palmer Drought Severity Index (PDSI) for Brazilian Municipalities
sus_grid_smvi(): Import Soil Moisture Volatility Index (SMVI / Flash Drought) Data
sus_grid_era5(): Import ERA5-Land Daily Climate Data for Brazilian Municipalities
sus_grid_pollution_cams(): Import CAMS Pollution Data for Brazilian Municipalities
sus_grid_pollution_ghap(): Import GHAP High-Resolution Pollution Data for Brazilian Municipalities
sus_grid_fires(): Import Fire Hotspot Data for Brazilian Municipalities
sus_grid_chirps(): Import CHIRPS Rainfall Data for Brazilian Municipalities
sus_grid_koppen(): Assign Kopppen-Geiger Climate Zones to Brazilian Municipalities
sus_grid_prodes(): Import PRODES Deforestation Data from TerraBrasilis for Brazilian Municipalities
sus_grid_join(): Join Gridded Environmental Data to Health Data
sus_grid_pollution_merra2(): Import MERRA-2 Pollution Data for Brazilian Municipalities
sus_grid_plot(): Plot a Raster Grid (GeoTIFF/NetCDF) as a Publication-Ready Map

Spatial Analysis & Cluster Detection

Spatial autocorrelation (LISA), cluster detection (Kulldorff), Bayesian CAR/BYM mapping, and spatial regression (SAR/SEM)

sus_mod_spatial_weights(): Build Spatial Weights from Municipality Polygons
sus_mod_spatial_moran(): Global Moran's I and LISA Local Autocorrelation for Health Outcomes
sus_mod_spatial_scan(): Kulldorff Circular Scan Statistic for Spatial Cluster Detection
sus_mod_spatial_bayes(): Bayesian CAR / BYM Disease Mapping with Climate Covariates
sus_mod_spatial_reg(): Spatial Regression (SAR / SEM / SDM / SAC) for Climate-Health Associations
sus_mod_spacetime_bayes(): Bayesian Spatiotemporal Hierarchical Model with INLA
sus_mod_spacetime_exceedance(): Posterior Exceedance Probabilities from a Spatiotemporal Bayesian Model
sus_mod_spacetime_predict(): Generate Predictions from a Fitted Space-Time Bayesian Model
sus_mod_plot_spacetime(): Visualizations for Space-Time Bayesian Disease Mapping Results
sus_mod_plot_spatial_moran(): Two-Panel LISA Visualisation: Cluster Map and Moran Scatter Plot
sus_mod_plot_spatial_scan(): Choropleth Map of Kulldorff Spatial Scan Cluster Results
sus_mod_plot_spatial_bayes(): Visualizations for Bayesian Spatial Disease Mapping Results

Quality & Metadata

Data quality reports and disease dictionaries

sus_data_quality_report(): Pipeline-aware data quality report for health data
sus_data_ts_quality(): Time-Series Quality Control for Daily Municipal Health Counts
print(<climasus_ts_quality>): Print a climasus_ts_quality object
summary(<climasus_ts_quality>): Summarise a climasus_ts_quality object
sus_census_select(): Interactive Census Variables Explorer
sus_data_cid_select(): Interactive Disease Groups Explorer
sus_meta(): Manage climasus_df S3 Class Metadata and Storage Backends
sus_welcome(): Display the climasus4r Pipeline Overview
print(<climasus_df>): Print method for climasus_df
`[`(<climasus_df>): Subsetting method for climasus_df
`[[`(<climasus_df>): Column extraction for climasus_df
`$<-`(<climasus_df>): Column assignment for climasus_df
rbind(<climasus_df>): Row binding for climasus_df objects
as.data.frame(<climasus_df>): Coerce climasus_df to plain data.frame (strips metadata)
write_duckdb_climasus(): Write a climasus_df to a persistent DuckDB file
write_parquet_climasus(): Write a climasus_df directly to a Parquet file
sus_as_arrow(): Convert a climasus_df to an Arrow Table with embedded metadata
sus_as_duckdb(): Convert a climasus_df to a DuckDB lazy tbl with embedded metadata

Reproducible Analytical Pipelines (RAP)

Export, read, inspect, run and share pipelines as Reproducible Analytical Pipelines. Integration with targets, Shiny GUI, YAML recipes and project templates.

sus_rap_export(): Export a climasus4r Pipeline as a Reproducible Analytical Pipeline (RAP)
sus_rap_read(): Read an Exported RAP File
sus_rap_inspect(): Inspect or Diff rap_object(s)
sus_rap_run(): Re-execute an Exported RAP
sus_rap_update(): Update Parameters in an Exported RAP File
sus_rap_targets(): Generate a targets Pipeline from a climasus4r RAP
sus_rap_make(): Execute a targets Pipeline from a RAP
sus_rap_recipe(): Export a RAP as a Compact YAML Recipe
sus_rap_from_recipe(): Import and Optionally Run a RAP Recipe
sus_rap_template(): Scaffold a Reproducible Analytical Pipeline Project
sus_rap_gui(): Launch an Interactive GUI for Editing and Running RAPs

AI and Cache Management

Manage local storage of downloaded files

sus_install_deps(): Install Optional Dependencies for climasus4r
sus_cache_clear(): Clear the Climasus4r cache
sus_cache_info(): Get cache information
sus_chat(): Launch the climasus4r AI Assistant

DLNM Modeling

Fit and visualize Distributed Lag Non-Linear Models (DLNM), which simultaneously capture the non-linearity and delayed effects of environmental exposures (temperature, air pollution) on health outcomes. Use these functions to estimate exposure-response and lag-response curves from time-series data on mortality or morbidity.

sus_mod_dlnm(): Distributed Lag Non-linear Model (DLNM) for Climate-Health Analyses
sus_mod_plot_dlnm(): Scientific Plots and Tables from a DLNM Fit

Attributable Fraction & Number

Estimate the Attributable Fraction (AF%) and Attributable Number (AN) of health outcomes due to heat, cold, or their combined effect, with bootstrap confidence intervals. Includes excess-risk analysis under counterfactual scenarios (e.g., no additional warming) and dedicated visualizations for communicating public health impact to non-specialist audiences.

sus_mod_af(): Attributable Fraction and Number from a DLNM Fit
sus_mod_excess(): Excess Mortality and Morbidity from Climate-Health Time Series
sus_mod_plot_af(): Plots and Tables from an Attributable Fraction Analysis

Pooled Meta-Analysis

Two-stage pooling of city-specific DLNM fits using mvmeta, producing pooled exposure-response curves and city-level Best Linear Unbiased Predictions (BLUPs). Meta-regression identifies municipal-level covariates (latitude, HDI, urbanization) that explain heterogeneity across cities. Use this block for multi-city analyses and national-scale climate-epidemiological surveillance studies.

sus_mod_pool(): Two-Stage Multi-City Pooling of DLNM Estimates
sus_mod_metaregression(): Meta-Regression of Pooled DLNM Estimates with City-Level Covariates
sus_mod_plot_pool(): Plots and Tables from a Pooled DLNM Meta-Analysis

Disease Burden, Sensitivity & Vulnerability

Quantify the climate-attributable disease burden (AN, AF, Lorenz inequality), perform comparative sensitivity analyses across population strata, and construct the IPCC three-pillar Vulnerability Index (Exposure, Sensitivity, Adaptive Capacity). These functions translate point epidemiological estimates into actionable public health indicators for territorial and environmental planning.

sus_mod_burden(): Ranked Disease Burden Table Across Cities or Strata
sus_mod_sensitivity(): Stratified Climate-Health Sensitivity Analysis from DLNM Fits
sus_mod_vulnerability_index(): IPCC 3-Pillar Composite Vulnerability Index for Climate-Health Analysis
sus_mod_plot_burden(): Plots and Tables from a City-Level Disease Burden Analysis
sus_mod_plot_sensitivity(): Plots and Tables from a Multi-Stratum Sensitivity Analysis
sus_mod_plot_vulnerability(): Plots and Tables from an IPCC Vulnerability Index Analysis

Epidemiological Study Designs

Implementations of classic observational designs: the time-stratified case-crossover study (within-individual control, ideal for acute short-term exposures) and Interrupted Time Series (ITS) analysis, which evaluates the causal effect of health interventions or policy changes on temporal trends in health outcomes.

sus_mod_casecrossover(): Time-Stratified Case-Crossover Analysis for Climate-Health Data
sus_mod_its(): Interrupted Time Series Analysis for Health Outcome Counts

Climate-Health SWOT Analysis

Synthesize pre-processed climasus model objects into a SWOT framework (Strengths, Weaknesses, Opportunities, Threats) with configurable numeric (0–100) and categorical scores per quadrant, plus radar, matrix, and bar visualizations.

sus_mod_swot(): SWOT Analysis for Climate-Health Surveillance
sus_mod_plot_swot(): Plots and Tables from a Climate-Health SWOT Analysis

Spatial Cluster Detection

Kulldorff circular scan statistic for detecting geographic clusters of disease excess. Identifies the most-likely cluster and significant secondary clusters via Monte Carlo hypothesis testing, mapping results back to municipality codes.

sus_mod_spatial_scan(): Kulldorff Circular Scan Statistic for Spatial Cluster Detection

Climate Machine Learning

Predictive modeling with XGBoost, featuring out-of-fold cross-validation, feature importance ranking, and graphical diagnostics (observed vs. predicted, CV loss curve, variable importance chart). Use when the goal is forecasting health outcomes from climate and socioeconomic predictors, complementing the causal DLNM framework with high-dimensional predictive capacity.

sus_mod_ml(): XGBoost Machine Learning for Climate-Health Outcome Prediction
sus_mod_plot_ml(): Plots and Tables from an XGBoost Machine Learning Model

S3 Model Methods

Exported S3 methods (print, summary, coef, vcov, tidy, predict) for all output classes of the modeling module (climasus_dlnm, climasus_af, climasus_pool, climasus_excess, climasus_casecrossover, climasus_its, climasus_sensitivity, climasus_metaregression, climasus_burden, climasus_vi, climasus_ml).

coef(<climasus_dlnm>): Extract coefficients from a climasus_dlnm model
coef(<climasus_metaregression>): Extract pooled coefficients from a climasus_metaregression object
coef(<climasus_pool>): Extract pooled coefficients from a climasus_pool object
coef(<climasus_vi>): Extract VI scores as a named numeric vector
predict(<climasus_ml>): Generate predictions from a climasus_ml model on new data
print(<climasus_af>): Print a climasus_af object
print(<climasus_burden>): Print a climasus_burden object
print(<climasus_casecrossover>): Print a climasus_casecrossover object
print(<climasus_dlnm>): Print a climasus_dlnm model
print(<climasus_excess>): Print a climasus_excess object
print(<climasus_its>): Print a climasus_its object
print(<climasus_metaregression>): Print a climasus_metaregression object
print(<climasus_ml>): Print a climasus_ml object
print(<climasus_pool>): Print a climasus_pool object
print(<climasus_sensitivity>): Print a climasus_sensitivity object
print(<climasus_spacetime_bayes>): Print method for climasus_spacetime_bayes objects
print(<climasus_spacetime_exceedance>): Print method for climasus_spacetime_exceedance objects
print(<climasus_spacetime_pred>): Print method for climasus_spacetime_pred objects
print(<climasus_spatial_bayes>): Print method for climasus_spatial_bayes objects
print(<climasus_spatial_moran>): Print method for climasus_spatial_moran objects
print(<climasus_spatial_reg>): Print method for climasus_spatial_reg objects
print(<climasus_spatial_scan>): Print a climasus_spatial_scan object
print(<climasus_swot>): Print a climasus_swot object
print(<climasus_vi>): Print a climasus_vi object
print(<climasus_weights>): Print method for climasus_weights
summary(<climasus_af>): Summarise a climasus_af object
summary(<climasus_burden>): Summarise a climasus_burden object
summary(<climasus_casecrossover>): Summarise a climasus_casecrossover object
summary(<climasus_dlnm>): Summarise a climasus_dlnm model
summary(<climasus_excess>): Summarise a climasus_excess object
summary(<climasus_its>): Summarise a climasus_its object
summary(<climasus_metaregression>): Summarise a climasus_metaregression object
summary(<climasus_ml>): Summarise a climasus_ml object
summary(<climasus_pool>): Summarise a climasus_pool object
summary(<climasus_sensitivity>): Summarise a climasus_sensitivity object
summary(<climasus_spacetime_bayes>): Summary method for climasus_spacetime_bayes objects
summary(<climasus_spacetime_exceedance>): Summary method for climasus_spacetime_exceedance objects
summary(<climasus_spacetime_pred>): Summary method for climasus_spacetime_pred objects
summary(<climasus_spatial_bayes>): Summary method for climasus_spatial_bayes objects
summary(<climasus_spatial_moran>): Summary method for climasus_spatial_moran objects
summary(<climasus_spatial_reg>): Summary method for climasus_spatial_reg objects
summary(<climasus_spatial_scan>): Summarise a climasus_spatial_scan object
summary(<climasus_swot>): Summarise a climasus_swot object
summary(<climasus_vi>): Summarise a climasus_vi object
summary(<climasus_weights>): Summary method for climasus_weights
tidy(<climasus_af>): Reduce climasus_af to a tidy one-row tibble
tidy(<climasus_burden>): Tidy a climasus_burden object into a flat tibble
tidy(<climasus_casecrossover>): Tidy a climasus_casecrossover object
tidy(<climasus_dlnm>): Reduce climasus_dlnm to a tidy one-row tibble
tidy(<climasus_excess>): Tidy a climasus_excess object into a one-row summary tibble
tidy(<climasus_its>): Tidy a climasus_its object
tidy(<climasus_metaregression>): Tidy a climasus_metaregression object into a summary tibble
tidy(<climasus_ml>): Tidy a climasus_ml object into a flat predictions tibble
tidy(<climasus_pool>): Tidy a climasus_pool object into a one-row summary tibble
tidy(<climasus_sensitivity>): Tidy a climasus_sensitivity object
tidy(<climasus_swot>): Tidy a climasus_swot object into a flat scores tibble
tidy(<climasus_vi>): Tidy a climasus_vi object into a flat tibble
vcov(<climasus_metaregression>): Extract variance-covariance from a climasus_metaregression object
vcov(<climasus_pool>): Extract pooled variance-covariance from a climasus_pool object