This track walks through the climasus4r analytical workflow, from preparing DATASUS data to environmental integration and epidemiological modeling. Each stage corresponds to an auditable methodological decision: data source, standardization, filters, spatial unit, climate exposure, temporal window, and statistical model.
| Stage | Title | Main Functions |
|---|---|---|
| 1 | Import | sus_data_import() |
| 2 | Encoding & Standardization | sus_data_clean_encoding(), sus_data_standardize() |
| 3 | Filtering | sus_data_filter_cid(), sus_data_filter_demographics() |
| 4 | Variables & Aggregation | sus_data_create_variables(), sus_data_aggregate() |
| 5 | Spatial Join | sus_spatial_join() |
| 6 | Socioeconomic Linkage | sus_socio_add_census() |
| 7 | Station-Based Climate (INMET) | sus_climate_inmet(), sus_climate_fill_inmet() |
| 8 | Gridded Climate & Environmental | sus_grid_era5(), sus_grid_chirps(), sus_grid_join() |
| 9 | Epidemiological Modeling | sus_mod_dlnm(), sus_mod_plot_*() |
Import
Download and read health data from DATASUS (SIM, SIH, SINAN, SINASC) and climate time series, preparing the base for the pipeline.
Start Now →Encoding & Standardization
Fix character encoding and standardize variable names and values into a consistent format.
View Tutorial →Filtering
Select cases by ICD-10 code and by demographic criteria (age, sex) to define the study population.
View Tutorial →Variables & Aggregation
Create derived variables and aggregate records over time and space to build analytical time series.
View Tutorial →Spatial Join
Link the data to geographic meshes (municipalities, states) for spatial analyses and mapping.
View Tutorial →Socioeconomic Linkage
Integrate Census indicators and socioeconomic data to contextualize population vulnerabilities.
View Tutorial →Station-Based Climate (INMET)
Obtain meteorological time series from INMET stations, with quality control and gap-filling.
View Tutorial →Gridded Climate & Environmental
Use gridded climate data (ERA5-Land) and environmental layers as an alternative and complement to weather stations.
View Tutorial →Epidemiological Modeling
Relate climate exposure and health outcomes with epidemiological models (e.g., DLNM) and estimate effects.
View Tutorial →