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Prepare CNES microdata

Source: R/process_cnes.R
process_cnes.Rd

Uses the official DataSUS TabWin definitions to label all thirteen CNES file families supported by fetch_datasus(). The shared ZIP is downloaded on first use and cached for the rest of the R session. For "CNES-SR", the service/classification definition is selected by record competence because DataSUS changed that table in March 2008.

Usage

process_cnes(
  data,
  information_system = c("CNES-ST", "CNES-PF"),
  nomes = FALSE,
  municipality_data = TRUE,
  labels = c("factor", "character", "none"),
  diagnostics = FALSE
)

Arguments

data

A data frame returned by fetch_datasus() for a supported CNES file family, or another data frame with a compatible layout.

information_system

CNES file family represented by data. If omitted, "CNES-ST" is used, preserving the first value of the historical default.

nomes

Logical scalar. If TRUE, add FANTASIA using the establishment-name DBF declared by the official definition. The CNES identifier itself is preserved.

municipality_data

Logical scalar. If TRUE, add municipality names and available territorial attributes. Professional files prefer UFMUNRES; other layouts use the establishment field CODUFMUN.

labels

Output type for categorical labels: "factor" (the default), "character", or "none" to retain the original codes.

diagnostics

Logical scalar. If TRUE, attach a processing report, including codes absent from official conversion tables. Retrieve it with processing_diagnostics().

Value

A tibble. Full dates are returned as Date, quantities as integer, labelled categorical fields as factors, and identifiers, reference months, and free text as character.

Performance and cache

Processing uses vectorized code padding and CNV thresholds, parses repeated dates once per field and format, and unescapes only text containing backslashes. UTF-8 conversion is still performed for text; values marked as "bytes" bypass text normalization. Historical relation selection subsets only the source columns it needs. These optimizations are automatic.

Dictionaries are reused within the R session. For reuse across sessions, set options(microdatasus.cache_dir = datasus_cache_dir(create = TRUE)); calling datasus_cache_dir() alone does not enable persistent caching. The first processing call can include dictionary downloads and parsing. labels = "none" controls categorical output, not network access: some processors still need DEF metadata or relations for field semantics.

diagnostics = FALSE avoids collecting the optional report, and municipality_data = FALSE omits territorial enrichment when it is not needed. For requests spanning many files, use fetch_datasus() with process = TRUE, collect = FALSE, and destination to save each file separately. A processor called directly still holds its input and output in memory. See the processing guide.

Territorial enrichment uses the fixed tabMun snapshot identified by datasus_reference_tables(), not an automatically selected edition for each observation year. Enable diagnostics to record that version in the report.

References

Saldanha, R. F. (2026). CNES – Cadastro Nacional de Estabelecimentos de Saúde.

See also

fetch_tabwin_dictionary(), fetch_datasus(), fetch_cadger()

Examples

if (FALSE) { # interactive() && curl::has_internet()
process_cnes(cnes_st_sample, information_system = "CNES-ST")
process_cnes(cnes_pf_sample, information_system = "CNES-PF")
}