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Filter Health Data by Demographic Variables

Source: R/sus_data_filter_demographics.R
sus_data_filter_demographics.Rd

Filters health data based on demographic characteristics such as sex, race, age range, education level, region, and municipality. This function complements sus_data_filter_cid() by enabling stratified analyses by population subgroups.

Usage

sus_data_filter_demographics(
  df,
  sex = NULL,
  race = NULL,
  age_range = NULL,
  education = NULL,
  region = NULL,
  municipality_code = NULL,
  drop_ignored = FALSE,
  lang = "pt",
  verbose = TRUE
)

Arguments

df

A data frame containing health data.

sex

Character vector specifying sex categories to include. Accepts values in English, Portuguese, or Spanish (e.g., "Male", "Masculino", "Masculino"). If NULL (default), includes all sexes.

race

Character vector specifying race/color categories to include. Accepts IBGE standard categories in multiple languages. If NULL (default), includes all races.

age_range

Numeric vector of length 2 specifying the age range c(min_age, max_age). Use Inf for no upper limit. If NULL (default), includes all ages.

education

Character vector specifying education levels to include. If NULL (default), includes all education levels. to include. If NULL (default).

region

A string indicating a predefined group of states or regions (supports multilingual names PT, EN, ES). See below in details.

municipality_code

Character or numeric vector specifying municipality codes (IBGE 6 or 7-digit codes) to include. If NULL (default), includes all municipalities.

drop_ignored

Logical. If TRUE, explicitly removes rows where demographic variables (sex, race and education) contain missing values (NA) or DATASUS ignored codes (e.g., "9", "Ignorado"). Default is FALSE.

lang

Character string specifying the language for messages. Options: "en" (English), "pt" (Portuguese, default), "es" (Spanish).

verbose

Logical. If TRUE (default), prints filtering summary.

Value

A filtered data frame containing only rows that match all specified demographic criteria.

Details

The function automatically detects column names in different languages and standardizations. It handles both original DATASUS column names and standardized names from sus_data_standardize().

Sex categories (case-insensitive):

  • English: "Male", "Female"

  • Portuguese: "Masculino", "Feminino"

  • Spanish: "Masculino", "Femenino"

Race/Color categories (IBGE standard):

  • English: "White", "Black", "Yellow", "Brown", "Indigenous"

  • Portuguese: "Branca", "Preta", "Amarela", "Parda", "Indigena"

  • Spanish: "Blanca", "Negra", "Amarilla", "Parda", "Indigena"

IBGE Macro-regions:

  • "norte": c("AC", "AP", "AM", "PA", "RO", "RR", "TO")

  • "nordeste": c("AL", "BA", "CE", "MA", "PB", "PE", "PI", "RN", "SE")

  • "centro_oeste": c("DF", "GO", "MT", "MS")

  • "sudeste": c("ES", "MG", "RJ", "SP")

  • "sul": c("PR", "RS", "SC")

Biomes (Ecological Borders):

  • "amazonia_legal": c("AC", "AP", "AM", "PA", "RO", "RR", "MT", "MA", "TO")

  • "mata_atlantica": c("AL", "BA", "CE", "ES", "GO", "MA", "MG", "MS", "PB", "PE", "PI", "PR", "RJ", "RN", "RS", "SC", "SE", "SP")

  • "caatinga": c("AL", "BA", "CE", "MA", "PB", "PE", "PI", "RN", "SE", "MG")

  • "cerrado": c("BA", "DF", "GO", "MA", "MG", "MS", "MT", "PA", "PI", "PR", "RO", "SP", "TO")

  • "pantanal": c("MT", "MS")

  • "pampa": c("RS")

Hydrography & Climate:

  • "bacia_amazonia": c("AC", "AM", "AP", "MT", "PA", "RO", "RR")

  • "bacia_sao_francisco": c("AL", "BA", "DF", "GO", "MG", "PE", "SE")

  • "bacia_parana": c("GO", "MG", "MS", "PR", "SP")

  • "bacia_tocantins": c("GO", "MA", "PA", "TO")

  • "semi_arido": c("AL", "BA", "CE", "MA", "PB", "PE", "PI", "RN", "SE", "MG")

Health, Agriculture & Geopolitics:

  • "matopiba": c("MA", "TO", "PI", "BA")

  • "arco_desmatamento": c("RO", "AC", "AM", "PA", "MT", "MA")

  • "dengue_hyperendemic": c("GO", "MS", "MT", "PR", "RJ", "SP")

  • "sudene": c("AL", "BA", "CE", "MA", "PB", "PE", "PI", "RN", "SE", "MG", "ES")

  • "fronteira_brasil": c("AC", "AM", "AP", "MT", "MS", "PA", "PR", "RO", "RR", "RS", "SC")

Examples

if (FALSE) { # \dontrun{
library(climasus4r)

# Filter by sex only
df_women <- sus_data_filter_demographics(df, sex = "Female")

# Filter by age range (elderly, 65+)
df_elderly <- sus_data_filter_demographics(df, age_range = c(65, Inf))

# Filter by multiple criteria (elderly women)
df_elderly_women <- sus_data_filter_demographics(
  df,
  sex = c("Feminino"),
  age_range = c(65, Inf),
  lang = "pt"
)

# Filter by race and municipality
df_subset <- sus_data_filter_demographics(
  df,
  race = c("Branca", "Parda"),
  municipality_code = c("3550308", "3304557"),  # Sao Paulo, Rio de Janeiro
  lang = "pt"
)

# Complex filtering (children under 5, both sexes, specific municipality)
df_children <- sus_data_filter_demographics(
  df,
  age_range = c(0, 5),
  region = "Norte",
  lang = "en"
)
} # }