Import an INFOSIGA-SP dataset

Downloads (if necessary) and imports one of the three INFOSIGA-SP datasets as a tidy tibble. The source archive is cached locally, so the first call triggers a download and subsequent calls read from disk.

Usage

read_infosiga(
  dataset = c("sinistros", "pessoas", "veiculos"),
  clean = TRUE,
  year = NULL,
  download_if_missing = TRUE,
  quiet = FALSE,
  ...
)

Arguments

dataset

Which dataset to import. One of:

"sinistros": Crash events (one row per event).
"pessoas": Victims / people involved (one row per person).
"veiculos": Vehicles involved (one row per vehicle).

clean

Logical. If TRUE (default), return a processed dataset: text is trimmed, the "NAO DISPONIVEL" marker becomes NA, ordinal columns become ordered factors, crash-type flags become logical, and impossible coordinates are dropped (see clean_infosiga() for the full list of steps). If FALSE, return the raw data exactly as published, with all text columns as character vectors.

year

Optional integer vector used to filter rows by year of the crash (ano_sinistro). If NULL (default), all available years are returned. For example, year = 2020:2023.

download_if_missing

Logical. If TRUE (default), download the archive when it is not already cached. If FALSE and the archive is missing, an informative error is raised.

quiet

Logical. If FALSE (default), report progress.

...

Additional arguments passed to infosiga_download() (for example overwrite = TRUE to force a refresh).

Value

A tibble with one row per record. The columns keep the original INFOSIGA-SP names (in Portuguese); see the package data dictionary via infosiga_dictionary(). The three datasets can be joined on id_sinistro (and id_veiculo, where present).

Details

Source files are encoded in Latin-1 (ISO-8859-1), use ; as the field separator, , as the decimal mark and DD/MM/YYYY dates. read_infosiga() handles all of these and returns UTF-8 text, Date columns and numeric coordinates. Each dataset is distributed across two period files inside the archive (2015-2021 and 2022 onward); they are read and row-bound transparently.

By default (clean = TRUE) the result is then processed by clean_infosiga(): text columns are whitespace-trimmed, the "NAO DISPONIVEL" ("not available") marker becomes NA, ordinal columns (dia_da_semana, turno, gravidade_lesao, the age bands) become ordered factors, the ano_mes_* year-month strings are parsed to first-of-month Dates, the binary tp_sinistro_* crash-type flags become logical, tempo_sinistro_obito becomes integer, and latitude/longitude values outside the bounding box of Sao Paulo state are dropped to NA. See clean_infosiga() for the complete, ordered list. Pass clean = FALSE to obtain the raw data exactly as published – every text column kept as a character vector, with "NAO DISPONIVEL" and the source's fixed-width whitespace padding preserved verbatim.

A small fraction of rows in the source contain data-quality issues (for example, an unescaped ; inside a street name, or mis-encoded coordinates). Any value that cannot be parsed to its declared column type is set to NA and recorded by readr::problems(). Empty fields are read as NA in both modes. In the raw data (clean = FALSE) the crash-type flag columns (tp_sinistro_*) hold "S" when the flag applies and NA otherwise; with clean = TRUE they are converted to logical.

Examples

if (FALSE) { # \dontrun{
# Import all crash events, processed (downloads the archive on first use)
sinistros <- read_infosiga("sinistros")
levels(sinistros$dia_da_semana)

# Only victims from 2022 and 2023
vitimas <- read_infosiga("pessoas", year = 2022:2023)

# The raw data, exactly as published
raw <- read_infosiga("sinistros", clean = FALSE)
} # }

# A bundled sample (no download required) illustrates the structure:
sample_path <- system.file(
  "extdata", "sinistros_sample.csv",
  package = "infosigasp"
)
if (nzchar(sample_path)) head(readr::read_delim(sample_path, ";"))
#> Rows: 100 Columns: 48
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr  (25): tipo_registro, data_sinistro, mes_sinistro, dia_sinistro, ano_mes...
#> dbl  (13): id_sinistro, ano_sinistro, numero_logradouro, cod_ibge, qtd_pedes...
#> num   (2): latitude, longitude
#> lgl   (7): qtd_bicicleta, qtd_caminhao, qtd_veic_outros, qtd_gravidade_ileso...
#> time  (1): hora_sinistro
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 6 × 48
#>   id_sinistro tipo_registro data_sinistro ano_sinistro mes_sinistro dia_sinistro
#>         <dbl> <chr>         <chr>                <dbl> <chr>        <chr>       
#> 1     1265457 NOTIFICACAO   01/01/2022            2022 01           01          
#> 2     1301846 NOTIFICACAO   01/01/2022            2022 01           01          
#> 3     1352130 NOTIFICACAO   01/01/2022            2022 01           01          
#> 4     2279865 NOTIFICACAO   01/01/2022            2022 01           01          
#> 5     1444289 NOTIFICACAO   01/01/2022            2022 01           01          
#> 6     1322053 SINISTRO NAO… 01/01/2022            2022 01           01          
#> # ℹ 42 more variables: hora_sinistro <time>, ano_mes_sinistro <chr>,
#> #   dia_da_semana <chr>, turno <chr>, logradouro <chr>,
#> #   numero_logradouro <dbl>, tipo_via <chr>, tipo_local <chr>, latitude <dbl>,
#> #   longitude <dbl>, cod_ibge <dbl>, municipio <chr>,
#> #   regiao_administrativa <chr>, administracao <chr>, conservacao <chr>,
#> #   circunscricao <chr>, tp_sinistro_primario <chr>, qtd_pedestre <dbl>,
#> #   qtd_bicicleta <lgl>, qtd_motocicleta <dbl>, qtd_automovel <dbl>, …

Usage

Arguments

Value

Details

See also

Examples