Ibamam

This package aims to provide the dataset for environmental fines and notices from the Brazilian Institute for the Environment and Renewable Natural Resources (IBAMA).

You can visit the IBAMA repository in the Brazilian Open Data Portal.

How to install?

This package can be installed via GitHub using the following code in R:

# install.packages("remotes")
remotes::install_github("cccneto/Ibamam")

How to use Ibamam?

The {Ibamam} package has one function available: get_dataset_ibamam(). This function returns a tibble, containing several columns described in the documentation of the function. This function has three arguments: dataset , uf and clean.

• dataset can receive one of the two values: distribuidas or arrecadadas. Use distribuidas to get data of the environmental fines applied by Ibama. Use arrecadadas to get data of the environmental fines collected by Ibama (which means that the fine has been paid).

• uf is the abreviation of the name of the states (two letters in lower case) that data will be obtained. The default value is "all", so all data from all the states of Brazil will be returned by default. Please notice that, If you use the default value, it might take several minutes to execute.

• clean is whether the dataset should be cleaned or not. If TRUE, the dataset will be cleaned. If FALSE, the returned dataset will be the original version, without modifications. Default is TRUE.

Examples on how to use this function:

library(Ibamam)

# get fines applied by IBAMA in Pará
fines_applied_Para <- get_dataset_ibamam(dataset = "distribuidas", uf = "PA")

dplyr::glimpse(fines_applied_Para)
#> Rows: 59,703
#> Columns: 16
#> $ dataAuto                   <date> 2021-04-23, 2020-09-28, 2021-02-10, 2020-0…
#> $ nomeMunicipio              <chr> "PLACAS", "RUROPOLIS", "MARAPANIM", "PRAINH…
#> $ nomeMunicipio_geobr        <chr> "Placas", "Rurópolis", "Marapanim", "Prainh…
#> $ codigoMunicipio            <chr> "1505650", "1506195", "1504406", "1506005",…
#> $ numAI                      <chr> "ABYX5X27 - ", "AB1FQ5MJ - ", "AD6DF079 - "…
#> $ tipoInfracao               <fct> Outras, Flora, Unidades de conservação, Flo…
#> $ ultimaAtualizacaoRelatorio <dttm> 2021-05-29 19:15:00, 2021-05-29 19:15:00, …
#> $ uf                         <chr> "PA", "PA", "PA", "PA", "PA", "PA", "PA", "…
#> $ situacaoDebito             <chr> "Para homologação/prazo de defesa", "Para h…
#> $ tipoAuto                   <fct> Multa, Multa, Multa, Multa, Multa, Multa, M…
#> $ moeda                      <fct> Real, Real, Real, Real, Real, Real, Real, R…
#> $ enquadramentoLegal         <fct> "Decreto 6514/2008 - Artigo 79", "Decreto 6…
#> $ nomeRazaoSocial            <chr> "ILSON ALVES DE FARIA", "ABIDIEL CELESTINO …
#> $ cpfCnpj                    <chr> "651.179.132-72", "516.819.352-87", "667.55…
#> $ valorAuto                  <dbl> 10000.0, 8000.0, 2000.0, 180729.9, 2015000.…
#> $ enquadramentoJuridico      <fct> CPF, CPF, CPF, CNPJ, CPF, CPF, CPF, CNPJ, C…

# get fines collected by IBAMA in Pará
fines_collected_Para <- get_dataset_ibamam(dataset = "arrecadadas", uf = "PA")

dplyr::glimpse(fines_collected_Para)
#> Rows: 73,686
#> Columns: 21
#> $ dataAuto                   <date> 2019-12-01, 2020-03-20, 2020-03-10, 2020-0…
#> $ nomeMunicipio              <chr> "PACAJA", "ANANINDEUA", "ANANINDEUA", "ANAN…
#> $ nomeMunicipio_geobr        <chr> "Pacajá", "Ananindeua", "Ananindeua", "Anan…
#> $ codigoMunicipio            <chr> "1505486", "1500800", "1500800", "1500800",…
#> $ dataPagamento              <date> 2020-12-16, 2021-02-17, 2021-02-22, 2021-0…
#> $ valorPago                  <dbl> 8400.32, 863.86, 866.96, 863.86, 863.86, 10…
#> $ numAI                      <chr> "C7AZB2I8 - ", "DB3W3QKR - ", "GUN4XIPO - "…
#> $ tipoInfracao               <fct> Flora, Outras, Outras, Outras, Outras, Flor…
#> $ ultimaAtualizacaoRelatorio <dttm> 2021-05-29 19:26:00, 2021-05-29 19:26:00, …
#> $ parcela                    <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3…
#> $ quantidadeParcela          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 30, 30,…
#> $ uf                         <chr> "PA", "PA", "PA", "PA", "PA", "PA", "PA", "…
#> $ tipoAuto                   <fct> Multa, Multa, Multa, Multa, Multa, Multa, M…
#> $ moeda                      <fct> Real, Real, Real, Real, Real, Real, Real, R…
#> $ statusDebito               <chr> "Quitado. Baixa automática", "Quitado. Baix…
#> $ enquadramentoLegal         <fct> "Decreto 6514/2008 - Artigo 47", "Decreto 6…
#> $ nomeRazaoSocial            <chr> "SIMONE SOUZA MORAES", "JATEX TRANSPORTES L…
#> $ cpfCnpj                    <chr> "019.077.525-44", "39.124.797/0004-17", "43…
#> $ valorbaseParcela           <dbl> 11524.50, 1200.00, 1200.00, 1200.00, 1200.0…
#> $ valorAuto                  <dbl> 11524.5, 1200.0, 1200.0, 1200.0, 1200.0, 15…
#> $ enquadramentoJuridico      <fct> CPF, CNPJ, CNPJ, CNPJ, CNPJ, CPF, CNPJ, CNP…

If you want to get the data for the whole country (Brazil), do not write anything in the argument uf.

# get fines applied by IBAMA in Brazil
fines_applied_Brasil <- get_dataset_ibamam(dataset = "distribuidas")

# get fines collected by IBAMA in Brazil
fines_collected_Brasil <- get_dataset_ibamam(dataset = "arrecadadas")

If you want to get the data for more than one state, but not for the whole country, use a vector in the argument uf with the abbreviations of the name of the states.

# get fines applied by IBAMA in Southeast of Brazil
fines_applied_southeast <- get_dataset_ibamam(dataset = "distribuidas", uf = c("SP", "RJ", "ES", "MG"))

# get fines collected by IBAMA in Southeast of Brazil
fines_collected_southeast <- get_dataset_ibamam(dataset = "arrecadadas", uf = c("SP", "RJ", "ES", "MG"))

If you want to access the original data, without the cleaning steps, use the argument clean = FALSE. Example:

# get fines applied by IBAMA in São Paulo, without cleaning the dataset
untidy_fines_applied_SP <- get_dataset_ibamam(dataset = "distribuidas", uf = "SP", clean = FALSE)

dplyr::glimpse(untidy_fines_applied_SP)
#> Rows: 34,598
#> Columns: 13
#> $ dataAuto                   <chr> "22/04/2020", "17/10/2019", "27/01/2020", "…
#> $ municipio                  <chr> "SAO JOSE DO RIO PRETO", "MARILIA", "SAO JO…
#> $ numAI                      <chr> "AC6FODNB - ", "ADX7F7IM - ", "AEGEYX7V - "…
#> $ tipoInfracao               <chr> "Fauna", "Fauna", "Fauna", "Controle ambien…
#> $ ultimaAtualizacaoRelatorio <chr> "29/05/2021 19:23", "29/05/2021 19:23", "29…
#> $ uf                         <chr> "SP", "SP", "SP", "SP", "SP", "SP", "SP", "…
#> $ situacaoDebito             <chr> "Quitado. Baixa automática", "Cancelado", "…
#> $ tipoAuto                   <chr> "Multa", "Multa", "Multa", "Multa", "Multa"…
#> $ moeda                      <chr> "Real", "Real", "Real", "Real", "Real", "Re…
#> $ enquadramentoLegal         <chr> "Decreto 6514/2008 - Artigo 3º, Instrução N…
#> $ nomeRazaoSocial            <chr> "JOSE ELIEZER DO PRADO DOS SANTOS", "VALTER…
#> $ cpfCnpj                    <chr> "368.573.748-13", "063.360.978-11", "102.74…
#> $ valorAuto                  <dbl> 500, 500, 500, 45000, 1300, 1300, 400500, 5…

Data cleaning

By default, the function get_dataset_ibamam() will use clean = TRUE and perform the following cleaning steps:

• Colunms dataAuto, ultimaAtualizacaoRelatorio, dataPagamento are transformed from class character to class date.

• Colunms tipoInfracao, tipoAuto, moeda, enquadramentoLegal are transformed from class character to class factor.

• Column enquadramentoJuridico is created from the cpfCnpj: if cpfCnpj has 14 characteres or less, is “CPF”, else is “CNPJ”.

• Column municipio is mutated into nomeMunicipio, and several cleanings is made in order to be possible to join with the names of municipalities in the package geobr.

• Join the dataset with the result of the package geobr, so the cleaned dataset has a column with the IBGE code of the municipality, called codigoMunicipio.

If you want to access the original data (without the cleaning steps), use the argument clean = FALSE.

How to cite Ibamam Package

“CRUZ NETO, Claudiano C., MILZ, Beatriz, MACEDO, Samuel (2021). Ibamam: Pacote para tratamento da Base de dados para multas e autuações ambientais aplicadas pelo Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis - Brasil. https://doi.org/10.5281/zenodo.4509344”.