Functions for BrazilDataAPI

The BrazilDataAPI package provides several core functions to access real-time and structured information about Brazil from public APIs such as BrasilAPI and REST Countries. Below is a list of the main functions included in the package:

• get_brazil_banks(): Get List of Banks in Brazil

• get_brazil_cep(): Get Address Information by Brazilian CEP (Postal Code) Example: get_brazil_cep(“89010025”)

• get_brazil_cnpj(): Get Company Information by CNPJ (Brazil) Example: get_brazil_cnpj(“19131243000197”)

• get_brazil_municipalities(): Get Municipalities of a Brazilian State from IBGE Example: get_brazil_municipalities(“SP”)

• get_brazil_rate_name(): Get Specific Brazilian Economic Rate by Name Example: get_brazil_rate_name(“CDI”)

• get_brazil_rates(): Get Official Interest Rates and Indexes from Brazil

• get_brazil_vehicle_brands(): Get Vehicle Brands from BrasilAPI (FIPE Data) Example: get_brazil_vehicle_brands(“motos”),get_brazil_vehicle_brands(“caminhoes”)

• get_country_info(): Get essential information about Brazil or any other country by its full name Example: get_country_info(“Brazil”),get_country_info(“brazil”),get_country_info(“Peru”)

• view_datasets_BrazilDataAPI(): Lists all curated datasets included in the BrazilDataAPI package

These functions allow users to access high-quality and structured information on Brazil, which can be combined with tools like dplyr, tidyr, and ggplot2 to support a wide range of data analysis and visualization tasks. In the following sections, you’ll find examples on how to work with BrazilDataAPI in practical scenarios.

# Retrieves official interest rates and indexes from the BrazilAPI

brazil_rates_001 <- get_brazil_rates()

print(brazil_rates_001)
#> # A tibble: 3 × 2
#>   nome  valor
#>   <chr> <dbl>
#> 1 Selic 15   
#> 2 CDI   14.9 
#> 3 IPCA   5.32

# A string indicating the type of vehicle. Must be one of "carros", "motos", or "caminhoes".

brazil_vehicles <- get_brazil_vehicle_brands("motos")

print(brazil_vehicles)
#> # A tibble: 94 × 2
#>    nome     valor
#>    <chr>    <chr>
#>  1 ADLY     60   
#>  2 AGRALE   61   
#>  3 APRILIA  62   
#>  4 ATALA    63   
#>  5 BAJAJ    64   
#>  6 BETA     65   
#>  7 BIMOTA   66   
#>  8 BMW      67   
#>  9 BRANDY   68   
#> 10 byCristo 69   
#> # ℹ 84 more rows

# A two-letter string representing the Brazilian state abbreviation (e.g., "SP", "RJ", "BA").

brazil_Municipalities <- get_brazil_municipalities("SP")

print(brazil_Municipalities)
#> # A tibble: 645 × 2
#>    nome                   codigo_ibge
#>    <chr>                  <chr>      
#>  1 ADAMANTINA             3500105    
#>  2 ADOLFO                 3500204    
#>  3 AGUAÍ                  3500303    
#>  4 ÁGUAS DA PRATA         3500402    
#>  5 ÁGUAS DE LINDÓIA       3500501    
#>  6 ÁGUAS DE SANTA BÁRBARA 3500550    
#>  7 ÁGUAS DE SÃO PEDRO     3500600    
#>  8 AGUDOS                 3500709    
#>  9 ALAMBARI               3500758    
#> 10 ALFREDO MARCONDES      3500808    
#> # ℹ 635 more rows

# Summarize total deaths by age and year
df_plot <- Brasil_females_df %>%
  group_by(year1, age) %>%
  summarise(total_deaths = sum(deaths, na.rm = TRUE), .groups = "drop")

# Plot: Deaths by age group over time
ggplot(df_plot, aes(x = age, y = total_deaths, color = as.factor(year1))) +
  geom_line(size = 1) +
  labs(
    title = "Female Deaths by Age Group in Brazil",
    subtitle = "Aggregated by year (year1)",
    x = "Age",
    y = "Number of Deaths",
    color = "Year"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    legend.position = "bottom"
  )

Dataset Suffixes

Each dataset in BrazilDataAPI is labeled with a suffix to indicate its structure and type:

• _df: A standard data frame.

• _ts: A time series object.

• _list: A list object.

Datasets Included in BrazilDataAPI

In addition to API access functions, BrazilDataAPI provides several preloaded datasets offering insights into Brazil’s demographic structure, environmental conditions, cultural events, and public health records. Here are some featured examples:

• Brasil_females_df: Brazilian Female Demographics & Mortality A data frame containing population counts and mortality information for females in Brazil, disaggregated by federal states and abridged age groups, for the years 1991 and 2000.

• manaus_ts: Monthly Average Heights of the Rio Negro at Manaus A univariate time series of monthly average river heights of the Rio Negro at Manaus. The series contains 1080 observations spanning 90 years, from January 1903 to December 1992.

• Yellow_Fever_list: Yellow Fever Outbreak in Brazil A list object containing information on the flow of Yellow Fever cases between five Brazilian states during the outbreak period from December 2016 to May 2017.

Conclusion

The BrazilDataAPI package provides a robust set of tools to access open data about Brazil through RESTful APIs and curated datasets. It includes functions to retrieve information about postal codes, banks, economic rates, and company registrations via the BrasilAPI, as well as international country indicators through the REST Countries API. Additionally, it offers preloaded datasets on Brazil’s male and female population by state and year, film festivals, São Paulo’s emission factors, river data from Manaus, and records of yellow fever outbreaks.