library(BrazilDataAPI)
library(ggplot2)
library(dplyr)
#>
#> Adjuntando el paquete: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
The BrazilDataAPI
package provides a unified interface
to access open data from the BrasilAPI and the
REST Countries API, with a focus on Brazil. It
allows users to easily retrieve up-to-date information on postal codes,
banks, economic indicators, holidays, company registrations, and
international country-level data relevant to Brazil.
In addition to API-access functions, the package includes a collection of curated datasets related to Brazil, covering diverse domains such as demographics (male and female population by state and year), river levels in Manaus, environmental emission factors in São Paulo, Brazilian film festivals, and historical yellow fever outbreaks.
BrazilDataAPI
is designed to support research, teaching,
and data analysis focused on Brazil by integrating public RESTful APIs
with high-quality, domain-specific datasets into a single, easy-to-use R
package.
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.
# 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"
)
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.
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.
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.