Functions for ChileDataAPI

The ChileDataAPI package provides several core functions to access real-time and structured information about Chile from public APIs such as FINDIC, REST Countries, the World Bank API, and Nager.Date. Below is a list of the main functions included in the package:

• get_chile_bitcoin(): Retrieves the daily Bitcoin price in Chilean Pesos over the last month.

• get_chile_copper_pound(): Returns historical daily copper prices (per pound).

• get_chile_dollar(): Provides the exchange rate of the U.S. Dollar in CLP.

• get_chile_euro(): Provides the exchange rate of the Euro in CLP.

• get_chile_ipsa(): Retrieves daily values of the IPSA (Chile’s stock market index).

• get_chile_uf(): Returns daily values of the Unidad de Fomento (UF).

• get_chile_utm(): Returns monthly values of the Unidad Tributaria Mensual (UTM).

• get_chile_yen(): Provides the exchange rate of the Japanese Yen in CLP.

• get_country_info_cl(): Get essential information about Chile or any other country by its full name

• get_chile_holidays(): Get official public holidays in chile for a given year, e.g., get_chile_holidays(2025).

• get_chile_child_mortality(): Get Chile’s Under-5 Mortality Rate data from the World Bank.

• get_chile_cpi(): Get Chile’s Consumer Price Index (2010 = 100) data from the World Bank.

• get_chile_energy_use(): Get Chile’s Energy Use (kg of oil equivalent per capita) data from the World Bank.

• get_chile_gdp(): Get Chile’s GDP (current US$) data from the World Bank.

• get_chile_hospital_beds(): Get Chile’s Hospital Beds (per 1,000 people) data from the World Bank.

• get_chile_life_expectancy(): Get Chile’s Life Expectancy at Birth data from the World Bank.

• get_chile_literacy_rate(): Get Chile’s Adult Literacy Rate data from the World Bank.

• get_chile_population(): Get Chile’s Total Population data from the World Bank.

• get_chile_unemployment(): Get Chile’s Total Unemployment Rate data from the World Bank.

• view_datasets_ChileDataAPI(): Lists all curated datasets included in the ChileDataAPI package

These functions return real-time data in tidy tibble format and represent time series that are updated daily or monthly depending on the source.
The functions powered by the FINDIC endpoints provide economic time series such as the UF, UTM, Dollar, Euro, Yen, Copper price per pound, Bitcoin, and the IPSA index.

In addition, functions connected to the World Bank API supply structured information on macroeconomic indicators (GDP, CPI, unemployment, energy use, hospital beds), population trends, life expectancy, literacy rates, and other development indicators for Chile.

All outputs are delivered as tidy tibble objects, making them ready for immediate integration into reproducible analytical workflows.

These functions allow users to access high-quality and structured information on Chile, 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 ChileDataAPI in practical scenarios.

chile_copper_price <- head(get_chile_copper_pound(),n=10)

print(chile_copper_price)
#> # A tibble: 10 × 2
#>    fecha      valor
#>    <chr>      <dbl>
#>  1 2025-09-11  4.46
#>  2 2025-09-10  4.46
#>  3 2025-09-09  4.46
#>  4 2025-09-08  4.46
#>  5 2025-09-05  4.49
#>  6 2025-09-04  4.5 
#>  7 2025-09-03  4.44
#>  8 2025-09-02  4.46
#>  9 2025-09-01  4.42
#> 10 2025-08-29  4.38

chile_dollar_price <- head(get_chile_dollar(),n=10)

print(chile_dollar_price)
#> # A tibble: 10 × 2
#>    fecha      valor
#>    <chr>      <dbl>
#>  1 2025-09-11  963.
#>  2 2025-09-10  966.
#>  3 2025-09-09  967.
#>  4 2025-09-08  965.
#>  5 2025-09-05  971.
#>  6 2025-09-04  969.
#>  7 2025-09-03  974.
#>  8 2025-09-02  968.
#>  9 2025-09-01  965.
#> 10 2025-08-29  967.

chile_euro_price <- head(get_chile_euro(),n=10)

print(chile_euro_price)
#> # A tibble: 10 × 2
#>    fecha      valor
#>    <chr>      <dbl>
#>  1 2025-09-11 1127.
#>  2 2025-09-10 1132.
#>  3 2025-09-09 1135.
#>  4 2025-09-08 1130.
#>  5 2025-09-05 1131.
#>  6 2025-09-04 1131.
#>  7 2025-09-03 1133.
#>  8 2025-09-02 1134.
#>  9 2025-09-01 1129.
#> 10 2025-08-29 1131.

chile_gdp <- head(get_chile_gdp())

print(chile_gdp)
#> # A tibble: 6 × 5
#>   indicator         country  year         value value_label    
#>   <chr>             <chr>   <int>         <dbl> <chr>          
#> 1 GDP (current US$) Chile    2022 301226575540. 301,226,575,540
#> 2 GDP (current US$) Chile    2021 315325547162. 315,325,547,162
#> 3 GDP (current US$) Chile    2020 254042159309. 254,042,159,309
#> 4 GDP (current US$) Chile    2019 278285058719. 278,285,058,719
#> 5 GDP (current US$) Chile    2018 295857562992. 295,857,562,992
#> 6 GDP (current US$) Chile    2017 276154259981. 276,154,259,981

chile_life_expectancy <- head(get_chile_life_expectancy())

print(chile_life_expectancy)
#> # A tibble: 6 × 4
#>   indicator                               country  year value
#>   <chr>                                   <chr>   <int> <dbl>
#> 1 Life expectancy at birth, total (years) Chile    2022  79.2
#> 2 Life expectancy at birth, total (years) Chile    2021  78.9
#> 3 Life expectancy at birth, total (years) Chile    2020  79.3
#> 4 Life expectancy at birth, total (years) Chile    2019  80.3
#> 5 Life expectancy at birth, total (years) Chile    2018  80.6
#> 6 Life expectancy at birth, total (years) Chile    2017  80.6

# Clean data: remove missing values from key variables
health_clean <- chile_health_survey_df %>%
  filter(!is.na(age), !is.na(pas), !is.na(male))

# Create gender variable
health_clean <- health_clean %>%
  mutate(gender = ifelse(male == 1, "Male", "Female"))

# Plot: Systolic Blood Pressure vs Age by Gender
ggplot(health_clean, aes(x = age, y = pas, color = gender)) +
  geom_point(alpha = 0.4) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title = "Systolic Blood Pressure (PAS) by Age and Gender",
    x = "Age (years)",
    y = "Systolic Blood Pressure (mm Hg)",
    color = "Gender"
  ) +
  theme_minimal()

Dataset Suffixes

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

• _df: A standard data frame.

• _ts: A time series object.

• _tbl_df: A tibble data frame object.

Datasets Included in ChileDataAPI

In addition to API access functions, ChileDataAPI provides several curated datasets offering valuable insights into Chile’s recent history, population health, territorial divisions, electoral processes, and seismic activity. Here are some featured examples:

• census_chile_2017_df: Data frame containing microdata from the 2017 Chilean census, specifically from the commune of San Pablo. The dataset includes 7,512 observations, all variable names and data values are in Spanish.

• chile_earthquakes_tbl_df: Tibble containing information about significant (perceptible) earthquakes that occurred in Chile from January 1st, 2012 to the present.

• malleco_tree_rings_ts: Time series object (ts) containing the average annual tree ring width, measured in millimeters, for Araucaria Araucana trees located in the Malleco region of Chile.

Conclusion

The ChileDataAPI package provides a robust set of tools to access open data about Chile through RESTful APIs and curated datasets. It includes functions to retrieve real-time financial indicators—such as the value of the dollar, euro, yen, copper, UF, UTM, and Bitcoin—via the FINDIC API, as well as international country information through the REST Countries API.

In addition, the package integrates with the World Bank API to deliver structured macroeconomic and demographic indicators, including GDP, CPI, unemployment, energy use, hospital beds, population, life expectancy, and literacy rates, enabling comprehensive socio-economic analysis.
Through the Nager.Date API, users can access official Chilean public holidays, which facilitates temporal alignment of datasets with policy, economic, and cultural events.

Beyond APIs, the package also offers curated datasets covering Chile’s recent history and socio-political context, including the 2017 census sample, the 2021 presidential election, public health survey data, territorial codes, seismic events, and records of human rights violations during the Pinochet regime.

By combining real-time API calls with high-quality curated datasets, ChileDataAPI enables reproducible research and in-depth analysis of Chile’s economic, demographic, environmental, and political landscape.