Functions for SouthKoreAPIs

The SouthKoreAPIs package provides several core functions to access real-time and structured information about South Korea from public APIs such as the World Bank API, Nager.Date, and the REST Countries API.

Below is a list of the main functions included in the package:

• get_southkorea_child_mortality(): Get South Korea’s Under-5 Mortality Rate data from the World Bank.

• get_southkorea_cpi(): Get South Korea’s Consumer Price Index (2010 = 100) data from the World Bank.

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

• get_southkorea_gdp(): Get South Korea’s GDP (current US$) data from the World Bank.

• get_southkorea_holidays(): Get official public holidays in South Korea for a given year, e.g., get_southkorea_holidays(2025).

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

• get_southkorea_literacy_rate(): Get South Korea’s Adult Literacy Rate data from the World Bank.

• get_southkorea_life_expectancy(): Get South Korea’s Life Expectancy at Birth data from the World Bank.

• get_southkorea_population(): Get South Korea’s Total Population data from the World Bank.

• get_southkorea_unemployment(): Get South Korea’s Total Unemployment Rate data from the World Bank.

• get_country_info_kr(): Get key country information for South Korea.

• view_datasets_SouthKoreAPIs(): View available curated datasets included in SouthKoreAPIs.

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

southkorea_gdp <- head(get_southkorea_gdp())

print(southkorea_gdp)
#> # A tibble: 6 × 5
#>   indicator         country      year   value value_label      
#>   <chr>             <chr>       <int>   <dbl> <chr>            
#> 1 GDP (current US$) Korea, Rep.  2022 1.67e12 1,673,916,511,800
#> 2 GDP (current US$) Korea, Rep.  2021 1.82e12 1,818,432,106,880
#> 3 GDP (current US$) Korea, Rep.  2020 1.64e12 1,644,312,831,906
#> 4 GDP (current US$) Korea, Rep.  2019 1.65e12 1,651,422,932,448
#> 5 GDP (current US$) Korea, Rep.  2018 1.73e12 1,725,373,496,825
#> 6 GDP (current US$) Korea, Rep.  2017 1.62e12 1,623,074,183,502

southkorea_life_expectancy <- head(get_southkorea_life_expectancy())

print(southkorea_life_expectancy)
#> # A tibble: 6 × 4
#>   indicator                               country      year value
#>   <chr>                                   <chr>       <int> <dbl>
#> 1 Life expectancy at birth, total (years) Korea, Rep.  2022  82.7
#> 2 Life expectancy at birth, total (years) Korea, Rep.  2021  83.5
#> 3 Life expectancy at birth, total (years) Korea, Rep.  2020  83.4
#> 4 Life expectancy at birth, total (years) Korea, Rep.  2019  83.2
#> 5 Life expectancy at birth, total (years) Korea, Rep.  2018  82.6
#> 6 Life expectancy at birth, total (years) Korea, Rep.  2017  82.6

southkorea_population <- head(get_southkorea_population())

print(southkorea_population)
#> # A tibble: 6 × 5
#>   indicator         country      year    value value_label
#>   <chr>             <chr>       <int>    <int> <chr>      
#> 1 Population, total Korea, Rep.  2022 51672569 51,672,569 
#> 2 Population, total Korea, Rep.  2021 51769539 51,769,539 
#> 3 Population, total Korea, Rep.  2020 51836239 51,836,239 
#> 4 Population, total Korea, Rep.  2019 51764822 51,764,822 
#> 5 Population, total Korea, Rep.  2018 51585058 51,585,058 
#> 6 Population, total Korea, Rep.  2017 51361911 51,361,911

Evolution of Births in Seoul by Age Group

# Filter data for a specific region, e.g., "Seoul"
births_seoul <- SouthKoreaBirths_tbl_df %>%
  filter(region == "Seoul") %>%
  group_by(time, age) %>%
  summarise(total_births = sum(births), .groups = "drop")

# Plot the evolution of births by age group in Seoul
ggplot(births_seoul, aes(x = time, y = total_births, color = age)) +
  geom_line(size = 1) +
  labs(
    title = "Evolution of Births in Seoul by Age Group",
    x = "Year",
    y = "Total Births",
    color = "Age Group"
  ) +
  theme_minimal()

Dataset Suffixes

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

• _df: A standard data frame.

• _tbl_df: A tibble data frame object.

• _list: A list object.

• _sf: A sf (Simple Features) object.

Datasets Included in SouthKoreAPIs

In addition to API access functions, SouthKoreAPIs offers one of the largest curated collections of open datasets focused on South Korea. These preloaded datasets cover a wide range of topics including public health outbreaks, demographics, social surveys, elections, economic indicators, natural disasters, administrative divisions, air quality, climate data, energy consumption, cultural information, and financial markets. Below are some featured examples:

• MERSKorea2015_list: A list containing two data frames with information collected during the first weeks of the 2015 MERS-CoV outbreak in South Korea.

• KoreanSocialSurvey_tbl_df: A tibble containing a sample of data from the Korean General Social Survey (KGSS) conducted in 2023.

• SeoulMosquito_tbl_df: A tibble containing daily mosquito indicator data and weather measurements for Seoul, South Korea, from 2016 to 2019.

Conclusion

The SouthKoreAPIs package offers a unified interface for accessing both real-time data from public APIs and a rich collection of curated datasets about South Korea. Covering a wide range of topics from economic indicators, public holidays, and demographic statistics via international APIs, to detailed datasets on public health outbreaks, elections, natural disasters, climate data, energy consumption, and more, SouthKoreAPIs provides users with reliable, structured, and high-quality data.

Unlike tools that focus exclusively on API access, SouthKoreAPIs includes one of the most comprehensive collections of preloaded open datasets related to South Korea, enabling deeper exploration of the country’s historical, social, and economic landscape.

Designed to support reproducible research, education, and data journalism, the package empowers users to analyze and visualize South Korea–focused data directly within R, using tidy data formats and well-documented sources.