Functions for JapanAPIs

The JapanAPIs package provides several core functions to access real-time and structured information about Japan 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_japan_child_mortality(): Get Under-5 Mortality Rate in Japan from World Bank

• get_japan_cpi(): Get Japan’s Consumer Price Index from World Bank

• get_japan_energy_use(): Get Japan’s Energy Use (kg of oil equivalent per capita) from World Bank

• get_japan_gdp(): Get Japan’s GDP (Current US$) from World Bank

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

• get_japan_hospital_beds(): Get Hospital Beds per 1,000 People in Japan from World Bank

• get_japan_life_expectancy(): Get Japan’s Life Expectancy at Birth from World Bank

• get_japan_literacy_rate(): Get Japan’s Literacy Rate (Age 15+) from World Bank

• get_japan_population(): Get Japan’s Total Population from World Bank

• get_japan_unemployment(): Get Japan’s Unemployment Rate from World Bank

• get_country_info_jp(): Get Key Country Information About Japan from the REST Countries API

• view_datasets_JapanAPIs(): View Available Datasets in JapanAPIs

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

japan_gdp <- head(get_japan_gdp())

print(japan_gdp)
#> # A tibble: 6 × 5
#>   indicator         country  year   value value_label      
#>   <chr>             <chr>   <int>   <dbl> <chr>            
#> 1 GDP (current US$) Japan    2022 4.26e12 4,262,463,317,797
#> 2 GDP (current US$) Japan    2021 5.04e12 5,039,148,168,861
#> 3 GDP (current US$) Japan    2020 5.05e12 5,054,068,005,376
#> 4 GDP (current US$) Japan    2019 5.12e12 5,117,993,853,017
#> 5 GDP (current US$) Japan    2018 5.04e12 5,040,880,939,325
#> 6 GDP (current US$) Japan    2017 4.93e12 4,930,837,369,151

japan_life_expectancy <- head(get_japan_life_expectancy())

print(japan_life_expectancy)
#> # A tibble: 6 × 4
#>   indicator                               country  year value
#>   <chr>                                   <chr>   <int> <dbl>
#> 1 Life expectancy at birth, total (years) Japan    2022  84.0
#> 2 Life expectancy at birth, total (years) Japan    2021  84.4
#> 3 Life expectancy at birth, total (years) Japan    2020  84.6
#> 4 Life expectancy at birth, total (years) Japan    2019  84.4
#> 5 Life expectancy at birth, total (years) Japan    2018  84.2
#> 6 Life expectancy at birth, total (years) Japan    2017  84.1

japan_population <- head(get_japan_population())

print(japan_population)
#> # A tibble: 6 × 5
#>   indicator         country  year     value value_label
#>   <chr>             <chr>   <int>     <int> <chr>      
#> 1 Population, total Japan    2022 125124989 125,124,989
#> 2 Population, total Japan    2021 125681593 125,681,593
#> 3 Population, total Japan    2020 126261000 126,261,000
#> 4 Population, total Japan    2019 126633000 126,633,000
#> 5 Population, total Japan    2018 126811000 126,811,000
#> 6 Population, total Japan    2017 126972000 126,972,000

Japan Vehicle Production (1947–1989)

# Convert time series to a tibble
jpn_vehicle_prod_df <- tibble(
  year = as.numeric(time(jpn_vehicle_prod_ts)),
  production = as.numeric(jpn_vehicle_prod_ts)
)

# Plot the time series
jpn_vehicle_prod_df %>%
  ggplot(aes(x = year, y = production)) +
  geom_line(color = "steelblue", size = 1) +
  labs(
    title = "Japan Vehicle Production (1947–1989)",
    x = "Year",
    y = "Vehicles Produced (in thousands)"
  ) +
  theme_minimal()

Dataset Suffixes

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

• _df: A standard data frame.

• _tbl_df: A tibble data frame object.

• _ts: A time series object.

• _vec: A numeric vector object.

Datasets Included in JapanAPIs

In addition to API access functions, JapanAPIs offers one of the largest curated collections of open datasets focused on Japan. These preloaded datasets cover a wide range of topics including demography, natural disasters, public health, sports, centenarians, atomic bomb survivors, earthquakes, and administrative data. Below are some featured examples:

• jpn_usd_exchange_df: A Data frame containing weekly observations of the yen-dollar exchange rate from 1975 to 1989.

• jpn_prefectures_tbl_df: A tibble representing Japan’s 47 prefectures arranged in a 7x7 grid layout.

• kobe_quake_1995_ts: A time series containing data related to the 1995 Kobe earthquake.

Conclusion

The JapanAPIs package offers a unified interface for accessing both real-time data from public APIs and a rich collection of curated datasets about Japan. Covering a wide spectrum of topics from economic indicators, holidays, and demographic statistics via international APIs, to detailed datasets on natural disasters, public health, automotive production, administrative divisions, and more JapanAPIs provides users with reliable, structured, and high-quality data.

Unlike tools that focus exclusively on API access, JapanAPIs includes one of the most comprehensive collections of preloaded open datasets related to Japan, including information on centenarians, earthquake records, atomic bomb survivors, and detailed regional statistics. This enables deeper exploration of Japan’s historical, social, and economic landscape.

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