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  "Package": "ChinAPIs",
  "Type": "Package",
  "Title": "Access Chinese Data via Public APIs and Curated Datasets",
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  "Maintainer": "Renzo Caceres Rossi <arenzocaceresrossi@gmail.com>",
  "Description": "Provides functions to access data from public RESTful APIs\nincluding 'Nager.Date', 'World Bank API', and 'REST Countries\nAPI', retrieving real-time or historical data related to China,\nsuch as holidays, economic indicators, and international\ndemographic and geopolitical indicators. Additionally, the\npackage includes one of the largest curated collections of open\ndatasets focused on China and Hong Kong, covering topics such\nas air quality, demographics, input-output tables,\nepidemiology, political structure, names, and social\nindicators. The package supports reproducible research and\nteaching by integrating reliable international APIs and\nstructured datasets from public, academic, and government\nsources. For more information on the APIs, see: 'Nager.Date'\n<https://date.nager.at/Api>, 'World Bank API'\n<https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>,\nand 'REST Countries API' <https://restcountries.com/>.",
  "License": "MIT + file LICENSE",
  "Language": "en",
  "URL": "https://github.com/lightbluetitan/chinapis,\nhttps://lightbluetitan.github.io/chinapis/",
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  "Repository": "https://lightbluetitan.r-universe.dev",
  "Date/Publication": "2026-02-12 07:51:33 UTC",
  "RemoteUrl": "https://github.com/lightbluetitan/chinapis",
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    "User": "root"
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  "Author": "Renzo Caceres Rossi [aut, cre] (ORCID:\n<https://orcid.org/0009-0005-0744-854X>)",
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    "china_admin_divisions_df",
    "china_cars_tbl_df",
    "china_corruption_tbl_df",
    "china_io_2002_122_df",
    "china_io_2005_42_df",
    "china_io_2007_135_df",
    "china_io_2010_41_df",
    "china_io_2012_139_df",
    "china_io_2015_42_df",
    "china_io_2017_149_df",
    "china_io_2017_42_df",
    "china_io_2018_153_df",
    "china_io_2018_42_df",
    "china_io_2020_153_df",
    "china_io_2020_42_df",
    "chinese_cities_tbl_df",
    "chinese_dams_tbl_df",
    "COVID19_HongKong_df",
    "family_name_df",
    "get_china_child_mortality",
    "get_china_cpi",
    "get_china_energy_use",
    "get_china_gdp",
    "get_china_holidays",
    "get_china_hospital_beds",
    "get_china_life_expectancy",
    "get_china_literacy_rate",
    "get_china_population",
    "get_china_unemployment",
    "get_country_info_cn",
    "given_name_df",
    "health_family_life_df",
    "hk_councillors_tbl_df",
    "hk_districts_tbl_df",
    "hk_population_tbl_df",
    "hk_street_names_tbl_df",
    "panda_locations_df",
    "population_df",
    "sars_hong_kong_list",
    "shanghai_factories_df",
    "shanghai_pm25_df",
    "top1000name_prov_df",
    "top100name_year_df",
    "top50char_year_df",
    "view_datasets_ChinAPIs",
    "wenchuan_ptsd_matrix"
  ],
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      "title": "Beijing Air Quality Dataset (2015)",
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        "HOUR",
        "NO2",
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        "TEMP",
        "WIND"
      ],
      "rows": 7680,
      "table": true,
      "tojson": true
    },
    {
      "name": "china_admin_divisions_df",
      "title": "Administrative Divisions of China",
      "object": "china_admin_divisions_df",
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      "table": true,
      "tojson": true
    },
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      "name": "china_cars_tbl_df",
      "title": "Stated Car Choice Data from Chinese Buyers",
      "object": "china_cars_tbl_df",
      "class": [
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        "data.frame"
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        "bev150",
        "phevFastcharge",
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        "weights"
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      "table": true,
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    },
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        "prefecture_id",
        "county_id"
      ],
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      "title": "PM2.5 Pollution and Weather Data in Shanghai",
      "object": "shanghai_pm25_df",
      "class": [
        "data.frame"
      ],
      "fields": [
        "PM_Jingan",
        "PM_US.Post",
        "PM_Xuhui",
        "DEWP",
        "HUMI",
        "PRES",
        "TEMP",
        "Iws",
        "precipitation",
        "Iprec"
      ],
      "rows": 5000,
      "table": true,
      "tojson": true
    },
    {
      "name": "top1000name_prov_df",
      "title": "Top 1,000 Given Names by Province in Mainland China",
      "object": "top1000name_prov_df",
      "class": [
        "data.frame"
      ],
      "fields": [
        "name",
        "n.male",
        "n.female",
        "beijing",
        "tianjin",
        "hebei",
        "shanxi",
        "neimenggu",
        "liaoning",
        "jilin",
        "heilongjiang",
        "shanghai",
        "jiangsu",
        "zhejiang",
        "anhui",
        "fujian",
        "jiangxi",
        "shandong",
        "henan",
        "hubei",
        "hunan",
        "guangdong",
        "guangxi",
        "hainan",
        "chongqing",
        "sichuan",
        "guizhou",
        "yunnan",
        "xizang",
        "shaanxi",
        "gansu",
        "qinghai",
        "ningxia",
        "xinjiang",
        "others"
      ],
      "rows": 999,
      "table": true,
      "tojson": true
    },
    {
      "name": "top100name_year_df",
      "title": "Top 100 Given Names in 6 Birth Cohorts",
      "object": "top100name_year_df",
      "class": [
        "data.frame"
      ],
      "fields": [
        "top100",
        "name.all.1950",
        "name.all.1960",
        "name.all.1970",
        "name.all.1980",
        "name.all.1990",
        "name.all.2000",
        "n.all.1950",
        "n.all.1960",
        "n.all.1970",
        "n.all.1980",
        "n.all.1990",
        "n.all.2000",
        "name.m.1950",
        "name.m.1960",
        "name.m.1970",
        "name.m.1980",
        "name.m.1990",
        "name.m.2000",
        "n.m.1950",
        "n.m.1960",
        "n.m.1970",
        "n.m.1980",
        "n.m.1990",
        "n.m.2000",
        "name.f.1950",
        "name.f.1960",
        "name.f.1970",
        "name.f.1980",
        "name.f.1990",
        "name.f.2000",
        "n.f.1950",
        "n.f.1960",
        "n.f.1970",
        "n.f.1980",
        "n.f.1990",
        "n.f.2000"
      ],
      "rows": 100,
      "table": true,
      "tojson": true
    },
    {
      "name": "top50char_year_df",
      "title": "Top 50 Given-Name Characters in 6 Birth Cohorts",
      "object": "top50char_year_df",
      "class": [
        "data.frame"
      ],
      "fields": [
        "top50",
        "char.all.1950",
        "char.all.1960",
        "char.all.1970",
        "char.all.1980",
        "char.all.1990",
        "char.all.2000",
        "n.all.1950",
        "n.all.1960",
        "n.all.1970",
        "n.all.1980",
        "n.all.1990",
        "n.all.2000",
        "char.m.1950",
        "char.m.1960",
        "char.m.1970",
        "char.m.1980",
        "char.m.1990",
        "char.m.2000",
        "n.m.1950",
        "n.m.1960",
        "n.m.1970",
        "n.m.1980",
        "n.m.1990",
        "n.m.2000",
        "char.f.1950",
        "char.f.1960",
        "char.f.1970",
        "char.f.1980",
        "char.f.1990",
        "char.f.2000",
        "n.f.1950",
        "n.f.1960",
        "n.f.1970",
        "n.f.1980",
        "n.f.1990",
        "n.f.2000"
      ],
      "rows": 50,
      "table": true,
      "tojson": true
    },
    {
      "name": "wenchuan_ptsd_matrix",
      "title": "PTSD Symptoms of Wenchuan Earthquake Survivors",
      "object": "wenchuan_ptsd_matrix",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "intrusion",
        "dreams",
        "flash",
        "upset",
        "physior",
        "avoidth",
        "avoidact",
        "amnesia",
        "lossint",
        "distant",
        "numb",
        "future",
        "sleep",
        "anger",
        "concen",
        "hyper",
        "startle"
      ],
      "rows": 362,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "bj_air_quality_tbl_df",
      "title": "Beijing Air Quality Dataset (2015)",
      "topics": [
        "bj_air_quality_tbl_df"
      ]
    },
    {
      "page": "china_admin_divisions_df",
      "title": "Administrative Divisions of China",
      "topics": [
        "china_admin_divisions_df"
      ]
    },
    {
      "page": "china_cars_tbl_df",
      "title": "Stated Car Choice Data from Chinese Buyers",
      "topics": [
        "china_cars_tbl_df"
      ]
    },
    {
      "page": "china_corruption_tbl_df",
      "title": "China's Corruption Investigations",
      "topics": [
        "china_corruption_tbl_df"
      ]
    },
    {
      "page": "china_io_2002_122_df",
      "title": "Input-output Table for China, 2002 (122 Sectors)",
      "topics": [
        "china_io_2002_122_df"
      ]
    },
    {
      "page": "china_io_2005_42_df",
      "title": "Input-output Table for China, 2005 (42 Sectors)",
      "topics": [
        "china_io_2005_42_df"
      ]
    },
    {
      "page": "china_io_2007_135_df",
      "title": "Input-output Table for China, 2007 (135 Sectors)",
      "topics": [
        "china_io_2007_135_df"
      ]
    },
    {
      "page": "china_io_2010_41_df",
      "title": "Input-output Table for China, 2010 (41 Sectors)",
      "topics": [
        "china_io_2010_41_df"
      ]
    },
    {
      "page": "china_io_2012_139_df",
      "title": "Input-output Table for China, 2012 (139 Sectors)",
      "topics": [
        "china_io_2012_139_df"
      ]
    },
    {
      "page": "china_io_2015_42_df",
      "title": "Input-output Table for China, 2015 (42 Sectors)",
      "topics": [
        "china_io_2015_42_df"
      ]
    },
    {
      "page": "china_io_2017_149_df",
      "title": "Input-output Table for China, 2017 (149 Sectors)",
      "topics": [
        "china_io_2017_149_df"
      ]
    },
    {
      "page": "china_io_2017_42_df",
      "title": "China Input-Output Table (2017, 42 Sectors)",
      "topics": [
        "china_io_2017_42_df"
      ]
    },
    {
      "page": "china_io_2018_153_df",
      "title": "China Input-Output Table (2018, 153 Sectors)",
      "topics": [
        "china_io_2018_153_df"
      ]
    },
    {
      "page": "china_io_2018_42_df",
      "title": "China Input-Output Table (2018, 42 Sectors)",
      "topics": [
        "china_io_2018_42_df"
      ]
    },
    {
      "page": "china_io_2020_153_df",
      "title": "Input-output Table for China, 2020 (153 Sectors)",
      "topics": [
        "china_io_2020_153_df"
      ]
    },
    {
      "page": "china_io_2020_42_df",
      "title": "China Input-Output Table (2020, 42 Sectors)",
      "topics": [
        "china_io_2020_42_df"
      ]
    },
    {
      "page": "ChinAPIs",
      "title": "ChinAPIs: Access Chinese Data via APIs and Curated Datasets",
      "topics": [
        "ChinAPIs-package",
        "ChinAPIs"
      ]
    },
    {
      "page": "chinese_cities_tbl_df",
      "title": "List of Prominent Chinese Cities",
      "topics": [
        "chinese_cities_tbl_df"
      ]
    },
    {
      "page": "chinese_dams_tbl_df",
      "title": "Chinese Dams Dataset",
      "topics": [
        "chinese_dams_tbl_df"
      ]
    },
    {
      "page": "COVID19_HongKong_df",
      "title": "COVID-19 Offspring Cases in Hong Kong (Jan–Apr 2020)",
      "topics": [
        "COVID19_HongKong_df"
      ]
    },
    {
      "page": "family_name_df",
      "title": "Chinese Surnames and National Frequency (1930–2008)",
      "topics": [
        "family_name_df"
      ]
    },
    {
      "page": "get_china_child_mortality",
      "title": "Get Under-5 Mortality Rate in China from World Bank",
      "topics": [
        "get_china_child_mortality"
      ]
    },
    {
      "page": "get_china_cpi",
      "title": "Get China's Consumer Price Index from World Bank",
      "topics": [
        "get_china_cpi"
      ]
    },
    {
      "page": "get_china_energy_use",
      "title": "Get China's Energy Use (kg of oil equivalent per capita) from World Bank",
      "topics": [
        "get_china_energy_use"
      ]
    },
    {
      "page": "get_china_gdp",
      "title": "Get China's GDP (Current US$) from World Bank",
      "topics": [
        "get_china_gdp"
      ]
    },
    {
      "page": "get_china_holidays",
      "title": "Get Official Public Holidays in China for a Given Year",
      "topics": [
        "get_china_holidays"
      ]
    },
    {
      "page": "get_china_hospital_beds",
      "title": "Get Hospital Beds per 1,000 People in China from World Bank",
      "topics": [
        "get_china_hospital_beds"
      ]
    },
    {
      "page": "get_china_life_expectancy",
      "title": "Get China's Life Expectancy at Birth from World Bank",
      "topics": [
        "get_china_life_expectancy"
      ]
    },
    {
      "page": "get_china_literacy_rate",
      "title": "Get China's Literacy Rate (Age 15+) from World Bank",
      "topics": [
        "get_china_literacy_rate"
      ]
    },
    {
      "page": "get_china_population",
      "title": "Get China's Total Population from World Bank",
      "topics": [
        "get_china_population"
      ]
    },
    {
      "page": "get_china_unemployment",
      "title": "Get China's Unemployment Rate from World Bank",
      "topics": [
        "get_china_unemployment"
      ]
    },
    {
      "page": "get_country_info_cn",
      "title": "Get Key Country Information About China from the REST Countries API",
      "topics": [
        "get_country_info_cn"
      ]
    },
    {
      "page": "given_name_df",
      "title": "Chinese Given Name Characters and Frequency (1930–2008)",
      "topics": [
        "given_name_df"
      ]
    },
    {
      "page": "health_family_life_df",
      "title": "Chinese Health and Family Life Survey",
      "topics": [
        "health_family_life_df"
      ]
    },
    {
      "page": "hk_councillors_tbl_df",
      "title": "Hong Kong District Councillors Elected in 2019",
      "topics": [
        "hk_councillors_tbl_df"
      ]
    },
    {
      "page": "hk_districts_tbl_df",
      "title": "Hong Kong District Labels and Regional Classification",
      "topics": [
        "hk_districts_tbl_df"
      ]
    },
    {
      "page": "hk_population_tbl_df",
      "title": "Hong Kong Population by District and Age Group",
      "topics": [
        "hk_population_tbl_df"
      ]
    },
    {
      "page": "hk_street_names_tbl_df",
      "title": "Hong Kong Street Names as of 2020",
      "topics": [
        "hk_street_names_tbl_df"
      ]
    },
    {
      "page": "panda_locations_df",
      "title": "Giant Panda Location Data",
      "topics": [
        "panda_locations_df"
      ]
    },
    {
      "page": "population_df",
      "title": "Population Statistics from the Chinese Name Database",
      "topics": [
        "population_df"
      ]
    },
    {
      "page": "sars_hong_kong_list",
      "title": "Daily Incidence of the 2003 SARS Epidemic in Hong Kong",
      "topics": [
        "sars_hong_kong_list"
      ]
    },
    {
      "page": "shanghai_factories_df",
      "title": "Per Capita Output of Workers in Shanghai Factories",
      "topics": [
        "shanghai_factories_df"
      ]
    },
    {
      "page": "shanghai_pm25_df",
      "title": "PM2.5 Pollution and Weather Data in Shanghai",
      "topics": [
        "shanghai_pm25_df"
      ]
    },
    {
      "page": "top1000name_prov_df",
      "title": "Top 1,000 Given Names by Province in Mainland China",
      "topics": [
        "top1000name_prov_df"
      ]
    },
    {
      "page": "top100name_year_df",
      "title": "Top 100 Given Names in 6 Birth Cohorts",
      "topics": [
        "top100name_year_df"
      ]
    },
    {
      "page": "top50char_year_df",
      "title": "Top 50 Given-Name Characters in 6 Birth Cohorts",
      "topics": [
        "top50char_year_df"
      ]
    },
    {
      "page": "view_datasets_ChinAPIs",
      "title": "View Available Datasets in ChinAPIs",
      "topics": [
        "view_datasets_ChinAPIs"
      ]
    },
    {
      "page": "wenchuan_ptsd_matrix",
      "title": "PTSD Symptoms of Wenchuan Earthquake Survivors",
      "topics": [
        "wenchuan_ptsd_matrix"
      ]
    }
  ],
  "_pkglogo": "https://github.com/lightbluetitan/chinapis/raw/HEAD/man/figures/logo.png",
  "_readme": "https://github.com/lightbluetitan/chinapis/raw/HEAD/README.md",
  "_rundeps": [
    "askpass",
    "cli",
    "curl",
    "dplyr",
    "farver",
    "generics",
    "glue",
    "httr",
    "jsonlite",
    "labeling",
    "lifecycle",
    "magrittr",
    "mime",
    "openssl",
    "pillar",
    "pkgconfig",
    "R6",
    "RColorBrewer",
    "rlang",
    "scales",
    "sys",
    "tibble",
    "tidyselect",
    "utf8",
    "vctrs",
    "viridisLite",
    "withr"
  ],
  "_vignettes": [
    {
      "source": "ChinAPIs_vignette.Rmd",
      "filename": "ChinAPIs_vignette.html",
      "title": "ChinAPIs: Access Chinese Data via Public APIs and Curated Datasets",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Functions for ChinAPIs",
        "China's GDP (Current US$) from World Bank 2022 - 2017",
        "China's Life Expectancy at Birth from World Bank 2022 - 2017",
        "China's Total Population from World Bank 2022 - 2017",
        "Total Population by District in Hong Kong",
        "Dataset Suffixes",
        "Datasets Included in ChinAPIs",
        "Conclusion"
      ],
      "created": "2025-08-01 21:55:10",
      "modified": "2025-08-21 06:01:06",
      "commits": 2
    }
  ],
  "_score": 4.176091259055681,
  "_indexed": true,
  "_nocasepkg": "chinapis",
  "_universes": [
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}