Help for package ChinAPIs

Type:

Package

Title:

Access Chinese Data via Public APIs and Curated Datasets

Version:

0.1.0

Maintainer:

Renzo Caceres Rossi <arenzocaceresrossi@gmail.com>

Description:

Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to China, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on China and Hong Kong, covering topics such as air quality, demographics, input-output tables, epidemiology, political structure, names, and social indicators. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'Nager.Date' https://date.nager.at/Api, 'World Bank API' https://datahelpdesk.worldbank.org/knowledgebase/articles/889392, and 'REST Countries API' https://restcountries.com/.

License:

MIT + file LICENSE

Language:

URL:

https://github.com/lightbluetitan/chinapis, https://lightbluetitan.github.io/chinapis/

BugReports:

https://github.com/lightbluetitan/chinapis/issues

Encoding:

UTF-8

LazyData:

true

Depends:

R (≥ 4.1.0)

Imports:

utils, httr, jsonlite, dplyr, scales, tibble

Suggests:

ggplot2, testthat (≥ 3.0.0), knitr, rmarkdown

RoxygenNote:

7.3.2

Config/testthat/edition:

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2025-08-21 07:01:54 UTC; Renzo

Author:

Renzo Caceres Rossi

[aut, cre]

Repository:

CRAN

Date/Publication:

2025-08-26 19:40:07 UTC

ChinAPIs: Access Chinese Data via APIs and Curated Datasets

Description

This package provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to China, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of datasets focused on China and Hong Kong.

Details

ChinAPIs: Access Chinese Data via APIs and Curated Datasets

Access Chinese Data via APIs and Curated Datasets.

Author(s)

Maintainer: Renzo Caceres Rossi arenzocaceresrossi@gmail.com

COVID-19 Offspring Cases in Hong Kong (Jan–Apr 2020)

Description

This dataset, COVID19_HongKong_df, is a data frame containing data on 290 observations of offspring case numbers generated by individual seed cases during the COVID-19 outbreak in Hong Kong, China, from January to April 2020. It includes the number of offspring cases per seed and the type of transmission event.

Usage

data(COVID19_HongKong_df)

Format

A data frame with 290 observations and 2 variables:

obs: Number of offspring cases from a single seed case (numeric)
type: Type of transmission event (character)

Details

The dataset name has been kept as 'COVID19_HongKong_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the modelSSE package version 0.1-3

Beijing Air Quality Dataset (2015)

Description

This dataset, bj_air_quality_tbl_df, is a tibble containing hourly air pollutant and weather measurements from the Dongsi air quality monitoring site in Beijing, China. The data covers 320 complete days of the year 2015 and includes variables such as nitrogen dioxide (NO_2), ozone (O_3), temperature, and wind speed.

Usage

data(bj_air_quality_tbl_df)

Format

A tibble with 7,680 observations and 6 variables:

DATE: Date of observation (Date)
HOUR: Hour of the day (integer, from 0 to 23)
NO2: Nitrogen dioxide concentration (numeric)
O3: Ozone concentration (numeric)
TEMP: Temperature in degrees Celsius (numeric)
WIND: Wind speed in meters per second (numeric)

Details

The dataset name has been kept as 'bj_air_quality_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble (a modern form of data frame). The original content has not been modified in any way.

Source

Data taken from the gmgm package version 1.1.2

Administrative Divisions of China

Description

This dataset, china_admin_divisions_df, is a data frame containing the codes and names of China's administrative divisions. The dataset includes 3212 observations and 2 variables, providing identifiers and names for each administrative unit. This can be useful for geographic analysis, mapping, and linking statistical data to spatial boundaries.

Usage

data(china_admin_divisions_df)

Format

A data frame with 3212 observations and 2 variables:

ID: Administrative division code (integer)
name: Name of the administrative division (character)

Details

The dataset name has been kept as 'china_admin_divisions_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the cnmap package version 0.1.0

Stated Car Choice Data from Chinese Buyers

Description

This dataset, china_cars_tbl_df, is a tibble containing stated choice observations from a conjoint survey conducted by Helveston et al. (2015). The survey includes 448 choice observations from Chinese car buyers and 384 from U.S. car buyers. The surveys were administered in 2012 across four major Chinese cities (Beijing, Shanghai, Shenzhen, and Chengdu), online in the U.S. via Amazon Mechanical Turk, and in person at the Pittsburgh Auto Show. Participants were asked to choose a vehicle from a set of three alternatives in 15 choice tasks.

Usage

data(china_cars_tbl_df)

Format

A tibble with 20,160 observations and 20 variables:

id: Participant ID (numeric)
obsnum: Observation number (numeric)
choice: Indicates if the option was chosen (1 = yes, 0 = no) (numeric)
hev: Hybrid electric vehicle dummy variable (numeric)
phev10: Plug-in hybrid vehicle with 10-mile range dummy (numeric)
phev20: Plug-in hybrid vehicle with 20-mile range dummy (numeric)
phev40: Plug-in hybrid vehicle with 40-mile range dummy (numeric)
bev75: Battery electric vehicle with 75-mile range dummy (numeric)
bev100: Battery electric vehicle with 100-mile range dummy (numeric)
bev150: Battery electric vehicle with 150-mile range dummy (numeric)
phevFastcharge: Fast charging availability for PHEV (numeric)
bevFastcharge: Fast charging availability for BEV (numeric)
price: Price of the vehicle (numeric)
opCost: Operating cost (numeric)
accelTime: Acceleration time (numeric)
american: American brand dummy variable (numeric)
japanese: Japanese brand dummy variable (numeric)
chinese: Chinese brand dummy variable (numeric)
skorean: South Korean brand dummy variable (numeric)
weights: Survey weights (numeric)

Details

The dataset name has been kept as 'china_cars_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble (a modern form of data frame). The original content has not been modified in any way.

Source

Data taken from the logitr package version 1.1.2

China's Corruption Investigations

Description

This dataset, china_corruption_tbl_df, is a tibble containing information on officials investigated during Xi Jinping's anti-corruption campaign. The dataset includes 10 observations and 6 variables, covering administrative divisions such as provinces, prefectures, and counties, along with their corresponding codes. While the original dataset contains data on nearly 20,000 individuals, this version includes a simplified subset of administrative identifiers for illustrative purposes.

Usage

data(china_corruption_tbl_df)

Format

A tibble with 10 observations and 6 variables:

province: Province code (numeric)
prefecture: Name of the prefecture (character)
county: Name of the county (character)
province_id: Province identifier (numeric)
prefecture_id: Prefecture identifier (numeric)
county_id: County identifier (numeric)

Details

The dataset name has been kept as 'china_corruption_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble object. The original content has not been modified in any way.

Source

Data taken from the regioncode package version 0.1.2

Input-output Table for China, 2002 (122 Sectors)

Description

This dataset, china_io_2002_122_df, is a data frame that represents the national input-output table of China for the year 2002. It covers 122 economic sectors and provides inter-sectoral flows of goods and services. Data values are measured in 10,000 Chinese Yuan (CNY) and calculated at producers' prices.

Usage

data(china_io_2002_122_df)

Format

A data frame with 129 observations and 139 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
001: Intermediate demand from sector 001 (numeric)
002: Intermediate demand from sector 002 (numeric)
003: Intermediate demand from sector 003 (numeric)
004: Intermediate demand from sector 004 (numeric)
005: Intermediate demand from sector 005 (numeric)
006: Intermediate demand from sector 006 (numeric)
007: Intermediate demand from sector 007 (numeric)
008: Intermediate demand from sector 008 (numeric)
009: Intermediate demand from sector 009 (numeric)
010: Intermediate demand from sector 010 (numeric)
011: Intermediate demand from sector 011 (numeric)
012: Intermediate demand from sector 012 (numeric)
013: Intermediate demand from sector 013 (numeric)
014: Intermediate demand from sector 014 (numeric)
015: Intermediate demand from sector 015 (numeric)
016: Intermediate demand from sector 016 (numeric)
017: Intermediate demand from sector 017 (numeric)
018: Intermediate demand from sector 018 (numeric)
019: Intermediate demand from sector 019 (numeric)
020: Intermediate demand from sector 020 (numeric)
021: Intermediate demand from sector 021 (numeric)
022: Intermediate demand from sector 022 (numeric)
023: Intermediate demand from sector 023 (numeric)
024: Intermediate demand from sector 024 (numeric)
025: Intermediate demand from sector 025 (numeric)
026: Intermediate demand from sector 026 (numeric)
027: Intermediate demand from sector 027 (numeric)
028: Intermediate demand from sector 028 (numeric)
029: Intermediate demand from sector 029 (numeric)
030: Intermediate demand from sector 030 (numeric)
031: Intermediate demand from sector 031 (numeric)
032: Intermediate demand from sector 032 (numeric)
033: Intermediate demand from sector 033 (numeric)
034: Intermediate demand from sector 034 (numeric)
035: Intermediate demand from sector 035 (numeric)
036: Intermediate demand from sector 036 (numeric)
037: Intermediate demand from sector 037 (numeric)
038: Intermediate demand from sector 038 (numeric)
039: Intermediate demand from sector 039 (numeric)
040: Intermediate demand from sector 040 (numeric)
041: Intermediate demand from sector 041 (numeric)
042: Intermediate demand from sector 042 (numeric)
043: Intermediate demand from sector 043 (numeric)
044: Intermediate demand from sector 044 (numeric)
045: Intermediate demand from sector 045 (numeric)
046: Intermediate demand from sector 046 (numeric)
047: Intermediate demand from sector 047 (numeric)
048: Intermediate demand from sector 048 (numeric)
049: Intermediate demand from sector 049 (numeric)
050: Intermediate demand from sector 050 (numeric)
051: Intermediate demand from sector 051 (numeric)
052: Intermediate demand from sector 052 (numeric)
053: Intermediate demand from sector 053 (numeric)
054: Intermediate demand from sector 054 (numeric)
055: Intermediate demand from sector 055 (numeric)
056: Intermediate demand from sector 056 (numeric)
057: Intermediate demand from sector 057 (numeric)
058: Intermediate demand from sector 058 (numeric)
059: Intermediate demand from sector 059 (numeric)
060: Intermediate demand from sector 060 (numeric)
061: Intermediate demand from sector 061 (numeric)
062: Intermediate demand from sector 062 (numeric)
063: Intermediate demand from sector 063 (numeric)
064: Intermediate demand from sector 064 (numeric)
065: Intermediate demand from sector 065 (numeric)
066: Intermediate demand from sector 066 (numeric)
067: Intermediate demand from sector 067 (numeric)
068: Intermediate demand from sector 068 (numeric)
069: Intermediate demand from sector 069 (numeric)
070: Intermediate demand from sector 070 (numeric)
071: Intermediate demand from sector 071 (numeric)
072: Intermediate demand from sector 072 (numeric)
073: Intermediate demand from sector 073 (numeric)
074: Intermediate demand from sector 074 (numeric)
075: Intermediate demand from sector 075 (numeric)
076: Intermediate demand from sector 076 (numeric)
077: Intermediate demand from sector 077 (numeric)
078: Intermediate demand from sector 078 (numeric)
079: Intermediate demand from sector 079 (numeric)
080: Intermediate demand from sector 080 (numeric)
081: Intermediate demand from sector 081 (numeric)
082: Intermediate demand from sector 082 (numeric)
083: Intermediate demand from sector 083 (numeric)
084: Intermediate demand from sector 084 (numeric)
085: Intermediate demand from sector 085 (numeric)
086: Intermediate demand from sector 086 (numeric)
087: Intermediate demand from sector 087 (numeric)
088: Intermediate demand from sector 088 (numeric)
089: Intermediate demand from sector 089 (numeric)
090: Intermediate demand from sector 090 (numeric)
091: Intermediate demand from sector 091 (numeric)
092: Intermediate demand from sector 092 (numeric)
093: Intermediate demand from sector 093 (numeric)
094: Intermediate demand from sector 094 (numeric)
095: Intermediate demand from sector 095 (numeric)
096: Intermediate demand from sector 096 (numeric)
097: Intermediate demand from sector 097 (numeric)
098: Intermediate demand from sector 098 (numeric)
099: Intermediate demand from sector 099 (numeric)
100: Intermediate demand from sector 100 (numeric)
101: Intermediate demand from sector 101 (numeric)
102: Intermediate demand from sector 102 (numeric)
103: Intermediate demand from sector 103 (numeric)
104: Intermediate demand from sector 104 (numeric)
105: Intermediate demand from sector 105 (numeric)
106: Intermediate demand from sector 106 (numeric)
107: Intermediate demand from sector 107 (numeric)
108: Intermediate demand from sector 108 (numeric)
109: Intermediate demand from sector 109 (numeric)
110: Intermediate demand from sector 110 (numeric)
111: Intermediate demand from sector 111 (numeric)
112: Intermediate demand from sector 112 (numeric)
113: Intermediate demand from sector 113 (numeric)
114: Intermediate demand from sector 114 (numeric)
115: Intermediate demand from sector 115 (numeric)
116: Intermediate demand from sector 116 (numeric)
117: Intermediate demand from sector 117 (numeric)
118: Intermediate demand from sector 118 (numeric)
119: Intermediate demand from sector 119 (numeric)
120: Intermediate demand from sector 120 (numeric)
121: Intermediate demand from sector 121 (numeric)
122: Intermediate demand from sector 122 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
THC: Household consumption (numeric)
FU103: Final use category 103 (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
GCF: Gross capital formation (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
IM: Imports (numeric)
ERR: Statistical discrepancy (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2002_122_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

Input-output Table for China, 2005 (42 Sectors)

Description

This dataset, china_io_2005_42_df, is a data frame that represents the national input-output table of China for the year 2005. It covers 42 economic sectors and provides inter-sectoral flows of goods and services. Data values are measured in 10,000 Chinese Yuan (CNY) and calculated at producers' prices.

Usage

data(china_io_2005_42_df)

Format

A data frame with 49 observations and 55 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
01: Intermediate demand from sector 01 (numeric)
02: Intermediate demand from sector 02 (numeric)
03: Intermediate demand from sector 03 (numeric)
04: Intermediate demand from sector 04 (numeric)
05: Intermediate demand from sector 05 (numeric)
06: Intermediate demand from sector 06 (numeric)
07: Intermediate demand from sector 07 (numeric)
08: Intermediate demand from sector 08 (numeric)
09: Intermediate demand from sector 09 (numeric)
10: Intermediate demand from sector 10 (numeric)
11: Intermediate demand from sector 11 (numeric)
12: Intermediate demand from sector 12 (numeric)
13: Intermediate demand from sector 13 (numeric)
14: Intermediate demand from sector 14 (numeric)
15: Intermediate demand from sector 15 (numeric)
16: Intermediate demand from sector 16 (numeric)
17: Intermediate demand from sector 17 (numeric)
18: Intermediate demand from sector 18 (numeric)
19: Intermediate demand from sector 19 (numeric)
20: Intermediate demand from sector 20 (numeric)
21: Intermediate demand from sector 21 (numeric)
22: Intermediate demand from sector 22 (numeric)
23: Intermediate demand from sector 23 (numeric)
24: Intermediate demand from sector 24 (numeric)
25: Intermediate demand from sector 25 (numeric)
26: Intermediate demand from sector 26 (numeric)
27: Intermediate demand from sector 27 (numeric)
28: Intermediate demand from sector 28 (numeric)
29: Intermediate demand from sector 29 (numeric)
30: Intermediate demand from sector 30 (numeric)
31: Intermediate demand from sector 31 (numeric)
32: Intermediate demand from sector 32 (numeric)
33: Intermediate demand from sector 33 (numeric)
34: Intermediate demand from sector 34 (numeric)
35: Intermediate demand from sector 35 (numeric)
36: Intermediate demand from sector 36 (numeric)
37: Intermediate demand from sector 37 (numeric)
38: Intermediate demand from sector 38 (numeric)
39: Intermediate demand from sector 39 (numeric)
40: Intermediate demand from sector 40 (numeric)
41: Intermediate demand from sector 41 (numeric)
42: Intermediate demand from sector 42 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
FU103: Final use category 103 (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
EX: Exports (numeric)
IM: Imports (numeric)
ERR: Statistical discrepancy (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2005_42_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

Input-output Table for China, 2007 (135 Sectors)

Description

This dataset, china_io_2007_135_df, is a data frame that represents the national input-output table of China for the year 2007. It covers 135 economic sectors and provides inter-sectoral flows of goods and services. Data values are measured in 10,000 Chinese Yuan (CNY) and calculated at producers' prices.

Usage

data(china_io_2007_135_df)

Format

A data frame with 142 observations and 152 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
001: Intermediate demand from sector 001 (numeric)
002: Intermediate demand from sector 002 (numeric)
003: Intermediate demand from sector 003 (numeric)
004: Intermediate demand from sector 004 (numeric)
005: Intermediate demand from sector 005 (numeric)
006: Intermediate demand from sector 006 (numeric)
007: Intermediate demand from sector 007 (numeric)
008: Intermediate demand from sector 008 (numeric)
009: Intermediate demand from sector 009 (numeric)
010: Intermediate demand from sector 010 (numeric)
011: Intermediate demand from sector 011 (numeric)
012: Intermediate demand from sector 012 (numeric)
013: Intermediate demand from sector 013 (numeric)
014: Intermediate demand from sector 014 (numeric)
015: Intermediate demand from sector 015 (numeric)
016: Intermediate demand from sector 016 (numeric)
017: Intermediate demand from sector 017 (numeric)
018: Intermediate demand from sector 018 (numeric)
019: Intermediate demand from sector 019 (numeric)
020: Intermediate demand from sector 020 (numeric)
021: Intermediate demand from sector 021 (numeric)
022: Intermediate demand from sector 022 (numeric)
023: Intermediate demand from sector 023 (numeric)
024: Intermediate demand from sector 024 (numeric)
025: Intermediate demand from sector 025 (numeric)
026: Intermediate demand from sector 026 (numeric)
027: Intermediate demand from sector 027 (numeric)
028: Intermediate demand from sector 028 (numeric)
029: Intermediate demand from sector 029 (numeric)
030: Intermediate demand from sector 030 (numeric)
031: Intermediate demand from sector 031 (numeric)
032: Intermediate demand from sector 032 (numeric)
033: Intermediate demand from sector 033 (numeric)
034: Intermediate demand from sector 034 (numeric)
035: Intermediate demand from sector 035 (numeric)
036: Intermediate demand from sector 036 (numeric)
037: Intermediate demand from sector 037 (numeric)
038: Intermediate demand from sector 038 (numeric)
039: Intermediate demand from sector 039 (numeric)
040: Intermediate demand from sector 040 (numeric)
041: Intermediate demand from sector 041 (numeric)
042: Intermediate demand from sector 042 (numeric)
043: Intermediate demand from sector 043 (numeric)
044: Intermediate demand from sector 044 (numeric)
045: Intermediate demand from sector 045 (numeric)
046: Intermediate demand from sector 046 (numeric)
047: Intermediate demand from sector 047 (numeric)
048: Intermediate demand from sector 048 (numeric)
049: Intermediate demand from sector 049 (numeric)
050: Intermediate demand from sector 050 (numeric)
051: Intermediate demand from sector 051 (numeric)
052: Intermediate demand from sector 052 (numeric)
053: Intermediate demand from sector 053 (numeric)
054: Intermediate demand from sector 054 (numeric)
055: Intermediate demand from sector 055 (numeric)
056: Intermediate demand from sector 056 (numeric)
057: Intermediate demand from sector 057 (numeric)
058: Intermediate demand from sector 058 (numeric)
059: Intermediate demand from sector 059 (numeric)
060: Intermediate demand from sector 060 (numeric)
061: Intermediate demand from sector 061 (numeric)
062: Intermediate demand from sector 062 (numeric)
063: Intermediate demand from sector 063 (numeric)
064: Intermediate demand from sector 064 (numeric)
065: Intermediate demand from sector 065 (numeric)
066: Intermediate demand from sector 066 (numeric)
067: Intermediate demand from sector 067 (numeric)
068: Intermediate demand from sector 068 (numeric)
069: Intermediate demand from sector 069 (numeric)
070: Intermediate demand from sector 070 (numeric)
071: Intermediate demand from sector 071 (numeric)
072: Intermediate demand from sector 072 (numeric)
073: Intermediate demand from sector 073 (numeric)
074: Intermediate demand from sector 074 (numeric)
075: Intermediate demand from sector 075 (numeric)
076: Intermediate demand from sector 076 (numeric)
077: Intermediate demand from sector 077 (numeric)
078: Intermediate demand from sector 078 (numeric)
079: Intermediate demand from sector 079 (numeric)
080: Intermediate demand from sector 080 (numeric)
081: Intermediate demand from sector 081 (numeric)
082: Intermediate demand from sector 082 (numeric)
083: Intermediate demand from sector 083 (numeric)
084: Intermediate demand from sector 084 (numeric)
085: Intermediate demand from sector 085 (numeric)
086: Intermediate demand from sector 086 (numeric)
087: Intermediate demand from sector 087 (numeric)
088: Intermediate demand from sector 088 (numeric)
089: Intermediate demand from sector 089 (numeric)
090: Intermediate demand from sector 090 (numeric)
091: Intermediate demand from sector 091 (numeric)
092: Intermediate demand from sector 092 (numeric)
093: Intermediate demand from sector 093 (numeric)
094: Intermediate demand from sector 094 (numeric)
095: Intermediate demand from sector 095 (numeric)
096: Intermediate demand from sector 096 (numeric)
097: Intermediate demand from sector 097 (numeric)
098: Intermediate demand from sector 098 (numeric)
099: Intermediate demand from sector 099 (numeric)
100: Intermediate demand from sector 100 (numeric)
101: Intermediate demand from sector 101 (numeric)
102: Intermediate demand from sector 102 (numeric)
103: Intermediate demand from sector 103 (numeric)
104: Intermediate demand from sector 104 (numeric)
105: Intermediate demand from sector 105 (numeric)
106: Intermediate demand from sector 106 (numeric)
107: Intermediate demand from sector 107 (numeric)
108: Intermediate demand from sector 108 (numeric)
109: Intermediate demand from sector 109 (numeric)
110: Intermediate demand from sector 110 (numeric)
111: Intermediate demand from sector 111 (numeric)
112: Intermediate demand from sector 112 (numeric)
113: Intermediate demand from sector 113 (numeric)
114: Intermediate demand from sector 114 (numeric)
115: Intermediate demand from sector 115 (numeric)
116: Intermediate demand from sector 116 (numeric)
117: Intermediate demand from sector 117 (numeric)
118: Intermediate demand from sector 118 (numeric)
119: Intermediate demand from sector 119 (numeric)
120: Intermediate demand from sector 120 (numeric)
121: Intermediate demand from sector 121 (numeric)
122: Intermediate demand from sector 122 (numeric)
123: Intermediate demand from sector 123 (numeric)
124: Intermediate demand from sector 124 (numeric)
125: Intermediate demand from sector 125 (numeric)
126: Intermediate demand from sector 126 (numeric)
127: Intermediate demand from sector 127 (numeric)
128: Intermediate demand from sector 128 (numeric)
129: Intermediate demand from sector 129 (numeric)
130: Intermediate demand from sector 130 (numeric)
131: Intermediate demand from sector 131 (numeric)
132: Intermediate demand from sector 132 (numeric)
133: Intermediate demand from sector 133 (numeric)
134: Intermediate demand from sector 134 (numeric)
135: Intermediate demand from sector 135 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
THC: Household consumption (numeric)
FU103: Final use category 103 (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
GCF: Gross capital formation (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
IM: Imports (numeric)
ERR: Statistical discrepancy (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2007_135_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

Input-output Table for China, 2010 (41 Sectors)

Description

This dataset, china_io_2010_41_df, is a data frame that represents the national input-output table of China for the year 2010. It covers 41 economic sectors and provides inter-sectoral flows of goods and services. Data values are measured in 10,000 Chinese Yuan (CNY) and calculated at producers' prices.

Usage

data(china_io_2010_41_df)

Format

A data frame with 48 observations and 58 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
01: Intermediate demand from sector 01 (numeric)
02: Intermediate demand from sector 02 (numeric)
03: Intermediate demand from sector 03 (numeric)
04: Intermediate demand from sector 04 (numeric)
05: Intermediate demand from sector 05 (numeric)
06: Intermediate demand from sector 06 (numeric)
07: Intermediate demand from sector 07 (numeric)
08: Intermediate demand from sector 08 (numeric)
09: Intermediate demand from sector 09 (numeric)
10: Intermediate demand from sector 10 (numeric)
11: Intermediate demand from sector 11 (numeric)
12: Intermediate demand from sector 12 (numeric)
13: Intermediate demand from sector 13 (numeric)
14: Intermediate demand from sector 14 (numeric)
15: Intermediate demand from sector 15 (numeric)
16: Intermediate demand from sector 16 (numeric)
17: Intermediate demand from sector 17 (numeric)
18: Intermediate demand from sector 18 (numeric)
19: Intermediate demand from sector 19 (numeric)
20: Intermediate demand from sector 20 (numeric)
21: Intermediate demand from sector 21 (numeric)
22: Intermediate demand from sector 22 (numeric)
23: Intermediate demand from sector 23 (numeric)
24: Intermediate demand from sector 24 (numeric)
25: Intermediate demand from sector 25 (numeric)
26: Intermediate demand from sector 26 (numeric)
27: Intermediate demand from sector 27 (numeric)
28: Intermediate demand from sector 28 (numeric)
29: Intermediate demand from sector 29 (numeric)
30: Intermediate demand from sector 30 (numeric)
31: Intermediate demand from sector 31 (numeric)
32: Intermediate demand from sector 32 (numeric)
33: Intermediate demand from sector 33 (numeric)
34: Intermediate demand from sector 34 (numeric)
35: Intermediate demand from sector 35 (numeric)
36: Intermediate demand from sector 36 (numeric)
37: Intermediate demand from sector 37 (numeric)
38: Intermediate demand from sector 38 (numeric)
39: Intermediate demand from sector 39 (numeric)
40: Intermediate demand from sector 40 (numeric)
41: Intermediate demand from sector 41 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
THC: Household consumption (numeric)
FU103: Final use category 103 (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
GCF: Gross capital formation (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
IM: Imports (numeric)
ERR: Statistical discrepancy (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2010_41_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

Input-output Table for China, 2012 (139 Sectors)

Description

This dataset, china_io_2012_139_df, is a data frame representing the national input-output table of China for the year 2012. It covers 139 sectors and provides inter-sectoral flows of goods and services. Data values are measured in 10,000 Chinese Yuan (CNY) and calculated at producers' prices.

Usage

data(china_io_2012_139_df)

Format

A data frame with 146 observations and 155 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
001: Input from sector 001 (numeric)
002: Input from sector 002 (numeric)
003: Input from sector 003 (numeric)
004: Input from sector 004 (numeric)
005: Input from sector 005 (numeric)
006: Input from sector 006 (numeric)
007: Input from sector 007 (numeric)
008: Input from sector 008 (numeric)
009: Input from sector 009 (numeric)
010: Input from sector 010 (numeric)
011: Input from sector 011 (numeric)
012: Input from sector 012 (numeric)
013: Input from sector 013 (numeric)
014: Input from sector 014 (numeric)
015: Input from sector 015 (numeric)
016: Input from sector 016 (numeric)
017: Input from sector 017 (numeric)
018: Input from sector 018 (numeric)
019: Input from sector 019 (numeric)
020: Input from sector 020 (numeric)
021: Input from sector 021 (numeric)
022: Input from sector 022 (numeric)
023: Input from sector 023 (numeric)
024: Input from sector 024 (numeric)
025: Input from sector 025 (numeric)
026: Input from sector 026 (numeric)
027: Input from sector 027 (numeric)
028: Input from sector 028 (numeric)
029: Input from sector 029 (numeric)
030: Input from sector 030 (numeric)
031: Input from sector 031 (numeric)
032: Input from sector 032 (numeric)
033: Input from sector 033 (numeric)
034: Input from sector 034 (numeric)
035: Input from sector 035 (numeric)
036: Input from sector 036 (numeric)
037: Input from sector 037 (numeric)
038: Input from sector 038 (numeric)
039: Input from sector 039 (numeric)
040: Input from sector 040 (numeric)
041: Input from sector 041 (numeric)
042: Input from sector 042 (numeric)
043: Input from sector 043 (numeric)
044: Input from sector 044 (numeric)
045: Input from sector 045 (numeric)
046: Input from sector 046 (numeric)
047: Input from sector 047 (numeric)
048: Input from sector 048 (numeric)
049: Input from sector 049 (numeric)
050: Input from sector 050 (numeric)
051: Input from sector 051 (numeric)
052: Input from sector 052 (numeric)
053: Input from sector 053 (numeric)
054: Input from sector 054 (numeric)
055: Input from sector 055 (numeric)
056: Input from sector 056 (numeric)
057: Input from sector 057 (numeric)
058: Input from sector 058 (numeric)
059: Input from sector 059 (numeric)
060: Input from sector 060 (numeric)
061: Input from sector 061 (numeric)
062: Input from sector 062 (numeric)
063: Input from sector 063 (numeric)
064: Input from sector 064 (numeric)
065: Input from sector 065 (numeric)
066: Input from sector 066 (numeric)
067: Input from sector 067 (numeric)
068: Input from sector 068 (numeric)
069: Input from sector 069 (numeric)
070: Input from sector 070 (numeric)
071: Input from sector 071 (numeric)
072: Input from sector 072 (numeric)
073: Input from sector 073 (numeric)
074: Input from sector 074 (numeric)
075: Input from sector 075 (numeric)
076: Input from sector 076 (numeric)
077: Input from sector 077 (numeric)
078: Input from sector 078 (numeric)
079: Input from sector 079 (numeric)
080: Input from sector 080 (numeric)
081: Input from sector 081 (numeric)
082: Input from sector 082 (numeric)
083: Input from sector 083 (numeric)
084: Input from sector 084 (numeric)
085: Input from sector 085 (numeric)
086: Input from sector 086 (numeric)
087: Input from sector 087 (numeric)
088: Input from sector 088 (numeric)
089: Input from sector 089 (numeric)
090: Input from sector 090 (numeric)
091: Input from sector 091 (numeric)
092: Input from sector 092 (numeric)
093: Input from sector 093 (numeric)
094: Input from sector 094 (numeric)
095: Input from sector 095 (numeric)
096: Input from sector 096 (numeric)
097: Input from sector 097 (numeric)
098: Input from sector 098 (numeric)
099: Input from sector 099 (numeric)
100: Input from sector 100 (numeric)
101: Input from sector 101 (numeric)
102: Input from sector 102 (numeric)
103: Input from sector 103 (numeric)
104: Input from sector 104 (numeric)
105: Input from sector 105 (numeric)
106: Input from sector 106 (numeric)
107: Input from sector 107 (numeric)
108: Input from sector 108 (numeric)
109: Input from sector 109 (numeric)
110: Input from sector 110 (numeric)
111: Input from sector 111 (numeric)
112: Input from sector 112 (numeric)
113: Input from sector 113 (numeric)
114: Input from sector 114 (numeric)
115: Input from sector 115 (numeric)
116: Input from sector 116 (numeric)
117: Input from sector 117 (numeric)
118: Input from sector 118 (numeric)
119: Input from sector 119 (numeric)
120: Input from sector 120 (numeric)
121: Input from sector 121 (numeric)
122: Input from sector 122 (numeric)
123: Input from sector 123 (numeric)
124: Input from sector 124 (numeric)
125: Input from sector 125 (numeric)
126: Input from sector 126 (numeric)
127: Input from sector 127 (numeric)
128: Input from sector 128 (numeric)
129: Input from sector 129 (numeric)
130: Input from sector 130 (numeric)
131: Input from sector 131 (numeric)
132: Input from sector 132 (numeric)
133: Input from sector 133 (numeric)
134: Input from sector 134 (numeric)
135: Input from sector 135 (numeric)
136: Input from sector 136 (numeric)
137: Input from sector 137 (numeric)
138: Input from sector 138 (numeric)
139: Input from sector 139 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
FU103: Final use category 103 (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
GCF: Gross capital formation (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
IM: Imports (numeric)
ERR: Statistical discrepancy (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2012_139_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

Input-output Table for China, 2015 (42 Sectors)

Description

This dataset, china_io_2015_42_df, is a data frame representing the national input-output table of China for the year 2015. It covers 42 economic sectors and captures the inter-sectoral flows of goods and services. The values are calculated at producers' prices and are expressed in 10,000 Chinese Yuan (CNY).

Usage

data(china_io_2015_42_df)

Format

A data frame with 49 observations and 59 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
01: Input from sector 01 (numeric)
02: Input from sector 02 (numeric)
03: Input from sector 03 (numeric)
04: Input from sector 04 (numeric)
05: Input from sector 05 (numeric)
06: Input from sector 06 (numeric)
07: Input from sector 07 (numeric)
08: Input from sector 08 (numeric)
09: Input from sector 09 (numeric)
10: Input from sector 10 (numeric)
11: Input from sector 11 (numeric)
12: Input from sector 12 (numeric)
13: Input from sector 13 (numeric)
14: Input from sector 14 (numeric)
15: Input from sector 15 (numeric)
16: Input from sector 16 (numeric)
17: Input from sector 17 (numeric)
18: Input from sector 18 (numeric)
19: Input from sector 19 (numeric)
20: Input from sector 20 (numeric)
21: Input from sector 21 (numeric)
22: Input from sector 22 (numeric)
23: Input from sector 23 (numeric)
24: Input from sector 24 (numeric)
25: Input from sector 25 (numeric)
26: Input from sector 26 (numeric)
27: Input from sector 27 (numeric)
28: Input from sector 28 (numeric)
29: Input from sector 29 (numeric)
30: Input from sector 30 (numeric)
31: Input from sector 31 (numeric)
32: Input from sector 32 (numeric)
33: Input from sector 33 (numeric)
34: Input from sector 34 (numeric)
35: Input from sector 35 (numeric)
36: Input from sector 36 (numeric)
37: Input from sector 37 (numeric)
38: Input from sector 38 (numeric)
39: Input from sector 39 (numeric)
40: Input from sector 40 (numeric)
41: Input from sector 41 (numeric)
42: Input from sector 42 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
THC: Household consumption (numeric)
FU103: Final use category 103 (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
GCF: Gross capital formation (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
IM: Imports (numeric)
ERR: Statistical discrepancy (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2015_42_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

Input-output Table for China, 2017 (149 Sectors)

Description

This dataset, china_io_2017_149_df, is a data frame representing the national input-output table of China for the year 2017. It covers 149 economic sectors and captures the inter-sectoral flows of goods and services. The values are calculated at producers' prices and are expressed in 10,000 Chinese Yuan (CNY).

Usage

data(china_io_2017_149_df)

Format

A data frame with 156 observations and 165 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
001: Input from sector 001 (numeric)
002: Input from sector 002 (numeric)
003: Input from sector 003 (numeric)
004: Input from sector 004 (numeric)
005: Input from sector 005 (numeric)
006: Input from sector 006 (numeric)
007: Input from sector 007 (numeric)
008: Input from sector 008 (numeric)
009: Input from sector 009 (numeric)
010: Input from sector 010 (numeric)
011: Input from sector 011 (numeric)
012: Input from sector 012 (numeric)
013: Input from sector 013 (numeric)
014: Input from sector 014 (numeric)
015: Input from sector 015 (numeric)
016: Input from sector 016 (numeric)
017: Input from sector 017 (numeric)
018: Input from sector 018 (numeric)
019: Input from sector 019 (numeric)
020: Input from sector 020 (numeric)
021: Input from sector 021 (numeric)
022: Input from sector 022 (numeric)
023: Input from sector 023 (numeric)
024: Input from sector 024 (numeric)
025: Input from sector 025 (numeric)
026: Input from sector 026 (numeric)
027: Input from sector 027 (numeric)
028: Input from sector 028 (numeric)
029: Input from sector 029 (numeric)
030: Input from sector 030 (numeric)
031: Input from sector 031 (numeric)
032: Input from sector 032 (numeric)
033: Input from sector 033 (numeric)
034: Input from sector 034 (numeric)
035: Input from sector 035 (numeric)
036: Input from sector 036 (numeric)
037: Input from sector 037 (numeric)
038: Input from sector 038 (numeric)
039: Input from sector 039 (numeric)
040: Input from sector 040 (numeric)
041: Input from sector 041 (numeric)
042: Input from sector 042 (numeric)
043: Input from sector 043 (numeric)
044: Input from sector 044 (numeric)
045: Input from sector 045 (numeric)
046: Input from sector 046 (numeric)
047: Input from sector 047 (numeric)
048: Input from sector 048 (numeric)
049: Input from sector 049 (numeric)
050: Input from sector 050 (numeric)
051: Input from sector 051 (numeric)
052: Input from sector 052 (numeric)
053: Input from sector 053 (numeric)
054: Input from sector 054 (numeric)
055: Input from sector 055 (numeric)
056: Input from sector 056 (numeric)
057: Input from sector 057 (numeric)
058: Input from sector 058 (numeric)
059: Input from sector 059 (numeric)
060: Input from sector 060 (numeric)
061: Input from sector 061 (numeric)
062: Input from sector 062 (numeric)
063: Input from sector 063 (numeric)
064: Input from sector 064 (numeric)
065: Input from sector 065 (numeric)
066: Input from sector 066 (numeric)
067: Input from sector 067 (numeric)
068: Input from sector 068 (numeric)
069: Input from sector 069 (numeric)
070: Input from sector 070 (numeric)
071: Input from sector 071 (numeric)
072: Input from sector 072 (numeric)
073: Input from sector 073 (numeric)
074: Input from sector 074 (numeric)
075: Input from sector 075 (numeric)
076: Input from sector 076 (numeric)
077: Input from sector 077 (numeric)
078: Input from sector 078 (numeric)
079: Input from sector 079 (numeric)
080: Input from sector 080 (numeric)
081: Input from sector 081 (numeric)
082: Input from sector 082 (numeric)
083: Input from sector 083 (numeric)
084: Input from sector 084 (numeric)
085: Input from sector 085 (numeric)
086: Input from sector 086 (numeric)
087: Input from sector 087 (numeric)
088: Input from sector 088 (numeric)
089: Input from sector 089 (numeric)
090: Input from sector 090 (numeric)
091: Input from sector 091 (numeric)
092: Input from sector 092 (numeric)
093: Input from sector 093 (numeric)
094: Input from sector 094 (numeric)
095: Input from sector 095 (numeric)
096: Input from sector 096 (numeric)
097: Input from sector 097 (numeric)
098: Input from sector 098 (numeric)
099: Input from sector 099 (numeric)
100: Input from sector 100 (numeric)
101: Input from sector 101 (numeric)
102: Input from sector 102 (numeric)
103: Input from sector 103 (numeric)
104: Input from sector 104 (numeric)
105: Input from sector 105 (numeric)
106: Input from sector 106 (numeric)
107: Input from sector 107 (numeric)
108: Input from sector 108 (numeric)
109: Input from sector 109 (numeric)
110: Input from sector 110 (numeric)
111: Input from sector 111 (numeric)
112: Input from sector 112 (numeric)
113: Input from sector 113 (numeric)
114: Input from sector 114 (numeric)
115: Input from sector 115 (numeric)
116: Input from sector 116 (numeric)
117: Input from sector 117 (numeric)
118: Input from sector 118 (numeric)
119: Input from sector 119 (numeric)
120: Input from sector 120 (numeric)
121: Input from sector 121 (numeric)
122: Input from sector 122 (numeric)
123: Input from sector 123 (numeric)
124: Input from sector 124 (numeric)
125: Input from sector 125 (numeric)
126: Input from sector 126 (numeric)
127: Input from sector 127 (numeric)
128: Input from sector 128 (numeric)
129: Input from sector 129 (numeric)
130: Input from sector 130 (numeric)
131: Input from sector 131 (numeric)
132: Input from sector 132 (numeric)
133: Input from sector 133 (numeric)
134: Input from sector 134 (numeric)
135: Input from sector 135 (numeric)
136: Input from sector 136 (numeric)
137: Input from sector 137 (numeric)
138: Input from sector 138 (numeric)
139: Input from sector 139 (numeric)
140: Input from sector 140 (numeric)
141: Input from sector 141 (numeric)
142: Input from sector 142 (numeric)
143: Input from sector 143 (numeric)
144: Input from sector 144 (numeric)
145: Input from sector 145 (numeric)
146: Input from sector 146 (numeric)
147: Input from sector 147 (numeric)
148: Input from sector 148 (numeric)
149: Input from sector 149 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
THC: Household consumption (numeric)
FU103: Final use category 103 (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
GCF: Gross capital formation (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
IM: Imports (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2017_149_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

China Input-Output Table (2017, 42 Sectors)

Description

This dataset, china_io_2017_42_df, is a data frame that represents the national input-output table of China for the year 2017. It covers 42 sectors and provides inter-sectoral flows of goods and services. Data values are measured in 10,000 Chinese Yuan (CNY).

Usage

data(china_io_2017_42_df)

Format

A data frame with 91 observations and 53 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
Origin: Origin region or source (character)
01: Input from sector 01 (numeric)
02: Input from sector 02 (numeric)
03: Input from sector 03 (numeric)
04: Input from sector 04 (numeric)
05: Input from sector 05 (numeric)
06: Input from sector 06 (numeric)
07: Input from sector 07 (numeric)
08: Input from sector 08 (numeric)
09: Input from sector 09 (numeric)
10: Input from sector 10 (numeric)
11: Input from sector 11 (numeric)
12: Input from sector 12 (numeric)
13: Input from sector 13 (numeric)
14: Input from sector 14 (numeric)
15: Input from sector 15 (numeric)
16: Input from sector 16 (numeric)
17: Input from sector 17 (numeric)
18: Input from sector 18 (numeric)
19: Input from sector 19 (numeric)
20: Input from sector 20 (numeric)
21: Input from sector 21 (numeric)
22: Input from sector 22 (numeric)
23: Input from sector 23 (numeric)
24: Input from sector 24 (numeric)
25: Input from sector 25 (numeric)
26: Input from sector 26 (numeric)
27: Input from sector 27 (numeric)
28: Input from sector 28 (numeric)
29: Input from sector 29 (numeric)
30: Input from sector 30 (numeric)
31: Input from sector 31 (numeric)
32: Input from sector 32 (numeric)
33: Input from sector 33 (numeric)
34: Input from sector 34 (numeric)
35: Input from sector 35 (numeric)
36: Input from sector 36 (numeric)
37: Input from sector 37 (numeric)
38: Input from sector 38 (numeric)
39: Input from sector 39 (numeric)
40: Input from sector 40 (numeric)
41: Input from sector 41 (numeric)
42: Input from sector 42 (numeric)
TIU: Total intermediate use (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2017_42_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

China Input-Output Table (2018, 153 Sectors)

Description

This dataset, 'china_io_2018_153_df', is a data frame that represents the national input-output table of China for the year 2018. It covers 153 sectors and provides inter-sectoral flows of goods and services. Data values are measured in 10,000 Chinese Yuan (CNY) and calculated at producers' prices.

Usage

data(china_io_2018_153_df)

Format

A data frame with 160 observations and 169 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
001: Input from sector 001 (numeric)
002: Input from sector 002 (numeric)
003: Input from sector 003 (numeric)
004: Input from sector 004 (numeric)
005: Input from sector 005 (numeric)
006: Input from sector 006 (numeric)
007: Input from sector 007 (numeric)
008: Input from sector 008 (numeric)
009: Input from sector 009 (numeric)
010: Input from sector 010 (numeric)
011: Input from sector 011 (numeric)
012: Input from sector 012 (numeric)
013: Input from sector 013 (numeric)
014: Input from sector 014 (numeric)
015: Input from sector 015 (numeric)
016: Input from sector 016 (numeric)
017: Input from sector 017 (numeric)
018: Input from sector 018 (numeric)
019: Input from sector 019 (numeric)
020: Input from sector 020 (numeric)
021: Input from sector 021 (numeric)
022: Input from sector 022 (numeric)
023: Input from sector 023 (numeric)
024: Input from sector 024 (numeric)
025: Input from sector 025 (numeric)
026: Input from sector 026 (numeric)
027: Input from sector 027 (numeric)
028: Input from sector 028 (numeric)
029: Input from sector 029 (numeric)
030: Input from sector 030 (numeric)
031: Input from sector 031 (numeric)
032: Input from sector 032 (numeric)
033: Input from sector 033 (numeric)
034: Input from sector 034 (numeric)
035: Input from sector 035 (numeric)
036: Input from sector 036 (numeric)
037: Input from sector 037 (numeric)
038: Input from sector 038 (numeric)
039: Input from sector 039 (numeric)
040: Input from sector 040 (numeric)
041: Input from sector 041 (numeric)
042: Input from sector 042 (numeric)
043: Input from sector 043 (numeric)
044: Input from sector 044 (numeric)
045: Input from sector 045 (numeric)
046: Input from sector 046 (numeric)
047: Input from sector 047 (numeric)
048: Input from sector 048 (numeric)
049: Input from sector 049 (numeric)
050: Input from sector 050 (numeric)
051: Input from sector 051 (numeric)
052: Input from sector 052 (numeric)
053: Input from sector 053 (numeric)
054: Input from sector 054 (numeric)
055: Input from sector 055 (numeric)
056: Input from sector 056 (numeric)
057: Input from sector 057 (numeric)
058: Input from sector 058 (numeric)
059: Input from sector 059 (numeric)
060: Input from sector 060 (numeric)
061: Input from sector 061 (numeric)
062: Input from sector 062 (numeric)
063: Input from sector 063 (numeric)
064: Input from sector 064 (numeric)
065: Input from sector 065 (numeric)
066: Input from sector 066 (numeric)
067: Input from sector 067 (numeric)
068: Input from sector 068 (numeric)
069: Input from sector 069 (numeric)
070: Input from sector 070 (numeric)
071: Input from sector 071 (numeric)
072: Input from sector 072 (numeric)
073: Input from sector 073 (numeric)
074: Input from sector 074 (numeric)
075: Input from sector 075 (numeric)
076: Input from sector 076 (numeric)
077: Input from sector 077 (numeric)
078: Input from sector 078 (numeric)
079: Input from sector 079 (numeric)
080: Input from sector 080 (numeric)
081: Input from sector 081 (numeric)
082: Input from sector 082 (numeric)
083: Input from sector 083 (numeric)
084: Input from sector 084 (numeric)
085: Input from sector 085 (numeric)
086: Input from sector 086 (numeric)
087: Input from sector 087 (numeric)
088: Input from sector 088 (numeric)
089: Input from sector 089 (numeric)
090: Input from sector 090 (numeric)
091: Input from sector 091 (numeric)
092: Input from sector 092 (numeric)
093: Input from sector 093 (numeric)
094: Input from sector 094 (numeric)
095: Input from sector 095 (numeric)
096: Input from sector 096 (numeric)
097: Input from sector 097 (numeric)
098: Input from sector 098 (numeric)
099: Input from sector 099 (numeric)
100: Input from sector 100 (numeric)
101: Input from sector 101 (numeric)
102: Input from sector 102 (numeric)
103: Input from sector 103 (numeric)
104: Input from sector 104 (numeric)
105: Input from sector 105 (numeric)
106: Input from sector 106 (numeric)
107: Input from sector 107 (numeric)
108: Input from sector 108 (numeric)
109: Input from sector 109 (numeric)
110: Input from sector 110 (numeric)
111: Input from sector 111 (numeric)
112: Input from sector 112 (numeric)
113: Input from sector 113 (numeric)
114: Input from sector 114 (numeric)
115: Input from sector 115 (numeric)
116: Input from sector 116 (numeric)
117: Input from sector 117 (numeric)
118: Input from sector 118 (numeric)
119: Input from sector 119 (numeric)
120: Input from sector 120 (numeric)
121: Input from sector 121 (numeric)
122: Input from sector 122 (numeric)
123: Input from sector 123 (numeric)
124: Input from sector 124 (numeric)
125: Input from sector 125 (numeric)
126: Input from sector 126 (numeric)
127: Input from sector 127 (numeric)
128: Input from sector 128 (numeric)
129: Input from sector 129 (numeric)
130: Input from sector 130 (numeric)
131: Input from sector 131 (numeric)
132: Input from sector 132 (numeric)
133: Input from sector 133 (numeric)
134: Input from sector 134 (numeric)
135: Input from sector 135 (numeric)
136: Input from sector 136 (numeric)
137: Input from sector 137 (numeric)
138: Input from sector 138 (numeric)
139: Input from sector 139 (numeric)
140: Input from sector 140 (numeric)
141: Input from sector 141 (numeric)
142: Input from sector 142 (numeric)
143: Input from sector 143 (numeric)
144: Input from sector 144 (numeric)
145: Input from sector 145 (numeric)
146: Input from sector 146 (numeric)
147: Input from sector 147 (numeric)
148: Input from sector 148 (numeric)
149: Input from sector 149 (numeric)
150: Input from sector 150 (numeric)
151: Input from sector 151 (numeric)
152: Input from sector 152 (numeric)
153: Input from sector 153 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
THC: Household consumption (numeric)
FU103: Final use category 103 (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
GCF: Gross capital formation (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
IM: Imports (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2018_153_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

China Input-Output Table (2018, 42 Sectors)

Description

This dataset, china_io_2018_42_df, is a data frame containing the national input-output table of China for the year 2018. It includes 91 observations across 42 economic sectors. The values are expressed in units of 10,000 Chinese Yuan (CNY). The dataset records transactions between sectors, value added components, imports, exports, and other final demand categories.

Usage

data(china_io_2018_42_df)

Format

A data frame with 91 observations and 53 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
Origin: Type of entry (e.g., sector, total, final use) (character)
01: Intermediate demand from sector 01 (numeric)
02: Intermediate demand from sector 02 (numeric)
03: Intermediate demand from sector 03 (numeric)
04: Intermediate demand from sector 04 (numeric)
05: Intermediate demand from sector 05 (numeric)
06: Intermediate demand from sector 06 (numeric)
07: Intermediate demand from sector 07 (numeric)
08: Intermediate demand from sector 08 (numeric)
09: Intermediate demand from sector 09 (numeric)
10: Intermediate demand from sector 10 (numeric)
11: Intermediate demand from sector 11 (numeric)
12: Intermediate demand from sector 12 (numeric)
13: Intermediate demand from sector 13 (numeric)
14: Intermediate demand from sector 14 (numeric)
15: Intermediate demand from sector 15 (numeric)
16: Intermediate demand from sector 16 (numeric)
17: Intermediate demand from sector 17 (numeric)
18: Intermediate demand from sector 18 (numeric)
19: Intermediate demand from sector 19 (numeric)
20: Intermediate demand from sector 20 (numeric)
21: Intermediate demand from sector 21 (numeric)
22: Intermediate demand from sector 22 (numeric)
23: Intermediate demand from sector 23 (numeric)
24: Intermediate demand from sector 24 (numeric)
25: Intermediate demand from sector 25 (numeric)
26: Intermediate demand from sector 26 (numeric)
27: Intermediate demand from sector 27 (numeric)
28: Intermediate demand from sector 28 (numeric)
29: Intermediate demand from sector 29 (numeric)
30: Intermediate demand from sector 30 (numeric)
31: Intermediate demand from sector 31 (numeric)
32: Intermediate demand from sector 32 (numeric)
33: Intermediate demand from sector 33 (numeric)
34: Intermediate demand from sector 34 (numeric)
35: Intermediate demand from sector 35 (numeric)
36: Intermediate demand from sector 36 (numeric)
37: Intermediate demand from sector 37 (numeric)
38: Intermediate demand from sector 38 (numeric)
39: Intermediate demand from sector 39 (numeric)
40: Intermediate demand from sector 40 (numeric)
41: Intermediate demand from sector 41 (numeric)
42: Intermediate demand from sector 42 (numeric)
TIU: Total intermediate use (numeric)
TC: Total consumption (numeric)
FU201: Final use 201: government consumption (numeric)
FU202: Final use 202: household consumption (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2018_42_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

Input-output Table for China, 2020 (153 Sectors)

Description

This dataset, china_io_2020_153_df, is a data frame that represents the national input-output table of China for the year 2020. It covers 153 sectors and provides inter-sectoral flows of goods and services. Data values are measured in 10,000 Chinese Yuan (CNY) and calculated at producers' prices.

Usage

data(china_io_2020_153_df)

Format

A data frame with 160 observations and 169 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
001: Input from sector 001 (numeric)
002: Input from sector 002 (numeric)
003: Input from sector 003 (numeric)
004: Input from sector 004 (numeric)
005: Input from sector 005 (numeric)
006: Input from sector 006 (numeric)
007: Input from sector 007 (numeric)
008: Input from sector 008 (numeric)
009: Input from sector 009 (numeric)
010: Input from sector 010 (numeric)
011: Input from sector 011 (numeric)
012: Input from sector 012 (numeric)
013: Input from sector 013 (numeric)
014: Input from sector 014 (numeric)
015: Input from sector 015 (numeric)
016: Input from sector 016 (numeric)
017: Input from sector 017 (numeric)
018: Input from sector 018 (numeric)
019: Input from sector 019 (numeric)
020: Input from sector 020 (numeric)
021: Input from sector 021 (numeric)
022: Input from sector 022 (numeric)
023: Input from sector 023 (numeric)
024: Input from sector 024 (numeric)
025: Input from sector 025 (numeric)
026: Input from sector 026 (numeric)
027: Input from sector 027 (numeric)
028: Input from sector 028 (numeric)
029: Input from sector 029 (numeric)
030: Input from sector 030 (numeric)
031: Input from sector 031 (numeric)
032: Input from sector 032 (numeric)
033: Input from sector 033 (numeric)
034: Input from sector 034 (numeric)
035: Input from sector 035 (numeric)
036: Input from sector 036 (numeric)
037: Input from sector 037 (numeric)
038: Input from sector 038 (numeric)
039: Input from sector 039 (numeric)
040: Input from sector 040 (numeric)
041: Input from sector 041 (numeric)
042: Input from sector 042 (numeric)
043: Input from sector 043 (numeric)
044: Input from sector 044 (numeric)
045: Input from sector 045 (numeric)
046: Input from sector 046 (numeric)
047: Input from sector 047 (numeric)
048: Input from sector 048 (numeric)
049: Input from sector 049 (numeric)
050: Input from sector 050 (numeric)
051: Input from sector 051 (numeric)
052: Input from sector 052 (numeric)
053: Input from sector 053 (numeric)
054: Input from sector 054 (numeric)
055: Input from sector 055 (numeric)
056: Input from sector 056 (numeric)
057: Input from sector 057 (numeric)
058: Input from sector 058 (numeric)
059: Input from sector 059 (numeric)
060: Input from sector 060 (numeric)
061: Input from sector 061 (numeric)
062: Input from sector 062 (numeric)
063: Input from sector 063 (numeric)
064: Input from sector 064 (numeric)
065: Input from sector 065 (numeric)
066: Input from sector 066 (numeric)
067: Input from sector 067 (numeric)
068: Input from sector 068 (numeric)
069: Input from sector 069 (numeric)
070: Input from sector 070 (numeric)
071: Input from sector 071 (numeric)
072: Input from sector 072 (numeric)
073: Input from sector 073 (numeric)
074: Input from sector 074 (numeric)
075: Input from sector 075 (numeric)
076: Input from sector 076 (numeric)
077: Input from sector 077 (numeric)
078: Input from sector 078 (numeric)
079: Input from sector 079 (numeric)
080: Input from sector 080 (numeric)
081: Input from sector 081 (numeric)
082: Input from sector 082 (numeric)
083: Input from sector 083 (numeric)
084: Input from sector 084 (numeric)
085: Input from sector 085 (numeric)
086: Input from sector 086 (numeric)
087: Input from sector 087 (numeric)
088: Input from sector 088 (numeric)
089: Input from sector 089 (numeric)
090: Input from sector 090 (numeric)
091: Input from sector 091 (numeric)
092: Input from sector 092 (numeric)
093: Input from sector 093 (numeric)
094: Input from sector 094 (numeric)
095: Input from sector 095 (numeric)
096: Input from sector 096 (numeric)
097: Input from sector 097 (numeric)
098: Input from sector 098 (numeric)
099: Input from sector 099 (numeric)
100: Input from sector 100 (numeric)
101: Input from sector 101 (numeric)
102: Input from sector 102 (numeric)
103: Input from sector 103 (numeric)
104: Input from sector 104 (numeric)
105: Input from sector 105 (numeric)
106: Input from sector 106 (numeric)
107: Input from sector 107 (numeric)
108: Input from sector 108 (numeric)
109: Input from sector 109 (numeric)
110: Input from sector 110 (numeric)
111: Input from sector 111 (numeric)
112: Input from sector 112 (numeric)
113: Input from sector 113 (numeric)
114: Input from sector 114 (numeric)
115: Input from sector 115 (numeric)
116: Input from sector 116 (numeric)
117: Input from sector 117 (numeric)
118: Input from sector 118 (numeric)
119: Input from sector 119 (numeric)
120: Input from sector 120 (numeric)
121: Input from sector 121 (numeric)
122: Input from sector 122 (numeric)
123: Input from sector 123 (numeric)
124: Input from sector 124 (numeric)
125: Input from sector 125 (numeric)
126: Input from sector 126 (numeric)
127: Input from sector 127 (numeric)
128: Input from sector 128 (numeric)
129: Input from sector 129 (numeric)
130: Input from sector 130 (numeric)
131: Input from sector 131 (numeric)
132: Input from sector 132 (numeric)
133: Input from sector 133 (numeric)
134: Input from sector 134 (numeric)
135: Input from sector 135 (numeric)
136: Input from sector 136 (numeric)
137: Input from sector 137 (numeric)
138: Input from sector 138 (numeric)
139: Input from sector 139 (numeric)
140: Input from sector 140 (numeric)
141: Input from sector 141 (numeric)
142: Input from sector 142 (numeric)
143: Input from sector 143 (numeric)
144: Input from sector 144 (numeric)
145: Input from sector 145 (numeric)
146: Input from sector 146 (numeric)
147: Input from sector 147 (numeric)
148: Input from sector 148 (numeric)
149: Input from sector 149 (numeric)
150: Input from sector 150 (numeric)
151: Input from sector 151 (numeric)
152: Input from sector 152 (numeric)
153: Input from sector 153 (numeric)
TIU: Total intermediate use (numeric)
FU101: Final use category 101 (numeric)
FU102: Final use category 102 (numeric)
THC: Household consumption (numeric)
FU103: Final use category 103 (numeric)
TC: Total consumption (numeric)
FU201: Final use category 201 (numeric)
FU202: Final use category 202 (numeric)
GCF: Gross capital formation (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
IM: Imports (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2020_153_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

China Input-Output Table (2020, 42 Sectors)

Description

This dataset, china_io_2020_42_df, is a data frame containing the national input-output table of China for the year 2020. It includes 91 observations across 42 economic sectors. The values are expressed in units of 10,000 Chinese Yuan (CNY). The dataset records transactions between sectors, value added components, imports, exports, and other final demand categories.

Usage

data(china_io_2020_42_df)

Format

A data frame with 91 observations and 53 variables:

Code: Sector code (character)
Description: Sector description in English (character)
DescriptionInChinese: Sector description in Chinese (character)
Origin: Type of entry (e.g., sector, total, final use) (character)
01: Intermediate demand from sector 01 (numeric)
02: Intermediate demand from sector 02 (numeric)
03: Intermediate demand from sector 03 (numeric)
04: Intermediate demand from sector 04 (numeric)
05: Intermediate demand from sector 05 (numeric)
06: Intermediate demand from sector 06 (numeric)
07: Intermediate demand from sector 07 (numeric)
08: Intermediate demand from sector 08 (numeric)
09: Intermediate demand from sector 09 (numeric)
10: Intermediate demand from sector 10 (numeric)
11: Intermediate demand from sector 11 (numeric)
12: Intermediate demand from sector 12 (numeric)
13: Intermediate demand from sector 13 (numeric)
14: Intermediate demand from sector 14 (numeric)
15: Intermediate demand from sector 15 (numeric)
16: Intermediate demand from sector 16 (numeric)
17: Intermediate demand from sector 17 (numeric)
18: Intermediate demand from sector 18 (numeric)
19: Intermediate demand from sector 19 (numeric)
20: Intermediate demand from sector 20 (numeric)
21: Intermediate demand from sector 21 (numeric)
22: Intermediate demand from sector 22 (numeric)
23: Intermediate demand from sector 23 (numeric)
24: Intermediate demand from sector 24 (numeric)
25: Intermediate demand from sector 25 (numeric)
26: Intermediate demand from sector 26 (numeric)
27: Intermediate demand from sector 27 (numeric)
28: Intermediate demand from sector 28 (numeric)
29: Intermediate demand from sector 29 (numeric)
30: Intermediate demand from sector 30 (numeric)
31: Intermediate demand from sector 31 (numeric)
32: Intermediate demand from sector 32 (numeric)
33: Intermediate demand from sector 33 (numeric)
34: Intermediate demand from sector 34 (numeric)
35: Intermediate demand from sector 35 (numeric)
36: Intermediate demand from sector 36 (numeric)
37: Intermediate demand from sector 37 (numeric)
38: Intermediate demand from sector 38 (numeric)
39: Intermediate demand from sector 39 (numeric)
40: Intermediate demand from sector 40 (numeric)
41: Intermediate demand from sector 41 (numeric)
42: Intermediate demand from sector 42 (numeric)
TIU: Total intermediate use (numeric)
TC: Total consumption (numeric)
FU201: Final use 201: government consumption (numeric)
FU202: Final use 202: household consumption (numeric)
EX: Exports (numeric)
TFU: Total final use (numeric)
GO: Gross output (numeric)

Details

The dataset name has been kept as 'china_io_2020_42_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a base R data frame. The original content has not been modified in any way.

Source

Data taken from the ionet package version 0.2.2

List of Prominent Chinese Cities

Description

This dataset, chinese_cities_tbl_df, is a tibble that contains information about 367 prominent cities in China. Each row represents a city and includes geographic coordinates (latitude and longitude), administrative information, and population data. The dataset is a tibble (special type of data frame) that preserves the original structure from its source simplemaps.

Usage

data(chinese_cities_tbl_df)

Format

A tibble with 367 observations and 9 variables:

city: City name in English (character)
lat: Latitude coordinate (numeric)
lng: Longitude coordinate (numeric)
country: Country name (always "China" in this dataset) (character)
iso2: 2-letter country code (always "CN" in this dataset) (character)
admin_name: Administrative division name (province or equivalent) (character)
capital: Administrative capital status (character)
population: City population estimate (numeric)
population_proper: City proper population estimate (numeric)

Details

The dataset name has been kept as 'chinese_cities_tbl_df' to maintain consistency with the naming conventions in the ChinAPIs package. The suffix 'tbl_df' indicates that this is a tibble data frame. The original content has not been modified in any way.

Source

Data obtained from simplemaps: https://simplemaps.com/data/cn-cities

Chinese Dams Dataset

Description

This dataset, chinese_dams_tbl_df, is a tibble containing information about 158 dams in China. Each row represents a dam and includes location details, physical characteristics, and completion information. The dataset preserves the original structure from its source Kaggle.

Usage

data(chinese_dams_tbl_df)

Format

A tibble with 158 observations and 8 variables:

Name: Name of the dam (character)
Province: Primary province where the dam is located (character)
Second Province: Additional province if dam spans multiple regions (character)
Impounds: River or water body the dam impounds (character)
Height: Height of the dam in meters (numeric)
Type: Type of dam (e.g., "Arch-gravity", "Embankment") (character)
Complete: Year of completion (character)
Storage capacity (million m3): Water storage capacity in million cubic meters (numeric)

Details

The dataset name has been kept as 'chinese_dams_tbl_df' to maintain consistency with the naming conventions in the ChinAPIs package. The suffix 'tbl_df' indicates that this is a tibble data frame. The original content has not been modified in any way.

Source

Data obtained from Kaggle: https://www.kaggle.com/datasets/alexandrepetit881234/chinese-dams

Chinese Surnames and National Frequency (1930–2008)

Description

This dataset, family_name_df, is a data frame containing 1,806 Chinese surnames along with their frequency and distribution across China. The dataset includes 1806 observations and 7 variables, covering information such as whether a surname is compound, its initial, frequency ranks, and relative frequency between 1930 and 2008. This dataset is useful for sociolinguistic analysis, demography, and historical population studies.

Usage

data(family_name_df)

Format

A data frame with 1806 observations and 7 variables:

surname: Chinese surname (character)
compound: Indicates if the surname is compound (numeric)
initial: Initial letter of surname in Pinyin (character)
initial.rank: Rank of the initial letter (numeric)
n.1930_2008: Estimated number of people with the surname (1930–2008) (numeric)
ppm.1930_2008: Relative frequency per million (1930–2008) (numeric)
surname.uniqueness: Surname uniqueness score (numeric)

Details

The dataset name has been kept as 'family_name_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the ChineseNames package version 2023.8

Get Under-5 Mortality Rate in China from World Bank

Description

Retrieves China's under-five mortality rate (per 1,000 live births) for the years 2010 to 2022 using the World Bank Open Data API. The indicator used is SH.DYN.MORT.

Usage

get_china_child_mortality()

Details

This function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "Mortality rate, under-5 (per 1,000 live births)")
country: Country name ("China")
year: Year of the data (integer)
value: Under-5 mortality rate per 1,000 live births (numeric)

Note

Requires internet connection.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/SH.DYN.MORT

Examples

if (interactive()) {
  get_china_child_mortality()
}

Get China's Consumer Price Index from World Bank

Description

Retrieves China's Consumer Price Index (2010 = 100) for the years 2010 to 2022 using the World Bank Open Data API. The indicator used is FP.CPI.TOTL.

Usage

get_china_cpi()

Details

The function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "Consumer price index (2010 = 100)")
country: Country name ("China")
year: Year of the data (integer)
value: Consumer Price Index value in numeric form

Note

Requires internet connection. The data is retrieved in real time from the World Bank API.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/FP.CPI.TOTL

Examples

if (interactive()) {
  get_china_cpi()
}

Get China's Energy Use (kg of oil equivalent per capita) from World Bank

Description

Retrieves China's energy use per capita, measured in kilograms of oil equivalent, for the years 2010 to 2022 using the World Bank Open Data API. The indicator used is EG.USE.PCAP.KG.OE.

Usage

get_china_energy_use()

Details

This function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "Energy use (kg of oil equivalent per capita)")
country: Country name ("China")
year: Year of the data (integer)
value: Energy use in kilograms of oil equivalent per capita

Note

Requires internet connection.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE

Examples

if (interactive()) {
  get_china_energy_use()
}

Get China's GDP (Current US$) from World Bank

Description

Retrieves China's Gross Domestic Product (GDP) in current US dollars for the years 2010 to 2022 using the World Bank Open Data API. The indicator used is NY.GDP.MKTP.CD.

Usage

get_china_gdp()

Details

The function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "GDP (current US$)")
country: Country name ("China")
year: Year of the data (integer)
value: GDP value in numeric form
value_label: Formatted GDP value (e.g., "1,466,464,899,304")

Note

Requires internet connection. The data is retrieved in real time from the World Bank API.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD

Examples

if (interactive()) {
  get_china_gdp()
}

Get Official Public Holidays in China for a Given Year

Description

Retrieves the list of official public holidays in China for a specific year using the Nager.Date public holidays API. This function returns a tibble containing the date of the holiday, the name in the local language (Chinese), and the English name. It is useful for academic, planning, and data analysis purposes. The information is retrieved directly from the Nager.Date API and reflects the current status of holidays for the requested year. The field names returned are consistent with the API structure.

Usage

get_china_holidays(year)

Arguments

year

An integer indicating the year (e.g., 2024 or 2025).

Value

A tibble with the following columns:

date: Date of the holiday (class Date)
local_name: Holiday name in the local language (Chinese)
name: Holiday name in English

Source

Data obtained from the Nager.Date API: https://date.nager.at/

Examples

get_china_holidays(2024)
get_china_holidays(2025)

Get Hospital Beds per 1,000 People in China from World Bank

Description

Retrieves data on the number of hospital beds per 1,000 people in China from 2010 to 2022 using the World Bank Open Data API. The indicator used is SH.MED.BEDS.ZS.

Usage

get_china_hospital_beds()

Details

This function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "Hospital beds (per 1,000 people)")
country: Country name ("China")
year: Year of the data (integer)
value: Hospital beds per 1,000 people (numeric)

Note

Requires internet connection.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/SH.MED.BEDS.ZS

Examples

if (interactive()) {
  get_china_hospital_beds()
}

Get China's Life Expectancy at Birth from World Bank

Description

Retrieves China's life expectancy at birth (in years) for the years 2010 to 2022 using the World Bank Open Data API. The indicator used is SP.DYN.LE00.IN.

Usage

get_china_life_expectancy()

Details

The function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "Life expectancy at birth, total (years)")
country: Country name ("China")
year: Year of the data (integer)
value: Life expectancy value in numeric form (years)

Note

Requires internet connection. The data is retrieved in real time from the World Bank API.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/SP.DYN.LE00.IN

Examples

if (interactive()) {
  get_china_life_expectancy()
}

Get China's Literacy Rate (Age 15+) from World Bank

Description

Retrieves China's literacy rate for adults aged 15 and above, expressed as a percentage, for the years 2010 to 2022 using the World Bank Open Data API. The indicator used is SE.ADT.LITR.ZS.

Usage

get_china_literacy_rate()

Details

The function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "Literacy rate, adult total (
country: Country name ("China")
year: Year of the data (integer)
value: Literacy rate as numeric percentage

Note

Requires internet connection. The data is retrieved in real time from the World Bank API.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/SE.ADT.LITR.ZS

Examples

if (interactive()) {
  get_china_literacy_rate()
}

Get China's Total Population from World Bank

Description

Retrieves China's total population for the years 2010 to 2022 using the World Bank Open Data API. The indicator used is SP.POP.TOTL.

Usage

get_china_population()

Details

The function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "Population, total")
country: Country name ("China")
year: Year of the data (integer)
value: Population as a numeric value
value_label: Formatted population with commas (e.g., "1,412,600,000")

Note

Requires internet connection. The data is retrieved in real time from the World Bank API.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/SP.POP.TOTL

Examples

if (interactive()) {
  get_china_population()
}

Get China's Unemployment Rate from World Bank

Description

Retrieves China's Unemployment, total ( for the years 2010 to 2022 using the World Bank Open Data API. The indicator used is SL.UEM.TOTL.ZS.

Usage

get_china_unemployment()

Details

The function sends a GET request to the World Bank API. If the API request fails or returns an error status code, the function returns NULL with an informative message.

Value

A tibble with the following columns:

indicator: Indicator name (e.g., "Unemployment, total (
country: Country name ("China")
year: Year of the data (integer)
value: Unemployment rate as percentage in numeric form

Note

Requires internet connection. The data is retrieved in real time from the World Bank API.

Source

World Bank Open Data API: https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS

Examples

if (interactive()) {
  get_china_unemployment()
}

Get Key Country Information About China from the REST Countries API

Description

Retrieves selected, essential information about China using the REST Countries API. The function returns a tibble with core details such as population, area, capital, region, and official language(s).

See the API documentation at https://restcountries.com/. Example API usage: https://restcountries.com/v3.1/name/china?fullText=true.

Usage

get_country_info_cn()

Details

The function sends a GET request to the REST Countries API. If the API returns data for China, the function extracts and returns selected fields as a tibble. If the request fails or China is not found, it returns NULL and prints a message.

Value

A tibble with the following 8 columns:

name_common: Common name of the country.
name_official: Official name of the country.
region: Geographical region.
subregion: Subregion within the continent.
capital: Capital city.
area: Area in square kilometers.
population: Population of the country.
languages: Languages spoken in the country, as a comma-separated string.

Note

Requires internet connection. The data is retrieved in real time from the REST Countries API.

Source

REST Countries API: https://restcountries.com/

Examples

get_country_info_cn()

Chinese Given Name Characters and Frequency (1930–2008)

Description

This dataset, given_name_df, is a data frame containing 2,614 Chinese characters commonly used in given names, along with nationwide frequency data. The dataset includes 2614 observations and 25 variables, providing information such as stroke count, gender distribution, historical usage, frequency per million, uniqueness, and perceived name traits such as warmth and competence.

Usage

data(given_name_df)

Format

A data frame with 2614 observations and 25 variables:

character: Chinese character used in given names (character)
pinyin: Pronunciation in Pinyin (character)
bihua: Number of strokes in the character (numeric)
n.male: Number of males with this character in their name (numeric)
n.female: Number of females with this character in their name (numeric)
name.gender: Gender index (numeric)
n.1930_1959: Number of occurrences between 1930–1959 (numeric)
n.1960_1969: Number of occurrences between 1960–1969 (numeric)
n.1970_1979: Number of occurrences between 1970–1979 (numeric)
n.1980_1989: Number of occurrences between 1980–1989 (numeric)
n.1990_1999: Number of occurrences between 1990–1999 (numeric)
n.2000_2008: Number of occurrences between 2000–2008 (numeric)
ppm.1930_1959: Frequency per million (1930–1959) (numeric)
ppm.1960_1969: Frequency per million (1960–1969) (numeric)
ppm.1970_1979: Frequency per million (1970–1979) (numeric)
ppm.1980_1989: Frequency per million (1980–1989) (numeric)
ppm.1990_1999: Frequency per million (1990–1999) (numeric)
ppm.2000_2008: Frequency per million (2000–2008) (numeric)
name.ppm: Overall frequency per million (numeric)
name.uniqueness: Uniqueness score of the name (numeric)
corpus.ppm: Frequency in linguistic corpus (numeric)
corpus.uniqueness: Uniqueness in corpus (numeric)
name.valence: Emotional valence of the name (numeric)
name.warmth: Perceived warmth of the name (numeric)
name.competence: Perceived competence of the name (numeric)

Details

The dataset name has been kept as 'given_name_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the ChineseNames package version 2023.8

Chinese Health and Family Life Survey

Description

This dataset, health_family_life_df, is a data frame from the Chinese Health and Family Life Survey, which sampled 60 villages and urban neighborhoods to represent the full geographical and socioeconomic range of contemporary China. The dataset includes 1,534 observations and covers variables related to age, education, income, health, and well-being, both for respondents and their partners.

Usage

data(health_family_life_df)

Format

A data frame with 1,534 observations and 10 variables:

R_region: Region of respondent (factor with 6 levels)
R_age: Age of respondent (numeric)
R_edu: Education level of respondent (ordered factor with 6 levels)
R_income: Income of respondent (numeric)
R_health: Self-reported health status of respondent (ordered factor with 5 levels)
R_height: Height of respondent (numeric)
R_happy: Self-reported happiness level of respondent (ordered factor with 4 levels)
A_height: Height of respondent’s partner (numeric)
A_edu: Education level of respondent’s partner (ordered factor with 6 levels)
A_income: Income of respondent’s partner (numeric)

Details

The dataset name has been kept as 'health_family_life_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the HSAUR3 package version 1.0-15

Hong Kong District Councillors Elected in 2019

Description

This dataset, hk_councillors_tbl_df, is a tibble containing public domain information about the 452 District Councillors elected in Hong Kong during the 2019 election. It includes demographic, political, and contact information, along with details on electoral performance and constituency classification.

Usage

data(hk_councillors_tbl_df)

Format

A tibble with 452 observations and 33 variables:

ConstituencyCode: Constituency code (character)
Constituency_ZH: Constituency name in Chinese (character)
Constituency_EN: Constituency name in English (character)
District_ZH: District name in Chinese (character)
District_EN: District name in English (character)
Region_ZH: Region name in Chinese (character)
Region_EN: Region name in English (character)
Party_ZH: Political party name in Chinese (character)
Party_EN: Political party name in English (character)
DC_ZH: Name of councillor in Chinese (character)
DC_EN: Name of councillor in English (character)
FacebookURL: Link to councillor's Facebook page (character)
DCPageURL: Link to official councillor page (character)
Address: Office address (character)
Phone: Phone number (character)
Fax: Fax number (character)
Email: Email address (character)
WebsiteURL: Personal or campaign website URL (character)
DCProjectPageURL: Project page URL (character)
ElectionYear: Year of election (numeric)
ElectionDate: Date of election (Date)
CandidateNum: Number of candidates in the race (numeric)
Occupation: Occupation of councillor (character)
Political_ZH: Political position or orientation in Chinese (character)
Political_EN: Political position or orientation in English (character)
Camp_ZH: Political camp in Chinese (character)
Camp_EN: Political camp in English (character)
Vote: Number of votes received (numeric)
VotePercentage: Vote percentage received (numeric)
Gender_ZH: Gender in Chinese (character)
Gender_EN: Gender in English (character)
Tag_ZH: Additional tags or classifications in Chinese (character)
Tag_EN: Additional tags or classifications in English (character)

Details

The dataset name has been kept as 'hk_councillors_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble (a modern form of data frame). The original content has not been modified in any way.

Source

Data taken from the hkdatasets package version 1.0.0

Hong Kong District Labels and Regional Classification

Description

This dataset, hk_districts_tbl_df, is a tibble summarizing the region classification and abbreviated labels of the 18 administrative districts in Hong Kong. It provides English and Chinese names for each district, along with their corresponding region and abbreviation. This dataset is useful for geographic mapping and administrative categorization.

Usage

data(hk_districts_tbl_df)

Format

A tibble with 18 observations and 6 variables:

Code: District code (character)
District_EN: District name in English (character)
District_ZH: District name in Chinese (character)
Region_EN: Region classification in English (character)
Region_ZH: Region classification in Chinese (character)
Abbrev: Abbreviation of the district (character)

Details

The dataset name has been kept as 'hk_districts_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble (a modern form of data frame). The original content has not been modified in any way.

Source

Data taken from the hkdatasets package version 1.0.0

Hong Kong Population by District and Age Group

Description

This dataset, hk_population_tbl_df, is a tibble containing the land-based non-institutional population of Hong Kong, broken down by District Council district and age group. It provides population counts for five age brackets and the total population for each of the 18 districts.

Usage

data(hk_population_tbl_df)

Format

A tibble with 18 observations and 8 variables:

District_ZH: District name in Chinese (character)
District_EN: District name in English (character)
Age_0_14: Population aged 0 to 14 (numeric)
Age_15_24: Population aged 15 to 24 (numeric)
Age_25_44: Population aged 25 to 44 (numeric)
Age_45_64: Population aged 45 to 64 (numeric)
Age_65: Population aged 65 and over (numeric)
TotalPopulation: Total population of the district (numeric)

Details

The dataset name has been kept as 'hk_population_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble (a modern form of data frame). The original content has not been modified in any way.

Source

Data taken from the hkdatasets package version 1.0.0

Hong Kong Street Names as of 2020

Description

This dataset, hk_street_names_tbl_df, is a tibble containing street names in Hong Kong as of the year 2020. It includes English and Chinese names for each street and logical indicators of whether a street is located within one of the 18 administrative districts of Hong Kong. This dataset is useful for geographic, linguistic, and administrative analysis.

Usage

data(hk_street_names_tbl_df)

Format

A tibble with 4,603 observations and 21 variables:

DC: District code or abbreviation (character)
StreetNames_EN: Street name in English (character)
StreetNames_ZH: Street name in Chinese (character)
TM: Tuen Mun district indicator (logical)
ST: Sha Tin district indicator (logical)
E: Eastern district indicator (logical)
S: Southern district indicator (logical)
WC: Wan Chai district indicator (logical)
C&W: Central and Western district indicator (logical)
Is: Islands district indicator (logical)
YL: Yuen Long district indicator (logical)
SK: Sai Kung district indicator (logical)
KC: Kowloon City district indicator (logical)
YTM: Yau Tsim Mong district indicator (logical)
KT: Kwun Tong district indicator (logical)
SSP: Sham Shui Po district indicator (logical)
N: North district indicator (logical)
TP: Tai Po district indicator (logical)
K&T: Kwai Tsing district indicator (logical)
TW: Tsuen Wan district indicator (logical)
WTS: Wong Tai Sin district indicator (logical)

Details

The dataset name has been kept as 'hk_street_names_tbl_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'tbl_df' indicates that the dataset is a tibble (a modern form of data frame). The original content has not been modified in any way.

Source

Data taken from the hkdatasets package version 1.0.0

Giant Panda Location Data

Description

This dataset, panda_locations_df, is a data frame containing giant panda location data. The dataset includes 147 observations and 4 variables, representing spatial and temporal coordinates of tracked panda movements. This dataset can be used for spatial analysis, movement modeling, or wildlife tracking applications.

Usage

data(panda_locations_df)

Format

A data frame with 147 observations and 4 variables:

time: Timestamp of location observation (numeric)
x: X coordinate (numeric)
y: Y coordinate (numeric)
z: Z coordinate (integer)

Details

The dataset name has been kept as 'panda_locations_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the mkde package version 0.3

Population Statistics from the Chinese Name Database

Description

This dataset, population_df, is a data frame containing population statistics derived from the Chinese name database. The dataset includes 40 observations and 3 variables, representing raw and corrected counts for various demographic items related to naming patterns and coverage. It supports analyses of representativeness, name distribution, and scaling adjustments.

Usage

data(population_df)

Format

A data frame with 40 observations and 3 variables:

item: Demographic or classification item (character)
n: Raw count (numeric)
n.corrected: Corrected count (numeric)

Details

The dataset name has been kept as 'population_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the ChineseNames package version 2023.8

Daily Incidence of the 2003 SARS Epidemic in Hong Kong

Description

This dataset, sars_hong_kong_list, is a list containing two components: the daily number of reported SARS cases and the serial interval distribution during the 2003 SARS epidemic in Hong Kong. The incidence data covers 107 days, and the serial interval distribution is provided for 25 days.

Usage

data(sars_hong_kong_list)

Format

A list with 2 components:

incidence: Daily number of SARS cases reported in Hong Kong (numeric vector of length 107)
si: Serial interval distribution (numeric vector of length 25)

Details

The dataset name has been kept as 'sars_hong_kong_list' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'list' indicates that the dataset is a list object. The original content has not been modified in any way.

Source

Data taken from the EpiLPS package version 1.3.0

Per Capita Output of Workers in Shanghai Factories

Description

This dataset, shanghai_factories_df, is a data frame containing data on per capita output of workers in 17 factories located in Shanghai. It includes measures of output along with three associated input variables, providing a concise snapshot of factory-level productivity indicators.

Usage

data(shanghai_factories_df)

Format

A data frame with 17 observations and 4 variables:

Output: Per capita output of workers (numeric)
SI: Input variable SI (numeric)
SP: Input variable SP (numeric)
I: Input variable I (numeric)

Details

The dataset name has been kept as 'shanghai_factories_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the SenSrivastava package version 2015.6.25.1

PM2.5 Pollution and Weather Data in Shanghai

Description

This dataset, shanghai_pm25_df, is a data frame containing information about PM2.5 air pollution and weather conditions in Shanghai. The data originates from a broader study on fine particle pollution in five Chinese cities. For this dataset, lines containing missing values were removed, and the first 5,000 complete observations were selected. Only pollution-related and weather-related variables were retained.

Usage

data(shanghai_pm25_df)

Format

A data frame with 5,000 observations and 10 variables:

PM_Jingan: PM2.5 concentration at Jingan station (integer)
PM_US.Post: PM2.5 concentration at the U.S. Consulate station (integer)
PM_Xuhui: PM2.5 concentration at Xuhui station (integer)
DEWP: Dew point temperature (integer)
HUMI: Relative humidity (numeric)
PRES: Barometric pressure (numeric)
TEMP: Temperature in degrees Celsius (integer)
Iws: Wind speed (numeric)
precipitation: Precipitation amount (numeric)
Iprec: Cumulative precipitation index (numeric)

Details

The dataset name has been kept as 'shanghai_pm25_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the slm package version 1.2.0

Top 1,000 Given Names by Province in Mainland China

Description

This dataset, top1000name_prov_df, is a data frame containing the 1,000 most common given names across 31 provinces in mainland China. The dataset includes 999 observations and 35 variables, reporting name counts by gender and by individual province. This dataset enables geographic comparisons of name popularity and sociocultural naming trends across Chinese regions.

Usage

data(top1000name_prov_df)

Format

A data frame with 999 observations and 35 variables:

name: Given name (character)
n.male: Number of males with this name (numeric)
n.female: Number of females with this name (numeric)
beijing: Name frequency in Beijing (numeric)
tianjin: Name frequency in Tianjin (numeric)
hebei: Name frequency in Hebei (numeric)
shanxi: Name frequency in Shanxi (numeric)
neimenggu: Name frequency in Inner Mongolia (numeric)
liaoning: Name frequency in Liaoning (numeric)
jilin: Name frequency in Jilin (numeric)
heilongjiang: Name frequency in Heilongjiang (numeric)
shanghai: Name frequency in Shanghai (numeric)
jiangsu: Name frequency in Jiangsu (numeric)
zhejiang: Name frequency in Zhejiang (numeric)
anhui: Name frequency in Anhui (numeric)
fujian: Name frequency in Fujian (numeric)
jiangxi: Name frequency in Jiangxi (numeric)
shandong: Name frequency in Shandong (numeric)
henan: Name frequency in Henan (numeric)
hubei: Name frequency in Hubei (numeric)
hunan: Name frequency in Hunan (numeric)
guangdong: Name frequency in Guangdong (numeric)
guangxi: Name frequency in Guangxi (numeric)
hainan: Name frequency in Hainan (numeric)
chongqing: Name frequency in Chongqing (numeric)
sichuan: Name frequency in Sichuan (numeric)
guizhou: Name frequency in Guizhou (numeric)
yunnan: Name frequency in Yunnan (numeric)
xizang: Name frequency in Tibet (numeric)
shaanxi: Name frequency in Shaanxi (numeric)
gansu: Name frequency in Gansu (numeric)
qinghai: Name frequency in Qinghai (numeric)
ningxia: Name frequency in Ningxia (numeric)
xinjiang: Name frequency in Xinjiang (numeric)
others: Name frequency in unspecified or other regions (numeric)

Details

The dataset name has been kept as 'top1000name_prov_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the ChineseNames package version 2023.8

Top 100 Given Names in 6 Birth Cohorts

Description

This dataset, top100name_year_df, is a data frame containing the top 100 given names in China across six birth cohorts: 1950, 1960, 1970, 1980, 1990, and 2000. It includes rankings and frequencies for all individuals, as well as separately for males and females. The dataset provides insights into naming trends and gender differences over time.

Usage

data(top100name_year_df)

Format

A data frame with 100 observations and 37 variables:

top100: Ranking from 1 to 100 (numeric)
name.all.1950: Most common name (all genders) in 1950 (character)
name.all.1960: Most common name (all genders) in 1960 (character)
name.all.1970: Most common name (all genders) in 1970 (character)
name.all.1980: Most common name (all genders) in 1980 (character)
name.all.1990: Most common name (all genders) in 1990 (character)
name.all.2000: Most common name (all genders) in 2000 (character)
n.all.1950: Number of people with the name in 1950 (numeric)
n.all.1960: Number of people with the name in 1960 (numeric)
n.all.1970: Number of people with the name in 1970 (numeric)
n.all.1980: Number of people with the name in 1980 (numeric)
n.all.1990: Number of people with the name in 1990 (numeric)
n.all.2000: Number of people with the name in 2000 (numeric)
name.m.1950: Most common male name in 1950 (character)
name.m.1960: Most common male name in 1960 (character)
name.m.1970: Most common male name in 1970 (character)
name.m.1980: Most common male name in 1980 (character)
name.m.1990: Most common male name in 1990 (character)
name.m.2000: Most common male name in 2000 (character)
n.m.1950: Number of males with the name in 1950 (numeric)
n.m.1960: Number of males with the name in 1960 (numeric)
n.m.1970: Number of males with the name in 1970 (numeric)
n.m.1980: Number of males with the name in 1980 (numeric)
n.m.1990: Number of males with the name in 1990 (numeric)
n.m.2000: Number of males with the name in 2000 (numeric)
name.f.1950: Most common female name in 1950 (character)
name.f.1960: Most common female name in 1960 (character)
name.f.1970: Most common female name in 1970 (character)
name.f.1980: Most common female name in 1980 (character)
name.f.1990: Most common female name in 1990 (character)
name.f.2000: Most common female name in 2000 (character)
n.f.1950: Number of females with the name in 1950 (numeric)
n.f.1960: Number of females with the name in 1960 (numeric)
n.f.1970: Number of females with the name in 1970 (numeric)
n.f.1980: Number of females with the name in 1980 (numeric)
n.f.1990: Number of females with the name in 1990 (numeric)
n.f.2000: Number of females with the name in 2000 (numeric)

Details

The dataset name has been kept as 'top100name_year_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the ChineseNames package version 2023.8

Top 50 Given-Name Characters in 6 Birth Cohorts

Description

This dataset, top50char_year_df, is a data frame containing the top 50 most common Chinese characters used in given names across six birth cohorts: 1950, 1960, 1970, 1980, 1990, and 2000. It includes rankings and frequencies for all individuals, as well as separately for males and females. The dataset provides insights into naming character trends and gender differences over time.

Usage

data(top50char_year_df)

Format

A data frame with 50 observations and 37 variables:

top50: Ranking from 1 to 50 (numeric)
char.all.1950: Most common given-name character (all genders) in 1950 (character)
char.all.1960: Most common given-name character (all genders) in 1960 (character)
char.all.1970: Most common given-name character (all genders) in 1970 (character)
char.all.1980: Most common given-name character (all genders) in 1980 (character)
char.all.1990: Most common given-name character (all genders) in 1990 (character)
char.all.2000: Most common given-name character (all genders) in 2000 (character)
n.all.1950: Number of people with the character in 1950 (numeric)
n.all.1960: Number of people with the character in 1960 (numeric)
n.all.1970: Number of people with the character in 1970 (numeric)
n.all.1980: Number of people with the character in 1980 (numeric)
n.all.1990: Number of people with the character in 1990 (numeric)
n.all.2000: Number of people with the character in 2000 (numeric)
char.m.1950: Most common male given-name character in 1950 (character)
char.m.1960: Most common male given-name character in 1960 (character)
char.m.1970: Most common male given-name character in 1970 (character)
char.m.1980: Most common male given-name character in 1980 (character)
char.m.1990: Most common male given-name character in 1990 (character)
char.m.2000: Most common male given-name character in 2000 (character)
n.m.1950: Number of males with the character in 1950 (numeric)
n.m.1960: Number of males with the character in 1960 (numeric)
n.m.1970: Number of males with the character in 1970 (numeric)
n.m.1980: Number of males with the character in 1980 (numeric)
n.m.1990: Number of males with the character in 1990 (numeric)
n.m.2000: Number of males with the character in 2000 (numeric)
char.f.1950: Most common female given-name character in 1950 (character)
char.f.1960: Most common female given-name character in 1960 (character)
char.f.1970: Most common female given-name character in 1970 (character)
char.f.1980: Most common female given-name character in 1980 (character)
char.f.1990: Most common female given-name character in 1990 (character)
char.f.2000: Most common female given-name character in 2000 (character)
n.f.1950: Number of females with the character in 1950 (numeric)
n.f.1960: Number of females with the character in 1960 (numeric)
n.f.1970: Number of females with the character in 1970 (numeric)
n.f.1980: Number of females with the character in 1980 (numeric)
n.f.1990: Number of females with the character in 1990 (numeric)
n.f.2000: Number of females with the character in 2000 (numeric)

Details

The dataset name has been kept as 'top50char_year_df' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the ChineseNames package version 2023.8

View Available Datasets in ChinAPIs

Description

This function lists all datasets available in the 'ChinAPIs' package. If the 'ChinAPIs' package is not loaded, it stops and shows an error message. If no datasets are available, it returns a message and an empty vector.

Usage

view_datasets_ChinAPIs()

Value

A character vector with the names of the available datasets. If no datasets are found, it returns an empty character vector.

Examples

if (requireNamespace("ChinAPIs", quietly = TRUE)) {
  library(ChinAPIs)
  view_datasets_ChinAPIs()
}

PTSD Symptoms of Wenchuan Earthquake Survivors

Description

This dataset, wenchuan_ptsd_matrix, is a matrix containing items measuring symptoms of post-traumatic stress disorder (PTSD) in survivors of the Wenchuan earthquake. Participants were 362 Chinese adults who lost at least one child in the disaster. The matrix includes 362 observations and 17 variables, each representing a symptom of PTSD as assessed by McNally et al. (2015).

Usage

data(wenchuan_ptsd_matrix)

Format

A matrix with 362 observations and 17 variables:

intrusion: Symptom: Intrusive thoughts (numeric)
dreams: Symptom: Distressing dreams (numeric)
flash: Symptom: Flashbacks (numeric)
upset: Symptom: Psychological distress (numeric)
physior: Symptom: Physiological reactivity (numeric)
avoidth: Symptom: Avoidance of thoughts (numeric)
avoidact: Symptom: Avoidance of activities (numeric)
amnesia: Symptom: Inability to recall aspects of trauma (numeric)
lossint: Symptom: Loss of interest (numeric)
distant: Symptom: Feeling distant from others (numeric)
numb: Symptom: Emotional numbness (numeric)
future: Symptom: Foreshortened future (numeric)
sleep: Symptom: Sleep disturbances (numeric)
anger: Symptom: Irritability or anger (numeric)
concen: Symptom: Concentration difficulties (numeric)
hyper: Symptom: Hypervigilance (numeric)
startle: Symptom: Exaggerated startle response (numeric)

Details

The dataset name has been kept as 'wenchuan_ptsd_matrix' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the ChinAPIs package and assists users in identifying its specific characteristics. The suffix 'matrix' indicates that the dataset is a matrix object. The original content has not been modified in any way.

Source

Data taken from the bgms package version 0.1.4.2

ChinAPIs: Access Chinese Data via APIs and Curated Datasets

Description

Details

Author(s)

See Also

COVID-19 Offspring Cases in Hong Kong (Jan–Apr 2020)

Description

Usage

Format

Details

Source

Beijing Air Quality Dataset (2015)

Description

Usage

Format

Details

Source

Administrative Divisions of China

Description

Usage

Format

Details

Source

Stated Car Choice Data from Chinese Buyers

Description

Usage

Format

Details

Source

China's Corruption Investigations

Description

Usage

Format

Details

Source

Input-output Table for China, 2002 (122 Sectors)

Description

Usage

Format

Details

Source

Input-output Table for China, 2005 (42 Sectors)

Description

Usage

Format

Details

Source

Input-output Table for China, 2007 (135 Sectors)

Description

Usage

Format

Details

Source

Input-output Table for China, 2010 (41 Sectors)

Description

Usage

Format

Details

Source

Input-output Table for China, 2012 (139 Sectors)

Description

Usage

Format

Details

Source

Input-output Table for China, 2015 (42 Sectors)

Description

Usage

Format

Details

Source

Input-output Table for China, 2017 (149 Sectors)

Description

Usage

Format

Details

Source

China Input-Output Table (2017, 42 Sectors)

Description

Usage