Package 'ChineseNames'

Compute multiple indices of surnames and given names.

Description

Compute all available name features (indices) based on familyname and givenname. You can either input a data frame with a variable of Chinese full names (and a variable of birth years, if necessary) or just input a vector of full names (and a vector of birth years, if necessary).

• Usage 1: Input a single value or a vector of name (and birth, if necessary).

• Usage 2: Input a data frame of data and the variable name of var.fullname (or var.surname and/or var.givenname) (and var.birthyear, if necessary).

Caution: Name-character uniqueness (NU) for birth year >= 2010 is estimated by forecasting and thereby may not be accurate.

Usage

compute_name_index(
  data = NULL,
  var.fullname = NULL,
  var.surname = NULL,
  var.givenname = NULL,
  var.birthyear = NULL,
  name = NA,
  birth = NA,
  index = c("NLen", "SNU", "SNI", "NU", "CCU", "NG", "NV", "NW", "NC"),
  NU.approx = TRUE,
  digits = 4,
  return.namechar = TRUE,
  return.all = FALSE
)

Arguments

data	Data frame.
var.fullname	Variable name of Chinese full names (e.g., "name").
var.surname	Variable name of Chinese surnames (e.g., "surname").
var.givenname	Variable name of Chinese given names (e.g., "givenname").
var.birthyear	Variable name of birth year (e.g., "birth").
name	If no data, you can just input a vector of full name(s).
birth	If no data, you can just input a vector of birth year(s).
index	Which indices to compute? By default, it computes all available name indices: NLen: full-name length (2~4). SNU: surname uniqueness (1~6). SNI: surname initial (1~26). NU: name-character uniqueness (1~6). CCU: character-corpus uniqueness (1~6). NG: name gender (-1~1). NV: name valence (1~5). NW: name warmth (1~5). NC: name competence (1~5).
NU.approx	Whether to approximately compute name-character uniqueness (NU) using the nearest two birth cohorts with relative weights (which would be more precise than just using a single birth cohort). Defaults to TRUE.
digits	Number of decimal places. Defaults to 4.
return.namechar	Whether to return separate name characters. Defaults to TRUE.
return.all	Whether to return all temporary variables in the computation of the final variables. Defaults to FALSE.

Details

https://psychbruce.github.io/ChineseNames/

Value

A new data frame (class data.table) with name indices appended. Full names are split into name0 (surnames, with compound surnames automatically detected), name1, name2, and name3 (given-name characters).

Examples

## Prepare ##
sn = familyname$surname[1:12]
gn = c(top100name.year$name.all.1960[1:6],
       top100name.year$name.all.2000[1:6],
       top100name.year$name.all.1960[95:100],
       top100name.year$name.all.2000[95:100])
demodata = data.frame(name=paste0(sn, gn),
                      birth=c(1960:1965, 2000:2005,
                              1960:1965, 2000:2005))
demodata

## Compute ##
newdata = compute_name_index(demodata,
                             var.fullname="name",
                             var.birthyear="birth")
newdata

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1,806 Chinese surnames and nationwide frequency.

Description

1,806 Chinese surnames and nationwide frequency.

Usage

data(familyname)

Format

A data frame with 7 variables:

surname

surname (in Chinese)

compound

0 = single surname, 1 = compound surname

initial

initial letter (a-z)

initial.rank

initial order (1-26)

n.1930_2008

total counts in the database

ppm.1930_2008

proportion in population (ppm = parts per million)

surname.uniqueness

surname uniqueness

Details

https://psychbruce.github.io/ChineseNames/

---

2,614 Chinese characters used in given names and nationwide frequency.

Description

2,614 Chinese characters used in given names and nationwide frequency.

Usage

data(givenname)

Format

A data frame with 25 variables:

character

character used in given names (in Chinese)

pinyin

pinyin (pronunciation)

bihua

number of strokes in a character

n.male

total counts in male

n.female

total counts in female

name.gender

difference in proportions of a character used by male vs. female

n.1930_1959, n.1960_1969, n.1970_1979, n.1980_1989, n.1990_1999, n.2000_2008

total counts in a birth cohort

ppm.1930_1959, ppm.1960_1969, ppm.1970_1979, ppm.1980_1989, ppm.1990_1999, ppm.2000_2008

proportion (parts per million) in a birth cohort

name.ppm

average ppm (parts per million) across all cohorts

name.uniqueness

name-character uniqueness (in naming practices)

corpus.ppm

proportion (parts per million) in contemporary Chinese corpus

corpus.uniqueness

character-corpus uniqueness (in contemporary Chinese corpus)

name.valence

name valence (positivity of character meaning) (based on subjective ratings from 16 raters, ICC = 0.921)

name.warmth

name warmth/morality (based on subjective ratings from 10 raters, ICC = 0.774)

name.competence

name competence/assertiveness (based on subjective ratings from 10 raters, ICC = 0.712)

Details

https://psychbruce.github.io/ChineseNames/

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Population statistics for the Chinese name database.

Description

Population statistics for the Chinese name database.

Usage

data(population)

Details

https://psychbruce.github.io/ChineseNames/

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Top 1,000 given names in 31 Chinese mainland provinces.

Description

Top 1,000 given names in 31 Chinese mainland provinces.

Usage

data(top1000name.prov)

Details

https://psychbruce.github.io/ChineseNames/

---

Top 100 given names in 6 birth cohorts.

Description

Top 100 given names in 6 birth cohorts.

Usage

data(top100name.year)

Details

https://psychbruce.github.io/ChineseNames/

---

Top 50 given-name characters in 6 birth cohorts.

Description

Top 50 given-name characters in 6 birth cohorts.

Usage

data(top50char.year)

Details

https://psychbruce.github.io/ChineseNames/

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