compute_name_index {ChineseNames}

Compute multiple features 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).

For details, see https://psychbruce.github.io/ChineseNames/

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). Default is TRUE.

digits

Number of decimal places. Default is 4.

return.namechar

Whether to return separate name characters. Default is TRUE.

return.all

Whether to return all temporary variables in the computation of the final variables. Default is FALSE.

Value

A new data frame (of 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).

Citation

Bao, H.-W.-S. (2023). ChineseNames: Chinese Name Database 1930-2008. R package version 2023.8. https://CRAN.R-project.org/package=ChineseNames

Bao, H.-W.-S., Cai, H., Jing, Y., & Wang, J. (2021). Novel evidence for the increasing prevalence of unique names in China: A reply to Ogihara. Frontiers in Psychology, 12, 731244. doi:10.3389/fpsyg.2021.731244

Note

For details and examples, see https://psychbruce.github.io/ChineseNames/

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