Compute multiple features of surnames and given names. — compute_name

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).

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
#>      name birth
#> 1  王秀英  1960
#> 2    李军  1961
#> 3    张平  1962
#> 4  刘建华  1963
#> 5    陈伟  1964
#> 6    杨勇  1965
#> 7    黄鑫  2000
#> 8    赵杰  2001
#> 9    吴涛  2002
#> 10   周浩  2003
#> 11   徐宇  2004
#> 12 孙俊杰  2005
#> 13 王秀花  1960
#> 14 李新华  1961
#> 15 张雪梅  1962
#> 16   刘荣  1963
#> 17   陈峰  1964
#> 18 杨春花  1965
#> 19   黄娟  2000
#> 20 赵志豪  2001
#> 21 吴天宇  2002
#> 22 周文博  2003
#> 23 徐子怡  2004
#> 24   孙灿  2005

## Compute ##
newdata = compute_name_index(demodata,
                             var.fullname="name",
                             var.birthyear="birth")
newdata
#>       name birth name0 name1 name2 name3 NLen    SNU SNI     NU    CCU      NG
#>  1: 王秀英  1960    王    秀    英          3 1.1257  23 1.3158 3.5975 -0.8840
#>  2:   李军  1961    李    军                2 1.1325  12 1.8763 3.0228  0.8241
#>  3:   张平  1962    张    平                2 1.1529  26 1.6493 2.9196  0.1704
#>  4: 刘建华  1963    刘    建    华          3 1.2753  12 1.5465 3.1711  0.2838
#>  5:   陈伟  1964    陈    伟                2 1.3415   3 2.0655 3.8865  0.6859
#>  6:   杨勇  1965    杨    勇                2 1.4725  25 2.2930 3.8916  0.9313
#>  7:   黄鑫  2000    黄    鑫                2 1.6292   8 1.9089 5.2161  0.4574
#>  8:   赵杰  2001    赵    杰                2 1.6754  26 1.7429 4.1740  0.5029
#>  9:   吴涛  2002    吴    涛                2 1.6992  23 2.0333 4.4778  0.8763
#> 10:   周浩  2003    周    浩                2 1.7071  26 1.8644 4.4124  0.8955
#> 11:   徐宇  2004    徐    宇                2 1.8247  24 1.5635 4.0210  0.5025
#> 12: 孙俊杰  2005    孙    俊    杰          3 1.8370  19 1.7784 4.3870  0.4234
#> 13: 王秀花  1960    王    秀    花          3 1.1257  23 1.6076 3.4917 -0.9282
#> 14: 李新华  1961    李    新    华          3 1.1325  12 1.6567 3.1363  0.1740
#> 15: 张雪梅  1962    张    雪    梅          3 1.1529  26 1.9998 3.8696 -0.8495
#> 16:   刘荣  1963    刘    荣                2 1.2753  12 1.6599 3.8888 -0.1303
#> 17:   陈峰  1964    陈    峰                2 1.3415   3 2.4063 4.1185  0.8346
#> 18: 杨春花  1965    杨    春    花          3 1.4725  25 1.7471 3.3374 -0.5918
#> 19:   黄娟  2000    黄    娟                2 1.6292   8 2.2167 4.7391 -0.9959
#> 20: 赵志豪  2001    赵    志    豪          3 1.6754  26 1.9377 3.7412  0.7828
#> 21: 吴天宇  2002    吴    天    宇          3 1.6992  23 1.8800 3.3503  0.5210
#> 22: 周文博  2003    周    文    博          3 1.7071  26 1.7989 3.4332  0.5658
#> 23: 徐子怡  2004    徐    子    怡          3 1.8247  24 1.8541 3.7488 -0.2639
#> 24:   孙灿  2005    孙    灿                2 1.8370  19 2.7219 4.7010  0.4339
#>       name birth name0 name1 name2 name3 NLen    SNU SNI     NU    CCU      NG
#>         NV   NW   NC
#>  1: 4.0938 3.80 3.50
#>  2: 3.9375 3.40 3.70
#>  3: 3.6875 3.30 3.10
#>  4: 4.2188 3.65 3.30
#>  5: 4.2500 3.50 3.40
#>  6: 4.1875 3.60 3.80
#>  7: 3.9375 3.20 3.60
#>  8: 4.6250 3.90 4.30
#>  9: 3.7500 3.80 3.60
#> 10: 4.2500 3.90 3.60
#> 11: 3.7500 3.70 3.60
#> 12: 4.5625 3.85 3.95
#> 13: 3.8438 3.80 3.00
#> 14: 4.1250 3.50 3.20
#> 15: 3.8750 3.25 3.25
#> 16: 4.1875 3.90 3.80
#> 17: 4.1250 3.40 3.80
#> 18: 3.9375 3.80 2.65
#> 19: 4.1250 3.60 2.80
#> 20: 4.2812 3.75 3.65
#> 21: 3.7812 3.60 3.45
#> 22: 4.5312 3.80 3.80
#> 23: 3.6875 3.45 2.90
#> 24: 4.1875 4.20 3.30
#>         NV   NW   NC