Enable Chinese as origin language

[turbanisch]

So far, there seems to be no way of converting country names from Chinese, not even by left-joining any of the dataframes that come with countrycode. I assume the reason for this is the lack of a corresponding set of regexes? If there is any interest, I would like to offer to come up with one!

[vincentarelbundock]

If you can come up with regexes, that would lovely!

But have you tried the countryname function? If so, what problem did you run into?

[cjyetman]

Since {countrycode} does include CLDR names in Chinese, one could (rather easily) create a custom dictionary using the included codelist to achieve exact matching, though it will not do fancy regex matching, e.g...

library(countrycode)

custom_dict <-
  unique(countrycode::codelist[, c("cldr.short.zh", "country.name.en", "iso3c")])

countrycode(
  "阿拉伯联合酋长国",
  origin = "cldr.short.zh",
  destination = "country.name.en",
  custom_dict = custom_dict
)
#> [1] "United Arab Emirates"

countrycode(
  "阿拉伯联合酋长国",
  origin = "cldr.short.zh",
  destination = "iso3c",
  custom_dict = custom_dict
)
#> [1] "ARE"

But yes, fancy Chinese regexes would be very interesting!

[turbanisch]

Ah, good idea, @cjyetman ! I think eventually it would be nice to have a more lenient regex matching because even data from Chinese authorities is messy, for some countries it may list the full country name, for others just an abbreviated one.

I had quickly discarded countryname but apparently I just picked the wrong examples: neither Germany nor France have Chinese versions and the one for China matches only the full country name (aka "People's Republic of China"):

library(tidyverse)
library(countrycode)

# no matches
countryname("德国")
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: 德国
#> [1] NA
countryname("中国")
#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: 中国
#> [1] NA

# there are Chinese country names in the data but not for all countries
# some match the official country name and may be too strict
countrycode::countryname_dict %>% 
  as_tibble() %>% 
  filter(str_detect(country.name.alt, "\\p{script=Han}")) %>% 
  filter(country.name.en %in% c("Germany", "France", "China"))
#> # A tibble: 2 × 2
#>   country.name.en country.name.alt
#>   <chr>           <chr>           
#> 1 China           中华人民共和国  
#> 2 China           中華人民共和國

^{Created on 2022-08-26 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.0 (2022-04-22) #> os macOS Monterey 12.5 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz Europe/Berlin #> date 2022-08-26 #> pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> broom 1.0.0 2022-07-01 [1] CRAN (R 4.2.0) #> cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.2.0) #> cli 3.3.0 2022-04-25 [1] CRAN (R 4.2.0) #> colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.2.0) #> countrycode * 1.4.0 2022-05-04 [1] CRAN (R 4.2.0) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> DBI 1.1.2 2021-12-20 [1] CRAN (R 4.2.0) #> dbplyr 2.2.0 2022-06-05 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> dplyr * 1.0.9 2022-04-28 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.15 2022-02-18 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> forcats * 0.5.1 2021-01-27 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0) #> ggplot2 * 3.3.6 2022-05-03 [1] CRAN (R 4.2.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0) #> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.2.0) #> haven 2.5.0 2022-04-15 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.2.0) #> httr 1.4.3 2022-05-04 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.39 2022-04-26 [1] CRAN (R 4.2.0) #> lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.2.0) #> lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> modelr 0.1.8 2020-05-19 [1] CRAN (R 4.2.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0) #> pillar 1.8.0 2022-07-18 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> readr * 2.1.2 2022-01-30 [1] CRAN (R 4.2.0) #> readxl 1.4.0 2022-03-28 [1] CRAN (R 4.2.0) #> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0) #> rlang 1.0.4 2022-07-12 [1] CRAN (R 4.2.0) #> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0) #> rvest 1.0.2 2021-10-16 [1] CRAN (R 4.2.0) #> scales 1.2.0 2022-04-13 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> stringi 1.7.8 2022-07-11 [1] CRAN (R 4.2.0) #> stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.2.0) #> tibble * 3.1.8 2022-07-22 [1] CRAN (R 4.2.0) #> tidyr * 1.2.0 2022-02-01 [1] CRAN (R 4.2.0) #> tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.2.0) #> tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.2.0) #> tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.2.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0) #> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0) #> xfun 0.31 2022-05-10 [1] CRAN (R 4.2.0) #> xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.0) #> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library #> #> ────────────────────────────────────────────────────────────────────────────── ```

[turbanisch]

I took a bit of a deep dive and came with regular expressions for Chinese by scraping Wikipedia. I outlined the issue (in short: Chinese has many variants, not just simplified vs. traditional scripts but also depending on regions) here and implemented a function as a proof of concept here.

Do let me know if you would like to incorporate the regular expressions into countrycode and I will try to come up with a PR! I assume this is how I would have to prepare the codes? https://github.com/vincentarelbundock/countrycode#adding-a-new-code

There is one issue though that you might want to handle differently than I did: my function converts the input into simplified characters before matching via regular expressions. I discuss alternative implementations in the README mentioned above (https://github.com/turbanisch/chinese-countryname-regex). In short, for maintenance reasons I would suggest only adding the regular expresssions for simplified Chinese and perhaps add a note in the countrycode documentation telling the user to apply a traditional-to-simplified conversion herself as needed.

[cjyetman]

@turbanisch this is really cool, thanks for all your effort so far

just a suggestion if/when you start building a PR... I would suggest following the model of adding known variants of each country to a data file for the tests, and then testing each one of them in a {testthat} test, as done here... https://github.com/vincentarelbundock/countrycode/blob/main/tests/testthat/data-known-name-variations.R https://github.com/vincentarelbundock/countrycode/blob/main/tests/testthat/test-known-name-variations.R

[vincentarelbundock]

That all sounds great! Thanks for putting this together.

I wont be able to look at anything in the near term, but feel free to open a PR whenever you are ready, and Ill try to review when possible.

Also agree with the know variations tests. Those would be super useful.

[turbanisch]

Thanks a lot to both of you! I will see how far I get and prepare a PR in the coming days.