Introducing the iso3c_kr Function in the kdiplo R Package for Converting Korean Country Names into iso3c Country Codes
Kadir Jun Ayhan
Tuesday, April 23, 2024
In my research, I often work with country-year data from Korean sources, including data on diplomatic visits, trade, aid and so on. One of the fundamental difficulties I have had is the lack of universal country codes across different datasets. Further complicating matters is the inconsistency of country names in these datasets. For example, Democratic Republic of the Congo has five different spellings across different official sources that I could find: 콩고 민주공화국, 자이르, 콩고민주공화국, 콩고 민주 공화국, 콩고민주공화국(DR콩고).
To address this issue, I have created a function in my kdiplo package that converts Korean country names into ISO 3166-1 alpha-3 (iso3c) country codes. This function, iso3c_kr, is designed to assign universal iso3c country codes to Korean-language country names that will make it easier to join different kinds of data.
One still needs to check if the output is correct, especially for countries that have gone through political transitions such as Germany, Yugoslavia, Russia, Vietnam, Yemen and so on.
Sometimes the Korean government sources have overlapping data for Yugoslavia and Serbia, for example. In such cases, one needs to check the data and make sure that the data is correct.
For example, the following is sample Korean trade data from Korean Statistical Information Service (KOSIS):
| 국가별 | 2018 년 | 2019 년 | 2020 년 | 2021 년 | 2022 년 | 2023 년 |
|---|---|---|---|---|---|---|
| 잠비아 | 26241 | 16087 | 17619 | 28356 | 14068 | 15459 |
| 잠비아 | 108344 | 54542 | 15164 | 100606 | 82198 | 53867 |
| 자이르 | NA | NA | NA | NA | NA | NA |
| 자이르 | 618 | 8 | 113 | 4 | NA | NA |
| 짐바브웨 | 25964 | 14088 | 15514 | 20404 | 16083 | 19563 |
| 짐바브웨 | 4909 | 13098 | 11377 | 9627 | 10415 | 20862 |
And, the following is sample Korean aid data from Korea’s ODA portal:
| country_kr | sector | 사업개수 | 약정액[USD] | 약정액[KRW] |
|---|---|---|---|---|
| 베트남 | 통신정책, 계획 및 행정(voluntary code) | 2 | 232334 | 270736486 |
| 캄보디아 | 11321 | 1 | 85815 | 99999361 |
| 미얀마 | 사회보호/보장 | 1 | 103460 | 120560903 |
| 라오스 | 비정규 농업훈련 | 1 | 107958 | 125802378 |
| 몽골 | 의료서비스 | 5 | 511824 | 596423389 |
Wide format is quite common in official Korean data sources. Trade data is in wide format. Before using the iso3c_kr function, let’s first transform the trade data into a long (country-year) format to make it in the same format as the aid data. This will make joining the two datasets more feasible.
export <- trade
import <- trade
export %<>% select(-`...63`)
export_long <- export %>% pivot_longer(4:62, names_to = "year", values_to = "export_kosis")
export_long %<>% set_names(c("country_kr", "type", "unit", "year", "export_kosis"))
export_long %<>% filter(type == "수출액[천달러]") %>%
mutate(export_kosis = as.numeric(export_kosis) * 1000,
year = parse_number(year)) %>%
select(-type, -unit)
import %<>% select(-`...63`)
import_long <- import %>% pivot_longer(4:62, names_to = "year", values_to = "import_kosis")
import_long %<>% set_names(c("country_kr", "type", "unit", "year", "import_kosis"))
import_long %<>% filter(type == "수입액[천달러]") %>%
mutate(import_kosis = as.numeric(import_kosis) * 1000,
year = parse_number(year)) %>%
select(-type, -unit)
trade <- export_long %>% left_join(import_long, by = c("country_kr", "year"))Using the iso3c_kr function, we can simply convert Korean country names into iso3c country codes. For example, the following is the output of the iso3c_kr function for the Korean trade data:
| country_kr | iso3c | year | export_kosis | import_kosis |
|---|---|---|---|---|
| 계 | NA | 2014 | 572664607000 | 525514506000 |
| 아랍에미리트 연합 | ARE | 1996 | 1377933000 | 2259205000 |
| 앤티가바부다 | ATG | 1978 | NA | NA |
| 앵귈라 | AIA | 2019 | 817000 | 1000 |
| 아르메니아 | ARM | 2001 | 1255000 | 43000 |
| 앙골라 | AGO | 1983 | 235000 | NA |
We see that in this example, “계” (gyae) did not get any iso3c country code. This is because the iso3c_kr function could not find the iso3c country code for this entry. This is because, it is not a country name. “계” means total. It is best to check the data to see which entries did not get an iso3c code.
[1] "계, 국제통화기금, 기타, 기타국"They mean “total”, “IMF”, “other”, and “other countries” in Korean. In other words, we are not missing any countries, which is good.
Now let’s convert the Korean country names in the aid data into iso3c country codes:
| country_kr | iso3c | sector | no_of_projects | aid_usd | aid_krw |
|---|---|---|---|---|---|
| 베트남 | VNM | 통신정책, 계획 및 행정(voluntary code) | 2 | 232334 | 270736486 |
| 캄보디아 | KHM | 11321 | 1 | 85815 | 99999361 |
| 미얀마 | MMR | 사회보호/보장 | 1 | 103460 | 120560903 |
| 라오스 | LAO | 비정규 농업훈련 | 1 | 107958 | 125802378 |
| 몽골 | MNG | 의료서비스 | 5 | 511824 | 596423389 |
| 필리핀 | PHL | 농업용수자원 | 2 | 0 | 0 |
Once you know the iso3c country codes, you can get the English country names, or other country codes (such as Correlates of War country codes) using the countrycode package, for example.
trade <- trade %>% mutate(country_name = countrycode::countrycode(iso3c, origin = "iso3c", destination = "country.name"))
trade[c(50, 150, 250, 350, 450, 550),c(1, 5, 6, 2:4)] %>% gt::gt()| country_kr | iso3c | country_name | year | export_kosis | import_kosis |
|---|---|---|---|---|---|
| 계 | NA | NA | 2014 | 572664607000 | 525514506000 |
| 아랍에미리트 연합 | ARE | United Arab Emirates | 1996 | 1377933000 | 2259205000 |
| 앤티가바부다 | ATG | Antigua & Barbuda | 1978 | NA | NA |
| 앵귈라 | AIA | Anguilla | 2019 | 817000 | 1000 |
| 아르메니아 | ARM | Armenia | 2001 | 1255000 | 43000 |
| 앙골라 | AGO | Angola | 1983 | 235000 | NA |
More importantly, this function allows users to be able to join different datasets that have Korean country names. For example, one can join the trade data with the aid data using the iso3c country codes. In this example, I will join the trade data with the aid data using the iso3c country codes.
# now that I think about it, this sample data is only 2019.
aid$year <- 2019
trade_aid <- trade %>% left_join(aid, by = c("iso3c", "year"), suffix = c("", "_aid"))
trade_aid %>%
filter(year == 2019 & !is.na(iso3c)) %>%
slice(c(50, 150, 250, 350, 450, 550)) %>%
select(c(1, 5, 6, 2:4, 8, 10)) %>%
gt::gt()| country_kr | iso3c | country_name | year | export_kosis | import_kosis | sector | aid_usd |
|---|---|---|---|---|---|---|---|
| 아르메니아 | ARM | Armenia | 2019 | 12729000 | 16743000 | 전문대,대학(원) 교육 | 119069 |
| 방글라데시 | BGD | Bangladesh | 2019 | 1282342000 | 404703000 | 건설정책 및 행정관리 | 46251 |
| 볼리비아 | BOL | Bolivia | 2019 | 30434000 | 450576000 | 환경정책 및 행정관리 | 80969 |
| 코트디부아르 | CIV | Côte d’Ivoire | 2019 | 136494000 | 5264000 | 교육정책 및 행정관리 | 30096 |
| 콜롬비아 | COL | Colombia | 2019 | 1143075000 | 718214000 | 성인 기초생활교육 | 62976 |
| 알제리 | DZA | Algeria | 2019 | 700918000 | 1746239000 | 레크리에이션 및 스포츠(voluntary code) | 22312 |
Voilà! Now we have a dataset that has both trade and aid data, both of which originally did not have consistent country names or country codes. I plan to add warning messages to the iso3c_kr function to make it easier to spot potential issues with the conversion of Korean country names. I will continue to update the Korean country name dataset in the kdiplo package as I come across new data sources. Feel free to report unavailable country names in the iso3c_kr function to me using the issue tracker on Github.
---
title: "Working with Korean Diplomatic Datasets"
subtitle: "Introducing the `iso3c_kr` Function in the `kdiplo` R Package for Converting Korean Country Names into iso3c Country Codes"
author: "Kadir Jun Ayhan"
format:
html:
embed-resources: true
code-fold: true
code-summary: "Show the code"
code-tools: true
date: "2024-04-23"
editor: visual
echo: true
warning: false
image: hanguel.png
draft: false
comments:
hypothesis: true
categories:
- Korea
- Korean dataset
- dataset
- R
- R package
- diplomacy
- kdiplo
---
```{r}
#| include: false
library(kdiplo)
library(tidyverse)
library(magrittr)
```
In my research, I often work with country-year data from Korean sources, including data on diplomatic visits, trade, aid and so on. One of the fundamental difficulties I have had is the lack of universal country codes across different datasets. Further complicating matters is the inconsistency of country names in these datasets. For example, Democratic Republic of the Congo has five different spellings across different official sources that I could find: `r paste(unique(kdiplo::iso3c_data$country_kr[kdiplo::iso3c_data$iso3c == "COD"]), collapse = ", ")`.
To address this issue, I have created a function in my `kdiplo` package that converts Korean country names into ISO 3166-1 alpha-3 (*iso3c*) country codes. This function, `iso3c_kr`, is designed to assign universal iso3c country codes to Korean-language country names that will make it easier to join different kinds of data.
One still needs to check if the output is correct, especially for countries that have gone through political transitions such as Germany, Yugoslavia, Russia, Vietnam, Yemen and so on.
Sometimes the Korean government sources have overlapping data for Yugoslavia and Serbia, for example. In such cases, one needs to check the data and make sure that the data is correct.
For example, the following is sample Korean trade data from [Korean Statistical Information Service (KOSIS)](https://kosis.kr/statHtml/statHtml.do?orgId=360&tblId=DT_1R11006_FRM101&conn_path=I3):
```{r}
trade <- readxl::read_xlsx("../../../korea_visits/data/kosis_trade_240330.xlsx")
trade[533:538,c(1,57:62)] %>% gt::gt()
```
And, the following is sample Korean aid data from [Korea's ODA portal](https://stats.odakorea.go.kr/portal/odakorea/detail):
```{r}
aid <- readxl::read_xlsx("../../../covid determinants 220818/data/korea_total_aid_2019_230709.xlsx")
aid %<>% select(1:5)
aid[c(50, 150, 250, 350, 450),] %>% gt::gt()
```
Wide format is quite common in official Korean data sources. Trade data is in wide format. Before using the `iso3c_kr` function, let's first transform the trade data into a long (country-year) format to make it in the same format as the aid data. This will make joining the two datasets more feasible.
```{r}
export <- trade
import <- trade
export %<>% select(-`...63`)
export_long <- export %>% pivot_longer(4:62, names_to = "year", values_to = "export_kosis")
export_long %<>% set_names(c("country_kr", "type", "unit", "year", "export_kosis"))
export_long %<>% filter(type == "수출액[천달러]") %>%
mutate(export_kosis = as.numeric(export_kosis) * 1000,
year = parse_number(year)) %>%
select(-type, -unit)
import %<>% select(-`...63`)
import_long <- import %>% pivot_longer(4:62, names_to = "year", values_to = "import_kosis")
import_long %<>% set_names(c("country_kr", "type", "unit", "year", "import_kosis"))
import_long %<>% filter(type == "수입액[천달러]") %>%
mutate(import_kosis = as.numeric(import_kosis) * 1000,
year = parse_number(year)) %>%
select(-type, -unit)
trade <- export_long %>% left_join(import_long, by = c("country_kr", "year"))
```
Using the `iso3c_kr` function, we can simply convert Korean country names into iso3c country codes. For example, the following is the output of the `iso3c_kr` function for the Korean trade data:
```{r}
trade <- iso3c_kr(trade, "country_kr") #you copy paste the column name that has the Korean country names.
trade[c(50, 150, 250, 350, 450, 550), c(1,5, 2:4)] %>% gt::gt()
```
We see that in this example, "계" (*gyae*) did not get any iso3c country code. This is because the `iso3c_kr` function could not find the iso3c country code for this entry. This is because, it is not a country name. "계" means total. It is best to check the data to see which entries did not get an iso3c code.
```{r}
missing_iso3c <- trade %>% filter(is.na(iso3c)) %>% pull(country_kr) %>% unique()
paste(missing_iso3c, collapse = ", ")
```
They mean "total", "IMF", "other", and "other countries" in Korean. In other words, we are not missing any countries, which is good.
Now let's convert the Korean country names in the aid data into iso3c country codes:
```{r}
aid %<>% set_names(c("country_kr", "sector", "no_of_projects", "aid_usd", "aid_krw"))
aid <- iso3c_kr(aid, "country_kr") #you copy paste the column name that has the Korean country names.
aid[c(50, 150, 250, 350, 450, 550),c(1, 6, 2:5)] %>% gt::gt()
```
Once you know the iso3c country codes, you can get the English country names, or other country codes (such as Correlates of War country codes) using the `countrycode` package, for example.
```{r}
trade <- trade %>% mutate(country_name = countrycode::countrycode(iso3c, origin = "iso3c", destination = "country.name"))
trade[c(50, 150, 250, 350, 450, 550),c(1, 5, 6, 2:4)] %>% gt::gt()
```
More importantly, this function allows users to be able to join different datasets that have Korean country names. For example, one can join the trade data with the aid data using the iso3c country codes. In this example, I will join the trade data with the aid data using the iso3c country codes.
```{r}
# now that I think about it, this sample data is only 2019.
aid$year <- 2019
trade_aid <- trade %>% left_join(aid, by = c("iso3c", "year"), suffix = c("", "_aid"))
trade_aid %>%
filter(year == 2019 & !is.na(iso3c)) %>%
slice(c(50, 150, 250, 350, 450, 550)) %>%
select(c(1, 5, 6, 2:4, 8, 10)) %>%
gt::gt()
```
Voilà! Now we have a dataset that has both trade and aid data, both of which originally did not have consistent country names or country codes. I plan to add warning messages to the `iso3c_kr` function to make it easier to spot potential issues with the conversion of Korean country names. I will continue to update the Korean country name dataset in the `kdiplo` package as I come across new data sources. Feel free to report unavailable country names in the `iso3c_kr` function to me using the [issue tracker on Github](https://github.com/kjayhan/kdiplo/issues).