1 OECD 국가

Wikipedia OECD 웹사이트에서 먼저 OECD 국가명을 추출한다.

library(tidyverse)
library(rvest)

oecd_countries <- read_html("https://en.wikipedia.org/wiki/OECD") %>% 
  html_nodes(xpath = '//*[@id="mw-content-text"]/div[1]/table[5]') %>% 
  html_table() %>% 
  .[[1]]

oecd_df <- oecd_countries %>% 
  as_tibble() %>% 
  janitor::clean_names() %>% 
  # mutate(id = row_number()) %>%
  select(content=country, start=membership_1, group = geographic_location) %>% 
  mutate(start = str_extract(start, "[0-9]{1,2}\\s[a-zA-Z]+\\s[0-9]{4}")) %>% 
  mutate(start = lubridate::dmy(start)) 

oecd_df
# A tibble: 37 x 3
   content        start      group        
   <chr>          <date>     <chr>        
 1 Australia      1971-06-07 Oceania      
 2 Austria        1961-09-29 Europe       
 3 Belgium        1961-09-13 Europe       
 4 Canada         1961-04-10 North America
 5 Chile          2010-05-07 South America
 6 Colombia       2020-04-28 South America
 7 Czech Republic 1995-12-21 Europe       
 8 Denmark        1961-05-30 Europe       
 9 Estonia        2010-12-09 Europe       
10 Finland        1969-01-28 Europe       
# ... with 27 more rows

OECD 가입순서대로 대륙별로 타임라인을 잡아보자. timeviz 팩키지를 통해 시간순으로 OECD 가입국가를 살펴봄으로서 대략적인 추세를 확인할 수 있다. 특히 대륙별로 가입순서를 나눠 보는 것도 흥미로운 사항을 파악할 수 있을 것이다.

library(timevis)

location <- oecd_df %>% 
  count(group) %>% 
  select(id = group) %>% 
  mutate(content = id)
  
timevis(oecd_df, groups = location, options = list(stack = FALSE)) %>% 
  setOptions(list(editable = TRUE)) %>%
  setSelection("South Korea") %>%
  fitWindow(list(animation = FALSE))

2 OECD 국가 비교

OECD 국가별 비교를 위해서 다양한 지표를 활용하여 평가가 이루지고 있다. 부폐관련 지표, 민주화 관련 지표, 국가 안정성 지표 등 다양한 지표를 통해 국가별 비교 작업을 수행할 수 있다. OECD 국가별 결측값이 일부 있어 이를 구글링을 통해 위키백과사전에 실린 오류를 수정하여 최대한 국가를 살리도록 한다.

oecd_fact <- read_html("https://en.wikipedia.org/wiki/OECD") %>% 
  html_nodes(xpath = '//*[@id="mw-content-text"]/div[1]/table[6]') %>% 
  html_table() %>% 
  .[[1]] %>% 
  as_tibble() %>% 
  janitor::clean_names()

oecd_fact_df <- oecd_fact %>% 
  set_names(c("country", "area", "population", "gdp", 
              "gdp_per_capita", "income_inequality", 
              "hdi", "fsi", "rli", "cpi", "ief", 
              "gpi", "wpfi", "di")) %>% 
  mutate(income_inequality = ifelse(income_inequality == "N/A", NA, income_inequality),
         fsi = ifelse(fsi == "N/A", NA, fsi),
         rli = ifelse(rli == "N/A", NA, rli),
         gpi = ifelse(gpi == "N/A", NA, gpi)) %>% 
  select(-rli) %>%  ## 결측값이 8개국
  mutate(income_inequality = ifelse(country == "New Zealand", 33, income_inequality),
         income_inequality = ifelse(country == "Poland", 31.8, income_inequality),
         fsi = ifelse(country == "Israel", 75.1, fsi)) %>% 
  filter(!str_detect(country, "Luxembourg|OECD|Country"))  %>% 
  mutate(across(.cols=area:di, parse_number))


## 변수 결측값
sapply(oecd_fact_df, function(y) sum(length(which(is.na(y))))) %>% 
  as.data.frame() %>% 
  rownames_to_column(var="country") %>% 
  as_tibble() %>% 
  set_names(c("country", "missings")) %>% 
  arrange(desc(missings))
# A tibble: 13 x 2
   country           missings
   <chr>                <int>
 1 country                  0
 2 area                     0
 3 population               0
 4 gdp                      0
 5 gdp_per_capita           0
 6 income_inequality        0
 7 hdi                      0
 8 fsi                      0
 9 cpi                      0
10 ief                      0
11 gpi                      0
12 wpfi                     0
13 di                       0
## 관측점 국가별 결측값
rowSums(is.na(oecd_fact_df)) %>% 
  as_tibble() %>% 
  bind_cols(oecd_fact_df %>% select(country)) %>% 
  rename(missings = value) %>% 
  arrange(desc(missings))
# A tibble: 36 x 2
   missings country       
      <dbl> <chr>         
 1        0 Australia     
 2        0 Austria       
 3        0 Belgium       
 4        0 Canada        
 5        0 Chile         
 6        0 Colombia      
 7        0 Czech Republic
 8        0 Denmark       
 9        0 Estonia       
10        0 Finland       
# ... with 26 more rows

정제된 데이터를 경제적인 측정지수 외에 다양한 지수를 바탕으로 일별해보자.

  • HDI: Human Development Index
  • FSI: Fragile States Index
  • CPI: Corruption Perceptions Index
  • IEF: Index of Economic Freedom
  • GPI: Global Peace Index
  • WPFI: Reporters Without Borders
  • DI: Democracy Index
oecd_fact_df %>% 
  arrange(desc(gdp)) %>% 
  DT::datatable() %>% 
  DT::formatRound(c("area", "population", "gdp", "gdp_per_capita"), digits = 0, interval = 3)

2.1 국가코드

위키백과사전에 담긴 OECD 국가정보와 World Economic Forum, IMD 국가 경쟁력 지표를 한데 묶는데 관계형 데이터베이스 모형에서 키값이 중요하기 때문에 countrycode 팩키지를 중간 다리로 삼아 최대한 정확히 국가명이 매칭되도록 준비한다.

library(countrycode)

oecd_fact_df <- oecd_fact_df %>% 
  mutate(flags = countrycode(country, origin = 'country.name', 'unicode.symbol'),
         iso3c = countrycode(country, origin = 'country.name', 'iso3c')) %>% 
  select(country, flags, iso3c, everything())

oecd_fact_df
# A tibble: 36 x 15
   country flags iso3c   area population     gdp gdp_per_capita income_inequali~
   <chr>   <chr> <chr>  <dbl>      <dbl>   <dbl>          <dbl>            <dbl>
 1 Austra~ "\U0~ AUS   7.74e6   24598933 1.19e12          48460             34.7
 2 Austria "\U0~ AUT   8.39e4    8809212 4.62e11          52398             30.5
 3 Belgium "\U0~ BEL   3.05e4   11372068 5.44e11          47840             27.7
 4 Canada  "\U0~ CAN   9.98e6   36708083 1.71e12          46705             34  
 5 Chile   "\U0~ CHL   7.56e5   18054726 4.45e11          24635             47.7
 6 Colomb~ "\U0~ COL   1.14e6   48901066 7.09e11          14507             49.7
 7 Czech ~ "\U0~ CZE   7.89e4   10591323 3.85e11          36327             25.9
 8 Denmark "\U0~ DNK   4.29e4    5769603 2.96e11          51364             28.2
 9 Estonia "\U0~ EST   4.52e4    1315480 4.18e10          31742             32.7
10 Finland "\U0~ FIN   3.38e5    5511303 2.47e11          44866             27.1
# ... with 26 more rows, and 7 more variables: hdi <dbl>, fsi <dbl>, cpi <dbl>,
#   ief <dbl>, gpi <dbl>, wpfi <dbl>, di <dbl>

3 Digital Transformation

디지털 전환(Digital Transformation) 대전환기에 WEF(World Economic Forum)에서 측정한 국가별 경쟁력 지표와 IMD 디지털 경쟁력(IMD Digital Competitiveness) 국가별 경쟁력 지표를 핵심 지표로 삼도록 한다.

3.1 weforum Global Competitiveness1

World Economic Forum, “Global Competitiveness Index 4.0”에서 보고서를 받아 국가별 핵심 정보만 추출한다.

한국 참여자

  • Korea, Republic of: Korea Development Institute
    • Joonghae Suh, Executive Director, Economic Information and Education Center
    • Youngho Jung, Head, Public Opinion Analysis Unit
    • Jiyeon Park, Senior Research Associate, Public Opinion Analysis Unit

library(pdftools)

wef <- pdftools::pdf_data("data/WEF_TheGlobalCompetitivenessReport2019.pdf")

wef_tbl <- wef[[15]] # The Global Competitiveness Index 4.0 2019 Rankings

## #1 ~ #47 국가
wef_tbl_01 <- wef_tbl %>% 
  filter(x > 82, x < 220, y > 140, y < 720)

wef_tbl_01 <- wef_tbl_01 %>% 
  mutate(x = round(x/5),     #reduce resolution to minimise inconsistent coordinates
         y = round(y/5)) %>% 
  arrange(y, x) %>%                        #sort in reading order
  mutate(group = cumsum(!lag(space, default = 0))) %>%  #identify text with spaces and paste
  group_by(group) %>% 
  summarise(x = first(x),
            y = first(y),
            text = paste(text, collapse = " ")) %>% 
  group_by(y) %>% 
  mutate(colno = row_number()) %>%         #add column numbers for table data 
  ungroup() %>% 
  select(text, colno, y) %>% 
  pivot_wider(names_from = colno, values_from = text) %>% #pivot into table format
  select(-y) %>% 
  mutate(rank = row_number()) %>% 
  set_names(c("country", "score", "rank_change", "score_change", "rank"))

## #48 ~ #94 국가
wef_tbl_02 <- wef_tbl %>% 
  filter(x > 246, x < 380, y > 138, y < 720)

wef_tbl_02 <- wef_tbl_02 %>% 
  mutate(x = round(x/5),     #reduce resolution to minimise inconsistent coordinates
         y = round(y/5)) %>% 
  arrange(y, x) %>%                        #sort in reading order
  mutate(group = cumsum(!lag(space, default = 0))) %>%  #identify text with spaces and paste
  group_by(group) %>% 
  summarise(x = first(x),
            y = first(y),
            text = paste(text, collapse = " ")) %>% 
  group_by(y) %>% 
  mutate(colno = row_number()) %>%         #add column numbers for table data 
  ungroup() %>% 
  select(text, colno, y) %>% 
  pivot_wider(names_from = colno, values_from = text) %>% #pivot into table format
  select(-y) %>% 
  mutate(rank = 47 + row_number()) %>% 
  set_names(c("country", "score", "rank_change", "score_change", "rank"))

wef_top_country <- wef_tbl_01 %>% 
  bind_rows(wef_tbl_02) %>% 
  mutate(score = parse_number(score)) %>% 
  select(wef_rank = rank, country, wef_score = score) %>% 
  mutate(iso3c = countrycode(country, origin = 'country.name', 'iso3c'))

wef_top_country
# A tibble: 93 x 4
   wef_rank country        wef_score iso3c
      <dbl> <chr>              <dbl> <chr>
 1        1 United States       83.7 USA  
 2        2 Hong Kong SAR       83.1 HKG  
 3        3 Netherlands         82.4 NLD  
 4        4 Switzerland         82.3 CHE  
 5        5 Japan               82.3 JPN  
 6        6 Germany             81.8 DEU  
 7        7 Sweden              81.2 SWE  
 8        8 United Kingdom      81.2 GBR  
 9        9 Denmark             81.2 DNK  
10       10 Finland             80.2 FIN  
# ... with 83 more rows

3.2 IMD Digital Competitiveness

마찬가지 방법으로 The 2020 IMD World Digital Competitiveness Ranking 웹사이트에서 The 2020 IMD World Digital Competitiveness Ranking 보고서를 받아 필요한 데이터를 추출하도록 한다.

library(tabulizer)

# locate_areas("data/digital_2020.pdf", pages = 24, copy = TRUE)

## USA ~ Poland 국가
digital_01 <- extract_tables("data/digital_2020.pdf", pages = 24, guess = FALSE, method = "decide", output = "data.frame",
                          area = list(c(155.45691, 44.26341, 590.53436, 297.63800)), encoding = "LATIN-1") %>% 
  .[[1]] %>% 
  set_names(c("country_econ", "z1", "z2", "2019")) %>% 
  select(country_econ, z1, `2019`) %>% 
  mutate(country = str_extract(country_econ, "[a-zA-Z \\.]*"),
         digital_rank = str_extract(country_econ, "[0-9]{1,2}")) %>% 
  select(country, digital_rank)
  
## Spain ~ Venezuela 국가
digital_02 <- extract_tables("data/digital_2020.pdf", pages = 24, guess = FALSE, output = "data.frame",
                          area = list(c(155.4569, 300.6664, 574.3830, 554.0410 ))) %>% 
  .[[1]] %>%
  set_names(c("country_econ", "z1", "z2")) %>% 
  mutate(country = str_extract(country_econ, "[a-zA-Z \\.]*"),
         digital_rank = str_extract(country_econ, "[0-9]{1,2}")) %>% 
  select(country, digital_rank)

digital_df <- bind_rows(digital_01, digital_02) %>% 
  mutate(country = ifelse(str_detect(country, "Kong SAR"),"Hong Kong", country),
         country = ifelse(str_detect(country, "Sdwitzerland"),"Switzerland", country),
         country = ifelse(str_detect(country, "Nsetherlands"),"Netherlands", country),
         country = ifelse(str_detect(country, "K.orea Rep."),"Korea Rep.", country),
         country = ifelse(str_detect(country, "Ugndiotemd Kingdom"),"United Kingdom", country),
         country = ifelse(str_detect(country, "Nnedw Zealand"),"New Zealand", country),
         country = ifelse(str_detect(country, "Lrguxembourg"),"Luxembourg", country),
         country = ifelse(str_detect(country, "Saudi ArabSiaudi Arabia"),"Saudi Arabia", country),
         country = ifelse(str_detect(country, "hTianiawan"),"Taiwan", country),
         country = ifelse(str_detect(country, "South AfricSaouth Africa"),"South Africa", country)) %>% 
  mutate(iso3c = countrycode(country, origin = 'country.name', 'iso3c')) %>% 
  select(iso3c, digital_rank) %>% 
  mutate(digital_rank = parse_number(digital_rank)) %>% 
  as_tibble()

digital_df
# A tibble: 63 x 2
   iso3c digital_rank
   <chr>        <dbl>
 1 USA              1
 2 SGP              2
 3 DNK              3
 4 SWE              4
 5 HKG              5
 6 CHE              6
 7 NLD              7
 8 KOR              8
 9 NOR              9
10 FIN             10
# ... with 53 more rows

3.3 옥스퍼드 AI 준비 보고서

Oxford Insights 웹사이트에서 AI Readiness Index 2020 PDF 파일을 가져와서 국가별 순위를 추출한다.

oxford <- pdftools::pdf_data("data/AI+Readiness+Report.pdf")

## Full Rankings: 1-24
oxford_01 <- oxford[[129]]

oxford_01_tbl <- oxford_01 %>% 
  filter(x > 137, x < 460, y > 178, y < 795)

oxford_01_tbl <- oxford_01_tbl %>% 
  mutate(x = round(x/5),     #reduce resolution to minimise inconsistent coordinates
         y = round(y/5)) %>% 
  arrange(y, x) %>%                        #sort in reading order
  mutate(group = cumsum(!lag(space, default = 0))) %>%  #identify text with spaces and paste
  group_by(group) %>% 
  summarise(x = first(x),
            y = first(y),
            text = paste(text, collapse = " ")) %>% 
  group_by(y) %>% 
  mutate(colno = row_number()) %>%         #add column numbers for table data 
  ungroup() %>% 
  select(text, colno, y) %>% 
  pivot_wider(names_from = colno, values_from = text) %>% #pivot into table format
  select(-y) %>% 
  set_names(c("rank", "country", "score"))

## Full Rankings: 25-51
## Full Rankings: 52-78, ... "160-172"

get_page_rank <- function(page) {
  oxford_page <- oxford[[page]]

  oxford_page_tbl <- oxford_page %>% 
    filter(x > 137, x < 460, y > 99, y < 795)
  
  oxford_page_tbl <- oxford_page_tbl %>% 
    mutate(x = round(x/3),     #reduce resolution to minimise inconsistent coordinates
           y = round(y/3)) %>% 
    arrange(y, x) %>%                        #sort in reading order
    mutate(group = cumsum(!lag(space, default = 0))) %>%  #identify text with spaces and paste
    group_by(group) %>% 
    summarise(x = first(x),
              y = first(y),
              text = paste(text, collapse = " ")) %>% 
    group_by(y) %>% 
    mutate(colno = row_number()) %>%         #add column numbers for table data 
    ungroup() %>% 
    select(text, colno, y) %>% 
    pivot_wider(names_from = colno, values_from = text) %>% #pivot into table format
    select(-y) %>% 
    set_names(c("rank", "country", "score"))
  
  return(oxford_page_tbl)
}

oxford_02_tbl <- get_page_rank(130)
oxford_03_tbl <- get_page_rank(131)
oxford_04_tbl <- get_page_rank(132)
oxford_05_tbl <- get_page_rank(133)
oxford_06_tbl <- get_page_rank(134)
oxford_07_tbl <- get_page_rank(135)

oxford_ranking <- bind_rows(oxford_01_tbl, oxford_02_tbl) %>% 
  bind_rows(oxford_03_tbl) %>% 
  bind_rows(oxford_04_tbl) %>% 
  bind_rows(oxford_05_tbl) %>% 
  bind_rows(oxford_06_tbl) %>% 
  bind_rows(oxford_07_tbl)

oxford_ranking_df <- oxford_ranking %>% 
  mutate(across(.col = c(rank, score), parse_number)) %>% 
  select(ai_rank = rank, country) %>% 
  mutate(iso3c = countrycode(country, origin = "country.name", 'iso3c'))

oxford_ranking_df
# A tibble: 172 x 3
   ai_rank country                  iso3c
     <dbl> <chr>                    <chr>
 1       1 United States of America USA  
 2       2 United Kingdom           GBR  
 3       3 Finland                  FIN  
 4       4 Germany                  DEU  
 5       5 Sweden                   SWE  
 6       6 Singapore                SGP  
 7       7 Republic of Korea        KOR  
 8       8 Denmark                  DNK  
 9       9 Netherlands              NLD  
10      10 Norway                   NOR  
# ... with 162 more rows

3.4 데이터 경제2

Harvard Business Review에서 데이터 총생산 (GDP, Gross Data Product)를 제시하는데 다음 4가지 기준을 제시하고 있다.

  • Volume: Absolute amount of broadband consumed by a country, as a proxy for the raw data generated.
    • 원데이터 생산량을 추정하는 대안으로 한 국가에서 소비되는 광대역통신 절대량
  • Usage: Number of users active on the internet, as a proxy for the breadth of usage behaviors, needs and contexts.
    • 인터넷에서 실제 활동중인 사용자수.
  • Accessibility: Institutional openness to data flows as a way to assess whether the data generated in a country permits wider usability and accessibility by multiple AI researchers, innovators, and applications.
    • 데이터 흐름의 제도적 개방성
  • Complexity: Volume of broadband consumption per capita, as a proxy for the sophistication and complexity of digital activity.
    • 인당 광대역 소비량
hbr_url <- "https://hbr.org/2019/01/which-countries-are-leading-the-data-economy"

data_economy <- hbr_url %>% 
  read_html() %>% 
  html_nodes(xpath ='//*[@id="main"]/div[4]/article-content/div/div[2]/div/div[3]/p[16]') %>% 
  html_text()

data_economy_df <- data_economy %>% 
  str_split(., pattern = "\\d{1,2}\\.\\s") %>% 
  .[[1]] %>% 
  as_tibble() %>% 
  rename(country = value) %>% 
  filter(country != "") %>% 
  mutate(country = str_trim(country)) %>% 
  mutate(data_rank = row_number()) %>% 
  mutate(iso3c = countrycode(country, origin = "country.name", 'iso3c')) %>% 
  select(data_rank, country, iso3c)

data_economy_df
# A tibble: 30 x 3
   data_rank country        iso3c
       <int> <chr>          <chr>
 1         1 United States  USA  
 2         2 United Kingdom GBR  
 3         3 China          CHN  
 4         4 Switzerland    CHE  
 5         5 South Korea    KOR  
 6         6 France         FRA  
 7         7 Canada         CAN  
 8         8 Sweden         SWE  
 9         9 Australia      AUS  
10        10 Czech Republic CZE  
# ... with 20 more rows

3.5 디지털 정부평가

경제협력개발기구(OECD)에서 처음으로 실시한 디지털 정부평가(The OECD 2019 Digital Government Index)는 각 나라의 디지털 전환 수준과 디지털정부 성숙도를 측정하기 위해 총 33개국을 대상으로 디지털 우선 정부, 플랫폼 정부, 열린 정부, 데이터 기반 정부, 국민주도형 정부, 선제적 정부 등 6가지 항목을 평가하는 것으로 구성되어 있다.

Australia, Hungary, Mexico, Poland, Slovakia, Switzerland, Turkey, the United States 은 데이터가 존재하지 않는다.

digital_gov <- extract_tables("data/The_OECD_2019_Digital_Government_Index.pdf", pages = 55, guess = FALSE, method = "decide", 
                             output = "data.frame",
                             area = list(c(142.68229, 62.84842, 694.51312, 535.30634)), encoding = "UTF-8") %>% 
  .[[1]] %>% 
  set_names(c("country", "디지털우선", "디지털우선순위", "데이터기반", "데이터기반순위", "플랫폼", "플랫폼순위", "열린정부", "열린정부순위", "국민주도", "국민주도순위", "선제적정부", "선제적정부순위", "종합점수", "gov_rank")) %>% 
  select(country, gov_rank) %>% 
  as_tibble() %>% 
  na.omit() %>% 
  mutate(iso3c = countrycode(country, origin = 'country.name', 'iso3c'))

digital_gov
# A tibble: 33 x 3
   country        gov_rank iso3c
   <chr>             <int> <chr>
 1 Korea                 1 KOR  
 2 United Kingdom        2 GBR  
 3 Colombia              3 COL  
 4 Denmark               4 DNK  
 5 Japan                 5 JPN  
 6 Canada                6 CAN  
 7 Spain                 7 ESP  
 8 Israel                8 ISR  
 9 Portugal             10 PRT  
10 France               11 FRA  
# ... with 23 more rows

3.6 산업용 로봇 밀도

국제로봇연맹(IFR, International Federation of Robotics)에서 매년 1만명당 로봇 댓수를 매년 국가별 순위를 매겨 발표하고 있다. 제조업 로봇 밀도 2019에 따르면 싱가포르와 더불어 한국이 만명당 가장 많은 로봇을 보유한 것으로 나타나고 있다.

library(readxl)

robot_dat <- read_excel("data/Robot_density.xlsx")

robot_df <- robot_dat %>% 
  mutate(iso3c = countrycode(country, origin = 'country.name', 'iso3c'))

robot_df
# A tibble: 20 x 4
   robot_rank country        density iso3c
        <dbl> <chr>            <dbl> <chr>
 1          1 Singapore          917 SGP  
 2          2 South Korea        855 KOR  
 3          3 Japan              364 JPN  
 4          4 Germany            346 DEU  
 5          5 Sweden             277 SWE  
 6          6 Denmark            243 DNK  
 7          7 Hong Kong          242 HKG  
 8          8 Chinese Taipei     234 TWN  
 9          9 United States      228 USA  
10         10 Italy              212 ITA  
11         11 Belgium            211 BEL  
12         12 Netherlands        194 NLD  
13         13 Spain              191 ESP  
14         14 Austria            189 AUT  
15         15 China              187 CHN  
16         16 France             177 FRA  
17         17 Slovakia           169 SVK  
18         18 Canada             165 CAN  
19         19 Switzerland        161 CHE  
20         20 Slovenia           157 SVN

4 OECD 통합 데이터

마지막 단계로 위키백과사전 OECD 국가별 중요 지표와 디지털 경쟁력을 담고 있는 World Economic Forum(WEF) 글로벌 경쟁력, IMD 디지털 경쟁력 지표를 결합시켜 후속 작업을 위한 데이터로 준비한다.

library(fuzzyjoin)

oecd_full_df <- oecd_fact_df %>%
  left_join(wef_top_country %>% select(-country), by="iso3c") %>% 
  left_join(digital_df, by = "iso3c") %>% 
  select(-wef_score) %>% 
  left_join(oxford_ranking_df %>% select(-country), by="iso3c") %>% 
  left_join(data_economy_df %>% select(-country),  by="iso3c") %>% 
  left_join(digital_gov %>% select(-country), by="iso3c") %>% 
  left_join(robot_df %>% select(-country, -density), by="iso3c")

oecd_full_df 
# A tibble: 36 x 21
   country flags iso3c   area population     gdp gdp_per_capita income_inequali~
   <chr>   <chr> <chr>  <dbl>      <dbl>   <dbl>          <dbl>            <dbl>
 1 Austra~ "\U0~ AUS   7.74e6   24598933 1.19e12          48460             34.7
 2 Austria "\U0~ AUT   8.39e4    8809212 4.62e11          52398             30.5
 3 Belgium "\U0~ BEL   3.05e4   11372068 5.44e11          47840             27.7
 4 Canada  "\U0~ CAN   9.98e6   36708083 1.71e12          46705             34  
 5 Chile   "\U0~ CHL   7.56e5   18054726 4.45e11          24635             47.7
 6 Colomb~ "\U0~ COL   1.14e6   48901066 7.09e11          14507             49.7
 7 Czech ~ "\U0~ CZE   7.89e4   10591323 3.85e11          36327             25.9
 8 Denmark "\U0~ DNK   4.29e4    5769603 2.96e11          51364             28.2
 9 Estonia "\U0~ EST   4.52e4    1315480 4.18e10          31742             32.7
10 Finland "\U0~ FIN   3.38e5    5511303 2.47e11          44866             27.1
# ... with 26 more rows, and 13 more variables: hdi <dbl>, fsi <dbl>,
#   cpi <dbl>, ief <dbl>, gpi <dbl>, wpfi <dbl>, di <dbl>, wef_rank <dbl>,
#   digital_rank <dbl>, ai_rank <dbl>, data_rank <int>, gov_rank <int>,
#   robot_rank <dbl>

작업한 데이터를 국가별로 정리하여 로컬 파일로 저장시킨다.

oecd_full_df %>% 
  write_rds("data/oecd_full_df.rds")

  1. stackoverflow, “Using the pdf_data function from the pdftools package efficiently”↩︎

  2. Bhaskar Chakravorti , Ajay Bhalla and Ravi Shankar Chaturvedi (January 24, 2019), “Which Countries Are Leading the Data Economy?” Harvard Business Review.↩︎

 

데이터 과학자 이광춘 저작

kwangchun.lee.7@gmail.com