풍성하게, 그리고 dplyr

dplyr로 하는 프로그래밍

Dr. Chester Ismay

Educator, Data Scientist, and R/Python Consultant

수강 선행 조건

dplyr로 데이터 조인
R 함수 작성 입문

강의 개요

1장

dplyr 파이프라인 복습
패턴으로 열 선택

2장

열 순서 재배치
여러 열에 걸친 변환

3장

dplyr 조인 심화
여러 데이터 소스에 집합 연산을 적용해 프로그래밍 역량 향상

4장

dplyr와 ggplot2 코드를 함수로 감싸기
rlang으로 타이디 평가 이해

world_bank_data 티블

country	region	year	infant_mortality_rate	fertility_rate	perc_rural_pop
Saudi Arabia	Western Asia	2013	13.3	2.64	17.260
Greece	Southern Europe	2014	3.7	1.54	22.298
Latvia	Northern Europe	2014	7.2	1.62	32.048
Romania	Eastern Europe	2014	10.1	1.43	46.100
Netherlands	Western Europe	2015	3.2	1.78	9.827

world_bank_data의 열

names(world_bank_data)

 [1] "iso"                   "country"               "continent"            
 [4] "region"                "year"                  "infant_mortality_rate"
 [7] "fertility_rate"        "perc_electric_access"  "perc_college_complete"
[10] "perc_cvd_crd_70"       "unemployment_rate"     "perc_rural_pop"

world_bank_data에서 일부 열 선택

world_bank_data %>%
    select(country, continent, region, year, perc_rural_pop, perc_college_complete)

# A tibble: 300 x 6
   country      continent region           year perc_rural_pop perc_college_complete
   <chr>        <fct>     <fct>           <dbl>          <dbl>                 <dbl>
 1 Portugal     Europe    Southern Europe  2000          45.6                   7.26
 2 Armenia      Asia      Western Asia     2001          35.6                  20.4 
 3 Bulgaria     Europe    Eastern Europe   2001          30.8                  18.0 
 4 Portugal     Europe    Southern Europe  2001          45.0                   7.57
 5 Qatar        Asia      Western Asia     2004           2.91                 20.9 
 6 Saudi Arabia Asia      Western Asia     2004          19.2                  14.9 
 7 Pakistan     Asia      Southern Asia    2005          66.0                   3.92
# ... with 293 more rows

대륙 값으로 행 필터링

continents_vector <- c("Africa", "Asia")

asia_africa_results <- world_bank_data %>%
    select(country, continent, region, year, perc_rural_pop, perc_college_complete) %>%
    filter(continent %in% continents_vector)

행 필터 결과

asia_africa_results

# A tibble: 111 x 6
   country      continent region              year perc_rural_pop perc_college_complete
   <chr>        <fct>     <fct>              <dbl>          <dbl>                 <dbl>
 1 Armenia      Asia      Western Asia        2001          35.6                  20.4 
 2 Qatar        Asia      Western Asia        2004           2.91                 20.9 
 3 Saudi Arabia Asia      Western Asia        2004          19.2                  14.9 
 4 Pakistan     Asia      Southern Asia       2005          66.0                   3.92
 5 Nigeria      Africa    Western Africa      2006          60.1                   9.04
 6 Pakistan     Asia      Southern Asia       2006          65.8                   6.30
 7 Singapore    Asia      South-Eastern Asia  2006           0                    19.6 
 8 Azerbaijan   Asia      Western Asia        2007          47.2                  14.9 
 9 Qatar        Asia      Western Asia        2007           2.08                 25.1 
10 Singapore    Asia      South-Eastern Asia  2007           0                    20.1 
# ... with 101 more rows

새 열 추가(mutate)

asia_africa_results <- asia_africa_results %>%
    mutate(perc_urban_pop = 100 - perc_rural_pop)

mutate 결과

# A tibble: 111 x 7
   country      continent region              year perc_rural_pop perc_college_complete perc_urban_pop
   <chr>        <fct>     <fct>              <dbl>          <dbl>                 <dbl>          <dbl>
 1 Armenia      Asia      Western Asia        2001          35.6                  20.4            64.4
 2 Qatar        Asia      Western Asia        2004           2.91                 20.9            97.1
 3 Saudi Arabia Asia      Western Asia        2004          19.2                  14.9            80.8
 4 Pakistan     Asia      Southern Asia       2005          66.0                   3.92           34.0
 5 Nigeria      Africa    Western Africa      2006          60.1                   9.04           39.9
 6 Pakistan     Asia      Southern Asia       2006          65.8                   6.30           34.2
 7 Singapore    Asia      South-Eastern Asia  2006           0                    19.6           100  
 8 Azerbaijan   Asia      Western Asia        2007          47.2                  14.9            52.8
 9 Qatar        Asia      Western Asia        2007           2.08                 25.1            97.9
10 Singapore    Asia      South-Eastern Asia  2007           0                    20.1           100  
# ... with 101 more rows

지역별 도시 비율 분석

asia_africa_results %>%

    group_by(region) %>%

    summarize(
      mean_urban = mean(perc_urban_pop)
    )

# A tibble: 9 x 2
  region             mean_urban
  <fct>                   <dbl>
1 Central Asia             49.2
2 Eastern Africa           19.5
3 Eastern Asia             74.2
4 Middle Africa            42.4
5 South-Eastern Asia       79.8
6 Southern Africa          64.8
7 Southern Asia            40.0
8 Western Africa           39.6
9 Western Asia             78.9

연습해 봅시다!

dplyr로 하는 프로그래밍