list.do

koweps

한국복지패널 2017년 12차 머지 데이터(가구용, 가구원용, 장애인설문용 데이터 통합본)입니다.
데이터에 대한 설명은 아래 자료를 참고하세요.
- 코딩북 : https://www.koweps.re.kr:442/data/book/list.do
- 유저가이드 : https://www.koweps.re.kr:442/data/guide/list.do

# install.packages("remotes")
remotes::install_github("youngwoos/koweps")

library(koweps)
str(welfare, list.len = 20)
#> Classes 'tbl_df', 'tbl' and 'data.frame':    15422 obs. of  1193 variables:
#>  $ h12_id          : num  1 2 3 4 4 6 6 6 6 6 ...
#>  $ h12_ind         : num  1 1 1 1 1 1 1 1 1 1 ...
#>  $ h12_sn          : num  1 1 1 1 1 1 1 1 1 1 ...
#>  $ h12_merkey      : num  10101 20101 30101 40101 40101 ...
#>  $ h_new           : num  0 0 0 0 0 0 0 0 0 0 ...
#>  $ h12_cobf        : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ p12_wgl         : num  953 1239 1282 1066 1391 ...
#>  $ p12_wsl         : num  0.287 0.373 0.386 0.321 0.418 ...
#>  $ p12_wgc         : num  948 1239 1282 1066 1383 ...
#>  $ p12_wsc         : num  0.285 0.373 0.386 0.321 0.416 ...
#>  $ h12_reg5        : num  1 1 1 1 1 1 1 1 1 1 ...
#>  $ h12_reg7        : num  1 1 1 1 1 1 1 1 1 1 ...
#>  $ h12_cin         : num  1643 1304 978 4232 4232 ...
#>  $ h12_din         : num  1642 1172 953 4172 4172 ...
#>  $ h12_flag        : num  0 0 0 0 0 0 0 0 0 0 ...
#>  $ h12_hc          : num  1 2 2 1 1 1 1 1 1 1 ...
#>  $ nh1201_1        : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ nh1201_2        : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ h1201_1         : num  1 1 1 2 2 5 5 5 5 5 ...
#>  $ h12_pind        : num  1 1 1 1 4 1 1 1 1 1 ...
#>   [list output truncated]

library(dplyr)
welfare %>%
  select(1:10)
#> # A tibble: 15,422 x 10
#>    h12_id h12_ind h12_sn h12_merkey h_new h12_cobf p12_wgl p12_wsl p12_wgc
#>     <dbl>   <dbl>  <dbl>      <dbl> <dbl>    <dbl>   <dbl>   <dbl>   <dbl>
#>  1      1       1      1      10101     0       NA    953.   0.287    948.
#>  2      2       1      1      20101     0       NA   1239.   0.373   1239.
#>  3      3       1      1      30101     0       NA   1282.   0.386   1282.
#>  4      4       1      1      40101     0       NA   1066.   0.321   1066.
#>  5      4       1      1      40101     0       NA   1391.   0.418   1383.
#>  6      6       1      1      60101     0       NA   2348.   0.706   2322.
#>  7      6       1      1      60101     0       NA   2726.   0.820   2702.
#>  8      6       1      1      60101     0       NA   1761.   0.530   1714.
#>  9      6       1      1      60101     0       NA    910.   0.274    908.
#> 10      6       1      1      60101     0       NA   1025.   0.308   1019.
#> # ... with 15,412 more rows, and 1 more variable: p12_wsc <dbl>

# 연령 및 성별 인구 밀도
welfare <- welfare %>%
  mutate(sex = ifelse(h12_g3 == 1, "Male", "Female"),
         age = 2017 - h12_g4)

library(ggplot2)
ggplot(welfare, aes(x = age, fill = sex)) + 
  geom_density(alpha = 0.3)