Skip to contents

5-Minute Quickstart

Source: vignettes/quickstart.Rmd
quickstart.Rmd

Install and load 

# install.packages("remotes")
remotes::install_github("kyusik-yang/assemblykor")
library(assemblykor)
#> 
#>   ┌───────────────────────────────────────────────────────────────┐
#>   │ assemblykor 0.1.1                                             │
#>   │ Korean National Assembly Data for Political Science Education │
#>   └───────────────────────────────────────────────────────────────┘
#> 
#>   7 built-in datasets:
#>     legislators    947 recs   MPs (20-22nd)
#>     bills       60,925 recs   Bills proposed
#>     wealth       2,928 recs   Asset declarations
#>     seminars     5,962 recs   Policy seminars
#>     speeches    15,843 recs   Committee speeches (22nd, Sci & ICT)
#>     votes        7,997 recs   Plenary vote tallies
#>     roll_calls 368,210 recs   Member-level votes (22nd)
#> 
#>   Downloadable:
#>     get_bill_texts()           Bill propose-reason texts
#>     get_proposers()            Co-sponsorship records
#> 
#>   Tutorials:
#>     list_tutorials()           See all 9 tutorials
#>     run_tutorial(1)            Launch in browser (interactive)
#>     open_tutorial(1)           Copy Rmd to working directory
#> 
#>   Korean font for ggplot2:     set_ko_font()
#> 
#>   https://github.com/kyusik-yang/assemblykor

Seven datasets, one line each 

# All datasets load lazily - just call their name
head(legislators, 3)
#>   member_id assembly   name name_hanja      name_eng        party party_elected
#> 1  XQ98168F       20 강길부     姜吉夫  KANG GHILBOO       무소속        무소속
#> 2  60490713       20 강병원     姜炳遠 KANG BYUNGWON 더불어민주당  더불어민주당
#> 3  IH436704       20 강석진     姜錫振  KANG SEOGJIN   자유한국당      새누리당
#>                        district district_type
#> 1                   울산 울주군  constituency
#> 2                 서울 은평구을  constituency
#> 3 경남 산청군함양군거창군합천군  constituency
#>                                                                                            committees
#> 1 산업통상자원중소벤처기업위원회, 4차 산업혁명 특별위원회, 예산결산특별위원회, 교육문화체육관광위원회
#> 2                                                                                                    
#> 3                      농림축산식품해양수산위원회, 국회운영위원회, 보건복지위원회, 예산결산특별위원회
#>   gender birth_date seniority n_bills n_bills_lead
#> 1      M 1942-06-05         4     312           21
#> 2      M 1971-07-09         2    1078           94
#> 3      M 1959-12-07         1     694           53
head(bills, 3)
#>                              bill_id bill_no assembly
#> 1 PRC_K2F0C0Y5B2D2C1Z5B2F6T4S2K9N8V8 2024996       20
#> 2 PRC_R2A0X0T5I2I2R1I4B5A6P3G2S7W8P0 2024995       20
#> 3 PRC_Z2T0H0A5G1H8J1M0Q4W4S0F4E6G6Y4 2024933       20
#>                                            bill_name      committee
#> 1 집합건물의 소유 및 관리에 관한 법률 일부개정법률안 법제사법위원회
#> 2                            지방세법 일부개정법률안 행정안전위원회
#> 3                                       상장회사법안     정무위원회
#>   propose_date       result proposer proposer_id
#> 1   2020-05-22 임기만료폐기   김병관    QB390802
#> 2   2020-05-22 임기만료폐기   김병관    QB390802
#> 3   2020-05-18 임기만료폐기   채이배    GDN7894V
head(wealth, 3)
#>   member_id year   name total_assets total_debt net_worth real_estate building
#> 1  0135473I 2015 이군현      1856381     304644   1551737     1373000  1373000
#> 2  0135473I 2016 이군현      1807027     330719   1476308     1514998  1514998
#> 3  0135473I 2017 이군현      1855777     321020   1534757     1597000  1597000
#>   land deposits stocks n_properties has_seoul_property has_gangnam_property
#> 1    0   160576  64074            5               TRUE                 TRUE
#> 2    0   141139  24233            5               TRUE                 TRUE
#> 3    0   154218  41873            5               TRUE                 TRUE

Quick analysis: gender and legislative productivity 

library(dplyr)

legislators %>%
  filter(assembly == 22) %>%
  group_by(gender) %>%
  summarise(
    n = n(),
    median_bills_led = median(n_bills_lead, na.rm = TRUE)
  )
#> # A tibble: 2 × 3
#>   gender     n median_bills_led
#>   <chr>  <int>            <dbl>
#> 1 F         64               54
#> 2 M        241               44

Quick analysis: bill survival rates 

bills %>%
  count(result, sort = TRUE) %>%
  head(5)
#>         result     n
#> 1 임기만료폐기 30678
#> 2 대안반영폐기 13692
#> 3         <NA> 12171
#> 4     수정가결  2222
#> 5     원안가결  1048

Only about 5% of bills pass in their original form.

Quick analysis: wealth inequality 

# Net worth in billion KRW
hist(wealth$net_worth / 1e6, breaks = 50, col = "steelblue",
     main = "Legislator Net Worth", xlab = "Billion KRW")

Quick analysis: voting consensus 

votes$yes_rate <- votes$yes / votes$voted
hist(votes$yes_rate, breaks = 40, col = "coral",
     main = "Distribution of Yes-Vote Share", xlab = "Proportion yes")

Most bills pass near-unanimously; a handful are fiercely contested.

Joining datasets

All datasets share member_id for easy merging:

# Do wealthier legislators propose more bills?
leg_wealth <- legislators %>%
  filter(assembly == 22) %>%
  inner_join(wealth %>% filter(year == 2024),
             by = "member_id")

cor(leg_wealth$n_bills_lead, leg_wealth$net_worth,
    use = "complete.obs")
#> [1] -0.05360641

Tutorials

Nine Korean-language tutorials cover the full methods sequence:

list_tutorials()           # See all 9
run_tutorial(1)            # Interactive in browser
open_tutorial(1)           # Copy Rmd to your directory

Next steps