US Congress

How to explore US political data

Let’s learn how to access, explore and visualize US political data via publicly available resources according to (jaytimm). # Basic infomation on lawmakers You can find basic information on lawmakers via (DATA and LAB).

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

# load the data
# 全URLを書いてもよいですが、長くなるので共通部分を取り出している。
git <- 'https://raw.githubusercontent.com/CivilServiceUSA/'
senate_lawmakers <- jsonlite::fromJSON(url(paste0(git, "us-senate/master/us-senate/data/us-senate.json")))
house_lawmakers <- jsonlite::fromJSON(url(paste0(git, 'us-house/master/us-house/data/us-house.json')))
# show the five lawwmakers
# selectで列を選んで、filterで行を選ぶ。
house_lawmakers %>%
  select(name, state_code, party, gender, ethnicity, twitter_handle) %>%
  filter(name %in% # name列の4人の名前を含む行を取り出す。
           c("Rashida Tlaib",
             "Ilhan Omar",
             "Ayanna Pressley",
             "Alexandria Ocasio-Cortez")
         )

##                       name state_code    party gender               ethnicity
## 1          Ayanna Pressley         MA democrat female          white-american
## 2            Rashida Tlaib         MI democrat female   multi-racial-american
## 3               Ilhan Omar         MN democrat female middle-eastern-american
## 4 Alexandria Ocasio-Cortez         NY democrat female   multi-racial-american
##   twitter_handle
## 1 housedemocrats
## 2 housedemocrats
## 3 housedemocrats
## 4 housedemocrats

Avarage, maximum and minimum ages of Congress

1.House

# age of the House
house_lawmakers %>%
  mutate(yearsHouse =
           lubridate::year(as.Date(Sys.Date())) -
           lubridate::year(as.Date(date_of_birth))) %>% 
  as_tibble() %>% # summarizeを使用するためにtibbleに変換する。
  summarize("平均" = mean(.$yearsHouse),
            "最高" = max(.$yearsHouse),
            "最低" = min(.$yearsHouse)
            )

## # A tibble: 1 x 3
##    平均  最高  最低
##   <dbl> <dbl> <dbl>
## 1  58.7    88    26

2.Senate

# age of the Senate
senate_lawmakers %>%
  mutate(yearsSenate =
           lubridate::year(as.Date(Sys.Date())) -
           lubridate::year(as.Date(date_of_birth))) %>%
  as_tibble() %>% # summarizeを使用するためにtibbleに変換する。
  summarize("平均" = mean(.$yearsSenate),
            "最高" = max(.$yearsSenate),
            "最低" = min(.$yearsSenate)
            )

## # A tibble: 1 x 3
##    平均  最高  最低
##   <dbl> <dbl> <dbl>
## 1  64.8    88    42

Histograms for ages

1.House

# histogram for the house
house_lawmakers %>%
  mutate(yearsHouse =
           lubridate::year(as.Date(Sys.Date())) -
           lubridate::year(as.Date(date_of_birth))) %>%
  ggplot (aes(yearsHouse)) +
  geom_histogram(bins=20, fill = 'steelblue', alpha = .85) +
  labs(title = 'The 117th House composition by age',
       caption = 'Data source: CivilServiceUSA')

2.Senate

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

DATA, MIT ELECTION, and SCIENCE LAB. “MIT ELECTION DATA and SCIENCE LAB.” https://electionlab.mit.edu/.

jaytimm. “American Political Data & r.” https://github.com/jaytimm/American-political-data-and-R#1-Lawmaker-details.