U.S. Congress members and their voting records

Propublica Congress API

The ProPublica Congress API provides information about the U.S. Congress members and their voting records. In this example, we’ll fetch data about the current Senate members and calculate the number of members in each party.

R: In R, we’ll use the ‘httr’ and ‘jsonlite’ packages to fetch and process data from the ProPublica Congress API.

R code:

# load necessary libraries
library(httr)
library(jsonlite)

# Replace 'your_api_key' with your ProPublica API key

#

# Fetch data about the current Senate members
url <- "https://api.propublica.org/congress/v1/117/senate/members.json"
response <- GET(url, add_headers(`X-API-Key` = api_key))

# Check if the request was successful
if (http_status(response)$category == "Success") {
  data <- content(response, "parsed")
  members <- data$results[[1]]$members

  # Calculate the number of members in each party
  party_counts <- table(sapply(members, function(x) x$party))
  print(party_counts)
} else {
  print(http_status(response)$message)
}

 D  I ID  R 
49  1  2 51

Python: In Python, we’ll use the ‘requests’ library to fetch data from the ProPublica Congress API and ‘pandas’ library to process the data.

python code:

# Install necessary libraries

import requests
import pandas as pd

# Replace 'your_api_key' with your ProPublica API key
api_key = "your_api_key"
headers = {"X-API-Key": api_key}

# Fetch data about the current Senate members
url = "https://api.propublica.org/congress/v1/117/senate/members.json"
response = requests.get(url, headers=headers)

# Check if the request was successful
if response.status_code == 200:
    data = response.json()
    members = data["results"][0]["members"]

    # Calculate the number of members in each party
    party_counts = pd.DataFrame(members)["party"].value_counts()
    print(party_counts)
else:
    print(f"Error: {response.status_code}")

In conclusion, both R and Python offer efficient ways to fetch and process data from APIs like the ProPublica Congress API. The ‘httr’ and ‘jsonlite’ libraries in R provide a straightforward way to make HTTP requests and parse JSON data, while the ‘requests’ library in Python offers similar functionality. The ‘pandas’ library in Python can be used for data manipulation and analysis, and R provides built-in functions like table() for aggregating data. Depending on your preferred programming language and environment, both options can be effective for working with the ProPublica Congress API.

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Last update: 2024-05-09