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R House Hunter

R-CMD-check test-coverage codecov

R package for searching for housing on Craigslist.

  • Author: Ela Bandari, Junting He, Ling (Elina) Lin, Alex Truong

Overview

Hunting for rentals can be an exhausting and frustrating experience in Canada, but this process can be made easy with a simple installation of our package. This R package intends to facilitate the house hunting process by scraping the listing information from Craigslist and organizing the extracted data for the user. Instead of having to manually go on the website to catch up with individual new listings, the user will be updated through email with new results as per their selection criteria.

Functions

Function Name Input Output Description
scraper url tibble Scrape data from rental websites into a csv file.
data_cleaner tibble tibble Clean the extracted data
data_filter tibble, min_price, max_price, sqrt_ft, num_bathroom, num_bedroom, neighbourhood tibble Filter the cleaned data set based on user inputs
send_email tibble, email address None Send the organized listing information to user email

Our Package in the R Ecosystem

To the best of our knowledge, there is currently no existing R package that simplifies the entire rental searching process with such a comprehensive functionality. This package takes care of all the steps including scraping rental websites, processing the data, and emailing users with the updated listing information. Plenty of general scraper packages exist in the R ecosystem, but they lack the focus on house rental in Canada and emailing functionality, such as the following two: https://github.com/adletaw/craigr and https://github.com/jayfeng1/Craigslist-Pricing-Project.

Installation

Please install package devtools before installing rhousehunter as follows

install.packages("devtools")
devtools::install_github("UBC-MDS/Rhousehunter")

Features

The pyhousehunter package contains the following four functions:

  • scraper() The scraper function will scrape all listings available on a given craigslist housing url (e.g. https://vancouver.craigslist.org/d/apartments-housing-for-rent/search/apa).
  • data_cleaner() The data_cleaner function will transform raw data from scraper() to a cleaned tibble object containing the scraped information.
  • data_filter() The data_filter function will filter the cleaned tibble based on the user's specifications. Users can specify their price range, minimum size, number of bedrooms, bathrooms, and desired municipality.
  • send_email() The send_email function sends end-users housing listing records meeting their specifications in a .csv format. The user must specify a valid email address and has the option to change the email subject.

Dependencies

  • polite
  • rvest
  • tibble
  • xml2
  • testthat
  • dplyr
  • stringr
  • httr
  • readr
  • rlang
  • emayili
  • qpdf

Usage

Please refer to the package vignette for more details

Documentation

The official documentation is hosted here

Contributors

The names and GitHub handles of core development team is listed below.

Name Github Handle
Alex Truong Hai Yen athy9193
Ela Bandari elabandari
Elina Lin elina-linglin
Junting He juntinghe

You can see a list of current contributors in the contributors tab.

We welcome and recognize all contributions. You can see how to contribute here

Credits

This package was created with reference from devtools package, R package guide by Hadley Wickham and Jenny Bryan and polite package by Dmytro Perepolkin