Comments (3)
Function 2 takes in the scraped tibble scarping from Function 1 and returns a cleaned tibble object containing information like listing url, price, number of bedroom, area in sqft, and city and ready for filtering.
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Ideas/notes that are related to the cleaner function .
- convert the price column into integer data types
- extract the information about the number of bedroom of the housing and the area of the housing from the house_type column
- extract the information about which city are the housing located in from the listing_url column
- select the columns (listing url, price, number of bedroom, area in sqft, and city) which are useful for future filtering
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In designing the function, I tried to use databases of the cities like world.cities or canada.cities to verify they is actually city information in the city
column but in fact, some cities like "Burnaby" are missing in those datasets. Hence, I decided to use city list instead of those database.
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Related Issues (8)
- R integration
- R Documentation
- Licensing discussion HOT 6
- Software review HOT 4
- Function 1 : Web Scraper HOT 1
- Function 3 : Filter HOT 1
- Function 4 : Emailer HOT 2
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