Closed liu431 closed 5 years ago
Thanks for this @liu431 ! I wrote this up in R— find it here if you're interested.
@smiklin Great! How does this work for your numbers? When I just conditioned on "valid"==true, only 15 are filtered out. Then I looked at these JSON lists again and added more conditions that carriers, locations, line_type are not empty. Then 140 of them are invalid.
Only a few got three that were "valid" == FALSE, and those are the three that have empty 'line_type'. If carrier/location are empty though, does that mean an invalid number, or is it simply unknown/missing data?
From reading the JSON Formatting part of the documentation, I think the ones with missing information are likely to be fake (without identity authentication). I tried dialing some and none was successful.
Oh, that is useful! I might have to get a new key and re-run it. I decided not to call over the weekend so I'll see tomorrow!
Edit: I just ran it again, making sure to pull the carrier and the location info. Here is a representative snapshot:
It looks like the carrier is empty for all landlines, so you might've ended up with a list of mobile phones only? I'll see tomorrow
Hah, turns out the majority of numbers are not in service, and that is not something registered by numverify... Still it was good practice writing the code :-)
I notice there are some websites where you can validate the phone numbers. This could be useful for filtering out invalid and out-of-service numbers. I used one called "numverify". When you type in a number, it will provide a JSON file containing the carrier, validate or not, location, etc. In order to validate 200 numbers, I wrote a simple function to scrape the JSON files and added a label to the numbers. Check out this if you have not called yet. Cheers!