R bindings to Retsly API
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README.md

Retsly

R bindings to get data from the Retsly API. More information about the Retsly API here: https://rets.ly/docs/platform.html

Retsly API

You must sign up for an account with Retsly at https://rets.ly . After you sign up you must request access to different MLS datasets and the public datasets.

Installation

Install with devtools. If you don't have devtools, install it using install.packages("devtools"). After you have devtools installed you can install the Retsly package using: devtools::install_github("ALShum/Retsly").

Basic functionality

Security Token:

Before you can use the Retsly API make sure you have your access token. You should be able to get an access token after registering a Retsly account. The easiest way to use the Retsly R API is to set your access token as an environment variable.

To create/modify an .Renviron file see here for instructions: http://astrostatistics.psu.edu/su07/R/html/base/html/Startup.html

Once you find your .Renviron file add the following line:

retsly_api_token = "INSERT YOUR ACCESS TOKEN HERE"

If you absolutely can't be bothered to find your .Renviron file you can set your environment variables every time you restart R:

Sys.setenv(retsly_api_token = "INSERT YOUR TOKEN HERE")

Alternatively you can just include your access token in all your function calls:

opts = retsly_query(access_token = "INSERT YOUR TOKEN HERE")

The above will create an options list and you will need to include this in all of your function calls. For example, to make a grab parcels data with the above manually specified access token:

parcels(opts = opts)

If you go this route, you will need to include your access token as part of the options list for every API function call.

Available Datasets:

list_datasets()

The above will list all of the datasets available through Retsly -- this is not necessarily the same set of datasets you will have access to. You must request access to the different datasets (besides the datasets listed as 'test').

MLS and Public

Datasets can be roughly split into 2 categories: MLS data and Public listings data. Public listings data includes information on parcels, assessments and transactions -- these functions should request data from the pub dataset. The MLS functions can pull data from different MLS providers -- these functions should request data from one of the available MLS datasets.

Parcels

To get public parcel data:

parcels()

Assessments

To get public assessment data:

assessments()

Transactions

To get public transactions data:

transactions()

Listings

To pull listings data from the test_sf dataset:

listings(dataset = "test_sf")

To pull a specific listing by a listing id from the test_sf dataset:

listings(dataset = "test_sf", list_id = "PUT LISTING ID HERE")

To pull the real estate office or agent associated with a specific listing id from the test_sf dataset:

listings_agent(dataset = "test_sf", list_id = "PUT LISTING ID HERE")
listings_office(dataset = "test_sf", list_id = "PUT LISTING ID HERE")

Agents

To pull data about real estate agents from the test dataset:

agents(dataset = "test")

To pull data about a specific real estate agent or to find all the listings associated with a specific agent from the test dataset:

agents(data = "test", agent_id = "PUT AGENT ID HERE")
agents_listings(data = "test", agent_id = "PUT AGENT ID HERE")

Offices

To pull data about real estate offices from the test_sd dataset:

offices(dataset = "test_sd")

To pull data about a specific real estate office, agents or listings from a specific office from the test_sd dataset:

offices(dataset = "test_sd", office_id = "PUT OFFICE ID HERE")
offices_agents(dataset = "test_sd", office_id = "PUT OFFICE ID HERE")
offices_listings(dataset = "test_sd", office_id = "PUT OFFICE ID HERE")

Media

To get a list of media from the test dataset:

media(dataset = "test")

Vendors

To get a list of approved vendors (MLS vendors and other datasets which you have access to):

vendors_aproved()

Advanced functionality

By default, when you make a request to the Retsly API it will return the first 100 rows of the dataset. To grab additional data you can also specify the ordering, number of rows returned and many other parameters.

Query parameters

Query parameters can be set using retsly_query. The following query parameters can be specified:

  • offset: Skips the first n listings. For example an offset of 100 means to skip the first 100 listings.
  • limit: Limits the size of the result set (maximum is 100 listings).
  • sortBy: Which column to sort query by.
  • order: Order responses by "asc" or "desc".
  • near: Find listings near a specific location. Either coordinates like "73.98,40.73" or a place name like "San Diego".
  • radius: Search radius in miles, km or degrees.

Here is an example of specifying some query parameters:

opts = retsly_query(
    offset = 100,
    limit = 100,
    order = "asc"
)

Any or all of the above query parameters can be specified this way.

Custom queries

The Retsly API also includes a much more advanced way to filter results from the various datasets using "custom queries". You can filter listings by things like number of bedrooms, number of bathrooms, the square footage and many other possible variables.

A full of the custom queries can be seen on the Retsly API explorer: https://rets.ly/docs/retsly/index.html#listings

The custom queries do not need to be a strict equality constraint; custom queries can be specified by the following constraints:

  • "greater than": >
  • "less than": <
  • "greater than or equal to": >=
  • "less than or equal to": <=
  • "not equal to" types of constraints: !=

For example, here is how to find listings from the test dataset with "greater than or equal to" 2 bedrooms:

opts = retsly_query(
    bedrooms = c(">=", 2)
)

listings(opts, dataset = "test")

Here is how to find the first 50 listings from the test dataset with bathrooms not equal to 3:

opts = retsly_query(
    limit = 50,
    baths = c("!=", 3)
)

listings(opts, dataset = "test")