cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v
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Query the Data Delivery Network

Query the DDN

The easiest way to query any data on Splitgraph is via the "Data Delivery Network" (DDN). The DDN is a single endpoint that speaks the PostgreSQL wire protocol. Any Splitgraph user can connect to it at data.splitgraph.com:5432 and query any version of over 40,000 datasets that are hosted or proxied by Splitgraph.

For example, you can query the covid19_cases_tests_and_deaths_by_zip_code table in this repository, by referencing it like:

"cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest"."covid19_cases_tests_and_deaths_by_zip_code"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "case_rate_weekly", -- Case rate per 100,000 population in the week.
    "cases_weekly", -- Number of cases in the week. Values are removed for privacy until the cumulative total for the ZIP Code has reached 5 cases. A blank indicates a suppressed number from 0 to 4. 
    "week_end", -- The last date of the week.
    "week_start", -- The first date of the week.
    "zip_code", -- Home ZIP Code of the cases and people tested.
    "population", -- ZIP Code population.
    "death_rate_weekly", -- Death rate per 100,000 population in the week.
    "deaths_weekly", -- Number of deaths in the week.
    "tests_weekly", -- Number of tests in the week. Please note data limitations in the dataset description.
    "case_rate_cumulative", -- Total case rate per 100,000 population through the week.
    "test_rate_weekly", -- Test rate per 100,000 population in the week. Because, as described in the dataset description, not all tests are reported, this rate is a low-end estimate. The true rate may be higher. 
    "week_number", -- A sequential count of weeks, starting at the beginning of 2020. These numbers are aligned to CDC MMWR weeks.
    "zip_code_location", -- A point within the ZIP Code to allow for geographic analysis. The precise point shown has no other meaning.
    ":@computed_region_6mkv_f3dw",
    ":@computed_region_bdys_3d7i",
    ":@computed_region_43wa_7qmu",
    ":@computed_region_vrxf_vc4k",
    ":@computed_region_rpca_8um6",
    "row_id",
    "death_rate_cumulative", -- Death rate per 100,000 population through the week.
    "deaths_cumulative", -- Number of deaths through the week.
    "percent_tested_positive_cumulative", -- Percentage of tests returning positive results through the week based on specimen collection date. Because, as described in the dataset description, some not-positive tests may not be received, this rate is a high-end estimate. The true rate may be lower. Values are removed for privacy until the cumulative total for the ZIP Code has reached 5 cases.
    "percent_tested_positive_weekly", -- Percentage of tests returning positive results in the week based on specimen collection date. Because, as described in the dataset description, some not-positive tests may not be received, this rate is a high-end estimate. The true rate may be lower. Values are removed for privacy until the cumulative total for the ZIP Code has reached 5 cases.
    "test_rate_cumulative", -- Total test rate per 100,000 population through the week. Because, as described in the dataset description, not all tests are reported, this rate is a low-end estimate. The true rate may be higher. 
    "tests_cumulative", -- Total number of tests through the week.  Please note data limitations in the dataset description.
    "cases_cumulative" -- Total number of cases through the week. Values less than 5 are removed for privacy. A blank indicates a suppressed number from 0 to 4.
FROM
    "cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest"."covid19_cases_tests_and_deaths_by_zip_code"
LIMIT 100;

Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v with SQL in under 60 seconds.

Query Your Local Engine

Install Splitgraph Locally
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"
 

Read the installation docs.

Splitgraph Cloud is built around Splitgraph Core (GitHub), which includes a local Splitgraph Engine packaged as a Docker image. Splitgraph Cloud is basically a scaled-up version of that local Engine. When you query the Data Delivery Network or the REST API, we mount the relevant datasets in an Engine on our servers and execute your query on it.

It's possible to run this engine locally. You'll need a Mac, Windows or Linux system to install sgr, and a Docker installation to run the engine. You don't need to know how to actually use Docker; sgrcan manage the image, container and volume for you.

There are a few ways to ingest data into the local engine.

For external repositories, the Splitgraph Engine can "mount" upstream data sources by using sgr mount. This feature is built around Postgres Foreign Data Wrappers (FDW). You can write custom "mount handlers" for any upstream data source. For an example, we blogged about making a custom mount handler for HackerNews stories.

For hosted datasets (like this repository), where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.

Cloning Data

Because cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest is a Splitgraph Image, you can clone the data from Spltgraph Cloud to your local engine, where you can query it like any other Postgres database, using any of your existing tools.

First, install Splitgraph if you haven't already.

Clone the metadata with sgr clone

This will be quick, and does not download the actual data.

sgr clone cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v

Checkout the data

Once you've cloned the data, you need to "checkout" the tag that you want. For example, to checkout the latest tag:

sgr checkout cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest

This will download all the objects for the latest tag of cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v and load them into the Splitgraph Engine. Depending on your connection speed and the size of the data, you will need to wait for the checkout to complete. Once it's complete, you will be able to query the data like you would any other Postgres database.

Alternatively, use "layered checkout" to avoid downloading all the data

The data in cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest is 0 bytes. If this is too big to download all at once, or perhaps you only need to query a subset of it, you can use a layered checkout.:

sgr checkout --layered cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v:latest

This will not download all the data, but it will create a schema comprised of foreign tables, that you can query as you would any other data. Splitgraph will lazily download the required objects as you query the data. In some cases, this might be faster or more efficient than a regular checkout.

Read the layered querying documentation to learn about when and why you might want to use layered queries.

Query the data with your existing tools

Once you've loaded the data into your local Splitgraph Engine, you can query it with any of your existing tools. As far as they're concerned, cityofchicago/covid19-cases-tests-and-deaths-by-zip-code-yhhz-zm2v is just another Postgres schema.

Related Documentation:

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