Query the Data Delivery Network
Query the DDNThe 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 risky_prescribing_measures_quarter_3_2016_current table in this repository, by referencing it like:
"pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58:latest"."risky_prescribing_measures_quarter_3_2016_current"or in a full query, like:
SELECT
":id", -- Socrata column ID
":@computed_region_amqz_jbr4",
"time_period", -- Timeframe for calculated rate in the format of Year and Calendar Quarter (YYYY QQ)
"rate_or_count", -- Number of or calculated population rate for each county and PENNSYLVANIA for risky prescribing measures.
"gender", -- Indicates whether data represents both genders (All Genders) or separate values for Males and Females.
"class", -- Risky Prescribing Measures
"risky_measure_type", -- Identifies the Risky Prescribing Measures being reported on Number/Rate of Individuals Seeing 5+ Prescribers and 5+ Dispensers: Number of individuals, or rate per 10,000 population, who received prescriptions from 5 or more prescribers AND 5 or more dispensers for any Schedule II-V substance in a 3-month period. This measure is also referred to as Multiple Provider Episodes. County rates are calculated based on the location of the patient's residence. Number/Rate of Individuals Seeing 4+ Prescribers and 4+ Dispensers: Number of individuals, or rate per 10,000 population, who received prescriptions from 4 or more prescribers AND 4 or more dispensers for any Schedule II-V substance in a 3-month period. This measure is also referred to as Multiple Provider Episodes. County rates are calculated based on the location of the patient's residence. Number/Rate of Individuals Seeing 3+ Prescribers and 3+ Dispensers: Number of individuals, or rate per 10,000 population, who received prescriptions from 3 or more prescribers AND 3 or more dispensers for any Schedule II-V substance in a 3-month period. This measure is also referred to as Multiple Provider Episodes. County rates are calculated based on the location of the patient's residence. Number/Rate of Individuals with an Average Daily MME >50, >90 or >120: Average Daily MME is calculated as the sum of the total MME* on each day in a time period based on all prescriptions an individual has filled divided by the number of days in the prescription(s). The Interactive Data Report displays the number of individuals, or rate per 10,000 population, who are receiving high dose opioid prescription(s). Measures include the number and rate of individuals prescribed greater than 50 MME per day, greater than 90 MME per day, or greater than 120 MME per day and is based on the patient’s county of residence. Number/Rate of Individuals with Overlapping Opioid/Benzodiazepine Prescriptions per 10,000 Population: Number of individuals, or rate per 10,000 population, receiving overlapping opioid and benzodiazepine prescriptions during a given quarter. This measure is based on the patients’ county of residence. Number/Rate of Individuals with >30 days of Overlapping Opioid/Benzodiazepine Prescriptions per 10,000 Population: Number of individuals, or rate per 10,000 population, receiving overlapping opioid and benzodiazepine prescriptions during a given quarter. This measure is based on the patients’ county of residence. *Morphine Milligram Equivalents (MMEs): MME is a standardized way to calculate the strength of an opioid prescription. MME is calculated as (Quantity / Days Supply) * Strength per Unit * Conversion Factor. Opioids are the only pharmaceutical class possible to convert to MME units. This measure is also referred to as Morphine Equivalent Doses (MED). Buprenorphine is excluded from MME calculations. Please see CDC website for additional details: https://www.cdc.gov/drugoverdose/pdf/calculating_total_daily_dose-a.pdf. Blank field indicates data was suppressed due to count between 1 and 5.
"county_code_text", -- There are 67 counties in Pennsylvania. They are number 01 through 67 in alphabetical order; 00 identifies the statewides totals. This column has the codes formatted as text fields to integrate with other files where county codes are used in place of names and for easier coding within certain software
"notes", -- Provides information about data suppression.
"geocoded_column", -- This includes a generic georeferenced point of Latitude and Longitude for each county and a generic point for the state of PA to help with creating visuals such as maps. If the statewide total for PA should be shown on the map, this centroid provides the means to show the statewide total at the SE part of PA actually in Maryland, so that the statewide total can be shown on on the map without adding to the total within another county.
"state_fips_code", -- These are the first 2 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the State association. Each State has its own 2-digit number and each County within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county. For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.). Information gathered from census data - https://www.census.gov/library/reference/code-lists/ansi.html
":@computed_region_nmsq_hqvv",
":@computed_region_d3gw_znnf",
":@computed_region_r6rf_p9et",
":@computed_region_rayf_jjgk",
"county", -- Pennsylvania County name; includes PENNSYLVANIA for the statewide rates
"age_group", -- Indicates whether data represents total age population (All Ages) or specific Age Groups, including 0-14 years of age, 15-24 years of age, 25-34 years of age, 35-44 years of age, 45-54 years of age, 55-64 years of age or 65 and older (65+).
"time_measure", -- Identifies whether the Rate or Count Column displays the count of individuals or the rate of individuals per 10,000 Residents
"year", -- Calendar Year the data is based on
"quarter_date_start", -- Start date for the Quarter for which the data is being reported
"county_code_number", -- There are 67 counties in Pennsylvania. They are number 01 through 67 in alphabetical order; 00 identifies the statewide totals.
"county_fips_code", -- The FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code (FIPS 6-4) which uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. This is the 3-digit part of the 5-digit county FIPS code specifically standing for the county.
"latitude", -- This includes a generic point of Latitude for each county and a generic point for the state of PA to help with creating visuals such as maps. If the statewide total for PA should be shown on the map, this latitude provides the means to show the statewide total at the SE part of PA actually in Maryland, so that the statewide total can be shown on on the map without adding to the total within another county.
"longitude" -- This includes a generic point of Longitude for each county and a generic point for the state of PA to help with creating visuals such as maps. If the statewide total for PA should be shown on the map, this latitude provides the means to show the statewide total at the SE part of PA actually in Maryland, so that the statewide total can be shown on on the map without adding to the total within another county.
FROM
"pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58:latest"."risky_prescribing_measures_quarter_3_2016_current"
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 pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58 with SQL in under 60 seconds.
Query Your Local Engine
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 pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58: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 pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58Checkout 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 pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58:latestThis will download all the objects for the latest tag of pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58 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 pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58: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 pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58:latestThis 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, pa-gov/risky-prescribing-measures-quarter-3-2016-current-7m4f-4k58 is just another Postgres schema.