https://github.com/anthonydb/advanced-sql-nicar15

Code and data for the NICAR 2015 Advanced SQL with PostgreSQL hands-on class.
https://github.com/anthonydb/advanced-sql-nicar15

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Code and data for the NICAR 2015 Advanced SQL with PostgreSQL hands-on class.

• Host: GitHub
• URL: https://github.com/anthonydb/advanced-sql-nicar15
• Owner: anthonydb
• Created: 2015-02-11T23:15:52.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2015-03-06T20:59:00.000Z (over 9 years ago)
• Last Synced: 2023-03-13T06:25:25.023Z (over 1 year ago)
• Language: SQLPL
• Size: 1000 KB
• Stars: 13
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NICAR 2015: Advanced SQL with PostgreSQL

The code and data examples here were created for the 2015 National Institute for Computer Assisted Reporting conference held in Atlanta, Ga., and sponsored by [Investigative Reporters and Editors](www.ire.org).

## Getting started

* This hands-on class will use the [pgAdmin](www.pgadmin.org) client to edit and run queries on PostgreSQL. Depending on your operating system and how you installed PostgreSQL, pgAdmin may need to be installed separately.

* In pgAdmin, load the `create-tables.sql` script and run it. (You may need to change the location of the data files in the three `COPY` statements.) When finished, you'll have three tables loaded with data:

  * **counties**: 2010 Census PL94 data.

  * **meat_poultry_inspect**: USDA data on plants that produce meat and poultry.

  * **acs_2012_stats**: Derived data from the 2012 American Community Survey.

## Basic math

We'll start with math review before moving on.

* Load the `basic-math.sql` script and work through each example.

## Interview and clean data

This segment covers how to check your data for problems and how to clean it up safely.

* Load the `interview-create.sql` script and work through the examples.

## Creating ranks and rates

Journalists generally like to discover which X is (highest/most/smallest/least/etc.). Here's how to do that with SQL.

* Load the `ranks-rates.sql` script to work the examples.

## Statistical functions

PostgreSQL can't match the power of R, Pandas or even Excel for stats, but a few built-in functions can give you a quick read on your data.

* For examples, load the `stats-functions.sql` script.