168 lines
4.5 KiB
SQL
168 lines
4.5 KiB
SQL
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-- Practical SQL: A Beginner's Guide to Storytelling with Data
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-- by Anthony DeBarros
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-- Chapter 11 Code Examples
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--------------------------------------------------------------
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-- Listing 11-1: Create Census 2011-2015 ACS 5-Year stats table and import data
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CREATE TABLE acs_2011_2015_stats (
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geoid varchar(14) CONSTRAINT geoid_key PRIMARY KEY,
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county varchar(50) NOT NULL,
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st varchar(20) NOT NULL,
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pct_travel_60_min numeric(5,3) NOT NULL,
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pct_bachelors_higher numeric(5,3) NOT NULL,
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pct_masters_higher numeric(5,3) NOT NULL,
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median_hh_income integer,
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CHECK (pct_masters_higher <= pct_bachelors_higher)
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);
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COPY acs_2011_2015_stats
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FROM 'C:\YourDirectory\acs_2011_2015_stats.csv'
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WITH (FORMAT CSV, HEADER, DELIMITER ',');
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SELECT * FROM acs_2011_2015_stats;
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-- Listing 11-2: Using corr(Y, X) to measure the relationship between
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-- education and income
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SELECT corr(median_hh_income, pct_bachelors_higher)
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AS bachelors_income_r
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FROM acs_2011_2015_stats;
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-- Listing 11-3: Using corr(Y, X) on additional variables
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SELECT
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round(
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corr(median_hh_income, pct_bachelors_higher)::numeric, 2
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) AS bachelors_income_r,
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round(
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corr(pct_travel_60_min, median_hh_income)::numeric, 2
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) AS income_travel_r,
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round(
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corr(pct_travel_60_min, pct_bachelors_higher)::numeric, 2
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) AS bachelors_travel_r
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FROM acs_2011_2015_stats;
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-- Listing 11-4: Regression slope and intercept functions
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SELECT
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round(
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regr_slope(median_hh_income, pct_bachelors_higher)::numeric, 2
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) AS slope,
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round(
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regr_intercept(median_hh_income, pct_bachelors_higher)::numeric, 2
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) AS y_intercept
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FROM acs_2011_2015_stats;
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-- Listing 11-5: Calculating the coefficient of determination, or r-squared
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SELECT round(
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regr_r2(median_hh_income, pct_bachelors_higher)::numeric, 3
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) AS r_squared
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FROM acs_2011_2015_stats;
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-- Bonus: Additional stats functions.
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-- Variance
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SELECT var_pop(median_hh_income)
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FROM acs_2011_2015_stats;
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-- Standard deviation of the entire population
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SELECT stddev_pop(median_hh_income)
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FROM acs_2011_2015_stats;
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-- Covariance
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SELECT covar_pop(median_hh_income, pct_bachelors_higher)
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FROM acs_2011_2015_stats;
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-- Listing 11-6: The rank() and dense_rank() window functions
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CREATE TABLE widget_companies (
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id bigserial,
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company varchar(30) NOT NULL,
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widget_output integer NOT NULL
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);
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INSERT INTO widget_companies (company, widget_output)
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VALUES
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('Morse Widgets', 125000),
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('Springfield Widget Masters', 143000),
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('Best Widgets', 196000),
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('Acme Inc.', 133000),
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('District Widget Inc.', 201000),
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('Clarke Amalgamated', 620000),
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('Stavesacre Industries', 244000),
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('Bowers Widget Emporium', 201000);
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SELECT
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company,
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widget_output,
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rank() OVER (ORDER BY widget_output DESC),
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dense_rank() OVER (ORDER BY widget_output DESC)
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FROM widget_companies;
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-- Listing 11-7: Applying rank() within groups using PARTITION BY
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CREATE TABLE store_sales (
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store varchar(30),
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category varchar(30) NOT NULL,
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unit_sales bigint NOT NULL,
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CONSTRAINT store_category_key PRIMARY KEY (store, category)
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);
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INSERT INTO store_sales (store, category, unit_sales)
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VALUES
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('Broders', 'Cereal', 1104),
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('Wallace', 'Ice Cream', 1863),
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('Broders', 'Ice Cream', 2517),
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('Cramers', 'Ice Cream', 2112),
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('Broders', 'Beer', 641),
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('Cramers', 'Cereal', 1003),
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('Cramers', 'Beer', 640),
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('Wallace', 'Cereal', 980),
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('Wallace', 'Beer', 988);
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SELECT
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category,
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store,
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unit_sales,
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rank() OVER (PARTITION BY category ORDER BY unit_sales DESC)
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FROM store_sales;
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-- Listing 11-8: Create and fill a 2015 FBI crime data table
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CREATE TABLE fbi_crime_data_2015 (
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st varchar(20),
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city varchar(50),
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population integer,
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violent_crime integer,
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property_crime integer,
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burglary integer,
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larceny_theft integer,
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motor_vehicle_theft integer,
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CONSTRAINT st_city_key PRIMARY KEY (st, city)
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);
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COPY fbi_crime_data_2015
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FROM 'C:\YourDirectory\fbi_crime_data_2015.csv'
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WITH (FORMAT CSV, HEADER, DELIMITER ',');
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SELECT * FROM fbi_crime_data_2015
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ORDER BY population DESC;
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-- Listing 11-9: Find property crime rates per thousand in cities with 500,000
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-- or more people
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SELECT
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city,
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st,
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population,
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property_crime,
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round(
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(property_crime::numeric / population) * 1000, 1
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) AS pc_per_1000
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FROM fbi_crime_data_2015
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WHERE population >= 500000
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ORDER BY (property_crime::numeric / population) DESC;
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