---------------------------------------------------------------------------
-- Practical SQL: A Beginner's Guide to Storytelling with Data, 2nd Edition
-- by Anthony DeBarros

-- Chapter 13 Code Examples
----------------------------------------------------------------------------

-- Listing 13-1: Using a subquery in a WHERE clause

SELECT county_name,
       state_name,
       pop_est_2019
FROM us_counties_pop_est_2019
WHERE pop_est_2019 >= (
    SELECT percentile_cont(.9) WITHIN GROUP (ORDER BY pop_est_2019)
    FROM us_counties_pop_est_2019
    )
ORDER BY pop_est_2019 DESC;

-- Listing 13-2: Using a subquery in a WHERE clause for DELETE

CREATE TABLE us_counties_2019_top10 AS
SELECT * FROM us_counties_pop_est_2019;

DELETE FROM us_counties_2019_top10
WHERE pop_est_2019 < (
    SELECT percentile_cont(.9) WITHIN GROUP (ORDER BY pop_est_2019)
    FROM us_counties_2019_top10
    );

SELECT count(*) FROM us_counties_2019_top10;

-- Listing 13-3: Subquery as a derived table in a FROM clause

SELECT round(calcs.average, 0) as average,
       calcs.median,
       round(calcs.average - calcs.median, 0) AS median_average_diff
FROM (
     SELECT avg(pop_est_2019) AS average,
            percentile_cont(.5)
                WITHIN GROUP (ORDER BY pop_est_2019)::numeric AS median
     FROM us_counties_pop_est_2019
     )
AS calcs;

-- Listing 13-4: Joining two derived tables

SELECT census.state_name AS st,
       census.pop_est_2018,
       est.establishment_count,
       round((est.establishment_count/census.pop_est_2018::numeric) * 1000, 1)
           AS estabs_per_thousand
FROM
    (
         SELECT st,
                sum(establishments) AS establishment_count
         FROM cbp_naics_72_establishments
         GROUP BY st
    )
    AS est
JOIN
    (
        SELECT state_name,
               sum(pop_est_2018) AS pop_est_2018
        FROM us_counties_pop_est_2019
        GROUP BY state_name
    )
    AS census
ON est.st = census.state_name
ORDER BY estabs_per_thousand DESC;

-- Listing 13-5: Adding a subquery to a column list

SELECT county_name,
       state_name AS st,
       pop_est_2019,
       (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY pop_est_2019)
        FROM us_counties_pop_est_2019) AS us_median
FROM us_counties_pop_est_2019;

-- Listing 13-6: Using a subquery in a calculation

SELECT county_name,
       state_name AS st,
       pop_est_2019,
       pop_est_2019 - (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY pop_est_2019)
                       FROM us_counties_pop_est_2019) AS diff_from_median
FROM us_counties_pop_est_2019
WHERE (pop_est_2019 - (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY pop_est_2019)
                       FROM us_counties_pop_est_2019))
       BETWEEN -1000 AND 1000;

-- Listing 13-7: Creating and filling a retirees table

CREATE TABLE retirees (
    id int,
    first_name text,
    last_name text
);

INSERT INTO retirees
VALUES (2, 'Janet', 'King'),
       (4, 'Michael', 'Taylor');


-- Listing 13-8: Generating values for the IN operator

SELECT first_name, last_name
FROM employees
WHERE emp_id IN (
    SELECT id
    FROM retirees);
    
-- Listing 13-9: Using a correlated subquery with WHERE EXISTS

SELECT first_name, last_name
FROM employees
WHERE EXISTS (
    SELECT id
    FROM retirees
    WHERE id = employees.emp_id);

-- Listing 13-10: Using a correlated subquery with WHERE NOT EXISTS

SELECT first_name, last_name
FROM employees
WHERE NOT EXISTS (
    SELECT id
    FROM retirees
    WHERE id = employees.emp_id);

-- Listing 13-11: Using LATERAL subqueries in the FROM clause

SELECT county_name,
       state_name,
       pop_est_2018,
       pop_est_2019,
       raw_chg,
       round(pct_chg * 100, 2) AS pct_chg
FROM us_counties_pop_est_2019,
     LATERAL (SELECT pop_est_2019 - pop_est_2018 AS raw_chg) rc,
     LATERAL (SELECT raw_chg / pop_est_2018::numeric AS pct_chg) pc
ORDER BY pct_chg DESC;

-- Listing 13-12: Using a subquery with a LATERAL join

ALTER TABLE teachers ADD CONSTRAINT id_key PRIMARY KEY (id);

CREATE TABLE teachers_lab_access (
    access_id bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
    access_time timestamp with time zone,
    lab_name text,
    teacher_id bigint REFERENCES teachers (id)
);

INSERT INTO teachers_lab_access (access_time, lab_name, teacher_id)
VALUES ('2022-11-30 08:59:00-05', 'Science A', 2),
       ('2022-12-01 08:58:00-05', 'Chemistry B', 2),
       ('2022-12-21 09:01:00-05', 'Chemistry A', 2),
       ('2022-12-02 11:01:00-05', 'Science B', 6),
       ('2022-12-07 10:02:00-05', 'Science A', 6),
       ('2022-12-17 16:00:00-05', 'Science B', 6);

SELECT t.first_name, t.last_name, a.access_time, a.lab_name
FROM teachers t
LEFT JOIN LATERAL (SELECT *
                   FROM teachers_lab_access
                   WHERE teacher_id = t.id
                   ORDER BY access_time DESC
                   LIMIT 2) a
ON true
ORDER BY t.id;

-- Common Table Expressions

-- Listing 13-13: Using a simple CTE to count large counties

WITH large_counties (county_name, state_name, pop_est_2019)
AS (
    SELECT county_name, state_name, pop_est_2019
    FROM us_counties_pop_est_2019
    WHERE pop_est_2019 >= 100000
   )
SELECT state_name, count(*)
FROM large_counties
GROUP BY state_name
ORDER BY count(*) DESC;

-- Bonus: You can also write this query as:
SELECT state_name, count(*)
FROM us_counties_pop_est_2019
WHERE pop_est_2019 >= 100000
GROUP BY state_name
ORDER BY count(*) DESC;

-- Listing 13-14: Using CTEs in a table join

WITH
    counties (st, pop_est_2018) AS
    (SELECT state_name, sum(pop_est_2018)
     FROM us_counties_pop_est_2019
     GROUP BY state_name),

    establishments (st, establishment_count) AS
    (SELECT st, sum(establishments) AS establishment_count
     FROM cbp_naics_72_establishments
     GROUP BY st)

SELECT counties.st,
       pop_est_2018,
       establishment_count,
       round((establishments.establishment_count / 
              counties.pop_est_2018::numeric(10,1)) * 1000, 1)
           AS estabs_per_thousand
FROM counties JOIN establishments
ON counties.st = establishments.st
ORDER BY estabs_per_thousand DESC;

-- Listing 13-15: Using CTEs to minimize redundant code

WITH us_median AS
    (SELECT percentile_cont(.5)
     WITHIN GROUP (ORDER BY pop_est_2019) AS us_median_pop
     FROM us_counties_pop_est_2019)

SELECT county_name,
       state_name AS st,
       pop_est_2019,
       us_median_pop,
       pop_est_2019 - us_median_pop AS diff_from_median
FROM us_counties_pop_est_2019 CROSS JOIN us_median
WHERE (pop_est_2019 - us_median_pop)
       BETWEEN -1000 AND 1000;


-- Cross tabulations
-- Install the crosstab() function via the tablefunc module

CREATE EXTENSION tablefunc;

-- Listing 13-16: Creating and filling the ice_cream_survey table

CREATE TABLE ice_cream_survey (
    response_id integer PRIMARY KEY,
    office text,
    flavor text
);

COPY ice_cream_survey
FROM 'C:\YourDirectory\ice_cream_survey.csv'
WITH (FORMAT CSV, HEADER);

-- view the data
SELECT * 
FROM ice_cream_survey
ORDER BY response_id
LIMIT 5;

-- Listing 13-17: Generating the ice cream survey crosstab

SELECT *
FROM crosstab('SELECT office,
                      flavor,
                      count(*)
               FROM ice_cream_survey
               GROUP BY office, flavor
               ORDER BY office',

              'SELECT flavor
               FROM ice_cream_survey
               GROUP BY flavor
               ORDER BY flavor')

AS (office text,
    chocolate bigint,
    strawberry bigint,
    vanilla bigint);

-- Listing 13-18: Creating and filling a temperature_readings table

CREATE TABLE temperature_readings (
    station_name text,
    observation_date date,
    max_temp integer,
    min_temp integer,
    CONSTRAINT temp_key PRIMARY KEY (station_name, observation_date)
);

COPY temperature_readings
FROM 'C:\YourDirectory\temperature_readings.csv'
WITH (FORMAT CSV, HEADER);

-- Listing 13-19: Generating the temperature readings crosstab

SELECT *
FROM crosstab('SELECT
                  station_name,
                  date_part(''month'', observation_date),
                  percentile_cont(.5)
                      WITHIN GROUP (ORDER BY max_temp)
               FROM temperature_readings
               GROUP BY station_name,
                        date_part(''month'', observation_date)
               ORDER BY station_name',

              'SELECT month
               FROM generate_series(1,12) month')

AS (station text,
    jan numeric(3,0),
    feb numeric(3,0),
    mar numeric(3,0),
    apr numeric(3,0),
    may numeric(3,0),
    jun numeric(3,0),
    jul numeric(3,0),
    aug numeric(3,0),
    sep numeric(3,0),
    oct numeric(3,0),
    nov numeric(3,0),
    dec numeric(3,0)
);

-- Listing 13-20: Re-classifying temperature data with CASE

SELECT max_temp,
       CASE WHEN max_temp >= 90 THEN 'Hot'
            WHEN max_temp >= 70 AND max_temp < 90 THEN 'Warm'
            WHEN max_temp >= 50 AND max_temp < 70 THEN 'Pleasant'
            WHEN max_temp >= 33 AND max_temp < 50 THEN 'Cold'
            WHEN max_temp >= 20 AND max_temp < 33 THEN 'Frigid'
            WHEN max_temp < 20 THEN 'Inhumane'
            ELSE 'No reading'
        END AS temperature_group
FROM temperature_readings
ORDER BY station_name, observation_date;

-- Listing 13-21: Using CASE in a Common Table Expression

WITH temps_collapsed (station_name, max_temperature_group) AS
    (SELECT station_name,
           CASE WHEN max_temp >= 90 THEN 'Hot'
                WHEN max_temp >= 70 AND max_temp < 90 THEN 'Warm'
                WHEN max_temp >= 50 AND max_temp < 70 THEN 'Pleasant'
                WHEN max_temp >= 33 AND max_temp < 50 THEN 'Cold'
                WHEN max_temp >= 20 AND max_temp < 33 THEN 'Frigid'
                WHEN max_temp < 20 THEN 'Inhumane'
                ELSE 'No reading'
            END
    FROM temperature_readings)

SELECT station_name, max_temperature_group, count(*)
FROM temps_collapsed
GROUP BY station_name, max_temperature_group
ORDER BY station_name, count(*) DESC;