-- Install spatial extension
INSTALL spatial; 
LOAD spatial; 

-- Represent a latitude and longitude as a point
-- The Eiffel Tower in Paris, France has a 
-- latitude of 48.858935 and longitude of 2.293412
-- We can represent this location as a point
SELECT st_point(48.858935, 2.293412) AS Eiffel_Tower;
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-- Distance between the Eiffel Tower and the Arc de Triomphe in Paris
-- Using the EPSG spatial reference systems: 
-- EPSG:4326 geographic coordinates as latitude and longitude pairs
-- EPSG:27563 projection that covers northern France and uses meters 

SELECT
    st_point(48.858935, 2.293412) AS Eiffel_Tower, 
    st_point(48.873407, 2.295471) AS Arc_de_Triomphe,
    st_distance(
        st_transform(Eiffel_Tower, 'EPSG:4326', 'EPSG:27563'), 
        st_transform(Arc_de_Triomphe, 'EPSG:4326', 'EPSG:27563')
    ) AS Aerial_Distance_M;
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-- Load the geometry outline for each country
-- Save the country name and "geom" border in table world_boundaries
CREATE OR REPLACE TABLE world_boundaries
AS
SELECT *
FROM st_read('https://public.opendatasoft.com/api/explore/v2.1/catalog/datasets/world-administrative-boundaries/exports/geojson');

-- Find the enclosing country for a given point
-- We can which country the Eiffel Tower is in 
SELECT name, region
FROM world_boundaries
WHERE ST_Within(st_point(2.293412, 48.858935) , geom);
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-- DuckDB can directly query a public REST API returning a JSON 
-- Query the "tvmaze.com" API for the TV show "South Park"

SELECT name, type, summary
FROM read_json('https://api.tvmaze.com/singlesearch/shows?q=South%20Park', 
auto_detect=true, format='newline_delimited');
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-- Format the response from a public REST API 
-- Retrieve a list of temperature readings for Sydney, Australia 
-- The returned payload has hourly temps for 24 hours
-- We can use JSONPath to extract data from a JSON documemnt 
-- to find the most recent temperature

SELECT json_extract(hourly, '$.temperature_2m[1]')
from read_json('https://api.open-meteo.com/v1/forecast?latitude=-33.8678&longitude=151.2073&hourly=temperature_2m&forecast_days=1', auto_detect=true, format='newline_delimited');
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ATTACH 'md:_share/sample_data/23b0d623-1361-421d-ae77-62d701d471e6' AS sample_data;
USE sample_data;
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SELECT
    city,
    year,
    CASE
        WHEN
            AVG(pm25_concentration) <= 10
            AND AVG(pm10_concentration) <= 20
            AND AVG(no2_concentration) <= 40
            THEN 'Good'
        WHEN
            AVG(pm25_concentration) > 10
            AND AVG(pm10_concentration) > 20
            AND AVG(no2_concentration) > 40
            THEN 'Poor'
        ELSE 'Moderate'
    END AS airqualityrating
FROM
    sample_data.who.ambient_air_quality
GROUP BY
    city,
    year
ORDER BY
    city,
    year;
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SELECT
    year,
    AVG(pm25_concentration) AS avg_pm25,
    AVG(pm10_concentration) AS avg_pm10,
    AVG(no2_concentration) AS avg_no2
FROM sample_data.who.ambient_air_quality 
WHERE city = 'Berlin'
GROUP BY year
ORDER BY year DESC;
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ATTACH 'md:_share/music/af2a4a73-d4ef-4c82-98ef-e49f60adccd2' AS music;
USE music;
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-- Switch to the attached music database
use music;

-- Top 25 songs at number 1, Billboard Hot 100
SELECT song, performer, 
  COUNT(*) AS weeks_at_no_1,
  chart_debut,
  MIN(chart_date) AS first_chart_date_as_no_1, 
  MAX(chart_date) AS last_chart_date_as_no_1
FROM billboard_hot_100
WHERE chart_position = 1
GROUP BY ALL
ORDER BY weeks_at_no_1 DESC, chart_debut
LIMIT 25;
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