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DataAnalysis.sql
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DataAnalysis.sql
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-- Number of trips per rider type
SELECT
member_casual,
count(*) as num_of_trips
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`
group by
member_casual
order by
member_casual
-- Number of trips per bike type per rider type
SELECT
member_casual,
rideable_type,
COUNT(*) AS num_of_trips
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`
GROUP BY
member_casual, rideable_type
ORDER BY
member_casual, num_of_trips;
-- Average ride length per rider type
Select member_casual,
FORMAT_TIMESTAMP
('%T',
TIMESTAMP_SECONDS(CAST(AVG(TIME_DIFF(ride_length, '00:00:00', SECOND)) AS
INT64)))
AS avg_ride_length
From
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`
Group By
member_casual
-- Average ride length per month per rider type
With combined as
(SELECT
*,
extract(month from started_at) as month
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`)
Select
member_casual,
month,
FORMAT_TIMESTAMP
('%T',
TIMESTAMP_SECONDS(CAST(AVG(TIME_DIFF(ride_length, '00:00:00', SECOND)) AS
INT64)))
AS avg_ride_length
From combined
group by
member_casual, month
order by
member_casual, month
-- Average ride length per day per rider type
Select
member_casual,
day_of_week,
FORMAT_TIMESTAMP
('%T',
TIMESTAMP_SECONDS(CAST(AVG(TIME_DIFF(ride_length, '00:00:00', SECOND)) AS
INT64)))
AS avg_ride_length
From cyclistic_2022_tripdata.combined_data
group by
member_casual, day_of_week
order by
member_casual, day_of_week
-- Average ride length per hour per rider type
With combined as
(SELECT
*,
extract(hour from started_at) as hour
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`)
Select
member_casual,
hour,
FORMAT_TIMESTAMP
('%T',
TIMESTAMP_SECONDS(CAST(AVG(TIME_DIFF(ride_length, '00:00:00', SECOND)) AS
INT64)))
AS avg_ride_length
From combined
group by
member_casual, hour
order by
member_casual, hour
-- Number of trips per rider type
SELECT
member_casual,
count(*) as num_of_trips
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`
group by
member_casual
order by
member_casual
-- Number of trips per month per rider type
With combined as
(SELECT
*,
extract(month from started_at) as month
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`)
Select
member_casual,
month,
count(*) as num_of_trips
FROM
combined
group by
member_casual, month
order by
member_casual, month
-- Number of trips per day per rider type
Select
member_casual,
day_of_week,
count(*) as num_of_trips
FROM
cyclistic_2022_tripdata.combined_data
group by
member_casual, day_of_week
order by
member_casual, day_of_week
--Number of trips per hour per rider type
With combined as
(SELECT
*,
extract(hour from started_at) as hour
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`)
Select
member_casual,
hour,
count(*) as num_of_trips
FROM
combined
group by
member_casual, hour
order by
member_casual, hour
--Most popular Start Stations per rider type
SELECT
member_casual,
start_station_name,
AVG(start_lat) as start_lat,
AVG(Start_lng) as start_lng,
count(*) as num_of_trips
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`
group by
member_casual, start_station_name
order by
member_casual, num_of_trips desc
-- Most popular end stations per rider type
SELECT
member_casual,
end_station_name,
AVG(end_lat) as end_lat,
AVG(end_lng) as end_lng,
count(*) as num_of_trips
FROM
`capstone-case-study-389823.cyclistic_2022_tripdata.combined_data`
group by
member_casual, end_station_name
order by
member_casual, num_of_trips desc