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Joining

It is frequently the case that the data we need is spread across multiple tables in a database. For example, along with the trip_data table you have already seen with data on each trip, there might be a table storing additional information the locations of the start and end stations, called bikeshare_stations.

Field Name	Description
station_id	Unique identifier of the station
name	Public name of the station
status	Status of the station (either ‘open’ or ‘closed’)
latitude	latitude of the station
longitude	longitude of the station

SELECT * FROM bikeshare_stations LIMIT 10

In Sheets, when you need to join data across multiple spreadsheets, you use VLOOKUP. In SQL, you use the JOIN keyword. JOIN is used to join multiple tables on a common column. You specify the common column on which you wish to join using the ON keyword.

Suppose you want to find the number of bikeshare trips that originated at the Van Ness Metro / UDC station. You can join the start_station field from the trip_data table to the station_id field in the bikeshare_stations table.

SELECT COUNT(*) AS n_trips FROM trip_data AS trips INNER JOIN bikeshare_stations AS stations ON trips.start_station = stations.station_id WHERE stations.name = 'Van Ness Metro / UDC'

There is a lot of new material to unpack in this query.

• Both tables are aliased using the AS keyword. (The trip_data table is named trips and the bikeshare_stations table is named stations.) Whenever you use JOIN, it is good practice to alias all tables used in the join. That way, columns can always be according to which table they appear in.

• An INNER JOIN returns only rows for which the join key appears in both tables. For example, if a row in the trips table has station 12345 as the start_station but it does not appear as a station_id in the stations table, this row will not be returned. The converse is also true. You can read more about other types of joins here.

• The ON clause uses the table names to reference the columns. SQL looks for rows in the stations table where the station_id matches each of the start_station values in the trips table.

• The WHERE clause limits the query to count rows where the start station is Van Ness Metro / UDC. Note that the table alias trips is used here again to reference the column.

Q-3: What question is the following query answering?

SELECT
  trips.start_station,
  AVG(duration) AS mean_duration
FROM
  trips_data AS trips
INNER JOIN
  bikeshare_stations AS stations
ON
  trips.start_station = stations.station_id
WHERE
  stations.status = 'closed'

How many trips were started at Adams Mill & Columbia Rd NW?

==== 0,0 == 9883

Write a query to display the mean duration of trip for each start station name. For example, one row could read as White House Station | 12345. sort the result by the average duration in ascending order.

==== assert 0,0 == 15th St & Massachusetts Ave SE assert 0,1 == 446.11764705882354

What is the name of the station has the most trips by riders with member type as casual?

==== assert 0,0 == USDA / 12th & Independence Ave SW assert 0,1 == 3609

Write a query to compare, in terms of number of trips and mean duration, stations that are listed as open and closed.

==== assert 0,0 == closed assert 1,0 == open assert 0,1 == 13440 assert 1,1 == 395482

Extension: Subqueries

It is sometimes the case that you can’t accomplish everything you want to within one query. In these cases, in the same way that you can wrap one Sheets function within another, you can wrap one query within another. The inner query is often referred to as a subquery.

One use case for a subquery is within the WHERE clause. This can be used instead of joining, and can often be more efficient. For example, the following query calculates the mean duration of trips that start at an open station.

SELECT COUNT(*) AS n_trips FROM trip_data WHERE start_station IN ( SELECT station_id FROM bikeshare_stations WHERE status = 'open' )

This could equivalently be done by joining the trips table to the stations table on trips.start_station = stations.station_id, then filtering on WHERE stations.status = 'open'. However, this involves joining every row of both tables. While using the subquery means having two distinct SELECT steps, each step involves less data, since each WHERE clause filters each table down to much fewer rows.

Another common use case for subqueries involves aggregating functions. For example, suppose you want to calculate the percentage of trips that start at each station. While you can calculate the number of trips that start at each start station in a single query, and the the total number of trips in a single query, it is complex to calculate both in a single SELECT statement. However, a subquery can be used to simply and logically accomplish this.

SELECT start_station, 100.0 * n_trips / (SELECT COUNT(*) FROM trip_data) AS percentage_trips FROM ( SELECT start_station, COUNT(*) AS n_trips FROM trip_data GROUP BY start_station )

The above query contains three SELECT statements. The “inner query” counts the number of trips for each start station. There is also a one-line subquery that sums the total number of trips. The “outer query” combines the two subqueries to calculate the percentage of total trips that start at each start station.

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