Trips And Users

                                            
/* Write your T-SQL query statement below */

WITH CountCancellation AS (
    SELECT request_at, 
           SUM(CASE WHEN status LIKE 'cancelled_%' THEN 1 ELSE 0 END) AS cancelled_requests,
           COUNT(*) AS total_requests
    FROM Trips t
    JOIN Users uc ON t.client_id = uc.users_id AND uc.banned = 'No'
    JOIN Users ud ON t.driver_id = ud.users_id AND ud.banned = 'No'
    WHERE request_at BETWEEN '2013-10-01' AND '2013-10-03'
    GROUP BY request_at
)
SELECT request_at AS Day, 
       ROUND(CAST(cancelled_requests AS FLOAT) / NULLIF(total_requests, 0), 2) AS 'Cancellation Rate'
FROM CountCancellation;

To solve this coding challenge, we need to calculate the cancellation rate of taxi trip requests by unbanned users on a daily basis between the specified dates "2013-10-01" and "2013-10-03". This challenge involves fetching and processing data from two tables:

Trips

and

Users

Explanation

Here’s a step-by-step breakdown of the solution:

Filtering Banned Users :

We need to filter out any trips made by banned users. This includes both clients and drivers. This means we join the
Trips

table with the
Users

table twice: once to filter out banned clients and the second time to filter out banned drivers.

Cancelling and Counting Trips :

For each day, we need to sum up the number of cancelled trips. Trips can be cancelled by either the client or the driver. We use a conditional statement to count trips with the status 'cancelled_by_driver' or 'cancelled_by_client'.
We also need to count the total number of trips made each day by unbanned users.

Calculating Cancellation Rate :

The cancellation rate is calculated by dividing the number of cancelled trips by the total number of trips. This value is rounded to two decimal points.

Output Format :

The result should be presented with columns "Day" (containing the date of the request) and "Cancellation Rate" (representing the rounded cancellation rate).

Now let's convert this outline into detailed pseudocode with comments explaining each step.

Detailed Steps in Pseudocode

Step 1: Join the Tables to Filter Out Banned Users

                                            
# We need both client and driver to be unbanned
join trips_table with users_table as client_table on trips_table.client_id = client_table.user_id 
join trips_table with users_table as driver_table on trips_table.driver_id = driver_table.user_id

# Only consider rows where both clients and drivers are not banned
filter where client_table.banned = 'No' and driver_table.banned = 'No'

Step 2: Filter the Date Range

                                            
# We are only interested in trips between '2013-10-01' and '2013-10-03'
filter where trips_table.request_at is between '2013-10-01' and '2013-10-03'

Step 3: Aggregate both Cancelled and Total Requests per Day

                                            
# Aggregate the data we need for each day
group by trips_table.request_at

# count the total number of trips per day
count the rows as total_requests

# count the number of cancelled trips per day
sum up rows where trips_table.status is either 'cancelled_by_driver' or 'cancelled_by_client' as cancelled_requests

Step 4: Calculate Cancellation Rate and Format Output

                                            
# Calculate the cancellation rate
for each date group:
    if total_requests > 0:
        cancellation_rate = cancelled_requests / total_requests
    else:
        cancellation_rate = 0

# Round cancellation rate to 2 decimal places
round cancellation_rate to 2 decimal places

# Format the date and rate into the output table
select trips_table.request_at as Day, cancellation_rate as "Cancellation Rate"
order by Day

Full Pseudocode with Comments

                                            
# Step 1: Join Trips and Users tables to filter out banned users and fetch relevant trip data
for each record in trips_table:
    # Only include clients who are unbanned
    if client_table.user_id = trips_table.client_id and client_table.banned = 'No':
        # Only include drivers who are unbanned
        if driver_table.user_id = trips_table.driver_id and driver_table.banned = 'No':
            
            # Step 2: Filter trips based on date range
            if trips_table.request_at between '2013-10-01' and '2013-10-03':

                # Step 3: Calculate total and cancelled trips for each date
                group records by trips_table.request_at:
                    # Count all valid trips for the date
                    total_requests += 1

                    # Count cancelled trips by the status
                    if trips_table.status = 'cancelled_by_driver' or trips_table.status = 'cancelled_by_client':
                        cancelled_requests += 1

                # Step 4: Calculate and format the cancellation rate
                if total_requests > 0:
                    cancellation_rate = cancelled_requests / total_requests
                else:
                    cancellation_rate = 0

                # Format the result
                add to result (trips_table.request_at, round(cancellation_rate, 2))

By following this detailed and structured approach, you should be able to accurately compute the cancellation rates for the specified dates, ensuring that only the trips involving unbanned users are considered. This provides a comprehensive solution to the given problem.