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select
    cohort,
    count(user_id) as cnt_users,
    sum(cnt_sundae_orders) as total_sundae_orders,
    sum(cnt_orders) as total_orders,
    sum(total_order_value) as total_order_value,
    sum(is_user_converted::int) as cnt_converted_users,
    total_sundae_orders / cnt_users as avg_sundae_orders,
    total_orders / cnt_users as avg_orders,
    total_order_value / total_orders as avg_order_value, 

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create table experiments.sundae_wizard_users as

-- Get all users exposed to the experiment
with exposed_users as (

    select
        experiment_name,
        user_id,
        feature_flag_evaluation,
        evaluated_at

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/* 
Suppose tb1 contains 31 rows:
  day (Jan 1 to Jan 31) 
  mins (mins generated that day)

And I want to know the percent distribution of mins for each day in January (i.e., (mins / total_Jan_mins) ). 
*/

-- Query 1 (CTE)

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library(prophet)

df   # A data frame with columns ds & y (datetimes & metrics)

# Create a prophet object
m <- prophet(df) 

# Extend dataframe 100 days into the future
future <- make_future_dataframe(m, periods = 100)