RamaharTime distribution
Updated 2023-01-25
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-- Only consider those developers who have done multiple pull requests. Taking the average frequency of each developers to measure for the distribution.
With first_t as (select
id,
updatedat,
author,
row_number () over (partition by author order by updatedat asc) as row_number
from near.beta.github_activity
where updatedat::date >= '2019-01-01'),
second_t as (select
id,
updatedat,
author,
row_number () over (partition by author order by updatedat asc) as row_number
from near.beta.github_activity
where updatedat::date >= '2019-01-01'),
time_diff as (select
a.author,
timediff (day,a.updatedat,b.updatedat) as day_difference
from first_t a join second_t b on a.author = b.author and b.row_number = a.row_number + 1
),
filtered_freq as (select
author,
avg(day_difference) as avg_freq,
median(day_difference) as median_freq
from time_diff
group by 1)
select
CASE
when avg_freq = '0' then 'Within a day'
when avg_freq between 1 and 7 then 'Within A week'
when avg_freq between 7 and 14 then 'Within 2 weeks'
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