issue_comments: 495085021
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/simonw/datasette/issues/485#issuecomment-495085021 | https://api.github.com/repos/simonw/datasette/issues/485 | 495085021 | MDEyOklzc3VlQ29tbWVudDQ5NTA4NTAyMQ== | 9599 | 2019-05-23T06:27:57Z | 2019-05-26T23:15:51Z | OWNER | I could attempt to calculate the statistics needed for this in a time limited SQL query something like this one: https://latest.datasette.io/fixtures?sql=select+%27name%27+as+column%2C+count+%28distinct+name%29+as+count_distinct%2C+avg%28length%28name%29%29+as+avg_length+from+roadside_attractions%0D%0A++union%0D%0Aselect+%27address%27+as+column%2C+count%28distinct+address%29+as+count_distinct%2C+avg%28length%28address%29%29+as+avg_length+from+roadside_attractions ``` select 'name' as column, count (distinct name) as count_distinct, avg(length(name)) as avg_length from roadside_attractions union select 'address' as column, count(distinct address) as count_distinct, avg(length(address)) as avg_length from roadside_attractions ``` | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | 447469253 |