text: 
  -- we want to control for the effect of bots in our studies
  -- this is because it can severely bias the number of shared repos
  -- (i.e. the edges in our collaboration networks) or the total
  -- number of commits in our cost estimations
  
  
  -- step 1: create a table with all the known bots
  -- this info is taken from acctype in bayoan's scrape
  -- and all of the logins with a "bot" at the end
  CREATE MATERIALIZED VIEW gh.bots_table AS (
  SELECT A.login
  FROM gh.desc_ctrs_summary A -- update to commits_dd
  LEFT JOIN gh_2007_2020.test_usr B
  ON A.login = B.login
  WHERE A.login SIMILAR TO '(%bot|%-bot)' OR A.login LIKE '%[bot]%' OR B.acctype = 'Bot');
  -- this gives us a table of 3,337 total bots
  
  
  -- step 2: create commits_dd_nbots as the deduped, debotted commits table
  CREATE MATERIALIZED VIEW gh.commits_dd_nbots AS (
  SELECT DISTINCT slug, committed_date, login, additions, deletions
  FROM gh.commits_dd
  WHERE login NOT IN (SELECT * FROM gh.bots_table));
  
  
  -- bonus code in case filtering bots out is needed
  -- creates a table with all the bots removed
  SELECT *
  FROM gh.____
  WHERE login NOT IN (SELECT * FROM gh.bots_table);
  
  -- filtering bots from edgelist
  SELECT * FROM gh.____
  WHERE ctr1 NOT IN (SELECT * FROM gh.bots_table)
    AND ctr2 NOT IN (SELECT * FROM gh.bots_table);
  