Showing posts from June, 2020

PostgreSQL Date table

Using recursive in SQL, I can create a sequence that make up a date table. Basic recursive With   recursive  a  as   ( Select   1   as  Num union Select  Num +  1 From  a Where  Num +  1  <  11 ) Select  *  from  a Postgres SQL bucket date to 5 mins interval WITH   recursive  dimdatecreator( date )  AS   (          SELECT  (to_timestamp( '2020-01-01' , 'YYYY-MM-DD HH24:MI:SS' ))  AS   date   -- Start Date          UNION ALL           SELECT   date  + interval  '00:05'           FROM    dimdatecreator          WHERE    date  <= (to_timestamp( '2020-01-31' , 'YYYY-MM-DD HH24:MI:SS' ))  -- End Date  )  SELECT   date ,         date_part( 'hour' , date )+ 1         AS  hr,         (date_part( 'minute' , date )/ 5 )+ 1   AS  bucket  FROM    dimdatecreator;

PTO Tracker

I built this project using R. The project came from my friend need of tracking PTO so he can plan for vacation. PTO Calculator Shinny App   Project repo:

Data Join and Blend in Tableau

Joining and Blending Data in Tableau There are 2 main ways to combine data: Joins Combine tables by adding more columns of data across similar row structures. This can cause data loss or duplication if tables are at different levels of detail Blends Unlike joins, never truly combine the data   Blends query each data source independently, the results are aggregated to the appropriate level, then the results are presented visually together in the view.   Because of this, blends can handle different levels of detail and working with published data sources. Blends are also established individually on every sheet and can never be published, because there is no true “blended data source”, simply blended results from multiple data sources in a visualization. Differences between joins and data blending Data blending simulates a traditional left join. (The Primary Data  left join  Secondary Data) The main difference between the two is when the aggregation is perfor