CountIf in BeastMode or ETL ?
Good Morning Community
I have a 5M+ row dataset where I need to develop a "COUNTIF" function based on a 'Category" column (image enclosed/table below). Any help would be appreciate
Data contains too many distinct categories to list individually in beast-mode by name - kindly assist.
Date | Price | Category | Report | COUNTIF GOAL |
2019-Feb | 10,000.00 | Type 89 | Report 32 | 5 |
2019-Feb | 10,000.00 | Type 89 | Report 107 | 5 |
2019-Feb | 10,000.00 | Type 89 | Report 17 | 5 |
2019-Feb | 10,000.00 | Type 89 | Report 53 | 5 |
2019-Feb | 10,000.00 | Type 89 | Report 19 | 5 |
Comments
-
Just to clarify, Are you looking to check the Unique no. of reports under each category against the goal of 5?
0 -
No, the goal is not dependent on unique report names - only the number of times the category (i.e. Type 89) occurs in the column.
0 -
Try this beast mode:
COUNT(DISTINCT category)
This will give you a count of unique categories.
0 -
tried this, the result shows row data only not the column data. i.e. Goal = 1 not 5
0 -
Its most likely doing that because of the report component which is different it appears on each row.
If you sum the count(Distinct) it should give you the value of 5 that your looking for.
If I'm interpreting this correctly.
Randy
0
Categories
- All Categories
- 1.8K Product Ideas
- 1.8K Ideas Exchange
- 1.5K Connect
- 1.2K Connectors
- 300 Workbench
- 6 Cloud Amplifier
- 8 Federated
- 2.9K Transform
- 100 SQL DataFlows
- 616 Datasets
- 2.2K Magic ETL
- 3.9K Visualize
- 2.5K Charting
- 738 Beast Mode
- 56 App Studio
- 40 Variables
- 685 Automate
- 176 Apps
- 452 APIs & Domo Developer
- 47 Workflows
- 10 DomoAI
- 36 Predict
- 15 Jupyter Workspaces
- 21 R & Python Tiles
- 394 Distribute
- 113 Domo Everywhere
- 275 Scheduled Reports
- 6 Software Integrations
- 124 Manage
- 121 Governance & Security
- 8 Domo Community Gallery
- 38 Product Releases
- 10 Domo University
- 5.4K Community Forums
- 40 Getting Started
- 30 Community Member Introductions
- 108 Community Announcements
- 4.8K Archive