Analysis of Data Distribution Across Multiple Columns

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In certain situations, it may be necessary to calculate the combined value of multiple columns that relate to a shared factor. Understanding the distribution of such values across various columns in a dataset is crucial for effective insights.

The objective is to conduct a comprehensive analysis of the frequency of each status category across different columns, displaying the status values for each column and indicating the total count of each status category.

Example:

In the below sample data, there are three distinct columns with options like “Completed,” “In Progress,” and “Scheduled”. If we wish to determine the combined total of Completed, In Progress, and Scheduled processes regardless of their respective columns, we can generate expressions for this purpose.

Expressions:

Completed status count Expression:

SUM(IF([BIA_Status]='Completed', 1, 0)) + SUM(IF([bcp_Status]='Completed', 1, 0)) + SUM(IF([exercise_status]='Completed', 1, 0))

In Progress status count Expression:

SUM(IF([BIA_Status]='In Progress', 1, 0)) + SUM(IF([bcp_Status]='In Progress', 1, 0)) + SUM(IF([exercise_status]='In Progress', 1, 0))

Scheduled status count Expression:

    SUM(IF([BIA_Status]='Scheduled', 1, 0)) + SUM(IF([bcp_Status]='Scheduled', 1, 0))  + SUM(IF([exercise_status]='Scheduled', 1, 0))

Explanation:

It calculates the total count of Completed/ In Progress/ Scheduled statuses across three different columns (‘BIA_Status’, ‘bcp_Status’, and ‘exercise_status’) by summing up the occurrences where each status is marked as ‘Completed/ In Progress/ Scheduled’.

Summary of ‘Status’ distributed Across Multiple Columns:

Charts provide a clear and intuitive way to visualize this information, allowing stakeholders to quickly identify relative proportions and trends, ultimately improving decision-making processes.