When presented with a table of data that requires analysis, one common requirement is to calculate a running total or what some might call a cumulative sum of individual values. One application might be a table containing the amount of money spent across a number of transactions and there is a need to provide a running total to show the total spend across the data set. There are a couple of different methods to achieve this but the easiest way is to use the function =sum($B$2:B2) where column B contains the values that you wish to add in a running total. This gif (below) is the above function being added to a table containing values. I've used the keyboard shortcut F4 to set the cell reference to absolute so that when it fills down, the second part of the cell range increments with each row.
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I recently blogged some instructions detailing the process to create a Date table using DAX in Power BI which is one way to create the foundation that enables the time intelligence functionality of PowerBI. If any of you have tried to use a date table you'll probably be aware of a couple of 'nuances' that can result in an incorrect result when you begin to visualise the data. I've described the 4 issues I've often had to rectify when working with time series data, hopefully they'll help you with similar issues I've incurred and found solutions to. Lets assume that you've created the date table and imported the sales data (more detailed explanation and link to files below). You then try and make a bar chart showing the number of sales by date and you end up with the following: I think most people who've used PowerBI have seen something similar, all of the columns are the same height and are of a magnitude that immediately feels incorrect. If you see t