1. Tables and scatter charts
Let's start off by looking at a few Power BI visuals. First, let’s create a table. On it, we’ll add Retailer_Name, Retailer_Channel, and the total orders made, dragging the table down along the way.
Now we want to add a slicer for the year range. We will use the Order_Date column to find the year orders were placed. Power BI has automatically detected the Order_Date as a date data type. Power BI has also created a date hierarchy for this column, which means we can add the year part of the order date to the Field section. The visual can then be resized appropriately. This helps us easily control the different years of operations.
Next, we want to check if there is a correlation between the total order quantity and the distance a retailer is from the company's warehouse; based on the regions in the UK where the retailers are located.
Let’s create a scatter chart using the average distance from the warehouse as the X-Axis, the total order quantity as the Y-Axis, and the region for the values.
It does not look like there is a strong correlation between the variables we have investigated. We might have expected that the greater the distance retailers are from the warehouse, the more items they would order so that transportation costs could be reduced. But this is not the case - which is still an important finding for the company and could help for future strategies.
Our final visual is a bubble chart, which is a scatter plot with varying bubble sizes. Here, we would like to see if there are any relationships we can determine between the average price of products, the total quantities ordered, and the total sales amount associated with each product. For example, are cheaper products being ordered in great quantities or not?
On this plot, the total order quantities will be our Y-Axis, the average price of products our X-Axis, and the product name will be the Values. We also want to see how the total sales amount relates to the other variables. To do this, we can use the Sales_Amount column and set it as the Size.
This is an instructive visual precisely because it’s so messy. We can see some groups of data points in the visual that probably relate to the product categories, but there is no real correlation that is visible. It is also hard to make out individual points because the chart has too many large bubbles. For this reason, be sure to use bubble charts in places where the size is important, you have relatively few bubbles on the chart, and you can still differentiate individual data points.
This has been a look at some of the most common visuals in Power BI. Now it’s your turn to take these visuals and tell a story.
2. Let's practice!