Bar charts and small multiples
1. Bar charts and small multiples
We will now cover using bar and column charts and formatting visuals. Currently, three types of column charts are available in Power BI; let’s start by creating a clustered bar chart. Clustered bar charts are great for showing a variety of data over a single period of time. In this case, we will look at the number of orders by retailer. We can now see a listing of retailers and determine which have placed the most orders. However, we'd like to filter down to see only franchise retailers in 2018. To do that, we'll add a pair of slicers. The first slicer will allow us to narrow down the retailers by channel. After adding the slicer, we will select the desired channel. Then, let’s add another slicer and use it to filter by year, including only information from 2018. These visuals are functional, but we can help users understand things better if we format the visuals to include more appropriate titles and axis values. If I select this bar chart and navigate to the Format menu, I have several options available. Let’s click on general, drill down into Title, and change it to “Total Orders by Retailer and Year”. We’ll make the X-axis label “Total Orders” and let's remove the Y-axis title altogether to save a little bit of space. For the two slicers, the set of formatting options available will differ. Let’s drill down into the Slicer header and change this text to Channel. We’ll do the same with the other slicer, changing it to Operating Year. There are a wealth of other options, and it is recommended that you experiment with them to learn more. Formatting options aren’t the only way to make it easier to understand the data. We can also navigate to the Analytics menu and add lines to help visibility. It would be good to add a constant line representing the desired number of orders by retailers. Suppose five orders are normal for a franchise retailer to make in one year. To make it easier to see the line, we’ll change the color to Black, 20% lighter. Now let’s look at stacked charts on a new page. Let's add page-level filters to set the year to 2018 and the retailer country to England. Add a 100% stacked bar chart. We’ll make the area the legend and add the total orders to the X-axis. This gives us the percentage of orders by area across England in 2018. The South East had the most orders throughout England in 2018, but is this true across all retailer channels? We can add retailer channel to the Small multiples field to find out. Small multiples let us break out our chart by some additional fields, making this analysis easy. We can format the small multiples to change the rows, columns, and even gridlines. This packs a lot of information on the screen but can be very difficult to compare. To make that easier, right-click on the visual and show it as a table. Now we have a tabular view of the data, and we can see that not every channel had its most orders carried out in the South East of England. Select “Back to report” to close the table. The last thing we will do is switch the visual to a stacked bar chart. The 100% stacked chart lets you compare proportions more easily. Switching to a stacked chart lets us compare aggregates, making it easy to see that franchise retailers had much higher orders than the other channel types. These are some things we can do to make visuals easier to understand. In the following exercises, you’ll take the reins and build out reports for your audience.2. Let's practice!
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