 Let's review some of the concepts that we've gone over through this demonstration. We can add a lot of dimensionality to our analysis within the pivot table application. We want to select our three core data dimensions whenever we're doing analysis in DHIS2. This includes our data, which reflects our what aspect, our periods, which is where, and our organization units, which is when. Let's work through an example, working through some of the things that we demonstrated earlier. If I'm working with a data element, then I'm working with a raw piece of data. This is numerical data that has been entered during data entry, typically. I next want to select my period. My period can be any period I want to select. It can be predefined using the available types up top. And it can be relative using the period types available at the bottom. Relative periods are particularly useful as they update over time. Next I select my organization unit. We discussed two methods to select the organization units. One method was actually selecting the individual organization units that we want to appear in our analysis. The other was changing the selection mode to select levels. And then selecting the level in the organization unit hierarchy that we want to appear in the pivot table. Underneath these three core data dimensions, we can add in additional dimensions. This allows us to separate the segregated values in our analysis. Before we create a table, we should generally check on the layout to make sure data is appearing in the places that we want it to. By default, the organization units are filtered, so if we do want them to appear in the pivot table, we should make sure to change our layout accordingly. When I'm ready to display the table, I can click on update. We can see in this case the organization units don't appear because I have not yet moved them. Let's move them to the row. We can move our periods over as well. And then we can update our table. There are a number of extra items on this table that we might want to remove. For example, we see this duplicated total. In this case, it's useful to have the row total, but we might want to remove the subtotal. We can do this by clicking on options, and then adjusting the items that we want to remove. We can save our items as well by adding them to favorites. You can click on favorites, click on save, and provide the favorite with a name in a description. We always want to follow the convention where we are using our username, our program name, and then what, where, and when, covering the data dimensions that are contributing to the output of our favorite. It's also good to put a description of the item, so people can see what you're referring to, in particular, for that favorite that you've created. It gives some more context and information about the item. In the options, we can also add in the hierarchy. The hierarchy allows us to download this offline with the hierarchy intact. It's also just generally useful to see where those organization units are coming from. This ends the pivot table session. We'll move on to the data visualizer in the next session. But first, please give the exercises a try and let us know if you're having any trouble.