 In this Data Visualizer session, we will start by introducing the Data Visualizer interface. We will then describe examples of the various chart types that are available. Next, we will start by creating some of these various chart types within DHIS2. We will show you how the chart layout can be applied to different chart types. We will describe the chart options, show you how to save chart favorites, download the charts offline, and end the session with a review of the various concepts that we've gone over in this demonstration. Let's go ahead and get started. In this session, we're going to go over the Data Visualizer. The Data Visualizer allows you to make a variety of different charts. In order to access this, just like the pivot table, go to our apps, find the Data Visualizer application, and select it. When the Data Visualizer application loads, we notice that the interface is quite similar to the pivot table. The main difference is now that we have different chart types that we can select at the very top of the visualizer interface. This includes vertical and horizontal bar charts, line charts, area charts, pie charts, radar charts, and dashboard charts. Let's go through making an example of a couple different charts within the Data Visualizer. We still want to go through the same selection process of selecting our what, when, and where aspects of our output. This corresponds to our data elements or indicators, our periods, and our organization units. Let's create a bar chart, which is the default chart type that is selected in Data Visualizer. We'll work with the data element from the ANC group. You can compare the first and fourth visits. For the period, let's select the last 12 months. For the organization unit, we're going to select training land. We can see that selecting the data, periods, and organization units is exactly the same when compared to the pivot table. Let's go ahead and update our chart in order to see what the output looks like. You can see when I update the chart, I have a very quick visualization of the data that I've selected. We have the ANC first visit in green, the ANC fourth visit in blue. We have our periods down on the x-axis of this particular chart. And then we have the number of visits going up our y-axis. In this case, we're just looking at these values for the entirety of training land. But what if we want to compare districts? The method of selection is the same as in the pivot table. We still change our selection mode to select levels. And we can still select our districts. If we update, just like our previous example in pivot tables, we see the name of the districts that have been selected, but we don't actually see them on the chart. In order to add them to the chart, we have to alter the layout. Let's explain the layout a bit more thoroughly. Here we have a diagram which corresponds to the type of bar chart that we are creating in DHIS2. There is some different terminology when compared to the pivot table. Here we see the bottom listed as a category and each of the individual items corresponding to a series. How we can think of this in these types of horizontal bar charts is that the x-axis is the category. The y-axis is the series. The series divides each of our data options up into its separate parts in the graph itself. We can see here, for example, each of the individual items are separated on the graph via different colors. This is defined by what we select as the series. Let's go back to the data visualizer and see what this looks like in practice. Back in the data visualizer, we need to now adjust the layout. If we click on layout, now we see those two terms that we were just introduced to. Category, series, and we still have a filter just like we did before. The filter acts a little bit differently and we'll discuss this as we go through this session. We want the districts to appear on the bottom axis. For this type of graph, we can consider the x-axis the category. Currently, the organization units are being filtered out of the graph. As they're acting as our filter, we can see that these values for A and C first and fourth visit are aggregated for the entirety of training land. The filter often defines how the data is aggregated. In this case, it's by our organization unit. If we take our organization unit and drag it down to the category, so this is the same process as before where we're just dragging and dropping that dimension item, we can see that organization units and periods swap. Unlike pivot tables, we cannot have multiple dimensions in our category or our series. In the pivot table, we actually didn't need any filter. We could have as many or as few dimensions appear in either the row or the column. This is one of the significant differences between the tools. Now that we have our organization units on the category, which we can refer to as our x-axis in this example, we can click on update. Now we can see that actually the districts appear in the bottom axis. This is because they are selected as our category in the layout. We can see all those periods now appear at the top. They're being filtered out of this particular visualization. The values have changed. For bird district, for example, the value of 18,592 is the A and C first visit for bird district for the entire period. So this means from November 2015 through to October 2016, it is actually aggregating that data value for that entire period. Before it was aggregating the data values for the entirety of training land. Now it's aggregating those values for the period, which is acting as our filter.