 So, as I mentioned earlier, today we'll be looking at the data visualizer, which is one of the DHS2 application specifically used for data analysis in terms of reproducing different analytical reports in the categorization of pivot tables as well as different charts. So I know some of you who have at some point used DHS2 previously, we had this pivot table as a standalone DHS2 analytical app, but starting from version 2.29, then it's now integrated within the DHS2 data visualizer app. So what are the pivot tables? This is one of the module in DHS2 that is more or less used in creating analytical reports and have them presented in table format. So of course, it has a couple of functionalities that are more or less the same as Excel, but not actual replicate of an Excel. And of course, it's mostly used when you have a very huge amount of data that you want to analyze and have a presentation of it, but of course, also it allows you to have different type of dimensions in one analytical report. So how can that be done? How do we do that? That's the whole point of having this session where we'll be looking at on how we can play around with the pivot tables, different dimensions. How do we create them? How do we play around with the layout in a manner that we get to have a presentation that makes sense, but also meet our needs? So through all this session, I'll be walking you through on that. And to start with, I'll just let us look at that kind of a pivot table. So as I said, this is one of the analytical reports that can be produced within the Data Visualizer app. And of course, the data presentation is in terms of table or more or less of a tabular format. And of course, it has the flexibility or it allows you to have a flexibility of presenting different type of data dimensions in a single report. When I speak of different data dimensions, the data elements, it could be the indicators all combined in a single analytical report. But regardless of any type or any kind of information you wish to have it presented in the pivot table, there are very three basic principles that you have to abide to. And of course, those are what, when and where. I'm pretty sure these are not new terminologies as far as you have been dealing with gages too. So whether it's an input part or an output part, you have to make sure that these three dimensions are clearly defined so that you could probably input or retrieve an information that is very meaningful. And of course, it can make sense when it comes to data or information use. So pivot table, as the name portrays, it's a table. And when you speak of tables, we have rows and columns. But with THS2, we also have another dimension which is called a filter. And once we get into the system, we could see exactly how these items could be selected and how they can get or be arranged in a table. So if you look at that example of a pivot table trying to give an information on HIV tests performed, we could clearly see the what, which is exactly now we are looking at the HIV test performed, but we also see when the period of this information, whatever figures that we are looking at, when, what is the time frame of these figures? Which is 2018, 2019, and 2020. Still, that doesn't make sense unless we clearly state where are these figures coming from. So that comes or take us back to the last part of the what, which is more or less the organization unit or the location of where this data was captured or the data is coming from. I understand you could see more of these female and male, but we'll see forward. How do we get or how do we reach into this point of breaking down this information into male and female? So as I said, creating a pivot table, it requires it's very simple and it needs you or it requires you to just accomplish the three steps. And what are those steps? It's first of all to define or select your data dimensions, which is what, when and where. And of course, if at some point you have further dimensions, like the male and female that we've just seen in this table, but also the second part will be to play around with the layout to make sure that data gets arranged in a way that once presented, it's easy to interpret. And of course, we have the other part, which is the options and we'll be also looking into what we can find into options, what is available with the options. So again, as I said, the three W's are really crucial, what, when and where. And of course, when we speak of what, like the table here is. This is the table with regards to DHS2, it could be a data, other kind of information that captured in the DHS2. pivot table under the word dimension indicators with regards to DHS2, these are all you would sometimes be using the data that are being captured in the data collection forms. We also have the reporting rates. These are more or less coming from the data sets when you want to monitor the reporting rates of all the data sets within the organization units that are used for tracking that data set. We also have the tracker data, but this is not covered in this, in this top, this module. This is more or less used when you're dealing with individual data. And the second part is the when, which is the period, as we all know, or the ones who have been using DHS2. We have two categories of periods. We have the fixed period, and we have the relative periods. What's the difference? How can they be accessed? We'll be looking them once we get into the demonstration of the system. And of course, the word part, which is the organization unit, and it could be the, how you retrieve it, it all depends on what you collect in your reporting hierarchy. So with the table, I'm pretty sure it's now clear what is when, what is what, and what are the organization units. So we said, apart from those key three dimensions that have to be clearly defined, sometimes you could be in a position that you want your data to be analyzed and presented with more granular information, and all these can be typed as further dimensions. And you can access them when we get into the pivot table, you can see how you could access them, but they are more or less used to break down or to further break down the information that you have already retrieved from the three dimensions that you have selected. So taking an example of these HIV tests performed, pivot table, you could see the female and male, all these are coming from these further dimensions which are being captured under the sex dimension. So as I said, there are more or less of disaggregations to further break down the already analyzed information. But with pivot table, you need to manage the layouts. How do you manage the layout? This is more or less of now getting things arranged in terms of columns and roles as well as the filters. So by doing this arrangement, you're basically telling the system, you, from the information that I have, I want my organization units to be represented or to be aligned in terms of roles, and I want my period to be in terms of columns, and I want my data to be in terms of filter. So it all depends on how you want, the assignment depends on how you want the table to look like. So apart from the layout now, we also have further navigations that we could do with pivot table, which is the options. So what do we have in the options? So under the options, it's more or less again to modify the data that we have already retrieved from the what, when, and where. The modifications that you're speaking of could be, let's say you need to see the roles, every the total, total, it could be rows or columns within the table. Because once you just retrieve the data as it is, it will only give you the granular information as per the dimensions that have been selected. But if at all you wish to see the total, or whatever that you have selected, you'll have to go into options and be able to select if at all you need the column totals, the row totals, or sometimes even the sub totals, if you have your pivot table with a cover of information that could be summed in terms of sub. But also with pivot table, as I said, it's normally used when you analyze a large amount of data. So sometimes the interpretation could be a little bit difficult, the fact that you could have a very massive data. So we have what we call the legends. With legends, this is more of color coding where, of course it has to be configured in the system, but the importance of legend is used to sort of color code your information for easy interpretation once you have the pivot table presented. We have the series, but this is more applied when you're dealing with charts. But also have the styles. This is more or less of putting in the titles of the chart, I mean the titles of the table and so many other ones we get into there, we'll see what is available. But we also have the limit values. Sometimes you could have your information, but your interest is only see the data that have a certain range of figures. So this is where now you could also go and define the ranges that you want to be presented within the pivot table. You have the parameters. Again, this is also more applicable once you're dealing with the standard reports. It's all of this coverage. So you have done all the analysis with DHS too, but what happens if you happen to log out the system? That means you could lose all the information that you have tried to analyze. But if you wanted to be able to use it in the future, then we also have the options of serving so that whenever you're into the system, you'll be able to retrieve what you had previously analyzed and be able to use it in a different way. And we have different options of serving. Could serve us once you're serving it for the first time. But also if you want to sort of make a clone of the available pivot table, you could make a clone of it by also using servers. But if at all you have made some changes to the existing pivot table, you could also go back and serve, which means it's going to overwrite the information that was available on the previous pivot table. Or sometimes you would wish to change your name of the pivot table. Also have that functionality of renaming. And of course you could also share it to other members within the system. And of course you could give them more limited access if at all you need just for them to be able to access or access and be able to do the changes. And if at all you think that pivot table is no longer valid to you, you can also get rid of it by using the delete functionality. So at that moment I've spoken a lot of the functionalities that are available with the pivot table. And I think it's high time we get into the system and see all the news that we have been talking about. So once you have logged in into the system, of course the first step would be to go and look for the actual DHS in analyzing data in terms of pivot table. And it's now within or it has been embedded within the visualizer app. So if you just scroll or turn it, you could just search, select data visual. Once that is opened, as I said, at this time we support two types of application, which is the charts as well as the pivot table. By default, if you open up this app, it will always pick the column chart. But if you want, since we are looking into pivot table, so you have to make sure that you select the appropriate type of visualization, which is the pivot table. So you just have to click and go to pivot table. So I can where this is where you could find all of them. And of course we have further dimensions. For instance, the sex, if you want to further break.