Yeah, so for our next sessions, we are going to look at the other more types of charts. And now that we have been able to cover, we've been able to look at the column, the stacked column, the bar, the stacked bar, and the line. These, as I said, all up to the area and stack area, they use the same layout. So the x-axis is going to be the category, while the y-axis is going to be the series. So that's what the audience, and that's what you know.  So those that we have been able to look at, we have now been able to come up here. I will come again to the area graph later on in the session, but also important now we want to look at the pie chart. Now the pie chart, most of you have moved it, and particularly pie chart is one of the easy and quick charts to make. It's usually very good when you are displaying data in percentages. So you have percentage coverage, percentage reporting, percentage work, and you are comparing also, it's also a type of chart that allows us to be able to compare. And to look at this, I will take it with a malaria use case. So I'm going to just completely make this new. So the team wanted to compare the different age groups in terms of the positivity of malaria cases using Arrabiti testing. This is the type of testing, they're going out to do the testing and all they have is they want to look at, you know, what is the percentage of the positive cases in the different age groups in terms of testing. So it is a number. And I said it's always good with the, with the percentage, but what they, what they, what they, the DHS is going to do for you against the number and the percentage. So to do this, we'll start by setting the chart type as pie chart. Again, this we can even select it afterwards, but so it's good to start with an ending. What we want to end up with, I want to end up with a pie chart. And that pie chart is, is all data, which is malaria cases positive in Arrabiti. So again, I can be able to search here in malaria. Yes, yes. Positive. And I said this is in the road that is data element. And again, I can be able to filter down to be able to make sure that I'm dealing with that. So this is the one I'm talking about. So I can be able to type. And for the period, we want to look at the last 10 months. So I wrote the period. Last year, months. Well, it is sorry. I didn't hide it. Then, in terms of. Organization unit, we want to look at the whole training line. So I want the chart for the whole training line. But, but it's really here. I want to see the different. So there is what we call it in malaria. So I'll be able to add this. In malaria to be able to see the different. How they, how they, this. Hi chat. So come here. And then I will put in all the different. I want to see the percentage within the. So I will add this to the series. And this is the sky. I'm able to present so I am able to present the team that you know, in this, we almost have. 3935 with the five. So the highest number. This is using. I want the. 15 years. Plus, that's where we are. The highest number. So I could also change this. And say, maybe we want to see where, where they like the highest. So this is where I was given cases in terms of last year, which, which month had the highest. So this is where I get, I will pull. Now you see, we didn't change the layout. When I'm dealing with five charts, I'm going to be with the filter and the series, which is the categorization. The, the, the, the, the, the, the, the display for the series. So I don't have the category here. So I'm going to deal with two, two, two layouts. So I move, I can move this one here and I put the period to be the one that shows me the, the different area. So I want to see which, which period has the highest, the highest. Now regardless of the age group, but I want to show which period has the highest. My positive cases. So when I did this, you will see that it's now giving me all the years. Is the hand being raised as a question or. I think those are hands from before. Okay, sorry, sorry about that. Okay, so you see with now the pie chart, you are able to be now play around with the series. For example, this is showing us the different months now that which month has had the highest Malaya case so I can be able to see maybe now you are using eyes you remember if it was the, the bar graph will be able to solve. But here, I have to use my eyes to see which, which pie is high, which pie is higher. And I think this is the main is where we had a lot of cases, which are positive, which is also showing that you know last year, we had high cases what we made. Maybe you may also want to use this to compare the regions. So I can be able to pull this back. And then I put this put in what region. At this, this is the organization in the city, but when I update, it gives me the whole plan because I'm going to be with what we want to do with what one. So I may come down here and I want to say, let me see this. You can see it's in the animal region where you had the malaria, the positive cases, the positive cases in using other people who are higher in the animal region. No wonder the animals spread most people so that's why it was, it was hard. Okay, so that's the beauty about the pie chart very easy. You can do it with two dimensions, you can be able to move them around and keep, you know, changing according to what you want. So that was going to do with the pie chart. Okay, so the next, the next chart type that we are going to look at is the radar, which is a spider in most of the other packages, it's a spider kind of data presentation. Again, we are going to look at it using the malaria cases. And then we want to see by age, so I bring this back, and I look at my malaria cases, and then I will just update. So this is what it has been showing me, but we want to use this to be able to show. So this is from the spider or the radar type of chat. So I just come here, and I can choose that from here. And then update. So, again, this is the, it's now bringing in the category point of view, which is now because it's now creating the kind of X axis in here. And now the category has come in, and we need to see what could be the category for this we want to see the malaria cases by age on the different. For the different. So when I update, this is how it looks like. So you are able to see by the different groups. How this is, you know, working around in terms of malaria cases for different months, the highest being in May, I think we have seen that for everything. And then you can also see where the spread. Of course, some months which have no data, the main plan, remember, this is using the relative period so we don't have data for February, we don't have data for January for this particular. So this is how we'll be able to show. So, again, this is the, again, on this. So, still, if you wanted to read from a fresh, not starting from what I had before, it's quickly be able to do that. You will be able to choose from data from the chapter. That's the type that you want from the data. You want malaria, malaria cases. And it's not an element. Which is a little bit. This is the one. This is a little bit. So, there are height or height, which was the period to say the period last three months. And the organization means we wanted to now look at for the whole for the whole. Now you have selected everything, then update. And when you update, you will see you are not done. I'm showing you the highest, the more opposed to the end, will be the highest and then the inner one will be from zero to the highest of that. So, basically, that is how this works. And again, if you want to be able to get and see by the different groups of malaria, you come up here. And then you have all of them. You are in the cities, cities, and then you will see that. So that's being able to make it from, from a blank slate. Okay, so that was looking at a pie chart. And I think now you can be able to see where you want to be able to use your pie charts.  The radar, you will see it also gives a good nice of performance. Basically, you need to have a mixture of, you know, visuals on your dashboard so that people don't get bored by the same chat tables, tables, you know, and if you have any tables, you're going back. So you need to mix and spice up your dashboard. So we don't see that on the dashboard and put all these types of charts. And then the dashboard really looks attractive. But the next one we are going to look at is the coach, which is the speedometer for most of you that have, that have uses other and includes we are trying to bring all these different types of charts that we see. That we see out there into the DHIS. So the, the board is a very easy and nice tool to use when you're going to show performance.  So the point is also something that is going to be able to help you monitor your performance. This could be voting rates, it could be coverage, this could be proportioned, this could be ratios. So that, that's that nice display can be able to do that. And particularly for this, I'm going to use an example of where the, the immunization program, the EPI or EPI program. I want to keep a check of OPV coverage. Even for, for the HIV team, you could also want to say, I want to keep a check on my dashboard, I want this video that show me how far I have gone in terms of, you know, whether it's the case or the protein or, or test and treat those kinds of indicators. So for this particular one, I'm going to use an example of an EPI indicator that looks at OPV coverage. So this is a number of children that have been able to see OPV, that were able to see OPV. So you are in a campaign and you want to keep checking when you are on your board to see how, how far and how good the coverage is going. And you can even have the national competition compared to the districts and you see how it's showing. So let me, let me start fresh. So again, I've said I need a charge, which is a weekend, which is here. So here's percentage indicators at 100 scale, that's the best for 100 scale. So you select it. So I've selected it, and I need now to select the data to be able to present. The next step is what data am I going to be able to share in my data. So I'll click on the data. And then I am going to look for OPV coverage. So it is OPV3 coverage. So that's an indicator, it's a percentage. Again, say this type of chart is really good for percentages for ratios or, you know, or when I add it in here, then I can help to be able to select the other options. So this particular team is monitoring for last year. So it's also going to be for this year, because you want to be able to see this grow every day. And I think this is, this type of chart is very good for the current, for the current period. If you say one month this month, one month this year and you see how the performance is going. Otherwise, if it's last year, it's probably not going to change because that will start there. So it's always good to use this grid in the current period. So as data comes in and it's improving, you are able to see the progress. But for this particular exercise, since we don't have data for this year, let me use the last year. But again, I've decided that it's always good to use it for an ongoing activity to be able to come pick here, select last year. Otherwise, it will be this year, which will be the next year. So I hide it. Then, and again, this is a chart type that only deals with settings and filter.  So you will have the category and then I update. Again, what has really improved here is depending on the chart type that you have, you will see that this is now coming and it's really, really working, working very well. As opposed to the previous questions, the previous questions, you still had all the layout that you mentioned, and it was really hard to tell when do I use this, when does I use this, and when might I use this. So in this new version, we are using the, there has been an effort only limit to what is applicable in there and you also be able to see the options. There are some of the things that which are going to appear, as opposed to the way they were appearing before. So with this, I can be able to see that last year, my more favorite coverage is stopped at 72%. But again, I said, if it was like, you know, tomorrow you will come and see, okay, you will recognize more children, then the speedometer will still keep on moving. And again, we can also be able to add a legend to this, we can add a legend on this by going to options and say we are tired of seeing this group all the time. So we want to see that if it's a rage, it's probably before, before, so we can come here and say, go to a legend, options, legend, and then you can add a legend and this legend I'm going to add here. Of course, I want it to be the background color, but the text color, the text color will just be on 72. And then I want to use a select not been fine. And then I want to use in the coverage as a legend. And then I want to show you to key, which I will update. So you will see we are in the arrow. Or it's because we are now in the 70 to 70 when it's 18 and 80 to the next level, it will show you like to be able to show the whole of it. And then when it comes to 90. It was about 100. And we shall have it as always. So that's the way it works. Very simple and very easy to use. Okay. Okay, good. So, um, yeah, those are the two charts that have been able to quickly show you in terms of So the next time we are going to look at is what is called a single value. This has become very popular in many other charts outside the highest to where you just want to have a single value. Say, I want to have my population figure displayed in my chart in my dashboard. I just want to have a single value. We have seen this really work very well in the COVID like you want to see the number expected number tested as single value because the table sometimes confuses, and the people can see it very well sometimes I may not want to compare so I want it as a single value. So we have this chart that has been introduced, I think after 56, 2.36, and it's called a single value. So again, I will make new here, and I go and I pick that single value. It's just here. Again, as a single value, it takes one value. So for this, um, I will say probably I want to just push a percentage, or PV percentage, let's say, or PV, so I come to that time, and I will choose or PV, or PV three coverage. I want to have a single value. And again, you will see again, this one is also dealing with already the filter, the series and the filter. So we don't have the category option here. And again, I will just say update here, because I wanted it for last year. Last year, yes. And then I update. So I'll get my single value. You can be able to see. And again, this is also still new that we can be able to put the percentage if it was an indicator, we put a percentage on it. And it comes with a percentage, 72%. So my PV coverage last year was 72, and this looks very smart on your dashboard. I see this with some examples that you require you to do. Can you define the chart that gives you to the number of HIV cases. And you can get to the number of people enrolled in school like that, learners in school and all that. So single values are going to be very good when you have many systems in this level. You can also add an agent. Again, I can just say predefined. And it's a quick recovery. And then you can also have it. So how nice this will be looking like the numbers will change from red to green to up to that. So that is the single value chart type. Okay. Good. So, um, okay, I've added a single value there. Okay, the next one we are going to look at the chat we're going to cut is what we call a cumulative chat, a cumulative kind of line in the chat. In most cases, you will be asked to present a chart that shows me progressively the total number. So what was January. If we had one client last in January, in February, we had the second one. So January should show tools are the cumulative kind of a chat. So this is also very good presentation in terms of monitoring your progress. The numbers should always go up once if they go down and then we have a problem. That's, that's, that's, that's, they know to actually happen. So we're going to cut to one other chart that is all to break. And this is mostly going to be easily presented by either a column chart or a line in graph. And in this to be able to demonstrate this, I am going to look at people who are newly initiated on a team. I remember every month we are doing testing and getting positive and we're starting them on medication. So you want to have this nice chat that really shows you the cumulative numbers, not necessarily what was that each month just wanted to make it out that shows you your upward trend as every month. So the difference between the last month and this month is what has been implemented. Okay, so I will reflect this and just start with you. So again, I will use a line graph for demonstrate this, and I will just be able to look for data around the people living with HIV new on their team. So I'll just come here and I'll come and say new or a. So it's people living with HIV new on their team, not the person, not the indicator, the person. So I can hide that. And I'll go to the period I look for the last month. I know, and I want to see the, the, the cumulative numbers for last month or last month. And again, I wanted to my core training that of a different region for these people given group of facilities, but for this that's the news hour. So what this is giving me it is giving me numbers. Each, each month, these are the numbers that were studied worldwide, but we want to be able to see a progress, because this is what this is giving you every month this month we had this number. This one, this one, this one, so that hasn't been our trade, we've been having more numbers more numbers. But we want to see this are cumulative how the numbers are. So to help us do that. So we have those options. And options, and the data, we have what we call cumulative values. So you just keep on that. And basically we see that it has this is how the numbers have been going back. So we started it in first March with the 5,700, but that's from December, we have now 61. Our program has really been growing. Now you see where it has become flat. It means that this month, we do not have any number so it can become flat. I don't think it will be able to drop down to just become like that because there is more. There is no data on that. And you can also be able to add more dimensions. And for example, I want to see how many are male, how many are female. So I can be able to add in a gender or sex dimension, which I can go from here. Let's have sex, I don't hear, which is male and female, and then I don't get the settings. And so what you will be able to see, you will be able to see the cumulative numbers per day per sex. So the male. So obviously we're looking to have more females in our program and then male, then males, but also the cumulative numbers are each. Good. So that is, that is, in terms of being with cumulative numbers or cumulative path or cumulative line graph, they will show you the progress and they will be able to, you know, be able to monitor that from between this period and this period. This was our possible increment increase. This is, you can see, it's steepness for the gradient. It also gives the level of the increment in those numbers. Okay, now let's go to monitoring over different years. So we have a chat what we call the year over year performance or monitoring. So, for example, let's use a case of malaria as an example you want to see the trends of malaria cases for the different years. You want to be able to see the lines for each year, line one, this is 2019 over the different parts of malaria. Because what this helps with, and I think for disease surveillance, for some of you are involved in disease surveillance, it helps you to be able to identify some kind of trend or some kind of seasonality of a disease. For example, if it's during the dry season or the wet season or the summer or winter, you want to see what happens, like you want to see in May or in December, what happens in December in terms of our, you know, our program for the different years. We've been operating for five years, but what has been our trend, what has been the data collection, what has been the numbers, what has been our coverage in terms of particular months, maybe it's a whole demand or maybe it's a migration month or maybe it's a school month and all that. So we use what we call year over year charts to be able to demonstrate this kind of, this kind of analysis, and this is also still new to GIS, this is also new to GIS too. I don't know how, before we were able to do it, to be able to look at multiple years and look at the different months. And a good example is where we're looking at seasonality or some kind of events that happen every year. We have some, please, you know, some traditional ceremonies that happen in different years. For example, people in the Safe Mere Circumcision, they probably take it some season and they want to see whether our trend has been the same over the years. And to do this, I am going to use PCD coverage, I would have used another indicator, but then use PCD coverage, but again, feel free to use it for all the other indicators that you want to monitor year by year. So they give it different months, but the whole concept is the same. Okay, I hope we are all together on that in terms of how the year over year. So like we're trying to be able to see school performance in given months of the year, whatever you want to do and then compare so many years at once. We want to be able to clear this and start over. And again, the chat type I'm trying to use here is what we call year over year column. You can even read the line graph, let's say year over year. No, it is year over year line graph, you can even use the chat. So let's use the year over year line graph. So you select it. And again, you'll see what has changed already, things are beginning to change here when I select the type of chat. We're still dealing with series category and filter, but you see some of these directions have changed the way we do the selection. Don't get worried, we want to get there. We want the data. Data we want is PCD coverage. And I want to see our PCD coverage over different so many years. So I select that, and I can hide or I can update, but I don't want to update because it's going to confuse you before you see exactly what you want until we select all the parameters we need. Remember, we can still move them around. And I will go to the, to the organization in it, let me still read it for the whole training and for the whole country. And now what I'm seeing is, is a this year and last year. That's what I've been selected. But because we don't have this year we don't have data, this is 2021, that's why you will not see any big line here. So let's move this and then select the periods that I want. I want to be able to look at my data. I want to compare 2021 by month, 2020, 2019, and 2018. Then I can be able to update.  So you can see that we were all low in 2019 and so on and all that and moving around. So this is showing you over here. It would be good because this data is made up. It would have been good to see some spikes. And you see if the spike is uniform. And if you are careful, I mean, because this is made up better, you will see that the trend is almost the same. Over here, the trend is almost the same, the increment is almost the same, but it would have been good to really see some of these big spikes. Obviously here, I'm just putting on 18, but I don't want to move 21. So. So, again, this is really, really something interesting in terms of being able to look at this kind of analysis. And again, I'm saying, very key for this analysis for monitoring programs, you know, like which year are we really at the highest, which year are we doing badly, you know, like you'll see some, but because this data is really made up, it does not work. Okay. Otherwise, the other way you do it, you will be able to put everything all the years from the time from the beginning, all the years, all this way up to down. Okay, so let's be able to change this a little bit and see what if I want to just change this back to maybe my three. I just want to see maybe pottery. This has been my theory. You can change this. Now you see the period that we have now brought in is months by year by month by month by year. So this is now at the time that you can look because I have used the chart of year over year. So you will see that again, this is changing depending on the chart that we are using. You've not seen this before. Okay, so let's say we're going to add your maybe a measles campaign, I'm going to take it back to months. And then we probably want to add now to data elements. Let's see how the data elements can compare in here. Month by year, it's by year. But say we want now to add in the data elements, which we're going to add here or just click there, and this is going to be called MR1, measles, rubella one. We're going to add in the data elements for the measles. Okay, it's saying that this cannot be compared. So we need to adjust our period. We're going to adjust our period so that we can be able to change this. Sorry, the year over year can only compare one, sorry, I think I was moving faster. The year over year can only compare one element at a time. So basically this is now what we can be able to do with our, our charts. So next is we're going to look at a chart where we are now going to be mixing up the categories. We're going to be having different accesses being introduced in here. And an example is where we want to be able to compare some dimensions. Our dimensions say you want to compare them with the different months and then for the different data elements. And an example I'm going to give you, I'm going to open up a chart that has been sent for us. This chart is a institutional, institutional deliveries in the last 12 months. This is a kind of chart, I don't know how many of you have been able to develop such a chart. And here what we are trying to display is two category charts. This is a two category chart. It's a normal column chart, but you're trying to bring in two categories. The category of, of, for this case is rural and urban. And also the category of the different months for this case. So we want to be able to make such a chart. As you can see, we have for, you know, we have the months here and each month we are going to compare the peri-urban and rural for each month we want to compare the peri-urban and rural and so on and so on. And for each month we want to compare two data elements. So this is also a new type of chart, very key to analyze different categories. You can have the male and female for each month and you are doing some comparison. So how this is being created, we will be able to select the, our data, our chart type, which is called, as you can see there. And then we will be able to select our period, which is the last 12 months. And then we add in there. So we can be able to sub-categorize. So it's basically sub-categorizing. So let me just read something. So I'll come here, it's a form chart. It's not in a special chart. And when it comes to data, we want to look at, this is institutional delivery by locality, institutional delivery rate. We want to see the, the delivery is in rural and the delivery is against urban. So you can hide, maybe put a period, it is the last 12 months. Then you can hide, the organization is the role of training lab, but when we do update. So we want the data into the down category here and then we want the recent app in the service and we update. So you see what it has now given us is the, no, the period is up there. So this is what it has given us, but we want to bring in the dimension into the same, into the category of the location. So we have rural part of urban. So we add all this and then add this into the category. And then again, we are able to have organizational category and then we are able to see our data up there. The data is up here. Then the period is down here and the period is sub-categorized by that and then we update. So we have one data in the service, which we are showing here. In our category, we have demands, but now if I move this one up before, you will see the difference. You see the difference. So it will be able to sub-categorize the rural, first the sub-categorize the rural and then give me all the demands. If I push this one up there, it will be able to first sub-categorize, first give me all the demands and then sub-categorize the activity. Just as simple as that. That is also some type of chart that helps in analysis from period to period, looking at the different dimensions. How it together? Good. So if I change this one up, you see how it changes. It will be able to give me the sub-categorization up here. I'm looking at the rural category and I can see the trend over time. And when I look at the rural, I can see the trend over time. So for those who are benefited from the rural clinics, this is the trend. So the trend is not really showing very well, but with the line graph, can we just add a line graph here? Maybe just take the line graph. So it will also show you a trend within each category, how the difference will be. The period can be changed to what you want and so on and so on. Okay, so that was the categorization of being able to categorize the categories. So next, you can look at an area chart. So probably you want an area chart within the different categories. The next and almost the second last chart we will be looking at out here for the day. And this is going to be a new also entry into the charts. And this is going to be looking at two having two axes. So remember in Excel, you can have one y-axis here and another y-axis here and you present the same data together. And this happens mostly when you are presenting aggregate data together with indicators. So we will be able to see that the numbers are shooting up and leaving the aggregate data below. So the DHS2 also has been provided for us to be able to have data presented into more than one axis, the y-axis. So we are going now to be able to look at this. And to do this, I am going to use again an example from immunization. In these examples, you can use them, it could be the number of HIV positive cases and the percentage of new cases initiated on BRT. That could be one. We could go to malaria, number of malaria cases and presented together on the same chart with percentage of positivity rate. All that can now be possible into one chart. I remember most of you have been downloading data to Excel and be able to achieve that kind of chart. But let's see how we can be able to achieve this. So I am going to again flash this. Members you can save as you want, but just for the purposes of the demo, I am only making clear to them as I move. So we are going to have a chart where we want to show two y-axis. And so for this one, let me use an example of the BCD doses given, so that means you are counting the number of people who have received the doses together with the percentage of the BCD coverage. And I am going to use a bar graph and then probably I can make it a line graph. You can just be able to mix them up which I see. So for start, let me choose a column graph for that. And then the data I am going to choose, I said I am going to choose two indicators or two items, data items. One is an indicator which must not go 100%. The other one is numbers which can even go to 1 million address. But when I put them in one bar side by side, I will see the numbers up and the indicator way down and very difficult for us. So let me see, I am going to go BCD doses given, which is this one here. Together with BCD coverage, which is this one here. And again, I am still selecting what I want to show. And then I want to look at it for the last three months. So I want to compare these two for the last three months. And then I want to first of all see for my whole country, for the whole country, which is the training ground, which is selected here. Now I cannot date. So you see what I was telling you is it's very difficult for you to be able to, this would be really non-MID chart presented. Where the numbers are up there, then they, for somebody to be able to see the percentage properly, you may probably have a ton of these, then a ton of that, a ton of these. So basically, this is not what you want to present. This would really look a bad chart. So for us to be able to do this, we are going to modify this so that we can present the coverage on this one. So as I did, we can be able to see the line come out straight and this is what we have been able to achieve with a lot of packages. So let's see how to do this. We will go to options and we go to settings. So under settings, you will see that now I have my two data items. One is the BCT coverage. So I can say to put this on access one, access one, and then probably this one I put on access two. And I can say whether I'm going to use a bad chart or for the other, I can use the line graph. Those are the two options now that we have for this, for this. So if I had a 30 indicator, it will also be able to show that and for now deal with two. So again, remember, I am trying to be able to use another access to be able to make my video look very attractive and be able to have some meaningful presentation. So that we can go to see the percentage is very well and also the number.  And then by default, this is what it is, all of them are bar graphs, but you want one of them to be an indicator and this one you want to present in an access one. We have four, so you can have four data items here and then update. And when you update, you will just be able to see that, you know, this upper one is presenting percentages. And again, this is not really what we should have been able to, we can increase this scale here so that it can be able to push more. This one has to be a hundred, but you can go to increase this scale and if you go to options, remember you have your access, you can go to the access one and then you will be able to make the maximum. So something like that so that you can be able to be able to differentiate them. So you can increase your maximum here and then you're able to see this. So I pushed it really, really far. I can put it back in here. Yeah, so so that I can be able to see this. I made it so short and go around. Yeah, so you can play around with this and see that you at least you are able to have a very smart layout. One layout of this. So that is basically what we can be able to do. So let's keep changing the scale. Whatever you can keep playing around and see. So this is now the one access and access and you can even be able to label them. So in this case, we are, we are going to label them on our first access, which is the first one. Yeah, we are making it. They're going to say it's the big, the DCG those given that's the one that is showing in the center side. So come here and make it custom. And this is DCG those days. Even. And again, I just wish to be able to start by changing the car. And then the access to that is going to be changed. Oh, that is BCG. That's also changing the car room. Okay, so that was dealing with two access. And you can easily now see that my chart is really displaying my my perspective very well. So this is also showing the numbers very well. Again, this is that which is made up. I wish it was having different spikes. So this is the also one of the new ways to be able to create your data. And again, you can. So if I want to add in my room and up and that's categorization also come in here. You don't have a band then I need to. As it's going to happen here. And then that is up there. So you can still have your, you know, your other contribution. So if you want to look at for each month by different categories for what just time that. So these are all new ways of being able to, you know, to look at your data. So this looks really also quite cool. You have different dimensions. Okay. So the last chart we are going to cut is the scatter plot. The last chart we're going to cut is the scatter plot, which again I'm also going to start from a fresh given the time and see how we can be able to use this. So this scatter plot for some of you are going to have, let me start by opening and already existing and then we have to see how to create it. So I'm going to open a chart which is a NC first visit first visit, compared with a NC for this is outlier. So here we are trying to do some outlier analysis and outlier analysis, trying to see which facilities are off. I mean, of the, the, in terms of data collection. So, so in terms of the two data elements we're looking at one compare the NC first visit, compared to the NC for the list are using this. So this is really going to use the outliers to be able to apply it in the prototype range, Z scores over five days for us into this. So this is also a new entry into the highest to mostly for those who have been working with the initial problems. This was one of the apps that we had originally that was separate. And so this has been brought me to the core for us to be able to have at least this. This of course, goes the wrong way to just presenting the data, but more about analysis and being able to know which, which, which charts you are able to compare. And then we see that because of this catapult, it has just changed their dimensions of work with a change. We have things like what for me to be introduced in here. It really takes an understanding of analysis.  And all this, all the ones that I read are all outliers and then the ones that I know that and you need to go back and check that because it's not aligned with what. So just to be able to be able to produce this. It's also as simple as being able to save the nation. But again, an understanding of analysis is very key. And then all the time I'm not going to be able to go through that. And I'm just going to be able to show you how to be able to create this analysis. This is used for indicators that are already related or that are immediately related. For example, first list and say, and first and foremost, we have some kind of relationship.  So for that particular case, I am going, I'm going to flash this. So the first step is to select the type of chat, which is this kind of chat. And when I say it already, you can see that it has already changed our layout. And I will now start looking at the data that you want to be able to put into this. You can choose what to put in the data by hitting on the data. And now we're just able to see that even our way we select data has changed. We have the particle and the horizontal. And in the vertical, let's put our NC first list and see one, which is this first one here. And then we have selected that then we go to the front row to put NC first list. And then we can hide or update. We go to the home units. And because this is going to be analyzed at source, the home, the period, the period we can use last year, let's keep it last year. Okay, but because this is going to be analyzed at the facility level, we want this, this, and I would try analysis is, is, this is a source of data. We are going to be able to put this a facility. And I will choose, I will not go into each of the facilities, but I'll just come and say, try training and I say, and then I update. So you will be able to see that now my scatter chart is coming in and I can go to see this, but for me to be able to do the outlier and the. The period is last year, we're going to get this, we need to be able to go to the options of outliers and specify some of the other extra selections. So when I got options, again, the options, this is new, you are just seeing this coming in, because we are in the chat of outliers. So we'll just click here, and then you just select outlier analysis. And again, you will just use the detection mode. So what do you want to use, you want to use the prototype rate. Again, this is the knowledge from our trial analysis, it's not the details we are trying to be able to spray this. So let's not get into what is this or what's the private score when we use this and all that. So for example, for this one, let's use the prototype rate. And then the threshold factor set at 1.5. And then we want to be able to look at the extreme lines, we want to be able to show the extreme lines in this and then last day we want the one percentage extreme detection. Again, this is the information that will give you when the outlier analysis for it. And when you update, you will be able to see your chart and you will see the outliers have come in red. So what is the rate you point to it and to give you the facility name. Otherwise the rest are all in here and I'm able to see that we've been able to draw a plot for us, our scatter plot that shows our trial analysis for LZ and first width and first width. And again, you can be able to zoom out. Sorry, I lost my connection. Okay, so I was going to just be able to zoom out. When you people in it, you can be able to zoom out. You can be able to highlight, but you shouldn't be able to highlight this. So when you do the highlight here, it allows you to be able to zoom and do your analysis in this facility so that you can see even the nearest by facility. So the zoom, this allows you to be able to zoom in to the facilities to the level that if you want to take it back up a bit. So this is just for the purposes of zoom. Click and click and zoom out. So basically this is the end of our charts. There have been quite, quite many and I think we've been able to look at all the charts. We've been able to look at the column graph, the bar charts, area charts, pie chart, or weather. Here over the scatter plot. So basically you have all everything that is in the DHIS2 latest version being introduced to you and highlighted. I will take this time to really stop here and maybe allow for a few questions since we are almost full time, but I do encourage you to go over and be able to go via using the user manual, the guide that is brought into Mundo and then go step by step. I will be going over the weekend so that you are able to go through the chat. But most importantly, I would encourage you to try this with your own instincts if you are using the same project because there is a training here, but there is also translating this to what has been installed in your system. So we can't stop here and then we will take questions and I think we have a few minutes, about seven minutes for a few questions and then see. So the one of the day, Daisy, you can share the one of the day. The one of the day is Chigali. The one of the day is Chigali and we will put it in the chat as well.