 Right. Welcome back. So in the third part, what we are planning to do is we will go through a few other chart types. For example, we will go through pie charts, radar charts, single value charts. So that we'll be able to cover most of the charts that are there in DHIS too. And what will be remaining after that is to discuss on some advanced or new features in the charts such as having charts with multiple axis and of course the year over year charts. Right. So, let us start with the third session of the day. Let me share my screen. I'm going to open the data visualizer. And I can select. Create a pie chart. Okay, so if you can remember we discussed in detail about when to use pie charts and you have to remember like whatever the components that are there have to add up to 100% for us to create a pie chart. So to do that, let me select one important thing you have to select you have to notice when we select pie chart you can see that the two dimensions we can define our series and filter. Okay, so let me select. For the data. I will select. Data elements. And the malaria. We have. This is positive. This is positive RAT. I will select this one. Right. I can hide in the organization unit I will keep it as training land and the period. I will keep it last 12 months. Right. Okay. And also, let me add this to series. And we have three age types, zero to four, more than 15 and five to four, five to 14. And click on update. Right. And this is what I get. So you can see now that we can only have one item the series and right now it's the disaggregation of positive cases. So if you can remember that now we have three items for the filter so that means whatever the data that is coming into this visualizer. The decision will be from training land for the past 12 months for malaria cases positive from our PT, and that has been desegregated and visualized based on the three categories, which is represented under the series. Is that clear. So that's why we are seeing three of them. So basically series determines which slices that formulate the pie chart. Okay. Let me just try to swap this one with something else like maybe. Yeah, sorry. Period. And click on update. And this is what I see. So here now you can see it's a very vivid pie chart that that we are seeing but obviously, as we discussed in detail before. This is not the ideal kind of pie chart that you should have. Why, because there are too many items which which we have, and it is, I mean most of these items are of same size. So we can see, I mean only thing that we can you know perceive by looking at this is like there are so many. Like, there are months and most of the months are more or less similar. This is all what we can say. Right. But there are of course clear winners, but it's very difficult to compare when you have so many items in the series, or as the slices. Right. Okay. Any questions on pie charts. We have discussed in detail before so I just showed you what the key differences so we have only one item in the series, we can, we don't have a category here, we can put whatever the filter criteria under the field. Any questions. So let's see if we can move to the next chart type. It looks like there are no questions. All right, so let us look at this new chart type. I mean, it has been there for a while called radar chart. Right. So what you see here, we are seeing this radar chart for training land EPI BCG coverage, right. So in this radar chart, what we can see is we have like the outer layer outer circle in this radar, which kind of represent all the months for the period dimension. Right. And from the center to the outer radius. This is where it. Demonstrate the intensity of the value. So you can see the, like it is from zero to 100 right so more closer the value is to zero, you will see that the radius of the line is lesser and you will see it around the zero, but here now it's closer to 100 and that's why you are seeing the values here closer to the hundred. Right. So in a way in the radar chart, it kind of gives us this two dimensions that we can compare at a given time. So, we have this outer circle, which kind of separates the entire data into different categories as you can see here we have different months and from the center how far closer to the outer circle is the actual value of the data item that is represented in these lines. Right. So, all in all, it's a good chart to visualize two values, and you can make a comparison, but the thing is, it's a bit of a complicated process and this is exactly something a person can, you know, define or person can go into depth and say this is what is meant by this chart. I mean just by looking at it. So it's a kind of a complicated chart and it becomes more complicated, especially like if you have values which are very close to each other. So, I mean, ideally for this visualization radar chart is not a great one. So for example, here we can see the slight variations, which are they are imperiable rural and urban communities. Okay. And this is another example, like here of course you have only two items, the cases positive and the positive is microscopy, which is much easier to define compared to the previous one. And if it is just one data item we try to visualize in the radar chart, it is much much easier. So here what we can easily do is we can now just by looking at it, what can you say, like, if I mean, let me ask this question from one of you, like, how can you interpret this chart that you are seeing here. What do you see. Anybody. So, in fact, what we can actually interpret is like just looking at it. Yeah. Yes, I just want to say that as we just only have one and Kira. We may see that in April to June 2018 is where we have had a good high number and compared to the lowest number it was in, it was in January to March 2016. So, yeah, that's what I can say. Exactly. So I mean, like, this is very easy to like this. For example, if there is a seasonal variation or like from some variations based on the period dimension you can easily demonstrate by using this kind of a chart, you can do it because it is just one indicator that we are comparing, but if it is too crowded it may be relatively difficult. Okay. Let's quickly try to draw one radar chart. Right. Excuse me. Yeah, sometimes for some conditions like in my area. Sometimes we need to compare different periods. Let me call them seasons because some analysis requires to consider some seasonal factors. So, using the previous slide you are presenting can we compare like three years. Each and every quarter is put on the same side so that we can see the output of the same season like from earlier. I don't know if you understand what I mean. Yeah, yeah, I do understand what you're meaning. So it's like, if you can think, if you remember like I mentioned the line chart is a very good way to compare the trends. If you try to convert this one to a line chart where you are comparing quarterly data across three years, what you still do is you will have one single X axis and all the periods will be listed in that single X axis. So, like if you want to compare with the January 2017 and January 2018 or maybe January 2017, 18 and 19, whether each of them are different. It's a difficult thing to compare because you are still looking at the same X axis, maybe a different point of time. So to do that, doing next something called year over year analysis where you are actually seeing a line over. I mean like you will see multiple lines for the same time period across the years. So I will get back to it when I'm presenting that which is a much easier way of comparing this season variations of data across the months. Yes. Right. I will quickly draw one chart. So I will just keep this malaria test positive just like that I mean the only this one and maybe I will just get rid of this one, and I will select radar. So here I have to have one in the series and then again in the category dimension. So let me move period to categories so that I will see this entire circle, and I will move data to series, and I will click on update. And this is what I see. Okay, now let's try to demonstrate. Let's try to analyze what we are seeing here. So for example, we are seeing all the months in this outer circle. Okay, so that means right now the periods are in category dimension. So that means, whatever you are seeing in this outer circle has to go to category dimension. And the series is this line right so this green color line the data data dimension is represented by the series and the data is filtered from the training land for the entire training land we have the day. Okay, so in a nutshell in a radar chart what you have to do is you have to put under category whatever we are having this outer circle and the data or like whatever you want to have in the line here. One line or multiple lines you can have it in series dimension. Right. Hope that is clear. So, what we can actually do is like if you want to show a very crowded radar chart, for example, we can select this malaria age and try to add it to series. So, as I told you before, whatever we add to series will appear as lines right so what I'm going to do is I will select one additional dimension called malaria age and add it to this. Here. Sorry, I will just put it here. Okay, so the data which comes here and I click on update. And then this is what I see. Okay. So, if I explained to you what I did here, the periods is clear it's it's all, you know, in the outer circle, and data is filtered to the training land. We also filter all the malaria positive cases. Under the filter. So, for the scope of this visualization we have all data from training land and the malaria cases positive. Right. And that data we have disaggregated based on these three ages. And because it is in the series, all three options that are there in this disaggregation will appear in each of the lines right so we have like the three age groups, which are appearing here. Now, please note, this is a kind of a trial I mean, this somewhat involved trial and error as well there is no harm trying to wish you I mean like, put something, I mean, a wrong or inappropriate thing in any of these dimensions and try to update. You can't break DHS to by doing that, you might sometimes even see errors. So if you see a real so for example, let's try to update now. I mean, sometimes it gives us very specific error like this series is empty right, but it might change sometimes if the error is not specific. So you just have to click like this and then finally you will get a visualization and you can decide whether it is appropriate or not. All right. Any questions on radar. If not, I think we can move to the next chart which is gauge. Let me create a new visualization. And I've liked from the chart type gauge. Okay. And here let me select for the data data from immunization. Or PV, right, I will take order for your three coverage or PV three coverage and hide it. And the category, I will also take the last 12 months and the organization unit test training that right and I click on update. So before I click on update you have to know you can you can see what happens here. We don't have a category to configure here we just have the series and whatever we put in the series will appear like this. You see, so it's just that one single value we are trying to display. So here you are seeing the valued 72.5, which is coming from this data which is order or PV three coverage for training land for last 12 months. Right. So this is a nice way of demonstrating a single value, which is kind of like I mean, we can just focus the induces attention on one very important value. We can just expand this further. So right now we have one random color here. What we can do is we can go to legend and click on display legend. And then click here, select a single legend for entire visualization, and we can select one legend which has been predefined right so for example we have one full API API coverage so I click and select that one. And I click on update, and you will see the background color of this visualization, the gauge shot is getting changed. So here now we are seeing yellow color. Right. So how this color comes is because we have already configured in DHS to a legend is which says like there is this I mean between a particular range of values, you should apply this particular color. Right. So that's exactly what we did by going into this legend and configuring which legend that that should be visualized here. Please can you apply the baseline and the target line to see how it looks. The line and target lines okay something interesting now ideally you can't okay so let's put a target line of course you want to put an on but it's maybe you want a baseline maybe. And so you see here right, you are seeing just just like a tick here target and baseline. Right. I think this is quite simple to understand the gauge shot. So in the same case let's move on to the next chart type any questions. I'm kind of rushing these of these two topics because these are kind of simpler ones. Are there any questions major questions. No right. Let me move on to the next chart type which is single value right so I will click on single value sing. Sorry, this one, and I click on update. Right. Let's see what happens. There you go. Right so previously we had the value 72.5 in the gauge chart, because basically gauge chart and single value chart is the same thing. Right. So it's just that it's a number single value that you are showing. And most important thing to know is like these values. I mean so for example the 72.5 it actually now the text color of the single value chart is also having the same color. Based on the legend that we apply. Right. This is something that you see from 2.1. I mean 2.35. So for example if I just select the display legend, and it becomes just plain black color, but you when you apply the legend, it becomes the color of the text is becoming. I mean it's getting changed based on the legend that we have applied. Okay, this is in fact very useful this this kind of single value charts, especially when you want to you know display the striking figures in dashboards say for example, COVID-19 deaths in the country cumulative deaths or cumulative cases is for these just the plain number really matters. And these are the, I mean, so this is one common use cases where use case where you are using single value charts. All right. I think. So we have covered most of the content area for the part three of today's session. So any questions up to this point. If not, like, what's your preference we can go and do the ungraded exercises, or else if you're fine. Maybe we will take five minutes break. And then the final break we are going to have to be. If someone wants we can take maybe, I don't know five minutes break and move on to the last or I mean the final part of today's session. We will be discussing few advanced visualizations and configurations we can do in data visualizing. But I think you can proceed on things. Yeah, are there any objections so if there are any objections. Please let us know in the chat. Okay, sorry, I was not focusing on chat. There are a couple. There is one question. What is the advantage of radar chart than a line chart. Why should we use radar chart instead of line charts. Right. Good question. Thing is, so what happens is, you can actually say one is superior right so basically thing is like radar chart. It's the way you look at it right the radar chart. If you're putting it in a dashboard it consumes less space because you I mean that particular line that single line that you are seeing in a plane line chart. You kind of call it so that it's it covers the entire 360 degrees so in a small screen area you can, you know maybe put that visualization that you that usually occupies the entire screen with if you do it in a line chart. Other than that, I don't see any major, major differences maybe like it's a good question anybody in the audience who have better idea as to like when some radar charts maybe superior to line charts. In the meantime, if anybody feels that we should take a baby couple of minutes break please let me know in the chat. We can proceed and give me a couple of seconds to display the word of the day. So, you can take at least couple of seconds break. Right. So the most awaited bit of word of the day for today is this one. Right. This is something from the today's session itself. Right.