 Right. I see that there are some comments in the chat as well. Yes, it's really good to see so many comments. So as like, I mean, I think the general agreement is that so basically how this line charts and radar charts those are kind of complimentary to each other. Claritivize it's always line charts are superior because you have, I mean, it's basically because it occupies larger screen area because you have larger screen area you can put things more clearly, but then again that that itself becomes a disadvantage. If you are trying to have dashboard with so many items right so it's always the way you interpret. I think the entire discussion around data visualizer session today and which chart type to use it's mostly yes there are some key elements that you have to compare some, some like fundamental concepts, which are applicable to each of the different types, but other than that, it's mostly about the specific use case, the scenario that you try to apply this. Right. Okay. So, let me share my screen again. So, I'm going to open the data visualizer, and let's create a line. So I will select line. And for the data, let me select data element from the HIV group. I will search for PL HIV, new on ART, this one. Okay, selected. And period I will select last 12 months and the organization unit, I will keep it at training land, and he'll also add this one to the series. And let's try to update and see what happens. Right, there we go. So what do we see here, if we try to interpret we will start with filter we are seeing data for training land, PL HIV on ART and for the x axis we are seeing the periods last 12 months. And the series we are seeing the gender desegregation of the PL HIV new one ART. Okay, right. So, what can we say about this I mean, like if I just ask you to interpret. Okay. Now, what happens, like if you okay so I will just ask it in a simple way and let's see how this direction you will interpret. So if can can someone interpret the data, just looking by looking at this chart what can you say about me. Yes, I see something about this chart. Yeah, actually for me they are two things. And when you see the trend, the both of the two trends for person living the HIV for new who has been put on for a new one ART, you see that there is a big difference between females and the males where females are being infected by HIV are getting new infections on high rates comparing to the males and the first observation. Then, not only that the other things that yeah for females for males there is a lower number comparing to the females. That's what I can say. And also there is an increase that is an increase from May up to May 2020 up to April 2021 for both male and females there you can see that there is an increase for a new HIV infections. That's fine. So, can you also say something about by looking at this, this visualization can you say, say something about the gap between male and female. Like, what can you say about it, maybe like by May 2020 and you can compare it with the value of April 2021. What do you think about the gap. So, one thing you have to notice like say, we have like, say around 1000 gap, which is there by May 2020, and this gap. This gap has persisted right throughout the year. And by April 2020, we are seeing again a gap of around 1100 right, but like, again, do we have any idea about what must have happened because all these people are living right so, I mean, not all this but like most of them are living if they're all living. What may have happened to the cumulative like I mean when you take the entire number of people with HIV. Would that be different for the entire training. And we get that idea. Come again please. Yeah, what I would say is like several thousand gap here around 1100 right all this while the gap remains. So when we consider about the cumulative total people living and maybe you have males and female. What type of visualization. You know like, does it explain it well, whether the gap of cumulative have been increasing or not. Sure, maybe. When you consider the increase of the variation for cumulative of those person having HIV. We may also consider other movements in and out because these are new but you may have some have been died or some have been who have, who have maybe have been lost out so. So considering that for other say the movements, there were nothing happened you may see that there's also an increase between female and males, even the difference will mean the same between 1000 or between 1000 and 1100 something. So if you don't if you we consider that other movements means zero. We will still have the difference of 1000. Okay, so let me show you something different right now I'm going to click on options. I wanted to give my view and a little bit on the data. Yeah, people living with HIV new on ART. Even though the gap has met team within the range of 1000 between me and females who are new on ART, but I'm not sure what a graph or the visualization is saying that the infection rate is higher in females than male. So I'm thinking maybe there's a possibility that males could be higher than female but maybe females are always going for the ART treatment and meals are a bit deliquent because from what I can see the gravity saying people living with HIV new on ART. I agree. So in fact like this is how you interpret right so when you get a visualization, the way you interpret is going to be always different. So to do that we always have to apply the context parameter to the visualization so I think this is exactly what we will be discussing on I think on date in interpretations. I think like you were heading somewhere down that direction so I will just post it there because that's a separate will keep that discussion for a great day. But what I wanted to highlight was that let me apply the cumulative values here and see what happens like even though the same gap continues, what happens to the gap between the males and female cumulative. So I'm going to update update it and you can see here right so these are the cumulative values right and now you can see because we have this gap the cumulative difference between the two are keep keeping on increasing right. So this may be one visualization that you want to highlight right this gap, because the same gap between male and female persist the cumulative gap keeps on widening right widening. Alright, so I will just stop it there interpretation part of it we will certainly do on day. Okay. Right so the time is, yeah, 350 here. So today, as I told you before, we will exceed the mark of three hour that we usually do unfortunately because there are too many sessions to cover. Right so let us quickly move on to the next section. So we will be discussing one disadvantage of line charts and how we are going to overcome it. So I think there were a couple of examples that you will discuss. Where you felt that line charts may not be ideal, especially to compare the differences between same time period or between same months across the years. Whether there was difference, I mean, whether we know something different between January months of January and month of October, across three different years right. So to do this kind of a visualization, we have something called year over year chart visualization. Okay, right. So let me open up one favorite. I will just click on open and see whether it's there. Yeah. So we open BCG coverage year over year three year comparison. And we can see the type is year over year line. Right. I click and open it. And this is what I see. Okay. Right, so let's try to interpret what we are seeing here. So first of all, we are seeing year over year line chart and the parameters that we can configure have now changed. So we are seeing filter category and series filter we can see it's the organization unit and the data which comes into this entire visualization and when we are visualizing actually in the category dimension because this again is a specialized line chart the category dimension represent month per year months per year right there are so many several options which are available we once we selected is months per year. So it's always from January to December. And importantly, the series dimension. It represents each of the years. So we have 289 and 2020. Okay, so that means each of these lines are representing individual years. So, by looking at this, what can we say. So, obviously, any questions. So, obviously, from a high level perspective when we look at this, we always see that the lines do not cross each other. So that means 20 this BCG coverage has generally been improving right. So you have the green light here 2018 this is 2019 and 2020 so it has been improving. But then again, we usually see that there are some like in January it's usually a low value right and then there are spikes in between, for example, June and September. It's it always seems to be going up. Right. So there's some pattern do you see, there is always a pattern that follows. So these patterns are very important to note so that you can do interpretation and even when you are planning your health services orders maybe maybe when you are trying to improve the services you can identify what are the areas where you are having bottlenecks I mean if for for an example if in January if it is reducing what is the reason so all this we will talk again. Maybe I mean definitely during the interpretation sessions that we are doing on day eight but I just wanted to highlight what I mean what are the advantages of having these kind of a year over year line chart. If you don't have this visualization, we are talking about a very lengthy chart spanning across all the three years, and it's very difficult to compare individual months of different years right in a simple line chart. That's the advantage of having this kind of a chart. Okay, so in fact we can try and compare it to it. Let me open another one. This is my month last three years. Yeah, this one. So, hope you remember the previous chart I showed you about year on year, and let's contrast that with this one. There you go. Okay, now it's the same data, right we have 36 months, the same data element, right, and we have the occurrence, right, as a filter. So you are seeing all the 3336 months in this x axis. Now what's the problem. It's very difficult to compare January 2018 which is here with January 2019 which is there right so we can yeah we can feel that this is 6965.9 and this is 68.7 so it's a very tedious task so that's what is facilitated by having year over year line charts. Any questions. Right. And here, what we selected before was year over year line chart, but we also have the year over year column chart right there are different types. There are actually two different types that are available for year over year comparison. Okay. Let me try to open that one again. Are there any questions up to this point. So, what I'm going to do is to just talk a bit more about the different options that we have in series and categories parameters when we are using year over year chart. So let's look at the series. So series is always, it's always the years right, which are available so for example it could be relative years like this year last year last five years or else we have the fixed years. If it comes to the category we have like it doesn't have to be years like it can be last three days last seven days last 12 months right quarters per year. So, but only thing that you have to remember is both the series and categories are representing something to do with time period. And that's the main difference when you talk about year over year charts. So the flexibility we had previously to apply series and I mean to apply data dimensions to series and categories. It doesn't happen in the year over year charts. Okay, that's a special thing about that's a special thing we notice when we are doing year over year charts. Right. Let us try to now here we are seeing only one data item which is BCG coverage. Okay, let's see what happens if we try to put another one right so for example, I'm just trying to put here. Maybe MR to MR to coverage right I just put them and I'm going to click on update. Let's see what happens. What happens. It says there's a problem with the layout, a single indicator must be the only data item when using indicators as data in field. Okay, so here it only allows us to use one indicator one data item. Either I mean say MR to BCG one BCG coverage. So here you can put multiple data values, just like we used to do with line charts, we can't do it here because I mean it kind of makes the interpretation very difficult when you have a lot of data items that are visualized so you have to remember that right now it only allows one data item to be included in the field. Okay, so I assume it is. Are there any questions about year over year how to use it and what are the advantages. I think we don't use year over year column, we have just been using yet over year line on right. Okay, let's try to do year over year column. Yeah, so I change it to a year over year column and I update. And this is what I see. Right. So, let's again, that's exactly what I was doing to do next. So, you know, yeah, so here we have what what what changed from line to column obviously lines have been replaced by columns. And what do we see here in the x axis we have the months, January, February, all the way up to December, and the series we are seeing each of the year. Right. So, good thing. Now for this particular visualizations that we that that that for this special data requirement that we wanted to address here. Which one do you prefer line or column. You prefer line. What's the reason why you don't like column. Actually, when you actually as I have said before, when you are called, you are using this year via comparison. You don't you want you, it's not only to compare the trend of from the beginning up to the end but also you want to compare some kind of decision analysis. So, when you want to compare like Kotas and Kotas and the changes or variations and do you want to talk about those specific factors related to the seasonal issues. This kind of columns will not really help to visualize when you are you want to visualize in your eyes. It's not easy to capture what you want to cut. So the thing is just going back to this fact I noted when I was comparing chart types when you are always thinking about trend analysis lines are always better. So here we can see say for example, we wanted to see like last three during last three years in the month of April. What kind of differences do we know like I mean is it like has been has it been improving yeah that kind of analysis we can take. Again, if if you want to compare January with October, like we have to look at we have to focus this part of the screen right at the beginning to an area which is towards the end in the time dimension right so to humanize this kind of comparisons in column column charts is always difficult. If our objective is to see whether the values have been changing over over the period in last three years across months lines are always much better. So that I mean an easily understood by people, but still we have this option available in case you are trying to you know like just focus on one particular period, something like that. Alright, any more questions or else we can move on to category charts. Right, so I assume there are no questions are there any questions. Okay. Let me open a favorite item which is, I will open this one institutional delivery by locality last 12 months and the chart type is column let me open. Right. This is what I see. So, what do you understand just by looking at this visualization what is different to what you have seen before. How many access are there. 123 123 123 how many. Okay, yeah, that's a better way of putting it so it's not. You know what I said three axis yeah. Two x axis right so you see one in the bottom one at the top. So let's see what has happened right so here, I mean like we are very familiar so if you just forget about this. Whatever the axis that is appearing here at the top. It's going to be just a plain column chart right so you have the x axis that are having data values and here, it was just going to be this very urban rural and urban chart right if you just come here or concentrate on this one. But what has actually happened is as he correctly mentioned we have two x axis one axis at the bottom so like you can see the one here. At the top is a period right last 12 months so you are seeing this last 12 months, and you have another axis, right, which is very urban rural and urban, which is at the bottom. So essentially what happens is if you just concentrate on one segment here. You can see data from May 2020 desegregated by very urban rural and urban right so for each of the month, the data that is in the x axis has been desegregated. Okay, so this this is what what we call as two category chart. So you have the category dimension here, and you also have a category dimension right at the top. Okay, so I mean these two category dimension charts are applicable to column stack column bar stacked bar line area stacked area so all these types of charts, this two categories are. Right, so whatever the column charts bar charts line charts, you can apply this two categories. Can we apply two categories to pie charts. Yeah, sorry yes. So, did you say yes, obviously not right so so to pie charts you can't have so thing is like the whatever the charts that are that I mean categories are applied you can try to category charts, the ones that I mentioned right the plane bar and stacked bar column and stacked column and the line. Okay, and which like now how do we know which one goes in the top as the category dimension and which one comes to the bottom. So that in fact is selected, based on the order that we place them in the category parameter. So here we have the periods at the beginning and then the urban rural so let's try to stop it like this, and click on update and see whether whether anything changes so I click update. And this is what I see. So what actually happened. We are now seeing the rural urban peri urban at the top and period at the bottom. So, how it is configured is whichever one but which is at the beginning of this, I mean whichever plays first in the category parameter is going to come here at the top. Which are, which is the second one, what comes after that is placed at the bottom. Okay, hope that is clear. Alright, so let us try to make a category chart from this stretch, rather than opening one that was already saved so do that I will click on new. And I click yes leave. Right. And for the data dimension I will select an indicator institutional delivery rate and the period. I will keep us 12 months and the organization unit I will keep training land. And let me update. So when I do that, this is what happens right. So this is the plane chart that we had before. So in this one we have one by axis, which is representing all the values, and we also have one x axis, where we have all the periods of last periods. Right. And what we can do is we can complicate this chart by adding urban rural peri urban I click here, and I can select add to category, right. I will do that. And I select these three. I just hide it so that I see period and urban rural coming here. So, based on this. Where do you think we will have the period dimension, we will have it at the bottom of the top. If I click on update. Here on the top, period on the top, anybody say period on the bottom. Okay, let's try to update. And you're correct. Yes, we see the period at the top right here. And if you just stop it. And update, you see the period goes to the bottom and the peri urban rural urban category comes at the top. And this way you can, you know, like, have a kind of a complex chart, which has multiple dimensions in the category under the category parameter. Okay, we can also, for example, try to apply this. Maybe, let's try to replace it with area and click on update. This is what we see, right. This is the state was it was previously called my update to area. Let's try to see, try and see what happens if we select bar. Right. See, we have, like, here now, I mean, the thing is like, you can remember right column and bar the X and Y axis swaps right so here, of course, category goes to the Y axis. When you are applying it to bar. I do like this and you're seeing the line charts. So, you can see it is applicable to column stacked column bar stack bar line area right for these jobs. All right. Any questions up to this point. If not, we are moving on to the final topic that we are going to do today which is combination charts. Are there any questions in slack or chat. If you have any questions related to the exercises, please ask in chat. Our facilitators will support you so few questions have already been raised. All right. Let me share the screen again. Yeah, here. Let me open a favorite item. ECG doses coverage last 12 months. Let me open this one. What do we see here. What is different here in this chart compared to the previous charts we have seen seen this. What's the main difference that you see here. What's the line and column. You have a line and a column together so interpret this chart I mean like, how do we like know which axis is representing the values for each of the chart type. How many axis are there in here. I must. Two y axis. Okay, I like that. I'll say yes. So it's not three axis. So you prefer to say yes, we have two y axis and only one access access right so yes. So here, we are seeing axis one, and we also have access to for the y axis so we have to y axis axis one and access to right. Basically what has happened here is now if you look at the value ranges in each of the axis, we can see axis one ranges from zero to 40,000 and access to ranges from 75 here or like even below and 87. So basically, we are also seeing the values of each of this chart type so we are seeing 30 30 so what I can do is I can hide one chart type. I want to just look at the, the column charts, we are seeing the values ranges from like 30,000 and 31,000 so these are the range that the values are spanning across across these periods right so basically, you can see that it's an axis number one, which is a y axis here, that is representing the data visualized in the column charts. Similarly, if you look at the line chart, it's the axis to which is actually having data ranging from 76 to 80 is 88 that is represented here. Okay. So another interesting thing to note is that is the starting value of the axis might change when you are using this combination chart by default. But together, this is a very good visualization and there are so many use cases where we where we want to visualize two data items in two different chart types, but in same visualization. So this type of visualization, we refer to them as combination charts. Okay, right. So, let's try to create this chart from the chart from the scratch. So what I'm going to do is I will click on new. For data, I will select from the data elements I will select BCG doses given. And from indicators, I will select BCG coverage. I have two of them hide. And the period I will leave as 12 months and organization unit training land right and I click on update. And obviously, this is the simple output that I will get because the chart type I have selected this column. So what's the problem that you see here. What is the issue is this chart okay or is there a problem. I cannot understand the bottom below 79.2. Why, why that happens, what's the reason it happens like that. Because of the low percent is, is percentages so quite low is committed to the alpha figures of the closest given. The root cause for this issue is that we are having just one axis, right. So this one single axis, which is here, it has to cover the data representation of both these chart types, rather than chart types, both the series data data series that you're seeing here. So for example, the green ones are the BCG doses, and the blue ones is the coverage. So because it's a coverage and it's a percentage, it's only going to range between zero and 100. But whereas these doses given it can, you know, like virtually range from zero to infinity. So the more the higher the doses you have for any of these months. The range of these y axis is going to, you know, like significantly vary and because of that, some most, I mean, like this value 100. You are usually not going to even see it's just that because the values have been displayed. So for example, if I just hide value labels. The problem is then, I mean, like, we can't actually even see that there's another chart that exists, right, to represent this BCG coverage. So that's a problem. So because of that it's always good to have values at least to see like there is something here down here. But to address this practical problem, the solution is to use a combination chart. So let us see how we are going to do that. What we have to do is we have to click on options, right, and then we move on to this step called series. So when we click on series tab, we are seeing our two data items the roses given which is a data element and the BCG coverage, which is the percentage. So at the moment, both of them are in the same chart type column and line, right, and both of them are in the same axis. So what we can do is to get the similar visualization as I showed you before. I mean, the one I say when I opened the saved favorite item. What we can do is I can convert one of these data items to a line. So let me convert this one to a line chart. Right, I do this. Right, I just do this one. Okay, and I click on update. Let's see what happens. Okay, so I selected a different chart type. But is it the expected output that people hoping to see. No, no, the reason being, still our root cause remains because we only have one single axis. So come this what we can do is the series step. We can add this line chart to the second axis. Right. In fact, we can even add third and fourth axis. So if you are wondering where we are this third and fourth axis are going to be. Now, if you can focus your attention here these are the locations we are going to have access. So basically, all these four axis are going to be in the y axis, the main y axis. It's just that the axis one is going to be on left side axis to is going to be on right side axis three is going to be again on left side outer left and axis four is going to be out right. Okay, so I just select this one axis to and click on update. There we go. We have the expected visualization that we wanted to see. Okay. So what if you put this access to the axis three. Okay, let's do that. You want to put this to access three. This one right. Let's see. What do you think this is okay, but the thing is, you might confuse the end user with the two axis. Like, when they look at this y axis, they will see there are two value ranges. So, obviously, because based on the color they can identify that okay the blue one axis one is the one that is ranging from zero to 40,000. Whereas the green one from 75 to 87. But most standard though acceptable, I would say the standard base you will you can have it here. It's a possible to maintain the column chat and and and and and and change the axis. Like you have them on separate axis but they in the same column shot. Not a lot. Same. Yeah, okay, let me do that. Yeah. Right. So here. So you want me to keep it here, but make it make the axis like this right so it's. This is what I get. It is possible. Right. So only thing, the person who's interpreting will have to, you know, like, because the only difference is, it's all about how your users interpret if they are really familiar with seeing DHS to column charts, which have multiple data items. They might try to interpret as just a plain column chart without focusing on this access. Right. That's the kind of downside. You understand. Yeah, users are so much familiar with our traditional DHS to column charts, they will totally know this. They will think that obviously I mean, I'm not saying like this will happen all the time. But like if you actually look at these data values, they will understand but in case if this data values are hidden. Then of course it's it can be a disaster right. They might think okay coverage, 30,000. That's what can happen. So, but yeah, it's a good question. So let me again. This one update. And let's make this one further more complicated right so now we are dealing with the was to access but we have to y axis. So let's make it to y axis and to x axis right we have already dealt with to x axis. So, let's try to do that. So what we can do is we can apply this urban rural dimension to which one. Series or category. What should I do. What do you think series or category. Okay, let's try that category. Yeah, hello. Shall I update. Okay. Let me update. And this is what we see. It's a very complex chart. Right. So now, this is where we can confuse the users right so when they look at it, they will first, I mean, try to figure out what they're like, what is this right so because here. I mean, it's not very easy to interpret. So you are seeing the periods x axis and peri urban rural y axis. Right. And for each of them, you have y axis of BCG doses and a y axis of BCG coverage. Right. So, you can do it, but always just think whether this is something really good to do, because like, sometimes even though you can have this complex visualization. Thinking of the time people might take to interpret this, you might as well create two different charts so that you know interpretation is much simpler. So you have the tools to use it but how to use it and to apply to your context is all what matters. So I mean this is what you're going to discuss in the interpretation so I really hope in that day you come up you can compare each of these charts and come in critically why you did not use this, you know, multiple two category combination charts, as opposed to a simple column chart. Okay. So, basically, we have covered everything. So, just to summarize what we have covered in the day. Let me stop sharing and going back to the object before you finish. Yes, yes, on the same one. Can you have a look at it as when you choose the series instead of category. Let me share my screen again. Yeah, what do you want me to do. So this one you want to move it to serious right here. And you want to have data also here. Sorry. We have to allow only one in the series right see. Oh, okay. Okay. I mean that's how the chart type works so you can't have data items so dimensions in the series. You can decide so for example if I, let's see what happens if I do this. You will have to decide how to interpret. It's very difficult right. Thank you. Okay, let me go back. Are there any questions. Okay, going back to the objectives of the day. What we covered in part one was. We went through the overview of data visualize interface. So, we just quickly recap what we did in pivot table sessions yesterday. Okay, and then we went through different charts, like the what are the inputs for the chart. I mentioned the organization units and periods right and also how to rearrange the layout of the chart using category series and filter so we defined category series and filter. This is a very important concept to understand category series and filter and also remember, you cannot say category appears in x axis right category appears in y axis no it's not like that it totally depends on the chart type in the adjust. Right, but you just you can understand categories the primary grouping, and then series will further subgroup, what is appearing in the category. Right, that's how it works in bar column and line charts and area, right, but in pie charts is totally different and you don't talk about. And then, of course, how to work with the disaggregations in the chart, we talked about it, and what are the different chart types and when to apply what are the best practices in applying each of the chart that we discussed, right. And then we discuss about several chart options like adding titles hundred percent stack values, how to sort and I, if you can remember I mentioned like it's the first item that is there based on which the sorting happens if you have multiple items and how to put the target line baseline right and also how to download charts. And then in the part three we discuss about high charts, red and spider charts and the gate charts as well as the single value charts. Okay. And then finally, the last session we talked about how to mention how to have the cumulative values, right, especially. And like, if you can remember that male and female example how the cumulative value application, change the visualization, and then we talked about year over year chart, which different charts which are there. Right. And then, of course, finally we talked about the combination charts, right, we have two y axis, right, and also we talked about multiple categories like where you can have to x axis. And we talked about the possible application of two y axis and two x axis, and of course the implications of having these kind of complication complicated visualization, right. So I guess that's all we discussed for today, and we can conclude the session. Of course there is a created exercise which is available to you, which you have to submit. And please also do the ungraded exercise, which will grow through all the steps that I have covered in the day. And complete the ungraded one and the graded exercise that would be good enough. And you will be able to achieve the expected level of skills and knowledge that is required for the data visualizer section. So any questions. And we can conclude for the day, and you can continue with the exercises and our facilitators will stay online on Slack channel to assist you all. And two reminders before we conclude, please complete your attendance, the word of the day, and of course we really appreciate the feedback. So if you have forgotten to give feedback in the previous days, please give us the feedback we always go through them and try to. I mean, try to change the way we present and we conduct the academy. Yeah, so thank you so much. We don't have any sessions tomorrow. Isn't that so sorrow. Yeah, you're not having any sessions tomorrow. And hopefully, yeah. So up there. We meet on Monday. If you really want we can try to try to have it on Saturday but I don't think anyone wants to have sessions on Saturday right. So that is the case then have a great weekend. See you all again at same time on Monday. Right. If there are any questions please type them and raise them in the Slack. If you have any issues with exercises, the graded or the ungraded, let us know and please give us a feedback. And on Monday we will do the map session again a bit of a lengthy topic. But then it's a very important one. Thank you so much. So anything else you want to communicate. No, just a happy weekend to everyone and see you all on Monday. Right, then thank you so much. See you all on Monday and have a great weekend. Bye bye. Thank you. Thank you.