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Power BI Desktop – Visualizations
5. 31. Identify and describe uses for visualization controls - basic types
Now, in this video, we're going to have a look at different types of visualisations you can do. So the first thing we've got here is a table. And by the way, we're using this data from a different course of mine because I've found it to be quite a nice source of visualizations. So this looks at six English regions over 22 years and says, "Okay, what were the numbers of house prices or sales volume," as you can see here. So this is a table. More accurately, this is a matrix. A matrix has columns with details going across, whereas an atable, such as this one, has columns going across, but those columns are just for different measures. So you can see, we've got these five different columns that we've explicitly said, whereas in a matrix, it's a bit like a pivot table. In Excel, we've got all of these fields going down. This is a hierarchy, so we're going from 1995 down to 2016, but only one field goes across. So there happen to be six columns plus a total because there are six regions. If there were 100 regions, then we would have 101 columns to include the total. So this is a table and a matrix. So the matrix is equivalent to a pivot table. Now, the thing about these matrixes is that they're good at numbers. The trouble with numbers is that you might get snowblinded; you can't really see all of the numbers in your head. So this is when visualisations such as bar charts could be useful. Now, this is a stacked bar chart. So we have got, yes, the total number of sales, but inside that we've also got the region name. So, for example, we have Greater Manchester with a sales form of $30,000. You've noticed that I've also got a drill through. I can drill through to another page of this report, having due regard to the fact that I've just clicked on 2008 and Greater Manchester. Now, I've called this a bar chart. Technically, this is a column chart. A bar chart would go the other way around. So there is your bar chart. Now, you can also have these bars not on top of each other, but right next to each other. So the difference between those is a stacked column chart. You have, say, all of 2006 all at once in the same vertical space. So we can see 61 plus 26 plus 27 in total, which adds up to about 240,000. or you can have them separate. So each of these individual bars will be on a separate vertical axis. So the question is, do you want to see the totality or is it more useful for you to be able to see, for instance, each of the individual regions starting at zero each time? Now, notice for the column and barcharts that we always start at zero. We don't start anywhere else. For instance, we don't start saying 100. And that's the case even if we get rid of the regionname. We still start at zero, even though there's a lot of space that we could save to be able to zoom in. If you did want to do that, then it would be better in a line chart. So in a line chart, we don't have to start at zero on the y axis for a good presentation. So now I can see the difference between 244 and 100. So the reason for that is because in a bar chart or column chart, I want to see these based on an area. With a line chart, not so much. We can visualise the area, but we might want to be able to zoom in. Now, a variant of this is the area chart. So that's just a filled-in bar chart. The only difference between a pie chart and a donut chart is whether there is a space in the middle. This is when it's useful to have a look at the totality of the data and divide it up. So here we've got all of the sales, and we can see what proportion each of these regions is. Now, I wouldn't recommend pie charts where you have more than about six different regions, or in this case, six different slices, because it can get really confusing. If I was to change this to the year, well, how useful is this really as a piece of analysis for me? not that useful.
6. 31. Identify and describe uses for visualization controls - advanced types
Now, just by turning away from this data set, you can also have visualizations, which are graphs. So here, for instance, we have a visualisation of Afghanistan by population. So again, as it's interactive, we can drill through it and click on bubbles. Maybe there'll be other visualisations we can take a look at as well. Now, scatter graphs are useful when you want to compare two numbers. So I've got the sales volume. So how many houses were sold and what changed? That is from the previous year, and I've got six bubbles, each of these being a different region. In addition, I have a third access, which is the average price. So the bigger the circle, the higher the price will be. So here, we've got fairly small prices compared to 20 years later. So the sales volume and the twelve-month change are roughly the same, but the actual prices are much higher. So the difference between a scatter chart and a bubble chart is whether you include this third dimension, the size. Now, as you can see, I'm also animating it using a fourth dimension, which is the date. So we can see how much house prices have changed over the years in a more playful way. The key influencers are the next visualisation I'd like to show you. Now this is a diagnostic analytics tool. So we know that sales volume is going up, but why is it going up? Well, the computer says that the region name is Greater Manchester, so you will have a greater sales volume increase. If it's in the early 2000s, then you'll have a greater sales volume increase equally.What causes it to decrease? If you're in Thailand, Manchester, or it's January, you might notice a drop in sales volume. So this is why things have happened in the past. So this is diagnostic as opposed to descriptive, telling us the numbers. The decomposition tree allows us to drill through. So here we have the total sales volume, and I can drill this down by a variety of different attributes and properties, allowing me to draw through by year. So we can see what each year happens to have in terms of sales volume. So we used to have a full bar representing all the sales volume. Now the full bar represents the highest number of units sold in a particular year, and then I can drill through further. For instance, I will drill down on the region name. So here are the six region names for my consideration. No, I don't want to do that. Let's start again. We'll enter the region name, and then I'll look at whatever value is highest and the computer says is the highest number of days. And then, because all of these are the first of each month, quarter three is the highest, and then July is the highest of the year 2004. So again, this is part of self-service analytics. You can upload this onto the Power Bi service, and then your end users can play with this and go, "Okay, what happens if we start again?" Now, there are many other visualisations that I could talk about. There are gauges; there are single- and multi-factor cards; there are KPIs. But I think this has given you a good indication of the various types of chart types that are available in Power BI. So the main ones are table and matrix, bar and column, line and area, pie and donut map, scatter, and graph key influences. So why in the past? and the decomposition tree.
7. 32. Describe types of filters
Now this is great. This actually shows you your visualisation the way you want. But suppose we don't want to show all of the data. Maybe we just want to concentrate on 2000 to 2009, where we can do this in a few ways. The first way we can do it is by using a filter. So if I expand this photo pane, and if you can't see it, go to view show panes, you'll be able to see it. Now there are three different types of photos. One photo will only affect visualisation on one page. So, one visualization, one page. So that is your visual filter. One filter will affect all the visualisations on one particular page, and another will affect the visualisations on all of the pages. So what I'm going to do isI'm going to duplicate this page. So we now have two pages, and I'll just rename it page two. So what I'm going to do is reduce the number of years that are shown on this one visualization. So I'll go to the photos on this visual, I'll go to date and year, I'll expand this, and I'm going to say I'm going to change this to basic filtering. And I'm just going to check 2000, 2001, and so forth. So you can see that this has now been filtered just for this one visualization. It has not affected the other visualization, and it has not affected the other page. So that is a filter for this one particular date visualization. Now you can change this to "advanced filtering." So we could say when it is greater than or equal to 2000, or when it is less than or equal to 2009. So we apply the filters. Again, you won't need to know the details of how to create the filters. You just need to know the concepts for this certification. Again, I just like to show you how to actually do it. I think it allows you to just cement in your mind what these photos are. So I'm going to erase this using the eraser. So, back to Iowa. Now, I'm going to do this not atthe filter level, but at the page level. So I'm going to drag date into this particular filter on the page section. So previously we had all the visualisations that were being used, and you can add additional ones there and here. It's not going to tell you everything that's on a particular page. So this is not dated. Yeah, this is dated. So what I'm going to do is click X, and I'm going to go into the hierarchy. So we have a date hierarchy here: year, quarter, month, day. So hierarchy are bigger things containing smallerthings, containing smaller things and so on. So I'm going to go into the year, I'm going to drag this into filters on a page, and I'm going to do exactly the same thing. So I'm going to do some basic filtering, and I'm going to say 20 00 20 01; notice what happens to both visualisations when I do so; you can see they've both been affected, and if there were any other visualisations on this page, they would simply be affected. However, page two has not been affected. It is only for this one particular page. And then finally, I'm going to do exactly the same thing, but with filters on all pages, and you probably won't be too surprised by the results. As soon as I start doing anything, then it affects all of the pages. So here we can see that in all of the visualisations on all of the pages, we have now only got 2000 to 2009. So this is one way of being able to filter, and I'll just click the Xs just to remove these. Another way of being able to filter is by using a different control called a slicer. So I'm just going to reduce the width of this just fractionally, and I'm going to insert a new visualisation called a slicer so you can see that. Again, I've got to populate this, and I won't use the filters pane this time. I use the visualisations pane, and I'm going to drag in the year again. So there are lots of different ways of showing these slices, but here's a nice way. And if I change this to 2000 and this to 2009, you can see that by default, the slice is affecting the visualisations on this page but not on any other pages. So it allows the end user to be able to say, "Actually, I want to only look at this particular range of results." So, different types of photos There are three different types of photos. Photos on a visual, individual visualisation photos on a specific page, photos on all pages And if you want the end user to have power over what they see, then you can use a slicer visualization. So that is another type of filter. Now, there is a third way of filtering, and it's not exactly filtering; it's called highlighting. And it's probably best for a specific example if I introduce a new slicer. So I'll just make this slicer a bit smaller, and I'm going to put in the region name in this field. So what I can do is say, "Okay, this is great, but I just want to see Greater Manchester, or just want to see Merseyside." So that is filtering; that's filtering using a slider. However, something else I could do is insert a table. So here we have our table, and again, I'm going to put the region name here. So what I can do is click on this, and it highlights just that particular element. Greater Manchester is in the West Midlands, as you can see from the highlighting and filtering. So there are ways to customise this. You can go into format and edit interactions, but I won't be covering that in this particular course. But this is another way to filter by having a table and clicking on it, and then one visualisation can interact with another. And this is called highlighting. So sometimes it doesn't filter the data specifically because, as you can see, all of the data is still here. There's just an element that is highlighted. very difficult, however, to get some of the data and take away some of the data in a line graph. As you can see, this line graph filters. So this is a third way of filtering, which can also highlight where one visualisation interacts with another. Now, a fourth way would be a drill-through report. So what's that? Well, let's have another page. And we'll have our table and our region name on the table. Let's say this was part of a dashboard and I wanted to go from South Yorkshire to another report. I wanted to drill through to another page. But on this page, all I wanted to see was South Yorkshire's results. And that can be done fairly easily. So we got the page, and in the drill through, I just dragged down what I wanted to use as my photo under the visualisation section. So in this case, the region name doesn't actually adjust anything on this page. It's all exactly as is, except that there is a new "go back" button, which you can maybe adapt your visualisations to accommodate. So maybe I can move that up there or raise the button. So all I have to do now is select South Yorkshire and drill down to page one. And now page one just shows Yorkshire. So you can see, we've got a height of around 28,000 if I go back. So I need to press Control and go back. If this was loaded onto the cloud, onto the PowerBI service, then I could just click on it rather than control, click, and say, "Look at Greater Manchester." Right-click and drill through page one. This gives me the information for Greater Manchester. So we've got a height of about 610, and so on. So a drill-through report allows me to go from one page or report to another and apply a filter that I have set on that first page. When we get to the Power Bi service, you'll see that there's also such a thing as a "cross report drill through," which means going from a page in one report to a page in a second report. So that's what the cross-report is all about. and drilling through filtering as well. So this is a fourth way of filtering, and that is using a drill-through.
8. 34. Describe uses for custom visuals including charts or controls
In this video what we're going to dois have a look at custom visuals. So we've had a look at all of these different types of visualizations, but there are many, many more, and if I click on "Get more visualizations," I have to do that twice to get into the app source. So why would you use custom visuals? It's basically when these visualisations that you've got are not sufficient.So for instance, we don't have any word cloud visualizations, but they can be quite useful to show how important certain words are. For instance, here you can see that social is very important in terms of number of occurrences, then media is less important than social, but still there, so a bullet chart. You can see additional visual elements to the standard charts that we have, and you can just go down and have a look around all of these visuals. Basically, you should only use these when you actually have a need for them. If you've got no need, stick with the original visualizations. They're incorporated into Power BI much more strongly. These have been written by others, so these are some that the app stores have suggested that you might particularly want to have a look at. So whichever one you want to have a look at, you can see some of them may require additional purchases, so you might just have a demo version or a restricted version to start with. Just click on what you want and you can read more about it. You can see that the visuals have been certified by Power Bi. If it has, click Add, and it is downloaded into your report, which you can see here. If you no longer want it, click Remove a visual, and then click Remove. So custom visuals are there for when the visualisations that you have are not sufficient enough.Another possible meaning of custom visuals If you go to the Insert tab, you can see these elements. So we've got text boxes, buttons, shapes, and images, and so these are little things that you can insert. So I'll insert a text box. We can add some text—this is my text—and we can add graphics. We can add controls. So maybe we want a back button or click here button, and we'd be able to go through and add actions to these and lots more things. So you know how we've got this fields section while we've also got a formatting section, which we will not be going into in this course, but when we go into this button, you can see that all we've got is the formatting section, and we can do things like actions. So if I go down to the action section and switch it on, you can see what sort of action it can do. So these elements can be quite useful for just adding to your desktop and maybe making some notes, or if you were to add actions, making the clarity of navigation a lot clearer. So these are elements that you might consider controlling. and so it might go into custom visuals. And you get these from the insert section of PowerBI Desktop.
Power BI Desktop - Managing Data
1. Loading queries into the Power Query Editor
Hello and welcome to this new section. In the previous section, we had a look at Power BI Desktop, where we concentrated on visualizations. In this section, we'll be looking at how to import data that might not be 100% straight and straightforward and combine it with other pieces of data. So what I've done is put three new spreadsheets in this workbook. This workbook is the source data you can download as a resource right near the beginning of this course. So there are electrical resources, and this is attached to them. Now, it's fairly straightforward data. We've got some expenses for January. So on the 14th of January, I bought some stationery 7.90, some more for $6, and miscellaneous for $14.88. And then later on in that month, I got an ebook for $7.99. I then just had one expense in February. So an ebook is $16.57. And these subtypes I have categorized, so we know whether a particular subtype is an ebook. So stationery, no. Miscellaneous, no. And ebooks, yes. Now, deliberately using very simplistic data, in reality, I might do this as a formula or something like that, but we don't need to cover that in this particular course. So let's just take a step back. We've got data for January and February. They need to be combined together. Additionally, we have had some problems with our data. For instance, we've got a leading space herein the subtype here, which will stop thesetwo from being grouped together, perhaps. And then once we combine January and February and clean it up, we've then got categories. And we need to associate this column, the subtype, with this column, the subtype, to enable us to get the category. So, first things first, we need to load all of this data. I'm going to do this in the same filethat we got here in Power Bi Desktop. So, now that I've closed my Excel, I'm going to get data. so I'll get data from Excel. I could have clicked on the more dot. I'm going to get source data F and add the categories February and January. You notice that these are in alphabetical order. Now, I could just load them. Instead, I'm going to transform them. So if I loaded them, they would all appear here. But I want to do some work on them. So I'll click Transform Data, and that opens a new window called the Power Query Editor. You may have seen this. In Excel there is called Get and Transform. So I'm just going to get rid of everything except the table. So we've got our previous table. That's fine. We're not going to touch that one bit. You notice that all of these empties are now called nulls. And then we've got our January data, our February data, and our category. Let's just have a look and see what we've got here in this Power Query Editor. First of all, we've got the queries. So these are the individual data sources, which I can collapse and uncle. Then when I click on each, we've got the current state of the table, and then on the right hand side, we've got query settings, and if you don't see that, if I close it, for instance, I can go to view layout query settings. Now I want you to notice what we've got on this right-hand side. We've got a list of applied steps. So, for example, since January, the computer has taken the source. So our source is a workbook containing multiple spreadsheets and tables. So here we can see all of the spreadsheets and tables that are there. We've now navigated to one particular spreadsheet, so it's important to a lot.So you can see we currently have column one, column two, and column three as the headings. It's then promoted the headers, so it's intelligent enough to say, "Okay, this first bit looks like they're going to be headers," so we now promote the type, the subtype, and the out as the heading type. So now they're no longer data, and that's good because previously in our first column we had something that looked like a date but also something that was text, and having two different types of fields is not great. And then the computer changed the field type. So at the moment we have this ABC one, two, three, which just means a general, but we can change it to a decimal, a fixed decimal, a whole number, a date, a time, a text, and so forth. The computer has done that for me. Now it's also important to note that we can go back over any of these steps and change what we're looking at. So if I click on this wheel next to source, I get: Where do you want to get this from? Dialog box. Again, if I click on navigation and click on the wheel, it says OK, we've got the workbook, but which particular spreadsheet do you want? So you can see that we've got options here on the right-hand side. Now why is this important? If I load this data into Power Bi Desktop and then change the data, all of these steps will still happen to the new data. All I've got to do is click on this refresh button to redownload the data and have all of these steps happen. So this isn't a one-time deal. If I go in and add an extra item and click Refresh, all we're seeing is a preview of the top 999 rows or so, then it will reload the data and redo all of these steps to automate the importing for you. So this is what we've got in a Power Query Editor. In the next video, we'll have a quick look at what all of these different menu items are all about.
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