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These are the user uploaded subtitles that are being translated: 1 00:00:05,340 --> 00:00:09,600 In this lesson, I'd like to speak a little bit more about different types of calculations that we can 2 00:00:09,600 --> 00:00:10,600 do in our tables. 3 00:00:10,620 --> 00:00:14,910 So, so far in the course, we've been summing everything, even this activity that we've got on the 4 00:00:14,910 --> 00:00:18,510 screen at the moment, you can see that we've got the sum of sales and we've just basically been selling 5 00:00:18,510 --> 00:00:19,330 those sales. 6 00:00:19,350 --> 00:00:23,220 But what if you wanted to know what was your average sale value or what was your highest sale value 7 00:00:23,220 --> 00:00:26,480 or your lowest sale value, or you wanted to come to the number of records. 8 00:00:26,490 --> 00:00:29,420 So you might be used to these types of calculations in Excel. 9 00:00:29,430 --> 00:00:33,210 So let's have a quick look and see how we're going to do these in Power BI. 10 00:00:33,360 --> 00:00:37,710 So I'm actually going to go back to my page one and I'm actually going to delete this existing matrix 11 00:00:37,710 --> 00:00:38,720 that we were using. 12 00:00:38,760 --> 00:00:42,720 Let's start off with a table again just to show something quite simple. 13 00:00:42,810 --> 00:00:44,460 Let's use our manufacturing sales. 14 00:00:44,460 --> 00:00:48,720 We've seen this table quite a bit in the course so far, so you should be quite familiar with the values 15 00:00:48,720 --> 00:00:49,560 that we're seeing. 16 00:00:49,830 --> 00:00:55,560 But what would happen now if you wanted to know what was my average sales value for each of my manufacturers? 17 00:00:55,560 --> 00:00:56,910 Some some manufacturers. 18 00:00:56,910 --> 00:01:01,440 You might have products that are actually selling at a much lower sales value than other manufacturers. 19 00:01:01,440 --> 00:01:02,670 And you want to compare these. 20 00:01:03,030 --> 00:01:07,740 Now, what you'll see is that when you go to your sum of sales is that you actually get a little dropdown 21 00:01:08,070 --> 00:01:11,850 and when you select that, you get a lot of options that you can do with this field. 22 00:01:11,940 --> 00:01:15,690 So you can see the top, we've got to remove fields, so that's quite useful if you want to remove it. 23 00:01:15,690 --> 00:01:20,820 Although the X are easier to use, you can actually rename for this visual and that's something I'm 24 00:01:20,820 --> 00:01:24,840 going to be showing you just now, that it's actually useful sometimes that you might want to rename 25 00:01:24,840 --> 00:01:28,620 the title of this so that in your header you get a different name. 26 00:01:29,160 --> 00:01:30,450 You can actually move this as well. 27 00:01:30,450 --> 00:01:32,250 Let's just actually have a look at a couple of these. 28 00:01:32,280 --> 00:01:37,320 And remember when I picked it up and I moved it up and down, depending where I wanted it to be placed. 29 00:01:37,320 --> 00:01:40,080 So you could use the up and down here to do that. 30 00:01:40,140 --> 00:01:44,940 There are different visualizations that you can add to this as well, such as spark lines, conditional 31 00:01:44,940 --> 00:01:45,660 formatting. 32 00:01:45,660 --> 00:01:47,790 So I'm going to have a look at those a little bit later. 33 00:01:47,790 --> 00:01:52,440 But the ones that I wanted to get to was these set of calculations here. 34 00:01:52,740 --> 00:01:57,660 Now the first one is actually don't summarize, so we could actually tell the system, don't summarize, 35 00:01:57,660 --> 00:02:01,980 show me all the individual values that are in the that are actually in the database. 36 00:02:01,980 --> 00:02:07,500 So this is now going and it's row by row now just showing you all of the values for each of the sales 37 00:02:07,500 --> 00:02:09,419 that are in the actual database. 38 00:02:09,479 --> 00:02:11,009 So you'll see there'll be quite a lot of these. 39 00:02:11,790 --> 00:02:15,060 So this is the first one we could do is we could tell it to don't summarize. 40 00:02:15,210 --> 00:02:19,140 Otherwise we've got the sum which will now sum up the values, which is our default. 41 00:02:20,480 --> 00:02:23,360 Also, we could say that you want to average this. 42 00:02:23,360 --> 00:02:26,990 So when I click on average, you'll see straight away that we get average of sales. 43 00:02:26,990 --> 00:02:30,680 And now it is showing me what each of the average values is. 44 00:02:30,680 --> 00:02:35,150 So what it's doing here is actually taking the total sum of sales and is dividing it by the number of 45 00:02:35,150 --> 00:02:39,800 transactions or number of rows that are actually in the data set for this manufacturer. 46 00:02:39,800 --> 00:02:42,440 And that's really how it's getting the average of sales. 47 00:02:42,950 --> 00:02:46,040 Let's continue let's just look at some of the other options we've got here. 48 00:02:46,190 --> 00:02:47,960 So we've also got a minimum amount. 49 00:02:47,960 --> 00:02:51,200 So when it goes to the minimum amount, what it does is it goes through the data set. 50 00:02:51,200 --> 00:02:56,870 It looks at the range of values for each of the manufacturers and it picks out the lowest value within 51 00:02:56,870 --> 00:02:57,650 that range. 52 00:02:57,650 --> 00:03:02,780 So in this case, what it's saying for a datum is the lowest sale that it has is 439. 53 00:03:03,140 --> 00:03:07,180 Now, one thing I do want to note is just how your grant totals are changing. 54 00:03:07,190 --> 00:03:11,780 Now, you may have seen on the average what happened was it actually now shows you an average for the 55 00:03:11,780 --> 00:03:13,370 entire data set here. 56 00:03:13,370 --> 00:03:16,790 It is showing you the lowest value that is actually in the data set. 57 00:03:17,000 --> 00:03:20,720 So basically, just be careful of when you're looking at these totals. 58 00:03:20,720 --> 00:03:24,130 It is according to the method of aggregation that we that we're looking at. 59 00:03:24,140 --> 00:03:28,880 So you can see that the lowest value is at five and that we've got that five value there. 60 00:03:29,540 --> 00:03:31,460 You'll also see that we can do a max. 61 00:03:31,670 --> 00:03:36,440 So if we go to Max, it's going to go through your data set again for each of your manufacturers. 62 00:03:36,440 --> 00:03:39,200 It's going to pull out the highest individual range. 63 00:03:39,200 --> 00:03:43,790 So as I said previously, you can imagine if you're working with Excel, you're working with equal sum, 64 00:03:43,790 --> 00:03:46,310 equals average equals min equals max. 65 00:03:46,310 --> 00:03:48,050 That's really what we're working with here. 66 00:03:48,050 --> 00:03:50,570 We're working with the same type of functions. 67 00:03:50,930 --> 00:03:54,260 Now in Excel, you do get another function which is called equals count. 68 00:03:54,440 --> 00:04:00,650 So equals counts will go through your data set and it will count the number of rows that each of the 69 00:04:00,650 --> 00:04:01,520 items of God. 70 00:04:01,520 --> 00:04:06,290 So in this case, each of the manufacturers, how many transactions or how many rows do they have in 71 00:04:06,290 --> 00:04:07,130 the data set? 72 00:04:07,370 --> 00:04:13,940 So if we go across here, we get a equals count and you'll see now that we know that there's 15,000 73 00:04:13,940 --> 00:04:17,959 rows in this dataset, because we saw that right at the beginning of the course, but now you can see 74 00:04:17,959 --> 00:04:22,910 that a datum, for example, has 1342 transactions. 75 00:04:23,090 --> 00:04:26,300 You can see that a venture works 1125. 76 00:04:26,510 --> 00:04:30,050 So this is now doing a count of the number of transactions. 77 00:04:30,650 --> 00:04:35,210 Now, this is really useful when you want to know the number of transactions, but let's say, for example, 78 00:04:35,210 --> 00:04:40,970 for each of the manufacturers, what I wanted to know was how many products, how many unique products 79 00:04:40,970 --> 00:04:43,940 does each manufacturer actually provide to me? 80 00:04:43,940 --> 00:04:50,540 So my data set, how many product names are against each of the actual manufacturers. 81 00:04:50,810 --> 00:04:55,100 So if we were to take our product name and let's say we dropped it in here and we wanted to now do a 82 00:04:55,100 --> 00:04:59,270 count of this, I can see by default what it's doing is it's actually using the text. 83 00:04:59,270 --> 00:05:04,040 So it's actually show me actually manufacturer by product what my cost of sales would be. 84 00:05:04,520 --> 00:05:07,220 So I'm going to change that now and you'll see that there's some options. 85 00:05:07,220 --> 00:05:10,310 What you could do is you could just say, I want to see the first product name. 86 00:05:10,310 --> 00:05:15,290 So if I said the first product name and now we'll just show that first product name or you could show 87 00:05:15,290 --> 00:05:16,970 I want to see the last product name. 88 00:05:17,450 --> 00:05:18,920 That would be the last one that it's got. 89 00:05:18,920 --> 00:05:21,260 But what I'm more interested in here is to do a count. 90 00:05:21,260 --> 00:05:27,820 So when I do a count, you'll see that I get exactly the same amount as I would if I just did a count 91 00:05:27,830 --> 00:05:28,700 of my sales. 92 00:05:28,820 --> 00:05:34,220 Because, as I said, this is now telling you how many rows or how many transactions are in the system 93 00:05:34,460 --> 00:05:37,030 or in the data set for that manufacturer. 94 00:05:37,040 --> 00:05:42,080 So in this case, it's telling me I've got 1342 products for a data. 95 00:05:42,080 --> 00:05:43,340 Now, I know that's not true. 96 00:05:43,340 --> 00:05:44,960 I've got a lot less products. 97 00:05:45,380 --> 00:05:50,630 So what I want to highlight here is a really useful feature which is called Count Distinct. 98 00:05:50,960 --> 00:05:55,490 And what this will do is it will go through your data set and it will then count the product. 99 00:05:55,670 --> 00:05:58,130 So once it comes across the product, it will count at once. 100 00:05:58,130 --> 00:06:00,890 But if it comes across it again, it does not count it again. 101 00:06:00,890 --> 00:06:05,360 So this will tell you how many unique products you've actually got in your data set. 102 00:06:05,870 --> 00:06:09,470 So this is really useful calculation when you're often doing your data analysis. 103 00:06:09,470 --> 00:06:11,390 So let's see the result of this, right? 104 00:06:11,390 --> 00:06:15,500 So you can see now that a datum actually has 131 products. 105 00:06:15,710 --> 00:06:21,470 And overall in your whole data set, you have 1638 products, which makes a lot more sense. 106 00:06:21,470 --> 00:06:24,230 So this is really useful, this distinct count. 107 00:06:24,380 --> 00:06:29,450 And the next part that I want to show you in this table is the ability to be able to combine these methods 108 00:06:29,450 --> 00:06:30,380 of aggregation. 109 00:06:30,530 --> 00:06:31,880 So this can be really useful. 110 00:06:31,880 --> 00:06:36,650 If you wanted to have a table and say for our manufacturer, we wanted to know what is our total sales 111 00:06:36,650 --> 00:06:41,630 value, what was the average sales value, what was our highest sale, what was our lowest sale, how 112 00:06:41,630 --> 00:06:44,480 many transactions were there and how many products are there? 113 00:06:45,140 --> 00:06:48,680 So what I'm going to do is I'm actually just going to remove these two fields. 114 00:06:48,680 --> 00:06:53,210 We're going to start off with our manufacturer and we're going to drag the first sales back in again. 115 00:06:53,390 --> 00:06:57,890 Now, by default, we get the sum of sales and this would give me my total sales value. 116 00:06:58,100 --> 00:07:02,120 Now what we can do is we can actually use the sales value again in our table. 117 00:07:02,120 --> 00:07:07,670 So if I take my sales and I actually drag it and I take it into my columns again, remember when the 118 00:07:07,670 --> 00:07:13,820 thick line appears and I can drop it, drop it in there, I now get a second some of sales appearing, 119 00:07:13,820 --> 00:07:17,150 which currently because it's summing up, the sales will show you exactly the same. 120 00:07:17,880 --> 00:07:23,370 So when I click on the dropdown now though, and I say I want to show this as my average sales, you'll 121 00:07:23,370 --> 00:07:28,290 see now that I can have my sum of sales and my average sales being displayed in the same table, so 122 00:07:28,290 --> 00:07:29,460 I can use this for quite a bit. 123 00:07:29,460 --> 00:07:34,470 So what I'm going to do is we quickly just going to add our high sell and our lowest sell. 124 00:07:34,470 --> 00:07:36,980 So again, we're just going to add our sales value. 125 00:07:36,990 --> 00:07:41,100 And this time, though, we're going to say we want a maximum value that will give me my highest sell. 126 00:07:41,250 --> 00:07:43,080 And again, let's just do another one. 127 00:07:43,080 --> 00:07:47,310 In this case, we're going to do minimum and we'll get our min of sales. 128 00:07:47,310 --> 00:07:51,930 So as I say, you can add the same field more than once and you can do a different method of aggregation 129 00:07:51,930 --> 00:07:52,410 on it. 130 00:07:53,110 --> 00:07:56,740 Now, at this point, I just want to pause slightly, just to talk a little bit about the ability to 131 00:07:56,740 --> 00:08:01,330 change the name of your field, because when we've got a table and we're presenting it to our users, 132 00:08:01,330 --> 00:08:04,630 summer sales, average of sales, Max of sales, not great headings. 133 00:08:04,990 --> 00:08:10,360 If you go across to your columns and you double click on the field, it now allows you to actually change 134 00:08:10,360 --> 00:08:11,730 the name that is going to be shown. 135 00:08:11,740 --> 00:08:15,250 So I could say I want to call this my total sales. 136 00:08:17,080 --> 00:08:20,390 I could say this is going to be my average sales. 137 00:08:20,860 --> 00:08:22,870 All you do is you just double click on the field. 138 00:08:23,290 --> 00:08:26,470 You type in what you want and you press enter. 139 00:08:27,390 --> 00:08:28,590 And there we have it. 140 00:08:29,320 --> 00:08:30,610 I'm going to have lowest sale. 141 00:08:31,850 --> 00:08:32,470 So there we go. 142 00:08:32,480 --> 00:08:36,320 Now we've got a much better looking table with the names that we're using. 143 00:08:36,770 --> 00:08:41,900 Now, the last part that I wanted to do here was to have that count of the sales so I can actually put 144 00:08:41,900 --> 00:08:43,100 my sales in here. 145 00:08:43,100 --> 00:08:47,330 Again, I could now change this and say, I want this just be a count. 146 00:08:47,600 --> 00:08:51,770 And again, I can just double click on this and just said number of transactions. 147 00:08:52,260 --> 00:08:54,440 And that would show me the number of transactions. 148 00:08:54,440 --> 00:08:57,500 And the last one was we were going to reshare that product name again. 149 00:08:57,500 --> 00:08:58,670 So I'm going to drop that in. 150 00:08:59,700 --> 00:09:01,050 And one another number of products. 151 00:09:01,050 --> 00:09:03,720 So just remember, I just got rid of it. 152 00:09:03,720 --> 00:09:05,250 So we're just going to drop it again. 153 00:09:05,280 --> 00:09:06,200 Let's try that again. 154 00:09:06,290 --> 00:09:07,230 Going to drop down. 155 00:09:07,320 --> 00:09:10,980 So in this case, just remember we're using the count distinct. 156 00:09:11,160 --> 00:09:13,190 So we're going to click count distinct on this. 157 00:09:13,200 --> 00:09:15,780 And we got count of product name, not name. 158 00:09:15,790 --> 00:09:19,770 So we're going to say this is my number of products. 159 00:09:20,710 --> 00:09:21,880 So there we go, Inter. 160 00:09:21,910 --> 00:09:22,560 So there we go. 161 00:09:22,570 --> 00:09:24,950 We've got a really useful looking table. 162 00:09:24,990 --> 00:09:29,110 And just remember, if you change a manufacturer here, you can get all sorts of calculations, could 163 00:09:29,110 --> 00:09:32,410 remove this and then it will show you the totals for the data set. 164 00:09:32,920 --> 00:09:36,640 Or you could add another field and you could say, I want to see the same by my country. 165 00:09:37,030 --> 00:09:39,040 Let's drop that in the front there and you'll see. 166 00:09:39,040 --> 00:09:44,560 Now all of these will be now recalculated for each of the countries to show you each of those calculations. 167 00:09:45,400 --> 00:09:47,110 So it's really using a table. 168 00:09:47,140 --> 00:09:51,240 What I want to show you just quickly is that we can also do this for a matrix. 169 00:09:51,250 --> 00:09:57,130 So let's say, for example, we set up a matrix and let's create, say, one with our product category 170 00:09:57,130 --> 00:10:01,180 in row, our channel in our columns and our sales value. 171 00:10:02,550 --> 00:10:04,590 And again, it's defaulted to some sales. 172 00:10:04,590 --> 00:10:08,640 And you'll see just using the same theory we can just click on the dropdown could change this to an 173 00:10:08,640 --> 00:10:09,840 average sales value. 174 00:10:10,930 --> 00:10:13,740 Or we could click to this and drop down to a minimum. 175 00:10:13,750 --> 00:10:16,240 So you can see how this works very, very easily. 176 00:10:16,720 --> 00:10:22,780 And the final part to show you is that each of your fields can actually have a default summarization. 177 00:10:23,110 --> 00:10:27,850 If I go back to my cells, you'll remember from earlier that my column tools popped up and I was able 178 00:10:27,850 --> 00:10:29,320 to change the formatting. 179 00:10:29,350 --> 00:10:34,100 What you can do as well is actually change the way that the summarization works on this. 180 00:10:34,120 --> 00:10:40,090 So you could say, I don't want this to sum or I could average min max count, so you could easily change 181 00:10:40,090 --> 00:10:42,730 what the default is if you wanted to. 182 00:10:43,240 --> 00:10:44,180 So there we go. 183 00:10:44,200 --> 00:10:47,800 Hopefully that gives you a pretty good idea of the different methods of aggregation. 184 00:10:47,830 --> 00:10:49,170 We're going to conclude the lesson there. 185 00:10:49,180 --> 00:10:50,290 I will see you in the next one. 19387

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