Practice Exams:

DA-100 Microsoft Power BI – Part 2 Level 2 – Getting Multiple files

  1. Merge Queries and Expand Table

In this video we’re going to have a look at merge queries, the first of the combine. And what I’m going to do is I’m going to get rid of almost everything that we did in the last few sections. Just have it so that we have our raw data. So these are our filenames, this is the path which contains the album. Now you can categorize these albums. This one for instance is a sampler. It’s got music from all over the place. Further down we have an audiobook, further down we have opera and that sort of thing. Popular music maybe. So what I’m going to do is I have in my Power bi spreadsheet, a spreadsheet called CD Categorization and that categorizes the various paths, the albums into these CD types. So what I want is an extra column in this query which has that type.

So in Excel this would be the equivalent of a vertical lookup. So what I’m going to do first of all is import get the data of the CD categorization as a second query. So let’s get from a recent source and this time we’re getting the CD Categorization click OK and it loads no problems. So how do we combine these two together? Well, first of all I want to promote this first raws headers. So how do we get these two queries together? We use merge queries and we’ve got two options merge one query into an existing query or merge them into a third query.

So I’m going to merge from CDs, I’m going to get in CD categorization so I’m going to merge queries. So this opens a dialog box with my existing query at the top and a second query at the bottom. So I’m going to call the second query the CD Categorization and it says okay, how do you want me to match them? Where are your columns which are going to be the same? Well, it’s the path in this column and it’s the path in this query. Now there are various join types. If you are used to SQL Server then you will understand immediately what left join, right join, full join in a join means alternatively have a look at what’s in the brackets. So left outer join says it’s going to take all of the rows from the first table and any matching rows in the second so where they don’t match. Where I’ve accidentally deleted one of the categorizations, I haven’t categorized it and it’s completely blank

in both path and CD type. We’re still going to keep our existing table. So you can see all from the second table and matching for the first all from both. That would mean that it would have to be either in the first table or the second table. Inner it has to be in the first and the second left ante and right anti it’s where there is a match you get rid of it essentially. So it’s used for trying to exclude certain data. So maybe you had something that said grand total and you wanted to exclude the grand totals. If none of this is making much sense, then just know the major ones you will be using are left outer where you’re keeping your original data, always inner, while you’re only keeping your original data where it matches something with the second data. And full outer where you’re keeping both sets of data regardless of how they match. So let’s have a look at left outer.

And here you can see that the two queries have been merged. Now they’ve been merged together in a table. So because each match doesn’t just have one column, it has multiple columns. Now we can expand that quite easily by clicking on this expansion button. This is also here in the transform structured column expand. So if I click on that it asks you what do you want to expand it to? What columns do you want to keep? So this is the equivalent of choose columns. So I could keep the path, but I’ve already got the path, so I don’t need the path again. So I’m just going to keep the CD type. Now, if it says use original column name as prefix then the column would be CD categorization CD type. Well, I don’t need that in this particular case, so I’m going to destick that. Now, if these contained numerical values and you wanted to sum them up, then you could use the aggregation instead, so you could sum the columns instead of having them as separate columns. So let’s click OK, and our CD categorization is now replaced by the CD type. So here we have the sampler, audiobooks, opera and so forth. So obviously what you can now do is close and apply that and because we have this new column called categorization CD type, we can put CD type in there and now we can see the difference between sampler and Christmas and classical and so forth.

  1. Merge Queries with Group By, and different types of Joins

Now, what else could we do with merging queries? Well, as I said, you can also merge the queries as new. You remember a few videos ago we had this group by so I created a new query based on this original query. So I am managing and in this case I’ll duplicate eight. And then I’m going to group by and I’m going to group by the CD type and I’m going to count how many rows there are in each. So similar sort of thing to what we’ve just seen in our visualization. What I’m going to do now is introduce discount into our original query by merging it. So we merge this query and I’ll merge it as a new just to show you that you can have a new query at the end of it. So it’s merging this CDs with CDs too. Obviously I could change the name and I’m doing a let out a join and selecting which particular columns I want. Expand the table, I just want the count.

So we can see that there are 351, samplers, 68, opera and so forth. So if you wanted to use that information to say, well, what proportion, what percentage of samplers is this? One particular row, then you can see it’s one divided by 351 multiplied by 100%. Now I’m just going to remove this and I’m going to change this CD categorization from CD categorization to CD categorization short which only contains two lines of detail. And the reason I’m doing this is just to demonstrate the various different types of joins. So if I change this, the navigation we’re going to change from CD categorization to CD categorization short. So there’s my end table.

So at the moment we have lots of nos, so we only have not knowles for these very first two items, the path and the CD type. And I’m just going to select path and CD type and I’m just going to remove all other columns just so we can see what’s there. So this is a left join. So it’s taking all from the first table and just those that exist in the second table. A right join will take all of those which exist in the first table and only those which match in the second table. So you can see my first table is now down to 35 rows because it’s only these two paths which exist in my first table. So this explanation might just be a bit simpler if we call these tables A and B. So a left outer join is all of those in table A and where they match in B, which happens to be all of the albums. So there’s 52 albums there B.

A right outer join is all those in B and all those in A which happen to match. So what I’m going to do actually is put in a third album. So this one is in a Nonexistent album and who knows what the CD type is. So I’ll just save that and I’m going to refresh my data. So my right join contains all three of my rows in CD type and only the data which exists in A. Now, in other words, it is this section here. Now a full outer join is both, it’s all rows from both. So we’ve got the entirety of table A and if I scroll to the very bottom it’s still within this preview. We will also have this who knows from B. So that’s a full outer join. So that’s why you don’t want any particular data at all to be deleted. And inner join is just where they match. So you can see this matches 35 rows from table A and just two rows from table B. So it won’t match the who knows because that exists in table B but doesn’t exist in table A. And then we have a look at the antijoins and this removes what’s in table B.

So this will exclude the 35 rows that is initially there. So anything in table B has now been removed, as I say the vast majority of the time. You will be using a left outer join which contains your original data plus anything that matches in the new data. You’ll be using a full outer join where you don’t want any of the data to be lost. And you’ll be using an inner which just gets the bit in the middle where A and B overlap. So merge queries. We have created a new query, we have merged it or we’ve imported it from another data source, or we’ve used the group by to create a new query, we have merged it together by selecting the columns that we want to merge it with. And we’ve used the various type of joins so that we get the information back that we want. And then we expanded the tables to get us just the columns from the new table, table B, that we want. So it matches sound a bit complicated, but just no merge queries gets information into a query from another query.

  1. Appending two queries together

Now in this video we’re going to look at Append queries. So Merge queries, which we’ve just looked at, was about adding extra columns from different queries. Append queries is about getting additional rows. So combining them, it’s like a union in SQL Server. So what I’m going to do, first of all is I’m going to delete my two existing queries and start again. So I’m going to delete my B table and you’ll find that I can’t my B table is being referenced by the A table. It’s because of this join here, this merged queries. So I’m going to now just delete A and delete B. So the data that I’m going to be using is in this another folder called Monthly CSV Files.

And if you’re following along with the resources that’s very near the beginning of this video series, then create a new folder called Monthly CSV Files and you’ve got January and February. I’m also going to create another folder outside of this that I’m just going to call Spare. So I’m going to have January and February in this monthly CSV files. And I’m going to move March and April into this bare folder. So all we’ve got are these two Monthly CSV files. You’ll see why a few videos later. So now I’m going to get new queries and you can see all the different ways that I’ve got of just getting good new queries from Text joke CSV. These are CSV files. So here is my January data.

And you can see the computer has interpreted it correctly. It’s comma based, it could be tab based. Those are the two main types. It’s trying to find what sort of data types each column is based on the first 200 rows. The reason for this is so that let’s say you had a file of a million rows, it wouldn’t need in this preview setting to download the entire data set if the data types were fairly consistent throughout.

On the other hand, if halfway down you’ve got some raw data that would change the data set, then you wouldn’t need to base it on the entire data set. Generally, the first 200 roles is fine. However, if you’ve got header roles, for instance, like we had in our previous example that weren’t part of the data type, then you could say do not detect the data types. So based on the first 200 rows, you can see that it has promoted that the first row to the headers if I don’t detect data types, and it’s not going to. And now I’d have to do that myself. So let’s see what happens when we import January. So you can see it’s got the source, its sources from here. It’s promoted the headers and it’s changed the types. So it got date and text and financial money. Right, let’s get a second query again from this folder.

We’ll have it as February and we’ll just import it in. It’s only contains one row. So what we have is two tables with the same sort of information date subtype out four rows and one row. We want to merge these two queries together to produce an overall, and that we can do using append queries. Again, we can append one query into another or we can append two queries into one.

So I’m going to do that. And here you can see we have got some options. Are we going to have two queries, two tables, or are we going to have three or more? So let’s just have these two tables, january and February. We started off of January and then at the bottom we’re going to put February, click OK. And we get a third query, which is the amalgam of these two. Now, if I did it the other way around and had February 1 and then January, I think you can guess the results. We’ll have February at the top and then all of the January ones at the bottom.

  1. Appending three or more queries together + resolving a problem with data types

Now let’s get in a third query. So I’m going to now get my March data back in. So I’m going to march and drag that in. So there we go. And now let’s delete my Pendant query. And now I want to have multiple queries appended. So append queries as new. Notice that I’m not offered that if I’m not actually on a specific query. So if I create a blank query and then just delete it, I’m not looking at any particular query, I can’t click a pen query. So just click on one query if you’ve got that is a problem. So Pen queries is new and I’m going to say three or more tables, and this is fairly simple, straightforward. Say which ones you want and say which order you would like them in.

And if you make a mistake, you can click the X button as well. So that now puts all three of these queries into one. So if I call this the total overall and close and apply, you can see we’re making all of these connections. I’ll delete the previous visualization and here we have the grand total. So if I have a table and put in all my dates, actually I’ll make it a matrix table. So let’s just change, let’s add it as a matrix table. And I have dates going across in rows and I have the type going down in the columns and the subtype going down in rows and the prices going down in values. And I want to change it so that it says sum and I can’t at the moment. So let’s just drill down. And there we can see we’ve got January and February and March as figures in there.

Now, why can’t I have out as a sum? It must be the wrong sort of data type. So let’s just go back and edit the query, see what data type we’ve got. And you can see that it is an ABC, it’s not being interpreted as a number. So let’s change this to a decimal number. No, that’s not going to work. You can see all the errors. So what we need to do is we place values, we’ll get rid of the pound sign, replace it with nothing, and now we should be able to change this to a decimal number. Close and apply. And now we can change this to a sum. There we go, that’s looking better. Now, the pound sign is the British unit of currency. You might not have that on your keyboard. So if you were following along and don’t have a pound sign on your keyboard, then here’s how to get it. Just go to Total, go to your source, click on any of these pounds, highlight the pound sign and go to Copy.

And then now you can replace the value with your paste control and V. So we’re getting examples of how we can cope with data that we weren’t expecting to be in the wrong format, but we have now got it into the right format. We have combined all three of these files together using something called an Append query. And the good thing about this is if we later on have another month’s worth. So here we have April. Drag that in. So here we have another query. All I’ve got to do to my previous query is change the source. So if I change the source and now April is added, you can see it includes their clause and apply. And once it’s applied, April comes onto this matrix as well. So I’ve not had to change anything in my reports, I’ve not had to change any of the macros steps, save one, the source data, you might be going, well, that’s great, but I don’t want to see April and February and January and March. That’s fine, you can hide it. So now all of these are hidden. We’re left with just the one query. So Append append is useful for getting two or more queries together in the same query. So we’ve appended four different queries together. Just one small thing, what if they didn’t exactly have the same heading? So let’s change this heading to Out Money.

What happens to our total? Well, you can see we now have four columns, so we have out and Out Money. Out is being used in January, February and April. And out money is being used in March. At this stage, what you can do is use some more advanced M formulas if you need to, so that it can be combined into one column. We’ll have a look at that a bit later. Or if you could go back to your source data or the query, if that wasn’t reliable enough, if you weren’t able to go back to your source data and change the heading, and then we can have a combined version. So you need to fear too much if the headings don’t exactly match the unlike a union in SQL Server, the append query will add as many columns that is needed to have all of the available data. So that is the Append query.

  1. Combine Files (getting information from a folder)

Now, combining queries together is great, but do you have to really load each individual month together so you can use the append queries and you might not be too surprised to find out the answer is no, you don’t. So what I’m going to do again, I’m going to move March and April out of this folder into a spare folder. So we just have January and February instead of loading, instead of loading text CSV, I’m going to click on the more dot or dot and the fifth item down is a folder. So this imports information about your folder. So I’m going to click Connect and it wants your folder path. Now, as you click on Browse, it’s not the best way to browse through your fold structure. It’s just not something I like. So I’ve done it to get to there. But what I generally do is just go to Microsoft Explorer and just copy the path from there and paste it into here. So just as a reminder, currently this contains two files. So click OK and the computer then says, oh, there are two files and it gives the name, gives the extension, that’s the bit, the last dot, data access, date modified, date created attributes.

Those are things like read only archive and the entire folder path. Now, I can combine and transform data and in fact I will, just to show you what happens. So the computer then imports all of those data together, all of those data files, and makes it into one big query. And you can see there’s all these sampler files, these helper queries as well, sample file, transform file and so forth that it uses. And you can see at the end of the day, we’ve got a source name, so that’s the name of the file, the date subtype and out as we had previously. What I’m going to do is I’m going to delete all of those. I’ll also delete these two groups as well and do this again. But instead of combining them, I’m just going to have them, the raw data as it was. And you’ll see why in a few minutes because the computer does a lot of steps to combine those and maybe one of two of those steps just requires a bit of tweaking for what you want. So I’m just going to transform the data rather than combine and transform.

So here we have our data together. So we’ve got name extension, date access, date modified, everything that we’ve just seen. We’ve got attributes as a record, so we can expand that if you so wish, and you can see all of the various attributes which are available. So if I just expand, say, the size, the kind, whether it is hidden, you can see the sort of information that you can get. Now, the content, the actual data is in a binary. So we’ve had a record over here. Well, this is a binary and what we can do is we can combine the files by clicking on this double down arrow icon, either of them. So if I do that, it now opens up all of these files. So you can see it’s saying, well, what’s your sample file? And here are all of the data type detections. Because it’s a CSV, it’s giving me the limiter as before. And I can even say if there are any files that give you errors, skip those.

So here we have the end data source name, date, subtype, and out. But we’ve only got the source name. Remember, we started off with name extension, data access and so forth. Suppose you wanted the folder path because this process is recursive. So it goes through this folder and then goes through additional folders. So maybe I’ve got a folder called January from Finance, and I’ve got another folder, February from It, let’s say. And that’s where these folders were, all these datas were. If I go back and I refresh this, you’ll see that the folder path now contains January from Finance, february from It. But when we get through to the end result, it doesn’t suppose I really needed to have that folder name. Okay, no problem.

What I can do is go back over the steps that it was doing and you’ll notice one of those steps is removed other folders. So it was doing the other columns. It was doing the remove other columns here. So what I’m going to do is go into those settings and I’m going to put back the folder path. Click, okay, so now the folder path is part of this. And when I go all the way to the end, the folder path is still part of this. So what I’m going to do now is I’m going to split this column by the delimiter. And we’re just going to split at the most righthand mouse delimiter.

So obviously that just gets rid of the last slash that was there. So I don’t want this. I’m going to remove this column. You’ll notice I’m using the right hand mouse button. There are plenty of these activities, mostly in the home tab that are there on the right hand column. I’m now going to transform this again. I’m going to split the column again by the backslash. Again, I just want the right and Morse delimiter. I’m going to get rid of everything else. And that’s what I want. That is my subfolder.

So I can see the February information is from It, january is from Finance. And the great thing is, because this is a macro, because this is a series of actions, if I add more information to it, to the source data, I just need to click on Refresh and it gets added into my final thing. How much time might you be saving on this? So again, I’ll just add one more. So maybe April was also from It refresh and instantly we get the information. So now we can close and apply. So I want a matrix. I want a matrix with the subfolder in the rows and the date in the columns and just drill down and the out in the values. And you notice I’ve got the same problem as I did previously.

So I’ve got to edit the query. I’ve got to replace the pound sign with blank and then transform this into a decimal number. Click save and apply. And I can now change this to a sum. But now, if I had new data, suppose I had new data in May, in June, all of these macros would automatically be done. I only have to set it up the once and I get the information here. So combined files, very useful when done in conjunction with new source more and folder. Now, I’ve been using CSV files. You don’t have to use CSV files. You could be using Excel files. You can be using other types of files. But the important thing is you can get a folder listing of any particular folder.

And you can do this from Excel as well. This isn’t specifically a Power bi thing. You can do this in Excel. And within just a few seconds, really, you get a listing of all of the information that’s there. And then if you want, you can combine the relevant files together. So that is combined files, getting information from a folder and combining it into one query.

  1. Practice Activity Number 10 – The Solution

So how did you do this practice activity? I hope you found it lots of fun. But equally, I’m hoping that you’re finding that it’s very easy to manipulate various sets of data. So, first of all, download the zip file attached to this practice activity and unzip the text files. So the zip file can be found here on the right hand side, or it could be found at the bottom of of the practice activity. So I’m going to download it. It’s going to download into my download folder. So here it is. In my version, it’s called drive safely. Some people are saying that it’s downloading as the word original, but however it’s downloaded, you now need to unzip it. So my version, I just right and click and extract all my version of Windows. So now it gets extracted into a folder and you can see that we’ve got Fatalities 2018, Mileage 2018, and then Driving Safely from 2007 to 2017. So let’s copy the path that I’m on. So the first task is to load the text files managed 2018 and Fatalities 2018.

So let’s get data. And I’m going to get text CSV data. So I’ll paste in the path that I’ve got and I’m going to load the Mileage 2018. Now I’m going to want to transform the data because I want to add in the Fatalities 2018 as well. So there it is. Now I’m going to get in the second bit of data. So that’s in this combined section. So depending on how your screen is set up, you might see it like this, or you might see it just as one single drop down. So I’m going to combine it with another CSV file. So I need to load that CSV file first. So I can either write and click in the queries and go on New Query and text, or I can say New source text here in the Power query window. Lots of different ways that I can get to the same point. So. I’ve already downloaded Mileage.

Let’s download fatalities. And now we need to merge these two together using State and Year. So let’s now go to the combine. We’re merging queries that’s adding extra columns as opposed to appending queries, which is adding extra rows. So I’m going to have this as a new query. You could merge it into an existing query if you wanted to. So I’m going to select first of all my two tables. Then I’m going to select State and Geo in each. And I hold down Shift after clicking the first to get here. So I’ll click hold down, shift, click release, shift. Now you can change the join kind, but in this particular instance it’s not necessary because all 51 rows are matched in each. So let’s click OK. And now we have State, Year and Miles, and all of that is from the Mileage. Then we have this Fatalities 2018 as a table. So we need to expand this Fatalities 2018. And I don’t want to use the original column name as a prefix, otherwise it would say Fatalities 2018 dot but I just want to get the Fatalities column. So I’m going to deselect the offers and say Fatalities.

Alternatively, I could select all, deselect all and then just select fatalities. So click OK. And so the end result of this query is four columns state Year, Miles, Fatalities, and I need to rename this as Driving Safely 2018. Now, an alternative way of getting here, if I delete this query, is to merge fatalities into our existing query. So to do that, we would combine, but instead of merging queries as new, we’ll just merge queries. So got miles 2018. You can’t change that. There is no drop down, but that’s the one that we are in. And then fatalities. 2018. And then we do the same highlighting, click OK. We have an extra column, we can expand this and save fatalities, and we’re not using the original column name as the prefix. And then we just need to rename this as Driving Safely 2018. So either way, you’re doing it absolutely fine. So now I want to load the Driving Safely 2017 file, which contains all four of these columns.

So this is one that I have processed earlier. So new source tech CSV driving safely 2018. So here we have Driving Safely 17, and here we have 18. Now, I want to append these queries into a new query. So this is a pendant query, so this is additional rows as opposed to additional columns. So I want it as a new query, which gives me two dropdowns. So driving Safety 2017 appending into or what? To Append on top 2018. So this is when we really do need the year as a field, because now we’ve got two Alabamas, one in 2017 and one in 2018.

So we’re going to be calling this Driving Safely 20, 17, 18. Now, we’re not going to use this in the final result. This is practice activities, just to show how you can actually append two tables together. So next I’m going to load these two files, 2015 and 2016. So there’s the 2015, and here is the 2016. And now what we need to do is append three queries together. So what we could do is append at 2015 to 2016 and then 2015 and 16 to another query. But instead, what I’m going to do is append as a new query. And again, if you want to use existing query, that’s fine, but I’m going to use three or more tables. So let’s remove any of the existing queries and I’m going to add 2015, 2016 and 2017 to 18.

So now we have three tables together, or three queries together, which together have four years. So I’m going to rename this Driving Safely 2015 to 18. So next we’re going to load the entirety of the folder. So instead of just loading each individually, we’re going to load the lot. So instead of going new source text, I’m going to say more. And then when this new dialog box comes up, I’m going to say folder. So let’s connect. So getting my path, that’s the start of my path.

Is that the actual path yet? It is. Click OK. And you can see that we have all of these names with binary content. Now I’m going to transform this data. I’m not going to combine the data yet, I’m just going to transform it. If you wanted the computer to go into each one and expand the data, you could combine it, but for now, I’m just going to transform. So what I’m going to do is filter out these two fatalities 2018, managed 2018. These all have four columns, whereas these two only have three. And the computer can handle that, but it doesn’t need to in this particular case because I’ve already combined those two into Driving Safety 2018. So there is our Driving Safely 2007 to 2017. Next we’re going to expand the content. So this is all of the binary files in avoid the text files, so expand it all. Compute is going to ask how I’m wanting to expand it. Well, tab delimited files.

Absolutely. And here is our results. So now we have five columns with the name of the file at the beginning and then source Year, Miles and fatalities. So what I’m going to do next is delete the source name column. So let’s just remove this one. I can right and click and remove lots of ways to do it. We’ve got to remove columns up here, for instance, lots of ways to do exactly the same thing. Now, you’ll notice we’ve had helper queries added lots of things that you can just basically ignore. So I’m going to rename this driving safely. 2017 but then I’m going to append on top of that into this query. Driving safely. 2018 So now we have got the full range of data from 2007 to 2018.

So I’m going to now rename this 2018. So now we can create a dashboard based on our finalized data. So I’ll click close and apply to get back to Power Bi desktop. Now it’s going to load all of those queries. We’re only interested in one of them, and that is the Driving Safety 2007 to 2018. So what you could do is hide all the others. So not delete them because we might need them for the combining. In fact, there’s a lot we don’t need. But this practice activity, we were doing lots of different appendings and combining, mergings, that sort of thing. So this is our finalized data.

So create a dashboard based on this data. So I’ve suggested a couple of clustered column charts, which would be this one, or stacked column charts. So I’m going to see what it looks like as a stacked column chart. So I’ll just get rid of filters for the moment and I’m going to put in fatalities and mileage over time. So what we could do for instance, is we could have fatalities as the values, year, as the axis state, as the legend, and then have a second one underneath for let’s do that, let’s just copy that, have a second one underneath which would be instead of fatalities, that would be number of miles driven. And then what we can also do is have a slicer. So let’s add a slicer, make sure nothing is selected. So here is my slicer and I’m going to be able to select the state or states. So maybe I was interested in the Southeast so I can select Florida, I can select Louisiana, holding down control to do so I can select South Carolina, that sort of thing. So that’s one possible dashboard.

Another dashboard is based on a clustered column chart, for example, so I can have this going across. So here’s just one and I’m going to have values as fatalities and number of miles in millions and then have the year going across and the state perhaps as a legend, maybe not needed in this, but in fact the computer won’t allow me to do it. So I was thinking wasn’t needed. So I don’t particularly like the fact that the axes are so different. So maybe we will change that to a line and clustered column chart and move the fatalities into the column series or actually the line values. So that’s looking a bit better. We’ve got two axes now, so we’ve got the number of miles in millions as the bar and the line being the fatalities. And that could be a better way of tracking things. So we probably want to add a title since it doesn’t say fatalities.

So it’s in the y axis and you’ll see the y axis because this is a line and clustered column chart, it’s a bit more complicated. We start off with a y axis column and then we have to really scroll down and scroll down. We need to scroll down the second scroll until we get to y axis line and then we can keep scrolling down. And here we have the title so we can put that on there and then let’s add the slicer again. So we’ve got slicer here, we’re going to put in the state and so we can have Florida for instance, and Louisiana and South Carolina. And we might also want to start the right hand axis at zero perhaps, although this might be a nice way of being able to show the differences. It could just be a very small line, largely mirroring the number of miles. But here you might want to really exaggerate it so entirely down to you how you want to present.

So in this practice activity, what we have done, we have practiced our merging, we have practiced our pending, we have practiced loading files, we’ve practiced loading an entire folder removing some of the files and then combining them all together and then appending some more. And then we have created various dashboards. So really with the Dashboards, the question is what is the end result that you are trying to give to the user? What are they trying to see? So with all of this, now we’ve reached part two. We’ll be creating dashboards at the end, but we’ll be doing that by transforming data at the beginning and then the Dashboards, they are now one that you should have all of the tools to be able to do. So I hope you enjoyed this practice activity with all of the transforming data from various queries. And I’ll see you in the next section where we’ll be going away from the whole menu and going to start to transform.