Include Level of Detail Calculation: Tableau Functions

0:00Hey, it's Tim here and in today's video

0:01what we're going to be doing is talking

0:03about the

0:03include function which is a level of detail

0:06calculation. This is the second of my

0:08videos

0:09on level of detail calculations. Be sure to

0:11check out the previous one I just let out

0:13on fixed level of detail calculation.

0:15Before we get stuck into the video it's

0:17also important to

0:18make sure you go to my channel and also

0:20check out the videos on granularity and

0:22also the order of

0:23operations. It doesn't have to be my

0:25content necessarily but just make sure you

0:26're familiar

0:27with these two concepts because we'll be

0:29talking about them in this particular video

0:31and in this

0:31video it makes a lot more difference in

0:34terms of your level of understanding if you

0:36don't know

0:37about those concepts. So check out those

0:39concepts before we get stuck in. Last but

0:41not least if you

0:42enjoy the content that I make on this

0:44channel be sure to share it with other

0:45people, like,

0:46subscribe and hit the notification bell so

0:48you find out as soon as I launch new videos

0:50out on

0:50this channel. Okay that's enough let's get

0:52stuck in. Okay so for this one I'm actually

0:55going to

0:55just dive right into tableau and set up an

0:57example. I'm going to open up the super

0:59store sales, I'm

1:00going to open up the American version but

1:01you can open up whichever version comes on

1:03your machine.

1:04To do this I'm actually going to build a

1:06very simple view. I'm going to bring region

1:09onto rows

1:10then I'm going to bring sales onto where it

1:12says ABC. That uses a feature called show

1:15me which then

1:16builds us this really nice table. Now what

1:18I'm going to do is I'm going to set this to

1:21an average

1:21okay and then I'm going to ask you this

1:23question what average is this actually

1:25showing me. It says

1:27average sales and so you might argue that

1:29this is the average sales for each region

1:32but if I then

1:33challenge you a little bit more on what

1:36exactly is that average of it's a really

1:39important thing to

1:39have clear. Some people might say that this

1:42is the order IDs data set so you see

1:44everything here is

1:45about orders and therefore this is the

1:47average of each order. That's not actually

1:49correct. Okay the

1:51level of detail for this whole entire data

1:53set the granularity the grain the level of

1:56sort of row

1:57level information that we actually have in

1:59this data set is actually at the product

2:01level. So if

2:02I just show you here let's just go into

2:05this table and I just bring sort this by

2:08order ID and let's

2:09just find an order with multiple items. You

2:11can see here I've got this order here with

2:14two items.

2:14You'll see here that if I just go across

2:17same order ID but the product name is

2:19actually what's

2:20different about these individual rows. So

2:22this data set although it's about orders

2:25actually tells me

2:25more about the products in each order

2:27rather than just the orders themselves. So

2:30whenever I do any

2:30aggregation I have to bear that in mind

2:33because what this average is is actually

2:35just the average

2:36of all the individual products in our

2:39orders totaled up and essentially just

2:42calculated

2:42across the whole entire table. So

2:44essentially what you do here to get this

2:46215 I'll just show you on

2:48a new sheet is you drag the order ID onto

2:50rows. I'm going to bring the customer name

2:53as well just

2:54because you know a customer can have

2:56multiple orders and then what I'll do is I

2:58'll also bring

2:59sales here onto the table and then I'll

3:01also bring product name because this is the

3:03actual grain of

3:04our data and you can see each and every

3:07individual one of these is actually the

3:10level of detail of

3:11our data set and if I just go into my table

3:13you'll see that in the orders table I have

3:169994 rows

3:18and in here we actually have 9986 there's

3:21actually some duplicate records somewhere

3:23in this data I

3:24know that because I've worked with it a lot

3:26but if I just bring the count of orders

3:27here which just

3:28counts the rows in the orders table you'll

3:30see that it's just one for the number of

3:32rows here.

3:33So this is the actual grain of our data so

3:35if we take that count out and we just look

3:38at this and

3:39we look at the specific grouping that we're

3:41talking about so if we take central as a

3:43very simple

3:44example let me drag region in front of

3:47order id and let's just keep central as the

3:50only data set

3:51in our view and then we're going to bring

3:53the worksheet and then show the summary and

3:56then

3:56you'll see here in the summary view that

3:58the average is actually 216 this is just

4:01computing

4:02the average of all these values so this is

4:04in fact the average of each and every one

4:07of these

4:07product cells if that makes sense and it

4:10matches that it's rounding up and down in

4:12in our in our

4:13in our calculator but it's more or less

4:15exactly the same thing so that's basically

4:18what that

4:18average is showing and so when it comes to

4:20fixed level of details and include level of

4:23details and

4:24exclude level of details it's really

4:26important to be aware of the question at

4:28hand. Now the question

4:29I'd really like to answer is what is the

4:31average order size in each of these regions

4:34and so in

4:34order to do that I have to bring some

4:36additional context into this visualization

4:39that isn't

4:40currently in there but what I don't want to

4:42do is have to show that context that's sort

4:44of the

4:44value add with level of detail calculations

4:47you can compute the different level to what

4:50you're

4:50actually looking at remember in my previous

4:52video I talked about the viz level of

4:54detail and so the

4:54viz level of detail is controlled by

4:56anything on columns and rows and then

4:59pretty much most things

5:00on the marks pane apart from the tool tip

5:02the tool tip is the only thing that doesn't

5:05change the viz

5:06level of detail it uses this capability

5:08called the attribute to sort of get around

5:10that and so

5:11pretty much everything else controls the

5:13viz level of detail. Let's just clear that

5:16and let's start

5:16looking at our level of detail calculation

5:18so let's open up the calculation window and

5:21let's

5:22just type the question we're trying to

5:24answer what is the average order size in

5:28each region okay so

5:32let's just put that in there so it's really

5:34really clear and you can have that in

5:35context and so

5:36let's go to a new line and I'm just going

5:38to type the calculation first then I'm

5:40going to show you

5:40how it solves the problem in order to write

5:43this calculation I'll just type an open

5:45curly brackets

5:46here and you can see it's very very simple

5:49I press the function key for emoji there we

5:51don't need

5:52that so if I just make this larger the

5:54first thing I need to do is type include

5:57okay and what the

5:58include level of detail does is it

6:00essentially brings a dimension into the

6:03visualization so

6:04unlike fix which is working independent of

6:07the visualization include and exclude work

6:10in

6:10conjunction with whatever is in our view so

6:13at the moment we have regions therefore if

6:15I then say

6:16order id what's going to happen is it's

6:19going to add order id into the view which

6:22means the level

6:23of detail for the view and this calculation

6:25will actually be region and a combination

6:28of order id

6:29not just region or not just order id okay

6:32and so I'll show you an example of that in

6:35a second so

6:35let's just finish typing this out and what

6:38we want to do is sum up all the cells and

6:40then what we're

6:41going to do after doing this is calculate

6:43the average of all these values so I'm

6:45always typing

6:46cells incorrectly here I should just use

6:48autocomplete it's a sort of a thing of

6:49habit

6:50and you can see that this calculation is

6:52now valid I'll just make sure I type this

6:53correctly

6:54with a capital I and now we have our

6:56include calculation so what this is doing

7:00is it's

7:00basically looking at the viz level of

7:02detail it's showing me that you know you're

7:04only working at

7:05the region so go ahead and add order id for

7:08that level of detail and then go ahead and

7:10calculate

7:11the total sum of cells okay so I'm just

7:13going to call this order size because that

7:16's essentially

7:17what it's calculating this is calculating

7:20the total order size for this particular

7:22setup so

7:23I'm going to hit apply and when I do that

7:25it shoots off over here to this left hand

7:28side pane

7:29and so now that I've done that I can bring

7:31that into the view and we can start to look

7:33at it and

7:34so you see you get the same total as we had

7:36before sort of nothing controversial there

7:38but if I then

7:39change this to the average order size you

7:42can see that we get a very different number

7:45426.6 for each

7:47and every one of these and so just to

7:49validate that let's go ahead and look at

7:51what the value is

7:52for central so for central we're seeing

7:55here that the value is 426.6 so let's go

7:58over ahead to my

7:59other visualization let's remove product

8:02name over here and you can see here that it

8:05's 427 so it's

8:06again doing some rounding up this is 426.6

8:10so it rounds up to 427 which is essentially

8:13what this is

8:14showing here which is kind of sort of good

8:16to know you can sort of add other you know

8:19aggregates to

8:20this summary window and it's just a you

8:22know nice thing to have it's like a

8:24calculator so you can

8:25now see that the number is correct and this

8:27is what is actually being calculated here

8:29so now

8:30that we've done this some of you might be

8:32saying well hey tim I could do this with

8:34fix couldn't I

8:35and you can so just bear with me now as I

8:38take a small tangent let's go and create a

8:41fixed

8:41calculation here and I'm going to do this

8:43exactly the same thing fix to the order id

8:46go and show me

8:47the sum of sales let's type in sales

8:51correctly here and just close that off and

8:54then do a close

8:55brackets at the end so basically what this

8:58is doing is remember the fixed level of

9:01detail

9:01doesn't work with anything in the

9:03visualization the calculation is

9:05independent and so unlike the

9:07include function it's going to work very

9:09differently and I'll show you an example of

9:11this in just a

9:11second so this is going to go ahead and

9:14aggregate all the summer cells for each

9:16order id okay so

9:17I'm just going to say this is the fixed

9:20version of the order size okay and so we're

9:24going to hit apply

9:25and click okay and then we're just going to

9:28drag that into the view and again it gets

9:30the same

9:31total as before and then we go in here and

9:33we set the average and there we go we get

9:35exactly the

9:36same answer 426.6 484.5 and so you're

9:40probably wondering well hey Tim so why did

9:43you go through

9:44all this effort to show me the include

9:46function when it does exactly the same

9:48thing as fixed

9:48well it actually depends on the question

9:51you're asking you see there's a subtle

9:53difference between

9:54these two things let me change this to

9:56include subcategory and then I'll try and

9:58explain to you

9:59why they're different let me just duplicate

10:01this sheet like this so we keep the other

10:03one there

10:04and then I'm going to go ahead and grab sub

10:06category and replace region with subcategory

10:08and then you'll see now these start doing

10:11completely different things they're not

10:13showing

10:13the same thing and if you can guess why

10:15this is before I explain it to you then let

10:18me know in

10:18the comments below and if you can then you

10:20are a pro user of Tableau you're totally in

10:23tune with

10:23everything that you need to know with gran

10:25ularity and you know level of detail

10:27calculations

10:29essentially what's going on here is that

10:32our orders are actually based on the

10:34product level

10:35and so when a customer makes an order let

10:37me just show you the table when a customer

10:39makes an order

10:40we capture a few bits of information okay

10:43and if I just sort of narrow down to these

10:46two

10:46that's actually not not a good example let

10:48me find another example here we go this is

10:51a big order here

10:52right here so this is a good example

10:53because it spans quite a few rows so there

10:56we have an order

10:57and you can see this customer is called uh

11:00or is it bracina hoffman okay and they are

11:04in

11:04los angeles in the united states and they

11:06've bought items across multiple subc

11:08ategories that's a really

11:10important thing to be aware of okay

11:12multiple categories and subcategories if I

11:14scroll across

11:15you'll see all the different products which

11:17makes total sense and then you'll see the

11:19region is west

11:20okay so the region is the same in this

11:22particular context the segment is the same

11:25as well and then

11:26you have the subcategories which are

11:28different and so this is actually why these

11:30are working

11:31differently when I switch from region to

11:33subcategory because at the region level

11:36the same customer is going to be ordering

11:38from the same region the region is just

11:40capturing the

11:41region that that customer lives in see this

11:44regional data this location data is

11:46actually

11:46belonging to the customer okay whereas my

11:50subcategory is different and so my fixed l

11:54od

11:54is actually going to be doing double

11:56counting in this view because what it's

11:58doing is independent

11:59of the visualization it's going out and

12:02totaling all the orders and it's basically

12:05it's going out

12:06and totaling all the products in my order

12:08and then apportioning them to the order id

12:10whereas my

12:11include function is doing that not just at

12:14the order level but at the order and sub

12:17category level

12:18so the order and subcategory level this

12:20number is actually correct what it's

12:22basically doing is it's

12:23almost creating a combined field between

12:26subcategory and order id then computing the

12:28average based on that so you can actually

12:31split an order in a slightly level of

12:33different level

12:34of detail compared to our fix which isn't

12:36doing that and that's why you can see here

12:39in accessories

12:40we're getting a rather high value even

12:42higher than what we had before for one

12:44particular region

12:45because actually what it's doing is it's

12:47just looking at the total order so even if

12:49they're

12:49from another category and then it's

12:52calculating the average okay and so an

12:54order can have multiple

12:55subcategories and that's why that's

12:58basically so high so in this case to get

13:00the right number

13:01and this time let's say we've changed the

13:03question to ask what's the average order

13:05size within each

13:06subcategory what we have to do is we have

13:08to basically split up our order and

13:10basically treat

13:11an order as being an order and subcategory

13:14combination because you don't want to sort

13:16of be

13:16merging orders from different subcategories

13:19and so we kind of create a new sort of let

13:20's think of it

13:21as a combined field essentially then we're

13:23going out and calculating this value and

13:26actually this

13:26this middle value is the correct one which

13:28is why we need to use the include function

13:30in the first

13:31place this outer one is actually incorrect

13:33but it ends up being correct when we look

13:35at it from

13:36a regional perspective if i just go back to

13:38this one because in this one the order does

13:41actually

13:41belong to the region and so when we do a

13:43fixed level of detail here it will give us

13:45the same as

13:46the include function okay so that's a

13:48really sort of important thing to be aware

13:51of the include

13:51function and the fixed uh lod works

13:53slightly differently and they although

13:55sometimes they

13:56can give you the same answer as soon as you

13:58change that question as soon as you just

14:00start dragging

14:01and dropping things into the view that

14:02question can very quickly change and so you

14:04need to keep

14:05asking that question about whether that's

14:07the correct context for your calculation

14:09okay so just

14:10to summarize the include level of detail we

14:13'll take our aggregation so let's bring this

14:17up

14:17let's bring up the order size function i'll

14:19open this up it'll take our visualization

14:24and it will

14:24understand what's in the visualization

14:26level of detail in this case it's sub

14:28category previously

14:29it was region okay and then it will also

14:31bring in order id into that level of detail

14:34so now think of

14:35subcategory and order id as two things and

14:38then it will use that to figure out the

14:40total cells

14:41at that level that new level that it's

14:43basically added then it will go and do the

14:45average um in

14:47this case we've done the average inside of

14:49this this sort of calculation window but we

14:52could

14:52actually end up doing the average here and

14:54then you can do the aggregation in the

14:56calculation but

14:56i'm just going to leave it like this

14:58because the order size is something that we

15:00might want to do

15:00flexibly in lots of different situations

15:03okay and so that is basically summarizing

15:05what's going on

15:06here in fact if i actually change this

15:08particular question what is the average

15:10order size in each

15:11subcategory because now that's what's

15:13correct and then you can see that um sort

15:17of working there

15:20okay so we've had a look at a very basic

15:22example the include function i can see some

15:25people still

15:26confused you know like when would i ever

15:28use this well this is a very good example

15:29of when you might

15:30use it but let's see what you might do if

15:32you want to use a more complex question

15:35maybe another

15:35example that you can also use let's get

15:37stuck into that okay so let me close this

15:40window and we're

15:41going to try and ask a new question here so

15:43the question i want to ask is which city

15:45has

15:46manufacturers that create the biggest cells

15:48okay it's a slightly more complex question

15:50because

15:51what we have to do is find out essentially

15:53the larger cells for each manufacturer and

15:57then try

15:57and you know do the average across each of

16:00these cities essentially okay um so let's

16:02go ahead and

16:03try and do that essentially so the first

16:05thing i'll do is open up a calculation and

16:08we're going

16:08to do a level of detail calculation of

16:10course we're doing the include one so let's

16:13go ahead

16:13open up the brackets i'll make this larger

16:16so you can see and i'll do include and in

16:19this case we

16:20need to do the average for the manufacturer

16:23first so let's go ahead and include the

16:26manufacturer

16:27okay and let's go do that then we need to

16:29go and find the largest cell for each

16:31manufacturer

16:32okay so let's just go do that and let's say

16:35we want the largest cell okay and just

16:40close that

16:40as well and then we're going to close out

16:42that bracket okay so that's sort of the

16:45first the first

16:46part and i never know how to sort of name

16:48these things but we'll just say um we're

16:50basically

16:51finding the largest cell for the

16:54manufacturer okay this should actually be a

16:58comment comment

16:59i don't know why i'm putting it in the name

17:01let's go ahead and put it in a comment here

17:02so it's

17:03easier to see okay uh finding the largest

17:05cell for the manufacturer okay and this is

17:08going to do it

17:09in context of the visualization okay so in

17:12context of the viz because it will do it

17:14for the largest

17:15manufacturer and whatever else we have in

17:17the viz so in context of the viz is what i

17:19'm going to put

17:20here in context of the viz let's just make

17:23sure that's clear okay so we've done the

17:26first uh sort

17:28of part of our question and i'm just going

17:31to say manufacturer manufacturer uh i can't

17:36even spell

17:37here i'm just going to call this um

17:41manufacturer manufacturer max cells include

17:47so we know what

17:48we're creating essentially okay then we're

17:50going to hit apply and now you'll see that

17:52that

17:53calculation has shut off over here and is

17:55now available for us to use okay the next

17:58thing i'm

17:58going to do is i'm going to start building

18:00my view and then we're basically going to

18:01try and answer

18:02this question properly okay so let's just

18:04hit uh control a just to clear out the

18:06annotations and

18:07hit close and let's just now start bringing

18:10in the question we're trying to answer so

18:12here's the

18:12cities okay you can see here are all the

18:14cities and then the last thing we're going

18:17to do is we're

18:17going to bring in that uh new calculation

18:20we've just created and which is just here

18:22manufacturer

18:23max cells include okay i'm going to bring

18:25that into the view and of course this first

18:27thing it's

18:28going to do is total it the thing we

18:30actually want to do is do an average okay

18:32so it's going to go

18:33and find on average the biggest cells for

18:35each manufacturer within the cities okay

18:38and then we're

18:38going to bring this actually we're going to

18:41just sort this from largest to smallest and

18:43there we go

18:44so it's jamestown is essentially the city

18:47in which the manufacturers on average

18:50generate the biggest

18:51cells okay so it's a slightly more complex

18:53question to ask and it involves a little

18:56bit more

18:56sort of thinking if that makes sense it

18:58involves us to sort of make sure we're

19:00asking the right

19:01question if i just go ahead here and open

19:03this up and you can sort of see the context

19:06of the four

19:06questions so what we had to go and do is go

19:09and find the max cells for each

19:10manufacturer and

19:11because this is doing in context of the viz

19:14it's like saying that you're doing a

19:16manufacturer and

19:17city combination okay then it's going to

19:20find the max cells so remember our data set

19:22is working at

19:23the product level of detail so it's gone

19:25off and found the max cells on a product

19:28level for each

19:29of those combinations okay and then it's

19:31basically got those ready to go it's sort

19:33of loaded them up

19:34and then what we're asking it to do is to

19:36take an average of that value and that will

19:39give us

19:40the value of food to these cities if we

19:42wanted to we could go into each one of

19:44these and just open

19:45this up and we get a summary view which

19:47tells us what's going on but also if we

19:49click on this

19:50we'll actually get this this view and so

19:53you can see this is a really good example

19:55because you can

19:56see that it's got the city james town it's

19:58got logitech and apple okay and this is the

20:00max cell

20:01for each of these manufacturers okay so

20:04this is the largest one and then what you

20:06can see here is

20:07that it's basically taking an average of

20:09these two and it's come back with 2354 so

20:12maybe that

20:12wasn't a bad question to ask because we

20:15just don't have that level of detail and

20:17sort of as much

20:19information in each row to really be asking

20:21this question in a meaningful way because

20:23the average

20:23is sort of taking two ends of our extreme

20:25and just drawing a line in the middle but

20:27hopefully you can

20:28see this question is working a little bit

20:30more realistically okay if i actually go

20:32ahead and do

20:33this let me just um go and bring in the

20:36sales value into the table as well you can

20:39actually

20:40see the total sales and if i go back in

20:42here and click the detail view for this one

20:44you'll see that

20:45we actually get three tables here at the

20:47bottom so we get the one we got before

20:49which shows us

20:50these two values and if we look at logitech

20:53and james town and we go to orders we'll

20:56see that it's

20:57the only value in here so logitech here the

21:00max is 159 and the max is 454 there so that

21:04is a pretty

21:05interesting sort of set of data if i just

21:07bring in the row count here well let's try

21:09and find

21:09something with lots and lots of detail j

21:12ames that only had sort of two values there

21:14if i go to

21:15lafayette i think is the correct way to say

21:17this let's go in here and let's go have a

21:20look at this

21:20more granular data okay so here we have the

21:23max values for each and every one of these

21:25manufacturers okay and essentially what's

21:28going on is it's basically gone and

21:30calculated the average

21:32across all of these manufacturers okay and

21:34that's coming back at 1006 that's not

21:37actually correct

21:38let's go back to the right one here 25,036

21:42is the total but the average is actually 12

21:4504 across all

21:46these values and so the way it's doing that

21:48if we go down to aviary for example and go

21:51into the

21:51orders you'll see the orders actually

21:53calculates across 31 rows we have all the

21:56information here

21:57and we can go across and just start looking

21:59at this in a little bit more detail so you

22:00can see

22:01the sales here and work at a slightly

22:02different level of detail and you can start

22:05to see how this

22:05is working okay so it's really nice that

22:07the summary actually gives you this so when

22:09you go

22:10into the tabular view tabular actually

22:12shows you what it's doing and how it's

22:14computing it

22:14and now this is basically doing what we

22:16asked it to do we are finding out the

22:18average max sales of

22:20all our manufacturers within each city and

22:22then saying which city on average creates

22:25the biggest

22:25ones of those opportunities okay now it's

22:28probably a good idea to have the count of

22:30rows here so you

22:31can just sort of get some context as to

22:33what's driving that average so here you can

22:35see new york

22:36city has 915 orders in this particular case

22:40sorry 915 rows in this particular case and

22:44if we actually

22:44did a count of orders the way we can do

22:46that is go to count of order id so just put

22:49that in there

22:50and make sure we do a count of distinctive

22:53order id then put that in our table so you

22:55can see here

22:56new york has 450 orders from for us to work

22:59with so when we go into that particular one

23:02you can see

23:02here that this is going to be working off a

23:05much bigger pool of manufacturers because a

23:07lot of

23:08a lot of manufacturers are selling products

23:11in this particular city or the customers

23:13are in this

23:14particular city and therefore these

23:16manufacturers are serving those customers

23:18in new york and then

23:18these are the max sales for each of those

23:21and then it's going to do the average okay

23:23so slightly

23:24obtrusive question but you can see how much

23:27we got into the weeds of just really

23:28understanding

23:29what the level of detail calculation is

23:32doing and more importantly what our data

23:34set is about

23:35really understanding the context of the

23:37question and really understanding what we

23:40're trying to ask

23:41to make sure we're getting this correct

23:42okay now before i close out the video i'm

23:44going to call out

23:45the resources that i mentioned earlier on

23:48go and check out this page on level of

23:50detail expressions

23:51by tableau it's really really good it goes

23:53into the detail of what they do how they do

23:55that and

23:55how to make sure you're doing them

23:57correctly this article here by bethany

23:59lions from tableau who's

24:00a senior product manager she talks about

24:02the 15 most used lod expressions and you'll

24:05find a few

24:06in there that use the include function and

24:08then lastly an overview of the lods and

24:11this one

24:11actually covers some things to be aware of

24:13so not only does it sort of tell you how

24:14how they work

24:15exactly but it also gives you some

24:17exceptions to be aware of and the last

24:19thing is the tableau's

24:20order of operations just understanding this

24:22is really really important to make sure

24:24that you know

24:24what's actually going on with your

24:26calculations i didn't touch too much in

24:28order of operations in

24:29the include function just because i think

24:31it was easier to see what was going on here

24:33and but if

24:34you ever wanted to do that actually there's

24:36another white paper which i'll put a link

24:37to in the

24:38description which actually shows a visual

24:40representation of how the calculations are

24:42working

24:42if i just sort of remind you here the

24:44include function happens after dimensional

24:48filters okay

24:49so they happen at a slightly different

24:51place in our data set and which is why it's

24:53really

24:54important to sort of get that context okay

24:55so that's it for this video it's been an

24:57introduction

24:58into the include function and the lods

25:00hopefully you start to understand why you

25:02might use them or

25:03what you might want to use them for and

25:04yeah i'll catch you in the next video where

25:06i'll be covering

25:07the exclude function which is actually very

25:09similar but just works in the opposite

25:11direction

25:12this one you bring data in you add

25:13something to the visualization level of

25:16detail with the

25:17absolutely take something out so it's

25:19almost the same but just works in reverse

25:21order okay catch

25:22you in the next video thanks for watching

25:24subscribe like and share the video with

25:26anyone you think you

25:27might enjoy it and i'll see you in the next

25:28video