Transcript for:
Introduction to Tableau Workbook Guide

hey guys and welcome to the video companion to the ultimate tableau workbook the ultimate tableau workbook is a workbook that i put together to help you get started with tableau the visualization software i believe that learning is a lot like flying in the sense that taking off is probably the hardest part but once you're airborne there really is nowhere you can't go this workbook and this course are designed to help you in that in that takeoff stage basically get to a point where you're familiar enough with most of tableau's important concepts so that you can go find the use the resources i give you at the end of the course and know what questions to ask and be able to ask the right questions in the right places this course is interactive so i highly recommend you follow along on your own device and i also recommend that you download the pdf of this workbook which is available in the description uh below one more thing the colored bar at the top of each page as you'll see over here tells you what the purpose of that page is orange means it's um the page is about information about the course uh this is a uh workbook that you're supposed to follow along so blue is for instructions on what you need to do we i offer step-by-step instructions in the guide the guide is standalone but the video will also help you yellow is for warnings and extra information you should consider these top three ones are these top three colors are the pages that you really should be reading in order to get the most out of the course red is purely for nerding out and this is content that is just there for your enjoyment and stuff that i thought was very interesting you won't miss anything mandatory by not reading this uh this content but it's always there for you if you need it with that thank you and let's get started all right so let's get started this is going to just be information that uh will help lay the groundwork for the course why why tableau is important what does it do who uses it uh and what can it be used for so what is tableau so tableau itself is actually a software company uh that's created a host of tools to explore visualize and present data uh their three main products are tableau desktop tableau prep and tableau server slash online and then within these products you usually have a couple of offerings so tableau desktop is the core product in tablet's portfolio and it's what most people mean when they refer to tableau it is the desktop application that runs on either mac or windows and it allows you to create professional grade visualizations very quickly at its core it's a visualization software but that means a lot more than people think uh than people usually think uh it means within tableau desktop you have tableau desktop public edition which is just a free version of the desktop software that contains all of the functionality of tableau desktop with the exception of a couple of things here and there but anything you publish on that is can only be saved to tableau's public cloud um so in exchange for having a completely free free version of their software um you can't save things to your desktop tableau prep is a tremendous tool that tableau released i think like a year or two ago um that is a data cleansing software if you've ever used alteryx then you'll get a basic idea of what some of tableau preps functionality is it comes free with every license of tableau desktop and it is tableau's answer to the question of um what do i do if my data is not fully prepared and fully cleaned so whenever you work in tableau you always bring in data and then you visualize the data but anyone that's worked with anyone that's worked with data will know that a lot of time is spent cleaning up data and getting it in a format to where it's easy to visualize tableau prep helps you take care of that it's completely it's a complete gui based interface no coding knowledge necessary and it's designed to let users easily and visually inspect data sets and clean them systematically before they're visualized in tableau desktop everything that's made in tableau prep is uh part of a workflow that uh allows you to link parts one after another to do different steps in your data cleaning workflow it is a tremendous tool and i do hope to make a course on it uh at a later time tableau server or online is uh it's basically a server that allows you to publish tableau visualizations and distribute them across the company um so if you work at a company that has a tablet deployment they will probably have a server and this is what allows people that design visualizations in tableau desktop to distribute them to other users in the company and this is key to a successful deployment of tableau without tablet server or online you're going to have a very hard time having a fully accessing the power of the visualizations you make in tableau so what is the importance of data visualization we call data we call tableau a data visualization tool why should you care well people the importance of data itself is not lost on anyone data visualization at its core is i'm going to skip to the end of here it's just the process of abstracting away the complexity of raw data and organizing it into a form which we can more easily consume that's a very powerful definition right over there because data has been called the oil of the 21st century and like oil it's not very useful uh right out of the ground in its raw format if you've ever seen crude oil no one can really use it for anything uh and it definitely um is not cannot be sold at its full value um right out of the ground the real value in oil or data is when it's been refined and when it's been organized that way um people can really derive uh in the case of oil power or in the case of uh data insights from it data itself is not useful it's what data can tell you that's useful and data visualization is one of the key ways that you can unlock insights when um uh unlike in unlock insights from data a really uh a great example of this is something called anscum's quartet so if you work in analysis you'll notice you'll know that a lot of um data analyses start off with the measures of central tendency where basically you'll take your table and then you'll organize you'll take your table and you'll take these measures right over here the mean the median the mode the mean is the average of all of your data points the median is the center of all of your data points um these are not necessarily not necessarily the same thing and the mode is the most um the the value that appears the most in your data and quartet is a series of four data sets that have the exact same mean median and mode while they have the exact same mean median and mode when you visualize the data when you actually plot it you'll notice that they are completely the distributions are completely different this is not something that a couple of statistics will immediately tell you uh and it's much but it's so much more obvious when you visualize the data the importance of data visualization is that it'll help you see patterns when uh where they may not obviously exist when looking at it in its raw format um and it is a key way to start any analysis of any data and is what tableau excels in so what makes tableau so great data visualization is not something that tableau is the only company to be working on but what makes tableau specifically so good at what it does um so for those of you that are not aware gartner is a research firm that specializes in giving it leaders information on where different software products are uh operating in their space basically how competitive are they and they release uh i believe every year for different uh types of software products something called the uh gartner magic quadrant it is a series of uh well four quadrants um that have two axes one called the completeness of vision and one called the ability to execute um the completeness of vision is basically where does this company see itself going in the next number of uh in the next x years ability to execute is how close are they to actually realizing that vision as you can see tableau is in the leaders quadrant this is usually the most desirable quadrant to be in uh gartner is very specific that um you should look at every product by uh its own merits in relation to what you need and that's tremendous advice um but this gives you a basic idea of how well tableau is competing against everyone not only are they in the leaders quadrant they're far ahead of everyone except for microsoft which uh my opinion on this is very much that gua that uh gartner biases companies with very large product portfolios um that's where the ability to execute in the complete destination come in um and microsoft you know being microsoft with azure especially in azure ml and all the new tools are releasing on that has a much bigger product portfolio than tableau but either way tableau is a best-in-class tool and something that is used all over the industry more specifically as a user as someone who's used tableau over the last couple of years my opinion is that consumer enthusiasm or sorry customer enthusiasm is probably where tableau really outshines its competitors um there are many tools in this uh there are many tools in this space uh a lot of which i've used and a lot of which i've heard other people use and it's not very often i hear people liking what they use as much as people like tableau the user groups that they have all over the country the forums the knowledge base the documentation everything is top notch and it is a tool that is not only well supported technically but is very well supported by its user community and by its uh by the company itself um the we were a smaller account for tableau in the dallas area um where i work when at my older company but the attention we got from the tableau account executives was amazing i absolutely loved it uh and i can say as a uh product and as a company tableau is great so why should you learn how to use tableau well um tableau specifically actually uh and this is just these are just salaries for a tablet developer but tableau um the job opportunities if you have good tableau skills and the pay that you can get if you have good tableau skills is very solid now obviously these are two very different numbers these are two very different numbers but as you can see a tablet developer gets paid a very solid salary this is not anything to laugh at and the job opportunities are quite numerous if you have tableau skills and probably the best thing about tableau is that almost anyone can benefit from it so they call data the oil of the 21st century and the number of insights that you're able to derive from it are such that data should not only be used by data professionals people in all walks of business really should be using it um when i went to tableau conference last year one of the most interesting stories was about a doctor who was using tableau to more accurately diagnose or find patterns with some of her patients to find out that they were being diagnosed uh not in the most accurate way not in the most accurate way possible i'll see if i can find the link to that story and link it below but just in my experience i've seen tableau used by account manager supply chain directors warranty managers and myself as a data analyst account manager um the example i have is that when i was working at my old company we had a account manager who was in charge of a very large very important account this account was many times larger than our actual company so keeping them happy was very important and they demanded a very high level of service because they were a very process oriented company with an excellent sense of process uh tableau helped us diagnose and or plot and prioritize any problems that we had with uh the account geographically that way we could service them to the best of our ability um when i was working at my old company also the uh uh c-level executive who came in to uh help out or that came in to lead our department he expected there to be a weekly report out from the directors on all the metrics that on any metrics that they decided to collect for their team and our team was able to automate the process of gathering calculating and visualizing these metrics using tableau and alteryx and you'll hear me mention all tricks a couple of times too because that's a tool that i'm very familiar with uh this reduced so we spent maybe you know a day or two working on this and this reduced what would probably have been a couple of hours long process every single week gathering gathering the data calculating and visualizing the metrics to something that just happened on a regular basis in fact this was so successful that we were asked to do this by the entire department and uh by the time i had left we had automated the metrics for all of the directors in our department uh we had a very special warranty metric um at my old company and tableau really helped us it helped us by automating that process of calculating this metric which took a full-time employee a quarter of the month in order to calculate on a regular basis but tableau made it to where we could um not only calculate this quickly but after we were able to calculate it very quickly uh and visualize it very quickly we could uh because the process was so much faster we could parlay this metric into other uh calculations that other people in the company were uh doing to rapidly speed up and improve the quality of our analysis across across the enterprise and then as a data analyst i would say probably the most surprising benefit of tableau is the ability to explore data data exploration and exploratory analysis are two big things that i've always been asked to do and tableau has made my job in that space much easier by allowing me to look at data in a very visual and intuitive format so vis-ql is a technology that forms one of the it's one of the core technologies that forms the back end of tableau we're not going to go too much into depth with it but basically just know that it's a querying language that tableau uses if you're interested feel free to read this page on the pdf and the hyper database system is just a database system that tablet bought out in 2016 i believe um that makes analyses much much faster in tableau and anyone that has done a lot of analysis will know that you can spend a lot of time just waiting for queries to run and just waiting for extracts to be taken out the hyper database system makes this process so much faster and it's amazing all right so to participate in this course i highly recommend you follow along um you will need to install tableau so a lot of you will have tableau installed because of your workplace um you can go to tableau.com and install it the a two week trial of the full version of the software for two weeks or you can install tableau public version just remember if you use tableau public version don't put any data in it that you wouldn't be comfortable uh going onto a public server because that's um all the visualizations you make of your can only be saved onto a public server the entire course can be done on tableau public i'm actually going to be demonstrating everything on tableau public so don't feel like if you're using this you're not getting the full experience all right with that let's get started with section one i will see you guys soon hey guys welcome back um so this is where we actually start section one of the course so um before we start make sure that you have tableau downloaded installed and open uh and also make sure that you have the data sets downloaded somewhere where you can easily reach them so assuming you have those two things we can get started so when you open up tableau actually let me close this oh whoops did not mean to do that how do i open that up again let's go here here hopefully just opens up the exact same way ah close so let's put that over there there we go and now i need to open up tableau there we go takes a second all right there we go oh looks like i sized it perfectly so when you first open up tableau you will be directed to somewhere called the connect menu and this is where you connect to data so if you have the pdf which again you can download right here you'll see a small anatomy of the uh connect menu and uh just to go over it basically this side over here on the left the this is a sample of the data sources you can connect to this is tableau public the version of tableau that i'm using is tableau public so because of that there's a limited number of data sets i can connect to um but that's okay that's uh this is all we'll need for the course today and if you click on that um then you can see that there are more things to connect to all right uh and then over here you'll see this will be populated with if you've used tableau any uh workbooks that you've created over um uh any yeah any workbooks that you've created and the right side is the discover tab where you can get a couple of how-to videos to get started the visualization of the day any updates that you might have to whenever you have to update tableau and then some resources one thing that's really cool is the sample data sets button over here so you can see that tableau has quite a few data sets that you can use for free i highly recommend this if you're looking for any practice with tableau but for this course i've created some custom data sets just for you guys so let's go ahead and get started so like i mentioned earlier we're going to be working with a data set um from the chicago department of transportation on filming permits that they have approved so it is a microsoft excel document and let's connect to it so over here click on microsoft excel and then find let's see there we go find the chicago filming films data set i believe that's what it's called chicago filming data set there we go should be about five megabytes large give tableau a second to process the data and here you go now you are on the data source page so earlier we were on the connect menu um and when you are working with tableau you have to um the way you think about your the way you think about your process is you first create a data source and then you start creating then you start making your visualization based on that data source so connecting to data is the first step of creating a data source now we imported a microsoft excel workbook and excel workbooks can have uh a large number of sheets on them i'm not sure if there's an actual limit but uh because of that you need to tell tableau which sheet to bring in so let's go over here uh we're going to want the chicago filming permits sheet so this one over here so all you do you take it and you drag it over here where it says drag sheets here and you can see now the screen is transformed a little bit to where um this is the canvas in which you create you create your data source in and for now we're just gonna have this over here but uh later in the course we'll do more complicated stuff with this area uh this section over here sorry let's start over here this section over here these are your connections uh you can have multiple connections as you can see i can add a connection over here and then i can bring in another data source uh again later in the course we'll talk about what exactly you would use that for and here are all the sheets associated with this connection right over here if the data you're bringing in doesn't look right or isn't imported correctly because tableau will by default just import it however it's saved which for example say the first five rows are completely empty then you'll see that over here in your data preview window you can use the data interpreter which what that will do is that will use it basically intelligently tries to determine what you're actually trying to import so i cleaned up this data set for you all so you won't need to worry about that but if the data over here doesn't look correct then using the data interpreter might be a good idea so let's see what else is there to show you guys you have your data preview over here and you can see that we have quite a few columns the columns are listed over here the rows are over here uh you first tableau by default will show you a thousand rows of data but you can change this to be whatever you want uh so i can make it ten thousand if i want uh or really as many as i want to but uh you wanna keep this as small as possible that way you're not just uh bringing in a ton of data for no reason you have your filters over here so these are what you call data source filters and later in the course we'll talk about what are all the different types of filters you can use a data source filter is uh basically if you know you don't want some data at all in your visualization then you can insert this filter right over here and tableau will filter out the data before bringing it in uh this can really really speed up the amount of time that it takes your data to load uh and this over here is just another way to cycle through your connections this uh little database over here so let's go ahead and get started click on sheet1 down here you can see tableau automatically tells you where to go i give you a small anatomy on this pdf right over here all right so now your screen should look something like this um you'll see this is where you'll probably spend most of your time in tableau so this is called the tableau workspace and it can look a little bit intimidating but it's actually quite simple and quite intuitive so let's go through it bit by bit so you'll notice over here on the left hand side uh you have this button over here this is the connect menu so you click on this and you go back to the connect menu uh which is how we started off tableau and then if you go down here you'll notice uh there are two tabs data and analytics data is what we're going to be sticking to right now here is the data source that we brought in chicago filming permits um so that is the name of the workbook this is the name of the sheet and then below that you'll see all the different columns that we have in our data and you'll see that they're split into two different sections dimensions and measures later we'll go over what the difference is between those two this button over here lets you this button over here lets you see all of your data um all at once so you can take a quick look at it if you need to and by default it shows you 10 000 rows of data that way you're not if you're connected to a large database you're not just bringing in a million rows all at once this over here is how you can create different fields and different types of tools that we'll be using later so we'll not worry about that right now but just know this is what these are where all of your columns are dimensions and measures are how you split them up uh over here you might notice this looks kind of like a pivot table this is where you can drag uh these columns in to create columns and to create rows in this area over here uh you can also drop the fields right over here if you want to if you want to make it a column if you want to make it a row or if you want tableau to figure out where it's best um where that data best belongs you have your filters over here and your marks card over here we'll go over these two a little bit later over here on the upper right left on the upper left you'll see uh this but this back button and this forward button this is an undo button and a redo button tableau has something called infinite undo where within one working session um basically from when you open up tableau you can undo an infinite number of times uh so this is a tremendous tool and just know that there's almost nothing you can do in tableau that you can't undo um which is incredibly helpful um and then the same applies for the redo obviously you have your save button right over here this is to connect to a new data source this is to create a new worksheet or a new dashboard or story we'll go over what all those are later um this is a sheet over here by the way as you can see sheet one this is to duplicate the current sheet and this is easily one of the most used functions i have in tableau it really helps me with exploratory analysis when i can just keep duplicating sheets and iterating through the data in different ways and then we have a couple of more buttons over here that we'll go through later and this is the show me button which basically given the data you put on the canvas over here it will um uh highlight the different types of visualizations that you can easily make and as you can see if you hover over things so for example like a bar chart you can see that it's asking for uh at least one measure uh and i don't necessarily need dimensions again we'll go over what all those terms are later all right so time to manipulate our data so although tableau is quite intelligent sometimes data is imported incorrectly uh so one example i can show you is wards over here so wards is actually you can see it's a number and it's under measures i'll explain what that is in a minute uh it's not supposed to be here i can tell you that so wards refers to basically a section of chicago i believe there's about 50 of them uh and each of them has a uh i believe alderman is the term for the head of award um but basically tableau imported this data incorrectly because it's completely and completely numerical i put it down here so what i want you to do is to click on it click and drag this up to dimensions and then drop it so you can see just by doing that we've moved it from measures to dimensions and i'll show you exactly what that does in a minute oh and just as a note inside this workbook all the column names will be inside brackets uh this just mirrors what sql and what tableau um how you uh specify a column in sql and in tableau so there are four types of data inside tableau you have uh dimensions and measures and then within dimensions and measures you might have noticed that some data is blue as in some of these labels are blue and some of these labels are green uh see if i can find an example okay so it looks like we don't have any green data here or blue data over here but basically a dimension is uh any qualitative data that you might have um so that's like names colors cities uh anything that's not numerical not a number that you can perform math on and perform math on is important because just because something is a number doesn't mean it's um quantitative data which is the exact opposite so you have dimensions which is your qualitative data your qualitative data and measures which is your quantitative data so this is basically anything that you can perform math on uh a good example of something that is a number but it is qualitative is a zip code your zip code might be 75251 um zip code our postal code for our international viewers um your postal code might be 75251 but that is that might be a number but that is not a number you can perform math on it is not qualitative data or it is not quantitative data it is qualitative data because there is no seven five to five point one uh you can't divide it infinitesimally that's one um determinant of a uh measure or quantitative data so population or profit are examples of measures or quantitative data and that's what will be classified over here so we actually moved wards from here measures up to dimensions because although wards are numbers like so you can live in the first through 50th ward uh in chicago it um you can't perform math on them because there's no 40.5 ward or you can't add up wards one two and three and get um ward six you know it's um it's just a number and the number itself doesn't really mean anything except for a classification it's the same as a name basically so that's why we moved it up to dimensions so one key thing to remember up here is that although tableau is quite smart whenever you work with data you always want to inspect it before you start working to make sure that the data was imported correctly so this is just a simple example of how we can fix that oh and sorry and then data can either be discrete or continuous um whoops there we go it can either be discrete or continuous in tableau blue so these blue pills over here or these blue icons over here represent discrete data whereas green represents continuous data continuous data is data that is infinitesimally divisible um whereas discrete data is data that is uh not infinitesimally um divisible so for example uh you can't have a 40.5 ward but a fee can be basically any number one through 100 um and same with waived fees and uh census trax is something that we should probably move up here too but we also won't be using that so it doesn't really matter uh i believe that and that can also be moved up there but we won't be using that in our analysis today so i wouldn't worry too much about it uh and let's see so given that data can be either a dimension or a measure and then it can be either a um either continue or sorry discrete or continuous you can then have discrete dimensions continuous measures continuous dimensions or discrete measures now the most common form of data you'll have are going to be discrete dimensions and continuous measures as you can see over here discrete blue dimension up here basically everything in dimensions or in this case everything in dimensions is um discrete it is blue whereas most of your measures are going to be continuous this is because a measure is purely something you can you know measure perform math on and most things you can perform math on are continuous uh and most um categories which is you know what a dimension is uh you can't perform math on them that being said that doesn't mean that you can have a continuous dimension or a discrete measure later in the course we'll probably see some examples of that but not for now so let's get started let's make our first visualization so the most basic form of visualization is a table something with two axes that just has all the data filled in inside it it's not the best type of visualization out there but it is something that gets the job done and is often necessary so let's get started all we need to do uh as you can see tableau is a drag and drop interface um you can just drag in whatever you want wherever you want and then stuff will automatically fill up as needed uh put that over there and then obviously you can undo it if you want to so let's get started tableau as you can see actually will imitate certain behaviors of a pivot table so let's drag application start date from dimensions to the columns shelf up here so application start date as you can see is a date and we're going to go over what these icons mean a little bit later so just drag and drop and you can see tableau automatically gave you um different columns with each uh at a at the year level of this date data you can change this as you please for now we'll just leave it as is then let's drag wards into rows so remember we converted wards let's drag that into rows so as you can see tableau expanded it to where our application start date is a column and the rows on the left-hand side are your wards uh and these abcs over here basically signify that there is no data inside this over uh in this section over here which makes sense we only uh defined our row and column but not what is actually inside the table so let's do something about that let's drag the column or the measure number of records to text under the marks card so if we go all the way down here number of records as you can see down here the marked card that's this part over here then we go to text put that over there and as you can see we very quickly populated our entire table so application start date under columns wards under rows and um number of records under your um in under your uh text area of your marks card and you can see we've created our first visualization congratulations if you have ever used excel pivot tables and you'll notice that this is very similar to how they work so that tableau borrows a couple of uh abstractions from um excel and from sql uh and these just make it very easy for you to switch back and forth between all these tools so i i find it quite uh useful so next let's try and create an actual visualization something like a bar chart and although they are very basic bar charts are some of the most effective visualizations you'll have in your toolbox don't ever skip on a bar chart just because you think it's boring they are tremendous for cal or for showing people uh data showing people insights very very quickly so if you look down here you'll notice that our sheet title is down there uh let us rename this to something more topical so let's call it applications by ward crosstab because this is what you call a cross tab so just double click sheet one and then type in applications by ward cross tab and you can see tableau automatically changed that title up here too uh you can always change this title up here too by double clicking this and you can see it just copies the sheet name right now i can change it to whatever i want to though if i want but for now we'll keep it as applications by word crosstab and if i change it to something else then tableau will reflect that change up here too so very convenient all right so now we're going to use the duplicate key uh that i was talking about earlier so go up here just hit duplicate or duplicate this is a duplicate that we made using the duplicate key let's call it that so as you can see down here we have a new sheet obviously tableau needed to call it something else uh so let's change it to applications by ward bar graph another way to make a duplicate is to right-click and then just click duplicate so you might think that we might have to go through some effort in order to make a bar chart not true tableau makes it really easy to iterate through different chart types like i said earlier this show me tool over here is amazing and we'll show you all the different types of graphs that you can make given what you have on the canvas right now click on this bar chart icon here and you'll see tableau created a bar chart for us from our table from our crosstab and then what we want to do is we want to sort this by the most recent year so go to 2019 as you can see when i hover over these they highlight and then click on that button right over there and then it'll sort everything in descending order by number of records uh by 2019 so we can see ward 42 uh it seems every year actually had the most applications turned into it for uh film permits and if you uh know what ward 42 is in chicago you'll not you'll understand why um this is basically the most where the most iconic scenery of chicago exists so that's why ward 42 would have the most application sent into it so as you can see with almost no effort at all we created a brand new visualization and was able to sort the data by 2019 uh in order to see exactly which ward brings in the most uh filming permits so congratulations you've created your first real visualization in tableau now okay so now let's do something a little bit more interesting we're going to work with something called geospatial data this is basically uh data that is plotted on the surface of the earth and geospatial analysis and geospatial data is all the rage right now people love seeing maps of things and i find that the very simple maps you can make in tableau are things that people find very useful and very um uh uh insightful so let's go ahead and duplicate this chart again oh sorry no let's not do that let's just create a new sheet by clicking on the new sheet button so you'll see down here where we have our sheets this left most button over here this is to create a new sheet so click on that and then let's drag latitude and longitude into rows and columns respectively so uh don't use latitude generated and longitude generated just use these two over here so latitude into rows whoops latitude into rows longitude into columns and you can see tableau will automatically turn it into a map now be careful if you do it the exact opposite way longitude into rows latitude into columns then you'll actually get a scatter plot which you don't want so remember latitude into rows longitude into columns and you can see what tableau does is it takes all of our points uh because we haven't told it um what exactly to graph yet we just told it latitude and longitude so it takes all of our points and it finds the center of every record we have in our database right now which is you know somewhere in the middle of chicago so what we want to do is we'll take application number um this should actually be in brackets into the details box of the marks card so application number where are you there we go and we'll drag it into the details or the detail box of the marks card and you might notice that uh this warning comes up this warning is designed to stop you from just adding way too many um records to a visualization all at once you might find that eventually you start working with so much data that you will actually lose hours of your day just waiting for things to process which is why tableau gives you this warning in our case we want all of the data so click on add all members and congratulations as you can see if you scroll out we've created a very basic geospatial visualization if you scrolled out and you want to get back to that view we were at just click on this pin over here there we go all right so we should have something like this uh this just goes over what happens if you switch these two i can just show you using this whoops button over here you'll get something like this because latitudes need to go into rows longitudes need to go into columns i'll leave you to figure out why that is but there's a there's a specific reason you need to do it that way all right so wait not yet let us go over here this okay ah okay so as you can see this map is obviously not particularly useful um it's just a bunch of blobs everywhere so let's see if we can visualize this uh ward by ward so let's give each of these dots a color corresponding to the ward that it that it is in now remember each of these dots also called a mark represents a application number as you can see in the tool tip over there department of transportation number number number number and then the latitude the launch tune so let us take wards and then drag it here into colors under the marks card add all members and as you can see we now have a much better view of um exactly where all of our uh film permits were where they were uh requested and then what you can actually do in tableau is you can click on any one of these and it'll highlight automatically all the marks corresponding to that uh number in the legend remember how i said ward 42 is where all the big stuff in chicago is yep as you can see right over there that's uh exactly where everything is if you think chicago you're probably thinking about this area over here so let's rename this sheet to application by ward map actually it's called applications by ward map all right uh and this is something i highly recommend you get in the habit of doing naming sheets accurately as you're creating them i can tell you from uh experience that it is very tempting to make a bunch of sheets with a bunch of different visualizations and not name things as you're going along or getting rid of sheets you're not using and that can lead to some very messy workbooks very quickly because tableau just makes it so easy to just keep iterating and iterating and iterating so i highly recommend that you uh name your sheets accurately as you go on i will be the first to admit it is a habit i need to build myself you might notice some errors inside this pdf i'm noticing them as we're going through the pdf right now i'll definitely try and get them fixed as uh before i release this fully and if you notice any improvements that i can make either to this course or to this pdf feel free to let me know all right let's make a dashboard so when you start working with tableau a lot of um you'll probably get a lot of requests to make dashboards uh of course dashboards are not the only thing that tableau can do um like we're showing over here you can do exploratory data analysis um but a dashboard is one of the most common deliverables that people are asked to create when using tableau so let's create a new dashboard click on the new dashboard button it is near the new sheet button this one over here the one with the four squares click on that and you might notice that you get this uh view over here that says drop sheets over here uh and this is what your dashboard will look like size-wise this is kind of weird we're creating something for the desktop and so we want something that is shaped a bit more accurately for a desktop view so the easiest way to fix that is to go to size under size click over here this might say something else just depending on your configuration of tableau choose automatic and you'll see tableau will dynamically resize everything and it'll resize any visualizations you put in there um to more accurately reflect uh or to accurately reflect whatever screen it's being viewed on all right so time to double click applications by word crosstab and applications by map uh by ward map uh in that order so if you can't see what it is obviously if you just highlight then you'll it'll show you a preview of what this visualization is very very useful for when you've created like 20 sheets and you need to know what's what very quickly alternatively you can just drag the sound over here so what did i say we needed we wanted a crosstab so just double click that and you can see tab will automatically put it up here and then applications by ward map double click so tableau will put it where it uh based on the size of your screen or your window where it thinks it'll best fit um because i shrunk this this window a little bit in order to make room for this pdf it put it down here if you want it on the side all you have to do is click go to this grab handle over here and look for this four-way arrow drag and one second drag and drop and you see it more closely resembles what i have over here and congratulations you've made your first dashboard so this is one of your very basic analytic deliverables this is the kind of stuff that people will be asking you for a lot of the time with tableau uh and the point of this was just to get your feet wet and show you and get you comfortable with uh how tableau works it's drag and drop for a lot of stuff um and almost everything can be done with just your mouse it's very easy to understand very easy to use if you want if you got caught up anywhere again remember tableau has unlimited undo you can undo all the way back to the beginning of this workbook as you can see over here where we have basically nothing and then redo all the way to that point too so if you feel like you've messed up or what you have doesn't look like this don't worry just undo go back in the video to where you need to and just copy what i did again if you do exactly what i did then you should get this same result over here or something very close to it really um i find that until you release things to the public you never really know how things are going to turn out so you might want to save your workbook over here um if you have tableau desktop just the the full edition then it's very simple just file and then you'll have a save as option over here just save it as a twb file um you can just save the workbook there's quite simple if you have a tableau public then you have to save it to the public cloud so you click down save to tableau public as and then a screen should pop up yep where you name it and another screen will pop up asking you to log in um i'm already logged in so i don't think that screen will pop up for me and i already have this saved so i don't need to save them right now so before we end section one let me explain what sheets dashboards and stories are so that's what those are what these three buttons down here are a sheet is the most basic um uh the most basic workspace in tableau this is where you will create your visualizations your base visualizations a dashboard is usually a collection of sheets and this is where you can put them together to create an interactive uh dashboard with many different sheets and many different visualizations different types of visualizations uh for any anyone that you're creating dashboards for a story is something that i'll be 100 honest i've never used i'm not saying it doesn't have a use but basically it um works kind of like a powerpoint presentation where you can um add like a sheet over here or even a dashboard and then continue to add different um let's see no need right now you can add a title over here and then just add more and more um uh what would you call them uh story points one after another and then you can present it kind of like a presentation so this is excellent if you end up presenting a lot of stuff a lot of analyses to people um usually i just present mine in the form of screenshots or i will build a dashboard for someone and i want them to play around with the dashboard not the story so that's what sheets stories and dashboards are for more detailed information obviously take a look at the pdf that i have uh it might have more detailed more um uh information laid out in a clearer way that'll help you go back and look at exactly what we are um what each of these things are the definition of each of these things is so there's something called the seven data stories and this is just a very interesting concept the basic idea being that there are basically um whenever you're doing anything with data you're really trying to tell a story um with it and there are seven different stories that you can tell you can basically show someone change over time you can drill down to a point you can zoom out for a point compare and contrast two different things show the intersection of two seemingly unrelated categories this in my opinion is one of the most interesting types of data stories show what drives a certain phenomena and um uh what what the what what are outliers in data again this is another one of the very interesting types of data um data stories um for a more expanded view on this feel free to follow this link but i want to stay on topic with this course and show you guys basically how to use tablet what i think is best communicated through a video but if you guys want to see more stuff like this like the more um abstract concepts of creating data products then let me know and uh you know maybe i'll create a video about it so congratulations you've completed section one of the course and in my opinion there's no better way to learn than to do especially something like tablet which is very drag and drop very kinesthetic i 100 believe that doing is the best way to learn uh if you have any questions about specific functions inside tableau um hopefully we'll go over them in this course if we don't then kb.tableau.com is your best friend this is the tableau knowledgebase and it took me way too long to figure out this thing existed but basically this has um document all of tableau's documentation on all the different things that you can do inside tableau and it's very very thorough um and i think it's it's one of the reasons tableau is such an amazing tool their documentation is excellent so this is going to be one of your best friends when you have questions this in the tableau forums um i have encountered maybe just a thing here and there that a problem that someone else hasn't had uh the user community is very active and if you have a question and you post it provided you did the research people will respond to you very quickly so congratulations we've finished section one and we've covered quite a bit we've gone over uh what exactly tableau is the importance of data visualization what makes tableau so great in my opinion it's the user community that's the the one of the best parts about it the part that's hard to duplicate um the ease of visual exploration and vis-ql this is that uh section that we kind of skipped over but is available in the pdf if you want to read more the hyper database system in my opinion this is a very interesting read um again not very not something that will that you'll benefit from hearing over video um i might leave a link to a excellent uh speech an excellent talk that was given a tableau conference about the hyper database system very interesting how to install tableau obviously if you've come this far you probably know how to install tableau now connecting to static data this course will only go over connecting to static data we're not going to connect to any live data or any server data only because that this is so it's so variable how this works um that you're i'm not sure i could communicate how to do that very well over a video uh the anatomy of a of the tableau data page the tableau data page is this page over here the anatomy of the tableau workspace that is any of these pages over here this sheet for example cleaning data that would be an example of that would be when we turned our wards from a measure into a dimension dimensions versus measures uh tables bar charts and geospatial data basic troubleshooting what to do if you have a problem where to go resizing dashboards that's where we change this size to be automatic saving your work sheets dashboards stories and the seven data stories so that is the end of section one if you want to take a break over here now is a great time to do so uh and i will see you in section two hey guys welcome to section two so the objective of this section is to try and create more advanced visualizations we're going to do a more uh we're gonna tackle a more advanced problem in chicago independent films only need to pay a permit price of 25 dollars per day per location whereas big budget films need to pay 250 dollars per day per location we're going to use this information to try and classify permits as either big budget or independent so if you open up a new sheet you'll notice that we actually don't have anything here that says big budget or independent basically there is no way given the data set we have currently to tell what we what films are independent or big budget so this section assumes that you've completed section one and that you have your workbook saved from there if you don't you can download a copy from here so this will just take you to tablet public and you can just download uh the workbook and you'll be exactly where we are right now um something might be renamed or something over here but it should be the exact same thing so make sure that you have all of section one done before you come here so there isn't actually a field that tells us if a film is a big budget or independent film when you don't have a the explicit field you need you might be able to derive it using what we call a calculated field and remember when i use the word field i'm referring to columns a field is just another word for a column if you look at our data set over here you'll notice each of these columns is what we call a field so let's go ahead and create a calculated field go up to oh whoops so what we do know is we know big budget films get charged 250 per day of shooting while independents get charged 25 dollars per day of per shooting we also know what fees a movie was charged through the column total fees and a quick pro tip you can see you can find a field by clicking on that magnifying glass over there and just typing in the name of the field this is really helpful for when you have a lot of fields that you need to look through so films can also have some some of their fees waived and this has to be taken into account through the waived fees field so create a new sheet i already have one and under the analysis tab sorry the analysis menu tab menu button click create calculated field so up here if you're on windows it should be like over here at the top of your menu bar click analysis and then go down to calculated field whoops if you're using multiple screens then it might show up on a different screen so you might get something that looks like this a separate window click on this arrow over here in order to expand it and show the assistant assist menu and then let's resize it a little bit all right so name your new calculated field days spent shooting because if we want to know if a uh film is big budget or independent we need to know the fees per day um we don't have a column over here that tells us how many days they've been shooting we only have um the start and end date so use the following formula you can always just copy and paste it and i will explain exactly what it is what we did in a minute all right and then we need to name the calculated field days spent shooting so this is where you name your calculated field all right there we go you can see the whole formula now so let me just go through what exactly this calculation does so this window over here is your calculated field window over here is where we name our calculated field this down here tells you if your calculation is valid or not if i for example take that comma out you'll notice that it says calculation contains errors this white space over here is where you actually write down your calculation you'll notice that it comes up in a different font a monospace font from the rest of your um uh the everything else in tableau basically this is how you know you're writing down a calculation you then have your apply button and your ok button uh the apply button is great because sometimes you'll want to modify a calculated field and see what the modifications you made due to your visualization so you just hit apply and the window will stay up but you can see what changes were made so you can see for example days spent shooting is now down here we created a calculated uh calculated field and uh tableau was smart enough to realize that this is a number and a measure so let's go through the actual calculation oh and another thing this over here explains exactly what the function you're using does and this over here is a list of all the functions available to you classified under this drop down so we use what we call the date diff from a function so date diff is just uh it is a function designed to tell you the difference between two dates at whatever level of um for whatever time period you want to determine it for so for example the first thing you put in is your date part either day month week or year so i can change this to month for example and that would still be valid now this function this date div function will tell us how many months have passed between our start and our end date but we want day you'll see over here application oh let's go back over here you'll see the second the second part of our formula and you'll see it has this awesome uh assist tool over here this is the start date for us that'll be the application start date then you have your end date so basically from when to when do you want to measure um the uh number of days uh that um have have occurred so one interesting thing about this data set uh this is actually a real data set you can download something very similar to this from the department of transportation website i've modified it slightly just to make it easier to use but in this data set the application end date and the application or the application expire date could be the date that the application lasts until so we want the later of these two dates so what i did is i wrapped these two inside this max function over here which you can see just returns the maximum of a single expression across all records or the maximum of two expressions for each record so basically what it does is it'll return whichever one of these is higher and it'll take that date all three of these are dates and it'll tell us how many days have passed between the application start date and the application end date or expired date we then have this plus one over here so this plus one over here exists because we want to make sure um so for example if an application started and ended on the same day because it was shot in one day um the date difference over there would be zero when really one day of shooting occurred so we want to add that one over there so here's just a quick explanation of exactly what is what in the in our calculated field so congratulations you've just created your first calculated field now calculated fields are easily some of the most powerful tools um in in tableau and as such they can also be some of the most confusing um i would probably i would not be lying if i um said i haven't spent just hours trying to figure out a single calculation before but they can really change the game as to what you are able to do with tableau there's really not a lot you can't accomplish with them all right so we know the number of days that were spent shooting and the fees we need to figure uh oh and the fees uh so we need to figure out the fees per day so if you see over here we have two fields for fees total fees and waived fees so let's create another calculated field so let me shrink this a little bit again you can just copy and paste here there we go and you'll see everything is exactly as it should be tableau will automatically highlight all your fields in orange and any formulas in blue there are a couple of other things that will are different colors in this window for example when you bring in a parameter uh which we will go over uh in section three uh i believe parameters are purple if i'm not mistaken um just to give you guys a small uh taste of what's coming up a parameter is basically a constant value that you can change whenever you want but let's not worry about that for now so what this function does is it takes the total fees and then subtracts any waived fees and then you notice how this is inside parentheses and then divides it by the number of days spent shooting so you basically get fees divided by days spent shooting to get the uh fees per day uh that way we can decide if it's 250 or if it's um uh would you call it if it's either if it's a 250 or it's uh 25 whether it's independent or big budget one really cool thing that tableau does is if you see day spent shooting over here this was a calculated field what tableau does is it over here will show us exactly what the calculated uh the calculation was for that field over here there are a lot of these little things that are designed to help you out in tableau this is uh the calculated field window is easily one of the best ones and you can press describe and it'll give you a little bit more information about this field so you might be wondering what the zn over here is so let me show you exactly what it is um let's see so let's put wave fees in here and i want to see the individual values application number hmm uh let's see there we go so you'll see over here for our application number um this is where the waived fees are you'll see that there's nothing there i think for some of uh i might be scrolling through it so fast but there are ah here we go yeah here are some you'll see that some um of the applications have wave fees some don't so the reason we have that zn in there so zn means zero if null so create calculated field paste and then what are we calling this we're calling this fees per day so what zero if null means means is if a null value appears then um replace it with a zero and the reason that this is important is because a null value means that there is no value no data inside that record that's not the same thing as a zero null and zero are not necessarily the same thing um depending on your data set they might be inside tableau they are not though so if for all those fields all those records that were null uh total fees minus null will get you no because you can't subtract a null from an actual number which is why we need the zn over here the zn will turn any nulls we have into zeros so this is something that you need to watch out for whenever you're subtracting adding or performing any math in tableau in a calculated field if you try to perform math on a null field um without the uh without the without turning the nulls into zeros then uh you may get a bunch more nulls than you expect now the reason i didn't wrap the total fees around this is because i know that there are no null values in there but we could be safer and do the exact same thing here all right returns if not null all right so we have fees per day and day spent shooting so now we know on a day-by-day basis how many fees a given movie was charged now we just need to turn this into a we need to turn this into a label all right so let's create one more calculated field analysis create calculated field and we're going to call this one independent or big budget and then copy this formula in there actually this one i'll type it out and then you'll see why in a minute so so you'll see tableau actually fills out stuff for me where it can so i'm hitting tab basically in order to finish commands oh let's see 250. there we go all right so what we just created is something called an if then statement uh and these are some very very powerful statements the basic um structure of an if-then statement if you look down here if condition one then action one otherwise if condition two then action two otherwise action three end so what this is basically saying is if the fees per day equals 25 then make this an independent film otherwise if the fees per day is 250 then make this a big budget film otherwise make this null you might be wondering why do we need this last else in here this is just a good practice you always should have an else in here just to take care of any instances that you might not any conditions that you might not have taken care of up here uh it's possible that our calculation outputs like say 35 uh and then tableau needs to know what to do with that for this exercise we're just going to make it null and you can see i wrapped the word independent and big budget inside these quotations and this makes it a string basically this is telling tableau output this as a string so say independent make um the column say independent if the fees per day is 25 and make the column say big budget if the fees for day are 250. and you can see our calculation is valid so we can go to we can hit ok and you'll see tableau intelligently put it over here because it's a dimension because the values are either independent big budget or um null they're basically categories and it's a string because again the word it's the word independent it's the words big budget or it's a null so what did we do we just created three calculated fields and these are like i said earlier some of the most powerful tools in tableau and they essentially allow you to add another column to your data set which you can use to expand what your visualization actually shows so earlier we had no way of knowing whether a film was an independent film or a big budget film now we do using data that was already available to us and one thing to notice is that when you create a calculated field the uh you'll have this little equal sign show up next to these little signs over here and that equal sign is uh basically telling you that you have a calculated field so this is just the anatomy of the calculated field uh window which i already went over and there's a lot more to them than this if you guys noticed you would you would see that uh there are a bunch of different formulas inside here i'd say some of the most interesting ones are probably uh regex ones and regex running and window formulas so but those are definitely for a different day and a lot of those are more advanced formulas that we won't really get into in this course let's see all right so using what you've learned try and recreate the sheet shown below so let me in case you're just watching this video let me expand this for you there we go so try and recreate this sheet below um and then i'll give you the answer in a minute all right so let me take a whack at it hopefully you paused the video and tried it out yourself oh you know what i need to see what's going on over here so the easiest way to recreate this sheet is to just look at what's inside the columns and the rows and the marks card if necessary but it looks like nothing is there so uh let's see this is application start date yep as you can tell over here application start date independent or big budget so that should say or big budget typo right there all right uh and then some of the number of records so let us take the application start date where are we there we go all right independent or big budget and for those of you that didn't see what i just did um i clicked on this magnifying glass and that shows this uh search bar that you can use to search for any field you want and then the sum of the number of records take that put it over here and you'll see tableau creates what i believe is the exact visualization you see here there we go okay yep looks about right and then oh looks like we got a title at something oh and we should probably title this too let's title this first dashboard i always switch my a's and my o's when typing in dashboard and then this is yeah independent movies by year all right good stuff good stuff so what this visualization tells us is it tells us that there were no big budget movies until 2019 so that's obviously incorrect what really happened was the um fees changed between 2019 and 2018 uh but for the sake of our uh visualization let's just say we only have them in 2019. all right so now let's work on filtering our data so one thing that you'll start to notice is that when you work with data most of it that you're looking at is just not necessary this goes back to that whole oil analogy i was using earlier where you really need to refine your data and clean it up so that people can actually use it for something this is one of those cases for our purposes there's no point in looking at anything before 2019 so let's get rid of all that data there are a couple of ways you can activate or you can create a filter in tableau easiest in my opinion when you have a visualization ready like this just click on 2019 [Music] and click keep only again if you didn't see i'm just going to undo that click 2019 click keep only and you'll see it only keeps that one you can do the exact opposite too where you can shift click so if you hold down shift click on 2018 and then click on 2013 you can exclude all those and the other way and this is actually the way i end up putting in my filters more often you can hold down control or command if you're using a mac like me and then drag application date here and let's see you'll see that these are all the different ways you can look at a date value in tableau uh we only want to look at the year so let's go to years and we want to look at the discrete year i only want 2019 so here is your normal filter view this is probably the way you'll be looking at filters most often but just know any way it works uh you'll start to realize in tableau there are a million different ways to do almost everything um and i don't see that as confusing more than i see it as um there when you become proficient enough then there are just a bunch of really fast ways to do things it's great all right um okay so now that we have uh we've limited our data to only include 2019 data let's try and make this graph a little bit more useful uh maybe it would be useful to see the distribution of these movies over months now whoops i think uh i accidentally see if i control click then so when you do that make sure that you're actually holding down the controller command key i must have let go at some point uh let's see so right click your date pill and then you'll notice that uh over here you have two different sections of what look like the same date values year quarter month day year quarter month day i'll explain the difference between these in a moment for now just click on the top one and you'll see our visualization oh one second our visualization now shows the progression of permits over month up until october of 2019. so under show me let's turn this into a line graph because we have um we have date data and two different categories a line graph might be very useful for us so you might have noticed two things happened when we switched it to a line graph this month pill over here turned from a discrete to a continuous so we have a continuous line graph over here so you should have something that looks similar to this and uh i clicked on this one over here this line graph there are a couple but this is the one that i generally end up using so this is something you need to be careful of when using dates and line graphs um so if you remember earlier uh actually let me go back uh no we'll use this there are two sections of dates over here you have your year quarter month day year quarter month day the top values over here highlighted in blue in the pdf those are discrete values whereas these are continuous values so explain why that's important so let's go ahead and do a little bit of experimentation let's go ahead and duplicate the sheet so remember you can either click this over here or the way i learned to do it really let's just right click oops come on okay right click right click duplicate there we go right click your filter okay yes so right click your filter edit the filter so if you didn't see that right click your filter edit the filter and then add in one year of the data 2018. so you might notice something we now have two years of data but i can't tell what year is what so and you might have noticed that the x-axis over here has not changed at all why is that we added an entire extra year of data and the x-axis is your date so you would think that there would be something over here right well the reason that is is because and this is where it gets a little bit confusing these values over here transform your dates into numbers basically meaning that it is when we have the month selected over here it is adding up all of the januaries together all of the februaries all of the marches maze it'll be a bit more obvious if i switch this to discrete there we go so you'll see over here we only have january for every february march april all the way until december but no year what tableau is doing is that it is bucketing all of these uh individual months together and adding up all the values over the two years inside these months so for example may this is 2018 and 2019 may june this is what here 2018 and 2019 june in order to see it more clearly i'm going to add 2017 and you'll see the same thing happen it just increased the size of the values if you notice this axis over here it changed uh if i take that out you'll notice that basically tableau just keeps stacking these values on top of each other and the reason that is is because it's treating these values as a discrete value sorry yes discrete values and so it's just taking may and putting all the maze together regardless of year now this is a little bit confusing because you have discrete and continuous down here what these represent is these just represent what the axis says so is the axis going to use discrete values which for dates is the uh named labels january february march april may or is it going to use numbers 0 1 2 3 4 5 6 7 8 9 10 11 12 and you can see it even has 0 and 13 even though there is no 0th or 13th month um assuming that there's a first month uh in the data because it's just treating this axis as numbers so what you actually want to do is you want to switch to the continuous date over here and you'll see tableau now extends the calculation to show you december 2017 comes before december 2018 comes before december 2019. the pdf also explains this idea but the basic idea is that all right there we go you have your uh discrete dates over here your continuous dates over here um and then this just refers to what the access shows you and uh this will group together all of your all every may and every june and every july regardless of the year together this one won't this will treat may 2015 as different than may 2014 as different than may 2016. so with a line graph you typically want to use this there are situations in which you might want to use the discrete version but for now i'd say i just suggest using the continuous version so go around and play go ahead and play around with this if you need to pause the video play around with this see if you can understand uh exactly what we did what happened again this is on your experimental sheet so it doesn't really matter so going back to the original sheet that we were using uh this these labels over here are not particularly helpful let's switch them to the discrete labels and it's only one year of data i think yes there's only one year of data so it's okay if we use the discrete values also uh and weirdly this is something i only uh learned i think maybe like two years into using tableau or something uh this menu over here will fit your sheet uh however you want uh want it to fit so standard is the normal way it just does whatever it thinks is best we're going to use fit entire view and you'll see it extends to occupy the entire view regardless of how big i make it congratulations that's the end of section two so section two was um we covered fewer independent things but we covered uh things of greater depth which is why i split this off into a separate section section two so as we can tell it looks like there was a large spike in big budget movies produced in august uh so i guess during the summer um versus earlier in the year and obviously dropped off again later in the year again if you want to see a copy of what i made over here feel free to download it at this link there will be the same workbook same as this one over here so in section two we went over calculated fields date calculations if then statements these things will save your life they are tremendously powerful but also don't um be careful about making if then statements that are like 20 lines long or something or even 10 lines long if you have too many if then um conditions then that might be a sign that there there might be a better way to handle this we learned how to filter data and then how to use line graphs especially in relation to continuous versus discrete dates of course so for this course or for this section of the course we will actually be using a different data set we're going to be looking at some player stat for um from the english premier league and i think these stats are from uh i think fifa 18 or fifa 19 or something like that so the epl is the highest level of soccer or football for uh for our international viewers played in england and is one of the most watched sports leagues in the world um and we'll be interested in plotting data on player salaries right now so first of all let's connect to our data if you are coming in from section two of the course you might have a screen like this after you've saved this which remember if you have tablet desktop you just go file and then there should be a save as button somewhere over here if you're on tableau public then you have to save it to the public gallery and remember anything you save to the public gallery is it's on a public cloud so anyone can see it so let's go ahead and open a new tableau workbook so under file click on new and it might take a second to open there we go so let's move this out of the way all right and you might see a screen like this or a screen like this either way let's go ahead and connect to our new data so it's going to be let's see another microsoft excel document and this one is going to be player data that's what we will be connecting to all right there we go here is our data source um page remember that so let us create our data source so you might notice that the um this excel document is actually split into two parts we have our english players on one sheet and our non-english players on another sheet and we actually want to combine these two data sets so if you take a look at them so again you can take a look at it by whenever you see this icon this icon means that this is how you look at the full data so let's look at the english players data you might notice if you just look at all these columns over here this looks like it's just some basic uh data on the players and if we go to non-english players then you'll see it is basically the same data but these two uh sets of data are coming on two different sheets so we probably need to stack them on top of each other in order to get a full data set so we're going to be performing something called a union bring in the english players data set first now right click over here and click convert to union there is more than one way to do this this is probably just the easiest way to illustrate it non-english players drag and drop here so what tableau will do right now is it will literally just stack these tables on top of each other and we should have about uh 1400 rows i think so you'll see if in tableau over here if you put in um so the default is a thousand rows if you put in something like 10 000 but there's only 1400 rows uh then it'll default to the maximum number of rows of 1400 so what we did over here is we literally just stacked the two tables on top of each other and this is something that uh in sql we call a union all right oh uh and this there's a potential bug over here um in tableau uh public if uh you if the window this window over here um if this doesn't show up it might just be behind whatever your your main tablet window so just go behind it in order to get to it looks like it didn't activate this time for me but a union literally means that we're taking two tables and basically stacking them on top of each other and we usually do this because oftentimes data sets are split up just to reduce the size of each individual data set you'll find a lot of financial statements um that are done in excel are actually split up by quarter or month or year so uh unioning data becomes something that is a sql function that you will execute on regularly inside tableau and like everything else with tableau there's really no need to write any code or anything obviously you can if you want to you can just use the visual editor to accomplish the same result so a union will take all of these column titles over here these column headers over here and it'll match them one for one based on the name of the column tableau doesn't care about the um data type inside the columns it just cares about the name so as long as there's a style over here color over here model over here it'll match all of those up and anything that doesn't match so for example you'll notice on this example over here the table on the left doesn't have a car column what tableau will do is it'll just put in null values for this uh part of the top part of the table over here so all the columns will come through at the end so let's go ahead and go to sheet1 this is the data set we'll be using so we connected to our data and then we constructed our data set over here by unioning all of our relevant data so a good practice is to always inspect your data before you actually start visualizing it just to make sure that everything um is uh everything is the way that you expect it to be so looking over here is there anything you notice that uh any column over here that you think probably probably should not be where it's currently classified as either a dimension or a measure or the data type that it's classified as so if you pause the video and guessed height then you'd be correct so if we take a look at uh height over here we can drag it into rows just to inspect it you'll see that the values are coming in and they're written in as five foot three inches five foot four inches six foot eight inches this format is great for just labels but it isn't good if we want to run any mathematical computations on it or if we want to compare heights across the data set so let's see if we can fix this go back to your data source tab so remember what we're doing right now is we want to edit this column over here so we're going to go back to this data source and edit the data source and then go all the way to the right in this data pane view over here you'll see height over here what we need to do is um we need to split this up and then create a new column that just uh gives us the inches the um inches that a player is you we could also do it in feet but uh it's recommended recommended that you just use the smallest uh dimension that you can sorry the smallest unit of measure that you can uh so with six feet one inch you'll notice with all these columns i mean all these rows uh there is an apostrophe that we can use to separate this column because we what we'll need is we'll need a column with the feet and a column with the inches that we can then add together so the way we will split these columns in tableau is to right click over here and click on custom split you can also just click on this regular split button but custom split i'll just walk you through the menu that we use over here so the separator we're trying to use we want to split this into uh one column that says six and one column that says one that way we can combine them together in a calculated field to calculate the total number of inches that a player is so the separator we'll use is the apostrophe so just type that in over there and then we will split off all of the rows or all of the columns this is just something i like to use to make it much simpler but you might sometimes want to only split off the first maybe like three or four columns but let's just split off all of the columns and hit okay and you'll notice that now six foot one turns into six and one and tableau will always just add this um uh this suffix at the end of any columns that it splits off split one split two uh all the way till you know how many other splits you end up having so let's return to sheet1 alright so first thing we need to do is we want to turn this into a measure that way we can compare heights add them up together take averages if we need to so take height split one and like we did with wards in our previous data set in our previous example just drag it down to um the measures area you'll see that it's still a string and you might even notice that there's a a calculated field sign over here that's because what tableau actually does when it splits off columns is that it makes a calculated field that uses some regex in order to split actually split the columns make the columns um and that's just how tablet handles splitting columns it doesn't actually create a brand new column it will create a calculated field so let's turn this into a whole number both of these number whole number so the way i did that was i just clicked on the data type and then turn it into a whole number and speaking of data types let's just go over what all the different data types we have can be so in tableau you have six different data types and they always appear uh to the left of your column names in your dimensions and measures menu uh menu bar so a string is basically just a catch-all data type um it's any text really and if tableau can't figure out what your data type is it basically almost always defaults to a string because a string can hold almost any data inside it at least any tabular data tabular data is just any data in the form of a table numerical data will have this hash symbol next to it um and that is any uh data that basically math can be be or let me rephrase that numerical data is actually any data that um uh is a number so for example a zip code or a postal code might be a might show up as a numerical data type date date um oh and the reason that i say math over here is because uh whenever i create visualizations or whenever i work with tableau i generally like to only classify things as numeric if i'm going to perform math on them otherwise i'll classify it as a string just to make things very clear date data is uh well we could have an entire course really not just that there's a link over here you can click on to go see more about date data but basically and there is none on this data set but date data is basically all right let's go back here it is any data in a date format and there is this is actually a lot uh harder to parse out than you would think it is uh tableau has a really cool system in its back end to actually parse out date data from a bunch of different formats that are out there um but for the sake of this course most data you encounter will be parsed out the correct way date time has this little clock next to it uh and basically it just means that a time is included as well i generally try and avoid using uh date times when i can um most data sets i personally work with uh there is no need to go to the time level of granularity and if a data set has a date time if i can convert it to just a regular date so just a calendar date i'll do that just because it's a lot easier to work with boolean data is really interesting so this is true false data so basically if something is a boolean it can either be true or false or like every data um it can be uh null also so a lot of very well constructed data sets in tableau so if you have like a a group of tableau users experienced tableau users in your company if they construct data sets really well constructed data sets will usually have a boolean value for common filters that people put in such as current fiscal year versus prior fiscal year so there might be a value over here that says um tf current fiscal year and basically you would just drag it into filters and click on true and that would filter out your table to only include values for the current fiscal year so uh in the future this might be something that you'll end up using more i don't use it that much unless i'm constructing a data set that is supposed to be used by the entire company geospatial data again this is something else that basically an entire course could be taught on this is just any uh data in the form of place names postal codes states countries cities or what we call geospatial objects which we'll go over in more detail later this is very interesting data and a lot of really cool stuff can be done with it but it's also very difficult to work with so we won't spend too much time in this course talking about geospatial data but those are your six basic data types all right so let's manipulate our data so we want to calculate the height of different players and we want that in one column that way we can easily add that to our visualizations so let's create a calculated field so if you remember how to do that click on analysis and create calculated field another way to do that and the way that i actually end up using more often is to right click whatever field i'm going to use in my calculated field and just click on create calculated field and tableau automatically adds this field in here for me so those are like many things in tableau there's more than one way to do it so let us name this height parentheses inches and we are going to take the height in feet so height in feet which is height split one if you remember multiply that by 12 because there's 12 feet in an inch sorry 12 inches in a feet and then add that to height split two and that should be our calculated field right there so that is ah so though that is basically this is the sum of all the so if you were to stack all of the players on top of each other they would be this many inches tall uh in total all right so another thing that um you might find interesting is that uh sometimes data that we bring in isn't encoded in a way that makes sense in a visualization one great example of that is this column over here called scale moves uh which you can right click and then click on aliases and you'll notice that it just lists numbers one through five with a null so this is actually a code that is used in fifa the game and basically we want to aliase these to make these more useful to uh for an end user um so the thing is uh what is an alias an alias is basically just a fake name for something so while the real value will still be one if we change the alias to for example weak skill then when we bring this in here the value will still be one but it'll just show weak skill on any visualization so this is just a very useful way to clean up your data and make it um much easier for people to understand what you're applauding below average so just copy these uh labels over here three should be average skill and then high skill all right click ok and then so now you'll notice that all of these change so that um although the base value is still five three or wait sorry five one uh two three four uh and null's null um the label has changed so that it uh is more informative to any user that uses or uses your data so aliases are incredibly useful and definitely something i recommend using whenever you can in the future alright so let's drag this new column that we created height inches into our columns and let's drag weight into rows where are we wait drag that into rows and then you might uh no you might have noticed that this uh whenever you drag in a measure uh let me bring in a uh dimension just to compare you might notice whenever you bring in a measure it's not just the column name that's listed up here it's also this thing over here it usually says sum sum is the default that it shows but if you bring in a dimension it just shows the dimension the reason for that is that this is what you call an aggregation again if you've used microsoft excel this might be familiar to you if you've used pivot tables especially and an aggregation basically tells tableau how to group together different measures so let's change the aggregation of these from sums to averages so you do that by right clicking the measure going down to measure parentheses sum and then clicking on average and we do the same over here right click measure sum average and you might notice that the uh values on these x axis the x and y axis changed so it used to be 110k and 240k and what this was was this was literally tableau adding up every single uh player's height and every single player's weight to get these two numbers which is not what we're going for but now i can tell you the average player is 71 inches tall and 166 pounds so how can we best explain aggregation so there's a small visual i put over here and basically say your data looks like this you have everything uh you have your years you have your months your sales and your revenue now say you want to look at the data at the year level well what can you do what you'll do is you'll take out this month column it's unnecessary information uh but then what do you do with the sales column well you'll need to combine this uh combine your measure somehow basically so the the way we combine measures is what we call an aggregation these are levels of aggregation so we're aggregating we're grouping at the year level and aggregating our data um uh through a sum so for example for sales if we are looking at everything at a year level we have 2020 over here and 2021 over here we need to add up our sales 22 so 10 plus 12 is 22 and then 14 plus 13 is 27 and we'll do the same thing for our revenue 100 plus 120 is 220 150 plus 120 is 270. that's just one way to aggregate our data you can use uh an average for an aggregation as well where what i'm showing you over here is that this is a sum aggregation versus an average aggregation you can do max's or mins where basically um tableau will just look for the maximum value within that group so the group we're defining as the year over here what is the maximum value um for revenue in 2020 it is 120 you see over here what is the minimum value in sales that's 10 uh or sorry 10 units these are units on dollars count and count d are interesting uh what count does is count will give you the number of values in a certain group so for example in each of these groups there are for 2020 and 2021 there are two values a piece uh count d will give you the count of distinct values so for example i think the example i used was over here ah sorry yes we're count we're uh looking at the month now um so what we're doing is that we're taking the count of the month there are two months in each of these years uh and in this data basically they're just having they we split january into two different uh columns but the count d for 2021 is only one because there's only one distinct month in 2021 so that's count and count d uh so i usually whenever i explain this to people i'll say that i'm either counting or counting the data attr or attribute is an is a very special aggregation function basically what it does is um it will look um if for a given measure or a given value or for a given measure in your uh column if there's only one value there then it'll output that value otherwise it'll output a star when you start using more complicated calculated fields this will become very important and very useful because in tableau you can't combine aggregate functions and non-aggregate functions and as you start to work with tableau a little bit more you'll you'll learn what that actually means but as an example if we are trying to aggregate the months at the year level then you'll see that the attribute aggregation can't output anything for 2020 because there's no single value over here uh we have january we have february it doesn't know what to put here so it just puts a star but in 2021 we only have january so because of that it's able to give us the attribute of january so this is something that you'll encounter as you use more advanced functions but for now it's not particularly useful all right let's go back to our data and let's create a scatter plot so drag position into colors on the marks card so where is position there we go so we need to drag position into colors on the marks card and then let's just add all members so you'll see we get a scatter plot that looks like this tableau by default starts off at zero but for this function that's not particularly useful as you can see we can barely see anything up here all the values are um uh grouped over here why is that well that's because there's probably no player who's only 10 inches tall and there are no players who are only 20 pounds large um and because of that basically this entire section of the graph over here this area or let's see over here this is not particularly useful information for us because there is going to be no player that is this small um and there's going to be no way that the average uh for the player's weights could be dragged down to this level just because this would be way too small for a uh player so let's go ahead and change our axes so the way you edit your axes in tableau is you literally just click on them right click and then click edit access so we clicked on our y-axis and we we right clicked it and we clicked on edit access here is our general information let us just uncheck this include zero checkbox and you'll see what happens tableau just starts it off near the lowest value um it starts it off a little bit above that that way you don't have a value on the edge of your graph which is something i highly recommend avoiding don't have values on the edges of your graph because it can just make things feel claustrophobic over here is also where you can change the range of the axes in general so this is the y-axis remember and even the title so we can change this to say to not contract it average weight and if i click on x you'll see it shows up right there let's do the same thing to our x-axis and you'll see we have a much more useful graph now now the reason we're able to do this is because if you're talking about fifa players you're talking about people who are relatively similar in size and relatively similar in weight versus for example if you were talking about the entire population of england um you would include like babies and kids and stuff like that and your graph would um probably benefit from having a zero value on the axes but because we're talking about people who are relatively similar in their physical build at least in comparison to the rest of the population it makes more sense to zoom in to where the change actually exists and this is a good practice to have you want to be able to actually show a differentiation in values on your graphs some people will say that axes have to start at zero that's just not true um although it it it all comes down to context uh one of the most uh one of the most obvious examples of abusing axes in order to prove a point is this graph over here so this graph is maybe like oh like 15 10 15 years old now um but it basically what they did is that they started the axes off at 34 over here and then went all the way up to 42 um and the change in what uh we're talking about the ta the top tactic up here looks monumental when it's really just a four percent change um not to say that that's small but the point is it's made to look a lot larger than it actually is uh and this graph actually misses out on a lot of context from you know previous administrations and everything where the top tax rate was all the way up to i think 50 at one point in time uh on top of that the access moved on to the right side of the screen where it's not at all obvious to a normal english reading audience where you know in english we read left to right that um what the what scale this graph is even on but another example um and this is where a lot of visualization really comes down to just understanding your audience uh is this graph over here this graph starts off at zero and then goes up to five but it's not particularly useful because i can barely tell what's going on over here this is an example of where you might want to show the um or you might want to take out the zero and start off at a higher value that way people can actually tell that there is a change at all and what i find is that usually in when you're measuring your metrics for your business or you know whatever you use a tableau and visualization tools for there is a um they're what we call bounds basically there is a minimum amount that a certain metric will go down to reasonably and a maximum amount that a metric can go up to reasonably and i usually set my axes to be a little bit below and above that in this and this is an excellent example of that again like we said earlier there's not going to be a player who's zero inches tall there's not going to be a player who weighs 10 pounds uh so because of that we and because um when you show someone the data you'll be explaining that to them you'll be showing them hey look this is the average weight and average height for fifa players um it's fairly obvious that they're going to be distributed in a certain small part of the graph um so as long as you set the context then it you can make the axes whatever you want but do be careful with how you do it because it can mislead people who don't know how to read or don't know how to interpret your graphs all right so another thing is while we're talking about moving the view of the graph and manipulating the view of the graph a couple of things if you want to pan around this graph basically drag it around hold down the shift key and then just drag and you'll be able to move this graph around as you please if you want to return to your old view then you can always just click undo or you can click on this pin over here so this pin basically means uh like just set a pin the graph to that view versus automatically um automatically try and fit everything inside the graph if you want to zoom in and out then hold down the control key if you're on windows or command key if you're on mac and just scroll so you can see this is how we can shrink or expand our data and then click on that to return to the default view and then obviously you can click on any axis and just edit the axes oh and let's duh let's rename the sheet average measurements by position all right grouping our data so sometimes the level of detail that we have is too granular um and in these situations grouping our data might be helpful so if you notice over here for our positions we have like 24 i believe 24 values over here um yes 24 marks there we go so we have 24 values over here um and that is not particularly useful for our viewer maybe we can reduce the number of positions over here by grouping them into groups that make logical sense so if you want to make groups with your data just right click on whatever column you want to make groups out of and click create group and you'll see tableau brings up every single position over here and then now we can start grouping them the way we want to so we're actually going to be grouping them based on um what type of position they are like midfielders defensives or forwards uh and this is where i always say knowing your data is key to making any data visualization if you don't know your data then visualizing the data is going to be much more difficult um and i can say that i've probably saved more time than i've lost by taking the time to ask people about uh ask people different questions about the data try and understand it before i try and um make a visualization versus trying to make a visualization on data i don't fully understand so let's start grouping our data the way i like to do it is i like to hold down the control key if i'm on windows or command key if i'm on mac and just select what i need another easier way to do it if you don't want to just hold down the key is to just select the first member you want to put in your group which is cam over here click group and then let's name this group mid fielders and this drag in any value you want into that group so cm we need lcm ldm lmlw rcm rdm rmrw all right so there are our midfielders let's get our defensive positions so these are the basic ways you can split up the positions and grouping your data is incredibly helpful because uh contrary to popular belief you usually don't want to just put a bunch of data in front of people you want to limit it to what will give them the most insights with the least amount of mental effort that's generally how i like to view my visualizations of course depending on who you're presenting the data to you might want to show them more or less generally the higher up you go the more or the less granular your data gets the higher up you go the the corporate food chain all right forwards okay cool and then just hit okay and then let's zoom out over here and let's go back to our data source there we go oh sorry before we do that let's actually let me show you what we just did actually so you can take this new group over here which what tableau did is it basically created a new column for our positions that grouped everything together the way we wanted it to take that and replace this position pill over here and you'll see on average it seems that defensive positions are the heaviest guys makes sense forwards are the um are medium in weight and height and midfielders are the smallest um now obviously take a look at your axis and see how much of a difference that actually makes we're talking like a difference of three inches and uh 13 pounds the 13 pounds might be substantial the three inches may or may not be but you can see we were able to clean up our visualization substantially and even the um legend over here is a lot cleaner and easier to look at now so let's go back to our data source so we're actually missing quite a bit of data um and what you'll notice is that um that i think i forgot who it was i think it was ibm that did a survey of a bunch of data scientists and they found out that data scientists tend to spend about 70 to 80 percent of their time just gathering and cleaning data if you work with data this is probably where you're going to spend a lot of your time uh which is why it's very important to get these skills down pat and sometimes your data comes in a unfortunate format or a format that's not easy to use like a pdf um and you know pdfs if anyone's ever tried using them to get data from them they can be very hard to get data from but tableau can make it quite easy for us through uh some technology they have in the background that's supposed to help you extract data from pdfs so let us add a connection we're going to connect to a pdf so we have our uh we're going to modify this data source over here to add more relevant data we're going to be performing a join but first let's bring in the data we want to join so under connections click add and let us add a pdf and then we have data to join.pdf and let's bring in all of the pages so this might take a second awesome okay so if we want to join this data over here then we just need to drag this to the right-hand side of our data of our current data you might notice that we now have two different connections we have a blue one and an orange one um and we're going to combine these connections and create our data source over here so it shows up as just one unified data source in our uh workspace so let us so this is actually another way to union your union your data so uh click on the first page then hold down shift click on the last page and then drag and drop and you'll see tableau unioned all of your pdf data and then brought it in for a join so in the venn diagram over here click on the venn diagram and then click on left we're going to be doing a left join and you can exit out and go back to the sheet that we were working on so what we just did is we joined on our data so like earlier we discussed when we needed more data one way to get more was to union it basically stacking tables on top of each other a join is another way to add more data to um your data set so while a union will add more rows to your data a join is usually used when you want to add more columns to your data now you can add more rows using the join too but it's uh um it's a way to add your data add more columns to your data that's usually how you we how we look at it we did what was called a left join if you go back to the if you go back to the data source tab you'll see that it's covering we're using the left side of the venn diagram that's a left join versus say an inner join a right join or a full outer join so what exactly is a join well in a join we have two tables that we need to combine right we have something called our left table and our right table we want to join them on the by the columns so what we do is we define a column to match it on in this case id and then we match it one for one so let's take a look at the types of joins we can do like i said earlier we can do inner left right and full joins um and you'll mostly be sticking to left and inner joins like you can do a right join but um usually people will just do a left we'll just switch the order of the data sources and do a left join so an inner join what is that so an inner join basically takes um the column you're matching on or you can also match up multiple columns but for the sake of this explanation we'll just stick with the one column and it'll find exact matches so for example in this uh with these two data sets with your left join over here and your right join over here there's only one column that matches between the two the one with id number one so our um result table ends up looking like this where you only have uh one row of data and then this top row is the uh header row if you were to do a left join a left join is the same as an inner join but it keeps all of the data from the left hand table so while we have the same match over here on id number one we also have to add in um id number three because that can all the data from the left-hand table has to stay and where we don't have data from the right table because there is no id3 here we just have two nulls now note when you um obviously if you have two columns that are named the same which you're matching one of them has to be renamed uh usually the right one is the one that's renamed a right join is just the exact opposite of a left join where you um you do an inner join and then you also add in all the columns from the right hand side so if you'll notice over here there's a reason we use venn diagrams to uh illustrate this an inner join basically keeps all the intersecting values or all the values that match on both tables a left join will keep the values that match on all tables plus i mean between both tables plus all values from the left hand table and the right join just does the opposite and then we have what's called a full outer join or just a full join which will basically just stick the two tables next to each other match what it can uh but keep everything so there's a common gotcha that um you can encounter in a join what happens when you're doing a join let's say we do an inner join and on your right hand table there are two id ones well what will happen is that id one on the left table will match to every id um uh one on the right hand table so you'll see we actually duplicated the values over here this blue and this 30x under color and model we actually duplicated it for a row so that's a common join gotcha i call it a gotcha because it's not an error necessarily i've used joins before because i intend to duplicate rows um and it is a function that can be used to your advantage if you need to for example match one value to like 13 different values on a different table you can do a join um and it wouldn't be considered an error so this is something to watch out for a well-designed table will have a key value that you can usually join to and the key value by definition has to be unique so if you have a a key column and a foreign key uh column in a table um that's usually what you'll be joining on so what we did over here was we brought in data from uh our pdf and we joined it on the id column using a left join because we want to make sure we keep all of this data even if there isn't necessarily a match over here we need to keep all of our player data and you'll see tableau very helpfully highlights our columns from our right table with an orange bar over here versus the blue that's the default when you're looking at um the columns from the left-hand table so you'll see we're bringing in a lot of player wage data so your value their value their wage release club clause all that stuff and let's go back to our average measurements by position table so one thing you might have noticed was that um the value wage and release clause they are being brought in as strings uh this is because they're being brought in through a pdf a lot of the pdf values won't default to the string value that you would expect them to be so let's correct these remember we just drag them down here because these should be measures and we will change these to let's see decimals or two oh actually not even that what we actually want to do is we want to change these to euro amounts so um this data set is supposed to be in euros so excuse me this data set is supposed to be in euros so we will go to default properties so right click any of them default properties um oh my bad uh you first need to convert it to a number so let's convert these to numbers first i was wondering now if we go to default properties you'll see number format so make sure you convert these to numbers first uh and we want a the currency standard will just give you the standard currency in the united states or you know just whatever your computer's configured to i guess uh i'm in the us but we are looking at euro data so i need to replace this with a euro value don't worry about this if you um um yeah don't worry about this if you if it's just if it's too much work but if you go to the set the next page on the pdf you can copy and paste that value here uh this is just to show international users or people that work across on teams across the globe that you can easily change the currency value in tableau so let's do that for our value remember default property so right click default property number format currency custom and then we'll change the prefix and let's do the same for wage all right and then let's see if you can recreate this visualization right here so pause the video let me expand this and try and whoops recreate okay try and recreate this visualization right here all right so let us see we can recreate the visualization so what we're trying to do here is we're trying to see how much each club spends on their players by position group those these position groups that we made over here so let's create a new sheet um and let us bring in the value and then let's see let us bring in the club and we're going to put that on our rows and we need to split up each of these bars into the different positions that we have so we'll put the position group into the marks card color um the colors might be a bit different than what we had earlier so oh this is why so when uh there's an instruction that i skipped under position group you want to right click edit the group and you want to include other and then these colors are still different so the way we change that is by going to this color card over here edit colors and then i believe defensive position should be blue so click on defensive positions click on the blue forward should be orange so forwards click on forwards click on orange and midfielders are red and other is gray there we go um and i like using these colors by default because they all contrast with each other while at the same time not um conflicting with each other so uh they contrast and don't conflict that's the best way to put it uh and these are the default colors tablet will put in automatically um so then let's sort this by clicking on this sort button up here in descending order so you might notice this still looks a little bit different from the graph we have over here do you know why well you might have noticed that the graph over here the level of aggregate i mean sorry the um method of aggregation of our value is a sum over here tableau used count d which means count distinct by default because this was converted from a string from a pdf so you can right click that go to measure count distinct and then change that to sum and we might have to sort again yep and this graph is the same as this one just it's been shrunk down a little bit now in order to prevent us from always having to change that aggregation for value because we're never going to want to see uh the countd you can right click the value you can right click the column go to default properties aggregation and then make sum the default aggregation now we can also do that by uh for whoops we can also do that for release clause and wage these are all numerical values that we're probably going to want to sum before anything else but if you don't want to change it over here you can always change it over here now whenever you change the default properties over here be careful it can be easy to get into the habit of changing the default properties whenever you want to just make a minor change on the graph itself uh so only change these default properties for things that you want to reflect across your visualization otherwise you'll be making a bunch of one-off changes or you'll make a permanent change to what might just be a one-off change um a good example of that would be these uh decimal places over here so there's no point in having the decimal place over here so there's two things i could do i could go to value and i could right click and go to default properties number format and i could change the decimal places to be zero and that would change it down there too so if you look over there oh i must have only made one so you can do that right but that'll change it across all visualizations for your entire workbook when you might not necessarily want to do that a better way would just be to change this one axis over here by right clicking edit access oh sorry actually we want to format the axis right click format and then under numbers change it over here so this isn't changing the default value this is just changing that one axis and then we have to make sure we use a euro over here too so that's just uh some professional advice make sure that you um if you're making a one-off change for just one graph like i want the i want table to keep the decimal values um for the value column but i don't want them over here just change them in the one graph you need to don't change the default properties unless you need to alright let's name this visualization player value by club so the marks card is something that we just use and it is uh one of the most useful parts of tableau um you'll hear me say that a lot this course is supposed to really just give you um a uh quick and dirty guide to the most useful parts of tableau um so the marx card is basically how you edit what is shown over here after you've already put your data in over here so you define where the data will actually actually show up so what's a column and what's a row uh up here and then you will put everything into the marks card over here uh sorry and you will edit how it's shown in the marks card over here so as you could see that's how we edited the color we clicked on the color and then edit colors and when we wanted to actually show the different bars over here in different colors we took the column that we want to split everything up on and then drag that into color there are a number of other changes you can do over here the one that we'll be working on next is how to label the ends of columns uh in your bar chart when you have multiple groups in a column another thing is you can also change the type of graph that shows up over here although i personally use the show me menu because it um will show you the different types of visualizations you can use with the data you currently have on your view so let's say we want to add labels to this graph you would think the most obvious way to do that would be just to drag value because we want to label the values to this label over here right so let's try that go to value under your measures drag and drop so you'll see tableau is only putting the values on certain marks over here remember a mark is basically an individual um group of rows uh shown in tableau so for example this is one group of rows this this is um these are all midfielders at liverpool um yeah so this represents all midfielders at liverpool so this represents all the rows of data where that condition is true uh and we call that a mark um so you notice that we put the label in here but these labels are not necessarily very helpful and the reason for that is that tableau doesn't want to create a big mess and have labels overlap each other you can click on label over here and under options click allow labels to overlap other marks and you'll see it's just a complete mess so that's not very helpful to us right now and you'll find that this is a problem you encounter regularly where you might want to label everything but you just don't have the room to do so so let's see if we can put a label mark at the end of our graph the reason we can't do that at the moment is because it is trying to label at the smallest um grouping we have which is the position group but we wanted to label the ends of these graphs so let's try something out click on the analytics tab up here on the upper left corner the analytics tab has a lot of useful things to analyze your data after you've actually visualized it double click on reference line down here under custom and then this menu pops up you can drag it to the side to see what the actual changes are because the changes are live let's click on the per cell option because right now what happens is it's um averaging everything uh across this pane over here but we wanted to do on a per cell basis uh and let's see we wanted to show the total sum not the average and let's see let's make the label instead of saying computation computation just means like what am i doing here i'm just summing it let's make it say value there we go so that's actually what we want and then remove value from the marks card so now you'll see this label is a lot more helpful it just tells you the total that every uh the total value of all every team's players obviously man city is the manchester city is um has the highest value players now you might have noticed something called reference um let's see if you want to edit a reference line you just have to click on it so i can click on this label and click format or sorry click edit uh you might have noticed something called pain over here per pane and entire table um so the scope basically just defines where the reference line is going to be when i did entire table it's adding up every single player on every single team all of their values all together per pane right now does the same thing because there's only one pane of data let me show you what a pain actually is so you don't have to follow along here this is just to demonstrate something uh let's see so what is a value i can use oh you know what actually i can do this uh position group okay so you'll notice that the data now that we have two different dimensions inside our rows is divided into what we would call a pane so this is one pane this is another pane and then here's your data so now if i go over here and i edit this and i click on per pane then you'll see what tableau does is that it's calculating the total value of all the players per pain which is the same value that we had earlier so this is what a pain is a pain is basically when you um like a window pane whenever you um have multiple dimensions you'll create a pain basically so that's not what we want right now that was just to demonstrate what a pain is okay perfect this is what you should have uh yes so here's what we end up with and so this this visualization is actually a very solid one this communicates exactly what we need um but it shows and it's great at communicating the value that of of players per team in comparison to other teams but what about the value per team in comparison to the whole in comparison to the entire league well this is where an average line might be useful so under analytics you can drag average line into the table section over here so you'll see what happened is when i start dr when i click and drag average line it'll ask me where do i want to add um out of the line now in this table remember a table and a pane are the same thing but we want it across a table then go down here to where your average line is click on it click on edit and then change this to custom under label change this to custom and let's make it i believe we want it to be yes computation value so computation is what we're doing and then let's add a colon over here and a space and then let's add value over here so you'll see what it did is it changed the old label from just saying average to now say average and then give you the value and then this looks a little bit cramped but when i um if i expand the amount of space that it has then you'll see it's a much better visualization so now we have a full featured visualization that communicates to your users a lot of useful information with just a couple of clicks we were able to show users this is uh how much each players this is the value of the players per team compared to the average and compared to all the other player i mean all the other teams and you can see where uh teams disproportionately spend their money on uh it's midfielders for a lot of teams then they spend the least amount of money on forwards so let's go ahead and add a little bit more data click on the connect menu icon that's this icon in the upper right corner upper left corner sorry up here and let's bring in a spatial file so this is a special type of data that is um you can't just use uh in any uh piece of software we're gonna be using something called a kml file this is a google earth file it's open source um and google actually submitted this uh this um data format because i think i think they're the ones that created it or they like acquired it or something um to the consortium that uh is in charge of you know standardizing these geospatial data formats so a dot kml format is a format that if you work with geospatial data you'll probably encounter quite a bit so we want to bring in the data to blend data set and then [Music] under name over here um what this data set includes that this includes the point the geometric point i mean sorry the uh a geographical point of every club's football stadium so let's right-click name and rename this value to club uh is it clubber clubs it's just club okay cool c l u b there we go uh and we can rename description to stadium okay come on all right good stuff let's create a new sheet and you can see now we have two different data sources over here we have this data source the one we were just working with english players plus and this is if we go back to this data source window so click on that and then go back to the data source window you'll see this is a data source we've constructed by unioning all the english players and non-english player data and then joining that to a union of all the uh i believe it was sorry financial data for the players from that pdf we imported and this is considered one data source so this one data source is um created from two different connections and now we've brought in a new data source data to blend which is a spatial file all right so it looks like we just imported um so we just imported our spatial files and uh the thing about spatial files is that um they are a very special data type that you can derive a lot of information from they can be a little bit complicated to work with and even harder to create depending on your expertise with gis data um but spatial files basically have three basic geospatial data types you have your points your lines your polygons and you can use them to create visualizations that would otherwise be very difficult to create so now we're going to be talking about a concept called the data blend the data blend is a concept that we use in tableau that is similar to a left join and i'll show you exactly what i mean in a second so we have the data we have our data set english players which has all of our player data and then we have data to blend which has all of the data on the locations of the stadiums for different players for different teams sorry so under the data menu tab click on edit blend relationships and you'll notice tableau will split the blend relationship into a primary data source and a secondary data source and we have clubs match up to clubs what is a blend so a blend like a join um or a union quite frankly is designed to help you combine multiple data sets um but the big difference is although a blend is like a left hand join i mean a left join um it doesn't uh it aggregates everything after it aggregates everything first and then joins the data instead of joining it uh row by row so whereas a left join what it does is um if we go back over here uh it's taking every single row and it's joining it uh joining the english players data um over here with our pdf data over here on a row by row basis so like we showed earlier it uh looks at i believe the id column and then joins everything row by row uh a blend what it will do is it will look at what you have on your visualization aggregate all of your data to that level and then try and do a left-hand join so it's a little bit different so you'll see over here this is an example of a left-hand join where we create some duplicates because we have a id of one over here and we have two ones over here right so we create the duplicate blue 30x blue 30x in the data blend we won't have this uh duplicate because it's going to aggregate all this data over here before it allows for the join to happen now what happens when you try and aggregate categorical data like this a dimension with two different values well you'll get a star like we had earlier with the um attribute function because it doesn't know how to combine these two numbers right these two uh strings you um there really is no way to do that if it was a measure then you could you know you could aggregate them in some way as and you could uh sum them or average them or count them but with a dimension there is no way to do that so in tableau you'll notice that uh blend is defined by these chains over here so if you go back to your tableau workbook you go to data edit blend relationships we've related the column club to the column club from both tables that's actually why we renamed the column in uh when we were bringing in the data to blend we renamed it that way we could um a tablet would automatically combine these two columns it would already associate these two columns and what we can do is we can double click so go to data to blend and you'll see when i click through these things i get the columns from the two different data sets because these are two different data sets so go to data to blend on a new sheet double click geometry and you'll see what it does is it plots all of the different uh stadiums across the uk under show me make sure that the symbol map over here is selected and then drag clubs into the details section so now go to the english players plus data set you'll see that tableau has automatically linked this column for us where if i if i click on this then these uh table these uh two data sets over here are no longer linked but if i click on this they're linked and i can now use data from this data set and it will combine them on the club field after it aggregates to the level of detail available on this visualization uh so let us take value let's see there we go and put that under size and you'll see over here that we have a blue circle check mark over here and an orange one over here blue signifies that data to blend is the primary data source on this visualization and orange signifies that this is a secondary data source on this visualization you can have multiple secondary data sources but only one primary data source um let's see so what you can do is you can go to size over here and then you can edit the size to something that you know maybe makes it easier to see what i also like to do is i like to go to color and i typically use 75 opacity and then i will make the border black and you'll see that pops a little bit more than what we had earlier but basically because we were able to uh use a blend we were able to combine these two data sources now could we have joined these two data sources yes we could have this is just to illustrate what a blend does because you can't join all data sources if you ever use tableau server data that data cannot be joined to other data sources you have to blend it um and sometimes a blend just makes more sense usually a blend makes more sense if you're just trying to add some uh uh descriptive columns to a data set which in this case we're just trying to add uh where is the stadium located so now we're going to work with something called parameters if you remember i believe it was section 2. i believe it was section two where i talked about how we were going to discuss parameters and parameter actions later um a parameter basically is a constant that you can insert almost anywhere in tableau and you can use it to edit um and you can edit it as you need to change say a filter or a calculated field to or dynamically so let's go ahead and see what this actually does let's go ahead and drag club to filters so club from the english players plus data set go to the top tab over here so you have general wildcard condition top these are just different ways to filter your data let's filter it by a field i want the top x by age or sorry by uh value sum so this x value over here you can make it whatever you want i can make it you know 12 i can make it two i can make it four or i can create a parameter and make it dynamic so let's change the parameters name to top teams by player value and then change allowable values to list so allowable values will basically say what values can you are we allowed to have in this for this parameter the value is the actual constant that will be um that will replace whatever value you wanted to replace and the display as will be what it's displayed asked so let's see i want one and i want this to be top team i want let's see 5 10 and 20. so now what we've done is we've created a parameter which is a constant that we can change whenever we want and i've said okay the parameter named top teams by player value can have the values 1 5 10 and 20. but i want them to be displayed as so this is kind of like an alias top team top five teams top 10 teams top 20 teams and what's going to happen is this 1 5 10 or 20 will replace this value over here so for example we are only going to show the top say one team one club by when i change this the value sum so click on ok and then change this to value the column value make this a sum and hit okay so you'll see only one value is shown over here this is manchester city the team the club with the highest combined value of players now i can dynamically change my parameter to show the top five teams the top 10 teams and the top 20 teams and what tableau is doing over here is that it's um when we go to say top five it's replacing um the filter over here it's telling the filter hey include only the top five teams by the value of the sum value of their players this is just one use of a parameter you could also use a parameter inside a calculated field so for example i could say um let's see three plus uh let's see what's the parameter same moves what top team yeah and then this value would change depending on whatever the parameter was so uh let's say parameter calculated field uh description parameter calculated field demonstration and you don't need to do this this is just to demonstrate what it does let me make this a dimension and then put that on our labels you'll see it says eight because the parameters value is currently five and i told uh tableau to calculate this field to be the value of three plus whatever the parameters now if i change this uh to ten you'll see it's thirteen if i change it to top team should be four so that is what a uh parameter can do it is a constant that we can change whenever we want to so let's get rid of this oh and if you don't see this control over here then you go all the way down here you'll notice your parameter is now down here right click the parameter all right right click show parameter control make sure that's checked if i uncheck it you'll see the parameter goes away this is a tremendous way to show stakeholders or to let stakeholders play around with different functions on your graph and maybe demonstrate the effect of a certain change that your team is experimenting on um to you to your companies to whatever system you're uh modeling so parameters are constants that can be used all over tableau you can use them to dynamically change a calculation to filter data by measures allow and to allow an audience to define certain criteria when exploring your data all right now let's make what we call a choropleth map uh for the longest time i used to call these chlorophyll chloropath chloropath maps or something like that but this is uh called a choropleth map uh and then let's change this to top teams by player value map and let's create a new sheet all right so nationality under english players right whoops right click geographic role country region so what this is doing is this is telling tableau to look at the nationality column which if you want to see the values inside it uh we can drag it to rows you can see these are all countries and we're telling tableau to instead of looking at it like a string this is a country so now double-click nationality and you'll see it'll plot a map so this is what we call a symbol map let's change it to a chloroplast or a choropleth map show me choropleth so now we see these are all the countries that um the uk gets its uh players for the uh premier league from so let us see how uh what country sends the most uh the highest value of players so under value or drag that a drag value to color okay good stuff make sure that the aggregation is at the is is a sum aggregation so you might have noticed in a couple of our visualizations there is this unknown tag over here and basically this is just saying that tableau doesn't know what some of the values are so we can click on that and click on edit locations i'll be the first to tell you tableau is not particularly smart when it comes to locating geographies uh and won't recognize a lot of stuff a lot of stuff is just not coded in there uh so let's correct what we can korea republic is actually south korea and macedonia i believe that's just north macedonia uh but you'll see for example for the different countries within within the united kingdom it doesn't know what to do with them so for the different countries within the united kingdom it doesn't know what to do with them so we will just leave them be so uh like i was saying earlier tableau is very strict in how it recognizes geographic locations so this is something that you'll find yourself doing quite often you'll have to correct locations if you want to hide this just right click and click hide indicator so let's name this map or the sheet map of countries by player value and then create a new sheet and see if you can recreate the visualization below so let me expand this so pause the video and see if you can recreate this visualization right here all right we are back so let me put this over here zoom in a little bit all right so what do we have it looks like we have top five players by position there we go and let's see we have player group name and some value so let's put that over there player group and name where are we so you'll see i just double clicked a bunch of things and then this is the exact opposite of what i want so i can click on this swap uh what i call the switch axes button and it switches axes uh and let's see if we can reorder this in a second um so a quick thing i wanted to uh let you guys know about so you might notice that i actually make a lot of horizontal graphs um instead of the what you're probably taught in school these vertical graphs and the reason i do this is because the way computer screens are designed these days um is that i mean they're wider than they are long um the the width is longer than the height so it makes more sense and it makes it easier to compare data when you especially with your bar graphs if you show it widthwise um and it's easier to compare and see that you know this is obviously longer than this one is longer than this one is longer than this one um from this angle so this is actually what i would call a best practice um there is a great book storytelling with data which talks about how we should actually be using horizontal bar graphs more often than we use vertical ones that's not to say vertical ones don't have a place just that horizontal ones are um usually seen as better so let's unsort that data all right so what we're going to try and do as you've probably figured out from the title of the visualization is we're going to try and find the top five players by position so you can see now we have a pane over here and we want to find the top five players per every position one way to do this is to use an index column so create a new calculated field let's just call it index and it will just be the index function all the index function does is it returns the index of the current row in the partition meaning in the pane that it's in let's make this a dimension or you know sorry not a dimension we want to convert it to a discrete value so we click on convert to discrete and you'll see this is an example of a discrete measure and let's put it between position and name so you'll see it's just incrementing up for every row in our table and it doesn't start over over here we're going to change that we're going to make sure it starts over every position group so right click index and one thing you might notice is that this is a there's this delta symbol over here that means that this is what we call a table calculation these can get quite complicated uh and they are beyond the scope of this course but eventually hopefully you start using them because they are quite useful so right click index and click edit table calculation and we're going to want to edit a couple of things so if you don't see the window it's probably behind [Music] let's see put that over there oh it showed up on my other screen there we go yeah that's one weird thing tableau does sometimes it'll just put windows on separate screens all right so this table calculation window what it does is it's going to um what is a table calculation a table calculation is basically a calculation that works instead of working on the data set like most other calculations do this one works on whatever you're showing on the screen so these become very useful when you want to sort your data or organize your data in a very specific way so let us go to specific dimensions as you can see there are multiple ways to calculate a table calculation you can do it uh you can calculate it one bro uh as a value at the cell level at the pain level where it goes across then down down then across or just use the whole table we're going to use specific dimensions we're going to have it operate at the deepest level meaning that it'll um increment per position and per name but i wanted to restart every position per group so you'll see now when i come down here i believe it was like 600 something it restarts when i go to the next position group this is what a table calculation is basically it's um this index only has a value in relation to this visualization over here it doesn't have any val or the value over here is uh uh not the same at all so this is what we call a table calculation and i want to sort it by custom value sum descending so now you'll notice what it did is it uh sorts the uh it sorts the table at the position group level so you'll notice if i go to like the 600s ah it restarts when we get to four words and you might see what we're starting to do here what we're doing is that we're now now we have a um column that tell that allows us to filter on say the top five players by position so grab index and drag it to filters one two three four five we're going to select the first five index indices click ok and you'll see tableau now shows us the first five position uh the five highest paid positions per um whoops there we go um per position group so that is how we use a um in a table function or a table calculation and we're using it to rank our marks so now one uh next thing we're going to cover is something called dashboard actions so let's create actually not a new sheet let's create a new dashboard and again you might want to make this one size automatically and let's add in um map of countries by player value oh looks like the first thing we need to do is duplicate this as a cross tab so let's go back here right click duplicate as duplicate as crosstab and you'll see it just duplicated the table as a crosstab or duplicated the map as a crosstab then let's go back to our dashboard one and you can scroll on this bar over here if you're running out of space very useful function oh looks like we need to do one more thing i should listen to my own instructions let us add the club value and the name of the player i think over here right yep on either side at all members there we go now for a given club for a given nationality we know or we know the player for a given club and nationality and let's go back to that dashboard we just created and recreate this below by simply double clicking that and that so double clicking double clicking map of countries by player value and map of countries by player value 2 which is our crosstab so actually let's rename this to map of countries by player value so actually we should actually call probably crosstab of countries right there we go and if you go here you'll see tableau dynamically renames it uh and like we said earlier because my windows size differently now i need to drag and drop that awesome so this is again a very simple dashboard um and what we want to do here is i want the dashboard to limit the size of this table based on what i'm clicking as you can see right now me clicking this does nothing to this sheet over here they're basically independent of each other so to do that we're going to do something use something called dashboard actions these are incredibly useful and they will really take your visualizations to the next level as they will allow your stakeholders to more easily explore the data themselves remember what i said you almost never want to have all of your data just there the point of data visualization is to abstract away the complexity of data and to show people what they need to see so under dashboard up here in the in the menu bar click actions and this menu will come up click add action and then click filter so these are all the different actions you can add basically what we can do is we can tell tableau for a given action say i select something i highlight something something something something i can um have one of these values change we're going to filter our data so under source sheets so source sheet is the sheet upon which uh the action is um what where we perform the action and the target sheet is what um the sheet that responds to that action so under source sheets uh let us only select map of countries by player value under under target sheets only select the cross tab make sure that you run the action on the select and then it should automatically choose show all values over here when you deselect that and then we're going to filter on the select on one field and that field is going to be nationality click ok click ok and then now say i click on mexico over here on the map we will see the two players and the clubs that they play for who are from mexico and if i unclick then you'll see it just shows all the data again this is just a very simple example of a dashboard action and how useful it can be to really flesh out the uh data you have so what a stakeholder would do is they would look at this map and they would be like oh hey looks like a lot of value is coming from brazil who's there oh looks like a lot of these players are coming from brazil and they're valued at this much and there's a good spread of the teams they're on versus for example say iran there are only three players from here all play all paid less than probably what looks like the average of the brazilian players this is how you can give your stakeholders more interactivity with your visualizations and this is something i find people like to do people like to feel like they're uh or i wouldn't even say feel like people like to see the insights that you're showing them and you don't always know what exactly they want to see so giving people this flexibility is a tremendous idea and will take your visualizations up to the next level so one thing you might notice is that these colors are not drastically different from each other so let's see if we can fix that in this legend over here go to the drop down um oh and quick thing this uh legend over here we can edit the title so uh usually if i don't know what to call it i just call it legend because that's what it is and you'll see that changes over there so click on this drop down and then click on edit colors and you'll see this is the span that we have so you'll see what where the obvious problem is the problem is that we have um uh countries that are only giving out a player that's worth 70 000 euros to 1.9 billion euros this spread is what makes it to where our data is not heavily differentiated over here you can't the colors aren't very different from one another so let's click on the advanced option and then we can change the start and the end points to whatever we want so let's make this uh 10 million and 500 million respectively and then we'll make this 500 million and you'll see the center automatically changes to be in between these two values and then let's make it a stepped color with five colors hit ok you'll see it's a little bit more differentiated now it's very obvious where the um a lot of the players are coming from uh obviously belgium france spain and brazil and argentina are sending out a lot of players um and if you know if you've watched it premier league you'll you'll see why oh you'll know why that's the case but let's see if we can make this even more specific go back to your sheet so let me show you what i just did you can go back to your sheet by clicking on the tab down here or you can click over here and click go to sheet this is what i usually use and then let's create something called a set so right click the nationality column create set so by the time um you're done with this course you'll have known how to create all of these things but we're going to create a set now and let's name this set top 10 countries by salary value or actually by player value so we'll name this top 10 countries by player value let's select the top option over here by field top 10 by i think value is what we want make sure that's a sum and hit ok so what we've done is we've created a set and what a set does is it is a value that you can make dynamic through a parameter that creates an in and out group so every every column is either in the set or it's out of the set so this is a very useful way to filter data as you please so now you see we've filtered the data to only show the top 10 teams as far as top 10 countries by player value sent so what i did is i dragged or set from down here all the way up to filters um these are quite useful in larger visualizations when you want to organize your data and that's it that's uh section three done for you guys thank you guys for joining me and with that we've reached the end of the ultimate tableau workbook thank you so much for watching this video and for downloading my workbook i really hope that it was helpful for you in uh getting you off the ground and really teaching you how to start using tableau there's so much more to learn and there's so much more depth to tableau as a tool so it looks like we've covered so we've gone over three sections and in these three sections we've covered what is tableau data data visualization tool at its core the importance of data visualization every great analysis honestly starts with a good data visualization uh what makes tableau so great great customer service um and a great user community um this is these two things are things you can read about inside the guide how to install tableau i'm assuming if you came this far you've learned how to install tableau connecting to static data so tableau can also connect to live data but this course does not go over how to do that because the process for connecting to live data can be really different different just depending on your enterprise architecture so we're not going to go over that in this course the anatomy of the tableau data page that is the page that you use to connect that you use to create your data source the anatomy of the tableau workspace your sheets your dashboards your stories cleaning data one of the most important um and for sure the most time consuming parts of any analysis dimensions versus measures uh dimensions or categories measures are things that you will actually you know measure tables bar charts geospatial data basic troubleshooting resizing dashboards saving your work sheets dashboards and stories and the seven types of data stories out there in section two we covered calculated fields uh especially the date div function and how to use that uh if then statements filtering data and line graphs and especially this is something that i recommend watching again the difference between continuous and discrete dates on your graph very important tableau also taught us that we can very easily extract pda or extract tables from pdf data so this was something that we learned how to do today unioning and joining data when you union data you basically stack two tables on top of each other when you join data you basically attach them at the side and blending data is when you aggregate tables first and then attach them on the side so these are the three ways you can easily or you can combine data sources aliasing data basically changing what a what a column comes out as so a one can show up as a first and a two can come up as a second splitting columns defining data types uh aggregate measures that's what we would call this uh editing axes making scatter plots grouping our data using the marks card uh adding labels to the ends of bar graphs this is uh surprisingly complicated actually um if you have multiple dimensions in your bar graph and eventually how to create dashboard actions sets and parameters so at the end of the guide i've listed a couple of more resources that i think would be useful to you all kb.tableau.com like i mentioned is the tableau knowledge base and this is a tremendous tool for any questions you may have and houses all of tableau's documentation storytelling with data is the first book that i read on my data career and it's a great book to get you started on learning how to present data accurately and efficiently uh it's platform agnostic but everything can be very easily applied to tableau and actually tableau as a company applies a lot of the logic in this book in their software an interesting underwear huh cool function makeover monday so makeover monday is a global movement where uh i think it's uh andy kreben ava green i believe they are two visualization experts and every monday they release a new data set along with a visualization uh and participants anyone can participate are asked to take that data set and visualize it in a new and interesting way and then post it on twitter with the hashtag makeovermonday the big book of dashboards is probably the best resource that i know of in order to actually help you build tableau dashboard specifically it goes over good color theory good design theory and gives you clear examples of the best practices when designing dashboards so i highly recommend it as a book this is one of my favorite books and then at the end i'll leave you with something i call my data checklist which is basically just a checklist of uh different process or different um uh things you should check before you start visualizing your data to make sure that you have the smoothest experience while visualizing i'm gonna be adding more to this and i'm gonna be updating this guide periodically so uh definitely feel free to check back to see if i've added anything uh at a later date as usual if you have any comments uh feel free to leave them down below and any suggestions on how i can improve this are greatly appreciated thank you guys so much for your time and i hope you guys have a great day thank you for learning tableau