Creating an Area Chart & Learning About Highlighting

Kirill Eremenko
A free video tutorial from Kirill Eremenko
Data Scientist
4.5 instructor rating • 44 courses • 1,750,216 students

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Tableau 2020 A-Z: Hands-On Tableau Training for Data Science

Learn Tableau 2020 for data science step by step. Real-life data analytics exercises & quizzes included. Learn by doing!

08:48:12 of on-demand video • Updated April 2021

  • Install Tableau Desktop 2020
  • Connect Tableau to various Datasets: Excel and CSV files
  • Create Barcharts
  • Create Area Charts
  • Create Maps
  • Create Scatterplots
  • Create Piecharts
  • Create Treemaps
  • Create Interactive Dashboards
  • Create Storylines
  • Understand Types of Joins and how they work
  • Work with Data Blending in Tableau
  • Create Table Calculations
  • Work with Parameters
  • Create Dual Axis Charts
  • Create Calculated Fields
  • Create Calculated Fields in a Blend
  • Export Results from Tableau into Powerpoint, Word, and other software
  • Work with Timeseries Data (two methods)
  • Creating Data Extracts in Tableau
  • Understand Aggregation, Granularity, and Level of Detail
  • Adding Filters and Quick Filters
  • Create Data Hierarchies
  • Adding Actions to Dashboards (filters & highlighting)
  • Assigning Geographical Roles to Data Elements
  • Advanced Data Preparation (including latest updates in Tableau)
English [Auto] Hello and welcome back to course on tableau. And in today's editorial we will learn how to create an area chart. And also I will show you a cool feature which you can use in your data discovery processes. So we'll start off of the feature right away in front of us here we've got the chart that we created last night. And by no means is this a final product can't go into a new report and it's way too cluttered to present any insightful information. But at the same time you might find yourself with a chart like this or something similar. While you're doing your daughter discovery process while you're looking for those anomalies trends and patterns you sometimes will find yourself a very very cluttered chart which you are trying to interrogate to find those answers. So here what we have is the representation of unemployment long term unemployment in the US between 2005 and 2015. So we've got this timeline here. It is shown to us by month it is the median of unemployment and the granularity level is gender plus age. So that means for every month there is several age groups seven age groups times two genders. So for every month there is 14 observations that are present on this chart. And as you can see from the column legend here men are represented in blue circles and women are represented with orange circles. So what happens if you want to interrogate one of these genders more specifically and understand exactly what's happening there. Well one way is to restructure the chart and just leave that one gender but there is another great approach which is called highlighting which helps you achieve the same result but much faster and all you have to do is go to a whole legend and just click on the gender that you're interested in. So if you're interested in looking at women you just click on women and there you go so that that is a representation of female unemployment for that same period for those same age groups and also same thing you can do with male. Just click on men and you'll see that the circles associated with male unemployment are highlighted for you right away. And a lot of people forget or don't even know about this feature and therefore they spend too much time restructuring their charts. But in reality if you're just doing data discovery and you just want those answers quickly and you want to understand exactly what's happening it's a very handy thing to know and to have. So definitely look into that. Well I'll just share another example now. Let's look at age for instance. We know that we have age in the chart as level of granularity and the way we got it there is we dragged it into the detail shell. So it is increasing or making the chart more granular but at the same time it's not represented anywhere in the chart it's not a color. It's not a shape it's nowhere. So if we want to be able to highlight age we need to give it some sort of representation visual representation or a chart. And so because colors are used let's drag age into shape right away you can see that now for every single age group that we have we've got a new shape and we would want to highlight say 35 to 44 years old and we would press here naturally. But nothing happens. So as we click around nothing happens nothing's been held. But that can be easily fixed. You just have to go to this button over here which says highlights selected items. And if you click on that now it will allow you to use this legend to highlight the items that you're interested in. And as we click through you can see that we're focusing on the different unemployment of different age groups. So once again very handy feature to know about and to use in your data discovery. Definitely take it into your data science arsenal. All right. So what are we going to do now is we going to create an area. Chad let's move back to just our original line charts. I'm going to take age and gender out. Gonna Change this to line and the sort of median unemployment we're going to look at the sum of unemployment. So now we're seeing the total unemployment or long term unemployment for every single month in the US between 2005 and 2015. So the child ranges from 0 million to 7 million. So now what we're going to do is we're going to take age so the age groups and we'll drag it into color and what that will do is it'll give us many line charts. Now the child ranges from zero to one point six million and every single line chart or line is here is independent. And so for instance like here you can see that for 35 to 44 years old people in April 2010 that employer was one point thirty eight million and once again we can use the college and to highlight the lines that we want to look at. So that's handy. But at the same time this visual is not very useful. You can. It's not easy to understand. And as we discussed it is our job as data scientists to make these visualizations very friendly and very easy to interrogate for people who are looking at them. So what we're going to do now is we're going to change this facilitation to a different type and one way of doing it is just trying to explore in finding out what position is going to work in this particular case. So for that you can use the Show-Me function and you can just click around. So for instance you look at a tree map is a tree map going to work right away. You can see that even though it looks bright quite hope advertising is not going to work for a report. It's that you can't really tell much from this exhilaration. How about a bubble chart. Once again very beautiful in terms of art. This is probably a 10 out of 10 but in terms of inside fulness not really can't really tell much from here. Too much information being thrown at me so you can just click around and find the best one that you like but the one I suggest going with in this case is an area chart. So we're going to click on that and right away you can see how this area chart has been created for us. And it looks pretty incredible and very I think insightful and we'll learn how to make it even better just now. But before we do what I'm going to do I'm going to show you how to create it from scratch report this show me bottom. So I'm just gonna count because that and I'm just gonna cancel these steps and go back to our line chart. So what you want to do here is if you go to the dropdown here instead of line just go to area and what you'll see is now all of these unemployment rates have been stacked on top of each other. So look at this axis here. I'll just go back to the line chart of click here. You can see here that it's only goes up to one point six million. And that's because each one of these lines is independent. Now if I go to area it goes all the way to seven million. Now that's because they're being stacked up and as they're stacked the total increases here as well. So the the actual unemployment for different groups different age groups is given by this pop up over here so for instance in this case it's one point five six million unemployment in that month for 25 to 54 year olds so that's how you create an area chart. And once again you can still highlight with color. So remember about that that's a very powerful feature. And what else we can do here is we probably can add some labels to make it really critical so we're going to take age press control and we'll drag it into label. And now you can see that the label has been added and once again we can as we know already we can format these labels maybe make it bold. And as you can see this is already starting to look much much better. There's no label for the top one. That's because it probably just doesn't fit. But it kind of makes sense they're all in like ascending order going downwards and so that sums it up for us for today. We learn how to create an area chart and we learned about highlighting in the next material. I will show you how to create a filter. We will use gender as a filter and I will show you also we'll recap a little bit on a level of detail and granularity and highlighting as well. So I look forward to seeing you next time and until then happy analyzing.