Learning Tableau

Tableau connects to data stored in a wide variety of files and databases. This includes flat files such as Excel and text files, relational databases such as SQL Server and Oracle, cloud-based data sources such as Google Analytics and Amazon Redshift, and OLAP data sources such as Microsoft SQL Server Analysis Services. With very few exceptions, the process of building visualizations and performing analysis will be the same no matter what data source you use. We'll cover the details of connecting to different data sources in later chapters.

For now, we will connect to an Access data source included in the resources supplied with this book. This chapter's workbook includes a connection to the data source, but we will walk through the steps of connecting using a new workbook first:

You should now be in the main work area of Tableau, which looks like this:

Locate the numbered features of the main workspace. We'll refer to these features throughout the book, so familiarize yourself with the terminology:

Having created your connection to the data, you are ready to begin to visualize and analyze the data. Over the course of the following examples, you'll take on the role of an analyst for the coffee chain. We'll build multiple visualizations that answer various questions and finally put everything together in an interactive dashboard. As we begin, let's consider a few of the foundational principles.

When you first connect to a data source such as the coffee chain data, Tableau will display the data connection and the fields in the Data window in the sidebar on the left-hand side. Fields can be dragged from the Data window onto various parts of the view on the right-hand side. Fields can be dropped onto the canvas area or onto various shelves, such as Rows, Columns, Color, or Size. The placement of the fields will result in different encodings of the data based on the type of field.

The fields from the data source are visible in the Data window and are divided into Measures and Dimensions. Understanding the difference between Measures and Dimensions is essential.

As an example, consider a view created using the Sales and Market fields from the coffee chain connection.

The Sales field is used as a measure in this view. Specifically, it is aggregated as an average. When you use a field as a measure in the view, the type aggregation (for example, SUM, MIN, and MAX) will be shown on the active field. Note in the preceding example that the active field on Rows clearly indicates the average aggregation of sales: AVG(Sales).

The Market field is a dimension with one of four values for each record of data: Central, East, South, or West. When the field is used as a dimension in the view, it slices the measure. So instead of an overall average, the view in the preceding example shows you the average sales for each market.

Another important distinction to make with fields is whether a field is being used as discrete or continuous. Tableau will give you a visual indication of the default for a field (the color of the icon in the Data window) and how it is being used in the view (the color of the active field on a shelf). Discrete fields are blue; continuous fields are green.

Whether a field is discrete or continuous, it determines how Tableau visualizes it based on where it is used in the view.

Note

Discrete (blue) fields have values that are shown as distinct and separate from each other. Discrete values can be reordered and still make sense.

When a discrete field is used on the Rows or Columns shelves, the field defines row or column headers.

When used for color, a discrete field defines a discrete color palette in which each color describes a distinct value of the field.

Continuous (green) fields have values that are shown as flowing from one field to another. Numeric and date fields are often used as continuous fields in the view. The values of these fields have an order that it would make no sense to change.

When used on Rows or Columns, a continuous field defines an axis.

When used for colors, a continuous field defines a gradient.

Most dimensions are discrete by default, and most measures are continuous by default. However, any numeric or date field, whether dimension or measure, can be used as a continuous field in the view. Any field, whether dimension or measure, can be used as a discrete field in the view.

As you work through the examples in this chapter, pay attention to the fields you are using to create the visualizations, whether they are dimensions or measures, and whether they are discrete or continuous. Experiment with changing fields in the view from continuous to discrete and vice versa to gain an understanding of the difference in the visualization.

A new connection to a data source is an invitation to explore. At times, you may come to the data with very well-defined questions and a strong sense of what you expect to find. Other times, you will come to the data with general questions and very little idea of what you will find. The data visualization capabilities of Tableau empower you to rapidly and iteratively explore the data, ask new questions, and make new discoveries.

The visualization examples in the following sections cover a few of the most foundational chart types. As you work through the examples, keep in mind that the goal is not simply to learn how to create each chart type. Rather, the examples are designed to help you think through the process of discovery, analysis, and storytelling. Tableau is designed to make this process intuitive, rapid, and transparent. Far more important than memorizing steps to create a bar chart is to understand how to use Tableau to create a bar chart and then iteratively adjust your visualization to gain new insight as you ask new questions.

Bar charts

Bar charts visually represent data in a way that makes comparisons of a value across different categories easy to compare. Length is the primary means by which you will visualize the data. You may also incorporate color, size, stacking, and order to communicate additional attributes and values.

Creating bar charts in Tableau is very easy. Simply drag and drop the measure you want to see on either the Rows or Columns shelf and the dimension that defines the categories onto the opposing Rows or Columns shelf.

As an analyst for the coffee chain, you are ready to begin a discovery process focused on profit. Create a new sheet for this and every subsequent example in the workbook you started. This chapter's workbook will contain the complete examples, so you can compare your results.

Begin your analysis with the following steps:

1. Drag and drop the Profit field from Measures on the Data window on the left to the Columns shelf. You now have a bar chart with a single bar representing the sum of profit for all the data in the data source.

2. Drag and drop the Product Type field from Dimensions in the Data window to the Rows shelf. This slices the data to give you four bars, representing the sum of profit for each product type.

   

You now have a horizontal bar chart. This makes the comparison of profit between the product types easy. Notice how the mark type dropdown on the Marks card is set to Automatic and shows that Tableau has determined that bars are the best visualization given the fields you have placed in the view. As a discrete dimension, the Product Type field defines row headers for each product type in the data. As a continuous measure, the Profit field defines an axis that causes each bar to be drawn from 0 to the value of the total profit for each product type.

Note

Typically, Tableau draws a mark (a bar, shape, circle, square, and so on) for every intersection of dimensional values in the view. In this simple case, Tableau draws a single bar mark for each dimensional value (Coffee, Espresso, Herbal Tea, and Tea) of Product Type. The type of mark is indicated and can be changed in the drop-down menu on the Marks card. The number of marks drawn in the view can be observed on the lower-left status bar.

Tableau draws different marks in different ways. For example, bars are drawn from 0 (or the end of the previous bar, if stacked) along the axis. Circles and other shapes are drawn at locations defined by the value(s) of the field defining the axis. Take a moment to experiment with selecting different mark types from the dropdown on the Marks card. Having an understanding of how Tableau draws different mark types will help you master the tool.

Extending bar charts for deeper analysis

Using the preceding bar chart, you can easily see that Coffee has more total profit than any other product type and Tea has less total profit than any other type. What if you want to further understand the profitability of product types in different markets?

Drag the Market field from Dimensions in the Data window to the left of the Product Type field that is already on the Rows shelf.

You still have a horizontal bar chart. But now you've introduced Market as another dimension that changes the level of detail in the view and further slices the aggregate value of profit. By placing Market before Product Type, you are able to easily compare sales for each product type within a given market.

Now you are starting to make some discoveries. For example, Coffee is making a bit more profit than any other product type in the East market but a bit less than other products in the West market.

Let's take a look at a different view using the same fields arranged differently.

Drag the Market field from the Rows shelf and drop it on the Color shelf.

Instead of a side-by-side bar chart, you now have a stacked bar chart. Notice how each segment of the bar is color-encoded by the Market field. Additionally, a color legend has been added to the workspace. You haven't changed the level of detail in the view, so the profit is still summed for every combination of market and product type.

Note

The level of detail of a view is a key concept when working with Tableau. In most basic visualizations, the combination of values of all the dimensions in the view defines the lowest level of detail for that view. All measures will be aggregated or sliced by the lowest level of detail. In the case of most basic views, the number of marks (indicated in the lower-left status bar) corresponds to the number of intersections of dimensional values.

If, for example, Product Type is the only field used as a dimension, you will have a view at the product type level of detail and all measures in the view will be aggregated per product.

If Market is the only field used as a dimension, you will have a view at the market level of detail and all measures in the view will be aggregated per market.

If you use both Product Type and Market as dimensions in the view, you will have a view at the level of product type and market. All measures will be aggregated per unique combination of product type and market.

Stacked bars are useful when you want to understand part-to-whole relationships. Now it is easy to see what portion of total profit for each product type is made in each region. However, it is very difficult to compare the profit for most of the markets across product types. This is because, with the exception of West, every segment of the bar has a different starting place. It is interesting to note that there is no bar segment for South for the Tea product type.

Conclude this example with the following steps:

1. Experiment by dragging the Market field from Color to the other various shelves on the Marks card, such as Size, Label, and Detail. Observe that in each case, the bars remain stacked but are redrawn based on the visual encoding defined by the Market field.

2. Finally, drag the Market field off the shelf and drop it in a gray area on the workspace. This will remove that field from the view. You should now have a bar chart that looks like the very first one you created. Later, you'll be able to use this simple bar chart in an interactive dashboard.

3. Right-click on the tab labeled Sheet 1 at the bottom of the screen and rename the sheet Profit by Product Type.

4. From the File menu, select Save and save your workbook as CoffeeChainAnalsyis.twb.

Tip

At the time of writing this, Tableau does not have an autosave feature. You will want to get into the habit of saving the workbook early and then pressing Ctrl + S or selecting Save from the File menu often to avoid losing your work.

Line charts

Line charts connect related marks in a visualization to show movement or the relationship between connected marks. The position of the marks and lines that connect them are the primary means of communicating the data. Additionally, you can use size and color to visually communicate additional information.

The most common kind of line chart is a time-series. Time series show the movement of values over time. They are very easy to create in Tableau and require only a date and a measure.

Continue your analysis of coffee chain profit using the workbook you just saved:

1. Create a new worksheet by clicking on the new worksheet button immediately to the right of the Profit by Product Type tab at the bottom. You should now have a new, blank worksheet named Sheet 2 by default. If you accidentally create a new dashboard or story, you can delete it or use the undo button.

2. Rename the sheet to Profit over Time by right-clicking on the tab and selecting Rename or by double-clicking on the text of the tab.

3. Drag the Profit field from Measures to Rows. This gives you a single, vertical bar representing the sum of all profit in the data source.

4. To turn this into a time series, you must introduce a date. Drag the Date field from Dimensions in the Data window and drop it in Columns.

5. Tableau has a built-in date hierarchy and the default level of detail has given you a line chart connecting 2 years. Let's say you want to see the profit by month. Right-click on the YEAR(Date) field in Columns and select Month from the second set of dates. Future chapters will discuss the details of the various date options.

   

Tip

You can right-click drag and drop a date field as a shortcut to selecting the date option available from this menu. Right-click dragging and dropping other types of fields is a shortcut to quickly selecting other customizations, such as the type of aggregation. If you are using Tableau on a Mac, the equivalent is Option + left-click + drag and drop.

You now have a time series that shows you the profit for each month.

Extending line charts for deeper analysis

Right now, you are looking at the overall profit over time. Let's perform some analysis at a slightly deeper level:

1. Drag the Market field from Dimensions to Color. Now, you have a line per market with each line in a different color and a legend indicating which color is used for which market. As with the bars, adding a dimension to Color splits the marks. However, unlike the bars where the segments were stacked, the lines are not stacked. Instead, the lines are drawn at the exact value for the sum of profit for each market and month. This allows an easy and accurate comparison.

   

2. Drag the Product field from Dimensions and drop it directly on top of the Market field currently in the Marks card. This replaces the Market field with Product. You now have 13 overlapping lines. Often, you'll want to avoid more than two or three overlapping lines. However, note that you can enable highlighting on the color legend using the icon in the upper-right section of the legend. Then, clicking on a product in the color legend will highlight the associated line in the view. This can be a good way to pick out a single item and compare it with all others.

3. Hold down the Ctrl key and then drag the Product field from Color in the Marks card and drop it in Rows. This copies the field in the view and you now have a line chart for each product. Now you have a way to compare each product over time without overwhelming the overlap. This is the start of a spark-lines visualization that will be developed more fully when advanced visualizations are discussed.

4. Remove the Product field from the Rows shelf to return to the first time series created in this exercise. Additionally, you may experiment with the undo button in the toolbar.

Often, you'll need more than a single visualization to communicate the full story of the data. In these cases, Tableau makes it very easy for you to use multiple visualizations together on a dashboard. In Tableau, a dashboard is a collection of views, filters, parameters, images, and other objects that work together to communicate a data story. Dashboards are often interactive and allow end users to explore different facets of the data.

Dashboards serve a wide variety of purposes and can be tailored for a wide variety of audiences. Consider the following possible dashboards:

Considerations for different audiences and advanced techniques will be covered in great detail in Chapter 7, Telling a Data Story with Dashboards. For now, let's consider an example that introduces foundational examples.

Continue with the CoffeeChainAnalysis dashboard you have been building, and click on the new dashboard button to the right of the Profit by Location tab. You now have a blank dashboard, and the sidebar on the left-hand side shows options to build a dashboard instead of the Data window that was visible in a worksheet. The sidebar should look like this:

The dashboard window consists of several key components. Techniques to use these objects will be detailed later. For now, focus on gaining some familiarity with the options that are available.

First is a list of all visible worksheets in the dashboard. You can add these to a dashboard by dragging and dropping them. A light gray shading will indicate the location of the sheet once it is dropped. You can also double-click on any sheet, and it will be added automatically.

The next section lists multiple additional objects that can be added to the dashboard. Horizontal and Vertical layout containers will give you finer control over the layout; Text allows you to add text labels and titles. Images and even embedded web content can be added. Finally, a Blank object allows you to preserve blank space in a dashboard or may serve as a placeholder.

Next, you can select whether new objects will be added as Tiled or Floating. Tiled objects will snap into a tiled layout next to other tiled objects or within layout containers. Floating objects will float on top of the dashboard in successive layers.

The Layout section gives you the ability to navigate through a hierarchical structure of objects that have been added to the dashboard. This can be useful for finding objects in complex dashboards.

The final section gives you control over the size of the dashboard as well as pixel-perfect sizing and positioning of floating objects.

Tableau's visual environment allows a rapid and iterative process of exploring and analyzing data visually. You took your first steps in understanding how to use the platform. You connected to an Access database and used it to explore and analyze the data using some foundational visualization types, such as bar charts, line charts, and geographic visualizations. Along the way, you focused on learning the techniques and understanding key concepts such as the difference between measures and dimensions and discrete and continuous fields. Finally, you put all the pieces together to create a fully functional dashboard that allows an end user, such as management, to understand your analysis and make discoveries of their own.

In the next chapter, we'll explore how Tableau works with data. You will be exposed to fundamental concepts and practical examples of how to connect to various data sources. Combined with the foundational concepts you just learned about building visualizations, you will be well equipped to move on to more advanced visualizations, deeper analysis, and telling fully interactive data stories.