When you first encounter a dataset, often the first thing you see is the raw data—numbers, dates, text, field names, and data types. Almost certainly, there are insights and stories that need to be uncovered and told, decisions to make, and actions to take. But how do you find the significance? How do you uncover the meaning and tell the stories that are hidden in the data?
Tableau is an amazing platform for seeing, understanding, and making key decisions based on your data! With it, you will be able to achieve incredible data discovery, data analysis, and data storytelling. You'll accomplish these tasks and goals visually using an interface that is designed for a natural and seamless flow of thought and work.
To leverage the power of Tableau, you don't need to write complex scripts or queries. Instead, you will be interacting with your data in a visual environment where everything that you drag and drop will be translated into the necessary queries for you and then displayed visually. You'll be working in real time, so you will see results immediately, get answers as quickly as you can ask questions, and be able to iterate through potentially dozens of ways to visualize the data to find a key insight or tell a piece of the story.
This chapter introduces the foundational principles of Tableau. We'll go through a series of examples that will introduce the basics of connecting to data, exploring and analyzing the data visually, and finally putting it all together in a fully interactive dashboard. These concepts will be developed far more extensively in subsequent chapters. But don't skip this chapter, as it introduces key terminology and key concepts, including the following:
- The cycle of analytics
- Connecting to data
- Foundations for building visualizations
- Creating bar charts
- Creating line charts
- Creating geographic visualizations
- Using Show Me
- Bringing everything together via a dashboard
Tableau allows you to jump to any step of the cycle, move freely between steps, and iterate through the cycle very rapidly. With Tableau, you have the ability to do the following:
- Data discovery: You can very easily explore a dataset using Tableau and begin to understand what data you have visually.
- Data preparation: Tableau allows you to connect to data from many different sources and, if necessary, create a structure that works best for your analysis. Most of the time, this is as easy as pointing Tableau to a database or opening a file, but Tableau gives you the tools to bring together even complex and messy data from multiple sources.
- Data analysis: Tableau makes it easy to visualize the data, so you can see and understand trends, outliers, and relationships. In addition to this, Tableau has an ever-growing set of analytical functions that allow you dive deep into understanding complex relationships, patterns, and correlations in the data.
- Data storytelling: Tableau allows you to build fully interactive dashboards and stories with your visualizations and insights so that you can share the data story with others.
All of this is done visually. Data visualization is the heart of Tableau. You can iterate through countless ways of visualizing the data to ask and answer questions, raise new questions, and gain new insights. And you'll accomplish this as a flow of thought.
Tableau connects to data stored in a wide variety of files and databases. This includes flat files, such as Excel documents, spatial files, 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 Analysis Services. With very few exceptions, the process of analysis and creating visualizations will be the same, no matter what data source you use.
We'll cover details of connecting to different types of data sources in Chapter 2, Working with Data in Tableau. And we'll cover data spanning a wide variety of industries in other chapters. For now, we'll connect to a text file, specifically, a comma-separated values file (
.csv). The data is a variation of the sample that ships with Tableau: Superstore, a fictional retail chain that sells various products to customers across the United States. Please use the supplied data file instead of the Tableau sample data, as the variations will lead to differences in visualizations.
Chapter 1 workbooks, included with the code files bundle, already have connections to the file, but for this example, we'll walk through the steps of creating a connection in a new workbook:
- Open Tableau. You should see the home screen with a list of connection options on the left and, if applicable, thumbnail previews of recently edited workbooks in the center, along with sample workbooks at the bottom.
To a File, click
- In the
Opendialogue box, navigate to the
\Learning Tableau\Chapter 01directory and select the
- You will now see the data connection screen, which allows you to visually create connections to data sources. We'll examine the features of this screen in detail in the Connecting to data section of Chapter 2, Working with Data in Tableau. For now, Tableau has already added and given a preview of the file for the connection:
For this connection, no other configuration is required, so simply click on the
Sheet 1 tab at the bottom to start visualizing the data! You should now see the main work area within Tableau, which looks like this:
We'll refer to elements of the interface throughout the book using specific terminology, so take a moment to familiarize yourself with the terms used for various components numbered in the preceding screenshot:
- The Menu contains various menu items for performing a wide range of functions.
- The Toolbar allows for common functions such as undo, redo, save, add a data source, and so on.
- The Side Bar contains tabs for
Analytics. When the
Datatab is active, we'll refer to the side bar as the data pane. When the
Analyticstab is active, we'll refer to the side bar as the analytics pane. We'll go into detail later in this chapter, but for now, note that the data pane shows the data source at the top and contains a list of fields from the data source below, divided into
- Various shelves such as
Filtersserve as areas to drag and drop fields from the data pane. The Marks card contains additional shelves such as
Tooltip. Tableau will visualize data based on the fields you drop on to the shelves.
Data fields in the data pane are available to add to a view. Fields that have been dropped on to a shelf are called in the view or active fields because they play an active role in the way Tableau draws the visualization.
- The canvas or view is where Tableau will draw the data visualization. You may also drop fields directly on to the view. You'll find the seamless title at the top of the canvas. By default, it will display the name of the sheet, but it can be edited or even hidden.
Show Meis a feature that allows you to quickly iterate through various types of visualizations based on data fields of interest. We'll look at
Show Metoward the end of the chapter.
- The tabs at the bottom of the window give you options for editing the data source, as well as navigating between and adding any number of sheets, dashboards, or stories. Many times, any tab (whether it is a sheet, a dashboard, or a story) is referred to generically as a sheet.
A Tableau workbook is a collection of data sources, sheets, dashboards, and stories. All of this is saved as a single Tableau workbook file (
.twbx). We'll look at the difference in file types and explore details of what else is saved as part of a workbook in later chapters. A workbook is organized into a collection of tabs of various types:
- A sheet is a single data visualization, such as a bar chart or a line graph. Since
Sheetis also a generic term for any tab, we'll often refer to a sheet as a view because it is a single view of the data.
- A dashboard is a presentation of any number of related views and other elements (such as text or images) arranged together as a cohesive whole to communicate a message to an audience. Dashboards are often designed to be interactive.
- A story is a collection of dashboards or single views arranged to communicate a narrative from the data. Stories may also be interactive.
- As you work, the status bar will display important information and details about the view, selections, and the user.
- Various controls allow you to navigate between sheets, dashboards, and stories, as well as view the tabs with Show Filmstrip or switch to a sheet sorter showing an interactive thumbnail of all sheets in the workbook. Now that you have connected to the data in the text file, we'll explore some examples that lay the foundation for data visualization and then move on to building some foundational visualization types. To prepare for this, please do the following:
- From the menu, select
- When prompted to save changes, select
- From the
\learning Tableau\Chapter 01directory, open the file
Chapter 01 Starter.twbx. This file contains a connection to the
Superstoredata file and is designed to help you walk through the examples in this chapter.
- From the menu, select
The files for each chapter include a
Starter workbook that allows you to work through the examples given in this book. If at any time, you'd like to see the completed examples, open the
Complete workbook for the chapter.
With a connection to the data, you are ready to start visualizing and analyzing the data. As you begin to do so, you will take on the role of an analyst at the retail chain. You'll ask questions of the data, build visualizations to answer those questions, and ultimately design a dashboard to share the results. Let's start by laying some foundations for understanding how Tableau visualizes data.
When you first connect to a data source such as the
Superstore file, Tableau will display the data connection and the fields in the data pane on the left
Side Bar. Fields can be dragged from the data pane onto the canvas area or onto various shelves such as
Size. As we'll see, 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 pane and are divided into Measures and Dimensions. The difference between measures and dimensions is a fundamental concept to understand when using Tableau:
- Measures are values that are aggregated. For example, they are summed, averaged, counted, or have a minimum or a maximum.
- Dimensions are values that determine the level of detail at which measures are aggregated. You can think of them as slicing the measures or creating groups into which the measures fit. The combination of dimensions used in the view define the view's basic level of detail.
As an example (which you can view in the
Chapter 01 Starter workbook on the
Measures and Dimensions sheet), consider a view created using the
Sales from the
AVG) will be shown on the active field. Note that in the preceding example, the active field on rows clearly indicates the sum aggregation of
Another important distinction to make with fields is whether a field is being used as discrete or continuous. Whether a field is discrete or continuous, determines how Tableau visualizes it based on where it is used in the view. Tableau will give a visual indication of the default for a field (the color of the icon in the data pane) and how it is being used in the view (the color of the active field on a shelf). Discrete fields, such as
Region in the previous example, are blue. Continuous fields, such as
Sales, are green.
In the screenshots in the printed version of this book, you should be able to distinguish a slight difference in shade between the discrete (blue) and the continuous (green) fields, but pay special attention to the interface as you follow along using Tableau. You may also wish to download the color image pack from Packt Publishing, available at: https://www.packtpub.com/sites/default/files/downloads/9781788839525_ColorImages.pdf
Discrete (blue) fields have values that are shown as distinct and separate from one another. Discrete values can be reordered and still make sense. For example, you could easily rearrange the values of
Region to be East, South, West, and Central, instead of the default order in the preceding screenshot.
When a discrete field is used on the
Columns shelves, the field defines headers. Here, the discrete field
Region defines column headers:
Here, it defines row headers:
When used for color, a
Discrete field defines a discrete color palette in which each color aligns with a distinct value of the field:
Continuous (green) fields have values that flow from first to last as a continuum. Numeric and date fields are often (though not always) used as continuous fields in the view. The values of these fields have an order that it would make little sense to change.
When used on
Columns, a continuous field defines an axis:
When used for color, a continuous field defines a gradient:
It is very important to note that continuous and discrete are different concepts from Measure and Dimension. While most dimensions are discrete by default, and most measures are continuous by default, it is possible to use any measure as a discrete field and some dimensions as continuous fields in the view.
To change the default of a field, right-click the field in the data pane and select
Convert to Discrete or
Convert to Continuous. To change how a field is used in the view, right-click the field in the view and select
Continuous. Alternatively, you can drag and drop the fields between
Measuresin the data pane.
In general, you can think of the differences between the types of fields as follows:
- Choosing between dimension and measure tells Tableau how to slice or aggregate the data
- Choosing between discrete and continuous tells Tableau how to display the data with a header or an axis and defines individual colors or a gradient.
As you work through the examples in this book, 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 differences in the visualization.
A new connection to a data source is an invitation to explore and discover! 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 visual analytics capabilities of Tableau empower you to rapidly and iteratively explore the data, ask new questions, and make new discoveries.
The following visualization examples cover a few of the most foundational visualization types. As you work through the examples, keep in mind that the goal is not simply to learn how to create a specific chart. Rather, the examples are designed to help you think through the process of asking questions of the data and getting answers through iterations of visualization. Tableau is designed to make that process intuitive, rapid, and transparent.
Something that is far more important than memorizing the steps to create a specific chart type is understanding how and why to use Tableau to create a bar chart, and adjusting your visualization to gain new insights as you ask new questions.
Bar charts visually represent data in a way that makes the comparing of values across different categories easy. The length of the bar is the primary means by which you will visually understand 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 onto either the
Columns shelf and the dimension that defines the categories onto the opposing
As an analyst for
Superstore, you are ready to begin a discovery process focused on sales (especially the dollar value of sales). As you follow the examples, work your way through the sheets in the
Chapter 01 Starter workbook. The
Chapter 01 Complete workbook contain, the complete examples so you can compare your results at any time:
- Click on the the
Sales by Departmenttab to view that sheet.
- Drag and drop the
Measuresin the data pane onto the
Columnsshelf. You now have a bar chart with a single bar representing the sum of sales for all the data in the data source.
- Drag and drop the
Dimensionsin the data pane to the
Rowsshelf. This slices the data to give you three bars, each having a length that corresponds to the sum of sales for each department:
You now have a horizontal bar chart. This makes comparing the sales between the departments easy. The mark type drop-down menu on the
Marks card is set to
Automatic and shows an indication that Tableau has determined that bars are the best visualization given the fields you have placed in the view. As a dimension, the
Department slices the data. Being discrete, it defines row headers for each department in the data. As a measure, the
Sales field is aggregated. Being continuous, it defines an axis. The mark type of bar causes individual bars for each department to be drawn from
0 to the value of the sum of sales for that department.
Typically, Tableau draws a mark (such as a bar, a circle, a square) for every intersection of dimensional values in the view. In this simple case, Tableau is drawing a single bar mark for each dimensional value (Furniture, Office Supplies, and Technology) of Department. 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 drop-down menu on the
Marks card. Having an understanding of how Tableau draws different mark types will help you master the tool.
Using the preceding bar chart, you can easily see that the technology department has more total sales than either the furniture or office supplies departments. What if you want to further understand sales amounts for departments across various regions? Follow these two steps:
- Navigate to the
Bar Chart (two levels)sheet, where you will find an initial view identical to the one you created earlier
- Drag the
Dimensionsin the data pane to the
Rowsshelf and drop it to the left of the
Departmentfield already in view
You should now have a view that looks like this:
You still have a horizontal bar chart, but now you've introduced
Region as another dimension that changes the level of detail in the view and further slices the aggregate of the sum of sales. By placing
Department, you are able to easily compare the sales of each department within a given region.
Now you are starting to make some discoveries. For example: the
Technology department has the most sales in every region, except in the
Furniture had higher sales.
Office Supplies never has the highest sales in any region.
Let's take a look at a different view, using the same fields arranged differently:
- Navigate to the
Bar Chart (stacked)sheet, where you will find a view identical to the original bar chart.
- Drag the
Regionfield from the
Rowsshelf and drop it on to the
Instead of a side-by-side bar chart, you now have a stacked bar chart. Each segment of the bar is color-coded by the
Region field. Additionally, a color legend has been added to the workspace. You haven't changed the level of detail in the view, so sales are still summed for every combination of region and department:
The View Level of Detail 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 simple views, the number of marks (indicated in the lower-left status bar) corresponds to the number of intersections of dimensional values. That is, there will be one mark for each combination of dimension values.
Departmentis the only field used as a dimension, you will have a view at the department level of detail, and all measures in the view will be aggregated per department.
Regionis the only field used as a dimension, you will have a view at the region level of detail, and all measures in the view will be aggregated per region.
- If you use both
Regionas dimensions in the view, you will have a view at the level of department and region. All measures will be aggregated per unique combination of department and region, and there will be one mark for each combination of department and region.
Stacked bars can be useful when you want to understand part-to-whole relationships. It is now fairly easy to see what portion of the total sales of each department is made in each region. However, it is very difficult to compare sales for most of the regions across departments. For example, can you easily tell which department had the highest sales in the
East region? It is difficult because, with the exception of
West, every segment of the bar has a different starting place.
Now take some time to experiment with the bar chart to see what variations you can create:
- Navigate to the
Bar Chart (experimentation)sheet.
- Try dragging the
Colorto the other various shelves on the
Markscard, such as
Detail. Observe that in each case the bars remain stacked but are redrawn based on the visual encoding defined by the
- Use the Swap button on the
Toolbarto swap fields on
Columns. This allows you to very easily change from a horizontal bar chart to a vertical bar chart (and vice versa):
- Drag and drop
Measuressection of the data pane on top of the
Regionfield on the
Markscard to replace it. Drag the
Colorif necessary, and notice how the color legend is a gradient for the continuous field.
- Experiment further by dragging and dropping other fields onto various shelves. Note the behavior of Tableau for each action you take.
- From the
Line charts connect related marks in a visualization to show movement or relationship between those connected marks. The position of the marks and the lines that connect them are the primary means of communicating the data. Additionally, you can use size and color to communicate additional information.
Continue your analysis of Superstore sales using the
Chapter 01 Starter workbook you just saved:
- Navigate to the
- Drag the
Rows. This gives you a single, vertical bar representing the sum of all sales in the data source.
- To turn this into a time series, you must introduce a date. Drag the
Order Datefield from
Dimensionsin the data pane on the left and drop it into
Columns. Tableau has a built-in date hierarchy, and the default level of
Yearhas given you a line chart connecting four years. Notice that you can clearly see an increase in sales year after year:
- Use the drop-down menu on the
YEAR(Order Date)field on
Columns(or right-click the field) and switch the date field to use
Quarter. You may notice that
Quarteris listed twice in the drop-down menu. We'll explore the various options for date parts, values, and hierarchies in the Visualizing Dates and Times section of Chapter 3, Venturing on to Advanced Visualizations. For now, select the second option:
- Navigate to the
over time (overlapping lines)sheet, where you will find a view identical to the one you just created.
- Drag the
Color. Now you have a line per region, with each line a different color, and a legend indicating which color is used for which region. 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 sales for each region and quarter. This allows for easy and accurate comparison. It is interesting to note that the cyclical pattern can be observed for each region:
With only four regions, it's fairly easy to keep the lines separate. But what about dimensions that have even more distinct values? The steps are as follows:
- Navigate to the
over time (multiple rows)sheet, where you will find a view identical to the one you just created.
- Drag the
Dimensionsand drop it directly on top of the
Regionfield currently on the
Markscard. This replaces the
Category. You now have 17 overlapping lines. Often, you'll want to avoid more than two or three overlapping lines. But you might also consider using color or size to showcase an important line in the context of the others. Also note that clicking an item in the color legend will highlight the associated line in the view. Highlighting is an effective way to pick out a single item and compare it to all the others.
- Drag the
Markscard and drop it into
Rows. You now have a line chart for each category. Now you have a way of comparing each product over time without an overwhelming overlap, and you can still compare trends and patterns over time. This is the start of a spark-lines visualization that will be developed more fully in the Advanced Visualizations section of Chapter 11, Advanced Visualizations, Techniques, Tips, and Tricks.
In Tableau, the built-in geographic database recognizes geographic roles for fields, such as
Congressional District, or
Zip Code. Even if your data does not contain latitude and longitude values, you can simply use geographic fields to plot locations on a map. If your data does contain latitude and longitude fields, you may use those instead of the generated values.
Tableau will automatically assign geographic roles to some fields based on a field name and a sampling of values in the data. You can assign or reassign geographic roles to any field by right-clicking the field in the data pane and using the
Geographic Role option. This is also a good way to see what built-in geographic roles are available.
Tableau can also read shape files and geometries from some databases. These and other geographic capabilities will be covered in more detail in the Mapping Techniques section of Chapter 11, Advanced Visualizations, Techniques, Tips, and Tricks. In the following examples, we'll consider some of the key concepts of geographic visualizing.
Geographic visualization is incredibly valuable when you need to understand where things happen and whether there are any spatial relationships within the data. Tableau offers three main types of geographic visualization:
- Filled maps (simply referred to as maps in the Tableau interface)
- Symbol maps
- Density maps
Filled maps fill areas such as countries, states, counties, or ZIP codes to show a location. The color that fills the area can be used to encode values, most often of aggregated measures but sometimes also dimensions. These maps are also called choropleth maps.
Let's say you want to understand sales for
Superstore and see whether there are any patterns geographically. You might take an approach similar to the following:
- Navigate to the
Sales by Statesheet.
- Double-click the
Statefield in the data pane. Tableau automatically creates a geographic visualization using the
Longitude (generated), and
- Drag the
Salesfield from the data pane and drop it on the
Colorshelf on the
Markscard. Based on the fields and shelves you've used, Tableau has switched the automatic mark type to
The filled map fills each state with a single color to indicate the relative sum of sales for that state. The color legend, now visible in the view, gives the range of values and indicates that the state with the least sales had a total of 3,543 and and the state with the most sales had a total of 1,090,616.
When you look at the number of marks displayed in the bottom status bar, you'll see that it is 49. Careful examination reveals that the marks consist of the lower 48 states and Washington DC; Hawaii and Alaska are not shown. Tableau will only draw a geographic mark, such as a filled state, if it exists in the data and is not excluded by a filter.
Observe that the map does display Canada, Mexico, and other locations not included in the data. These are part of a background image retrieved from an online map service. The state marks are then drawn on top of the background image. We'll look at how you can customize the map and even use other map services in the Mapping Techniques section of Chapter 11, Advanced Visualizations, Techniques, Tips, and Tricks.
Filled maps can work well in interactive dashboards and have quite a bit of aesthetic value. However, certain kinds of analyses are very difficult with filled maps. Unlike other visualization types, where size can be used to communicate facets of the data, the size of a filled geographic region only relates to the geographic size and can make comparisons difficult. For example: which state has the highest sales? You might be tempted to say Texas or California because they appear larger, but would you have guessed Massachusetts? Some locations may be small enough that they won't even show up compared to larger areas. Use filled maps with caution and consider pairing them with other visualizations on dashboards for clear communication.
With symbol maps, marks on the map are not drawn as filled regions; rather, marks are shapes or symbols placed at specific geographic locations. The size, color, and shape may also be used to encode additional dimensions and measures.
Continue your analysis of
Superstore sales by following these steps:
- Navigate to the
Sales by Postal Codesheet.
Dimensions. Tableau automatically adds
Postal Codeto the
Rows. The mark type is set to a circle by default, and a single circle is drawn for each postal code at the correct latitude and longitude.
Sizeshelf on the
Markscard. This causes each circle to be sized according to the sum of sales for that postal code.
Colorshelf on the
Markscard. This encodes the mark color to correspond to the sum of profit. You can now see the geographic location of profit and sales at the same time. This is useful because you will see some locations with high sales and low profit, which may require some action.
The final view should look like this, after making some fine-tuned adjustments to the size and color:
Sometimes, you'll want to adjust the marks on symbol map to make them more visible. Some options include the following:
- If the marks are overlapping, click the
Colorshelf and set the transparency to somewhere between
75%. Additionally, add a dark border. This makes the marks stand out, and you can often better discern any overlapping marks.
- If marks are too small, click on the
Sizeshelf and adjust the slider. You may also double-click the size legend and edit the details of how Tableau assigns size.
- If the marks are too faint, double-click the color legend and edit the details of how Tableau assigns color. This is especially useful when you are using a continuous field that defines a color gradient.
A combination of tweaking the size and using
Stepped Color and
Use Full Color Range, as shown here, produced the final result for this example:
Unlike filled maps, symbol maps allow you to use size to visually encode aspects of the data. Symbol maps also allow for greater precision. In fact, if you have latitude and longitude in your data, you can very precisely plot marks at a street address-level of detail. This type of visualization also allows you to map locations that do not have clearly defined boundaries.
Sometimes, when you manually select Map in the Marks card drop-down menu, you will get an error message indicating that filled maps are not supported at the level of detail in the view. In those cases, Tableau is rendering a geographic location that does not have built-in shapes. Other than cases where filled maps are not possible, you will need to decide which type best meets your needs. We'll also consider the possibility of combining filled maps and symbol maps in a single view in later chapters.
Density maps show the spread and concentration of values within a geographic area. Instead of individual points or symbols, the marks blend together, showing intensity in areas with a high concentration. You can control color, size, and intensity.
Let's say you want to understand the geographic concentration of orders. You might create a density map using the following steps:
- Navigate to the
Density of Orderssheet.
- Double-click the
Postal Codefield in the data pane. Just as before, Tableau automatically creates a symbol map geographic visualization using the
Longitude (generated), and
- Using the drop-down menu on the
Markscard, change the mark type to
Density. The individual circles now blend together showing concentrations:
Try experimenting with the
Size options. Clicking on
Color, for example, reveals some options specific to the
Density mark type:
Several color palettes are available that work well for density marks (the default ones work well with light color backgrounds, but there are others designed to work with dark color backgrounds). The
Intensity slider allows you to determine how intense the marks should be drawn based on concentrations.
This density map displays a high concentration of orders from the East Coast. Sometimes, you'll see patterns that merely reflect population density—in which case, your analysis may not be particularly meaningful. In this case, the concentration on the East Coast compared to the lack of density on the west coast is intriguing.
Show Me is a powerful component of Tableau that arranges selected and active fields into the places required for the selected visualization type. The
Show Me toolbar displays small thumbnail images of different types of visualizations, allowing you to create visualizations with a single click. Based on the fields you select in the data pane and the fields that are already in view,
Show Me will enable possible visualizations and highlight a recommended visualization.
Explore the features of
Show Me by following these steps:
- Navigate to the
- If the
Show Mepane is not expanded, click the
Show Mebutton in the upper-right of the toolbar to expand the pane.
- Press and hold the Ctrl key while clicking the
Profitfields in the data pane to select each of those fields. With those fields highlighted,
Show Meshould look like this:
Notice that the
Show Me window has enabled certain visualization types such as text tables, heat maps, symbol maps, filled maps, and bar charts. These are the visualizations that are possible given the fields already in the view, in addition to any selected in the data pane.
Show Mehighlights the recommended visualization for the selected fields and also gives a description of what fields are required as you hover over each visualization type. Symbol maps, for example, require one geographic dimension and 0 to 2 measures.
Other visualizations are greyed-out, such as lines, area charts, and histograms.
Show Me will not create these visualization types with the fields that are currently in the view and selected in the data pane. Hover over the greyed-out line-charts option in
Show Me. It indicates that line charts require one or more measures (which you have selected) but also require a date field (which you have not selected).
Tableau will draw line charts with fields other than dates.
Show Me gives you options for what is typically considered good practice for visualizations. However, there may be times when you know that a line chart would represent your data better. Understanding how Tableau renders visualizations based on fields and shelves instead of always relying on
Show Me will give you much greater flexibility in your visualizations and will allow you to rearrange things when
Show Me doesn't give the exact results you want. At the same time, you will need to cultivate an awareness of good visualization practices.
Show Me can be a powerful way to quickly iterate through different visualization types as you search for insights into the data. But as a data explorer, analyst, and story-teller, you should consider
Show Me as a helpful guide that gives suggestions. You may know that a certain visualization type will answer your questions more effectively than the suggestions of Show Me. You also may have a plan for a visualization type that will work well as part of a dashboard but isn't even included in Show Me.
You will be well on your way to learning and mastering Tableau when you can use
Show Me effectively, but feel just as comfortable building visualizations without it.
Show Me is powerful for quickly iterating through visualizations as you look for insights and raise new questions. It is useful for starting with a standard visualization that you will further customize. It is wonderful as a teaching and learning tool.
However, be careful not to use it as a crutch without understanding how visualizations are actually built from the data. Take time to evaluate why certain visualizations are or are not possible. Pause to see what fields and shelves were used when you selected a certain visualization type.
Show Me example by experimenting with
Show Me by clicking various visualization types, looking for insights into the data that may be more or less obvious based on the visualization type. Circle views and box-and-whisker plots show the distribution of postal codes for each state. Bar charts easily expose several postal codes with negative profit.
Now that you have become familiar with creating individual views of the data, let's turn our attention to putting it all together in a dashboard.
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 to suit a wide variety of audiences. Consider the following possible dashboards:
- A summary-level view of profit and sales to allow executives to have a quick glimpse into the current status of the company
- An interactive dashboard, allowing sales managers to drill into sales territories to identify threats or opportunities
- A dashboard allowing doctors to track patient readmissions, diagnoses, and procedures to make better decisions about patient care
- A dashboard allowing executives of a real-estate company to identify trends and make decisions for various apartment complexes
- An interactive dashboard for loan officers to make lending decisions based on portfolios broken down by credit ratings and geographic location
Considerations for different audiences and advanced techniques will be covered in detail in Chapter 7, Telling a Data Story with Dashboards.
When you create a new dashboard, the interface will be slightly different than it is when designing a single view. We'll start designing your first dashboard after a brief look at the interface. You might navigate to the
Superstore Sales sheet and take a quick look at it yourself.
The dashboard window consists of several key components. Techniques for using these objects will be detailed in Chapter 7, Telling a Data Story with Dashboards. For now, focus on gaining some familiarity with the options that are available. One thing you'll notice is that the left sidebar has been replaced with dashboard-specific content:
The left side-bar contains two tabs:
Dashboardtab, for sizing options and adding sheets and objects to the dashboard
Layouttab, for adjusting the layout of various objects on the dashboard
Dashboard pane contains options for previewing based on target device along with several sections:
- A Size section, for dashboard sizing options
- A Sheets section, containing all sheets (views) available to place on the dashboard
- An Objects section with additional objects that can be added to the dashboard
You can add sheets and objects to a dashboard by dragging and dropping. As you drag the view, a light-grey shading will indicate the location of the sheet in the dashboard once it is dropped. You can also double-click any sheet and it will be added automatically.
- Horizontal and Vertical layout containers will give you finer control over the layout
- Text allows you to add text labels and titles
- An Image and even embedded Web Page content can be added
- A Blank object allows you to preserve blank space in a dashboard, or it can serve as a place holder until additional content is designed
- A Button is an object that allows the user to navigate between dashboards
- An Extension gives you the ability to add controls and objects that you or a third party have developed for interacting with the dashboard and providing extended functionality
Using the toggle, you can select whether new objects will be added as
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.
When a worksheet is first added to a dashboard, any legends, filters, or parameters that were visible in the worksheet view will be added to the dashboard. If you wish to add them at a later point, select the sheet in the dashboard and click the little drop-down caret on the upper right. Nearly every object has the drop-down caret, providing many options for fine-tuning the appearance and controlling behavior.
You can resize an object on the dashboard using the border. The Grip, marked in the screenshot, allows you move the object once it has been placed. We'll consider other options as we go.
- Navigate to the
Superstore Salessheet. You should see a blank dashboard.
- Successively double-click each of the following sheets listed in the
Dashboardsection on the left:
Sales by Department,
Sales over time, and
Sales by Postal Code. Notice that double-clicking the object adds it to the layout of the dashboard.
- Add a title to the dashboard by checking
Show Dashboard titlein the lower left of the sidebar.
- Select the
Sales by Departmentsheet in the dashboard and click the drop-down arrow to show the menu.
Entire View. The Fit options describe how the visualization should fill any available space.
Be careful when using various Fit options. If you are using a dashboard with a size that has not been fixed or if your view dynamically changes the number of items displayed based on interactivity, then what might have once looked good might not fit the view nearly as well.
- Select the
Salessize legend by clicking it. Use the Remove UI element to remove the legend from the dashboard.
- Select the
Profitcolor legend by clicking it. Use the grip to drag the legend, and place it under the map.
- For each view (
Sales by Department,
Sales by Postal Code, and
Sales over time),select the view by clicking an empty area in the view. Then, click the Use as FilterUI element to make that view an interactive filter for the dashboard. Your dashboard should look similar to this:
- Take a moment to interact with your dashboard. Click on various marks, such as the bars, states, and points of the line. Notice that each selection filters the rest of the dashboard. Clicking a selected mark will deselect it and clear the filter. Notice also that selecting marks in multiple views causes filters to work together. For example, selecting the bar for
Sales by Departmentand the 2016 Q1 in
Sales over timeallows you to see all the ZIP codes that had furniture sales in the first quarter of 2016.
Congratulations! You have now created a dashboard that allows for interactive analysis!
As an analyst for the Superstore chain, your visualizations allowed you to explore and analyze the data. The dashboard you created can be shared with members of management, and it can be used as a tool to help them see and understand the data to make better decisions. When a manager selects the furniture department, it immediately becomes obvious that there are locations where sales are quite high but the profit is actually very low. This may lead to decisions such as a change in marketing or a new sales focus for that location. Most likely, this will require additional analysis to determine the best course of action. In this case, Tableau will empower you to continue the cycle of discovery, analysis, and storytelling.
Tableau's visual environment allows for a rapid and iterative process of exploring and analyzing data visually. You've taken your first steps toward understanding how to use the platform. You connected to data and then explored and analyzed the data using some key 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 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 key 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.