Tableau Training Resources
Why Choose Tableau for Your Data Visualizations?
Data Visualizations are an undeniable part of how we communicate information about our data. Nowadays, most teams use data visualization tools to get insight into their raw data, and it can be difficult to decide what tool to use within your organization. Having worked in the field of Data Visualization for the past eight years, I found myself more and more inclined to recommend Tableau to my clients looking for better ways to leverage their data.
It is fair to say that Tableau is an extremely powerful tool, and has no equal in data visualization capabilities and performances. Not only for its speed, scalability, but also for offering the best tools to create visual answers to most of your business questions, from bar charts to more complex visualization, including rich maps with tons of customizations. And best of all, Tableau is free to use for the Tableau Public version!
How does Tableau compare to other tools?
There are many reasons why a vast majority of businesses have adopted Tableau:
It takes much less time to create graphs in Tableau compared to Softwares like Microsoft Excel. Besides, unlike Excel, Tableau uses algorithms to format your graph intelligently, leveraging most of the heavy lifting in data analysis.
Tips: Select your data fields in Tableau, and use the Show Me Panel feature to quickly browse through possible graphs to represent your data.
Tableau allows you to work with your raw data in many formats: Excel file, text file, and even tables from pdf documents from which Tableau can extract the data. Tableau can also connect to your organization database.
Tip: Cleaning up the data using the data interpreter with a simple click.
Tableau has a functionality named the Data Interpreter. Use it to clean your imported Tables and remove any unnecessary information that is not part of your dataset.
While Excel cannot handle a large amount of data, Tableau is robust and doesn’t break when importing big data.
Tableau works like an all-in-one tool, and you do not need to jump to another tool to create your presentations for your data. In Tableau, it is all integrated.
Tableau goes further by allowing you to create Dashboards. Perhaps one of the most important parts of how Tableau works with the dashboard is that your data is interactive,
you can control to your dashboard and your dashboard automatically updates to show you the data you have queried.
Tip: Click on the Device Preview button in your Tableau dashboard to customize it to various devices sizes (desktop, tablet, or phone), with many layout sizes to choose from.
Getting started with Tableau
It might take you time to get up and running with Tableau, and you might consider taking a course to get you started. The software is a beast, packed with many tools and menus that it can be difficult at the beginning to keep a steady workflow on a project you are creating with Tableau. We recommend having experience with spreadsheets, at it will help you with data analysis.
Thanks to its WYSIWYG (What-You-See-Is-What-You-Get) interface, Tableau is the perfect environment to quickly connect to your dataset and interact with the data to create stunning visualizations on the fly.
It might be daunting to learn new software; however, Tableau is suitable for novice and experienced analyst as it is the most scalable solution for all your data, from a simple spreadsheet to big data. If you are not sure where to start, we offer courses which cover the main tools to help you tackle your data visualization needs.
Although other programs like Excel have started incorporating more visualization tools in their program, Tableau remains the leader in Data Visualization tools, and the ever-growing access we have to more data should convince you to shift to Tableau to improve your productivity and get more insight into your data.
There are many different versions of Tableau to fit your organization’s need. You can use Tableau Public the free version of the software as long as you first publish your visualization to the Tableau online platform. Check-out the website www.tableau.com to find out about Tableau offerings. Tableau also offers a Tableau Desktop and Tableau Server version for more extensive use.
Creating dual-axis charts in Tableau
What is a dual-axis chart?
A dual-axis chart is a chart representing two sets of data overlaid on top of one another, in which the resulting charts will have a shared axis. For example, you might have an X-axis for date (months) values, and two separate Y-axis representing separate measures, sales, and profit, on each side of your chart. This allows you to compare profit and sales figures over a period. In dual-axis, you can format your chart with separate marks card for each axis. In our example, we are representing sales figures using a bar chart while using a line chart for profit.
Blend measures vs. dual-axis
Dual-axis charts are particularly useful for analyzing two measures that have different scales. In the case of measures with the same scale, they can share the same axis using the blend method. Blending measures in Tableau will consist of dragging one measure or axis and dropping it onto an existing axis. Instead of adding rows and columns, Tableau will show your second measure on that same continuous axis.
How to create your dual-axis chart in Tableau
The first step in creating dual axis charts is to make a graph for one of your measures.
You then drag your second measure onto your row shelf. Tableau will generate a second graph which is technically a dual-axis chart at this point. But not a dual-axis combination chart.
Note: You can add up to four layered axes: two on the Columns shelf and two on the Rows shelf.
You will need to decide whether or not the two axes should be synchronized. If the unit of measure is the same for both measures, right click on the right axis and select Synchronize Axis. This aligns the scale of the secondary axis to the scale of the first axis.
In our example, the Profit axis is the secondary axis and the Sales axis is the primary axis.
You can also hide one of the axis and display the values on the graph area, or change the order of your fields in the row shelf to alternate which field to represent the primary and secondary axis.
Right-click the right-side axis and select Show Header to toggle off the labels on the right side. Switch one measure from one side to the other in the Rows shelf to move one measure forward.
Note: You can synchronize dual axes for numeric data types that don't match. For example, you can synchronize an axis that uses an integer data type and an axis that uses decimal data type.
Extending the possibilities of dual-axis
Understanding the logic behind dual-axis charts can be useful if you are looking to create more custom chart such as a ranking charts.
Dual-Axis (Layered) Maps
You can extend the possibilities of these charts in the context of maps: when two sets of geographic data are overlaid on top of each other, we obtain a dual-axis (layered) map.
For example, you might create a map with polygon areas filled with one measure and geographic data points for cities on which you mapped another measure. In the example below, we mapped CO2 emissions per borough of the city of London, and mapped the population of each borough on the second “layer” of our map using circle sizes (fig. ).
There are different ways to create these maps in Tableau, which we will explain in a separate post. Using one or the other methods will depend on the type of geographic data you collected (latitude and longitude generated, polygon data, or a combination).
While using Tableau, you will need to use dual-axis charts, either because blending axes for multiple measures into a single axis isn’t sufficient to analyze your data, or for the creation of custom charts such as ranking charts.
The ability to add a second axis to your chart unlocks many more possibilities in Tableau, making dual-axis charts some of the most useful charts. In particular, the fact that they retain the same level of customization as a single chart allows you to customize the level of detail, size, shape, and color encoding for each measure. They are also a great addition to maps as well.
Getting started with Maps in Tableau
Answering the “where” has become more and more important in the interpretation of your data. For that purpose, Tableau presents many possibilities when it comes to displaying your data on a map, and no other tool offers as much flexibility and integration with your data. This three-part article highlights the many tools and ways to create maps in Tableau.
Why Maps are relevant ?
Maps let you unravel information about your data even the most complex graph can’t. They allow you to compare your location data, and discover patterns of repartition, as well as how the geographic data evolve over time. When you combine the map information with other graphs, this leads to better answers to your data questions.
Did you know you can create a multitude of maps in Tableau?
The followings are some examples: Choropleth maps, proportional symbol maps, heat maps, dot density or point distribution maps, flow maps, spider maps, and many more.
Additionally, Tableau comes built in with a series of map layers: Census-based population, income, and other standard demographic datasets which can be overlayed on your data.
You can find these options in Tableau under the menu Map > Map Layers
Tableau also allows you to add details to the map using parameters like County borders, zip code area, and streets as well.
Tableau goes further…
Using Dual-Axis layer you can create multi-layered maps.
Combine for example a Choropleth (or filled map) with pie charts, to reflect percentage analysis across regions. Reflect relative repartition of your data with larger and smaller circles. Use color spectrums to reflect small or large numbers.
-> Read our previous article to familiarize yourself with using Dual-axis charts.
How do you create maps in Tableau?
Tableau is designed to make the most of geographical data. If your data has geographic coordinates, or locations Tableau recognizes, such as country or zip codes, for example, Tableau will use them to generate a map. All you have to do is double-click on the geographic field associated with your data. This is the field that displays an icon next to it. Once you have plotted the data on the map, you can share your map with a click.
Creating custom maps using images:
In Tableau, you can also work with an image as a map and plot data points on it.
Tableau will treat this image as a background image with coordinates based on the dimension of the image.
Access this feature in the menu Maps > Background Images.
Note: Although this method offers the advantage of using your own image which could be anything, a building floor you have created and saved as an image file (jpg, png) for example. It can also be more time consuming than using geographic data as you will have to add your point coordinates one by one in your data table.
Once you have created an image map and added your point coordinates, by removing the axis you will have the map left with your annotated points.
Geocoding your custom locations
Using addresses instead of zip codes requires to geocode your data. Geocoding is the process of determining geographic coordinates (latitude and longitude) for your addresses. There are many free tools offering geocoding. Google Geocoder is one of them. Note that some are limited in the number of places or addresses you can geocode.
Using Spatial Files for your maps
Expanding the scope of possibilities, you can also import geographic data from R or GIS (or other spatial files or custom geocode data you have available, such as the following examples which are customizable.
We will review how to use GIS (Geographic Information System) files in Tableau in part two of this article.
Adding maps to your analysis offers powerful ways to dissect your data and answer questions that would otherwise be impossible without the map. Tableau simplifies that process by treating maps like any other graph. As a result, with tools like the dual-axis, you can create rich data maps. The combination of map layers and customization with several map types opens new possibilities not available in traditional tools like Excel.
Fully integrated in Tableau, maps can also be used in a dashboard to filter data from other charts.
Expanding map possibility with spatial files
We have seen in a previous article that it is possible to create maps in
Tableau using various methods. Your starting point will always be that
Tableau will need to know the geographical coordinates of what is actually
drawn on your map, be it a city, the location of a building, or any
geographic place of importance for your data mapping.
As a result, and in many cases, you may not need to download a detailed map
to start your visual analysis. For instance, a map like the one below shows
too many irrelevant information like cities and rivers. A simpler one with
an outline of countries will be easier to use to overlay your data, and
that’s where the integration of GIS files in Tableau becomes handy.
Using Spatial files
Tableau can create maps using spatial files which are GIS files. For those
not familiar with GIS files, this type is the standard file format used by
most organizations that create maps, which makes GIS files widely
Note: Tableau will accept the following types of GIS files:
- MapInfo TAB files;
- KML (Keyhole Markup Language) files;
- GEOJSON files.
GIS files represent a map using lines, points, and polygons to define an
area on the map such as lakes, park boundaries, city boundaries, etc… In
Tableau, you can use the polygons to map data on the area itself, like in
the example below.
Where can you find Spatial files?
You can find spatial files on most government organization websites and data portals.
Here are some examples:
ESRI open Data
With most GIS files, several files are required to allow you to open the
map file in Tableau and you will need to download the entire folder of map
files. For example, if you are using ESRI Shape files, your folder must
contain .shp, .shx and .dbf files.
Connecting to your Spatial file
The process of connecting to your map data file is the same as with other
By going to Connect > Spatial file in the menu
And browsing to your files location.
As soon as Tableau reads the file, it converts it to latitude and longitude coordinates and creates a Geometry field. You
can then double-click the Geometry field to display the download map in
Adding your data to the map
Depending on the type of map you wish to create, you will proceed
For a choropleth map, this is done by dragging your data field
onto the color mark card.
Here we used red as an example.
You can then edit your color and choose between a monochrome palette to
represent your data. In other cases, if your data presents negative values,
you will want to use a diverging color palette.
Tableau goes further by allowing you to choose the number of steps, which
could be your number of individual polygons...
...and the range of values to narrow the scope of analysis from within your
This is particularly useful since you can see your values changes on your
map as you edit the color panel, unraveling geographic patterns for your
In other approaches, you might use the size card to map your data.
Combining multiple layers of data
If you wish to compare two or more variables (or measures) on the
same map, the best approach will be to use dual-axis layers to create
multi-layered maps. You can then use the size, color and shape mark cards to add the data to your map.
In some cases, if your measures are of the same order and you wish to
compare them side by side, a bivariate choropleth map might be useful. This
is a different approach that takes into account that bivariate choropleth
maps combine two datasets where we are gauging how more of each variable is
present in each polygon. I will detail both approaches in the next article
of this three-part series on maps.
While there are many routes to mapping your data, you will find that
starting with shapefile offer many possibilities. There are however some
limitations to using polygon maps files. As although letting our data speak
for itself is appealing, we often find it has too much to say, and we can
end up with overlaying too much data on our map, more than is necessary to
see the geographic patterns.