Menu
Tableau
January 19, 2019 4:44 pm Published by

– Fourth in a series for the 2019 Data VizArt Student Challenge –                                                                                                                  

Today in the blog we’re covering more of the most common functions of Tableau, and highlighting its capabilities from a graphical standpoint.  We posed these questions to several individuals from around the office on Friday and put together their responses below.

 

What are the advantages of Tableau?

 

Tableau delivers a ‘Wow!’ moment to you, your team and your clients.  It offers a more vivid way to showcase your data and is faster than to wrangle it in less specialized software.

 

What’s the difference between… ?

 

Worksheets and Stories

 

story visualization

Worksheets are individual visualizations such as a bar chart, series chart, pie chart, a map, etc.  They represent information pertaining to a specific set of attributes within a dataset.  A user can build several visualizations in multiple worksheets across all the attributes in a dataset.  All those visuals can be put together next to each other in a dashboard.  Such a form of representation gives a broader picture of the dataset by showing us what is going on in the data.  In certain cases, the dataset may be very broad and may have the ability to show the big picture across different aspects.  For example, to measure the impact of the aerospace industry on local business in a geographic area.  In such cases we need more than one dashboard to tell the complete story.  This is where Tableau stories come in.  The storyboard allows a user to combine multiple dashboards together in a linear fashion.

 

Dimensions and Measures

 

In Tableau, when we load data for the first time, the attributes are divided into two sets; Dimensions and Measures.  We can think of dimensions as those attributes containing qualitative information such as Name, City, Country, Product Type, etc.  Conversely, measures are attributes that contain quantitative information such as – age, height, length, width,  sales, revenue, number of employees, etc.  It is important to know your data before you load it into Tableau because sometimes, we can have qualitative information being represented as numerical codes, as in product type 1,2,3.  Tableau will pick it up as a measure, but it’s the user’s job to change that to a dimension during the data loading stage.  Both dimensions and measures can be aggregated however only measures can have a mathematical sum and an average.

 

Mapping: Coordinates vs FSA

 

tableau primer

As you may have noticed, Tableau can map your data on a virtual atlas.  The “centroid process” in this is called a “symbols map.”  This type of map is best used tor representing latitude-longitude information (individual addresses).  In order to represent something such as an FSA (a geographic area) in a map, first, Tableau has to recognize FSA as a geographic attribute.  Most times Tableau is pretty smart in identifying that by itself when you load the data for the first time.  Other times, you have to manually define it during the ETL stage.  When we choose to represent FSA in Tableau, normally we drop the field named FSA into the worksheet directly and Tableau picks it up.  By default, Tableau chooses to represent FSAs as dots on a symbols map.  This dot in theory is the average of the lat and long of all coordinates corresponding to a specific FSA.  However, intuitively, it makes more sense to represent FSA information as “filled maps” with each FSA marked by borders and filled with a color gradient.  To do that in Tableau, we can simply switch from the “symbols map” view to the “map” view.  Tableau simply overlays the polygons of each FSA on top of the main map.  This is because under the hood, Tableau already has a pre-built database of such information pertaining to postal area codes and polygon shapes for some of the most commonly used countries.

 

Maps vs Scatterplots

 

Maps are a great tool to present geographic patterns.  However, it is not the best tool to explore the correlation about data.  For this you may prefer to use a ‘scatterplot’ to present numeric data.  In Tableau there is a graph tool called Shape that can enrich a scatterplot function.  This way you can visualize your data in detail as scatters which can help you find the data density in different areas easily, through customizable symbols and color settings to distinguish your categories clearly.  In both maps and scatterplots, the details of the data may be shown when you hover on the dot.  Here’s an example to present the relation between the age of the business and the percentage of female directors in the companies:

tableau scatterplot

 

Bar Charts, Histograms, and TEXT

 

Another important graph is the histogram, which is a function of the bar chart.  To generate histograms, you first need create bins from the measure data.  Combining the bar chart and histogram provides us flexibility to enrich our plot.  We can use colors to see the component of each bar in the graph and see the trend of the changes.  Here is another example for the scatterplot data above, but using bar charts to present the same patterns:

histogram bar chart

Last, there is also an import function in Tableau called TEXT.  Text is also a powerful graphic tool, not only for labeling the data!  Going this route, one can easily create word clouds in a  designated style.  Just choose the text as the graph type (don’t put anything into Rows or Columns) and drag the measurement which can present the importance of the text.  Then, add dimensions that present the categoric information to the color.  You will get a vivid word cloud in your Tableau!

text

 

Pretty cool, hunh?

We hope this tutorial has been useful in your preparation for the 2019 Data VizArt Student Challenge (or for any Tableau novice).  If you haven’t registered for the competition, note there is just one week left before the submission deadline.  We still have a couple of blogs planned to help you through.  Early next week we will walk through the use of filtering in Tableau.  Check back soon!

 

Written by Ren Li, Pari Borah, Zack Dai, & Drew Fones.

Tags: , , , , , , , , , , , , ,
Categorised in: , ,

This post was written by rel8admin

Comments are closed here.