How to Create a Dashboard in Tableau

I took the time to produce a dashboard series that would get a relatively new Tableau user up to speed in very little time. I put together the “Goldilocks” videos I wish I had when I was a novice; not too short but long enough to hammer the concepts home.

In the first video, I dive head first into constructing four basic charts that I believe every data user should know how to put together. You will watch me demonstrate how to put together the following charts:

Line Chart with Forecast

Tableau Dashboard Line Chart

The shaded area is a time series forecast predicting the number of orders for the year 2020.

Map

Tableau Dashboard Map

Heat Map

Tableau Dashboard Heat Map

Bar Chart

Tableau Dashboard Bar Chart

In the second video, I’ll cover the layout and formatting of the dashboard, as well as adding a little interactivity. When the user hovers the cursor over the Line Chart, all of the other charts will update to reflect the number of orders represented since the selected month and year.

Full Dashboard

Tableau Dashboard

Watch Part 1 to Build the Component Charts

Watch Part 2 for Layout and Interactivity

What You need:

  • Either Tableau or Tableau Desktop
  • Data set: Tableau Superstore Data (can be found all over the internet with a simple Google search).

Do some great things with your data!

If you find this type of instruction valuable make sure to subscribe to my Youtube channel. All views and opinions are mine alone, independently researched and do not necessarily represent those of my employer.

Tableau Bar Chart: Combining Small Values

In this video we’ll learn how to build a bar chart visualization that combines values below a certain threshold into an “OTHER” bucket. This technique is very useful when limiting the number of bars to show on your visualization while not losing sight of all the smaller values.

  • Using standard Tableau Superstore data, we’ll calculate the percentage of sales that are generated by each individual state.
  • We’ll then use a parameter to set a percentage threshold where all states below this percentage will be combined.
  • This technique also requires the use of sets and Fixed LODs.

This technique allows us to combine all states below a certain threshold (e.g., 2%) into one single bar chart showing a combined 24%.

Bar Chart Below Threshold Thumb 01

I have to give credit where credit is due to Ann Jackson for sharing this technique at TC19!

Feel free to interact with the viz and download the workbook on Tableau public:

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

 

Build a Stacked Donut Chart in Tableau

Have you ever wanted to stack 4 pie charts on top of each other to build a visual? Let’s have some fun building out a stacked donut chart or a “TrailBlazer” chart as I call it due to its likeness to a particular NBA team’s logo.

Stacked Donut Chart Thumb

Portland Trailblazers anyone?

Stacked Donut vs Trailblazer

In order to build out this chart I used an innovative technique shared by Simon Runc on the Tableau forums. Feel free to check out that post here.

Using Tableau’s Sample Superstore Data, Simon came up with an innovative use of the INDEX() function and the Size functionality to create three different pie charts that each show a respective percentage of a measure (in this case Sales) to the Total amount of the measure.

For example, the chart highlights in red the percentage of Consumer Sales as a percentage of all segment sales (i.e., Consumer + Corporate + Home Office). The grey portions represent all other sales, other than the segment of interest.

The trick to this approach is using the Index function to create a pie chart per segment. For example Consumer is assigned a value of 1, Corporate a value of 2 and Home Office is assigned 3. When the INDEX() value is placed on size, the three different charts are assigned sizes where one is slightly larger than the next.

Stacked Donut Raw 2

With a little division and axis customization, the three segments are placed on top of each other to provide a stacked pie chart effect. The hole is courtesy of the standard methodology for creating a donut chart which involves a dual axis.

It makes much more sense when you see it in action so make sure to watch the video!

Here is an example of the raw stacked donut chart before the “TrailBlazer” formatting.

Stacked Donut Raw

Feel free to interact with the viz and download the workbook on Tableau public:

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Definitely check out other posts of interest for building donut charts in Tableau:

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Select Random Sample Values and Rows using Excel

In this video I help you solve the dual problems of selecting a random value from an Excel list and selecting a number of random rows from a range of data in Excel. At times when I am generating a data-set to use in my video tutorials, I want to select a random selection of rows. Typically, because my data values are clumped together and are too similar to the data on preceding and subsequent rows.

SELECTING A RANDOM VALUE FROM A LIST

Enter three Excel formulas to save the day for selecting a random value from a list:

  • ROWS()
    • Returns the number of rows in a reference or array.
  • RANDBETWEEN()
    • Returns a random integer number between the numbers you specify. A new random integer number is returned every time the worksheet is calculated.
  • INDEX()
    • The INDEX function returns a value or the reference to a value from within a table or range.

Randow Row in Excel Blog Screenshot

  1. In the screenshot above notice that the ROWS() function returns the value of 20, which corresponds to the number of names listed in the cell range of A6 to A25.
  2. The RANDBETWEEN() function generated a random number between 1 and the value returned from ROWS() (i.e., 20). In this case, RANDBETWEEN() combined with ROWS() returned a value of 3.
  3. By combining the results from the first 2 functions, the INDEX() function searches our list and returns the value of the 3rd cell in the list  (i.e., Flor McCard) because the RANDBETWEEN() function returned a value of 3.

When we put it all together it looks like the following:

=INDEX($A$6:$A$25,RANDBETWEEN(1,ROWS($A$6:$A$25)))

I choose to use the absolute cell reference notation with dollar signs although in this case it is not necessary since we are not copying our results to other cells.

SELECTING RANDOM ROWS FROM A LIST

We’ll only use 1 Excel formula to save the day for selecting random rows from a range:

  • RAND()
    • RAND() returns an evenly distributed random real number greater than or equal to 0 and less than 1. A new random real number is returned every time the worksheet is calculated.

By placing the RAND() function in a column co-located with your data, you will assign a random number to each row in your data-set or range.

Once that is done, all you have to do is sort your data by the RAND() column and then select however many rows you need. It’s that simple!

If you are like me, you probably need to see it in action to get a better understanding. Check out the video above and if you learned something, please go ahead and like it on my Youtube channel!

Thanks for your support!

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

References:

All Excel function definitions are from https://support.office.com

 

 

 

 

Build a Tableau Parameter Action Dashboard

This video is inspired by Filippos Lymperopoulos who has a great article on parameter action concatenation. Definitely check out his article here. I am a hands on person who learns from building and sharing, therefore I put together this video to explore, tweak and hammer home the concept.

In this video we’ll build a Sales Analysis dashboard in Tableau using Parameter Actions and the Concatenation Aggregation functionality. The great thing about this tip is that you can use it across multiple data sources. This is a must see!

This approach relies upon the use of two different data connections. In this manner our data tables are completely un-joined without an established relationship. We have a data-set comprised of a customer list and one comprised of customer transactions across three years.

The key to linking the data sets together relies upon the following calculated field which creates a de-facto set that we can use to highlight customer purchases:

Tableau Concat Calc Field

When we setup a concatenation parameter action on our dashboard, the very act of selecting a [Customer Name] will add that Customer Name to the parameter named [Selected Customer]. This will cause all selected customers to resolve to TRUE, which allows highlighting of the sales bar charts related to the user selected customers.

Tableau Concat Parameter Action Thumb1

In the screenshot above, notice the selected [Customer Name] values on the left hand side are also concatenated together at the bottom of the dashboard (i.e., Franciso Hernandez, Jose Garcia, and Terrye Marchi). All of their respective purchases are also highlighted in the middle of the dashboard.

Tableau Concat Calc Field Label

The above calculated field is used to only show the [QTY] purchased for the user selected customer and is placed on the bar chart label.

Feel free to interact with the viz and download the workbook on Tableau public:

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

How to Highlight the Bottom Bar Chart Values in Tableau

 

I decided to make this video after someone left a comment on another video I made titled “How to Highlight the Top 3 Bar Chart Values in Tableau” asking how to find the last three values.

Youtube Comment Highlight Bottom 3

In this video I will show you how to highlight the bottom three sales values on a bar chart. You’ll also learn how to use a parameter to dynamically change the number of lowest bars highlighted.

Bottom 3 Bar Chart Values Thumbnail

We can accomplish the highlighting of the bottom N bar chart values via two ways. We can either create a set or create a calculated field to accomplish this task. The set method is cleaner but has its limitations when multiple dimensions are used in the visual. Therefore, the calculated field approach serves us well when we add multiple dimensions.

Watch the video to see how it all comes together but the calculation boils down to this:

RANK(SUM(0-[Sales]))<=[Highlight Parameter]

By adjusting the [Highlight Parameter] control, the user can determine how many bottom sales values are highlighted in the visual. This method also maintains its functionality when an additional dimension is added to the visual.

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Create Multiple Bar Charts in Python using Matplotlib and Pandas

In this Python visualization tutorial you’ll learn how to create and save as a file multiple bar charts in Python using Matplotlib and Pandas. We’ll easily read in a .csv file to a Pandas dataframe and then let Matplotlib perform the visualization. As a bonus you’ll also learn how to save the plot as a file.

The key to making two plots work is the creation of two axes that will hold the respective bar chart subplots.

# define the figure container and the two plot axes
fig = plt.figure(figsize=(20,5))

# add subplots to the figure (build a 1x2 grid and place chart in the first or second section)
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)

Understanding the subplot nomenclature is essential. Adding axes to the figure as part of a subplot arrangement is simple with the fig.add_subplot() call. In this arrangement the first digit is the number of rows, the second represents the number of columns, and the third is the index of the subplot (where we want to place our visualization).

Of course you need to watch the video to see how all of the code comes together.

Also, keep this Matplotlib style sheet reference handy for changing up the style on your visual.

NBA Blocks Assists

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

See the following links for additional background:

https://matplotlib.org/3.1.0/gallery/subplots_axes_and_figures/subplots_demo.html

https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_subplot