What if the data you already have could reveal something you've never seen before? It can, and it starts with a technique most Tableau analysts have never tried: the frequency matrix. In this full keynote from Appalachian State University's Analytics Day, Andy Kriebel (Tableau Hall of Fame Visionary and founder of Next Level Tableau) builds a frequency matrix from scratch, then shows the same underlying data six different ways: Sankey diagrams, tree diagrams, network diagrams, geographic flow maps, box plots, and slope graphs. You'll walk away with a technique you can take straight back to your own data, whether you're working with enrollment numbers, customer flow, product usage, or anything else where you need to compare two categories at once. What's covered: - What a frequency matrix is and how to read one - Building a frequency matrix step by step in Tableau - Using calculated fields to highlight the most common and highest-performing combinations - Visualizing the same relationship with Sankey diagrams, tree diagrams, and network diagrams - Mapping geographic flow with Make Point and Make Line - Guiding principles for building dashboards stakeholders actually use - Live Q&A on accessibility, color palettes, and chart governance Want more? Grab my free 30-Day Tableau Skill-Building Accelerator: nextleveltableau.com/accelerator Download the workbook and data here: https://nlt.kit.com/frequency-matrix

Dot strip plots help visualize the frequency and distribution of small, discrete data sets. Dots placed in order on a strip are a space-efficient method of laying out ranks across multiple categories.

In this tip, I show you how to use both a level of detail expression and a table calculation to compute the most frequent value in a measure. 

Marginal histograms are a great choice when you want to display your data at different levels of aggregation in a single view.  Download the data here.

Dot matrix charts are useful for visualizing the distribution and frequency of discrete data. They help you understand the scale and proportion. The purchase is to understand how many, not how much. 

In this example, I'm using two colors to distinguish new vs. used cars. However, if you have only one variable or category, then stick to one color.

Resources:

1. Workbook - https://bit.ly/DotMatrixPlot
2. Data set - https://data.world/vizwiz/car-sales-mock-data

It seems to have been a while since I worked on a personal data analysis/visualization project. The one I'm going to take you through below was inspired by a piece of work I saw by Erin Davis (no contact info to link to). Check out her amazing portfolio on her website.

The piece I wanted to replicate in Tableau is based on her beautiful work Visualizing the Geography of FM Radio. Since she had already done this for radio, I thought I'd try to replicate her work, but with TV stations, that is, the strength and coverage that the broadcast signals from TV stations transmit.

First, I had to prep the data. Fortunately the raw data was easily accessible on the FCC website as are explanations of the fields and how to use them. The FCC also have information about which States fall into which FCC regions. I manually grouped the States into their regions in Tableau (it would have needed to be manually created data anyway).

From there, it was some data prep to get the signal boundaries for each state, ensure they are in the correct State (e.g., some stations that were listed in California actually plotted in other States), then export as a TDE (Hyper files don't work well with polygons).

Here's the Alteryx workflow:


For Tableau, I created a custom color palette based on the color legend on Erin's vizzes, replicated her maps as close as possible, and that's it!


Enjoy!

Week 29 has us analyzing the changing frequency of sex by Americans. Thank you to Pablo Gomez for bringing this chart to my attention.


What works well?

• Overall, the chart is really good. 
• The title and subtitle make it very clear what the viz is about.
• The labels focus you to the topic the creator wants you to focus on.
• I like how the first year is a filled dot and the last year is an open dot.
• Bolding the first and last years on the axis
• Including the % sign on the y-axis for only the top value

We begin this second half of Makeover Monday 2018 with data about rat sightings in New York City. I've been aware of this data set for quite a while and thought it would be funny to look at rats this week after analyzing London's bikes last week. It's kind of the same data, just replace the city and the subject. I'm curious to see how different the vizzes are this week from everyone.

The original article by Jowanza Joseph contains several fantastic visualization, most which look like they were created in R. For this week's makeover, we need to try to make this visualization better:

What works well?

What could be improved?

What I did

Alteryx Workflow

The workflow is pretty simple. It takes the individual sightings, converts them to spatial points, assigns them to a 1/2 mile grid based on shape files available for each borough, then I export it as a shape file.

Tableau Visualization

This week Ann Jackson stepped in to provide everyone the challenge. I think she's been seeking some revenge and she sure dished it out. Find all of the requirements here.

Easiest Parts

Hardest Parts

This week's tip comes from a request from Dan Cox on Twitter. Dan had seen Robert Crocker post about last week's Workout Wednesday and Dan wanted to know how to create this music waves or frequency chart. It's pretty simple with a few key parts:

1. Create percentile calcs and place them on the same axis.
2. The date field must be discrete.
3. Use Measure Names on the Path shelf.

In last week’s Makeover Monday about global warming I included a strip plot at the bottom of my final visualisation. You may hear these also called barcode charts or frequency charts, but whatever their “official” name, they are very useful for:

1. Seeing a lot of data at a glance
   
2. Understanding concentration of the data
   
3. Seasonal trends

In this week’s video tip, I walk you through five use cases for strip plots varying from global warming to the frequency of fires to distribution of sales to deprivation in Scotland.