VizWiz

Data Viz Done Right

May 25, 2022

#WOW2022 Week 20 - Monthly Sales vs. Target

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Today I ran a live #WatchMeViz session to show how I completed Workout Wednesday 2022 week 20. I messed up a bit, even though I had done the challenge before, but that's part of the learning process! I hope you find the session useful. If you have any questions, please leave a comment on YouTube.

Resources


Enjoy!



May 11, 2022

How to Color BANs by Positive or Negative Change

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In this tip, I show you two methods for coloring BANs based on whether there is a positive or negative change. The first method colors the entire BAN, whereas the second method only colors the value of the change itself.

April 21, 2022

#B2VB - 2022/W4 - Design Some KPIs

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April 19, 2022

How to Calculate YTD vs. Prior YTD Based on a Selected Date

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In this tip, I show you how to calculate year over year change based on a date selected with a parameter. The use case is to calculate YTD sales depending on the date selected and compare it to the prior YTD sales for the same period.

April 12, 2022

How to Show Data for Only Completed Periods

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In this tip, I show you how to filter your data to only completed weeks. This method can be extended to completed months, quarters or years. All you have to do is swap out the word 'week' in the filter calculation.

April 5, 2022

How to Always Sort a Dimension to the Bottom of a Chart

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In this tip, I show you how to use a calculated field to always sort a specific dimension to the bottom of a chart. You can also always have a dimension at the top, you simply change the sort from Descending to Ascending. This also works with column charts that sort left-to-right.


March 31, 2022

#B2VB 2022/W3 - Olympic Medal Tracker

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If you're not participating in the Back to Viz Basics community project yet, you should be. It's all about creating effective, simple charts, which is exactly what you need to be able to do before you build anything fancy. This will definitely help you master data visualization best practices.

For week 3, the objective was to build a text table of Olympic medal counts. Sounds simple, but unfortunately, building good looking tables isn't one of Tableau's strengths. 

I also started the #WatchMeViz livestreams again. You can find all of them on this playlist. Thank you to those that attended live and asked good questions and left thoughtful comments. Below is the livestream as well as my final viz.

Until next week...


March 29, 2022

How to Create an Alternative to a Merimekko Chart

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A Merimekko Chart, also known as a variable width bar chart, is useful for comparing both high-level data and low-level data in the same chart. However, since we have size variables for both the width of the bars AND the height of the bars, comparing different segments of the chart is more challenging than it needs to be. Read more about the problems with Merimekko charts here.

In this example, I show you how to make an alternative to a traditional Merimekko Chart. I show you how to compare both category sales and regions sales within each category at the same time.

This can be easily replicated by simply swapping out the dimensions and updating one LOD calculation. Alternatively, you could use two parameters to allow the user to pick both the high-level and low-level dimensions and a third parameter for the measure to compare.


March 22, 2022

Two Methods for Labeling the Top N Values in a Chart

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In this tip I show you how to use the INDEX and RANK functions to label the top N values in a chart. I also explain the difference in how they work, and how the RANK function is simpler to configure.


March 11, 2022

How to Include Multiple Dimensions on the Color Shelf

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In this tip, I show you how to include more than one field on the color shelp. 

A few notes:
  1. You can have more than two dimensions on the color shelf. Simply repeat the steps for adding a second color.
  2. This only works for dimensions, as I show in the video.
  3. You do not have to use Sets for this to work; I used that in my example for simplicity.

March 1, 2022

How to Create a Dual Axis Chart with One Dimension and One Measure

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This tip comes from a comment from a viewer. They wanted to know how they could build a dual-axis chart based on a single dimension (e.g., Order Date) and a single measure (e.g., Sales). In this tip I show you how to compare the monthly sales for the current year (as a line) to the prior year (as a bar).


February 22, 2022

How to Displays Multiple Measures on Multiple Rows

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In this tip, I show you how to create a chart with multiple rows, one row has three measures and the other row has two measures. AND all of the measures are on a single worksheet.

February 15, 2022

How to Create a Barbell Chart

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In this tip, I show you two methods for creating a barbell chart. The first method uses two measures and the second method uses one measure split up by a dimension.

February 8, 2022

How to Create an Enclosed Dot Plot

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In this tip, I show you how to create an enclosed dot plot, which is essentially a dot plot that is enclosed by a line. It's very similar to a barbell chart except the line connecting the dots surrounds to dots.

February 2, 2022

How to Create a Jitter Plot

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In this tip I show you how to create a jitter plot. A jitter plot is very similar to a dot strip plot other than it reduces the overlapping of the data points. The data is plotted like a dot plot and then we use either the RANDOM() or INDEX() functions to spread out the dots.

Data - https://data.world/vizwiz/mock-car-sales

January 25, 2022

How to Calculate the Distance Between Two Points

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In this tip, I show you how to use the Distance function in Tableau to calculate the distance between two points. I also show you how to use the Makepoint and Makeline functions to draw the map. 

Download the data set to follow along here - https://data.world/vizwiz/airport-to-airport-routes

January 19, 2022

Social Connectedness in the United States

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NOTE: The insights you see in this post are based on an article by The Upshot from September 2018. Some of the insights and use cases demonstrated are the same and are shown in Tableau for demonstration purposes.

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When I first saw the map that The Upshot created in How Connected Is Your Community to Everywhere Else in America? I was blow away by the simplicity of the map and how easy it is to understand the relationships of people in the United States via their Facebook friendships. The first thing you need to understand is the metric "Social Connectedness Index". You can access the data I used via the same link. 

Here's the formula Facebook uses to calculate the index:

From Facebook:

Social Connectednessi,j measures the relative probability of a Facebook friendship link between a given Facebook user in location i and a user in location j. Put differently, if this measure is twice as large, a Facebook user in i is about twice as likely to be connected with a given Facebook user in j.

In each dataset, we scale the measure to have a fixed maximum value (by dividing the original measure by the maximum and multiplying by 1,000,000,000) and the lowest possible value of 1. We also round the measure to the nearest integer.

I was not able to match the color scale in The Upshot exactly, so instead I used a table calculation that ranks each County in the U.S. compared to the County selected by the user.


Close enough for me! 

The data has columns for the State/County of the user and for State/County of the friend. To ensure that I was only looking at friends for the County selected, I used Parameter Actions to filter the user to the County and State selected. The rank calculation then only uses the SCI for the friends.

Now let's look through some of the use cases as described in The Upshot.


DISTANCE IS MOST IMPORTANT

People are more likely to be friends with people that live nearby. That makes sense. Consider these four counties that I lived in while I lived in the U.S. Clearly relationships on Facebook are more likely with people that lived near me.






STATE LINES ARE BOUNDARIES

In some counties (like the four below, friendships drop significantly outside State borders.




MIGRATION PATTERNS

People from certain areas of the country have migrated to other areas in the country over the course of many decades. We can see these patterns by looking at Chicago and Milwaukee. The southern counties were typically related to the slave trade, and the people in the south gradually migrated north after they were freed.



Migration patterns aren't limited to history. Consider counties in the Northeast. Nearly all of them have a strong relationship with coastal areas in South Carolina and Georgia and all of Florida. These are called snowbirds, people that migrate south for the winter.




PHYSICAL BOUNDARIES

Friendships in some counties are limited by geographical boundaries. For example, friendships for people living in Belmont County, Ohio don't cross the Appalachian Mountains in West Virginia.


While people in Scott County, Arkansas don't have friends on the other side of the Mississippi River.



Have some fun with the interactive version below.

January 18, 2022

How to Add a +/- Indicator to a Drill Down Action

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In this tip, I show you how to add an indicator before the selected dimension for a set action. This can also be done with a parameter action.

For example, if you click on the East region to drill down to the State level, you will see + East as an indicator. I also show you how to use • as an indicator.

January 11, 2022

How to Create a Calendar Widget for Filtering

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In this tip, I show you how to create a calendar to use as a filter on a dashboard. This calendar is a heatmap, thus providing additional information in your dashboard that a regular date filter cannot.

Download the mock data set used in this tip on data.world.

January 5, 2022

How to Create a User-Defined Moving Average

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In this tip, I show you how to use two parameters to create a view that allows the user to pick (1) the duration and (2) the date part to create a moving average calculation.

For example, this technique will allow the user to choose a 13-week moving average, a 6-month moving average, a 90-day moving average, etc.