# VizWiz

Data Viz Done Right

## Tableau Tip Tuesday: Table Calculations Overview

Table calcs - the beast of all things Tableau...for me at least! It's been nearly 10 years that I've been using Tableau and it wasn't until I was teaching last week that the light bulb went off.

I promised a blog post and here it is. I've recorded about a 30 minute video where I have two primary objectives:

1. Provide recommendations for how to approach table calcs
2. Teach you how to read table calcs in a plain English sentence

Here are two slides I created to help you read table calcs:

The trick is to read from the bottom up.

1. Start with the unchecked boxes and add the words "For each" before those.
2. Move to the Calculation type and add the words "compute the" before the item in the Calculation Type box.
3. Add the word "by" before each box that is checked at the bottom, again reading it from bottom to top.

I hope you find this helpful and that it makes table calculations easier for you to understand. Please let me know if this helps.

## Makeover Monday: How do I use my AMEX?

Well, this sure was an interesting experiment. Giving up your personal expense data sure opens pandora's box. But, you know, it's all in the spirit of learning.

I received my annual AMEX statement back in January. It included these two charts:

Normally I review what works and what doesn't. However, this week I'm incredibly short on time, so read Eva's review and you'd basically be reading what I would write.

For my viz, I knew I wanted something that I could view on my phone. Nearly every viz I created now, I start with the mobile version. It tends to lead to a better user experience. I drew inspiration from this dashboard by Money Dashboard:

In addition, I wanted to use the blue that in the AMEX Delta gold card. Ok, so without any more bantering, here's my makeover for this week. Clean, simple, tells me what I need to know. Good enough for me!

## What is the Merimekko calculation actually doing?

Table calcs have ALWAYS been my biggest weakness in Tableau. I find them incredibly difficult to understand and finding a plain language way of explaining them has always been tough. If I heard the words addressing and partitioning, they might as well have been speaking Swahili.

I'll have a more detailed blog coming soon on how I've learned to explain table calculations; I had a serious lightbulb go off Friday morning while I was teaching. In the meantime, I thought I'd look back at this week's Workout Wednesday and explain what the calculation for the columns is actually doing.

First up, here's the final viz:

The trickiest piece for me was understanding how the width of the bars worked. Here's the calculation we'll review:

Notice that I named the calculation "Columns". I do this when I know I need a calculation to be placed on a specific shelf. It's a way that I remind myself. When I choose "Edit Table Calculation" I'm presented with this view.

This setup tells me that for each Sub-type (i.e., gender in this case), calculate the Columns formula by Job Type. Ok, got it? Got it! Let's look back at the formula and explain what is actually being calculated.

Note: The Job Types are sorted by the % of female in descending order.

Hopefully this helps explain a bit about how the calcs work to get the bar widths correct in a Merimekko. I know it helped me think through it.

How might you explain it differently?

## Workout Wednesday: Merimekko Makeover

Wow! That this was a tough workout! I've never created a Merimekko chart before and I'm very, very thankful that Emma provided a link to a great how-to blog by Jonathan Drummey.

I read through Jonathan's post once and tried to create the Merimekko on my own, but that didn't work out so well. Apparently I was 99% of the way there, however, the order that I placed my dimensions in the view is what tripped me up. I decided to follow Jonathan's blog step-by-step and built a table first like he did to verify the calculations before changing the table into a chart.

This is a good practice in general when working with calculations, especially table calcs and LOD calcs. Build a table to verify the calculations are working as expected, then create your chart. It's much easier to identify problems.

Emma's rules were pretty straight forward:

• Show the 50% point of equality as a grid line
• Set the dashboard size to 660 * 650 pixels
• Add on the direction of less equality
• Label the columns of the Marimekko to match hers
• Label the first two segments Male and Female
• Make sure the tooltips match hers

In the end, I created the entire view in a single sheet. After looking at Emma's I see that she floated some text boxes in the dashboard. I used mark annotations for all of the annotations and used reference lines for the Male/Female labels and for the 50% line (Emma used reference lines too).

Overall, a really good challenge. Took me about 90 min to finish start-to-end.

## Tableau Tip Tuesday: How to Add Min/Max Indicators to Sparklines

The backlog of videos that I want to create is quite extensive and including min/max indicators on sparklines has been sitting there for a while. This week's #MakeoverMonday by Ann Jackson kicked me into gear.  Thanks Ann!

First, let's have a quick look at Ann's viz:

Notice the sparklines with the indicators on the right. In this week's tip, I'll walk you through how I would go about creating those. The video is under five minutes and easily replicable. Enjoy!

## Makeover Monday: Who's Winning Europe's Battle for Potato Supremacy?

Well, this certainly was a data set I never thought I'd see. Leave it to Eva to surprise us again. I'm really enjoying how she's mixing things up and allowing me to participate like everyone else. I also need to thank her for sending me her great color palettes again.

This week, we looked at the EU potato sector. Seriously! We're creating vizzes about potato production. The original website has things kind of all over the place. First there's this table:

Then there are a few donuts chart, most of them look more or less like this one:

They also included a few bars charts and a line chart. All in all, it's quite colorful.

What works well?
• Donut charts are sorted
• Tables are good for looking up specific values
• Line chart provides context by comparing to an index of 100 to make yearly change easier to understand

What doesn't work?
• Inconsistent colors
• Hard to identify the "story" in the data; The story is buried in the article.
• Pretty busy overall; too much going on
• Tables are terrible for finding insight in the data

For my version, I first read through the entire article to get a feel for their conclusions. I then focused in on the information about harvesting and decided to basically take their paragraph and turn it into a visual story. I used Eva's color palette to help highlight the important data points and I used Matt Chambers' shade slope charts blog post to create the second chart.

I wanted to create a beginning, middle and end to the story, and I feel like I did that. I used a question in the infographic title to help the reader understand what the viz is about. I used dividers to the viz into "parts" of the story and I used the chart titles as legends. Lastly, I used Roboto Condensed font to match the font used in the article.

## Workout Wednesday: Dynamic Trellis Chart

For Workout Wednesday week 7, I challenge you to build this dynamic trellis chart. Special thanks to Chris Love for explaining to me why the trellis works the way it does. He has a great blog post series over on The Information Lab blog.

In this example, I'm using the same Superstore Sales I've used in previous Workout Wednesdays. Here are the guidelines:

1. Match my colors.
2. The user should be able to choose the level of detail they want for the date.
3. The date axis format should not change, irrespective of the date level chosen.
4. The user should be able to pick from the list of dimensions shown.
5. The dimensions should be sorted from upper left to lower right based on the sales in the most recent time period.
6. Match my tooltips. Note that they change based on the options the user selects. Pay attention to the date formats in particular.
7. The title should update dynamically based on the date level and dimension selected.
8. The end of each line should be labeled to the right of the last point.
9. There should be a little circle on the end of each line.
10. Each section of the trellis should include a label for the value of the dimension for that section. E.g., California should be on the upper left when you selected quarters by state.
11. The dimension labels should be centered in each section.
12. There should be no gridlines, but the zero line should be included.
13. Each row should should have a light divider between them.
14. My final view is 900x700.

## Tableau Tip Tuesday: How to Add Space for Labels on the End of Lines and How to Create a Year/Quarter/Month Selector

This week is a double tip. First, I take you through adding a buffer to the end of sparklines so that you can have your labels next to the end of the lines. Second, I show you how to create a dynamic date selector, which I then use to create a dynamic buffer for the labels.

## Makeover Monday: How Much Do Americans Spend on Valentine’s Day?

Because I'm tend to forget Valentine's Day (I consider it a Hallmark Holiday), it almost passed me that this week included Valentine's Day and I had intended to use another data set for Makeover Monday. Fortunately we have people on Twitter to keep us straight and this tweet changed the theme for this week.

This meant spending my Sunday morning find a new viz and data set. A quick google search turned up this infographic from KarBel Multimedia:

What I like:
• Color choices that match the theme
• Simple title that tells me what I'm about to see
• Proper sourcing
• Nice description that include a question that explains what the viz is about
• Donut chart works well here as it's only 2 slices
• Clear labeling

What could be improved:
• Why use bubbles to compare the sizes of the spending? A bar chart would be way easier to read.
• There's very little context. Is this spending increasing or decreasing?
• While the color choices work for the theme, this sure is A LOT of pink.

For my viz, I wanted to create a mobile version that looks at the historical spending trends in two groups: significant others and everyone else. I don't lover my effort this week (pardon the pun), but there's only so much time in a day. Lastly, special thanks to Eva for the color palette.

## Workout Wednesday: How Will the UK population Change By 2039?

Another fun Workout Wednesday challenge from Emma this week. She asked us to build a butterfly chart, something I'd never done before.

Conceptually, I knew exactly what I needed to do:

1. Create separate measures for males and females
2. Create additional measures to make them percentages by dividing by the population estimate
3. Create an LOD for the national average for males and females
4. Create additional measure to make them percentages
5. Make the male axis reversed
6. Set all of the axes scales to be equivalent (Emma didn't do this, but I think the axes should be the same on each side of the center)
7. Create the calcs for the rows and columns of the trellis
8. Add a filter for local authority and add that to the title
9. Throw is all together in a dashboard size 1000x800
I tend to jot these things down in the Notes app as I think of them. It helps me remember what I need to do for weeks like this when I don't get it done on Wednesday.

I'm posting an image here, but if you want to see the interactive version, tap on the image.

## Tableau Tip Tuesday: How to Create an Aggregated Extract

### Aggregated extracts are an undervalued and underused feature in Tableau.

Week 6 of #MakeoverMonday allowed us to work with 105M Chicago taxi trips from an Exasol data source. This is fantastic, until you need to publish the viz to Tableau Public which has a 15M record limit. The way to work around the 15M limit is by creating an aggregated extract, for which Tableau has created a great quick start guide here. In the video below, I show you how I created an aggregated extract with the Chicago taxi data.

Keep in mind, though, that your data has to be aggregatable. For example, if you do cohort analysis, you likely won't be able to aggregate the data and maintain the cohorts.

### Why aggregated extracts?

1. Smaller extracts
2. Better performance
3. Only contains the necessary dimensions
4. Makes extract refreshes faster
5. Reduces resource burden on your Tableau Server

### Steps for Creating an Aggregated Extract

1. Hide unused dimensions
2. Add extract filters (optional)
3. Aggregate the data for the visible dimensions
4. Create the extract

## Makeover Monday: Are We Nearing the Death of Chicago Taxis?

Now THIS was a fun week! Eva and Exasol set us up with 105M records to play with. Often time, this can feel like an incredibly daunting exercise, but I took a very methodical approach. More about that in a minute. First, let's take a look at the viz we want to makeover:

What works well?

• Using a line chart to portray a time series rarely is a bad choice
• Nice simple labeling of the axes
• Title tells me what I'm looking at
• Red line on the grey background works well
• The y-axis is truncated, but the chart maintains a nice 3x2 ratio so that the line trends aren't too distorted.

What could be improved?
• Scale of the y-axis is a bit odd; I like nice rounded numbers that make the math easy to do in my head
• There is SO much more data to work with; why limit to only trips?
• Could use a more impactful title
• Could use more context

This week I decide to apply some of the training we received from Rhiannon Fox and do a bit of mood boarding, color choosing and seeking overall inspiration. I knew I wanted to create a summary dashboard of sorts that included lots of context, so after a bunch of Google image searching and pinning, I ended with this mood board.

I started by connecting to all 105M records live because I wasn't sure which dimensions I would end up using. When I finally finished (this took well over an hour), I created an aggregated extract. The trick to this is to hide the dimensions you aren't using before you create the extract and to rollup to the lowest date level in the view (month for my viz). This took the viz from 105M to 299K records. Incredibly, the extract was ready in less than 10 seconds. Exasol is crazy fast!

Overall, another fun week. Tonight I get to introduce this to a bunch of new Tableau users at the #MakeoverMonday Live session at Tableau HQ in London. Can't wait to see what they come up with!

## Workout Wednesday: The Distribution and Median of NFL Quarterbacks

Last week on my Data Viz Done Right site, I wrote about a distribution visualisation created by Harry Enten that shows the range of dates for snowfall at select U.S. cities. It's Super Bowl week, so I decided to recreated the style of Harry's viz in Tableau with the same NFL data that Emma used last week. Your challenge this week is to re-create my viz.

Below is the visualisation that I created. If you're reading this on a phone, tap on the image for the interactive version. Some requirements to keep in mind that are intentionally designed to make this tougher and to make you learn:

1. All of the elements must be floating on a dashboard sized 650x650.
2. You cannot use the Player dimension anywhere in the view.
3. Match my colors including the background
4. Create the legend (HINT: It's not an image)
5. Match the tooltip (Note the stats that are displayed in the tooltip. This will be a bit tricky. Essentially you need to count the number of players that are contained within each band.)
6. The viz should update based on the stat selected. The user should be able to choose between: Attempts, Completions, Interceptions, Touchdowns, and Yards
7. The title should update dynamically based on the stat the user selects.
8. Optional: Use Montserrat font (you can download it from Google fonts)

If you have any questions or get stuck, either leave a comment on this post or tweet me. Good luck!