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.

• Catchy title that quickly tells the story of the viz
• Bar charts are simple to understand and they show the pattern well
• Sorting the apps by the most recent value
• Including the axis, otherwise we wouldn't know what the labels mean on the top of the bars

• The fading colors across the time periods are unnecessary. Include a time axis instead.
• Change the numbers above each bar to percentages
• The color legend doesn't match any of the bar charts and should be removed.

I started by looking at slope graph comparing the starting and ending periods.

Data collection for week 27 was simpler than most, and I’m quite thankful for that since I forgot to track the data as I prepared for talks in Cincinnati and Vegas. I looked retroactively at my history on the apps & websites on which I consume stories and articles. I only counted articles that I actually read and not just skimmed over. I did not count the content on Facebook that you see, but don’t click on.

From there, I did some quick analysis in Tableau to understand the data before creating the postcard.

When I went into the project for this week, I fully expected Facebook to dominate other consumption methods. Yet to my great surprise, Feedly was far and away my preferred method for consuming stories. I’m not exactly sure why this is, surely it has something to do with the design of their product. In Feedly, I choose the content I want to follow, so it’s much more likely to be of interest to me. Whereas Facebook tends to be a lot of noise and little signal as far as stories and articles are concerned. I don’t look at my LinkedIn and Twitter timelines often, so it’s no surprise that those as so low.

The inspiration for the postcard came from Giorgia’s week 13 postcard about her desires. I wanted to use stars like she did, but I couldn’t find a stencil to use. I went with hexagons instead.

I didn’t have much time today for a makeover, so this will be a bit brief. I had tagged this article from the Facebook internet.org team as needing a makeover. It’s the cover of the report that caught my attention.


I guess what bothers me most is that this simple bar chart is completely unreadable. I can’t find any countries unless I use a microscope. I assume this was by design, but I don’t see the value in it.

Of course the data was not provided, so I did a quick Google search and found the data in Wikipedia. I then used the InterWorks Web Data Connector for import.io to extract the data. I blended that with another data set I had of country abbreviations.

I started by recreating the original in Tableau.


Ok, I still can’t read it, but I can hover over a bar at least. I wanted something better, something easier to understand, something people might want to quickly explore. I created this three chart layout which includes a map, bar chart (same as the original, but larger), and a slope graph.

I’m not totally sold that this is complete or great, but it’s definitely better than the original. And remember, I timebox myself on these makeovers, so once I reach my time limit I stop. Rules are rules.

Just realized this is my 500th post on this blog.  Feels like quite a milestone for some reason.  Anyway, for Dear Data Two | Week 15: Compliments, like Jeffrey, I looked at my content that people liked across social media platforms (assuming that I can take those like, favorites, reshares as compliments).

The irony of it all.

• I’ve been riding the Facebook shuttle to work for almost a year.
• I’ve been tracking my commute time via Swarm check-ins.
• I decided to drive to work yesterday for my last day.
• I got my first ever speed ticket on my way to work.

I started building the viz below a while ago and decided to finalize it since I had collected all of the data I could. I would like to thank Anya A’hearn for her feedback on my design (note to self: Anya hates lollipop charts). I also need to thank Jonathan Drummey for his help in getting my time calculation formatted correctly (he pointed me to this post).

Anyway, here is my analysis of my commute to work on the shuttle. There are filters for the time of the commute and the schedule. Use the drop down to select various displays: the default is a calendar view and the other views are various dot plots.

Click on the image for the interactive version.

Yes, I know it's Wednesday and I'm a day late with getting this post out, but I kind of having a lot going on right now, so please forgive me. Since Thursday is my last day in the Facebook office, I thought it would be fun to show how to answer a critical question:

How often do I go to the Sweet Stop and when?  HINT: It's not as often as you might think.

To collect the data, I used Swarm to check-in to the Sweet Stop. Each check-in is logged into Google Sheets via this IFTTT recipe:


I then exported the data to Excel and connected it to Tableau.  Download the data here and the workbook used to create this video here (requires Tableau 9).

Back in January, AdWeek published this infographic with their predictions for growth of various social media platforms in 2015.


Wow! That's quite a bit to digest. So much going on, from the donut/pie charts, to all of the annotations, to the sizing of the pies by overall growth. This infographic has it all.

I recreated the data in Excel, which you can download here, and decided to build a couple of different alternatives because the design choices depend on the question that's trying to be answered. This first version aims to show which social networks are predicted to grow the most in each demographic.


Looking at the data this way, it's clear that Facebook will continue to see the largest growth across all ages. This view also makes the following obvious:

1. Younger people are using Instagram
2. Older age groups are using Pinterest
3. Twitter is a middle of the road platform everywhere, which you could spin as a more diverse audience

Looking at the data this way allows you to compare within a single app, compare to the total and compare across apps.  Which version do you prefer? What would you do differently?

Download the Tableau workbook here (requires Tableau 9).

Last week on Facebook, I saw a link to this report by Deloitte. From what I can gather, Facebook commissioned Deloitte to estimate Facebook's impact on the economy.  Read the report for more details.

In the report, the first graphic that Deloitte shows was this donut chart that explodes into three slanted pie charts:


Some of the most striking problems:

1. Using a donut chart to represent parts-to-whole is a bad idea. Read this post by Steve Wexler for a good explanation on why you shouldn't use donut charts.
2. There's an implicit hierarchy from the ecosystem to the breakdown of each ecosystem, yet this view goes in the opposite direction by having the regional breakdown above the totals.
3. The pie charts, while not 3D are slanted, which distorts their proportions.
4. Pie charts are not the best way to compare proportions.
5. The title is super small. Looking at this as a stand-alone graphic, it's difficult to understand what the purpose of the graphic is in the first place.

In this version, I attempted to address the concerns I outlined above by basically changing everything to bar charts and rearranging the view.  Does this work better? Note that I added some interactivity as well.

I had a choice to make this evening, catch up on my backlog of Makeover Monday posts or work on my performance review.  Given that I enjoy writing about Tableau more than writing my performance review, allow me to present a second Makeover Monday of the day today.  In this post, I take a look at this chart from Cork Gaines of Business Insider:


I've reviewed a lot of Cork's charts and this one makes many of the same mistakes as his past charts:

• It's incredibly annoying to have to turn my head sideways to read the chart.
• The chart is in ascending order, yet the story emphasizes the descending order.
• The colors aren't distinct enough from each other for me. For example, the colors for Track & Field and Cricket and too similar.
• The chart, on it's own, doesn't capture the entire story that's in the article.

You can download the data here and the workbook here.

From Buzzfeed: "The social network (Facebook) may end TV’s long dominance of American politics — and open the door to a new kind of populism." The purpose of this article was to demonstrate that conversations on Facebook are becoming a dominant force in the U.S. political landscape. The article included this infographic about Facebook users' sentiment towards 2016 Presidential candidates.


There are several things about this infographic that I don't like:

1. The title doesn't tell us what the graphic is about.
2. The pie charts; simply too many of them making comparisons difficult.
3. There's no apparent order to the candidates.
4. Candidate names are in ALL CAPS...why?
5. The label for the Democrats section is off to the right, why?
6. Some of the pies add up to less than 100% and some to more than 100%.

Alex, I'll take No Assembly Required for $300 please. The answer is:

I wish I could expand a section in-line 

We all get frustrated with the inability to scroll synchronously across multiple worksheets on a dashboard in Tableau. At #DATA14, I showed how I address this problem by creating accordion views.

Download the Tableau workbook here.

Alex, I'll take Tipping The Scale for $200 please. The answer is:

My extract has been failing for the last 3 days and I just noticed. 

Anyone that uses Tableau Server can benefit from this hack. Our admin team created this awesome tool that monitors the extract refreshes on Server and sends us notifications once extracts complete or fail. Imagine a life where you have trigger kicking off your extracts and you have a service that monitors your extracts; that's what we have and it freaking awesome!

While we cannot share the code for this (it's built on our internal code stack anyway), the idea and the implementation is quite simple. A couple weeks ago, I was at Tableau HQ and did a deeper dive into this for them; here's to hoping they add this into Server as a standard feature.

Alex, I'll take No Assembly Required for $200 please. The answer is:

I want to build a waterfall chart, but it's taking 9 minutes for my 37 sheet dashboard to render. 

Waterfall charts are a great way to show system or process flow, but the typical method to do this in Tableau requires you to create lots of sheets and then strategically place them on a dashboard. Jonathan Wehrer on our team created a way to shape his data using the scaffolding technique that Joe Mako talks about often to come up with a method for viewing an entire waterfall chart in a single worksheet.

Here's the demo of the hack from #DATA14 (the hack starts at 17:14 if it doesn't start there automatically):
Download the Tableau workbook here.

Alex, I'll take No Assembly Required for $100 please. The answer is:

I like drilling down, but I hate losing context by switching tabs. 

One of our internal dashboarding tools is really good at pop-up charts, so Rob Koste on our team came up with this nifty trick for creating pop-up charts in Tableau. In the end, it's pretty simple:

1. Create a dashboard
2. Create a worksheet that you want to "pop-up"
3. Add this worksheet to the dashboard as a floating object
4. Create an action to trigger this worksheet to display. The action should exclude all value in order to hide the sheet when the action is deselected.

Download the Tableau workbook here.

At #DATA14, Bryan Brandow demoed the "Trigger" service that he built for our needs at Facebook. Basically, it's a snippet of python code that listens for data to lands in a database. Once Trigger "hears" that data has landed, it kicks off a Tableau extract refresh.  Get all of the details on his blog by clicking this image.

Here's the video of Bryan presenting the Trigger Service at #DATA14 (start the video at 7:05 if it doesn't start there automatically):

The Facebook Jeopardy presentation that Bryan Brandow and I gave at #DATA14 was by far the most fun presentation I have ever been a part of.

We squared off in a bout of Jeopardy. Topics included a wide range of pain points in Tableau that everyone faces today and we showed how to solve them by answering in the form of a Hack. The categories were: Sanity Savers, Life Changers, Tipping the Scale and No Assembly Required. If you want to be inspired to find new ways to think about solving problems, then watch this session!

Enjoy and let us know if you have any questions.