July 2, 2015
Tableau On Tour comes to London next week. This will be my first conference as a partner, so I’m interested to see how the experience is different. You’ll likely be able to find me hanging around The Information Lab booth, which you won’t be able to miss in all of its orangeness.
First, I know I won’t be able to compete in the Information Lab Speed Challenge.
But you can, and you can win a drone. This year’s challenge include a bit of Alteryx as well. If you’re not an Alteryx user yet, ask us for help. Don’t worry, the Alteryx piece will be simple enough for anyone to follow along, even a brand new user.
I’m very excited to be hosting a breakfast Wednesday morning with founder, boss and friend Tom Brown. We’re going to be talking about the Data School and how the experts we’re building will be instrumental in the world of data analytics. If you think you’re company would like to take on one of these consultants for a 6-month engagement after their training is complete or if you’d like to have the School do a 1-week project for free with your company’s data, email me and I’ll get you into the breakfast. Trust me, this isn’t a group of talent you want to miss out on.
The consultants at the School will be very visible throughout the conference. If you see one of them, stop them and ask how things are going. Ask them to show you what they’re working on. Find out how much they’ve learned in only two weeks of training.
Lastly, I thought I’d share my agenda. I’m hoping I get to go to this many sessions, but I’m fairly sure things will change. I’d always rather talk shop in the hallways than go to sessions. These types of conversations are what the Tableau community and Tableau conferences are all about; these are how you get value from them.
See you at The Brewery Monday!
June 30, 2015
The basic reasons behind this project were twofold:
- I was learning Alteryx and wanted a use case to apply what I was learning.
- I'm training for my first marathon and wanted a better way to see all of my runs in one place.
June 29, 2015
I found an infographic on Twitter this week from the Wall Street Journal that described the average American's day.
What Americans did with their time last year: more work, more sleep, more TV, less school http://t.co/MhLZkOBBdU pic.twitter.com/TIJB6FR4y1— Nick Timiraos (@NickTimiraos) June 25, 2015
There are several problems with this infographic:
- It's really hard to see easily what American's spend most and least of their time doing
- It's difficult to compare the years - the colour encoding helps, but you have to work out the actual change in your head
- Your eyes have to look around a lot to get the whole picture
- There's a lot of reading involved
- The squares equal one minute - but it's hard to compare values using area I had a go at making an alternative.
I have to admit I couldn't find the same data set that the Wall Street Journal used. I downloaded this one from the Bureau of Labor Stats (hence my numbers don't exactly match).
In this version I have:
- Changed the infographic to a bullet chart - the bars show the 2014 values and the reference line 2004 values.
- Sorted the bars by the 2014 value
- Coloured the bars by the change, to let you easily spot increases / decreases
- Added tool-tips to show the actual change (rather than doing the Math in your head)
You can download my viz from Tableau Public here.
Would you do anything differently?
June 25, 2015
As part of this exercise, we were building a dot plot and Laszlo Zsom asked how to connect two dots on the same row. I hadn't ever done it before, so I used a Gantt chart to connect them. Then Chris Love suggested using lines.
In this week's tip (two days late as it is), I demonstrate both of these methods. Click on the image below to enjoy the video.
June 22, 2015
A few weeks ago, the Guardian Datablog published this series of circular heatmaps to represent monthly rainfall across a 20 year period in three Australian cities.
There are several problems with using radial heatmaps:
- They are not too dissimilar from geographical heatmaps in that they tend to skew towards the segments that cover the most surface area, in this case 2015.
- It’s difficult to compare across years, across months, and across months and years.
- Your eyes are drawn all over the place.
- There’s very little sense for trends.
- Like a pie chart, you’re trying to compare the angles of the slices, which is nearly impossible.
There are some other issues with this particular implementation:
- The hover does not work once you get to the smaller segments.
- The labels are quite hard to read.
- When I hover, all I get is the value. This means that I have to look back to the labels to see which month and year it refers to.
Given these problems, I created this alternative version.
In this version, I have:
- Taken the radial heatmap and flattened it out. I liked their idea of using a heatmap, but needed it to be easier to read.
- Added two trend charts: (1) cumulative rainfall, (2) monthly rainfall
- Added a selector for the city
- Added a highlight action on the year
- Included informative tooltips
- Improved the title
You can download the viz from Tableau Public here. What would you do differently? What could be improved in my version?
June 17, 2015
A very common question and feature request I see on the Tableau Forums is to show the axis above a chart rather than below, as Tableau does by default.
When I was working on solving this, I started by looking at the XML for a workbook and there is a bit of code that controls whether an axis displays. I built a dual-axis chart to see if I could change the display in the code.
In the <style> section, note the value=‘false’ setting. This is what is hiding the axis. I changed only the top axis by setting it to ‘true', but when I opened the workbook again, both axes were displayed. Back to the drawing board I went.
Here are the steps to follow to display the axis above the bar chart and not below (sort of):
Step 1: Create a bar chart.
Step 2: Duplicate the measure that is shown in the chart by right-clicking on the measure in the Measures list and choosing Duplicate.
Step 3: Drag the duplicated measure onto the secondary axis.
You should now have a chart that looks like this:
Step 4: Change the mark type for both measures to Bar.
Step 5: Remove the Measure Names field from the Color shelf. This isn’t required, but you really don’t need to have two colors for the same measure.
Step 6: Right-click on the bottom axis and choose Format. Change the Font to white and set the Ticks to ‘None’. This doesn’t hide the axis (hence the reason I said ’sort of’ above), but it will give the appearance that the axis isn’t there.
Note: The reason that you have to duplicate the measure has to do with how formatting works. If I used the same measure again for the secondary axis (i.e., Sales in this case), then when I format Sales, it applies to both axes. However, when you duplicate the field, Tableau treats it like a totally separate measure with its own formatting.
Step 7: Double click on each of the axes and remove the titles. This isn’t required, but it makes the axis narrower.
That’s it! While this isn’t a perfect solution, it works. Also, be sure to clean up the tooltips since they will show the measure twice.
You can download the workbook used to create this viz here.
June 4, 2015
For week 3 Jeffrey sent me a donut chart by accident (so he says…) and while I was thinking about my ideas for this week, I started connecting some data points with lines and ended up with a radar chart. (See the draft version in the story points.) It’s funny how he and I both have gone against what we would consider best practices. What does that mean??
I decided to go with a clock them this week and split the data up between morning and afternoon. From there, I plotted each day going outward from the centre for that hour. For example, at 12am, Monday is closest to the middle and Sunday is farthest from the middle. This helped me see which hours were cumulatively the most frustrating for me for the week. Each dot is separated by the frustration level. If the frustration level was three, then the dot would be 6mm from the previous dot. I then sized the dots by the frustration level so as to double encode the values.
It’s no surprise that my sleeping hours were generally the least frustrating, except for 5am when jetlag kicked in. Overall, 9am was my worst hour in the morning and 1pm was the worst in the afternoon. The story points viz below goes into more of the explanations. Click on the image to read through the story.
June 2, 2015
Click on the image to launch the dashboard that contains the video.
June 1, 2015
The article is trying to emphasize the change in the share of Apple revenue in China compared to the Americas. Here are some problems I have with this chart:
- It’s very hard to compare stacks in a bar chart over time because each stack is influenced by those stacks below it.
- The title of the article and the chart don’t match. The article says China vs. the US, but the chart is China vs. the Americas.
- There is not enough emphasis on the comparison. The rest of the regions should fall to the background.
- I don’t like the legend above the chart in this case because I’m constantly having to go back and forth.
Here’s what I’ve done differently:
- I changed the title to reflect the purpose of the story.
- I changed the chart to a line chart to make it easier to see the trends for each region.
- I’m only emphasising the Americas and China. The rest of the regions are in a light grey.
- I’ve added annotations to make it easier for the reader to see the values.
- I removed the color legend as it’s not necessary since I’ve labeled the end of each line.
Which version do you prefer? What would you do differently? There are so many ways to redesign charts and no single way is 100% correct.
May 29, 2015
I'm a huge quantified self data collector, which you'll likely see throughout my Dear Data Two work. I wanted to see how I could use Alteryx to help me get the data into Tableau for analysis before creating my analogue version because I feel like the best way to learn a new tool is to find a practical application. This is the first workflow I built on my own in Alteryx. It might not be the most elegant or most efficient, but I sure did learn a lot along the way. You can download this workflow here.
One of the things I have started to like the most about Alteryx is that I can push all of the complicated row level calculations that I used to do in Tableau to Alteryx, which in the end makes Tableau much faster. For example, I used to multi-row tool to calculate the distance between two geographic points recorded by my watch.
From there, I created the dashboard below to explore the data. In particular I wanted to view the maps and see the summary stats. One thing I learned is that I need to figure out a way to account for times that I paused my watch; that data doesn't appear in the GPX files.
Click on the image to interact with the dashboard.
Exploring the data Tableau helped me quantify my runs for the week, but that didn't account for all of my physical activity for the week. To capture ALL of my activity:
- I noted my total daily steps from Fitbit.
- I calculated the number of steps for my runs by taking my stride rate of 184 strides per minute from TomTom and multiplying by the minutes I ran in Strava.
- I subtracted my running steps from the total steps to get my walking steps.
- I used the time of day that I ran and roughly calculated the proportion of walking steps before and after each run each day.
May 25, 2015
I first got a demo of Alteryx from George Mathew back in San Diego at TCC12. I was working for Facebook at the time, Mike Roberts from InterWorks set up the meeting, but I didn’t see a particularly good use case immediately for Facebook. Why? Facebook Data Engineers have always (and probably always will) code their own pipelines.
The day before heading to Inspire, I was sitting with Robin Kennedy and told him that I wanted to get a headstart on my training. Low and behold, he showed me all of the fabulous training modules that are built right into Alteryx. I had no idea! I completed about 15 of these on the flight to Boston.
After watching Arsenal draw 1-1 in a drab affair Sunday morning, I headed to the first of three training sessions: Predictive Analytics for Beginners. In this class I learned how to apply different data investigation techniques to help me understand how predictive a data source may be. The instructor showed us how to use the Association Analysis, Violin Plot, and Field Summary tools.
The workflow that we created...
…resulted in this series of violin plots (apologies for the blurry image).
The regression analysis workflow we created...
…resulted in a series of tables and this chart (which shows that charting is not in the Alteryx sweet spot).
The third and final class I attended on Sunday was Intermediate Macro Development. This was a pretty simple class in which we built a workflow + macro to strip heading from a messy Excel spreadsheet.
|The Information Lab team ALWAYS has fun!|
|Nice photobomb by the TIL team!|
|The quantified self work of Tim Ngwena of TIL was a keynote highlight!|
In the end, Inspire15 was a fantastic experience for me, a new Alteryx user. I’ve already started applying what I’ve learned and am working on two blog posts. My only regret is that I didn’t start using Alteryx sooner.
May 23, 2015
- Track every purchase that I make
- Categorize each purchase by the type of goods
- Locations each place where I made a purchase
Precise times were taken from purchase receipts, along with the categorisations. I then recorded the locations of each place by Swarm check-in, which were uploaded to a Google Sheet via IFTTT. I downloaded both sets of data into excel and manually joined them (there were only 19 records so it wasn’t much effort to do manually).
I then explored the data in Tableau, to see what stories I could find, if any. This week took me longer than I was expecting, mostly because I was having trouble finding anything interesting in the data. The one point that stuck out the most is that I spent more on ice cream than Mother’s Day. Oops! Please don’t tell my mom.
Click on the image below to explore the story.
May 19, 2015
May 18, 2015
Quick makeover this week (we have a Segway tour of Boston at #Inspire15 in 30 minutes). I saw this graphic on the LA Times about the amount of water it takes to produce a single ounce of food.
It’s cute and it’s interactive, but it’s not very good for making comparisons or ranking. Bubble plots are notoriously difficult this way. For example, tell me quickly which food uses the 3rd most water? Tough to tell, right? I also don’t understand why they grouped fruits and vegetables together.
I manually recreated the data in Excel, which you can download here. Hopefully I recorded everything correctly; if not, please let me know. I then quickly built a chart in Tableau. I’ve addressed the issues that bubbles present, ranking and comparison, by using a bar chart instead.
Going back to the previous question, using my viz, which food uses the 3rd most water? Simple right? How about the 10th most vegetable? That’s simple too; all you need to do is click the color on the right.
May 13, 2015
The first example is very basic; I did this intentionally so that the steps would be super easy to follow. The second example is only moderately more complex; it looks at Tableau's SEC financial filings from 2011-2014.