August 30, 2016
In this week’s tip, I walk you through how to create a two-color Pareto chart, a dynamic title, and customised tooltips based on my recent Makeover Monday about U.S. companies hoarding money offshore.
August 29, 2016
First, I must give credit for finding this week’s Makeover Monday to Jade Le Van. She’s a massive supporter of the work everyone has been doing in this project. Thank you Jade for finding this beauty from CNBC.
There are so many problems with this visualisation. However, the most egregious mistake is that the data is completely wrong. The author took the original data set, which has the total money held offshore by a company and allocated that same amount to every country that each country has an affiliate in. For example, General Electric has $199B stored in 18 offshore subsidiaries spread across seven countries. The author allocated $199B to EACH of those seven countries. That’s simply wrong!
What doesn’t work?
- The data is inaccurate.
- The pie charts are a ridiculous choice, especially when you show every single company in each country.
- The companies aren’t sorted.
- The size of the pies isn’t proportionate across the difference columns. In other words, they aren’t all based on the same range.
- It’s impossible to find a company you might be interested in investigating.
What works well?
- Nothing; the whole visualisation is a complete disaster.
I had a few minutes to explore this data set while on the bus home, so I opened up the data in Vizable. Within about 20 seconds I had two views that were far superior to the original.
Quickly and easily, I have a view of just how much money companies are keeping offshore and who they are in a nice, simple ranked list. Great! I already have something much better than the original.
Two more swipes in Vizable and I’m now looking at the companies with the most offshore subsidiaries.
At this point, I felt I basically had all I needed to tell the story in Tableau. I quickly reproduced the two charts from Vizable, then added a Pareto chart for more context. Lastly, I added some headers above each chart to tell a synchronous story.
August 24, 2016
After publishing my viz earlier today that used Google Sheets, Zen Master Chris Love posted this question on our collaboration tool:
So I told him I’d create a video that shows three things:
- How to scrape data from a web page into Google Sheets
- How to import that data into Tableau with the Google Sheets connector
- Ensuring it refreshes when you publish the viz to Tableau Public
UPDATE: Zen Master Jeffrey Shaffer sent me a link to this post on his blog. It’s important to note that the IMPORTHTML function does not seem to auto-refresh. You can force an auto-refresh in your Google Sheet by making the first cell an IF statement like this following -
Then you need to update your spreadsheet settings. Click "File" -> "Spreadsheet setting" and set the "recalculate" to "On change and every hour". This will keep the data refreshed.
It’s election time, so time for an election viz. You will notice this is very similar to the RealClearPolitics version the difference being that I am calculating the 2-week average of all polls in their data set.
This little project also provided me a chance to try out the new Google Sheets connector in Tableau 10 and allow the extract to refresh on Tableau Public. In addition, I learned how to scrape a table from the web in a Google Sheet via this blog post, which should (if I did it right) update automatically.
This was also another chance for me to test out the Device Designer that came with Tableau 10. Enjoy!
August 23, 2016
In this week’s tip, I show you how to compare your company to your competitors, but only for products that you both sell. In other words, you only want to compare products where in which you both compete.
August 22, 2016
This week for Makeover Monday we are tackling the malaria epidemic. The fact that countries still have to worry about malaria despite the prevention measures available is quite sad. Fortunately, the Tableau Foundation is helping and you can help too. Please visit visualizenomalaria.org to help.
Let’s look at the original visualisation on the World Health Organization website.
What works well?
- Really nice interactivity with both hover and click actions
- You can make any are full screen
- Consistent color palette
- Easy to understand
What doesn’t work well?
- Map is way too wide
- List of countries doesn’t aid in understanding
- Time series and bar chart don’t adjust for the data that is filtered
- Timeline is missing several years, even though the data exists
- Time series doesn’t display anything until you click on a country
I wanted my visualisation to fix the issues listed above, but also to be more focused on Africa. I also wanted it to serve as a call to action. I start with a summary and background information, dig a bit into some insights I found, and wrap it up with a way people can help.
August 17, 2016
This Data School Gym challenge is nothing more than a remake of an existing chart. I was reading Andy Kirk’s great series “The Little of Visualisation Design” and he showcased a chart from The Economist. From Andy’s blog:
"Typically, charts like this would have categorical value labels right-aligned to the left of the vertical axis. However, in this case, the labels are positioned with immediate proximity just to the right of the highest value - which is the value used to order the categories vertically. This approach aids readability, making it just that little bit more efficient to perceive the values and their associated categories."
According to The Economist, this chart shows:
"In every state where exit polling from this year’s primaries is comparable with the previous competitive cycle (2012 for the Republicans and 2008 for the Democrats), more voters have described themselves as “conservative” on the Republican side and “liberal” on the Democratic one."
I thought this would be a fun chart to try to rebuild in Tableau. I recreated the data manually, which you can download here. Note that the data probably isn’t 100% accurate to the original chart; I did the best I could and that’s not the point anyway. The point is to practice, practice, practice.
- This is three charts.
- I used 100% floating objects.
- The vertical and horizontal lines were added manually.
- Each event has its own sort: The top is sorted by Conservatives, the second by Liberals and the third by Moderates.
- The shading must span from the smallest to largest value for each event and state.
Give it a shot. I didn’t think this one was too terribly difficult. The toughest parts for me were getting the layout just right.
August 16, 2016
In this week’s tip, I show you how to shade the area under an area chart, when that are is around some type of baseline. A regular area chart won’t work in this case, so it requires a few simple steps to get it to look just right.
August 15, 2016
Several of the ladies that participate in Makeover Monday have been asking for a while now to look at a data set and viz that related to the original #MakeoverMonday hashtag. That is, something to do with beauty, make-up or hair.
This week, we look at this infographic from Raconteur Media, a publishing house and content marketing agency. Raconteur has a whole section devoted to their infographics if you’re looking to do more makeovers on your own and need interesting data sets, or if you’re looking for some creative inspiration.
In particular, the focus on this Makeover Monday is on the radial chart in center of the graphic. This chart attemps to show the year-over-year change in the UK market for various makeup products.
What works well?
- Each of the sections is sorted from largest to smallest
- Each bar has a clear label for the 2014 value and the change vs. 2013
- Design catches and retains your attention
- Simple summary in the middle
- Really crisp font that is easy to read
- Good color choices that contrast well
- Consistent decimal formatting
What doesn’t work well?
- The categories are not sorted, so it takes a lot of work to see which category is the largest.
- Comparing products across categories is basically impossible.
- Originally, I thought the white T-line extended out to the value for 2014, but it doesn’t mean anything. It’s simply to fit the text.
- This led me to think that the bars themselves were 2013 values, but they aren’t. So I’m not totally sure what the white line and bars represent.
- Some of the bars are cut off with squiggly lines to show they go farther than the viz shows. This can be misleading.
- At a glance, it’s very hard to tell which products are growing and which have declined.
For my version, I decided to go 100% floating in Tableau for the first time ever. I now understand why people design in Tableau this way. I can make my dashboard look EXACTLY the way I want and it’ll render exactly the same way on Public. Previously I would try to float only parts of the dashboard and they would shift a few pixels in Public. Not so with 100% floating. So, what I’ve learned is design Tableau dashboards 100% tiled or 100% floating, but don’t mix the two. This is the beauty (pardon the pun) of Makeover Monday; it gives me a chance to experiment and learn.
I deliberately chose colors that I thought looked more “make-upy”. I used an orange-to-purple diverging color palette from ColorBrewer and added it to my Tableau preferences file. Lastly, I also incorporated a phone version using Tableau 10’s new Device Designer.
My goals for this dashboard were simple:
- Make a dashboard that makes understanding and compare within and across categories easier
- Create a computer and phone version
- Use colors that represent the topic, but also clearly show products that are increasing or decreasing
Another fun week of Makeover Monday complete and, once again, I learned a TON! Enjoy!
August 12, 2016
Is Michael Phelps the greatest Olympic athlete of all-time? It’s be tough to argue against that. As of this writing, Michael Phelps was won four more gold medals at the Rio Olympics, only adding to his legend. To give this some context, consider this chart. Yeah, he’s pretty good!
Today at the Data School, Amanda Patist was giving an Alteryx demo and she used worldwide population data. That got me thinking about population pyramids; I’ve always wanted to build one. When I create this, it required a few tricks to get it just right. So I thought I’d post it as a Data School Gym challenge.
The small line chart show the % females and % males. The big chart shows the population. Post a link with your solution before you download mine.
Ignore the gray lines that are going across the viz. That’s a bug in Tableau 10.
August 10, 2016
A fun Data School Gym challenge for you today. Using this data source of human height since 1896, create this visualisation:
- Each country should show only 1896 and 1996 and they should be connected
- Label the midpoint with the country name
- Use an orange to blue diverging color palette based on the Avg Height
- Allow the user to filter by sex (no All option)
- Create a parameter to allow the user to pick a country; it should also include an (All) option
- Use the parameter to sort the visualisation; (1) If (All) is chosen then sort from largest to smallest based on the 1996 value. (2) If a specific country is chosen, that should be displayed at the top and the rest should be sorted from largest to smallest.
When the user picks Bermuda, for example, the visualisation should look like this:
Here’s the interactive version. Post a comment with a link to your solution. Good luck!
August 9, 2016
In this week’s tip, I walk you through how I’ve learned to use Tableau 10’s Device Designer feature, which makes it so much easier to create device specific designs.
Note: I ramble a bit at some points in this video as I learn some things along the way and I also hit some wifi issues, so I apologize in advance. Hopefully this helps you see that no one is perfect and we’re all on this journey of learning Tableau together.
August 8, 2016
With the Olympics starting this weekend, I thought we’d take a look at the most classic way that people display Olympic medal counts, as stacked bars. Being an American, I pretty much have only known NBC as the host of the Olympics, so when I went to their website and looked for historical medal counts, I was mortified. This viz is just about as bad as it can get.
What works well?
- The countries are ordered from most medals to least.
- There’s cute little actions when you click on the medals.
- They used appropriate colors for each medal type.
Seriously, that’s all I see that’s any good. This is an incredibly poorly done graphic.
What doesn’t work well?
- There’s no title.
- There aren’t any tooltips, so I have no idea how big each bar is; I’m forced to guess.
- I can only see seven countries at a time, and I can’t even see the name of the seventh country. I mean, who would ever want to compare only the 15th-21st ranked countries?
- When I click on the scroll button, it scrolls by an increment of 2. Why?
- Comparisons are nearly impossible with a stacked bar except for the total medal count and bronze.
Here are some of the changes I made:
- I separated out each medal into a dot plot and chose to show only the top 25.
- I included a summary next to each country to provide the exact medals counts.
- I included a mobile view, but in this view I remove this summary for a better visual look.
- I included informative tooltips.
- I included a title so you know what the chart is about.
- I included filters so the user can decide which Olympic games to include.
- The "sort by" option allows the user to pick the medal count to sort by making comparisons easier.
August 1, 2016
Definitely my quickest Makeover Monday ever. Forgive me, I’m busy at the bar at the beach.
This week we looked at a rather funny dataset from FiveThirtyEight about how the media tries to avoid saying “groin” when someone in a sporting event gets hits where it hurts. They presented their work in this table:
There’s nothing particularly terrible about this other than a table of numbers makes comparisons more difficult than necessary. The summary on the right helps it make a bit more sense.
For my viz, I wanted to create a chart that I’ve never built - a treemap bar chart. Basically this is a way to view the distribution of the words by each media outlet but also summarize them in bar chart form so that you can see which media outlet ranks the highest.