This week’s exercise looked at Valentine’s day spending in the US. I liked the original viz – the color scheme seemed appropriate for the topic. I liked the images and felt the size and images conveyed what they were intended.
After setting up the data set I started creating the calculated fields I needed:
- The first was to create a date field from the Year field in the dataset – DATE(“02″+ “/” + “14” + “/” + STR([Year])) .
- I then created a couple of measures fields for the % Buying and the Avg Net Spend – IF [Metric] = ‘Percent Buying’ THEN [Measure] END and IF [Metric] = ‘Net Average Spend’ THEN [Measure] END
I wanted a custom shape for whatever I ended up creating – I found a free clip art heart online bought that into PowerPoint did a couple of updates to it and saved it to my custom shapes file.
After creating a few different views I decided to keep it simple and focus on what people were buying for Valentine’s day from 2010 – 2016. I tried line charts and bar charts with the hearts and then thought this may be a good time to give a bump chart a try.
Matt Chambers has a great post on his site that walks you through how to create a bump chart and I used that as a refresher. In order to get the bump chart to work I had to create a couple of more calculated measures:
- Rank for the % Buying – RANK_UNIQUE(SUM([% Buying]))
- Prior Year Ranking – LOOKUP([Rank % Buying],-1)
- Difference From Prior Year – IF [Rank % Buying] > [Prior Year Ranking] THEN ‘down from the prior year’
ELSEIF [Rank % Buying] = [Prior Year Ranking] THEN ‘the same as the prior year’
ELSEIF [Rank % Buying] < [Prior Year Ranking] THEN ‘up from the prior year’
I wanted the prior year and difference from prior year for the tooltip.
After getting the bump chart working I tested out a couple of different color schemes and found the purple to be a bit easier on the eyes than the red I had intended on using.
There is a lot more I could have done with the dataset this week, but, overall I’m pretty happy with what I created. The color scheme is different for me and I was happy with the custom shape and the bump chart. For the next few months I’m going to experiment more with the design side.
I hadn’t been to a restorative class in awhile and hadn’t been to a Mary O’Toole restorative class in ages so last night was a treat. Mary is holding classes in a great spot in Quincy and I highly recommend attending her classes – the schedule is on her meetup site.
Trying to get caught up on the Makeover Monday exercises from the last couple of weeks. I just finished week 5 which was on employment in the G7 countries. The original viz showed two pie charts on employment share and net employment growth in the G7 countries from 2010 – 2016
From reading the article the more important data point appeared to be the share of net employment growth in the US. I decided to turn that pie chart into a bar chart because I find it easier to see the differences in values with a bar chart than in a pie chart. I also adjusted the thickness of the bar to correspond to the measure. I kept the share of total employment in my remake as a reference point – I wanted the focus to be on the net share of employment so I added the share of total employment as a table. You can view the workbook on my Tableau public site.
In both my MOM and work dashboards I am starting to use the same theme. I like the clean look and find it easier to read dashboards on a light background. I think the darker backgrounds can be beautiful when they are done right but I have a hard time reading them and sometimes find them distracting. I’m getting new glasses in a few weeks and maybe when I get my progressives I’ll change my mind but for now I’m sticking with the light background!
Back from vacation and catching up on the last two Makeover Monday exercises. I tried the New Zealand RTI one from week 4 first.
The first thing I created was a line chart that showed the RTI by visitor type by period. I filtered the dashboard to just the Total Region and excluded 2008 from the chart. The definitions listed RTI as the change in expenditure by Region comparing the month to the average month in 2008, so, I figured 2008 didn’t need to be included.
There were a lot of options with this data set and I spent longer than it may seem on this one. The thing I kept coming back to was the difference in the RTI in Winter between domestic and international visitors. So after reading a lot about New Zealand weather I decided to go with that.
The viz itself is simple – a line chart to show the RTI trends by visitor type and a list of the 10 Regions with the largest difference in RTI between domestic and international visitors during the winter months.
Let me know if you think I missed the mark here or have any suggestions or feedback!