Makeover Monday: Spending By Generation

Makeover Monday: Spending By Generation

For this weeks Makeover Monday we are taking a look at consumer spending by generation and category. In the original visualization below you can easily see how each individual generation spent in each category.

I thought it would be interesting to break the data visualization out by category. I wanted to easily scan the categories to see how each generation spent differently.

By breaking the visualization out by category I found it easier to see what categories each generation valued the most. For instance, you instantly see that Traditionalist are more likely to cook at home as they spend the most on Groceries and the least in Resturants. You can also see how the Millenials are the complete opposite of the Traditionalists in these categories with the lowest spending in Grocery complemented by the highest spending in Resturants. As a Millennial who rarely eats out and cooks most meals from scratch this one initially surprised me.

You can also see other interesting associations, like how Millennials spend the most on Hobbies, and the least on Furniture/Building. As a generation that values experiences over things, these spending patterns make sense.

After looking at both visualizations what questions do you have? Did you create a Makeover Monday visualization? Share the link, I would love to see it.

Make Over Monday: World Development Indicators – Health and Equality

Make Over Monday: World Development Indicators – Health and Equality

This week for Make Over Monday we are looking at data for World Development Indicators – Health and Equality. There was several different aspects of health and equality that we could have looked at with the data provided. I decided to focus on the Average Female Life expectancy world wide from 1960 to 2016.

I thought it would be interesting to see the trend and how this number has changed for different countries around the world.

For this first visualization I thought it would be interesting to explore the over all world trend. The over all trend is that the life expectancy for women has steadily increased. While this may not come as a surprise it was interesting to see that several countries have seen wild swings with lower life expectancy then rebounding.

When inspecting which countries experienced sever fluctuations we can see that these are countries that have had serious conflicts in recent history such as Rwanda and Sierra Leon. This would explain these rapid fluctuations shown in this visualization.

Now that we have an idea of how female life expectancy has been trending I thought it would be useful to see how different countries compare with one another in the most recent recorded year. This world map shows female life expectancy for 2016. We can see that developed countries have a longer life expectancy than developing nations. There is a wide gap between the countries with the highest life expectancy which is well into the 80’s and the countries with the lowest life expectancy, which are coming in at the low 50’s.

Finally I thought it would be interesting to be able to see visually how each countries life expectancy has changed over the years. While most countries follow the trend that we saw in the first visualization we can also see those countries that had dips in their life expectancy and how those numbers rose and fell over the years.

With the data this week I felt that it was important to look for over all trends as well individual variances. While we are becoming an increasingly integrated world economy the data shows that there is also large gaps between developing and developed nations.

Make Over Monday: Economic Value Of The Bicycle Industry

Make Over Monday: Economic Value Of The Bicycle Industry

This week for Make Over Monday we are looking at dataset for the economic value of the bicycle industry in the UK. Cycling is one of my favorite hobbies so I was excited to jump in and make some fun visualizations.

Because I love bicycles, and I thought they would make for great visualizations, I wanted to use custom shapes for my graphs this week. I imported a custom icon of a bicycle to be used across my visualizations.

For the first visualization I wanted to take a look at the number of manual bicycles that were imported each year. I added a dual axis with the same data to create this bicycle carousel effect. I felt that adding the bar graph element made it easier to see the change in trends and the significant decline that the bicycle industry experienced in 2016.

Next, I thought it would be interesting to see how manual bicycle imports compared with electric bicycle imports. This graphic shows that while electric bikes may have carved out their own portion of the market, manual bikes still make up the majority of the imports.

Finally, I thought that it was important to show electric bikes in their own visual which shows a sharp climb in imports in 2015

With a sharp rise in 2015 that then fell significantly in 2016 I was curious what could have caused this fluctuation in the market. For answers I went to my local bike shop pro. He explained to me a little of the history behind the e-bike and how it continued to develop over the years. He explained that this rise was likely due to the development of a better battery and ingratiation of that battery into the frame. E-bikes saw a rise in popularity after these improvements. He also had a possible explanation for the decline. He explained that in his experience E-bikes were a one time purchase, he did not see customers coming in the next year to upgrade to a new model. If a large population of riders interested in E-bikes bought theirs the year that the improved design came out it could explain how the number of E-bikes needed would then decline the next year.

I had a lot of fun this week learning some new skills in Tableau I focused on using custom shapes and dual axis in all of my visualizations and love the different stories in the data I was able to show using these techniques.

Did you participate in Make Over Monday? I’d love it if you would share a link to your visualization and share any useful insights you learned.


Make Over Monday: Which States Produce The Most Wind Energy

For Make Over Monday this week we are looking at data for wind energy produced by each state in 2018. The original visulisation was a bar graph similar to the one below.

I wanted to include a similar bar graph in my visualizations because the bar graph did a great job of illustrating the large range of equivalent house holds powered by wind energy. It clearly shows how large the difference is between the state that powers the most households with wind energy, Texas, even when compared with the next largest producer of wind energy Oklahoma. This visualization clearly shows that Texas is leading the way when it comes to powering homes with wind energy.

I also thought it would be useful to see this data displayed in a geographical context which is why I created this visualization of the continental united states.

The color gradient in this visualization also highlights Texas as the largest producer of wind energy in the United States. You can also see that there appears to be more wind energy produced in the midwest, followed by the west coast when compared to the east coast indicating regional differences. These differences by region could lead to further investigations, are these differences due to climate, space, or politics?

Finally I wanted a table of rankings for quick and easy reference which you can find below.

Did you create a Tableau visualization for Make Over Monday? I would love to see it, leave your link in the comments below.

Make Over Monday: Energy Use at 10 Downing Street

Make Over Monday: Energy Use at 10 Downing Street

I am just getting started with visualizations in Tableau and what better way to learn than to jump in with Make Over Monday. I am new to the software and still learning, so rather than try and make a high tech dashboard or complicated graphic I decided to work with the skills I have and instead focus on answering a question. I decided to go with this approach because the reality is a data visualization should answer a questions, otherwise it is just a pretty graphic.

After looking at the original graphic, which you can see here, I was left wondering what the energy use trends looked like. Was there a day of the week or time of day where energy use was significantly higher or lower? I felt that looking at this trend would tell us more about how the energy is used at Downing Street than simply showing a running log of watts used and carbon impact. With this in mind I created the following visualization.

Given that the Downing Street complex is both a home and a busy office it makes sense that power usage would be higher during the week when office staff would also be in the townhouse working. The energy usage data shows that is during business hours when energy usage is highest. If the goal where to reduce the carbon impact this would suggest that green solutions for the office area would have the highest impact.

After looking at the original graphic what question would you like answered? If you created a Make Over Monday Visualization of your own share a link in the comments, I would love to see what you did with the data. Until next next time, happy analyzing.