MakeoverMonday Week 26 – London Cycle Hire Usage

We’re halfway through the year for MakeoverMonday!

I can’t believe I’ve come this far (week after week). I’ll have to put up another post on what I’ve learned embarking on this journey but I’m glad I did.

I really like the original viz and I’ll leave it as is. What stories or patterns can you tell from the original viz? Hint: Click the image below for an interactive version.

I’ve been wanting to build an infographic type viz for a while and this large dataset presented the perfect opportunity!

*Click image for interactive version*

It’s been very fulfilling completing half a year of MakeoverMondayzz, but my schedule is about to switch up and I do not think I’d be able to continue for the rest of the year.

Let’s see how it goes!

Thanks for reading.

MakeoverMonday Week 25 – Influenza Surveillance Report

Data, Data, Data.

Not to preach to the choir or anything but having the right data really is key to effective decision making and I can only imagine what a better country Nigeria would be if we were deliberate on gathering data and obtaining insights. Case in point, this week’s MM. Andy noticed a lot of headlines surrounding a flu outbreak this year, so he did some googling and found this dataset.

The percentage of visits for influenza-like illness has been at an all-time high despite being only 36 weeks into the year and is the worst flu recorded since 2009/2010. This discovery can serve as a good starting point to discover what has changed and led to these results. #Datahastheanswers

My thoughts on the original viz below:

• It’s a good use case for a line chart as it’s easy to spot trends
• The use of multiple colors result in a clutter of some sort, maybe highlight just two years or use lighter colors for other seasons.
• I am not sure the purpose of the baseline as indicated here.

This week, I learned how to encode arrows in my viz to show up and down changes in trends.

*Click image below for interactive version*

For people in the States any thoughts on this flu outbreak? Do share.

Afterthoughts: I think coding the arrows in different colors (say black and white) would be more helpful.

 

 

MakeoverMonday Week 24 – Tourism Density Index

I’m a travel enthusiast and sometimes I spend my time fawning over locations on Instagram and plotting my next adrenaline activity on my next trip out, then I snap back to real life cos guess what? I ain’t living that lavish life yet with unending leave days and deep pockets to travel wherever I would want to.

Overtourism and Undertourism is a new term I discovered on this week’s MakeoverMonday and it has been interesting learning about these different countries and their approach to tourism. I did some additional research and found that Singapore is the most welcoming city in the world. *Bumps it up my list*

My thoughts on this week’s Viz:

• Yes to properly defined terms and title
• I like the colors used and I tried to retain that in my own viz
• The charts are labeled in detail, however, comparing the numbers would take some extra effort as it does not stand out.
• The circles are all the same size, which might come across as all the countries having the same number of locals.

While exploring the dataset, I discovered Singapore’s story and chose to share that. In addition to bumping Singapore up my travel list, Lee Kuan Yew’s book is now on my reading list.

*Click image for interactive version*

Any of these countries on your travel list? do share!

MakeoverMonday Week 23 – UK Gender Pay Gap

The dataset this week was quite difficult to get through, but this is such an important topic so I made sure to wiggle around it. Another win this week was me finally getting around to explore Tableau Prep, thanks to Andys tutorial.

My thoughts on the Viz above:

• I liked the use of icons to differentiate between the genders
• The header works well and the number is visible enough. However, I am not sure what the number represents. It does not exactly tell who earns more, I know I know Captain Obvious, but it may be rather presumptuous of us to immediately assume the men earned more if a reader did not bother to view the accompanying article.
• The filled up colors for both genders are at the same level and that does not exactly show any difference. You agree?

What I did:

• I decided to visualize the differences in the pay gap between company sizes and went ahead to recreate Danifornication’s viz.

*Click image for interactive version*

• 

Below is something interesting I read in the main article

The gender pay gap shows the difference in the average pay between all men and women in a workforce.

The gender pay gap is different to equal pay. Equal pay deals with the pay differences between men and women who carry out the same jobs, similar jobs or work of equal value. It is unlawful to pay people unequally because they are a man or a woman.

If you’re interested in reading the full article, link here.

 

MakeoverMonday Week 21 – Premier League Predictions

In other football related news, It’s World Cup Seasonnnn!

With the amount of time I’ve spent raving and ogling over Nigeria’s Kit to the World Cup, you’d think I knew a thing or two about football but I’m sorry to disappoint you, I don’t! The only time I garner any sort of interest in football is during The World Cup which only happens once in four years.

Ah well, on to MakeoverMonday for this week, it is a pretty straightforward viz so just a few comments here:

• Clear and easy to understand title.
• Maybe tone down on the logos? seems to cause a bit of clutter.
• Include the actual number positions so it is easy to see which team did better or worse than predicted.

A slope graph was my first point of call to recreate this viz (maybe ‘cos I also wanted this to double as my submission for Cole’s June SWD challenge) and I made sure to introduce the numbers on the column for final results to help with tracking.

*Click image for tooltip*

Any football lovers in the house? Were you expecting similar results at the end of the league? Do share!