Makeover Monday 2019 Week 43: Suicides by Age

Makeover Monday

Content warning: death, suicide

This post is going to discuss suicide and information presentation around suicide. If you are at risk, please stop here. You are not a number or a statistic. You are a life, a beautiful one, likely feeling a number of things. Please don’t dismiss it. There is life to live and you can:
Canada: 1-833-456-4566
US: 1-800-273-8255
UK: 116 123

~ Bridget Cogley

This week’s Makeover Monday dataset is from the Office for National Statistics in the UK. It shows trends in suicides in England and Wales from 1981 to 2017.

What works well?

  • The ridgeline chart effectively shows the shifting peak in suicides.
  • The gradient fill highlights the peaks, while still showing the full trend.

What could be improved?

  • Placing the year labels on the left would be more intuitive.
  • Adding more commentary would help explain the pattern being highlighted.

What Ryan did:

  • Created 7 ranges to group the number of deaths.
  • Used the ranges to build a horizon chart to show the increase in age over time.
  • Limited the chart to show only 1987-2017 so that the increase in age would be more apparent.
View in Tableau Public

What Marc did:

  • Built a custom ridgeline chart with D3.js as a community visualization in Data Studio.
  • Used a newspaper style with commentary taken from the original Office for National Statistics report.
View in Data Studio

Makeover Monday 2019 Week 18: Space Station Spacewalks

Makeover Monday

The topic of this week’s Makeover Monday is ISS spacewalks. The original chart is from NASA.

What works well?

  • Stacked bar chart makes it easy to compare between U.S. and Russia.
  • Bars are also labelled which helps with comparisons.
  • Colors are distinct.

What could be improved?

  • A legend could be added to make it clearer which color is for which country.
  • Background image is distracting and can be excluded.

What Ryan did:

  • I used the detail data set provided to show the top 25 astronauts by total spacewalk time.
  • Created a bar chart showing the total spacewalk time broken down by individual spacewalks.
  • Colored the bars by space suit used.
View on Tableau Public

What Marc did:

  • Created a diverging bar chart to allow each series to have an equal baseline. Oriented the axis vertically to avoid unintended positive/negative associations with bars above/below a horizontal axis.
  • Used light colours on a dark background in keeping with the space theme.
  • Excluded 2019, as there is only partial data.
  • Added annotations to highlight key events, including the Space Shuttle Columbia disaster and the end of the Shuttle program.
View in Data Studio

Makeover Monday 2019 Week 17: Stephen Curry’s Stadium Popcorn Rankings

Makeover Monday

This week’s chart comes from a New York Times article on Stephen Curry’s love for popcorn. He ranks each NBA stadium’s popcorn based on five categories.

What works well?

  • Stadium’s are sorted from highest to lowest total score.
  • Colour palette makes it easy to see the different ranks in the heatmap.

What could be improved?

  • The scale used for the rankings is not stated on the chart (it is stated in the article that a 1 to 5 scale was used).
  • Rather than encoding the data by colour saturation, a bar chart or dot plot would make differences in rating easier to perceive.

What Ryan did:

  • Remade the original chart using a dot plot.
  • Colored the columns by category.
View in Tableau Public

What Marc did:

  • Inspired by a video game “team select” screen, my viz allows two teams to be compared head-to-head.
  • The ratings across the 5 popcorn dimensions is shown in back-to-back bar charts.
View in Data Studio

Makeover Monday 2019 Week 14: Waste on UK Beaches

Makeover Monday

The data for this week’s Makeover Monday challenge comes from the Marine Conservation Society’s Great British Beach Clean in 2017. The original chart is by the BBC.

What works well?

  • Clear title and subtitle which provides enough context
  • Types of waste are sorted from most to least
  • Overall style is visually appealing

What could be improved?

  • Using a bubble chart makes it difficult to compare the types by size
  • The chart only depicts the top 10 types of waste which makes up about 69% of the total waste found per 100 meters of beach. This could be called out somewhere on the viz.

What Ryan did:

  • The BBC article emphasized how problematic plastic pollution is so I wanted to highlight that.
  • Turned the original into a bar chart showing items per 100m by type
  • Coloured the items made from plastic
View on Tableau Public

Makeover Monday 2019 Week 13: Consumer Spending by Generation

Makeover Monday

For week 13, we look at the the spending of different generations.

What works well?

  • Percentages are labelled clearly which makes comparisons easier.
  • Grid lines are simple.
  • Using a 100% stacked bar makes it easy to see part to whole relationships for each generation.

What could be improved?

  • There there is no indication of what the y-axis measures.
  • Some more context could be provided such as when the data was collected or what country the consumers are from.
  • Difficult to compare spending for categories across generations.

What Ryan did:

  • Converted the stacked bar into bar charts
  • Coloured each generation group
View on Tableau Public

What Marc did:

  • Created small multiple bar charts by category.
  • Colour-coded each generation.
  • Created desktop and mobile views on separate pages in Data Studio. Click the link to switch between versions.
View in Data Studio

Makeover Monday 2019 Week 12: The Reykjavik Index

Makeover Monday

The Reykjavik Index measures the extent to which men and women are perceived to be equally suited for positions of leadership. This week’s Makeover Monday chart shows the Reykjavik Index across the G7.

What works well?

  • Clean, uncluttered design
  • G7 Average is highlighted with a different colour

What could be improved?

  • Circular bars are misleading. A typical bar chart encodes data in the length of the bars. In a circular bar chart, the data is in fact encoded in the radial angle, which is more difficult to interpret.

What Ryan did:

  • Used a traditional bar chart.
  • Added a reference line to show the G7 average.
  • Colour-coded countries above average and below average.
View in Tableau Public

What Marc did:

  • Straightened the bars to allow easier comparison.
  • Added grey bars stacked to 100 to show the maximum extent of the index.
  • Kept the original colours and overall style.
View in Data Studio

Makeover Monday 2019 Week 8: Wind Power in the United States

Makeover Monday

This week’s Makeover Monday dataset looks at the status of wind power in the USA.

What works well?

  • The chart is sorted which makes it easy and quick to see the states with the highest wind power capacity.
  • The investment figures below the x-axis add some context to the viz.

What could be improved?

  • The turbine bar charts are unnecessary and distracting. It is harder to make comparisons since the shapes block the bars of other states. Using simple bars would be clearer.
  • The labels get very small on the right side of the chart making them hard to read. The chart could be widened to give the labels more space.
  • The y-axis scale is not accurate. The distance from 100 to 1,000 looks the same as from 5,000 to 10,000.

What Ryan did:

  • Created a hex map chart to show the wind power capacity of each state. I also wanted to bring attention to the states with zero capacity.
  • Looked into why the Southeast lacks wind power capacity and added an annotation (source).

What Marc did:

  • Created a bar chart focusing on the top 10 states by wind energy production.
  • Added scorecards to summarize the key wind energy metrics.
  • Applied chart interactions in Data Studio to allow the bar chart to be used a filter for the scorecards.
View in Data Studio

Makeover Monday 2019 Week 7: President Trump’s Official Schedule

Makeover Monday

This week’s Makeover Monday gives us a peek into how President Trump schedules his time, based on 3 months of leaked private schedules.

What works well?

  • Didn’t use a pie chart!
  • Minimalist design; High data-ink ratio
  • Clear titles and labels

What could be improved?

  • Honestly, not much.
  • Percentage labels would help more accurately compare the breakdown between categories.

What Marc did:

  • Recreated the original chart in Data Studio. There’s not much in need of a makeover.
  • Added percentage labels to the bars.
View in Data Studio

What Ryan did:

  • Created a Gantt chart to show Trump’s full schedule.
  • Coloured the periods of executive time in orange.
  • Added reference lines to frame the working hours.
  • Annotated some key events that took place from Nov-Feb.

Makeover Monday 2019 Week 6: Chinese New Year vs. American Thanksgiving

Makeover Monday

It’s Chinese New Year this week, and for Makeover Monday we get this chart that compares the Spring Festival in China with Thanksgiving in the United States.

What works well?

  • Colours are distinct and intuitive.
  • Values are labelled.
  • Circles are visually pleasing.

What could be improved?

  • Comparing the areas of circles is extremely difficult. Using bar charts would be much more effective.
  • Metrics are absolute totals and don’t account for the difference in population between China and the U.S.

What Ryan did:

  • Converted the total expenditure and trips to a per person amount and the TV viewership to percentage of population. Since China has a larger population, this makes comparisons easier.
  • Created bar charts for each measure comparing China and the USA.
View in Tableau Public

What Marc did:

  • I re-created the original graphic, converting the concentric circles to side-by-side bar charts.
View in Data Studio