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 38: Positive Impact Events

Makeover Monday

This week’s Makeover Monday is a special collaboration with the United Nations Sustainable Development Goals (SDGs) Action Campaign. It’s also notable that the original viz this week is built with Google Data Studio (which I believe is a first).

What works well?

  • Dropdown filters allow for interactivity.
  • Stacked bar charts show distribution of responses.

What could be improved?

  • The order of the segments in the stacked bars is not intuitive. It would make more sense to sort them in order of duration (Don’t know, 1 month, 6 months, Forever).
  • Since there are a different number of responses for each action, it would be more comparable to use a 100% stacked bar.
  • The treemaps are a disaster; absolutely unreadable.

What Marc did:

  • Used 100% stacked bar charts in a small multiple layout to enable fair comparison across all actions.
  • Applied colours, fonts, and icons from the UN SDG branding guidelines.
View in Data Studio

Makeover Monday 2019 Week 21: Fatal Bear Attacks

Makeover Monday

This week’s Makeover Monday focuses on fatal bear attacks in North America from 1900-2018. The original chart is the following bar chart from an article on Vox.

What works well?

  • Summarizing the data by month provides useful insight into the annual pattern of incidents.
  • Using a bar chart allows easy comparison between months.
  • The “X” on January and March helps emphasize that there were no attacks in those months.
  • The bears on top of each bar are gratuitous, but don’t distract from the overall message.

What could be improved?

  • Not much. The original chart is very clean and clear.
  • Month labels could be consistently abbreviated to 3 letters.

What Marc did:

  • Filtered the data to attacks occurring in Canada only.
  • Added subtle gridlines with axis labels.
View in Data Studio

Makeover Monday 2019 Week 16: Info We Trust by R.J. Andrews

Makeover Monday

This week’s Makeover Monday features Info We Trust by R.J. Andrews and a word cloud of the most frequently used words in the book.

What works well?

  • The word “data” stands out due to its size, colour, and orientation. It is clearly the most common word.

What could be improved?

  • Beyond “data”, it’s very difficult to perceive which words are 2nd, 3rd, 4th, etc. Almost any other chart type would be better.
  • Colour is used arbitrarily, without any meaning.

What Marc did:

  • Charted the top 10 words by frequency, broken down by page, chapter, and section.
  • Used a bubble plot; larger circles indicate a higher frequency of the word on the given page.
View in Data Studio

Makeover Monday 2019 Week 15: Ranking States by Fiscal Condition

Makeover Monday

This week’s Makeover Monday chart is from a 2018 report by the Mercatus Center at George Mason University, ranking each state by its fiscal condition.

What works well?

  • Clean design without any distracting map details.
  • Colour palette is distinct and visually pleasing.

What could be improved?

  • As with any geo map, the size of each state can skew our interpretation. A equal-sized tile map would address this problem.
  • The map shows only the ranking, but not the actual measure on which the rank is computed. Hence, we can’t assess how the states are distributed, i.e. how far above or below average is each state?

What Marc did:

  • The overall ranking is based on an aggregate fiscal condition index, which is composed of 5 underlying indices. I re-calculated all the index values based on the underlying data, according to the formulas defined in the full Mercatus report.
  • Using a jitter plot, I charted the distribution of the states on the overall index and each component index.
  • I kept the original colour scheme for each ranking group.
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 10: World Development Indicators

Makeover Monday

This week’s Makeover Monday challenge is in association with Operation Fistula. The original chart is the following map showing adolescent fertility rates in Africa. However, the dataset provided does not include the data needed to reproduce this chart:

What works well?

  • Clean and simple. Well-labelled. Easy to understand.
  • Colours are muted and not distracting. Visually-pleasing.

What could be improved?

  • Legend should be placed closer to the map to aid the viewer.

What Marc did:

  • Focused on female life expectancy in African countries.
  • Used a geo chart in Data Studio to map the data.
  • Added a jitter plot to show the distribution of life expectancy across countries. The filter allows you to select a particular country and see it in context of the distribution.
View in Data Studio

Makeover Monday 2019 Week 9: Bicycle Imports in the U.K.

Makeover Monday

This week’s Makeover Monday dataset comes from a report on the economic value of the bicycle industry in the United Kingdom. The chart shows the quarterly trend in bicycle imports from 2010 to 2016.

What works well?

  • Use of a line chart makes the seasonal trend easy to see.
  • Line markers clearly indicate the quarterly data points.

What could be improved?

  • Labels and gridlines can be re-formatted to eliminate some non-data ink.
  • Vertical axis title can be re-oriented to make it easier to read.

What Marc did:

View in Data Studio
  • Kept original line chart style.
  • Simplified labels on both axes and horizontal label on vertical axis.
  • Added shading for 2016 to correspond with the insight presented in the caption.

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.