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

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 can 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)

What Ryan did:

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

Makeover Monday Week 15: Ranking States by Fiscal Condition

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 Week 14: Waste on UK Beaches

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 Week 13: Consumer Spending by Generation

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 Week 12: The Reykjavik Index

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

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.

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 8: Wind Power in the United States

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

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

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