A CHART REDESIGN

1980 2008

Amanda Makulec JSI Center for Health Information, Monitoring & Evaluation @abmakulec

Excel has the capability of churning out incredibly hard to interpret charts and graphs, and automatically includes a lot of visual clutter. Thinking through the story you want to tell with your data, considering the many different chart types that can be created in Excel (or hacked with some creative formatting), and designing a chart that tells that data story effectively are essential for making sure your message is heard. Walk through the following simple steps for how to transform a hard-to-read graph into something that tells the data story well.

The message we want to tell to the general public from this data table is:

Around the world, the proportion of women* who are overweight or obese has increased across regions.

*Normal as defined by standard BMI measures and women aged 20-49 years. Data table from: Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, Ezzati M, Grantham-McGregor S, Katz J, Martorell R, Uauy R. Maternal and child undernutrition and overweight in low-income and middle-income countries” The Lancet 2013; (06 June 2013) DOI: 10.1016/S0140-6736(13)60937-X.

underweight normal overweight obese

1980 2008 1980 2008 1980 2008 1980 2008

Africa 18 12 64 58 14 19 4 11

LAC 4 2 66 43 22 31 8 24

Asia 19 17 68 62 11 17 2 4

Europe 4 4 61 55 25 28 10 13

Oceania 6 2 69 45 19 32 6 21

Change in BMI status of women 20-49 years from 1980 to 2008 by region

1980 2008

START The original Excel chart.

Original layout - doesn't facilitate comparisons by region very well

Africa LAC Asia Europe Oceania

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overweight

normal

underweight

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Africa LAC Asia Europe Oceania

The comparison is across regions and focused on changes in numbers that sum to 100%. So, a stacked bar chart is a better choice.

By clustering the stacked bars by region, you can better facilitate comparisons within regions.

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Africa LAC Asia Europe Oceania

Obese

Overweight

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Normal

Percent of women 20-49 years by body mass index (BMI) status

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Africa LAC Asia Europe Oceania

Obese

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By minimizing the spaces between the columns, you make it easy to see the pairs of regional data.

Percent of women 20-49 years by body mass index (BMI) status

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Africa LAC Asia Europe Oceania

Obese

Overweight

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Normal

Then, reduce the clutter by taking away the unnecessary tick marks and lines.

Percent of women 20-49 years by body mass index (BMI) status

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1980 2008 1980 2008 1980 2008 1980 2008 1980 2008

Africa LAC Asia Europe Oceania

Obese

Overweight

Underweight

Normal

Consider how much detail your audience needs. In this case, if the important trend is the overall change in percent above normal, then combine the two categories.

Percent of women 20-49 years by body mass index (BMI) status

Reorder categories to be logical. Starting with underweight at the base and overweight/obese at the top of the stacked bar.

Percent of women 20-49 years by body mass index (BMI) status

Add color that adds meaning. Bright colors call attention to the important categories while gray lets ‘normal’ fade a bit.

Percent of women 20-49 years by body mass index (BMI) status

Think about the level of detail required. Technical folks might want to know that this is based on BMI and the specific age range defined as “women” while a broader global audience may not need that level of detail as an axis title.

Percent of women 20-49 years by body mass index (BMI) status

The proportion of women who are overweight has increased in low and middle income countries.

Add a title that tells the key message you want your audience to takeaway. This is very important since the axis title has been removed.

Change in BMI status of women 20-49 years from 1980 to 2008 by region

1980 2008

A reminder of where we started.

The proportion of women who are overweight has increased in low and middle income countries.

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