T4N - Breakout Session 3: Making better figures: visualising data for different audiences
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This document provides an overview of best practices for visualizing data for different audiences. It discusses choosing the most appropriate visualization based on the key message, such as comparing numbers, showing how numbers relate to a total, or how numbers change over time. Specific graph types are recommended, such as bar charts for comparisons, pie or stacked bar charts to show parts of a whole, and line graphs to illustrate changes. The document also covers principles like emphasizing important numbers, showing uncertainty, and formatting graphs clearly. Overall, it aims to teach how to effectively communicate data visually for non-technical audiences.
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T4N - Breakout Session 3: Making better figures: visualising data for different audiences
- 1. Data for decision-making across the data value chain 11 February 2020 SESSION 4
- 2. Making better figures: visualising data for different audiences Tricia Aung & Youssouf Keita 26 February 2020
- 3. Session goals • Learn how to choose the most appropriate data visualization based on your key message • Identify what makes a good (and bad) data visualization 11 February 2020
- 4. What is the most appropriate graph based on key message? Exercise 26 February 2020
- 5. 26 February 2020 0% 20% 40% 60% 80% 100% 2015 DHS 2016 DHS 2017 DHS Breastfeeding status children <6 months in Senegal1 2017 DHS2016 DHS2015 DHS Breastfeeding status children <6 months in Senegal2 “The proportion of children <6 months exclusively breastfed in Senegal increased between 2015 and 2017.”
- 6. 26 February 2020 0 10 20 30 40 50 60 70 80 2018 DHS 2012/13 DHS 2006 DHS Percent Percentage of women age 15- 49 with any anaemia in Mali by wealth quintile 1 2 “Prevalence of anaemia among women varies by household wealth in Mali. The difference between anaemia prevalence among the poorest and richest increased between 2006 and 2013.”
- 7. “the graphic is only as useful as the audience finds it” 26 February 2020
- 8. How do we make good data visualizations? 1. Identify your key message. 2. Choose an appropriate type of visualization based on your key message. 3. Create your data visualization and double check that your visual supports your key point. 26 February 2020 What is a key message? A key message is a piece of information that you want your audience to remember. It is the “so what.”
- 9. Based on your key message, what do you want your visualization to show? 26 February 2020 Category Key Message Comparisons • “This is how two or more numbers are the same (or different)” • “This is how one number/numbers compare to the total” • “This is how things change over time” Small numbers, percentages, frequencies • “This one number is VERY important for you to remember” Adapted from Evergreen, Stephanie DH. Effective Data Visualization: The Right Chart for the Right Data. SAGE Publications, 2016 and Chart Chooser.
- 10. How 2 or more numbers are the same (or different) Comparisons 26 February 2020
- 11. Most effective ways to compare 2 or more numbers • For comparing 2 (or more) numbers… • Side by side column graph (bar chart) • Slope graph • Dot plot • Back-to-back bar chart 26 February 2020
- 12. Side by side column graph (bar chart) • Suitable for displaying categorical data and to compare discrete data in distinct categories • Each bar represents one category • Can be organized horizontally or vertically • Height of the bars are proportional to the number of events (e.g. cases) in the category • Variables in a bar graph can be discrete (e.g. sex, region, race) or continuous (e.g. age) but organized in categories (e.g. age groups) 26 February 2020
- 13. Side by side column graph (bar chart) • Side by side column graphs (bar charts) are the most frequently used approach to compare two or more numbers. However, they are often not the most effective option. 26 February 2020 Studies suggest that side by side column graphs are most effective for two categories. Why? Studies have shown that our brains can process 3-5 groups with 2 columns per group. Any more groups or columns are challenging to interpret.
- 14. Slope graph • Slope graphs are useful for comparing two categories and emphasizing how some categories may have changed faster than other categories. 26 February 2020 Our brains have a pretty easy time judging slope/rate of change!
- 15. Bar chart to slope graph transformation ! 26 February 2020 0 5 10 15 20 25 30 35 Breastfed Nonbreastfed All children age 6-23 months Percentofchildren6-23months Minimum dietary diversity among children 6-23 months in Nigeria (2013 DHS v. 2018 DHS) 2013 DHS 2018 DHS 0 5 10 15 20 25 30 35 2013 DHS 2018 DHS Percentofchildren6-23months Minimum dietary diversity among children 6-23 months in Nigeria (2013 DHS v. 2018 DHS) Breastfed Nonbreastfed All children age 6-23 months
- 16. Dot plot • Dot plots can be quickly read. • Great option for emphasizing gaps between numbers = “equiplots” 26 February 2020 Our brains can more accurately interpret dots on a line, and space on a common axis rather than length on varying axes (bar charts).
- 17. Dumbbell plot – a type of dot plot 26 February 2020
- 18. Lollipop plot – a type of dot plot 26 February 2020
- 19. Back-to-back bar chart • Back-to-back bar charts are two bar charts that are aligned back-to-back • Purpose is not to highlight specific values, but distributions • One familiar example is the population pyramid 26 February 2020 Our brains can easily assess symmetry.
- 20. Back-to-back bar chart example 26 February 2020 Number of case of stunting averted at national level and in Sikasso region if the target of nutrition indicators in plans are met between 2015 and 2023, in Mali (NEP-Mali, 2019).
- 22. How 1 number/numbers compares to the total Comparisons 26 February 2020
- 23. Two ways to compare parts of a whole • Pie chart • Stacked bar chart • 100% stacked bar chart 26 February 2020 Usually the better option !
- 24. Pie charts • Circular chart split into segments which show components of a larger group • Suitable for displaying categorical data, or discrete data in distinct categories • The size of a “slice” is proportional to the amount of data (e.g., number of cases) it represents • Proportion (%) of the total each component represents is frequently added on the slice • Different colors can be used to identify various slices 26 February 2020
- 25. When NOT to use a pie chart • When you have more than 5 slices • When the values of each slice are similar because it is difficult to see differences between slice sizes • For comparisons (one pie chart vs. another pie chart) 26 February 2020 Studies show that our brains have trouble quickly interpreting pie charts compared to bar charts and line graphs, which are composed of straight lines and 90 degree angles. Only choose a pie chart when you are sure that the data cannot be effectively visualized by a bar chart.
- 26. Stacked bar chart 26 February 2020 Number of case of stunting averted between 2015 and 2023 if the target set for nutrition indicators in ongoing plan are in Mali (NEP-Mali Team, 2019)
- 27. 100% stacked bar chart 26 February 2020 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2016 MIS 2014 DHS 2008 DHS 2003 DHS Distribution of anaemia status among children age 6-59 months in Ghana Mild anaemia Moderate anaemia Severe ananemia No anaemia
- 28. How numbers change over time Comparisons 26 February 2020
- 29. Most effective ways to show change over time • Line graph • Slope graph 26 February 2020 Usually what you should use ! 0 5 10 15 20 25 30 35 2013 DHS 2018 DHS Percent Minimum dietary diversity among children 6- 23 months in Nigeria (2013 DHS v. 2018 DHS) Breastfed Nonbreastfed All children age 6-23 months 0 10 20 30 40 50 60 70 80 90 100 2014 DHS 2015 DHS 2016 DHS 2017 DHS Percent Percentage of children age 6-59 months who received vitamin A supplements in the 6 months before the survey in Senegal
- 30. When one number is very important Small numbers, percentages, frequencies 26 February 2020
- 31. Single numbers can have a big impact • Occasionally, you may want to tell a story that highlights a single number for impact. Ways to visualize a single number: use a big font, icons, and/or color. 26 February 2020 Nigeria DHS 2018 Infographic Cameroon DHS 2011 Nutrition Fact Sheet IFPRI & GNR 2015 Infographic
- 32. Other tips 26 February 2020
- 33. Communicating uncertainty and statistical significance • Consider your audience. Most audiences don’t understand confidence intervals or standard deviation, and there is a risk that your graph will confuse your audience. • If you display variability or imply statistical significance in your graph, you must be able to explain it. Examples: • Confidence interval: “There’s a statistically high chance that the actual number falls within this range.” • Statistical significance: “Statistically, the trends shown did not happen by chance.” 26 February 2020 Steve Gesuale presentation on visualizing uncertainty Source: https://graphics.cs.wisc.edu/Papers/2014/CG14/Preprint.pdf
- 34. Accurate axes 26 February 2020
- 35. Showing targets • You can easily show targets using “x” symbols or lines. 26 February 2020 0 10 20 30 40 50 60 2017 SMART Percent Exclusive breastfeeding among infants <6 months in Burkina Faso (2017 SMART) Global nutrition target: at least 50%
- 36. Retain formatting dimensions • Remember to press the “Shift” key on the keyboard while resizing images to retain image proportions. • If making your graphs in Excel, when pasting the graph in PowerPoint or Word, select “paste as image” • Right click graph to get this pop up: 26 February 2020
- 37. What is the most appropriate graph based on key message? Revisiting exercise 26 February 2020
- 38. 26 February 2020 0% 20% 40% 60% 80% 100% 2015 DHS 2016 DHS 2017 DHS Breastfeeding status children <6 months in Senegal1 2017 DHS2016 DHS2015 DHS Breastfeeding status children <6 months in Senegal2 “The proportion of children <6 months exclusively breastfed in Senegal increased between 2015 and 2017.”
- 39. 26 February 2020 0 10 20 30 40 50 60 70 80 2018 DHS 2012/13 DHS 2006 DHS Percent Percentage of women age 15- 49 with any anaemia in Mali by wealth quintile 1 2 “Prevalence of anaemia among women varies by household wealth in Mali. The difference between anaemia prevalence among the poorest and richest increased between 2006 and 2013.”
- 40. Additional Resource: Data Visualization Society (DVS) 26 February 2020 Join 10,000+ individuals interested in data visualization (all experience levels welcomed)
- 41. Additional Reading • Aung T, Niyeha D, Shagihilu S, Mpembeni R, Kaganda J, Sheffel A, Heidkamp R. Optimizing data visualization for reproductive, maternal, newborn, child health, and nutrition (RMNCH&N) policymaking: data visualization preferences and interpretation capacity among decision-makers in Tanzania. Global health research and policy. 2019 Dec 1;4(1):4. [link] • Aung T, Niyeha D, Heidkamp R. Leveraging data visualization to improve the use of data for global health decision-making. Journal of Global Health. 2019 Dec;9(2). [link] • Aung, T. Data Visualization for Audiences in Low & Middle-Income Countries. Data Visualization Society Nightingale. 2019. [link] • Evergreen, Stephanie DH. Effective Data Visualization: The Right Chart for the Right Data. SAGE Publications, 2016. • Global Health eLearning Center. Data Visualization - An Introduction [Internet]. 2015. Available from: https://www.globalhealthlearning.org/course/data-visualization-brave-new-world • Graybeal, Carolyn. “Exploring a Culture of Health: How Can We Visualize Health Data for Better Communication?” PLOS CitizenSci Blog • Pandey, Anshul Vikram, et al. "The persuasive power of data visualization." IEEE transactions on visualization and computer graphics 20.12 (2014): 2211-2220. 26 February 2020
- 42. Thank you! More information on https://www.nationalevaluationplatform.org/ This training was developed with funding from the Government of Canada & the Bill & Melinda Gates Foundation
Editor's Notes
- The goal of today’s session is to: (1) learn how to choose the most appropriate data visualization based on your key message and (2) identify what makes a good (and bad) data visualization.
- First, we will do an exercise for you to think about your own preferences. We will then revisit these examples at the end of the presentation. This exercise is similar to an exercise that the National Evaluation Platform project conducted with government decision-makers in Tanzania. The objective of that study was to characterize data visualization interpretation capacity and preferences among RMNCH&N Tanzanian program implementers and policymakers (“decision-makers”) to design more effective approaches towards promoting evidence-based RMNCH&N decisions in Tanzania.
- Here is one key message and two different ways to visualize the data. <Read the key message>. Of the two graphs, which do you think better represents the key message? Raise your hand if you believe 1 is better. (Estimate number of hands). Raise your hand if you believe 2 is better. (Estimate number of hands).
- Here is another key message and two different ways to visualize the data. <Read the key message>. Of the two graphs, which do you think better represents the key message? Raise your hand if you believe 1 is better. (Estimate number of hands). Raise your hand if you believe 2 is better. (Estimate number of hands).
- Through the Data Visualization Society, a community of practice for those interested in data visualization, Tricia has led discussions on visualizing data for low- and middle-income audiences. One individual shared this comment, which is the key theme for today and should drive your data visualization choices. What does this mean? Making a beautiful, fancy graph is fruitless if the audience can’t understand it.
- How do we make sure we make a graph that an audience can understand? First, figure out what you are trying to get across – the key message. Key messages are short (1-2 sentences), relevant, easy to understand, and memorable. This presentation will focus on step 2 – choosing the right graph based on your key message. Step 3 accounts for the necessity to reflect on whether you’ve chosen the right graph, which can be done by yourself and/or with peers.
- Here is a summary of key message categories which correspond to the handout (decision tree) that you can keep. This presentation is organized based on these common categories. This presentation includes many examples using nutrition data from West Africa. Some graphs can be made in Excel, and other graphs are not Excel options but can be made using R or Tableau. Tricia can help those interested in making graphs in R or Tableau.
- This is the most frequently used approach to visualizing comparisons, but is sometimes not the most effective (and you’ll learn other ways to visualize comparisons). The brain symbol will appear in this presentation when referencing data visualization studies. This brain symbol will appear throughout the presentation. This symbol represents related research to this recommendation.
- This is the same data visualized two different ways. In the slope graph, it is more apparently how minimum dietary diversity has dropped among non-breastfed children (negative slope).
- Dot plots are often a great option because they are easy to read and emphasize a gap between numbers, which can be useful for equity graphs. University of Pelotas/Countdown to 2030 calls these “equiplots.”
- What do you see? (Pause) The gap in coverage of iodized salt between rural/urban has decreased between 2005 and 2017!
- Our brains are better at interpreting 90 degree angles and looking up-to-down and left-to-right. This is why stacked bar charts can be a better alternative than pie charts. Has anyone heard of a donut chart? (Pause and ask audience.) Donut charts are actually more challenging for our brains to interpret than even pie charts. This is because the center axis helps our brains figure out how big is the pie slice. Donut charts do not have a center axis, so can make interpretation more difficult.
- What does this graph tell us? (Pause and let people look at it.) Proportion with no anaemia has increased over time while proportion of severe anaemia has decreased over time. How can we see this? (Point up-and-down to yellow (no anaemia) and up-and-down to dark blue (severe anaemia)).
- The left is a line graph. The right is a slope graph.
- Making a single number very large/in a different color, can be the most effective way to make your audience remember one important number. In one study, a team found that when individuals were shown disease prevalence in four different ways (single large number, icon array, pie chart, bar chart), a single large number was the most accurately interpreted in terms of disease significance and scope.
- According to a study by Correll and Gleicher (2014), even individuals with a background in science have trouble interpreting graphs with confidence intervals, which is why they advised to only display them when necessary. Of the four options on the right, the one circled was most accurately interpreted.
- What’s wrong with the graph on the left? (Pause and see if there are any reactions.) Double check that any continuous data (like time) is accurately reflected on an axis.
- Let us revisit our exercise from the beginning.
- Let’s see if you’ve decided to change your mind after today’s session. Of the two graphs, which do you think better represents the key message? Raise your hand if you believe 1 is better. (Count number of hands). Raise your hand if you believe 2 is better. (Count number of hands).
- Of the two graphs, which do you think better represents the key message? Raise your hand if you believe 1 is better. (Count number of hands). Raise your hand if you believe 2 is better. (Count number of hands).
- The purpose of DVS: 1) Foster collaboration and discussion on data visualization 2) Serve as a resource for data visualization practioners 3) Encourage growth of the field. Over 10,000 members and membership is currently free with growing resources to help those interested in expanding and sharing their data visualization knowledge. DVS could particularly benefit from more members from West Africa based on this map!
- Here are additional resources if you want to learn more about the National Evaluation Platform’s data visualization work and data visualization research.