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# Data visualization presentation - Making charts with Excel - with notes - UC Berkeley Library IDP

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### Data visualization presentation - Making charts with Excel - with notes - UC Berkeley Library IDP

1. 1. Illustrating data Making charts with Microsoft Excel Preparation Write on board Objectives Identify the best type of chart to communicate a data analysis or story. Outline heuristics for the effective chart design. Create basic charts with Microsoft Excel. ---- ! Good morning. Thank you for joining our Instructor Development Program on making charts with Microsoft Excel. ! I’m excited because today we’re testing out a brand new lesson plan, which we hope to teach to students eventually. So I look forward to your comments in our post-session survey. ! === December 17, 2013 (Tuesday)
2. 2. Visuals are an innately quick form of communication. ! “Seeing comes before words. And the [baby] looks and recognizes before it can speak.” (Berger, 1972) ! ===
3. 3. a curved line with every point equal distance from the center We are able to infer meaning from pictures much more quickly than from text. ! Tests show that we can process the visual representation on the left 60,000 times faster than the text on the right. ! === ! Source: http://edwardsanchez.me/blog/13589712 !
4. 4. fruit < This phenomenon is known as the picture superiority effect. ! Viewing pictures is more easily and frequently recalled than concepts that are learned by viewing the written word. ! === Source: (Wikipedia, Picture Superiority Effect).
5. 5. And therein lies the beauty of the chart: ! “Charts display series of numeric data in a graphical format. They make it easier to understand large quantities of data and the relationship between the different series.” ! === Source: http://office.microsoft.com/en-us/excel-help/create-a-chart-from-start-to-finish-HP010342356.aspx
6. 6. Because of our innate visual communication abilities, there is a long history of graphs and charts for conveying data. ! Allow me to take you on a historical tour in great moments of charts. ! The year is approximately 16,500 BC. Paleolithic era humans in southwestern France painted the Lascaux cave paintings of animals. ! These dots on the wall === !
7. 7. turned out to be maps of the stars and the constellations: Orion, Taurus, and Pleiades. ! So one of the earliest forms of visual data communication is maps.
8. 8. Let’s jump to the 18th Century. This is William Playfair. ! Born in 1759 Scotland during the Enlightenment, he was a failed silversmith who then dabbled in a number of different professions. He was an investment broker, a translator, a land speculator, a journalist, and even a blackmailer and convict. ! He also found time to be a political economist. ===
9. 9. As an economist, he invented the line chart in 1786. ! Here it is: exports and imports between England and a few Nordic countries during the 1700s. ! === Source: Playfair’s trade-balance time-series chart, published in his Commercial and Political Atlas, 1786
10. 10. That same year, the first bar chart was born - illustrating Scottish exports and imports from Christmas 1780 to Christmas 1781. === Source: Exports and Imports of Scotland to and from different parts for one Year from Christmas 1780 to Christmas 1781
11. 11. And in 1801 he made the delicious innovation of a pie chart. ! === Source: Pie chart from Playfair's Statistical Breviary (1801), showing the proportions of the Turkish Empire located in Asia, Europe and Africa before 1789
12. 12. Let’s move Stateside to the Civl War Era. ! This painting by Francis Bicknell Carpenter in 1864 is of the First Reading of the Emancipation Proclamation of President Lincoln. ! If you look to the right, <click> there is a data visualization. === ! http://en.wikipedia.org/wiki/File:Emancipation_proclamation.jpg
13. 13. Here it is. Near the end of 1861, with the American Union crumbling, President Lincoln became obsessed with this three feet long visualization. It is a map of the southern states covered with shading, with the darkness of each county reflecting the number of slaves who lived there. ! Biographers noted that Lincoln had become obsessed with this infographic. ! In it, he saw testimony that the American South was not a uniform bloc. Areas of heavy slavery tended to be secessionist, but the areas in between held the hope of pro-Union sympathy. And in Lincoln’s mind, this map showed the moral terrain of the region and was critical to his war effort planning. ! So data visualizations have had an important role in the history of our nation. === Source: http://www.newyorker.com/online/blogs/elements/2013/10/why-abraham-lincoln-loved-infographics.html ! Map showing the distribution of the slave population of the southern states of the United States. Compiled from the census of 1860 Drawn by E. Hergesheimer. Engr. by Th. Leonhardt.
14. 14. More recently, on August 25, 2012 an infographic left the solar system for the first time carrying a message for intelligent aliens. ! On this day, the Voyager I space probe entered interstellar space. ! In the space probe, there are the Voyager Golden Records, which are LP records that contain sounds and images selected to portray the diversity of life and culture on Earth. ! They were intended for any intelligent extraterrestrial life forms, or for future humans, who may find them. The cover of the record had graphics with instructions for playing these records. === Source: New York Magazine, October 2013
15. 15. Efﬁcient communication ! ! Opportunity for instruction and research support digital literacies and scholarly communications ! ! Data are persuasive From these stories, we see that the visual representation of data is a quick form of communication that helps us understand the world more deeply because of their analytical insights. ! In academic libraries, data visuals can help us communicate effectively and efficiently in the reports we write. ! Data visualization is at the intersection of digital literacies and scholarly communication, which are increasingly at play for the instruction and research support services we provide. ! And academia is an analytical place, where data and evidence can be very persuasive, and presenting them visually is even more persuasive to promoting your vision or efforts. === !
16. 16. Determine if a chart is needed ! Pick the best chart for your data analysis and story ! Design the chart for ease of reading and comprehension ! Build the chart in Excel This beginners’ class will help you make charts in Microsoft Excel for your own reports ! or to consult with library patrons who are writing up a research project. ! By the end of today, you will be able to <point to board> Identify the best type of chart to communicate a data analysis or story. Outline heuristics for the effective chart design. Create basic charts with Microsoft Excel. ! To meet these objectives, we are going to explore four steps in building a chart. First, determine if a chart is needed Next, pick the best chart for your data analysis and story Afterwards, design the chart for ease of reading and comprehension Finally, build the chart in Excel and embed in a document
17. 17. 1. Determine if a chart is needed Table Chart for ease of looking up values when the message is contained in the shape of the values when precise values are required to reveal relationships among the values Sometimes tables are the best format to present your data, and sometimes charts are better. How do you know when to use a chart? ! You use a table for ease of looking up values and when precise values are required ! You use a chart when the message is contained in the shape of the values and when you want to reveal relationships among the values ===
18. 18. This beef temperature data are best presented in a table. The precise values need to be shown. And it’s easier to look up the values in a table. ===
19. 19. Here are Nick and Nora. Their caretakers wanted to monitor their growth, and created this chart. === Source: http://xiphias.livejournal.com/542646.html
20. 20. On the vertical axis is their weight. On the horizontal axis is the date from July to November. ! You can see how well Nick and Nora are turning cat food into cat flesh. ! The chart is helpful because you can see the shape and the relationships behind the data. For example, the two cats started at the same weight, and then Nick increased his weight at a faster rate than Nora. ! There was a slight plateau in September, but afterwards, their weight increased at a similar rate. ===
21. 21. Data Table Chart The birthdate of every student library employee The rising and falling numbers of student library employees working in each semester from 1995 to present Important historical events in British Columbia and the year they occurred The correlation between the number of hours worked by student library employees and their academic success At the bottom of page 1, we have a quick activity. Review the data and mark with an X whether you would use a table or a chart to represent them. ! <click> ! Here are the answers. To review, tables are great for looking up precise values. Charts illustrate relationships and shapes. ===
22. 22. 2. Pick the best chart for your data analysis and story Congratulations, you’ve determined a need for a chart. ! In step 2, pick the best chart for your data analysis and story. ===
23. 23. What’s your quantitative message? Value comparison Time-Series Ranking Part-to-Whole Deviation Distribution Correlation The best chart will match the story you want to tell through your data. ! What is your message? What is the relationship you want to display? ! In fact, there are seven types of quantitative messages you can express with charts. They differ in how the different values relate to one another. ! I’m going to give you some time to review the definitions on page 2 of your handout. ! <pause> ! Any questions? ===
24. 24. Common chart types Histogram Now let’s look at five commonly used charts. They’re listed on page 3. ! With the histogram, there are bins that represent a category, and the area of each bar represents the count of items in that category. ===
25. 25. Pie chart 7% 8% 35% 10% 11% 29% In the pie chart, each slice represents a percentage value of the whole pie.
26. 26. Column/Bar chart Next is the column chart, which is also known as a bar chart. ! The rectangular bars are proportional to the values they represent.
27. 27. Line chart 100 75 50 25 0 April May In a line chart, data points are in a sequence like time, and they’re connected by lines. June July
28. 28. Scatterplot Finally, the scatterplot displays data points for two variables along the horizontal and the vertical axis scales. ! Here is a plot of Old Faithful eruptions where each point shows the time between eruptions and its duration. ! ===
29. 29. We’ve looked at 7 quantitative messages and 5 common chart types. Let’s match them up on what chart to make for the data story you want to tell. Check out pages 5 and 6 of your handout. ! For a nominal comparison, the bar chart helps differentiate the various categories. ! Time series are well served by line graphs to track the changes over time. ! Ranking relationships benefit from a bar chart for the relative order of the different categories or items. ! For part-to-whole relationships, use the bar chart or the pie chart to show the different proportions. ===
30. 30. For deviations, bars can be used to emphasize the negative and positive difference from a reference point. ! Frequency distributions are good with histograms. ! And finally, scatterplots are good for correlations. The location of the dots encode values of the two different measures on the horizontal and the vertical axis. ! To review, there are 7 quantitative messages. And there are five charts that are helpful to communicating these messages. ===
31. 31. Before we move to the next step, let’s review the anatomy of a chart. ! I’ll give you a couple of seconds to review page 7 of your handout. ! <pause> ! I want to stress one part. Normally, the horizontal axis represents the independent variable. The independent variable is the measure that the researcher/observer has control over in the research design. In this case, it is date, the researcher picked these dates to observe, he has control over that. But he has no control over the rainfall on those days, that’s Mother Nature’s job. Other examples of independent variables include time or category. ! Before we continue, are there any questions about the parts of a chart? ===
32. 32. 3. Design the chart for ease of reading and comprehension In the previous steps, you determined your quantitative message and the best chart to communicate that. ! Before we build a chart, let’s look at principles that will help you design your chart for ease of reading and comprehension. ! I refer to kaiseki, the traditional multi-course Japanese dinner that is analogous to Western haute cuisine. ! Making these small, simple dishes is in fact complex with a lot of principles at play. === !
33. 33. We’re going to review design heuristics for keeping charts simple so that they are easy to understand. ! There is a lot going on in this chart. ! There are four scales across. There are lines criss-crossing. They are scores at the bottom. ! I’m not sure what all this means. ===
34. 34. The first heuristic is to limit the number of stories you tell with a chart. ! Creating separate charts for different narratives can be easier to understand. ! Here the earlier chart has been separated into four individual ones. Easier to understand. ! Descriptive titles are helpful. Write a title that articulates an analytical insight. Rather than “books checked out by month”, try “more books checked out during the fall semester at the library.” ===
35. 35. 20 15 10 5 0 0 3 6 Secondly, show the data - make them prominent. I can barely see the data points. === 9 12
36. 36. 20 15 10 5 0 0 3 Much better. The dots are prominent and the lines connect their path. ! === 6 9 12
37. 37. 20 15 10 5 0 0 3 6 Principle 3. I’ve used contrasting colors and I can easily see the different data series. ! But I’m confused as to what the axes mean. What do they depict? === 9 12
38. 38. Count of litter on the Vancouver test SkyTrain car 20 15 10 5 0 0 3 6 9 12 Hours of operation Millennium Line Expo Line Canada Line Number 4. Use text labels to add detail and clarity. ! We’ve labelled the axes with hours of operation and the count of litter on a subway car. There’s a legend at the bottom that explains the different line colors.
39. 39. 200 150 100 50 0 April May June July Principle 5. ! Help the reader to read quickly. ! Here is a stacked column chart, I have to conduct some mental math to determine the size of the green tops.
40. 40. 100 75 50 25 0 April May June It’s easier for the reader when we separate the stacked columns and cluster them together like so. ! === July
41. 41. 100 75 50 25 0 April May June Month Number 6. Let chart junk go. Remove design features that don’t help explain the data. ! Here we could remove some of the grid lines for a cleaner look. We could also remove the month label at the bottom since it is redundant. === July
42. 42. 100 75 50 25 0 April It looks a lot better with these changes. May June July
43. 43. Count of litter on the Vancouver test SkyTrain car 20 15 10 5 0 0 3 6 9 Hours of operation Millennium Line Principle 7. Explain the data by responding to journalistic questions Expo Line Canada Line 12
44. 44. Litter on sample Vancouver SkyTrain cars on Dec 17, 2013 Count of litter on the Vancouver test SkyTrain car Cleaning services needed by 9th hour of operation, particularly on Millennium and Expo lines 20 15 10 5 0 0 3 6 9 Hours of operation Millennium Line Expo Line Canada Line I’ve added a title that helps answer some who, what, when, where questions. Litter on sample Vancouver SkyTrain cars on Dec 17, 2013 ! I’ve also added a subtitle that articulates an analytical insight. Cleaning services are needed by the 9th hour of operation, particularly on the Millennium and Expo lines. === 12
45. 45. Sample A Sample B 70 200 52.5 160 35 120 17.5 80 0 40 April May June July Finally, be consistent to prevent distortions and disorientation. ! Use consistent intervals and use similar scales for separate charts that are related. ! These two charts in fact … ! === April May June July
46. 46. Sample A 70 52.5 Sample B 200 70 58 55 160 43 35 120 17.5 80 70 55 0 April May 40 April May June July 58 43 June July illustrate the exact same data, but adjustments to the scale can give a different impression of the two samples. ! There you have it, 8 principles for keeping charts easy to read and understand. ===
47. 47. 4. Build the chart in Excel and embed in document Enter data on a worksheet Insert tab, select chart Cut and paste chart With all your prep in the previous 3 steps, let’s build the chart. ! I’m going to demo the process. There are basically 3 parts: enter data into a worksheet, click the insert tab, and select a chart, and then cut and paste your chart into another document. ===
48. 48. Identify the data story. Use Excel; make visuals simple. Before I start with the demo, I want to summarize the discussion component quickly in the form of a haiku. ! Identify the data story. Use Excel; make simple visuals. ! ===