presentation graphics and exploratory graphics

Exploratory Data Analysis with R

5 principles of analytic graphics.

Watch a video of this chapter .

The material for this chapter is inspired by Edward Tufte’s wonderful book Beautiful Evidence , which I strongly encourage you to buy if you are able. He discusses how to make informative and useful data graphics and lays out six principles that are important to achieving that goal. Some of these principles are perhaps more relevant to making “final” graphics as opposed to more “exploratory” graphics, but I believe they are all important principles to keep in mind.

5.1 Show comparisons

Showing comparisons is really the basis of all good scientific investigation. Evidence for a hypothesis is always relative to another competing hypothesis. When you say “the evidence favors hypothesis A”, what you mean to say is that “the evidence favors hypothesis A versus hypothesis B”. A good scientist is always asking “Compared to What?” when confronted with a scientific claim or statement. Data graphics should generally follow this same principle. You should always be comparing at least two things.

For example, take a look at the plot below. This plot shows the change in symptom-free days in a group of children enrolled in a clinical trial testing whether an air cleaner installed in a child’s home improves their asthma-related symptoms. This study was conducted at the Johns Hopkins University School of Medicine and was conducted in homes where a smoker was living for at least 4 days a week. Each child was assessed at baseline and then 6-months later at a second visit. The aim was to improve a child’s symptom-free days over the 6-month period. In this case, a higher number is better, indicating that they had more symptom-free days.

Figure 5.1: Change in symptom-free days with air cleaner

There were 47 children who received the air cleaner, and you can see from the boxplot that on average the number of symptom-free days increased by about 1 day (the solid line in the middle of the box is the median of the data).

But the question of “compared to what?” is not answered in this plot. In particular, we don’t know from the plot what would have happened if the children had not received the air cleaner. But of course, we do have that data and we can show both the group that received the air cleaner and the control group that did not.

Figure 5.2: Change in symptom-free days by treatment group

Here we can see that on average, the control group children changed very little in terms of their symptom free days. Therefore, compared to children who did not receive an air cleaner , children receiving an air cleaner experienced improved asthma morbidity.

5.2 Show causality, mechanism, explanation, systematic structure

If possible, it’s always useful to show your causal framework for thinking about a question. Generally, it’s difficult to prove that one thing causes another thing even with the most carefully collected data. But it’s still often useful for your data graphics to indicate what you are thinking about in terms of cause. Such a display may suggest hypotheses or refute them, but most importantly, they will raise new questions that can be followed up with new data or analyses.

In the plot below, which is reproduced from the previous section, I show the change in symptom-free days for a group of children who received an air cleaner and a group of children who received no intervention.

Figure 5.3: Change in symptom-free days by treatment group

From the plot, it seems clear that on average, the group that received an air cleaner experienced improved asthma morbidity (more symptom-free days, a good thing).

An interesting question might be “Why do the children with the air cleaner improve?” This may not be the most important question—you might just care that the air cleaners help things—but answering the question of “why?” might lead to improvements or new developments.

The hypothesis behind air cleaners improving asthma morbidity in children is that the air cleaners remove airborne particles from the air. Given that the homes in this study all had smokers living in them, it is likely that there is a high level of particles in the air, primarily from second-hand smoke.

It’s fairly well-understood that inhaling fine particles can exacerbate asthma symptoms, so it stands to reason that reducing the presence in the air should improve asthma symptoms. Therefore, we’d expect that the group receiving the air cleaners should on average see a decrease in airborne particles. In this case we are tracking fine particulate matter , also called PM2.5 which stands for particulate matter less than or equal to 2.5 microns in aerodynamic diameter.

In the plot below, you can see both the change in symptom-free days for both groups (left) and the change in PM2.5 in both groups (right).

Figure 5.4: Change in symptom-free days and change in PM2.5 levels in-home

Now we can see from the right-hand plot that on average in the control group, the level of PM2.5 actually increased a little bit while in the air cleaner group the levels decreased on average. This pattern shown in the plot above is consistent with the idea that air cleaners improve health by reducing airborne particles. However, it is not conclusive proof of this idea because there may be other unmeasured confounding factors that can lower levels of PM2.5 and improve symptom-free days.

5.3 Show multivariate data

The real world is multivariate. For anything that you might study, there are usually many attributes that you can measure. The point is that data graphics should attempt to show this information as much as possible, rather than reduce things down to one or two features that we can plot on a page. There are a variety of ways that you can show multivariate data, and you don’t need to wear 3-D glasses to do it.

Here is just a quick example. Below is data on daily airborne particulate matter (“PM10”) in New York City and mortality from 1987 to 2000. Each point on the plot represents the average PM10 level for that day (measured in micrograms per cubic meter) and the number of deaths on that day. The PM10 data come from the U.S. Environmental Protection Agency and the mortality data come from the U.S. National Center for Health Statistics.

Figure 5.5: PM10 and mortality in New York City

This is a bivariate plot showing two variables in this dataset. From the plot it seems that there is a slight negative relationship between the two variables. That is, higher daily average levels of PM10 appear to be associated with lower levels of mortality (fewer deaths per day).

However, there are other factors that are associated with both mortality and PM10 levels. One example is the season. It’s well known that mortality tends to be higher in the winter than in the summer. That can be easily shown in the following plot of mortality and date.

Figure 5.6: Daily mortality in New York City

Similarly, we can show that in New York City, PM10 levels tend to be high in the summer and low in the winter. Here’s the plot for daily PM10 over the same time period. Note that the PM10 data have been centered (the overall mean has been subtracted from them) so that is why there are both positive and negative values.

Figure 5.7: Daily PM10 in New York City

From the two plots we can see that PM10 and mortality have opposite seasonality with mortality being high in the winter and PM10 being high in the summer. What happens if we plot the relationship between mortality and PM10 by season ? That plot is below.

Figure 5.8: PM10 and mortality in New York City by season

Interestingly, before, when we plotted PM10 and mortality by itself, the relationship appeared to be slightly negative. However, in each of the plots above, the relationship is slightly positive. This set of plots illustrates the effect of confounding by season, because season is related to both PM10 levels and to mortality counts, but in different ways for each one.

This example illustrates just one of many reasons why it can be useful to plot multivariate data and to show as many features as intelligently possible. In some cases, you may uncover unexpected relationships depending on how they are plotted or visualized.

5.4 Integrate evidence

Just because you may be making data graphics, doesn’t mean you have to rely solely on circles and lines to make your point. You can also include printed numbers, words, images, and diagrams to tell your story. In other words, data graphics should make use of many modes of data presentation simultaneously, not just the ones that are familiar to you or that the software can handle. One should never let the tools available drive the analysis; one should integrate as much evidence as possible on to a graphic as possible.

5.5 Describe and document the evidence

Data graphics should be appropriately documented with labels, scales, and sources. A general rule for me is that a data graphic should tell a complete story all by itself. You should not have to refer to extra text or descriptions when interpreting a plot, if possible. Ideally, a plot would have all of the necessary descriptions attached to it. You might think that this level of documentation should be reserved for “final” plots as opposed to exploratory ones, but it’s good to get in the habit of documenting your evidence sooner rather than later.

Imagine if you were writing a paper or a report, and a data graphic was presented to make the primary point. Imagine the person you hand the paper/report to has very little time and will only focus on the graphic. Is there enough information on that graphic for the person to get the story? While it is certainly possible to be too detailed, I tend to err on the side of more information rather than less.

In the simple example below, I plot the same data twice (this is the PM10 data from the previous section of this chapter).

Figure 5.9: Labelling and annotation of data graphics

The plot on the left is a default plot generated by the plot function in R. The plot on the right uses the same plot function but adds annotations like a title, y-axis label, x-axis label. Key information included is where the data were collected (New York), the units of measurement, the time scale of measurements (daily), and the source of the data (EPA).

5.6 Content, Content, Content

Analytical presentations ultimately stand or fall depending on the quality, relevance, and integrity of their content. This includes the question being asked and the evidence presented in favor of certain hypotheses. No amount of visualization magic or bells and whistles can make poor data, or more importantly, a poorly formed question, shine with clarity. Starting with a good question, developing a sound approach, and only presenting information that is necessary for answering that question, is essential to every data graphic.

5.7 References

This chapter is inspired by the work of Edward Tufte. I encourage you to take a look at his books, in particular the following book:

Edward Tufte (2006). Beautiful Evidence , Graphics Press LLC. www.edwardtufte.com

Statistical Graphics and more

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Presentation vs. Exploration

Giving a clear separation between presentation graphics and graphics for data exploration is not always easy, because many aspects of the graphics are shared by both types. I very much liked the distinction Antony Unwin gave during one of his latest talks.

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BUS610: Business Intelligence and Analytics

Why is data visualization important.

This paper discusses how graphics reveal data features that statistics and models may miss and why this is important. The paper also points out that we often accept graphics unquestioned as truth when sometimes they are incorrect or misrepresent the data by not giving the full perspective. Graphics raise questions that stimulate research and suggest ideas. After reading this paper, look at graphs used in news reports online and see if you can find at least one that fits each of the issues named.

Why is Data Visualization Important? What is Important in Data Visualization?

Column Editor's note: Data visualization, facilitated by the power of the computer, represents one of the fundamental tools of modern data science. Professor Antony Unwin from the University of Augsburg describes different ways in which data visualization is used, explores the opportunities for future research in the area, and looks at how data visualization is taught.

The What and Why of Data Visualization

Data visualization means drawing graphic displays to show data. Sometimes every data point is drawn, as in a scatterplot, sometimes statistical summaries may be shown, as in a histogram. The displays are mainly descriptive, concentrating on 'raw' data and simple summaries. They can include displays of transformed data, sometimes based on complicated transformations. One person's statistics may be another person's raw data. As with other aspects of working with graphics, it would be useful to have an agreed base of concepts and terminology to build on. The main goal is to visualize data and statistics, interpreting the displays to gain information.

Data visualization is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining to check data quality and to help analysts become familiar with the structure and features of the data before them. This is a part of data analysis that is underplayed in textbooks, yet ever-present in actual investigations. Look, for instance, at the one-sided peaks in the distributions of marathon finishing times.

Graphics reveal data features that statistics and models may miss: unusual distributions of data, local patterns, clusterings, gaps, missing values, evidence of rounding or heaping, implicit boundaries, outliers, and so on. Graphics raise questions that stimulate research and suggest ideas. It sounds easy. In fact, interpreting graphics needs experience to identify potentially interesting features and statistical nous to guard against the dangers of overinterpretation. Just as graphics are useful for checking model results, models are useful for checking ideas derived from graphics.

This overview concentrates on static graphics. Dynamic graphics and, more especially, interactive graphics are in an exciting stage of development and have much to add. They require an article of their own. Superb examples include Human Terrain, a dynamic graphic showing the world's population in 3-D, and the interactive NameVoyager.

'A Picture Is Worth a Thousand Words'

Famous sayings have a way of developing a life of their own. A picture is not a substitute for a thousand words; it needs a thousand words (or more). For data visualization you need to know the context, the source of the data, how and why they were collected, whether more could be collected, the reasons for drawing the displays, and how people with the necessary background knowledge advise they might be interpreted. There is a story that M. G. Kendall reviewed a book of R.A. Fisher's with the words: "No one should read this book who has not read it already". It is like that with graphics. If you have read all the supporting text, the display is often memorable and readily understandable. If you have not, it is not. Graphics on their own are insufficient, they are part of a whole. They complement text and are complemented by text. Student's reanalysis of the Lanarkshire Milk Experiment is an excellent example (and is also interesting as an early analysis of a large data set).

The potential synergy of text and graphics can be appreciated by talking through your own graphics, explaining them to others. Why have you drawn those graphics? How have you drawn them? What can be seen? Are there interesting patterns? What could be changed and improved? Which other graphics might be drawn? How can conclusions be checked? There should be more talking about graphics and less relying on the graphics to speak for themselves.

When it comes to graphics you have not drawn yourself, the same kinds of questions are still relevant, although they may be more difficult to answer. Edward Tufte described Charles Minard's display of Napoleon's Russian campaign as the best statistical graphic ever drawn. It is a magnificent graphic, fully deserving of the praise heaped on it, yet as Lee Wilkinson has pointed out in his book The Grammar of Graphics , there are inaccuracies and imprecisions in the display. Why did no one point them out before? We are too used to accepting graphics uncritically, not asking enough questions of them.

Presentation and Exploratory Graphics

Presentation and exploratory graphics are quite different animals. In presenting your results, you may have space for only one graphic and no idea how many people may see it. If it appears in a newspaper or on television or the Web, your audience could be millions of people. The graphic should be well-designed and well-drawn with an effective accompanying explanatory text. On the other hand, if you are exploring data, then you need many, many graphics and they are for an audience of one: yourself. The individual graphics need not be perfect, but they should provide alternative views and additional information. Presentation graphics are used to convey known information and are often designed to attract attention. Exploratory graphics are used to find new information and should direct attention to information.

Published graphics tend to be graphics for presentation, partly because they are for publication and partly because no one wants to see hundreds of quick graphics that may or may not have been helpful. It is rather like mathematical proofs: articles contain the elegant and concise final versions, not the scribbled notes and random ideas that came before. How many graphics may have been drawn before the striking display was chosen to show the resignations of U.K. cabinet ministers in recent years?

Exploratory graphics take advantage of how easy it is now to draw and redraw graphics. What used to be a slow and wearisome process, even including having to print out displays, has become fast and flexible. At the same time, new, additional skills are required. Identifying interesting features and knowing how to check them in more detail among a myriad of possible graphics is not just a matter of drawing many graphics, you need interpretative skills and an appreciation of which graphics will provide what kinds of information. There is so much that can be varied: the variables displayed, the types of graphics, the sizes of graphics and their aspect ratios, the colors and symbols used, the scales and limits, the ordering of categorical variables, the ordering of variables in multivariate displays. Selecting from the wide range of graphics wisely, and understanding how to gain insights, are not trivial tasks. The lack of a theory of data visualization to guide and build on is a key issue.

Data Visualization Has Become More Important

Better hardware has meant more precise reproduction, better color (including alpha-blending), and faster drawing. Better software has meant easier and more flexible drawing, consistent themes, and higher standards. Computer scientists have become much more involved, both on the technical side and in introducing new approaches. There has been progress in developing a theory of graphics, especially thanks to Wilkinson's Grammar of Graphics  and Hadley Wickham's implementation of it in the R package ggplot2. There is continuing work and better understanding of the problems of color and perception. Graphics that were rarely used and difficult to draw, such as parallel coordinate plots and mosaicplots, have been refined and developed. Much larger data sets can be analyzed and visualized and graphics can play a valuable role in diagnosing the strengths and weaknesses of complex models. Data visualizations can be found everywhere, in scientific publications, in newspapers and TV, and on the Web. There are many Web pages where graphics are discussed and debated. This is a huge improvement over the situation of even 20 years ago.

Research in Data Visualization

There are great opportunities for future research in data visualization. Principles are needed on how to decide which of many possible graphics to draw. It is not a matter of drawing a single, 'optimal' graphic, if such a thing even existed; it is a matter of choosing a group of graphics that will provide more information. It is like taking photographs of a complicated object, a single one would not be enough, and taking pictures from every possible angle and distance would be far too many. Sets of graphics are useful for providing context, as the scatterplots in Klimek, Yegorov, Hanel, and Thurner demonstrate.

More understanding of combining and linking graphics is needed, whether in static ensembles or in interactive displays, just as better software is needed for these. The value of alignment and common scaling for making effective comparisons, for instance, with small multiples and faceting (displaying many graphics of the same form conditioning on other variables) is one part of this. It is a historical curiosity that the current exciting work on interactive graphics on the Web still lags behind standalone systems that were already available more than 30 years ago in linking multiple windows. Data Desk and JMP were commercial examples at the time.

Published graphics are sometimes attractive and beautifully produced. The content does not always match. That may be because authors and publishers do not expect the graphics to be examined in any detail. They may be added as illustrations to balance the layout and make it look more agreeable. If you do not have a suitable photograph, cartoon, or map, you could use a colorful statistical graphic. I have many times heard people say that they do not understand numbers and were bad at mathematics in school. No one has ever said to me they do not understand graphics, perhaps because they regard them as illustrations and not as central parts of an argument. There is work to be done in educating researchers and readers in the value of graphics.

Research into new and innovative graphics is exciting and productive. Simultaneously, it is essential to make the best use of known and well-understood graphics. There is a risk of emphasis on novelty at the expense of familiarity. New, innovative graphics need instruction and experience to interpret them. Their designers have spent much time developing them and reasonably enough believe that what is obvious to them should be obvious to everyone. Just think of the humble scatterplot. It is only in recent years that scatterplots have appeared in the media, although they are one of the most important statistical graphics. If you have never seen one before, they can be intimidating, even more so when you are told 'It is clear that...' or 'You can easily see that...' We should build on the familiar to carry our readers along with us.

Examples and Sources

The visualizations I like may not be the visualizations you like. I urge you to search extensively and judge for yourselves. Much interesting and thought-provoking material can be found in Tufte's classic books, and in the displays by the New York Times over the years. Other newspapers and media have also produced excellent work. These are, of course, presentation graphics, but they offer much to engage with. It is difficult to make a choice among the many individual Web pages providing examples and discussion, but Visualising Data is one site that recommends highlights across the web. The current interest and activity in graphics are very welcome.

What Happens Now?

Educating people in choosing, drawing, and interpreting graphics is more difficult than you might think. Data visualization is not taught badly, it is just not taught very much at all. Ideally, there should be better theory, and consequently better graphics. That will take time. In the meantime, we should:

– discuss more graphics more; – interpret more graphics more; – teach more graphics more.

Leland Wilkinson. Presentation Graphics . In International Encyclopedia of the Social and Behavioral Sciences , vol. 9, pp. 6369--6379, 2001.

This paper surveys briefly the history of presentation graphics, principles of usage, and applications.

@Article{ wilkinson:2001:PG, author = {Leland Wilkinson}, title = {Presentation Graphics}, journal = {International Encyclopedia of the Social and Behavioral Sciences}, year = {2001}, volume = {9}, pages = {6369--6379}, }

References:

Principles of Analytic Graphics

Roger D. Peng, Associate Professor of Biostatistics Johns Hopkins Bloomberg School of Public Health

• Principle 1: Show comparisons
• Evidence for a hypothesis is always relative to another competing hypothesis.
• Always ask "Compared to What?"

Show Comparisons

Reference: Butz AM, et al. , JAMA Pediatrics , 2011.

Principle 2: Show causality, mechanism, explanation, systematic structure

• What is your causal framework for thinking about a question?

Show causality, mechanism

• Principle 3: Show multivariate data
• Multivariate = more than 2 variables
• The real world is multivariate
• Need to "escape flatland"

Show Multivariate Data

Principle 4: Integration of evidence

• Completely integrate words, numbers, images, diagrams
• Data graphics should make use of many modes of data presentation
• Don't let the tool drive the analysis

Integrate Different Modes of Evidence

Principle 5: Describe and document the evidence with appropriate labels, scales, sources, etc.

• A data graphic should tell a complete story that is credible
• Principle 6: Content is king
• Analytical presentations ultimately stand or fall depending on the quality, relevance, and integrity of their content
• Principle 2: Show causality, mechanism, explanation
• Principle 4: Integrate multiple modes of evidence
• Principle 5: Describe and document the evidence

Edward Tufte (2006). Beautiful Evidence , Graphics Press LLC. www.edwardtufte.com

Exploratory Graphics with R (Part 1)

Olusoji o. d., owokotomo o. e., ajayi t. o., obisesan, k. o., friday, september 11, 2015.

The primary goal of a statistician is to understand data and several tools have been developed to this end over-time. These tools have been used to do analyze several dataset arising from varying fields of human endeavour but one of the oldest tool known to statisticians in terms of understanding data is the use of pictures to depict data. If you are very conversant with Discovery World, you would agree with me that the early man used pictogram (a type of graph used in statistics) in representing information. There are several caves, rocks and even pyramids with different kinds of drawings showing how the early man lived and what they own. All this points out the fact that graphs have been in existent even before the formulation of the discipline statistics. So let us journey into this ancient statistical tool and see how it is to be properly used.

Introduction

Exploratory Graphs are often misunderstood as Analytic graphs but there is a difference. Analytic graphs aren’t the final graphs to be included in presentations or final documents, Exploratory graphs are. Analytic graphs are constructed to understand the nature and pattern of relationships existing in the data while Exploratory Graphs (a product of analytic graphs) are those final piece of art generated from data, included in the final document for the consumption of the reading public. Several analytic graphs are drawn but the best of them becomes exploratory graphs. While analytic graphs have principles, exploratory graphs have characteristics but they have the same aim and often achieve the same objective.

Constructing graphs also forms one of the most fundamental but crucial step in Data Analysis and Data Science. Exploratory graphs is the last step of what is popularly referred to as Exploratory Data Analysis and it sure has a lot of use and advantage.

Why Graphs?

It is quite common for a crude statistician to go straight to the modelling technique or build a machine learning algorithm that will help him better understand the dataset he is tackling (the act of using a gun to kill a mosquito) but this act is quite dangerous and can lead to misleading results and conclusions. The following are reasons why use of graphs is important;

• to better understand data properties,
• to uncover salient and hidden information about the data
• to discover patterns and trends in the data
• to suggest appropriate modeling strategy
• to have an idea of what to expect from the model to be used
• to debug data analysis or a prediction algorithm
• to communicate results

It isn’t surprising that the seventh reason is the most known advantage of using graphs but the first six reasons are far more important than the last. Nonetheless, graphs better communicate results than numbers especially if your reading public are strictly non-statisticians.

Principles of Analytic Graphics

According to Roger P. (2014) , the following are crucial (but not entirely all) principles of exploratory graphs;

• show comparisons: This is quite fundamental and almost all graphs tend to satisfy this principle. Most graphs tend to compare one level of a variable to the other while others compare one group to another. Asking the question of “Compared to What?” can actually help determine if a graph satisfies this principle. To illustrate how crucial this principle is, let us consider two plots using the FuelEconomy dataset contained in the AppliedPredictiveLearning package in R .

without much stress, you could see the advantage of comparison here. The first plot gives the overall description of displacement of cars surveyed in 2010 while the second plot compared the overall description of displacement of cars surveyed in 2010 to those surveyed in 2012. One can easily see a reduction in median (the thick black line in the box) displacement of cars between 2010 and 2012.

• Show Causality, Mechanism, Explanation and Systematic Structure: Unfortunately most graphs don’t satisfy this principle. Asking yourself what could be the causal framework for thinking about the question you seek to answer can be of help here. Understanding the question you seek to answer as an anlyst is key here. To illustrate this, let us consider the PhD dataset used in Olubusoye and Olusoji (2014) ;

while the first plot just showed the general properties of duration of studies for PhD students at the University of Ibadan, the other plot showed the properties of duration by gender since gender is a possible cause for variation in duration of studies.

• show multivariate data: as much as I hate to admit it, univariate problems rarely occur in practice. The real world is multivariate. Hence, the more variable you can invovle in a plot, the more information revealed. But, you need to becareful as involving too many variables in a plot can actually trade off the meaning of the plot. As an example, still using the dataset in Olubusoye and Olusoji (2014) , let us illustrate the importance of showing multivariate data;

while the first plot shows a general positive relationship between Age and duration but the second shows that this is reversed for respondents who didn’t supply their age.

don’t let the tool drive the analysis: as much as people think plots can be limited to already existing plots, but that is wrong. Advances in statistical programming and emergence of statistics oriented languages have made it possible to extend current graphs or even develop new ones. Integrate as many evidence as possible, make use of many modes of many data presentation and make sure to integrate enough evidence in form of words, numbers, images and diagrams.

desribe and document the evidence with appropriate labels, scales and sources: a data graphic should tell a complete story that is credible.

Remember that content is king: irrelevant content will lead to a failed graph. Graphs stand or fail depending on the quality, relevance and integrity of their content.

Characteristics of Exploratory Graphs

According to Roger, R. (2014) , the following are not conventional but characteristics of exploratory graphs;

• they are made quickly
• a large number are made
• the goal is understanding
• coluor/sizes are primarily used for information

This piece have actually introduced you to what analytical and exploratory graphs are, their characteristics and principles. In the next lecture, we will delve into creating plots using R . We hope this is insightful and rewarding, till next week, USE R .

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What are Presentation Graphics?

Pre-requisites: Introduction to Computer Graphics

Graphics are defined as any sketch or a drawing or a special network that pictorially represents some meaningful information. Computer Graphics is used where a set of images needs to be manipulated or the creation of the image in the form of pixels and is drawn on the computer. Computer Graphics can be used in digital photography, film, entertainment, electronic gadgets, and all other core technologies which are required. It is a vast subject and area in the field of computer science. Computer Graphics can be used in UI design, rendering, geometric objects, animation, and many more. 

Presentation graphics provides predefined backgrounds and simple page layouts to help in the creation of slides. These slides can be moved automatically within seconds.

Slides: 

Slides mean we have text, graphics, audio clips, video clips, animations, and other effects   Presentation graphics is also known as Presentation Software. The best example of presentation graphics is Microsoft PowerPoint. 

In recent days we have a new presentation tool that is: our Web browser and that is for creating Web pages, like Microsoft’s FrontPage and Adobe’s PageMill. 

Rules to Design Presentation:

• Keep it simple: Make it simple to understand. It only contains information regarding your topic.
• Correct Spelling:   We have to be careful with the spelling that we have written.
• Consistency: There is need to be continuous flow from one slide to another.
• Don’t put too much on a Slide: Don’t write too much. just give a brief description and important points.
• Use Fonts Properly: Only you can use two font styles in the presentation.
• Find Your Equipment: First, you have to be confident with your topic and details about it.

Uses: 

Presentation graphics programs are mainly concerned with the display of graphs and charts but now allow users to attach anything from text to animations. When Compared to other programs like Excel, PowerPoint follows a slideshow format.

Applications:  

In the following areas we can use presentation software:

• Google Slides
• Microsoft Office
• WPS Office 
• Photo Slideshow with Music

Advantages:

• Less Cost: The cost is low to use presentation software
• Logical Ideas: Tables and graphs are used to represent information then a logical order is created to give a clear idea for viewers.
• Acceptability: By using this busy person can easily get an idea about the topic.

Disadvantages:

• Time-taking process. It needs more time to prepare.
• Data can sometimes be lost.
• Errors and Mistakes can occur during design.

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Designing Data Visualizations by Noah Iliinsky, Julie Steele

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Chapter 1. Classifications of Visualizations

There are several ways to categorize and think about different kinds of visualizations. Here are four of the most useful. The first two are unrelated to the others; the last two are related to each other.

One way to classify a data visualization is by counting how many different data dimensions it represents. By this we mean the number of discrete types of information that are visually encoded in a diagram. For example, a simple line graph may show the price of a company’s stock on different days : that’s two data dimensions. If multiple companies are shown (and therefore compared), there are now three dimensions; if trading volume per day is added to the graph, there are four ( Figure 1-1 ).

Figure 1-1. Four data dimensions are shown in this graph. Adding more points within any of these dimensions won’t change the graph’s complexity.

This count of the number of data dimensions can be described as the level of complexity of the visualization. As visualizations become more complex, they are more challenging to design well, and can be more difficult to learn from. For that reason, visualizations with no more than three or four dimensions of data are the most common—though visualizations with six, seven, or more dimensions can be found.

Adding more volume or data points of the same data dimension doesn’t increase complexity. Showing 100 years of stock data for one stock isn’t more complex than one week of data, it’s just more voluminous. Showing 50 companies instead of two might make the display more crowded or complicated, but fundamentally it’s just more data points in the company dimension, and therefore isn’t making the graph more complex.

There are two main challenges to designing more complex visualizations. The first is that the more dimensions you need to encode visually, the more individual visual properties you need to use. Selecting properties is easy to do for the first few dimensions when most visual properties haven’t been used. However, as more dimensions are added, finding appropriate, unused visual properties becomes more difficult. (Bear in mind that a visualization shows not just types of information but also the relationships between and among those information types.) As this difficulty in design increases, intentionality in the decision-making process becomes ever more necessary.

The way to succeed in the face of this challenge is to be intentional about which property to use for each dimension, and iterate or change encodings as the design evolves. This is the subject of Part II .

The second challenge for designing more complex visualizations is that there are relatively few well-known conventions, metaphors, defaults, and best practices to rely on. Because the safety net of convention may not exist, there is more of a burden on the designer to make good choices that can be easily understood by the reader.

Infographics versus Data Visualization

You may have heard the terms infographics and data visualization used in different ways, or interchangeably in different contexts, or even casually by the same person in a single sentence. You may also have heard these terms used politically—that is, with positive or negative connotations attached. Some people use infographic to refer to representations of information perceived as casual, funny, or frivolous, and visualization to refer to designs perceived to be more serious, rigorous, or academic.

The truth is, even though the art of representing statistical information visually is hundreds of years old, the vocabulary of the field is still evolving and settling. Among the general public, there is still confusion over what these two terms mean, but within the information design community, definitions for these terms are solidifying.

In short: The distinction between infographics and data visualizations (or information visualizations ) is based on both form and origin (see Figure 1-2 ).

Figure 1-2. The difference between infographics and data visualization may be loosely determined by the method of generation, the quantity of data represented, and the degree of aesthetic treatment applied.

Infographics

We suggest that the term infographics is useful for referring to any visual representation of data that is:

manually drawn (and therefore a custom treatment of the information);

specific to the data at hand (and therefore nontrivial to recreate with different data);

aesthetically rich (strong visual content meant to draw the eye and hold interest); and

relatively data-poor (because each piece of information must be manually encoded).

Put another way, infographics are illustrations where the data representation is manually laid out or sketched, probably with drawing software such as Adobe Illustrator. Because of their manually-drawn process of creation, infographics have the option of being aesthetically rich (see Figure 1-3 [ 2 ] ). Another consequence of their manual origins is they tend to be limited in the amount of data they can convey, simply due to the practical limitations of manipulating many data points. Similarly, it is difficult to change or update the data in an infographic, as any changes must be implemented manually.

Figure 1-3. Flint Hahn’s Burning Man infographic is a great example of an aesthetically rich, manually-drawn piece.

This is not a complete, universal, or absolute definition, but may be a helpful way to think about and identify the category.

Data Visualization

By contrast, we suggest that the terms data visualization and information visualization (casually, data viz and info viz ) are useful for referring to any visual representation of data that is:

algorithmically drawn (may have custom touches but is largely rendered with the help of computerized methods);

easy to regenerate with different data (the same form may be repurposed to represent different datasets with similar dimensions or characteristics);

often aesthetically barren (data is not decorated); and

relatively data-rich (large volumes of data are welcome and viable, in contrast to infographics).

Data visualizations are initially designed by a human, but are then drawn algorithmically with graphing, charting, or diagramming software. The advantage of this approach is that it is relatively simple to update or regenerate the visualization with more or new data. While they may show great volumes of data, information visualizations are often less aesthetically rich than infographics.

As you will have inferred from the title of this book, it is this latter category of data visualizations with which we are primarily concerned here. However, the principles we present are relevant to the design of both infographics and data visualizations.

Exploration versus Explanation

Generally speaking, there are two categories of data visualization: exploration and explanation . The two serve different purposes, and so there are tools and approaches that may be appropriate only for one and not the other. For this reason, it is important to understand the distinction, so that you can be sure you are using tools and approaches appropriate to the task at hand.

Exploration

Exploratory data visualizations are appropriate when you have a whole bunch of data and you’re not sure what’s in it . When you need to get a sense of what’s inside your data set, translating it into a visual medium can help you quickly identify its features, including interesting curves, lines, trends, or anomalous outliers.

Exploration is generally best done at a high level of granularity. There may be a whole lot of noise in your data, but if you oversimplify or strip out too much information, you could end up missing something important. This type of visualization is typically part of the data analysis phase, and is used to find the story the data has to tell you .

Explanation

By contrast, explanatory data visualization is appropriate when you already know what the data has to say, and you are trying to tell that story to somebody else . It could be the head of your department, a grant committee, or the general public.

Whoever your audience is, the story you are trying to tell (or the answer you are trying to share) is known to you at the outset , and therefore you can design to specifically accommodate and highlight that story. In other words, you’ll need to make certain editorial decisions about which information stays in, and which is distracting or irrelevant and should come out. This is a process of selecting focused data that will support the story you are trying to tell.

If exploratory data visualization is part of the data analysis phase, then explanatory data visualization is part of the presentation phase. Such a visualization may stand on its own, or may be part of a larger presentation , such as a speech, a newspaper article, or a report. In these scenarios, there is some supporting narrative—written or verbal—that further explains things.

Hybrids: Exploratory Explanation

It’s worth noting that there is also a kind of hybrid category, which involves a curated dataset that is nonetheless presented with the intention to allow some exploration on the reader’s part. These visualizations are usually interactive via some kind of graphical interface that lets the reader choose and constrain certain parameters, thereby discovering for herself whatever insights the dataset may have to offer. These might even be insights the creator of the visualization hasn’t come across yet.

So in these hybrid designs there is a certain freedom-of-discovery aspect to the information presented, but it is usually not totally raw; it has been distilled and facilitated to some extent. See http://www.juiceanalytics.com/nfl-visualization/ for an example.

Informative versus Persuasive versus Visual Art

We posit that there are three main categories of explanatory visualizations based on the relationships between the three necessary players: the designer, the reader, and the data.

This section refers to explanatory (or hybrid) visualizations exclusively, because it discusses designing visualizations of data with known parameters and stories. If you don’t yet know the message you intend to convey, then you’re still in an exploration phase, and probably aren’t designing for the same styles of consumption as this section describes.

The Designer-Reader-Data Trinity

It is useful to think of an effective explanatory data visualization as being supported by a three-legged stool consisting of the designer, the reader, and the data. Each of these “legs” exerts a force, or contributes a separate perspective, that must be taken into consideration for a visualization to be stable and successful. Chapter 2 will address the considerations of each of the three in much more detail, but we find it helpful to introduce the concept here.

Each of the three legs of the stool has a unique relationship to the other two. While it is necessary to account for the needs and perspective of all three in each visualization project, the dominant relationship will ultimately determine which category of visualization is needed (see Figure 1-4 ).

Figure 1-4. The nature of the visualization depends on which relationship (between two of the three components) is dominant.

Informative

An informative visualization primarily serves the relationship between the reader and the data . It aims for a neutral presentation of the facts in such a way that will educate the reader (though not necessarily persuade him). Informative visualizations are often associated with broad data sets, and seek to distill the content into a manageably consumable form. Ideally, they form the bulk of visualizations that the average person encounters on a day-to-day basis—whether that’s at work, in the newspaper, or on a service-provider’s website. The Burning Man Infographic ( Figure 1-2 ) is an example of informative visualization.

A persuasive visualization primarily serves the relationship between the designer and the reader . It is useful when the designer wishes to change the reader’s mind about something. It represents a very specific point of view, and advocates a change of opinion or action on the part of the reader. In this category of visualization, the data represented is specifically chosen for the purpose of supporting the designer’s point of view, and is presented carefully so as to convince the reader of same. See also: propaganda .

While an informative visualization may not have an intentional point of view in the manner that a persuasive visualization does, all visualizations are going to be biased to some degree, based on the fact that designers are human and have to make choices.

A good example of persuasive visualization is the Joint Economic Committee minority’s rendition of the proposed Democratic health care plan in 2010, shown in Figure 4-14 .

The third category, visual art , primarily serves the relationship between the designer and the data . Visual art is unlike the previous two categories in that it often entails unidirectional encoding of information, meaning that the reader may not be able to decode the visual presentation to understand the underlying information.

Whereas both informative and persuasive visualizations are meant to be easily decodable— bidirectional in their encoding—visual art merely translates the data into a visual form. The designer may intend only to condense it, translate it into a new medium, or make it beautiful; she may not intend for the reader to be able to extract anything from it other than enjoyment.

This category of visualization is sometimes more easily recognized than others. For example, Nora Ligorano and Marshall Reese designed a project that converts Twitter streams into a woven fiber-optic tapestry ( Figure 1-5 [ 3 ] ; http://ligoranoreese.net/fiber-optic-tapestry ). A project like this is abstract enough that most people intuitively recognize it as art: something to be appreciated rather than explicitly decoded.

But a project like the Planetary app from Bloom Studios ( http://planetary.bloom.io/ ) is less easily categorized. Ostensibly, one may decode the information represented visually by noting the number of stars (representing artists), planets (representing albums), and moons (representing tracks) in a constellation or galaxy on the screen. But properties such as track length, encoded as the speed at which the each moon orbits its album-planet, are encoded too subtly for the average user to decode—at which point, it just becomes something pretty to look at. A worthy pursuit in its own right, perhaps, but better clearly labeled as visual art, and not confused with informative visualization.

Figure 1-5. Participants address the Fiber Optic Tapestry by tweeting #optictapestry and a primary color—the tapestry displays the colors in algorithmically-determined patterns.

[ 2 ] Flint Hahn (2010). Copyright © 2010, Flint Hahn. Permission to reproduce the likeness of Burning Man and the mark “Burning Man” granted by Burning Man.

[ 3 ] Nora Ligorano and Marshall Reese (2011). Copyright © 2011, Ligorano/Reese. http://ligoranoreese.net/fiber-optic-tapestry

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Handbook of Data Visualization

• © 2008
• Chun-houh Chen 0 ,
• Wolfgang Härdle ,
• Antony Unwin

Academia Sinica, Institute of Statistical Science, Taipei, Taiwan, Province of China

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• Complete overview of data visualization
• Comprehensive coverage of the field, both in theory and practice
• Includes supplementary material: sn.pub/extras

Part of the book series: Springer Handbooks of Computational Statistics (SHCS)

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Introduction to Data Visualization

Data Visualization

• Cluster analysis
• Projection pursuit
• data analysis
• multidimensional scaling
• visualization

Table of contents (35 chapters)

Front matter, introduction.

• Antony Unwin, Chun-houh Chen, Wolfgang Härdle

A Brief History of Data Visualization

• Michael Friendly

Good Graphics?

Static graphics.

• Paul Murrell

Data Visualization Through Their Graph Representations

• George Michailidis

Graph-theoretic Graphics

• Lelend Wilkinson

High-dimensional Data Visualization

• Martin Theus

Multivariate Data Glyphs: Principles and Practice

• Matthew O. Ward

Linked Views for Visual Exploration

• Adalbert Wilhelm

Linked Data Views

• Graham Wills

Visualizing Trees and Forests

• Simon Urbanek

Methodologies

Interactive linked micromap plots for the display of geographically referenced statistical data.

• Jürgen Symanzik, Daniel B. Carr

Grand Tours, Projection Pursuit Guided Tours, and Manual Controls

• Dianne Cook, Andreas Buja, Eun-Kyung Lee, Hadley Wickham

Multidimensional Scaling

• Michael A. A. Cox, Trevor F. Cox

Huge Multidimensional Data Visualization: Back to the Virtue of Principal Coordinates and Dendrograms in the New Computer Age

• Francesco Palumbo, Domenico Vistocco, Alain Morineau

Multivariate Visualization by Density Estimation

• Michael C. Minnotte, Stephan R. Sain, DavidW. Scott

Authors and Affiliations

Chun-houh Chen

Bibliographic Information

Book Title : Handbook of Data Visualization

Authors : Chun-houh Chen, Wolfgang Härdle, Antony Unwin

Series Title : Springer Handbooks of Computational Statistics

DOI : https://doi.org/10.1007/978-3-540-33037-0

Publisher : Springer Berlin, Heidelberg

eBook Packages : Mathematics and Statistics , Mathematics and Statistics (R0)

Copyright Information : Springer-Verlag Berlin Heidelberg 2008

Hardcover ISBN : 978-3-540-33036-3 Published: 18 December 2007

Softcover ISBN : 978-3-662-50074-3 Published: 23 August 2016

eBook ISBN : 978-3-540-33037-0 Published: 18 December 2007

Series ISSN : 2197-9790

Series E-ISSN : 2197-9804

Edition Number : 1

Number of Pages : XIII, 936

Topics : Computer Applications , Statistics and Computing/Statistics Programs , Computational Biology/Bioinformatics , Statistical Theory and Methods , Computer Imaging, Vision, Pattern Recognition and Graphics

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