- Pie charts from William Playfair's "Statistical Breviary", 1801
- One of the pie charts, 1801
- Minard's map, 1858
- Polar chart by Florence Nightingale, 1858

A **pie chart** (or a **circle chart**) is a circular statistical graphic, which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice (and consequently its central angle and area), is proportional to the quantity it represents. While it is named for its resemblance to a pie which has been sliced, there are variations on the way it can be presented. The earliest known pie chart is generally credited to William Playfair's *Statistical Breviary* of 1801.^{ [1] }^{ [2] }

- History
- Variants and similar charts
- 3D pie chart and perspective pie cake
- Doughnut chart
- Exploded pie chart
- Polar area diagram
- Ring chart, sunburst chart, and multilevel pie chart
- Spie chart
- Square chart / Waffle chart
- Example
- Use and effectiveness
- References
- Further reading
- External links

Pie charts are very widely used in the business world and the mass media.^{ [3] } However, they have been criticized,^{ [4] } and many experts recommend avoiding them,^{ [5] }^{ [6] }^{ [7] }^{ [8] } as research has shown it is difficult to compare different sections of a given pie chart, or to compare data across different pie charts. Pie charts can be replaced in most cases by other plots such as the bar chart, box plot, dot plot, etc.

The earliest known pie chart is generally credited to William Playfair's *Statistical Breviary* of 1801, in which two such graphs are used.^{ [1] }^{ [2] }^{ [9] } Playfair presented an illustration, which contained a series of pie charts. One of those charts depicted the proportions of the Turkish Empire located in Asia, Europe and Africa before 1789. This invention was not widely used at first.^{ [1] }

Playfair thought that pie charts were in need of a third dimension to add additional information.^{ [10] }

Florence Nightingale may not have invented the pie chart, but she adapted it to make it more readable, which fostered its wide use, still today. Indeed, Nightingale reconfigured the pie chart making the length of the wedges variable instead of their width. The graph, then, resembled a cock's comb.^{ [11] } She was later assumed to have created it due to the obscurity and lack of practicality of Playfair's creation.^{ [12] } Nightingale's polar area diagram,^{ [13] }^{: 107 } or occasionally the **Nightingale rose diagram**, equivalent to a modern circular histogram, to illustrate seasonal sources of patient mortality in the military field hospital she managed, was published in Notes on Matters Affecting the Health, Efficiency, and Hospital Administration of the British Army and sent to Queen Victoria in 1858. According to the historian Hugh Small, "she may have been the first to use [pie charts] for persuading people of the need for change."^{ [11] }

The French engineer Charles Joseph Minard also used pie charts, in 1858. A map of his from 1858 used pie charts to represent the cattle sent from all around France for consumption in Paris.

Early types of pie charts in the 19th century

- Pie charts from William Playfair's "Statistical Breviary", 1801
- One of the pie charts, 1801
- Minard's map, 1858
- Polar chart by Florence Nightingale, 1858

A 3d pie chart, or perspective pie chart, is used to give the chart a 3D look. Often used for aesthetic reasons, the third dimension does not improve the reading of the data; on the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. The use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts.^{ [7] }^{ [14] }

A doughnut chart (also spelled donut) is a variant of the pie chart, with a blank center allowing for additional information about the data as a whole to be included. ^{ [15] }^{ [16] } Doughnut charts are similar to pie charts in that their aim is to illustrate proportions.^{[ citation needed ]} This type of circular graph can support multiple statistics at once and it provides a better data intensity ratio to standard pie charts.^{ [16] } It does not have to contain information in the center.

A chart with one or more sectors separated from the rest of the disk is known as an *exploded pie chart*. This effect is used to either highlight a sector, or to highlight smaller segments of the chart with small proportions.

The polar area diagram is similar to a usual pie chart, except sectors have equal angles and differ rather in how far each sector extends from the center of the circle. The polar area diagram is used to plot cyclic phenomena (e.g., counts of deaths by month). For example, if the counts of deaths in each month for a year are to be plotted then there will be 12 sectors (one per month) all with the same angle of 30 degrees each. The radius of each sector would be proportional to the square root of the death count for the month, so the area of a sector represents the number of deaths in a month. If the death count in each month is subdivided by cause of death, it is possible to make multiple comparisons on one diagram, as is seen in the polar area diagram famously developed by Florence Nightingale.

The first known use of polar area diagrams was by André-Michel Guerry, which he called *courbes circulaires* (circular curves), in an 1829 paper showing seasonal and daily variation in wind direction over the year and births and deaths by hour of the day.^{ [17] } Léon Lalanne later used a polar diagram to show the frequency of wind directions around compass points in 1843. The wind rose is still used by meteorologists. Nightingale published her rose diagram in 1858. Although the name "coxcomb" has come to be associated with this type of diagram, Nightingale originally used the term to refer to the publication in which this diagram first appeared—an attention-getting book of charts and tables—rather than to this specific type of diagram.^{ [18] }

A ring chart, also known as a sunburst chart or a multilevel pie chart, is used to visualize hierarchical data, depicted by concentric circles.^{ [19] } The circle in the center represents the root node, with the hierarchy moving outward from the center. A segment of the inner circle bears a hierarchical relationship to those segments of the outer circle which lie within the angular sweep of the parent segment.^{ [20] }

A variant of the polar area chart is the spie chart, designed by Dror Feitelson.^{ [21] } The design superimposes a normal pie chart with a modified polar area chart to permit the comparison of two sets of related data. The base pie chart represents the first data set in the usual way, with different slice sizes. The second set is represented by the superimposed polar area chart, using the same angles as the base, and adjusting the radii to fit the data. For example, the base pie chart could show the distribution of age and gender groups in a population, and the overlay their representation among road casualties. Age and gender groups that are especially susceptible to being involved in accidents then stand out as slices that extend beyond the original pie chart.

Square charts, also called waffle charts, are a form of pie charts that use squares instead of circles to represent percentages. Similar to basic circular pie charts, square pie charts take each percentage out of a total 100%. They are often 10 by 10 grids, where each cell represents 1%. Despite the name, circles, pictograms (such as of people), and other shapes may be used instead of squares. One major benefit to square charts is that smaller percentages, difficult to see on traditional pie charts, can be easily depicted.^{ [22] }

The following example chart is based on preliminary results of the election for the European Parliament in 2004. The table lists the number of seats allocated to each party group, along with the derived percentage of the total that they each make up. The values in the last column, the derived central angle of each sector, Is found by multiplying the percentage by 360°.

Group | Seats | Percent (%) | Central angle (°) |
---|---|---|---|

EUL | 39 | 5.3 | 19.2 |

PES | 200 | 27.3 | 98.4 |

EFA | 42 | 5.7 | 20.7 |

EDD | 15 | 2.0 | 7.4 |

ELDR | 67 | 9.2 | 33.0 |

EPP | 276 | 37.7 | 135.7 |

UEN | 27 | 3.7 | 13.3 |

Other | 66 | 9.0 | 32.5 |

Total | 732 | 99.9* | 360.2* |

*Because of rounding, these totals do not add up to 100 and 360.

The size of each central angle is proportional to the size of the corresponding quantity, here the number of seats. Since the sum of the central angles has to be 360°, the central angle for a quantity that is a fraction *Q* of the total is 360*Q* degrees. In the example, the central angle for the largest group (European People's Party (EPP)) is 135.7° because 0.377 times 360, rounded to one decimal place, equals 135.7.

A flaw exhibited by pie charts is that they cannot show more than a few values without separating the visual encoding (the “slices”) from the data they represent (typically percentages). When slices become too small, pie charts have to rely on colors, textures or arrows so the reader can understand them. This makes them unsuitable for use with larger amounts of data. Pie charts also take up a larger amount of space on the page compared to the more flexible bar charts, which do not need to have separate legends, and can display other values such as averages or targets at the same time.^{ [7] }

Statisticians generally regard pie charts as a poor method of displaying information, and they are uncommon in scientific literature. One reason is that it is more difficult for comparisons to be made between the size of items in a chart when area is used instead of length and when different items are shown as different shapes.^{ [23] }

Further, in research performed at AT&T Bell Laboratories, it was shown that comparison by angle was less accurate than comparison by length. Most subjects have difficulty ordering the slices in the pie chart by size; when an equivalent bar chart is used the comparison is much easier.^{ [24] } Similarly, comparisons between data sets are easier using the bar chart. However, if the goal is to compare a given category (a slice of the pie) with the total (the whole pie) in a single chart and the multiple is close to 25 or 50 percent, then a pie chart can often be more effective than a bar graph.^{ [25] }^{ [26] }

In a pie chart with many section, several values may be represented with the same or similar colors, making interpretation difficult.

Several studies presented at the *European Visualization Conference* analyzed the relative accuracy of several pie chart formats,^{ [27] }^{ [28] }^{ [22] } reaching the conclusion that pie charts and doughnut charts produce similar error levels when reading them, and square pie charts provide the most accurate reading.^{ [29] }

A **chart** is a graphical representation for data visualization, in which "the data is represented by symbols, such as bars in a bar chart, lines in a line chart, or slices in a pie chart". A chart can represent tabular numeric data, functions or some kinds of quality structure and provides different info.

**Information design** is the practice of presenting information in a way that fosters an efficient and effective understanding of the information. The term has come to be used for a specific area of graphic design related to displaying information effectively, rather than just attractively or for artistic expression. Information design is closely related to the field of data visualization and is often taught as part of graphic design courses. The broad applications of information design along with its close connections to other fields of design and communication practices have created some overlap in the definitions of communication design, data visualization, and information architecture.

**Graph paper**, **coordinate paper**, **grid paper**, or **squared paper** is writing paper that is printed with fine lines making up a regular grid. The lines are often used as guides for plotting graphs of functions or experimental data and drawing curves. It is commonly found in mathematics and engineering education settings and in laboratory notebooks. Graph paper is available either as loose leaf paper or bound in notebooks.

A **bar chart** or **bar graph** is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a **column chart**.

A **small multiple** is a series of similar graphs or charts using the same scale and axes, allowing them to be easily compared. It uses multiple views to show different partitions of a dataset. The term was popularized by Edward Tufte.

**Chartjunk** refers to all visual elements in charts and graphs that are not necessary to comprehend the information represented on the graph, or that distract the viewer from this information.

**Infographics** are graphic visual representations of information, data, or knowledge intended to present information quickly and clearly. They can improve cognition by utilizing graphics to enhance the human visual system's ability to see patterns and trends. Similar pursuits are information visualization, data visualization, statistical graphics, information design, or information architecture. Infographics have evolved in recent years to be for mass communication, and thus are designed with fewer assumptions about the readers' knowledge base than other types of visualizations. Isotypes are an early example of infographics conveying information quickly and easily to the masses.

**William Playfair**, a Scottish engineer and political economist, served as a secret agent on behalf of Great Britain during its war with France. The founder of graphical methods of statistics, Playfair invented several types of diagrams: in 1786 the line, area and bar chart of economic data, and in 1801 the pie chart and circle graph, used to show part-whole relations. As secret agent, Playfair reported on the French Revolution and organized a clandestine counterfeiting operation in 1793 to collapse the French currency.

**Data visualization** is an interdisciplinary field that deals with the graphic representation of data. It is a particularly efficient way of communicating when the data is numerous as for example a time series.

**Chernoff faces**, invented by applied mathematician, statistician and physicist Herman Chernoff in 1973, display multivariate data in the shape of a human face. The individual parts, such as eyes, ears, mouth and nose represent values of the variables by their shape, size, placement and orientation. The idea behind using faces is that humans easily recognize faces and notice small changes without difficulty. Chernoff faces handle each variable differently. Because the features of the faces vary in perceived importance, the way in which variables are mapped to the features should be carefully chosen.

A **radar chart** is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. The relative position and angle of the axes is typically uninformative, but various heuristics, such as algorithms that plot data as the maximal total area, can be applied to sort the variables (axes) into relative positions that reveal distinct correlations, trade-offs, and a multitude of other comparative measures.

**André-Michel Guerry** was a French lawyer and amateur statistician. Together with Adolphe Quetelet he may be regarded as the founder of moral statistics which led to the development of criminology, sociology and ultimately, modern social science.

An **area chart** or **area graph** displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.

**Statistical graphics**, also known as **statistical graphical techniques**, are graphics used in the field of statistics for data visualization.

A **plot** is a graphical technique for representing a data set, usually as a graph showing the relationship between two or more variables. The plot can be drawn by hand or by a computer. In the past, sometimes mechanical or electronic plotters were used. Graphs are a visual representation of the relationship between variables, which are very useful for humans who can then quickly derive an understanding which may not have come from lists of values. Given a scale or ruler, graphs can also be used to read off the value of an unknown variable plotted as a function of a known one, but this can also be done with data presented in tabular form. Graphs of functions are used in mathematics, sciences, engineering, technology, finance, and other areas.

A **bubble chart** is a type of chart that displays three dimensions of data. Each entity with its triplet of associated data is plotted as a disk that expresses two of the *v _{i}* values through the disk's

The following **comparison of Adobe Flex charts** provides charts classification, compares Flex chart products for different chart type availability and for different visual features like 3D versions of charts.

In statistics, a **misleading graph**, also known as a **distorted graph**, is a graph that misrepresents data, constituting a misuse of statistics and with the result that an incorrect conclusion may be derived from it.

**Howard Gray Funkhouser** was an American mathematician, historian and Associate Professor of Mathematics at the Washington and Lee University and later at the Phillips Exeter Academy, particularly known for his early work on the history of graphical methods.

- 1 2 3 Spence (2005)
- 1 2 Tufte, p. 44
- ↑ Cleveland, p. 262
- ↑ Wilkinson, p. 23.
- ↑ Tufte, p. 178.
- ↑ van Belle, p. 160–162.
- 1 2 3 Stephen Few. "Save the Pies for Dessert", August 2007, Retrieved 2010-02-02
- ↑ Steve Fenton "Pie Charts Are Bad"
- ↑ "Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization".
*www.datavis.ca*. - ↑ Palsky, p. 144–145
- 1 2 "Who Made That Pie Chart?".
*www.nytimes.com*. - ↑ Dave article on this information on QI
- ↑ Cohen, I. Bernard (March 1984). "Florence Nightingale".
*Scientific American*.**250**(3): 128–137. Bibcode:1984SciAm.250c.128C. doi:10.1038/scientificamerican0384-128. PMID 6367033. (alternative pagination depending on country of sale: 98–107, bibliography on p. 114) online article – see documents link at left - ↑ Good and Hardin, chapter 8.
- ↑ Harris, Robert L. (1999).
*Information graphics : a comprehensive illustrated reference*([Nachdr.] ed.). Oxford: Oxford University Press. p. 143. ISBN 9780195135329. - 1 2 "Data Design by Juergen Kai-Uwe Brock on iBooks".
*iBooks*. Retrieved 2017-06-10. - ↑ Friendly, p. 509
- ↑ "Florence Nightingale's Statistical Diagrams" . Retrieved 2010-11-22.
- ↑ "Multi-level Pie Charts".
*www.neoformix.com*. - ↑ Webber Richard, Herbert Ric, Jiangbc Wel. "Space-filling Techniques in Visualizing Output from Computer Based Economic Models"
- ↑ "Feitelson, Dror (2003) Comparing Partitions With Spie Charts" (PDF). 2003. Retrieved 2010-08-31.
- 1 2 Kosara, Robert; Skau, Drew (2016). "Judgment Error in Pie Chart Variations".
*EuroVis*. - ↑ Krygier, John. "Perceptual Scaling of Map Symbols".
*makingmaps.net*. Retrieved 3 May 2015. - ↑ Cleveland, p. 86–87
- ↑ Simkin, D., & Hastie, R. (1987). An Information-Processing Analysis of Graph Perception. Journal of the American Statistical Association, 82(398), 454. doi : 10.2307/2289447. Kosara, Robert. "In Defense of Pie Charts" . Retrieved April 13, 2011.
- ↑ Spence, Ian; Lewandowsky, Stephan (1 January 1991). "Displaying proportions and percentages".
*Applied Cognitive Psychology*.**5**(1): 61–77. doi:10.1002/acp.2350050106. - ↑ "An Illustrated Tour of the Pie Chart Study Results".
*eagereyes*. 2016-06-28. Retrieved 2016-11-28. - ↑ Skau, Drew; Kosara, Robert (2016). "Arcs, Angles, or Areas: Individual Data Encodings in Pie and Donut Charts".
*EuroVis*. - ↑ "A Reanalysis of A Study About (Square) Pie Charts from 2009".
*eagereyes*. 2016-07-11. Retrieved 2016-11-28.

- Cleveland, William S. (1985).
*The Elements of Graphing Data*. Pacific Grove, CA: Wadsworth & Advanced Book Program. ISBN 0-534-03730-5. - Friendly, Michael. "The Golden Age of Statistical Graphics,"
*Statistical Science,*Volume 23, Number 4 (2008), 502-535 - Good, Phillip I. and Hardin, James W.
*Common Errors in Statistics (and How to Avoid Them)*. Wiley. 2003. ISBN 0-471-46068-0. - Guerry, A.-M. (1829). Tableau des variations météorologique comparées aux phénomènes physiologiques, d'aprés les observations faites à l'obervatoire royal, et les recherches statistique les plus récentes.
*Annales d'Hygiène Publique et de Médecine Légale*, 1 :228-. - Harris, Robert L. (1999).
*Information Graphics: A comprehensive Illustrated Reference*. Oxford University Press. ISBN 0-19-513532-6. - Lima, Manuel. "Why humans love pie charts: an historical and evolutionary perspective,"
*Noteworthy*, July 23, 2018 - Palsky Gilles.
*Des chiffres et des cartes: la cartographie quantitative au XIXè siècle*. Paris: Comité des travaux historiques et scientifiques, 1996. ISBN 2-7355-0336-4. - Playfair, William,
*Commercial and Political Atlas and Statistical Breviary*, Cambridge University Press (2005) ISBN 0-521-85554-3. - Spence, Ian. No Humble Pie: The Origins and Usage of a statistical Chart.
*Journal of Educational and Behavioral Statistics*. Winter 2005, 30 (4), 353–368. - Tufte, Edward.
*The Visual Display of Quantitative Information*. Graphics Press, 2001. ISBN 0-9613921-4-2. - Van Belle, Gerald.
*Statistical Rules of Thumb*. Wiley, 2002. ISBN 0-471-40227-3. - Wilkinson, Leland.
*The Grammar of Graphics*, 2nd edition. Springer, 2005. ISBN 0-387-24544-8.

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