Pie chart

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Image:English dialects1997.svg
Pie chart of populations of English-speaking people

A pie chart is a circular chart divided into sectors, illustrating relative magnitudes or frequencies or percents. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. Together, the sectors create a full disk. It is named for its resemblance to a pie which has been sliced.

While the pie chart is perhaps the most ubiquitous statistical chart in the business world and the mass media, it is rarely used in scientific or technical publications.[1] It is one of the most widely criticised charts,[1] and many statisticians recommend to avoid its use altogether[1][1], pointing out in particular that it is difficult to compare different sections of a given pie chart, or to compare data across different pie charts. Pie charts can be an effective way of displaying information in some cases, in particular if the intent is to compare the size of a slice with the whole pie, rather than comparing the slices among them.[1] Pie charts work particularly well when the slices represent 25 or 50% of the data,[1] but in general, other plots such as the bar chart or the dot chart, or non-graphical methods such as tables, may be more adapted for representing information.

The earliest known pie chart is generally credited to William Playfair's Statistical Breviary of 1801.[1][1]

Contents

Example

Image:Pie chart EP election 2004.png
A pie chart for the example data.
Image:Pie chart EP election 2004 exploded.png
An exploded pie chart for the example data, with the largest party group exploded.

The following example chart is based on preliminary results of the election for the European Parliament in 2004. The following 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
ERIC 276 37.7 135.7
AKINS 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 360Q degrees. In the example, the central angle for the largest group (EPP) is 135.7° because 0.377 times 360, rounded to one decimal place, equals 135.7.

Discussion on use

Image:Piecharts.svg
Three sets of data plotted using pie charts and bar charts.

Statisticians tend to regard pie charts as a poor method of displaying information. While pie charts are common in business and economics, they are uncommon in scientific literature. One reason for this 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. In Stevens' power law, visual area is perceived with a power of 0.7, compared to a power of 1.0 for length. This suggests that length is a better scale to use, since perceived differences would be linearly related to actual differences.

In research performed at AT&T Bell Laboratories, it was shown that comparison by angle was less accurate than comparison by length. This can be illustrated with the diagram to the right, showing three pie charts, and, below each of them, the corresponding bar chart representing the same data. Most subjects have difficulty ordering the slices in the pie chart by size; when the bar chart is used the comparison is much easier. [1]. Similarly, comparisons between datasets are easier using the barchart. 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%, say 25% or 50%, then the pie charts works better than the bar chart.

Variants and similar charts

Exploded pie chart

A chart with one or more sectors separated from the rest of the disk. This effect is used to either highlight at sector, or to highlight smaller segments of the chart with small proportions.

Perspective (3D) pie chart

Image:FarmlandinEU2.gif
An example of a three dimensional pie chart.

This style of pie chart is used to give the chart a 3D look-and-feel. Often used for aesthetic reasons, the third dimension does not improve the reading of the data; on the contrary, these plots are more 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.[1]

Polar area diagram

Image:Nightingale-mortality.jpg
"Diagram of the causes of mortality in the army in the East" by Florence Nightingale.

Florence Nightingale is credited with developing a form of the pie chart now known as the polar area diagram, or occasionally the Nightingale rose diagram and first published in 1858. The name "coxcomb" is sometimes used erroneously, but this was the name Nightingale used to refer to a book containing the diagrams rather than the diagrams themselves. [1]

The polar area diagram is similar to a usual pie chart, except that the sectors are each of an equal angle and differ rather in how far each sectors extends from the centre of the circle. It has been suggested that most of Nightingale's early reputation was built on her ability to give clear and concise presentations of data.


History

The earliest known pie chart is generally credited to William Playfair's Statistical Breviary of 1801, where two such graphs are used.[1][1] This invention was not widely used at first;[1] Charles Joseph Minard being one of the first to use it in 1858, in particular in maps where he needs to add information in a third dimension.[1]

Notes


References

  • Cleveland, William S. (1985). The Elements of Graphing Data. Pacific Grove, CA: Wadsworth & Advanced Book Program. ISBN 0-534-03730-5. 
  • Good, Phillip I. and Hardin, James W. Common Errors in Statistics (and How to Avoid Them). Wiley. 2003. ISBN 0-471-46068-0.
  • 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 0961392142.
  • van Belle, Gerald. Statistical Rules of Thumb. Wiley, 2002. ISBN 0471402273.
  • Wilkinson, Leland. The Grammar of Graphics, 2nd edition. Springer, 2005. ISBN 0-387-24544-8.

See also

External links

Wikimedia Commons has media related to:

de:Kreisdiagramm eu:Sektore diagrama fr:Diagramme circulaire he:דיאגרמת עוגה nl:Cirkeldiagram simple:Pie chart sv:Cirkeldiagram


Acknowledgement and Attribution Regarding Sources of Content

Some of the initial content on this page may be incorporated in part from copyleft sources in the public domain including wikis such as Wikipedia and AskDrWiki. Drug information for patients came from the The National Library of Medicine. Infectious disease information may have come from the Centers for Disease Control (CDC). Differential Diagnoses are drawn from clinicians as well as an amalgamation of 3 sources: 1.The Disease Database; 2. Kahan, Scott, Smith, Ellen G. In A Page: Signs and Symptoms. Malden, Massachusetts: Blackwell Publishing, 2004:3; 3. Sailer, Christian, Wasner, Susanne. Differential Diagnosis Pocket. Hermosa Beach, CA: Borm Bruckmeir Publishing LLC, 2002:7 .

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