R Dataset / Package HistData / Fingerprints

How To Create a Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Regression

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Embed
<iframe src="https://embed.picostat.com/r-dataset-package-histdata-fingerprints.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-38304.csv 430 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the Fingerprints data set which pertains to Waite's data on Patterns in Fingerprints. The Fingerprints data set is found in the HistData R package. You can load the Fingerprints data set in R by issuing the following command at the console data("Fingerprints"). This will load the data into a variable called Fingerprints. If R says the Fingerprints data set is not found, you can try installing the package by issuing this command install.packages("HistData") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the Fingerprints R data set. The size of this file is about 430 bytes.

Documentation

Waite's data on Patterns in Fingerprints

Description

Waite (1915) was interested in analyzing the association of patterns in fingerprints, and produced a table of counts for 2000 right hands, classified by the number of fingers describable as a "whorl", a "small loop" (or neither). Because each hand contributes five fingers, the number of Whorls + Loops cannot exceed 5, so the contingency table is necessarily triangular.

Karl Pearson (1904) introduced the test for independence in contingency tables, and by 1913 had developed methods for "restricted contingency tables," such as the triangular table analyzed by Waite. The general formulation of such tests for association in restricted tables is now referred to as models for quasi-independence.

Usage

data(Fingerprints)

Format

A frequency data frame with 36 observations on the following 3 variables, representing a 6 x 6 table giving the cross-classification of the fingers on 2000 right hands as a whorl, small loop or neither.

Whorls

Number of whorls, an ordered factor with levels 0 < 1 < 2 < 3 < 4 < 5

Loops

Number of small loops, an ordered factor with levels 0 < 1 < 2 < 3 < 4 < 5

count

Number of hands

Details

Cells for which Whorls + Loops>5 have NA for count

Source

Stigler, S. M. (1999). Statistics on the Table. Cambridge, MA: Harvard University Press, table 19.4.

References

Pearson, K. (1904). Mathematical contributions to the theory of evolution. XIII. On the theory of contingency and its relation to association and normal correlation. Reprinted in Karl Pearson's Early Statistical Papers, Cambridge: Cambridge University Press, 1948, 443-475.

Waite, H. (1915). The analysis of fingerprints, Biometrika, 10, 421-478.

Examples

data(Fingerprints)
xtabs(count ~ Whorls + Loops, data=Fingerprints)
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Dataset imported from https://www.r-project.org.

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