# R Dataset / Package datasets / HairEyeColor

On this R-data statistics page, you will find information about the HairEyeColor data set which pertains to Hair and Eye Color of Statistics Students. The HairEyeColor data set is found in the datasets R package. You can load the HairEyeColor data set in R by issuing the following command at the console data("HairEyeColor"). This will load the data into a variable called HairEyeColor. If R says the HairEyeColor data set is not found, you can try installing the package by issuing this command install.packages("datasets") and then attempt to reload the data with the library() command. 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 HairEyeColor R data set. The size of this file is about 851 bytes.

## Hair and Eye Color of Statistics Students

### Description

Distribution of hair and eye color and sex in 592 statistics students.

### Usage

HairEyeColor

### Format

A 3-dimensional array resulting from cross-tabulating 592 observations on 3 variables. The variables and their levels are as follows:

No | Name | Levels |

1 | Hair | Black, Brown, Red, Blond |

2 | Eye | Brown, Blue, Hazel, Green |

3 | Sex | Male, Female |

### Details

The Hair *x* Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by `Sex`

was added by Friendly (1992a) for didactic purposes.

This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

### Source

http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

Snee (1974) gives the two-way table aggregated over `Sex`

. The `Sex`

split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

### References

Snee, R. D. (1974) Graphical display of two-way contingency tables. *The American Statistician*, **28**, 9–12.

Friendly, M. (1992a) Graphical methods for categorical data. *SAS User Group International Conference Proceedings*, **17**, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

Friendly, M. (1992b) Mosaic displays for loglinear models. *Proceedings of the Statistical Graphics Section*, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

Friendly, M. (2000) *Visualizing Categorical Data.* SAS Institute, ISBN 1-58025-660-0.

### See Also

`chisq.test`

, `loglin`

, `mosaicplot`

### Examples

require(graphics) ## Full mosaic mosaicplot(HairEyeColor) ## Aggregate over sex (as in Snee's original data) x <- apply(HairEyeColor, c(1, 2), sum) x mosaicplot(x, main = "Relation between hair and eye color")

Dataset imported from https://www.r-project.org.