On this Picostat.com 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. 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
Distribution of hair and eye color and sex in 592 statistics students.
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
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.
Snee (1974) gives the two-way table aggregated over
Sex split of the ‘Brown hair, Brown eye’ cell was
changed to agree with that used by Friendly (2000).
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,
Friendly, M. (1992b)
Mosaic displays for loglinear models.
Proceedings of the Statistical Graphics Section,
American Statistical Association, pp. 61–68.
Friendly, M. (2000)
Visualizing Categorical Data.
SAS Institute, ISBN 1-58025-660-0.
## Full mosaic
## Aggregate over sex (as in Snee's original data)
x <- apply(HairEyeColor, c(1, 2), sum)
mosaicplot(x, main = "Relation between hair and eye color")
Dataset imported from https://www.r-project.org.