R Dataset / Package MASS / painters

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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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Embed
<iframe src="https://embed.picostat.com/r-dataset-package-mass-painters.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-18554.csv 1.33 KB
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 painters data set which pertains to The Painter's Data of de Piles. The painters data set is found in the MASS R package. You can load the painters data set in R by issuing the following command at the console data("painters"). This will load the data into a variable called painters. If R says the painters data set is not found, you can try installing the package by issuing this command install.packages("MASS") 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 painters R data set. The size of this file is about 1,361 bytes.

Documentation

The Painter's Data of de Piles

Description

The subjective assessment, on a 0 to 20 integer scale, of 54 classical painters. The painters were assessed on four characteristics: composition, drawing, colour and expression. The data is due to the Eighteenth century art critic, de Piles.

Usage

painters

Format

The row names of the data frame are the painters. The components are:

Composition

Composition score.

Drawing

Drawing score.

Colour

Colour score.

Expression

Expression score.

School

The school to which a painter belongs, as indicated by a factor level code as follows: "A": Renaissance; "B": Mannerist; "C": Seicento; "D": Venetian; "E": Lombard; "F": Sixteenth Century; "G": Seventeenth Century; "H": French.

Source

A. J. Weekes (1986) A Genstat Primer. Edward Arnold.

M. Davenport and G. Studdert-Kennedy (1972) The statistical analysis of aesthetic judgement: an exploration. Applied Statistics 21, 324–333.

I. T. Jolliffe (1986) Principal Component Analysis. Springer.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

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

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