R Dataset / Package MASS / biopsy


On this Picostat.com statistics page, you will find information about the biopsy data set which pertains to Biopsy Data on Breast Cancer Patients. The biopsy data set is found in the MASS R package. You can load the biopsy data set in R by issuing the following command at the console data("biopsy"). This will load the data into a variable called biopsy. If R says the biopsy 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 biopsy R data set. The size of this file is about 26,977 bytes.

Biopsy Data on Breast Cancer Patients


This breast cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. He assessed biopsies of breast tumours for 699 patients up to 15 July 1992; each of nine attributes has been scored on a scale of 1 to 10, and the outcome is also known. There are 699 rows and 11 columns.




This data frame contains the following columns:


sample code number (not unique).


clump thickness.


uniformity of cell size.


uniformity of cell shape.


marginal adhesion.


single epithelial cell size.


bare nuclei (16 values are missing).


bland chromatin.


normal nucleoli.




"benign" or "malignant".


P. M. Murphy and D. W. Aha (1992). UCI Repository of machine learning databases. [Machine-readable data repository]. Irvine, CA: University of California, Department of Information and Computer Science.

O. L. Mangasarian and W. H. Wolberg (1990) Cancer diagnosis via linear programming. SIAM News 23, pp 1 & 18.

William H. Wolberg and O.L. Mangasarian (1990) Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proceedings of the National Academy of Sciences, U.S.A. 87, pp. 9193–9196.

O. L. Mangasarian, R. Setiono and W.H. Wolberg (1990) Pattern recognition via linear programming: Theory and application to medical diagnosis. In Large-scale Numerical Optimization eds Thomas F. Coleman and Yuying Li, SIAM Publications, Philadelphia, pp 22–30.

K. P. Bennett and O. L. Mangasarian (1992) Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software 1, pp. 23–34 (Gordon & Breach Science Publishers).


Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Third Edition. Springer.


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

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Attachment Size
dataset-29890.csv 26.34 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0