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. Try to load the biopsy data set in R by issuing the following command at the console data("biopsy"). This may 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 with library("MASS") followed by data("biopsy"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing biopsy at the command-line which should display the entire dataset.
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).
uniformity of cell size.
uniformity of cell shape.
single epithelial cell size.
bare nuclei (16 values are missing).
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,
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
Dataset imported from https://www.r-project.org.