BreastCancer_na.rm {spinifex}

Wisconsin Breast Cancer Database

Description

The objective is to identify each of a number of benign or malignant classes. Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself. Each variable except for the first was converted into 11 primitive numerical attributes with values ranging from 0 through 10. Rows with missing attribute values and duplicate rows removed.

Usage

BreastCancer_na.rm

Format

A data frame with 675 observations of 8 numeric variables and target factor Class.

• Id, Sample code number

• Cl.thickness, Clump thickness

• Cell.size, Uniformity of cell size

• Cell.shape, Uniformity of cell shape

• Marg.adhesion, Marginal adhesion

• Epith.c.size, Single Epthelial cell size

• Bare.nuclei, Bare nuclei

• Bl.cromatin, Bland chromatin

• Normal.nucleoli, Normal Nucleoli

• Mitoses, Mitoses

• Class, Class of cancer, either "benign" or "malignant"

Details

This is a cleaned subset of mlbench's BreastCancer. See help(BreastCancer, package = "mlbench") for the original.

Replicating this dataset:

require("mlbench")
data(BreastCancer)

raw <- BreastCancer
## rownumber index of 8 duplicate 16 incomplete rows
idx <- !duplicated(raw) & complete.cases(raw) 
d <- raw[idx, 3:10]
d <- apply(d, 2L, as.integer)
d <- data.frame(d, Class = as.factor(raw$Class[idx]))
BreastCancer_na.rm <- d
## save(BreastCancer_na.rm, file = "./data/BreastCancer_na.rm.rda")

Source

J.W. Smith., el al. 1988. Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261–265). IEEE Computer Society Press.

mlbench, R package. F. Leisch & E. Dimitriadou, 2021. mlbench: Machine Learning Benchmark Problems https://CRAN.R-project.org/package=mlbench

Examples

library(spinifex)
str(BreastCancer_na.rm)
dat  <- scale_sd(BreastCancer_na.rm[, 1:8])
clas <- BreastCancer_na.rm$Class

bas <- basis_pca(dat)
mv  <- manip_var_of(bas)
mt  <- manual_tour(bas, mv)

ggt <- ggtour(mt, dat, angle = .2) +
  proto_default(aes_args = list(color = clas, shape = clas))

animate_plotly(ggt)