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# import Wisconsin Diagnostic Breast Cancer Dataset from UCI Machine Learning Repository
# citation: W.N. Street et al., 1993
library(tidyverse)
# import data
url <- "http://mlr.cs.umass.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data"
brca_nuclei <- read.csv(url, header = FALSE)
# add variable names - info from wdbc.names file at UCI ML repository
x <- c("radius", "texture", "perimeter", "area", "smoothness", "compactness", "concavity", "concave_pts", "symmetry", "fractal_dim")
y <- c("mean", "se", "worst")
temp <- expand.grid(x = x, y = y) # build variable names as all combos of x,y
names(brca_nuclei) <- c("id", "type", paste(temp$x, temp$y, sep = "_"))
# arrange by type to group like samples together
brca_nuclei <- brca_nuclei %>% arrange(type)
# extract numeric features
features <- brca_nuclei %>% select(-id, -type) %>% as.matrix()
# extract tumor type
type <- brca_nuclei$type
# combine features and type into brca object
brca <- list(x = features, y = type)
save(brca, file = "data/brca.rda", compress = "xz")
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