I think you need to be more specific about what kind of insights are you looking for, for example, if you want to see how variables correlate with each other you could use a correlation matrix.
library(corrplot)
library(tidyverse)
CancerData <- data.frame(stringsAsFactors=FALSE,
Id = c("1000025", "1002945", "1015425", "1016277", "1017023"),
Cl.thickness = c("5", "5", "3", "6", "4"),
Cell.size = c("1", "4", "1", "8", "1"),
Cell.shape = c("1", "4", "1", "8", "1"),
Marg.adhesion = c("1", "5", "1", "NA", "3"),
Epith.c.size = c("2", "7", "2", "3", "2"),
Bare.nuclei = as.factor(c("1", "10", "2", "4", "1")),
Bl.cromatin = as.factor(c("3", "3", "3", "3", "3")),
Normal.nucleoli = as.factor(c("1", "2", "1", "7", "1")),
Mitoses = as.factor(c("1", "NA", "1", "1", "1")),
Class = as.factor(c("benign", "benign", "benign", "benign",
"benign"))
)
CancerData <- CancerData %>%
mutate_at(vars(-Class), ~parse_number(as.character(.)))
CancerData %>%
select(-Id, -Class) %>%
cor() %>%
corrplot(method = "circle")
