breast_cancer_eda.Rpres

Breast Cancer data analysis
========================================================
author: Amazon Web Services
date: 09/07/2021
autosize: true

Dataset
========================================================

This is an exploratory analysis on [UCI Breast Cancer Wisconsin (Diagnostic) dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)) from [mlbench](https://cran.r-project.org/web/packages/mlbench/index.html) library. 

The data is collected from 699 people who were eligible of the study. 9 features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass, describing the characteristics of the cell nuclei present in the image. 

Descriptive Statistics
========================================================

We could see class imbalance between the *Benign* and *Malignant* cases. Summary statistics shown below.
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(mlbench)
library(ggplot2)
```

```{r breastcancer, echo=FALSE}
data(BreastCancer)
df <- BreastCancer
# convert input columns 2 to 10 from factor to numeric
for(i in 2:10) {
  df[,i] <- as.numeric(as.character(df[,i]))
}
summary(df)
```

Thicker clumps in malignant cases
========================================================

It turns out that *benign* cases tend to have smaller clumps as oppose to *malignant* cases who tend to have thicker clumps in the breasts.

```{r cl_thickness, dpi=100, fig.width = 10, echo=FALSE}
theme_set(theme_gray(base_size = 20))
ggplot(df, aes(x=Cl.thickness))+
       geom_histogram(color="black", fill="white", binwidth = 1)+
       facet_grid(Class ~ .)
```

Thank you
========================================================

This is the end of the presentation.