```{r global_options, include=FALSE}

library(knitr)
opts_chunk$set(fig.align = "center", fig.height = 5, fig.width = 6)
library(tidyverse)
theme_set(theme_bw(base_size = 12))
library(ggrepel)
library(plotROC)
library(ggthemes)

```

## Project 2
*Enter your name and EID here*

This is the dataset you will be working with:
```{r }

wine_features <- 
  read_csv("https://wilkelab.org/classes/SDS348/data_sets/wine_features.csv") 

```

### **Part 1**

**Question:** Can red and white wines be distinguished based on their physicochemical composition?

To answer this question, perform a principal component analysis. Make a scatterplot of PC2 vs. PC1, and a rotation matrix visualizing the influence of the input variables. *Hint: You must remove all categorical variables before creating the PCA object.*

**Introduction:** *Your introduction here.*

**Approach:** *Your approach here.*

**Analysis:**
```{r }

# your R code here
# remember to comment your code!

```

```{r }

# your R code here
# remember to comment your code!

```

```{r }

# your R code here
# remember to comment your code!

```

**Discussion:** *Your discussion here.*

### **Part 2** 

**Question:** *Your question here here.*

**Introduction:** *Your introduction here.*

**Approach:** *Your approach here.*

**Analysis:**
```{r }

# your R code here
# remember to comment your code!

```

```{r }

# your R code here
# remember to comment your code!

```

```{r }

# your R code here
# remember to comment your code!

```

```{r }

# your R code here
# remember to comment your code!

```

**Discussion:** *Your discussion here.*
