```{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.*