Project 2

Enter your name and EID here

This is the dataset you will be working with:

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

## Parsed with column specification:
## cols(
##   type = col_character(),
##   quality = col_double(),
##   quality_grade = col_character(),
##   alcohol = col_double(),
##   alcohol_grade = col_character(),
##   pH = col_double(),
##   acidity_grade = col_character(),
##   fixed_acidity = col_double(),
##   volatile_acidity = col_double(),
##   citric_acid = col_double(),
##   residual_sugar = col_double(),
##   chlorides = col_double(),
##   free_sulfur_dioxide = col_double(),
##   total_sulfur_dioxide = col_double(),
##   density = col_double(),
##   sulphates = col_double()
## )

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:

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

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

# 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:

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

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

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

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

Discussion: Your discussion here.