Principal Component Analysis on Meat Products

This project will explore the potential correlations of mineral nutrients contents in meat products, using the food nutrient information dataset from United States Department of Agriculture (USDA) (USDA, 2019). This includes beef products, finfish and shellfish products, Pork Products, Lamb, Veal, and Game Products and poultry products. The dataset includes the description and nutrients contents for 8,618 food products, categorized into different food groups. Other than the absolute value of nutrients, the dataset also includes the nutrients’ percentage of the United States Recommended Daily Allowance (USRDA), which will not be included in this analysis.

raw_df_2 <- read_csv("usda_nutrients.csv")

raw_df_meat <- raw_df_2 %>% 
  filter (FoodGroup %in% c("Finfish and Shellfish Products", "Poultry Products","Pork Products", "Lamb, Veal, and Game Products", "Beef Products")) %>% # Selecting all meat products in the dataset
  clean_names() %>% 
  select(food_group,calcium_mg, copper_mcg, iron_mg, magnesium_mg, manganese_mg, phosphorus_mg, selenium_mcg, zinc_mg) # Selecting all mineral nutrients in the dataset

# Selecting the numerical part of the dataset for PCA analysis
pca_df <- raw_df_meat[c(2:9)]

meat_pca <- prcomp(pca_df, scale = TRUE)

# Un-comment this if you need see the result of PCA
# summary(meat_pca)

#Finishing biplot
my_biplot <- autoplot(meat_pca, 
                      data = raw_df_meat,
                      colour = 'food_group',
                      alpha = 0.5,
                      size = 0.5,
                      loadings.colour = "#00A08A",
                      loadings.label = TRUE,
                      loadings.label.size = 3,
                      loadings.label.colour = "black",
                      loadings.label.repel = TRUE) +
  theme_minimal()+
  scale_color_manual(values=wes_palette(n=5, name="FantasticFox1"))+
  labs(color = "Food Group")+
  xlab("Principal Component 1 (25.62%)")+
  ylab("Principal Component 2 (17.27%)")
  

my_biplot

Figure 1. Principal Component Analysis Biplot Showing Mineral Nutrients Contents Associations in Meat Products. Principal component 1 and principal component 2 together can expalin 42.89% of the data variance. Color of the points indicates the type of the meat (Source: USDA, 2019).