Load Necessary Libraries

if (!requireNamespace("kableExtra", quietly = TRUE)) 
    install.packages("kableExtra", repos = "https://cran.rstudio.com/")
library(knitr)
library(kableExtra)

Create Data Frame

data <- data.frame(
  Age = c(30.83, 58.67, 24.50, 27.83, 20.17),
  Debt = c(0.000, 4.460, 0.500, 1.540, 5.625),
  YearsEmployed = c(1.25, 3.04, 1.50, 3.75, 1.71),
  CreditScore = c(1, 6, 0, 5, 0),
  Income = c(0, 560, 824, 3, 0)
)

kable(data) %>% kable_styling()
Age Debt YearsEmployed CreditScore Income
30.83 0.000 1.25 1 0
58.67 4.460 3.04 6 560
24.50 0.500 1.50 0 824
27.83 1.540 3.75 5 3
20.17 5.625 1.71 0 0

Convert Data to Matrix

numeric_data <- as.matrix(data)

# Hapus kolom dengan varians nol
numeric_data <- numeric_data[, apply(numeric_data, 2, var) > 0]

Compute Eigenvalues and Eigenvectors

cor_matrix <- cor(numeric_data, use = "complete.obs")
eigen_results <- eigen(cor_matrix)
kable(data.frame(Eigenvalues = eigen_results$values)) %>% kable_styling()
Eigenvalues
2.5048904
1.2162054
0.8937056
0.3851986
0.0000000
kable(as.data.frame(eigen_results$vectors)) %>% kable_styling()
V1 V2 V3 V4 V5
-0.5173164 0.3435734 -0.1580531 -0.6520227 -0.4052488
-0.2366539 -0.3405399 -0.8896621 0.1713247 0.0847152
-0.5423918 -0.2393841 0.2932737 0.5500118 -0.5098862
-0.6144324 -0.0402371 0.2391578 -0.0569615 0.7486072
-0.0682825 0.8408722 -0.2007850 0.4896495 0.0905545

Variance-Covariance Matrix

cov_matrix <- cov(numeric_data, use = "complete.obs")
kable(cov_matrix) %>% kable_styling()
Age Debt YearsEmployed CreditScore Income
Age 231.361400 9.345662 6.999375 33.30 2046.9725
Debt 9.345662 6.187675 0.605225 1.34 -112.3137
YearsEmployed 6.999375 0.605225 1.182050 2.81 -42.7750
CreditScore 33.300000 1.340000 2.810000 8.30 11.5500
Income 2046.972500 -112.313750 -42.775000 11.55 151957.8000

Correlation Matrix

kable(cor_matrix) %>% kable_styling()
Age Debt YearsEmployed CreditScore Income
Age 1.0000000 0.2470022 0.4232489 0.7599058 0.3452272
Debt 0.2470022 1.0000000 0.2237872 0.1869829 -0.1158264
YearsEmployed 0.4232489 0.2237872 1.0000000 0.8971175 -0.1009278
CreditScore 0.7599058 0.1869829 0.8971175 1.0000000 0.0102845
Income 0.3452272 -0.1158264 -0.1009278 0.0102845 1.0000000