Matriks Korelasi

# 1. Input data dari tabel
data <- data.frame(
  Y  = c(57.5, 52.8, 61.3, 67.0, 53.5, 62.7, 56.2, 68.5, 69.2),
  X1 = c(78, 69, 77, 88, 67, 80, 74, 94, 102),
  X2 = c(2.75, 2.15, 4.41, 5.52, 3.21, 4.32, 2.31, 4.30, 3.71),
  X3 = c(29.5, 26.3, 32.2, 36.5, 27.2, 27.7, 28.3, 30.3, 28.7)
)

# 2. Hitung matriks korelasi
matriks_korelasi <- cor(data)

# Tampilkan matriks korelasi di konsol
print(matriks_korelasi)

##            Y        X1        X2        X3
## Y  1.0000000 0.9470919 0.7611425 0.5603295
## X1 0.9470919 1.0000000 0.5340188 0.3899864
## X2 0.7611425 0.5340188 1.0000000 0.7844691
## X3 0.5603295 0.3899864 0.7844691 1.0000000

# -------------------------------------------------------------
# Opsi A: Menggunakan fungsi bawaan R (heatmap)
# -------------------------------------------------------------
heatmap(
  matriks_korelasi, 
  Rowv = NA, Colv = NA,            # Matikan dendrogram (pengelompokan)
  col = cm.colors(256),             # Skala warna
  scale = "none",                   # Tidak perlu normalisasi skala
  main = "Heatmap Matriks Korelasi"
)

# -------------------------------------------------------------
# Opsi B: Menggunakan package 'pheatmap' (Hasil lebih rapi)
# -------------------------------------------------------------
# Install package jika belum ada: install.packages("pheatmap")
library(pheatmap)

## Warning: package 'pheatmap' was built under R version 4.5.3

pheatmap(
  matriks_korelasi,
  display_numbers = TRUE,          # Menampilkan nilai koefisien korelasi
  number_format = "%.2f",           # Format 2 angka di belakang koma
  cluster_rows = FALSE,            # Nonaktifkan hierarki baris
  cluster_cols = FALSE,            # Nonaktifkan hierarki kolom
  color = colorRampPalette(c("blue", "white", "red"))(50), #Gradasi warna
  main = "Heatmap Korelasi Y, X1, X2, X3"
)