#1.load dataset setwd(“D:/coding/coding sem 4/Analisis Multivariat/tugas harian/tugas 1”) data <- read.csv(“Titanic-Dataset.csv”)

#2A. pilih beberapa row data_olah1 <- data[, c(“Age”, “SibSp”, “Parch”, “Fare”)] data_olah1

2B. hapus missing value

data_misval <- na.omit(data_olah1) data_misval print(dim(data_olah1)) print(dim(data_misval))

#3Ai. korelasi matrik cor <- cor(data_misval) cor

#3aii. cor plot library(corrplot) corrplot(cor, method = “color”, type = “full”, tl.col = “black”, tl.srt = 45, title = “Correlation Matrix Heatmap”, mar = c(0, 0, 2, 0))

#3Bi. varian covarian cov <- cov(data_misval) cov

#3Bii. plot variance covariance all panel.scatter.mean <- function(x,y,…){ points(x,y,pch = 19,col =“blue”) points(mean(y),pch = 19,col = “black”,cex = 1.8) points(mean(x),pch = 19,col = “red”,cex = 1.8) }

panel.diag.scatter.mean <- function(x, …) { points(x, x, col =“blue”, pch = 19) points(mean(x), mean(x), col = “red”, pch = 19, cex =2) }

data_misval_num <- data_misval[, sapply(data_misval, is.numeric)] par(mar = c(3,3,2,2)) pairs(data_misval_num, main = “Scatterplot Matrix”, lower.panel = panel.scatter.mean, upper.panel = panel.scatter.mean, diag.panel =panel.diag.scatter.mean)

#3Biii.plot variance kovariance fare age mean_age <- mean(data_misval\(Age) mean_fare <- mean(data_misval\)Fare) plot(data_misval\(Age, data_misval\)Fare, main=“Age vs Fare”, xlab=“Age”, ylab=“Fare”, pch=19, col=“blue”) points(mean_age, col = “red”, pch = 19, cex = 2) points(mean_fare, col =“black”, pch = 19, cex = 2)

#3Ci.eigen vekcyor dan eigen value eigen_res <- eigen(cov) eigen_res[[“values”]]

eigen_val <- eigen_res\(values eigen_vec <- eigen_res\)vectors

#3Cii. eigen value print(eigen_val) eigen_val #3Ciii. eigen vektor print(eigen_vec)

#3Civ.plot eigen eigen_val = eigen_res$values plot(eigen_val, type = “b”, pch = 19,
col = “blue”, xlab = “Principal Component”, ylab = “Eigenvalue”, main = “Scree Plot”)