library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.3
library(reshape2)
## Warning: package 'reshape2' was built under R version 4.5.3
data <- datasets::mtcars
data_mtcars <- data[, c("mpg", "disp", "hp", "wt", "qsec")]
# Matrix korelasi
cor_mtcars <- cor(data_mtcars)
cor_mtcars
## mpg disp hp wt qsec
## mpg 1.0000000 -0.8475514 -0.7761684 -0.8676594 0.4186840
## disp -0.8475514 1.0000000 0.7909486 0.8879799 -0.4336979
## hp -0.7761684 0.7909486 1.0000000 0.6587479 -0.7082234
## wt -0.8676594 0.8879799 0.6587479 1.0000000 -0.1747159
## qsec 0.4186840 -0.4336979 -0.7082234 -0.1747159 1.0000000
# Heatmap
data_cor <- melt(cor_mtcars)
ggplot(data_cor, aes(Var1, Var2, fill = value)) +
geom_tile(color = "black") +
geom_text(aes(label = round(value, 2)), color = "black") +
scale_fill_gradient2(
low = "red",
high = "green",
mid = "yellow",
midpoint = 0,
limit = c(-1, 1),
name = "Korelasi"
) +
theme_minimal() +
labs(
title = "Heatmap Dataset mtcars",
x = "Variabel 1",
y = "Variabel 2"
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(hjust = 0.5)
)

bern<-function(x,p){p^x*(1-p)^(1-x)}
bern(1,3/6)
## [1] 0.5
cmfbinom <- function(k, n, p) {
# Validasi input
if (k < 0 || n < 0 || p < 0 || p > 1) {
stop("input tidak valid")
}
# Inisialisasi CMF
cmf <- 0
# Menghitung CMF
for (x in 0:k) {
prob <- choose(n, x) * (p^x) * ((1 - p)^(n - x))
cmf <- cmf + prob
}
return(cmf)
}
# Peluang paling sedikit 2 orang sembuh = 1 - P(X <= 1)
Peluang <- 1 - cmfbinom(1, 12, 0.2)
Peluang
## [1] 0.7251221
# Poisson CMF
cmfpois <- function(x, lambda) {
# Validasi input
if (lambda <= 0) {
stop("Lambda Harus Bernilai > 0")
}
if (x < 0) {
return(0)
}
cmf <- 0
for (k in 0:x) {
prob <- (exp(-lambda) * (lambda^k)) / factorial(k)
cmf <- cmf + prob
}
return(cmf)
}
# Hitung peluang
peluang2 <- cmfpois(10, 2) - cmfpois(0, 2)
peluang2
## [1] 0.8646564
#Geometr
geo<-function(x,p){p*(1-p)^(x-1)}
geo(5,0.3)
## [1] 0.07203
hyper <- function(x, N, n, k) ((((factorial(k)) / (factorial(k - x) * factorial(x))) * (factorial(N - k) / (factorial((N - k) - (n - x)) * factorial(n - x)))) / (factorial(N) / (factorial(N - n) * factorial(n))))
hyper(2, 100, 10, 5)
## [1] 0.07021881