# Unduh dan pasang paket jika belum terinstal di perangkat:
# install.packages("ggplot2")
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.2
# Menggambar grafik kurva menggunakan fungsi stat_function
ggplot(data.frame(x = c(-4, 4)), aes(x = x)) +
stat_function(fun = dnorm,
args = list(mean = 0, sd = 1),
color = "blue",
linewidth = 1) +
labs(title = "Kurva Distribusi Normal",
subtitle = "Rata-rata = 0, Simpangan Baku = 1",
x = "Nilai",
y = "Kepadatan") +
theme_minimal() # Tema latar belakang bersih
library(ggplot2)
ggplot(data.frame(x = c(-8, 12)), aes(x = x)) +
stat_function(fun = dnorm, args = list(mean = 2, sd = sqrt(10)), color = "blue", linewidth = 1) +
labs(title = "Kurva Distribusi Normal", subtitle = expression(paste("Rata-rata (", mu, ") = 2, Varians (", sigma^2, ") = 10")), x = "Nilai", y = "Kepadatan") +
theme_minimal()
library(ggplot2)
ggplot(data.frame(x = c(-8, 12)), aes(x = x)) +
stat_function(fun = dnorm,
args = list(mean = 2, sd = sqrt(10)),
color = "blue",
linewidth = 1) +
labs(title = "Kurva Distribusi Normal",
subtitle = expression(paste("Rata-rata (", mu, ") = 2, Varians (", sigma^2, ") = 10")),
x = "Nilai",
y = "Kepadatan") +
theme_minimal()
library(ggplot2)
ggplot(data.frame(x = c(-8, 12)), aes(x = x)) +
# Kurva Pertama
stat_function(aes(color = "Distribusi 1 (Mu = 0, Var = 1)"),
fun = dnorm,
args = list(mean = 0, sd = 1),
linewidth = 1) +
# Kurva Kedua
stat_function(aes(color = "Distribusi 2 (Mu = 2, Var = 10)"),
fun = dnorm,
args = list(mean = 2, sd = sqrt(10)),
linewidth = 1) +
# Atur warna manual untuk legenda
scale_color_manual(name = "Keterangan Parameter",
values = c("Distribusi 1 (Mu = 0, Var = 1)" = "red",
"Distribusi 2 (Mu = 2, Var = 10)" = "blue")) +
labs(title = "Perbandingan Dua Kurva Distribusi Normal",
x = "Nilai",
y = "Kepadatan") +
theme_minimal() +
theme(legend.position = "bottom") # Pindahkan legenda ke bawah
library(ggplot2)
ggplot(data.frame(x = c(-10, 12)), aes(x = x)) +
# Kurva Pertama
stat_function(aes(color = "Distribusi 1 (Mu = 0, Var = 1)"),
fun = dnorm,
args = list(mean = 0, sd = 1),
linewidth = 1) +
# Kurva Kedua
stat_function(aes(color = "Distribusi 2 (Mu = 2, Var = 10)"),
fun = dnorm,
args = list(mean = 2, sd = sqrt(10)),
linewidth = 1) +
# Kurva Ketiga
stat_function(aes(color = "Distribusi 3 (Mu = -3, Var = 4)"),
fun = dnorm,
args = list(mean = -3, sd = 2),
linewidth = 1) +
# Atur warna manual untuk legenda
scale_color_manual(name = "Keterangan Parameter",
values = c("Distribusi 1 (Mu = 0, Var = 1)" = "red",
"Distribusi 2 (Mu = 2, Var = 10)" = "blue",
"Distribusi 3 (Mu = -3, Var = 4)" = "green")) +
labs(title = "Perbandingan Tiga Kurva Distribusi Normal",
x = "Nilai",
y = "Kepadatan") +
theme_minimal() +
theme(legend.position = "bottom")
library(ggplot2)
ggplot(data.frame(x = c(-10, 25)), aes(x = x)) +
stat_function(aes(color = "Kurva 1 (Mu=0, Sig=1)"),
fun = dnorm, args = list(mean = 0, sd = 1), linewidth = 1) +
stat_function(aes(color = "Kurva 2 (Mu=2, Sig=1)"),
fun = dnorm, args = list(mean = 2, sd = 1), linewidth = 1) +
stat_function(aes(color = "Kurva 3 (Mu=5, Sig=5)"),
fun = dnorm, args = list(mean = 5, sd = 5), linewidth = 1) +
stat_function(aes(color = "Kurva 4 (Mu=10, Sig=1)"),
fun = dnorm, args = list(mean = 10, sd = 1), linewidth = 1) +
stat_function(aes(color = "Kurva 5 (Mu=15, Sig=0.1)"),
fun = dnorm, args = list(mean = 15, sd = 0.1), linewidth = 1) +
scale_color_manual(name = "Keterangan Parameter",
values = c("Kurva 1 (Mu=0, Sig=1)" = "red",
"Kurva 2 (Mu=2, Sig=1)" = "blue",
"Kurva 3 (Mu=5, Sig=5)" = "green",
"Kurva 4 (Mu=10, Sig=1)" = "purple",
"Kurva 5 (Mu=15, Sig=0.1)" = "orange")) +
labs(title = "Perbandingan 5 Kurva Distribusi Normal",
x = "Nilai",
y = "Kepadatan") +
theme_minimal() +
theme(legend.position = "right")
# Parameter (mu dan sd)
params <- list(
c(mu = 0, sd = 2, col = "blue"), # Biru
c(mu = 2, sd = 1, col = "green"), # Hijau
c(mu = 5, sd = 5, col = "red"), # Merah
c(mu = 10, sd = 3, col = "purple"), # Ungu
c(mu = 1.5, sd = 0.1, col = "orange") # Oranye
)
# Rentang sumbu X
x <- seq(-11, 20, length.out = 2000)
# Plot Kurva 1 sebagai dasar
y1 <- dnorm(x, mean = 0, sd = 2)
plot(x, y1, type = "l", col = "#1F77B4", lwd = 2,
main = "Perbandingan 5 Distribusi Normal Bertumpuk",
xlab = "Nilai X", ylab = "Densitas",
ylim = c(0, 1.2), # Membatasi sumbu Y agar kurva lain tetap terlihat jelas
las = 1, bty = "l")
# Menambahkan kurva sisanya dengan perulangan (loop)
for (p in params) {
m <- as.numeric(p["mu"])
s <- as.numeric(p["sd"])
cl <- p["col"]
y <- dnorm(x, mean = m, sd = s)
lines(x, y, col = cl, lwd = 2)
polygon(c(x, rev(x)), c(y, rep(0, length(y))),
col = adjustcolor(cl, alpha.f = 0.15), border = NA)
}
grid()
legend("topright",
legend = c("N(0, sd=2)", "N(2, sd=1)", "N(5, sd=5)", "N(10, sd=3)", "N(1.5, sd=0.1)"),
col = c("#1F77B4", "#2CA02C", "#D62728", "#9467BD", "#FF7F0E"),
lwd = 2, bty = "n")
library(ggplot2)
# Kurva 1
ggplot(data.frame(x = c(-3, 3)), aes(x = x)) +
stat_function(fun = dnorm, args = list(mean = 0, sd = 1), color = "red", linewidth = 1) +
labs(title = "Kurva 1 (Mu=0, Sig=1)") + theme_minimal()
# Kurva 2
ggplot(data.frame(x = c(-1, 5)), aes(x = x)) +
stat_function(fun = dnorm, args = list(mean = 2, sd = 1), color = "blue", linewidth = 1) +
labs(title = "Kurva 2 (Mu=2, Sig=1)") + theme_minimal()
# Kurva 3
ggplot(data.frame(x = c(-10, 20)), aes(x = x)) +
stat_function(fun = dnorm, args = list(mean = 5, sd = 5), color = "green", linewidth = 1) +
labs(title = "Kurva 3 (Mu=5, Sig=5)") + theme_minimal()
# Kurva 4
ggplot(data.frame(x = c(7, 13)), aes(x = x)) +
stat_function(fun = dnorm, args = list(mean = 10, sd = 1), color = "purple", linewidth = 1) +
labs(title = "Kurva 4 (Mu=10, Sig=1)") + theme_minimal()
# Kurva 5
ggplot(data.frame(x = c(14.7, 15.3)), aes(x = x)) +
stat_function(fun = dnorm, args = list(mean = 15, sd = 0.1), color = "orange", linewidth = 1) +
labs(title = "Kurva 5 (Mu=15, Sig=0.1)") + theme_minimal()
# 1. Muat paket yang diperlukan
# install.packages(c("ggplot2", "dplyr", "patchwork")) # Jalankan ini jika belum install
library(ggplot2)
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.5.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(patchwork)
## Warning: package 'patchwork' was built under R version 4.5.3
# ==========================================
# GRAFIK 1: DISTRIBUSI BINOMIAL (Barchart)
# Mirip dengan grafik kiri atas di gambar
# ==========================================
df_binom <- data.frame(
x = rep(0:20, 3),
# Membuat 3 variasi probabilitas (p)
p = factor(rep(c(0.2, 0.5, 0.8), each = 21)),
prob = c(dbinom(0:20, size = 20, prob = 0.2),
dbinom(0:20, size = 20, prob = 0.5),
dbinom(0:20, size = 20, prob = 0.8))
)
plot_binom <- ggplot(df_binom, aes(x = x, y = prob, fill = p)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.8) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Distribusi Binomial", subtitle = "n = 20",
x = "Jumlah Sukses", y = "Probabilitas", fill = "Nilai p") +
theme_minimal() +
theme(legend.position = "right")
# ==========================================
# GRAFIK 2: DISTRIBUSI POISSON (Lollipop Chart)
# Mirip dengan grafik kanan atas di gambar
# ==========================================
df_poisson <- data.frame(
x = rep(0:15, 3),
# Membuat 3 variasi Lambda
lambda = factor(rep(c(1, 4, 8), each = 16)),
prob = c(dpois(0:15, lambda = 1),
dpois(0:15, lambda = 4),
dpois(0:15, lambda = 8))
)
plot_poisson <- ggplot(df_poisson, aes(x = x, y = prob, color = lambda)) +
geom_segment(aes(xend = x, yend = 0), linewidth = 1, alpha = 0.5) +
geom_point(size = 2) +
scale_color_brewer(palette = "Set1") +
labs(title = "Distribusi Poisson", subtitle = "Variasi Lambda",
x = "Jumlah Kejadian", y = "Probabilitas", color = "Lambda") +
theme_minimal()
# ==========================================
# GRAFIK 3: DISTRIBUSI t-STUDENT (Line Chart)
# ==========================================
df_t <- data.frame(x = c(-4, 4))
plot_t <- ggplot(df_t, aes(x = x)) +
stat_function(aes(color = "df = 1"), fun = dt, args = list(df = 1), linewidth = 1) +
stat_function(aes(color = "df = 5"), fun = dt, args = list(df = 5), linewidth = 1) +
stat_function(aes(color = "df = 30 (Mendekati Normal)"), fun = dt, args = list(df = 30),
linewidth = 1, linetype = "dashed") +
scale_color_manual(values = c("red", "blue", "black")) +
labs(title = "Distribusi t-Student", subtitle = "Berdasarkan Derajat Bebas (df)",
x = "Nilai t", y = "Kepadatan", color = "Parameter") +
theme_minimal()
# ==========================================
# GRAFIK 4: DISTRIBUSI F (Line Chart)
# ==========================================
df_f <- data.frame(x = c(0, 5))
plot_f <- ggplot(df_f, aes(x = x)) +
# Distribusi F memiliki 2 derajat bebas (df1 dan df2)
stat_function(aes(color = "df1=2, df2=5"), fun = df, args = list(df1=2, df2=5), linewidth = 1) +
stat_function(aes(color = "df1=5, df2=10"), fun = df, args = list(df1=5, df2=10), linewidth = 1) +
stat_function(aes(color = "df1=10, df2=50"), fun = df, args = list(df1=10, df2=50), linewidth = 1) +
scale_color_manual(values = c("orange", "purple", "darkgreen")) +
labs(title = "Distribusi F (Fisher-Snedecor)", subtitle = "Berdasarkan df1 dan df2",
x = "Nilai F", y = "Kepadatan", color = "Parameter") +
theme_minimal()
# ==========================================
# MENGGABUNGKAN GRAFIK MENJADI DASHBOARD (Menggunakan Patchwork)
# ==========================================
# Operator | untuk meletakkan bersebelahan, / untuk meletakkan di bawahnya
dashboard_visual <- (plot_binom | plot_poisson) / (plot_t | plot_f)
# Tampilkan hasil akhir
print(dashboard_visual)
# Muat paket yang diperlukan
library(ggplot2)
library(patchwork)
# ==========================================
# 1. DISTRIBUSI CHI-SQUARE
# Parameter utama: df (derajat bebas)
# ==========================================
plot_chisq <- ggplot(data.frame(x = c(0, 20)), aes(x = x)) +
stat_function(aes(color = "df = 2"), fun = dchisq, args = list(df = 2), linewidth = 1) +
stat_function(aes(color = "df = 5"), fun = dchisq, args = list(df = 5), linewidth = 1) +
stat_function(aes(color = "df = 10"), fun = dchisq, args = list(df = 10), linewidth = 1) +
scale_color_manual(values = c("red", "blue", "green")) +
labs(title = "Distribusi Chi-Square", x = "Nilai", y = "Kepadatan", color = "Parameter") +
theme_minimal()
# ==========================================
# 2. DISTRIBUSI GAMMA
# Parameter utama: shape (bentuk) dan rate/scale (skala)
# ==========================================
plot_gamma <- ggplot(data.frame(x = c(0, 15)), aes(x = x)) +
stat_function(aes(color = "Shape=1, Rate=1"), fun = dgamma, args = list(shape = 1, rate = 1), linewidth = 1) +
stat_function(aes(color = "Shape=2, Rate=1"), fun = dgamma, args = list(shape = 2, rate = 1), linewidth = 1) +
stat_function(aes(color = "Shape=5, Rate=1"), fun = dgamma, args = list(shape = 5, rate = 1), linewidth = 1) +
scale_color_manual(values = c("orange", "purple", "brown")) +
labs(title = "Distribusi Gamma", x = "Nilai", y = "Kepadatan", color = "Parameter") +
theme_minimal()
# ==========================================
# 3. DISTRIBUSI WEIBULL
# Parameter utama: shape (bentuk) dan scale (skala)
# ==========================================
plot_weibull <- ggplot(data.frame(x = c(0, 5)), aes(x = x)) +
stat_function(aes(color = "Shape=1, Scale=1"), fun = dweibull, args = list(shape = 1, scale = 1), linewidth = 1) +
stat_function(aes(color = "Shape=2, Scale=1"), fun = dweibull, args = list(shape = 2, scale = 1), linewidth = 1) +
stat_function(aes(color = "Shape=5, Scale=1"), fun = dweibull, args = list(shape = 5, scale = 1), linewidth = 1) +
scale_color_manual(values = c("cyan", "magenta", "black")) +
labs(title = "Distribusi Weibull", x = "Nilai", y = "Kepadatan", color = "Parameter") +
theme_minimal()
# ==========================================
# 4. DISTRIBUSI EKSPONENSIAL
# Parameter utama: rate (laju)
# ==========================================
plot_exp <- ggplot(data.frame(x = c(0, 5)), aes(x = x)) +
stat_function(aes(color = "Rate = 0.5"), fun = dexp, args = list(rate = 0.5), linewidth = 1) +
stat_function(aes(color = "Rate = 1.0"), fun = dexp, args = list(rate = 1), linewidth = 1) +
stat_function(aes(color = "Rate = 2.0"), fun = dexp, args = list(rate = 2), linewidth = 1) +
scale_color_manual(values = c("darkred", "darkblue", "darkgreen")) +
labs(title = "Distribusi Eksponensial", x = "Nilai", y = "Kepadatan", color = "Parameter") +
theme_minimal()
# ==========================================
# 5. DISTRIBUSI PARETO
# R tidak memiliki dpareto bawaan, jadi kita definisikan fungsinya
# ==========================================
dpareto <- function(x, scale, shape) {
# Mengembalikan 0 jika nilai x lebih kecil dari skala awal (xm)
ifelse(x < scale, 0, (shape * scale^shape) / (x^(shape + 1)))
}
plot_pareto <- ggplot(data.frame(x = c(0, 5)), aes(x = x)) +
stat_function(aes(color = "Scale=1, Shape=1"), fun = dpareto, args = list(scale = 1, shape = 1), linewidth = 1) +
stat_function(aes(color = "Scale=1, Shape=2"), fun = dpareto, args = list(scale = 1, shape = 2), linewidth = 1) +
stat_function(aes(color = "Scale=1, Shape=3"), fun = dpareto, args = list(scale = 1, shape = 3), linewidth = 1) +
scale_color_manual(values = c("gold", "tomato", "steelblue")) +
labs(title = "Distribusi Pareto", x = "Nilai", y = "Kepadatan", color = "Parameter") +
theme_minimal()
# ==========================================
# MENGGABUNGKAN GRAFIK (Dashboard Layout)
# ==========================================
# Mengatur 5 grafik ke dalam 3 baris. Baris terakhir untuk Pareto saja.
dashboard_lanjutan <- (plot_chisq | plot_gamma) /
(plot_weibull | plot_exp) /
plot_pareto
# Menampilkan hasil
print(dashboard_lanjutan)
library(ggplot2)
library(patchwork)
# ==========================================
# 1. DISTRIBUSI UNIFORM DISKRIT (Seperti di gambar)
# Contoh kasus: Pelemparan mata dadu (1 sampai 6)
# ==========================================
# Membuat dataframe dengan probabilitas yang sama rata (1/6)
df_uniform_diskrit <- data.frame(
x = 1:6,
prob = rep(1/6, 6)
)
plot_unif_diskrit <- ggplot(df_uniform_diskrit, aes(x = factor(x), y = prob)) +
geom_bar(stat = "identity", fill = "skyblue", alpha = 0.9, width = 0.6) +
# Mengatur batas sumbu y agar proporsional
scale_y_continuous(limits = c(0, 0.25)) +
labs(title = "Distribusi Uniform Diskrit",
subtitle = "Contoh: Hasil lemparan dadu (1-6)",
x = "Keluaran (x)",
y = "Probabilitas (P(X=x))") +
theme_minimal()
# ==========================================
# 2. DISTRIBUSI UNIFORM KONTINU
# Parameter utama: min (batas bawah) dan max (batas atas)
# ==========================================
# Membuat fungsi untuk menggambar garis seragam
plot_unif_kontinu <- ggplot(data.frame(x = c(-2, 12)), aes(x = x)) +
# Rentang dari 0 hingga 10
stat_function(aes(color = "Min = 0, Max = 10"),
fun = dunif, args = list(min = 0, max = 10), linewidth = 1.2) +
# Rentang dari 2 hingga 8
stat_function(aes(color = "Min = 2, Max = 8"),
fun = dunif, args = list(min = 2, max = 8), linewidth = 1.2, linetype = "dashed") +
scale_color_manual(values = c("Min = 0, Max = 10" = "blue", "Min = 2, Max = 8" = "red")) +
labs(title = "Distribusi Uniform Kontinu",
subtitle = "Berdasarkan rentang Minimum dan Maksimum",
x = "Nilai",
y = "Kepadatan (Density)",
color = "Parameter") +
theme_minimal() +
theme(legend.position = "bottom")
# ==========================================
# MENGGABUNGKAN GRAFIK
# ==========================================
# Meletakkan grafik secara berdampingan (kiri dan kanan)
dashboard_uniform <- plot_unif_diskrit | plot_unif_kontinu
# Menampilkan grafik
print(dashboard_uniform)
library(ggplot2)
# 1. Set data x
x_val <- seq(0.01, 10, length.out = 500)
# 2. Rumus manual Pareto Tipe I
dpareto <- function(x, xm = 1, alpha = 3) {
ifelse(x >= xm, (alpha * (xm^alpha)) / (x^(alpha + 1)), 0)
}
# 3. Gabungkan data 8 distribusi kontinu
df_all <- rbind(
data.frame(x = seq(-4, 4, length.out = 500), y = dt(seq(-4, 4, length.out = 500), df = 5), Distribusi = "t-Student (df=5)"),
data.frame(x = x_val, y = df(x_val, df1 = 5, df2 = 10), Distribusi = "F (df1=5, df2=10)"),
data.frame(x = seq(0, 15, length.out = 500), y = dchisq(seq(0, 15, length.out = 500), df = 4), Distribusi = "Chi-Square (df=4)"),
data.frame(x = x_val, y = dgamma(x_val, shape = 2, rate = 1), Distribusi = "Gamma (shape=2, rate=1)"),
data.frame(x = seq(0, 4, length.out = 500), y = dweibull(seq(0, 4, length.out = 500), shape = 2, scale = 1), Distribusi = "Weibull (shape=2, scale=1)"),
data.frame(x = seq(0, 5, length.out = 500), y = dexp(seq(0, 5, length.out = 500), rate = 1), Distribusi = "Eksponensial (rate=1)"),
data.frame(x = seq(1, 5, length.out = 500), y = dpareto(seq(1, 5, length.out = 500), xm = 1, alpha = 3), Distribusi = "Pareto (xm=1, alpha=3)"),
data.frame(x = seq(-1, 6, length.out = 500), y = dunif(seq(-1, 6, length.out = 500), min = 0, max = 5), Distribusi = "Uniform (0,5)")
)
# 4. Plot gabungan dengan Facet (dipisah per panel kecil)
ggplot(df_all, aes(x = x, y = y, color = Distribusi, fill = Distribusi)) +
geom_area(alpha = 0.25, show.legend = FALSE) +
geom_line(size = 1, show.legend = FALSE) +
facet_wrap(~ Distribusi, scales = "free", ncol = 4) + # Membagi jadi 8 kotak kecil (2 baris x 4 kolom)
labs(
title = "Perbandingan 8 Distribusi Kontinu",
x = "Nilai X",
y = "Kerapatan Probabilitas f(x)"
) +
theme_bw() +
theme(
strip.background = element_rect(fill = "#E9ECEF"),
strip.text = element_text(face = "bold", size = 9),
plot.title = element_text(face = "bold", size = 14, hjust = 0.5)
)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
library(ggplot2)
# ------------------------------------------------------------
# 1. Definisi Fungsi Pareto
# ------------------------------------------------------------
dpareto <- function(x, xm = 1, alpha = 3) {
ifelse(x >= xm, (alpha * (xm^alpha)) / (x^(alpha + 1)), 0)
}
# ------------------------------------------------------------
# 2. Sumbu X Bersama (-4 sampai 15 agar mencakup semua domain)
# ------------------------------------------------------------
x_vals <- seq(-4, 15, length.out = 1000)
# ------------------------------------------------------------
# 3. Membuat Data Frame Gabungan (Tidy Format)
# ------------------------------------------------------------
df_all <- rbind(
data.frame(x = x_vals, y = dt(x_vals, df = 5), Distribusi = "t-Student (df=5)"),
data.frame(x = x_vals, y = df(x_vals, df1 = 5, df2 = 10), Distribusi = "F (df1=5, df2=10)"),
data.frame(x = x_vals, y = dchisq(x_vals, df = 4), Distribusi = "Chi-Square (df=4)"),
data.frame(x = x_vals, y = dgamma(x_vals, shape = 2, rate = 1), Distribusi = "Gamma (shape=2, rate=1)"),
data.frame(x = x_vals, y = dweibull(x_vals, shape = 2, scale = 1),Distribusi = "Weibull (shape=2, scale=1)"),
data.frame(x = x_vals, y = dexp(x_vals, rate = 1), Distribusi = "Eksponensial (rate=1)"),
data.frame(x = x_vals, y = dpareto(x_vals, xm = 1, alpha = 3), Distribusi = "Pareto (xm=1, alpha=3)"),
data.frame(x = x_vals, y = dunif(x_vals, min = 0, max = 5), Distribusi = "Uniform (min=0, max=5)")
)
# Mengunci urutan label di legenda sesuai keinginan Anda
dist_levels <- c(
"t-Student (df=5)", "F (df1=5, df2=10)", "Chi-Square (df=4)",
"Gamma (shape=2, rate=1)", "Weibull (shape=2, scale=1)",
"Eksponensial (rate=1)", "Pareto (xm=1, alpha=3)", "Uniform (min=0, max=5)"
)
df_all$Distribusi <- factor(df_all$Distribusi, levels = dist_levels)
# ------------------------------------------------------------
# 4. Palet Warna Kustom (Sesuai Hex Code Kode Asli Anda)
# ------------------------------------------------------------
palet_warna <- c(
"t-Student (df=5)" = "#4361EE",
"F (df1=5, df2=10)" = "#F72585",
"Chi-Square (df=4)" = "#4CC9F0",
"Gamma (shape=2, rate=1)" = "#7209B7",
"Weibull (shape=2, scale=1)" = "#3A0CA3",
"Eksponensial (rate=1)" = "#4895EF",
"Pareto (xm=1, alpha=3)" = "#10B981",
"Uniform (min=0, max=5)" = "#F59E0B"
)
# ------------------------------------------------------------
# 5. Visualisasi 1 Frame
# ------------------------------------------------------------
ggplot(df_all, aes(x = x, y = y, color = Distribusi, fill = Distribusi)) +
# Layer 1: Isian area transparan bertumpuk
geom_area(alpha = 0.12, position = "identity") +
# Layer 2: Garis utama kurva
geom_line(linewidth = 0.9) +
# Layer 3: Pemetaan Warna Kustom
scale_color_manual(values = palet_warna) +
scale_fill_manual(values = palet_warna) +
# Layer 4: Penyesuaian Skala
# Batasi Y hingga 1.2 agar kurva Pareto (puncak y=3) tidak memipihkan kurva lainnya
coord_cartesian(ylim = c(0, 1.2)) +
scale_x_continuous(breaks = seq(-4, 15, by = 2)) +
scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
# Layer 5: Judul dan Label
labs(
title = "Perbandingan 8 Distribusi Kontinu",
subtitle = "Digabungkan dalam Satu Sumbu Koordinat",
x = "Nilai Variable (x)",
y = "Kepadatan Probabilitas f(x)",
color = "Jenis Distribusi",
fill = "Jenis Distribusi",
caption = "Dibuat dengan R & ggplot2"
) +
# Layer 6: Styling Tema
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", size = 15, color = "#111827"),
plot.subtitle = element_text(color = "#4B5563", margin = margin(b = 12)),
axis.title = element_text(face = "bold", color = "#374151"),
# Pengaturan Legenda di Samping
legend.position = "right",
legend.title = element_text(face = "bold", size = 10),
legend.background = element_rect(fill = "#F9FAFB", color = "#E5E7EB", linewidth = 0.5),
legend.margin = margin(6, 10, 6, 10),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "#F3F4F6")
)
