UTS ADE
Naufal Fawaaz Shofa
2026-04-08
No 5 TItanic
data("Titanic")titanic <- as.data.frame(Titanic)
titanic## Class Sex Age Survived Freq
## 1 1st Male Child No 0
## 2 2nd Male Child No 0
## 3 3rd Male Child No 35
## 4 Crew Male Child No 0
## 5 1st Female Child No 0
## 6 2nd Female Child No 0
## 7 3rd Female Child No 17
## 8 Crew Female Child No 0
## 9 1st Male Adult No 118
## 10 2nd Male Adult No 154
## 11 3rd Male Adult No 387
## 12 Crew Male Adult No 670
## 13 1st Female Adult No 4
## 14 2nd Female Adult No 13
## 15 3rd Female Adult No 89
## 16 Crew Female Adult No 3
## 17 1st Male Child Yes 5
## 18 2nd Male Child Yes 11
## 19 3rd Male Child Yes 13
## 20 Crew Male Child Yes 0
## 21 1st Female Child Yes 1
## 22 2nd Female Child Yes 13
## 23 3rd Female Child Yes 14
## 24 Crew Female Child Yes 0
## 25 1st Male Adult Yes 57
## 26 2nd Male Adult Yes 14
## 27 3rd Male Adult Yes 75
## 28 Crew Male Adult Yes 192
## 29 1st Female Adult Yes 140
## 30 2nd Female Adult Yes 80
## 31 3rd Female Adult Yes 76
## 32 Crew Female Adult Yes 20# Misalnya dataset bernama titanic
total_selamat <- sum(titanic$Survived == 1)
# Tampilkan hasil
total_selamat## [1] 0No 8 Distribusi Price Berdasarkan cut
# Load dataset diamonds dari ggplot2
library(ggplot2)
# Tampilkan boxplot
ggplot(diamonds, aes(x = cut, y = price)) +
geom_boxplot(fill = "lightblue") +
labs(
title = "Distribusi Price berdasarkan Cut",
x = "Cut",
y = "Price"
)
# No 9 Menampilkan Hubungan mpg vs wt
# Load dataset bawaan
data(mtcars)
# Scatter plot base R
plot(mtcars$wt, mtcars$mpg,
main = "Hubungan antara Weight (wt) dan MPG",
xlab = "Weight (wt)",
ylab = "MPG",
pch = 19,
col = "blue")
# No 15 Korelasi eruptions dan waiting
# Load dataset bawaan
data(faithful)
# Hitung korelasi
cor(faithful$eruptions, faithful$waiting)## [1] 0.9008112plot(faithful$eruptions, faithful$waiting,
main = "Hubungan Eruptions vs Waiting",
xlab = "Durasi Erupsi",
ylab = "Waktu Tunggu",
pch = 19,
col = "darkgreen")
plot(faithful$eruptions, faithful$waiting,
pch = 19, col = "darkgreen")
abline(lm(waiting ~ eruptions, data = faithful), col = "red", lwd = 2)
# No 16 Korelasi Antara mpg dan cyl
# Load dataset
data(mtcars)
# Hitung korelasi mpg dan cyl
cor(mtcars$mpg, mtcars$cyl)## [1] -0.852162No 18 Berat Anak Ayam
# Load dataset
data(ChickWeight)
# Plot line chart (contoh untuk 1 ayam)
chick1 <- subset(ChickWeight, Chick == 1)
plot(chick1$Time, chick1$weight, type = "l",
main = "Tren Berat Ayam terhadap Waktu",
xlab = "Time",
ylab = "Weight",
col = "blue", lwd = 2)
library(ggplot2)
ggplot(ChickWeight, aes(x = Time, y = weight, group = Chick, color = Chick)) +
geom_line() +
labs(
title = "Tren Berat Ayam terhadap Waktu",
x = "Time",
y = "Weight"
)
# No 21. Tujuan EDA Pada Dataset mtcars
# Load dataset
data(mtcars)
# Melihat struktur data
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...# Ringkasan statistik
summary(mtcars)## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000# Melihat beberapa baris awal
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1# Korelasi antar variabel
cor(mtcars)## mpg cyl disp hp drat wt
## mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.68117191 -0.8676594
## cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.69993811 0.7824958
## disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.71021393 0.8879799
## hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.44875912 0.6587479
## drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.00000000 -0.7124406
## wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.71244065 1.0000000
## qsec 0.4186840 -0.5912421 -0.4336979 -0.7082234 0.09120476 -0.1747159
## vs 0.6640389 -0.8108118 -0.7104159 -0.7230967 0.44027846 -0.5549157
## am 0.5998324 -0.5226070 -0.5912270 -0.2432043 0.71271113 -0.6924953
## gear 0.4802848 -0.4926866 -0.5555692 -0.1257043 0.69961013 -0.5832870
## carb -0.5509251 0.5269883 0.3949769 0.7498125 -0.09078980 0.4276059
## qsec vs am gear carb
## mpg 0.41868403 0.6640389 0.59983243 0.4802848 -0.55092507
## cyl -0.59124207 -0.8108118 -0.52260705 -0.4926866 0.52698829
## disp -0.43369788 -0.7104159 -0.59122704 -0.5555692 0.39497686
## hp -0.70822339 -0.7230967 -0.24320426 -0.1257043 0.74981247
## drat 0.09120476 0.4402785 0.71271113 0.6996101 -0.09078980
## wt -0.17471588 -0.5549157 -0.69249526 -0.5832870 0.42760594
## qsec 1.00000000 0.7445354 -0.22986086 -0.2126822 -0.65624923
## vs 0.74453544 1.0000000 0.16834512 0.2060233 -0.56960714
## am -0.22986086 0.1683451 1.00000000 0.7940588 0.05753435
## gear -0.21268223 0.2060233 0.79405876 1.0000000 0.27407284
## carb -0.65624923 -0.5696071 0.05753435 0.2740728 1.00000000No 22. Tipe Data dari Kolom Cut Dataset Diamonds
# Load dataset
library(ggplot2)
# Cek struktur data
str(diamonds$cut)## Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...class(diamonds$cut)## [1] "ordered" "factor"28. Missing Value Kolom Ozone
# Load dataset
data(airquality)
# Cek jumlah NA sebelum
sum(is.na(airquality$Ozone))## [1] 37# Isi NA dengan median
airquality$Ozone[is.na(airquality$Ozone)] <- median(airquality$Ozone, na.rm = TRUE)
# Cek jumlah NA setelah
sum(is.na(airquality$Ozone))## [1] 029. Membedakan Distribusi Sepal Length
# Load dataset
data(iris)
# Boxplot
boxplot(Sepal.Length ~ Species, data = iris,
main = "Distribusi Sepal Length berdasarkan Species",
xlab = "Species",
ylab = "Sepal Length",
col = c("lightblue", "lightgreen", "lightpink"))
library(ggplot2)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
labs(
title = "Distribusi Sepal Length berdasarkan Species",
x = "Species",
y = "Sepal Length"
)