Komstat Pertemuan 9
Fadil
2026-04-24
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shahohfwh kelas efjkabjbwj, Broadcasting dan Perfilman.
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komputasi statistik
View(mtcars)
#Soal 1a
data_mobil <- mtcars
data_mobil## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2View(data_mobil)
#Soal 1b
data_mobil$am <- factor(data_mobil$am,
levels = c(0, 1),
labels = c("Automatic", "Manual"))
data_mobil$am## [1] Manual Manual Manual Automatic Automatic Automatic Automatic
## [8] Automatic Automatic Automatic Automatic Automatic Automatic Automatic
## [15] Automatic Automatic Automatic Manual Manual Manual Automatic
## [22] Automatic Automatic Automatic Automatic Manual Manual Manual
## [29] Manual Manual Manual Manual
## Levels: Automatic Manual#Soal 1c
#1
nama_mobil <- rownames(mtcars)
nama_mobil## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710"
## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [7] "Duster 360" "Merc 240D" "Merc 230"
## [10] "Merc 280" "Merc 280C" "Merc 450SE"
## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"#2
df_mobil <- data_mobil
str(df_mobil)## '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 : Factor w/ 2 levels "Automatic","Manual": 2 2 2 1 1 1 1 1 1 1 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...df_mobil## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 Manual 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 Manual 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 Manual 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 Automatic 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 Automatic 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 Automatic 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 Automatic 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 Automatic 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 Automatic 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 Automatic 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 Automatic 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 Automatic 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 Automatic 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 Automatic 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 Automatic 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 Automatic 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 Automatic 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 Manual 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 Manual 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 Manual 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 Automatic 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 Automatic 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 Automatic 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 Automatic 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 Automatic 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 Manual 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 Manual 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 Manual 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 Manual 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 Manual 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 Manual 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 Manual 4 2#3
matriks_mobil <- as.matrix(head(data_mobil, 5))
matriks_mobil## mpg cyl disp hp drat wt qsec vs am
## Mazda RX4 "21.0" "6" "160" "110" "3.90" "2.620" "16.46" "0" "Manual"
## Mazda RX4 Wag "21.0" "6" "160" "110" "3.90" "2.875" "17.02" "0" "Manual"
## Datsun 710 "22.8" "4" "108" " 93" "3.85" "2.320" "18.61" "1" "Manual"
## Hornet 4 Drive "21.4" "6" "258" "110" "3.08" "3.215" "19.44" "1" "Automatic"
## Hornet Sportabout "18.7" "8" "360" "175" "3.15" "3.440" "17.02" "0" "Automatic"
## gear carb
## Mazda RX4 "4" "4"
## Mazda RX4 Wag "4" "4"
## Datsun 710 "4" "1"
## Hornet 4 Drive "3" "1"
## Hornet Sportabout "3" "2"#List laporan objek
laporan_objek <- list(
nama_mobil = nama_mobil,
data_frame = df_mobil,
matriks_5_baris = matriks_mobil
)
laporan_objek## $nama_mobil
## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710"
## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [7] "Duster 360" "Merc 240D" "Merc 230"
## [10] "Merc 280" "Merc 280C" "Merc 450SE"
## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"
##
## $data_frame
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 Manual 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 Manual 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 Manual 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 Automatic 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 Automatic 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 Automatic 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 Automatic 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 Automatic 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 Automatic 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 Automatic 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 Automatic 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 Automatic 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 Automatic 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 Automatic 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 Automatic 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 Automatic 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 Automatic 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 Manual 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 Manual 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 Manual 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 Automatic 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 Automatic 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 Automatic 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 Automatic 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 Automatic 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 Manual 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 Manual 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 Manual 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 Manual 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 Manual 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 Manual 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 Manual 4 2
##
## $matriks_5_baris
## mpg cyl disp hp drat wt qsec vs am
## Mazda RX4 "21.0" "6" "160" "110" "3.90" "2.620" "16.46" "0" "Manual"
## Mazda RX4 Wag "21.0" "6" "160" "110" "3.90" "2.875" "17.02" "0" "Manual"
## Datsun 710 "22.8" "4" "108" " 93" "3.85" "2.320" "18.61" "1" "Manual"
## Hornet 4 Drive "21.4" "6" "258" "110" "3.08" "3.215" "19.44" "1" "Automatic"
## Hornet Sportabout "18.7" "8" "360" "175" "3.15" "3.440" "17.02" "0" "Automatic"
## gear carb
## Mazda RX4 "4" "4"
## Mazda RX4 Wag "4" "4"
## Datsun 710 "4" "1"
## Hornet 4 Drive "3" "1"
## Hornet Sportabout "3" "2"#Soal 2a
n <- nrow(data_mobil)
Efisiensi <- character(n)
for(i in 1:n){
if(data_mobil$mpg[i] > 20){Efisiensi[i] <- "Irit"}
else {Efisiensi[i] <- "Boros"}}
data_mobil$Efisiensi <- Efisiensi
View(data_mobil)
#Soal 2b
i <- 1
n <- nrow(data_mobil)
while(i <= n){
if(data_mobil$hp[i] > 200){
print(rownames(data_mobil)[i])
break
}
i <- i + 1
}## [1] "Duster 360"#Soal 3a
konversi_berat <- function(wt){
hasil= wt * 1000 * 0.453
return(hasil)
}
konversi_berat(1.615)## [1] 731.595#Soal 3b
kategori_silinder <- function(cyl){
hasil <- switch(as.character(cyl),
"4" = "Hemat Pajak",
"6" = "Standar",
"8" = "Pajak Mewah",
"Tidak diketahui")
return(hasil)
}
kategori_silinder(4)## [1] "Hemat Pajak"#Soal 4a
data_numerik <- data_mobil[sapply(data_mobil, is.numeric)]
mean_kolom <- apply(data_numerik, 2, mean)
mean_kolom## mpg cyl disp hp drat wt qsec
## 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
## vs gear carb
## 0.437500 3.687500 2.812500#Soal 4b
tipe_kolom <- sapply(data_mobil, class)
tipe_kolom## mpg cyl disp hp drat wt
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## qsec vs am gear carb Efisiensi
## "numeric" "numeric" "factor" "numeric" "numeric" "character"#Soal 4c
mean_hp_am <- tapply(data_mobil$hp, data_mobil$am, mean)
mean_hp_am## Automatic Manual
## 160.2632 126.8462#Soal 4d
library(magrittr)
hasil <- data_mobil %>%
subset(cyl == 6) %>%
with(mean(mpg))
hasil## [1] 19.74286#Soal 5a
set.seed(123)
n <- nrow(data_mobil)
harga_sewa <- rnorm(n, mean = 500, sd = 100)
data_mobil$harga_sewa <- harga_sewa
data_mobil$harga_sewa## [1] 443.9524 476.9823 655.8708 507.0508 512.9288 671.5065 546.0916 373.4939
## [9] 431.3147 455.4338 622.4082 535.9814 540.0771 511.0683 444.4159 678.6913
## [17] 549.7850 303.3383 570.1356 452.7209 393.2176 478.2025 397.3996 427.1109
## [25] 437.4961 331.3307 583.7787 515.3373 386.1863 625.3815 542.6464 470.4929#Soal 5b
n <- nrow(data_mobil)
warna <- character(n)
for(i in 1:n){
if(data_mobil$cyl[i] == 4){warna[i] <- "blue"}
else if(data_mobil$cyl[i] == 6) {warna[i] <- "green"}
else {warna[i] <- "red"}
}
plot(data_mobil$hp, data_mobil$mpg,
col = warna,
pch = 16,
xlab = "Horsepower (hp)",
ylab = "Miles per Gallon (mpg)")
#Soal 5c
abline(h = mean(data_mobil$mpg), lty = 2)
title("Scatter Plot hp vs mpg berdasarkan Silinder")
legend("topright",
legend = c("4 Silinder", "6 Silinder", "8 Silinder"),
col = c("blue", "green", "red"),
pch = 16)