PERTEMUAN 9
Aisyah Humairah
2026-04-24
R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
You can also embed plots, for example:
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.
LATIHAN R MARKDOWN (tebal ** **) LATIHAN R MARKDOWN (miring * *) ## Kode Baru Kode baru adalah kodingan baru :p
#NOMOR 1
#memanggil dataset
data (mtcars)
View (mtcars)
data_mobil <- mtcars
#mengubah variabel am menjadi factor
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 #membuat list
laporan_objek <- list (
nama_mobil = rownames(data_mobil),
data_frame = data_mobil,
matriks = as.matrix(head(data_mobil, 5))
);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
##
## $matriks
## mpg cyl disp hp drat wt qsec vs am
## Mazda RX4 "21.0" "6" "160" "110" "3.90" "2.620" "16.46" "0" "Manual"
## Mazda RX4 Wag "21.0" "6" "160" "110" "3.90" "2.875" "17.02" "0" "Manual"
## Datsun 710 "22.8" "4" "108" " 93" "3.85" "2.320" "18.61" "1" "Manual"
## Hornet 4 Drive "21.4" "6" "258" "110" "3.08" "3.215" "19.44" "1" "Automatic"
## Hornet Sportabout "18.7" "8" "360" "175" "3.15" "3.440" "17.02" "0" "Automatic"
## gear carb
## Mazda RX4 "4" "4"
## Mazda RX4 Wag "4" "4"
## Datsun 710 "4" "1"
## Hornet 4 Drive "3" "1"
## Hornet Sportabout "3" "2"#NOMOR 2
#membuat kolom baru
bensin_category <- c()
for (i in 1:nrow(data_mobil)){
bensin <- data_mobil$mpg[i]
if(bensin > 20 ){
bensin_category[i] <- "Irit"
}else {
bensin_category[i] <- "Boros"
}
}
data_mobil$Efisiensi <- bensin_category
table(data_mobil$Efisiensi)##
## Boros Irit
## 18 14 View(data_mobil)
#mengecek kolom hp
i <- 1
while (i <= nrow(data_mobil)){
horsepower <- data_mobil$hp[i]
if(horsepower > 200){
print (rownames(data_mobil)[i])
break
}
i <- i + 1
}## [1] "Duster 360"#NOMOR 3
#membuat fungsi konversi berat
konversi_berat <- function (wt){
berat <- wt * 1000 * 0.453
return (berat)
}
konversi_berat (50)## [1] 22650 #membuat fungsi kategori silinder
kategori_silinder <- function (cyl){
silinder <- switch (as.character (cyl),
"4" = "Hemat Pajak",
"6" = "Standar",
"8" = "Pajak Mewah")
return (silinder)
}
kategori_silinder (4)## [1] "Hemat Pajak"#NOMOR 4
#hitung mean tiap kolom
kolom <- apply(data_mobil [,sapply(data_mobil, is.numeric)],2,mean);kolom## mpg cyl disp hp drat wt qsec
## 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
## vs gear carb
## 0.437500 3.687500 2.812500 #mengetahui tipe data
tipe_data <- sapply (data_mobil, class);tipe_data## mpg cyl disp hp drat wt
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## qsec vs am gear carb Efisiensi
## "numeric" "numeric" "factor" "numeric" "numeric" "character" #rata-rata hp berdasarkan am
ratahp <- tapply (data_mobil$hp, data_mobil$am, mean);ratahp## Automatic Manual
## 160.2632 126.8462 #operator pipe
library (magrittr)
rata_mpg <- data_mobil %>%
subset(cyl == 6) %>%
.$mpg %>%
mean();rata_mpg## [1] 19.74286#NOMOR 5
#data simulasi menggunakan rnorm()
set.seed(123)
data_mobil$Harga_sewa <- rnorm (n = nrow(data_mobil),
mean = 500, sd = 100);data_mobil$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 #scatter plot
warna <- c ()
for(i in 1:nrow(data_mobil)){
if(data_mobil$cyl[i] == 4){
warna[i] <- "orange"
} else if(data_mobil$cyl[i] == 6){
warna[i] <- "red"
} else {
warna[i] <- "blue"
}
}
plot(x = data_mobil$hp, y = data_mobil$mpg,
col = warna,
pch = 19,
cex = 1.5,
xlab = "Horsepower (hp)",
ylab = "Miles per Gallon (mpg)",
main = "Hubungan HP dan MPG dengan Kategori Jumlah Silinder")
#menambahkan garis rata-rata, judul dan legenda
rata_mpg <- mean (data_mobil$mpg)
abline(h = rata_mpg, col = "grey", lwd = 2)
legend("topright",title = "Silinder",
legend = c("4", "6", "8"),
col = c("orange", "red", "blue"),
pch = 19) 