Praktikum

1.8.1 Identifikasi Data Tipe

a <- 42L
b <- 3.14
c <- "Hello"
d <- FALSE
e <- c(1, 2, 3)
f <- list(name = "Alice", age = 25)

cat(
"Type a:", class(a), "\n",
"Type b:", class(b), "\n",
"Type c:", class(c), "\n",
"Type d:", class(d), "\n",
"Type e:", class(e), "\n",
"Type f:", class(f), "\n"
)
## Type a: integer 
##  Type b: numeric 
##  Type c: character 
##  Type d: logical 
##  Type e: numeric 
##  Type f: list

Jadi kenapa itu menggunakan 42L?, karena tanpa menggunakan variabel tambahan hasilnya akan numerik bukan integer. Saya juga akan memberi contoh jika tidak diberi variabel sesudah angka 42, Ini adalah contohnya:

a <- 42
b <- 3.14
c <- "Hello"
d <- FALSE
e <- c(1, 2, 3)
f <- list(name = "Alice", age = 25)

cat(
"Type a:", class(a), "\n",
"Type b:", class(b), "\n",
"Type c:", class(c), "\n",
"Type d:", class(d), "\n",
"Type e:", class(e), "\n",
"Type f:", class(f), "\n"
)
## Type a: numeric 
##  Type b: numeric 
##  Type c: character 
##  Type d: logical 
##  Type e: numeric 
##  Type f: list

1.8.2 Variabel dan Data Manipulasi

x <- 20
y <- 5
text1 <- "Data"
text2 <- "Science"

x <- x + 10
text_combined <- paste(text1, text2)
text_upper <- toupper(text_combined)

cat(
"Updated x:", x, "\n",
"Combined text:", text_combined, "\n",
"Uppercase text:", text_upper, "\n"
)
## Updated x: 30 
##  Combined text: Data Science 
##  Uppercase text: DATA SCIENCE

1.8.3 Operasi Aritmatika

a <- 15
b <- 4

jumlah <- a + b
selisih <- a - b
produk <- a * b
pembagian <- a / b
modulo <- a %% b
pangkat <- a ^ b
c <- as.integer(a / b)

cat(
"Sum:", jumlah, "\n",
"Difference:", selisih, "\n",
"Product:", produk, "\n",
"Division:", pembagian, "\n",
"Modulo:", modulo, "\n",
"Exponentiation:", pangkat, "\n",
"Integer division result:", c, "\n"
)
## Sum: 19 
##  Difference: 11 
##  Product: 60 
##  Division: 3.75 
##  Modulo: 3 
##  Exponentiation: 50625 
##  Integer division result: 3

1.8.4 Operasi String

text <- "Hello, Data Science!"

substring_text <- substr(text, 1, 5)
length_text <- nchar(text)
lower_text <- tolower(text)

cat(
"First 5 characters:", substring_text, "\n",
"Text length:", length_text, "\n",
"Lowercase text:", lower_text, "\n"
)
## First 5 characters: Hello 
##  Text length: 20 
##  Lowercase text: hello, data science!

1.8.5 Operator dan perbandingan logika

x <- 7
y <- 10

lebih_besar <- x > y
kurang_sama <- x <= y
tidak_sama <- x != y
ekspresi <- (x > 5) & (y < 20)

cat(
"x > y:", lebih_besar, "\n",
"x <= y:", kurang_sama, "\n",
"x != y:", tidak_sama, "\n",
"(x > 5) AND (y < 20):", ekspresi, "\n"
)
## x > y: FALSE 
##  x <= y: TRUE 
##  x != y: TRUE 
##  (x > 5) AND (y < 20): TRUE

1.8.6 Konversi Tipe Data

num_str <- "123"
num_float <- 45.67

num_int <- as.integer(num_str) + 10
num_float_int <- as.integer(num_float)
num_float_str <- as.character(num_float)

cat(
"Converted num_str + 10:", num_int, "\n",
"num_float as integer:", num_float_int, "\n",
"num_float as string:", num_float_str, "\n"
)
## Converted num_str + 10: 133 
##  num_float as integer: 45 
##  num_float as string: 45.67

1.9 Bonus

nama <- ("Zain Iqbal Saputra")
usia <- ("18")
kota <- ("Bekasi")

cat("Hello",nama, ", you are",usia , "years old and from", kota, ".\n")
## Hello Zain Iqbal Saputra , you are 18 years old and from Bekasi .
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