
Mengidentifikasi Tipe Data
Tentukan tipe data variabel berikut dalam Python dan R:
# R
a = 42
b = 3.14
c = "Hello"
d = FALSE
e = c(1, 2, 3)
f = list(name = "Alice", age = 25)
Pertanyaan:
- Identifikasi tipe data setiap variabel di atas.
- Cetak tipe data setiap variabel menggunakan
type()
(Python) dan class()
(R).
Jawaban
a = 42 -> numeric
b = 3.14 -> numeric
c = Hello -> character
d = FALSE -> logical
e = 1 2 3 -> numeric
f = name: Alice age: 25 -> list
Variabel dan
Manipulasi Data
Buat variabel berikut dalam Python dan
R :
# R
x = 20
y = 5
text1 = "Data"
text2 = "Science"
Pertanyaan:
Perbarui nilai x dengan menambahkan
10.
Gabungkan text1 dan text2 ke
dalam “Data Science”.
Mengubah teks gabungan menjadi huruf besar.
Jawaban
Nilai x setelah ditambah 10: 30
Teks gabungan: Data Science
Teks dalam huruf besar: DATA SCIENCE
Operasi Aritmatika
Diberikan variabel berikut:
Pertanyaan:
Hitunglah jumlah, selisih, produk, pembagian, dan modulo dari
a
dan b
.
Hitunglah a
pangkat b
.
Buat variabel baru c = a/b
dan ubah menjadi
integer
Jawaban
Jumlah: 19
Selisih: 11
Produk: 60
Pembagian: 3.75
Modulo: 3
Pangkat: 50625
c sebagai integer: 3
Operasi String
Diberikan teks berikut:
# R
Text = "Hello, Data Science!"
Petanyaan: 1. Ekstraks 5 Karakter pertama dari
teks.
Hitung jumlah karakter dalam teks.
Ubah teks menjadi huruf kecil.
Jawaban
5 karakter pertama: Hello
Jumlah karakter: 20
Teks dalam huruf kecil: hello, data science!
Operator Perbandingan
dan Logika
Diberikan variabel-variabel berikut:
Pertanyaan:
Periksa apakah x
lebih besar dari
y
Periksa apakah x
kurang dari atau sama dengan
y
Periksa apakah x
tidak sama dengan
y
Evaluasi ekspresi(x > 5) AND (y < 20)
Jawaban
[1] FALSE
[1] TRUE
[1] TRUE
[1] TRUE
Konversi Tipe Data
Diberikan variabel-variabel berikut:
# R
num_str = "1,2,3"
num_float = 45.67
Petanyaan:
Ubah num_str
ke bilangan bulat dan tambahkan
10
Ubah num_float
ke bilangan bulat
Konversikan num_float
kembali menjadi
string
Jawaban
[1] 133
[1] 45
[1] "45.67"
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