EXERCISE 1 VECTOR

Author

Nur Adilah Safiah

EXERCISE 1

Create a numeric variable ” num_var ” with the value “42.5”.
```
num_var <- 42.5
```
Create a character variable ” char_var ” with the value “R is fun!”.
```
char_var <- "R is fun!"
```
Print the data type ” char_var”
```
class(char_var)
```
```
[1] "character"
```
Create a list ” student_info” with the following elements:
1. “name”: “Mohammad Nasir Abdullah”
2. “age”: 18
3. ” grades”: a numeric vector with values “99, 100, 89”.
```
student_info <- list(   
  name = "Mohammad Nasir Abdullah",   
  age = 18,   
  grade = c(99,100,89) 
  )
```
Create a data frame “f_students” with the following columns
1. “Name”: “John”, “Pablo”
2. ” Age”: “22”, “30”
3. “Grade”: “A”, “C”
```
f_students <- data.frame(    
  name = c("John", "Pablo"),   
  age = c(22,30),   
  grade = c("A", "C")  
  )
```
Create a numeric vector ” vec_num” with the values “5,10,15,20”.
```
vec_num <- c(5,10,15,20)
```
Extract the second and third elements from ” vec_num”.
```
vec_num[c(2,3)]
```
```
[1] 10 15
```
Create a vector ” vec_seq” that contains a sequence of number from 1 to 10.
```
vec_seq <- 1:10
```
Create a vector ” vec_rand” with 5 random numbers between 1 and 100.
```
vec_rand <- sample(1:100, size = 5)
```
Create a character vector ” vec_char” with the values “apple”, “banana”, “cherry”.
```
vec_char <- c("apple", "banana", "cherry")
```
Use the substr() function to extract the first 3 characters from each elements of ” vec_char”.
```
vec_char <- substr(vec_char, 1,3)
```
Create a character vector ” vec _colors” with the values “red”, “blue”, “green”, “red”, “blue”.
```
vec_colors <- c("red", "blue", "green", "red", "blue")
```
Convert ” vec_colors” into a factor vector ” factor_colors”.
```
factor_colors <- as.factor(vec_colors)
```

Print the levels of “factor_colors”.

print(factor_colors)

[1] red   blue  green red   blue 
Levels: blue green red

Given a numeric vector ” vec_bonus = c(10,20,30,40,50) “, write a code snippet to :
1. Extract all elements greater than 25.
  vec_bonus <- c(10, 20, 30, 40, 50) vec_bonus[vec_bonus > 25]
```
[1] 30 40 50
```
2. Calculate the mean of the extracted elements.
```
extract <- vec_bonus[vec_bonus > 25]  
mean(extract)
```
```
[1] 40
```
Given the following vectors:

weights ‹- c(55, 68, 72, 61, 58, 75, 64, 70)

names ‹- c(“Alice”, “Bob”, “Charlie”, “Daisy”, “Eve”, “Frank”, “Grace”, “Henry”)
```
weights <- c(55, 68, 72, 61, 58, 75, 64, 70)  
names <- c("Alice", "Bob", "Charlie", "Daisy", "Eve", "Frank", "Grace", "Henry")
```
1. Identify the names of individuals whose weight is greater than 65kg.
  names[weights > 65]
```
[1] "Bob"     "Charlie" "Frank"   "Henry"  
```
2. Find the average weight of individuals weighting less than or equal to 60kg.
  mean(weights[weights<=60])
```
[1] 56.5
```
3. Extract the weights of individuals whose names start with the letter ‘A’ or ‘D’.
  weights[substr(names,1,1) %in% c("A","D")]
```
[1] 55 61
```
4. Determine the number of individuals whose weight is between 60kg and 70kg (inclusive).
  sum(weights >= 60 & weights <= 70)
```
[1] 4
```
Given the following vector of daily temperatures (in Celsius) for a week:
```
temperatures <- c(22, 25, 19, 21, 18, 24, 23) 
days <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")
```
a. Identify the days when the temperature was below 20°C.
```
days[temperatures < 20]
```
```
[1] "Wednesday" "Friday"   
```
b. Calculate the difference between the highest and lowest temperatures of the week.
```
max(temperatures) - min(temperatures)
```
```
[1] 7
```
c. Determine the number of days when the temperature was between 20°C and 24°C (inclusive).
```
sum(temperatures >= 20 & temperatures <= 24)
```
```
[1] 4
```

Given the following vector of exam scores:

scores <- c(85, 78, 92, 65, 88, 70, 95, 80, 60, 90) 
students <- c("Anna", "Ben", "Cara", "Dan", "Ella", "Finn", "Grace", "Hank", "Ivy", "Jay")

Identify the students who scored above 90.

students[scores > 90]

[1] "Cara"  "Grace"

Find the average score of students who scored below 80.
```
mean(scores[scores < 80])
```
```
[1] 68.25
```

Extract the scores of students whose names start with a vowel (A, E, I, O, U)

scores[substr(students, 1, 1) %in% c("A", "E", "I", "O", "U")]

[1] 85 88 60

Take Home Exercise

Basic Vector Creation: Create a numeric vector containing the numbers from 10 to 20 and a character vector containing the days of the week. Display both vectors.

num_vector <- 10:20
num_vector

 [1] 10 11 12 13 14 15 16 17 18 19 20

days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")
days_vector

[1] "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"    "Saturday" 
[7] "Sunday"

Vector Concatenation: Given two vectors: a ‹- c(1, 2, 3) and b ‹- c(4, 5, 6), concatenate them to form a single vector.
```
a <- c(1, 2, 3)
b <- c(4, 5, 6)

combine <- c(a, b)
```
Element Extraction: Given the vector temperatures ‹- c(15, 18, 20, 22, 19, 17, 16), extract the temperatures for the 2nd, 4th, and 6th days.
```
temperatures <- c(15, 18, 20, 22, 19, 17, 16)
temperatures[c(2, 4, 6)]
```
```
[1] 18 22 17
```
Omitting Elements: From the same temperatures vector, omit the temperature of the 5th day.
```
temperatures[-5]
```
```
[1] 15 18 20 22 17 16
```
Vector Arithmetic: Given the vector prices ‹- c(10, 20, 30, 40, 50), calculate the new prices after a 10% discount.
```
prices <- c(10, 20, 30, 40, 50)
prices * 0.9
```
```
[1]  9 18 27 36 45
```
Patterned Vectors: Create a vector that contains the numbers from 5 to 50, but only includes every 5th number (i.e., 5, 10, 15,…).
```
vec_num <- seq(5,50, by =5)
```
Random Patterns: Simulate the results of rolling a 6-sided die 10 times.
```
dice <- sample(1:6, size = 10, replace = TRUE)
```
Character Vector Onerations: Given the vector fruits <- c(“apple”, “banana”, “cherry”), extract the first three letters of each fruit.
```
fruits <- c("apple", "banana", "cherry")
substr(fruits, 1, 3)
```
```
[1] "app" "ban" "che"
```
Logical Vector Filtering: Given the vector ages ‹- c(25, 30, 35, 40, 45, 50), identify which ages are greater than 30 and less than 50.
```
ages <- c(25, 30, 35, 40, 45, 50)
ages[ages > 30 & ages < 50]
```
```
[1] 35 40 45
```
Relational Operators with Vectors: Given two vectors: vector1 ‹- c(5, 10, 15) and vector2 ‹- c(10, 10, 20), determine which elements of vector are less than or equal to the corresponding elements in vector2.
```
vector1 <- c(5, 10, 15)
vector2 <- c(10, 10, 20)
vector1 <= vector2
```
```
[1] TRUE TRUE TRUE
```