Exercise 1 - Vector

Author

Arfa Zuhaira Binti Zulroshdi

Exercise 1

Create a numeric variable “num-var” with the value “42.5”.
```
num_var <- 42.5
num_var
```
```
[1] 42.5
```
Create a character variable “char_var” with the value “R is fun!”.
```
char_var <- "R is fun!"
char_var
```
```
[1] "R is fun!"
```
Print the data type “char_var”.
```
class(char_var)
```
```
[1] "character"
```

Create a list “student_info” with the following elements:

“name”: “Mohammad Nasir Abdullah”
“age”: 18
“grades”: a numeric vector with values “99, 100, 89”.

student_info <- list(
  name = "Mohammad Nasir Abdullah",
  age = 18,
  grades = c(99, 100, 89)
)
student_info

$name
[1] "Mohammad Nasir Abdullah"

$age
[1] 18

$grades
[1]  99 100  89

Create a data frame “df_students” with the following columns.

“Name”: “John”, “Pablo”
“Age”: “22”, “30”
“Grade”: “A”, “C”

df_students <- data.frame(
  Name = c("John", "Pablo"),
  Age = c(22, 30),
  Grade = c("A", "C")
)
df_students

   Name Age Grade
1  John  22     A
2 Pablo  30     C

Create a numeric vector “vec_num” with the values “5, 10, 15, 20”.
```
vec_num <- c(5, 10, 15, 20)
vec_num
```
```
[1]  5 10 15 20
```
Extract the second and third elements from “vec_num”.
```
vec_num[c(2, 3)]
```
```
[1] 10 15
```
Create a vector “veq_seq” that contains a sequence of number from 1 to 10.
```
vec_seq <- seq(1, 10)
vec_seq
```
```
 [1]  1  2  3  4  5  6  7  8  9 10
```
Create a vector “vec_rand” with 5 random numbers between 1 and 100.
```
vec_rand <- sample(1:100, size = 5)
vec_rand
```
```
[1] 62 89 32 58 34
```
Create a character vector “vec_char” with the values “apple”, “banana”, “cherry”.
```
vec_char <- c("apple", "banana", "cherry")
vec_char
```
```
[1] "apple"  "banana" "cherry"
```
Use the substr() function to extract the first 3 characters from each elements of “vec_char”.
```
substr(vec_char, 1,3)
```
```
[1] "app" "ban" "che"
```
Create a character vector “vec_colors” with the values “red”, “blue”, “green”, “red”, “blue”.
```
vec_colors <- c("red", "blue", "green", "red", "blue")
vec_colors
```
```
[1] "red"   "blue"  "green" "red"   "blue" 
```

Convert “vec_colors” into a factor vector “factor_colors”.

factor_colors <- as.factor(vec_colors)
factor_colors

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

Print the levels of “factor_colors”.

levels(factor_colors)

[1] "blue"  "green" "red"

Given a numeric vector “vec_bonus = c(10,20,30,40,50)”, write a code snippet to:
```
vec_bonus <- c(10,20,30,40,50)
vec_bonus
```
```
[1] 10 20 30 40 50
```

Extract all elements greater than 25.
```
vec_bonus[vec_bonus>25]
```
```
[1] 30 40 50
```
Calculate the mean of the extracted elements
```
mean(vec_bonus[vec_bonus>25])
```
```
[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")
```
a. Identify the names of individuals whose weight is greater than 65kg.
```
names[weights>65]
```
```
[1] "Bob"     "Charlie" "Frank"   "Henry"  
```
b. Find the average weight of individuals whose weight is greater than 65kg.
```
mean(weights[weights>65])
```
```
[1] 71.25
```
c. 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
```
d. Determine the number of individuals whose weight is between 60kg and 70kg (inclusive).
```
length(weights>=60 & weights<=70)
```
```
[1] 8
```
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”)
```
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 20C.
```
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 20C and 24C (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”, “Jenny”)
```
scores <- c(85, 78, 92, 65, 88, 70, 95, 80, 60, 90)

students <- c("Anna", "Ben", "Cara", "Dan", "Ella", "Finn", "Grace", "Hank", "Ivy", "Jenny")
```
a. Identify the students who scored above 90.
```
students[scores>90]
```
```
[1] "Cara"  "Grace"
```
b. Find the average score of students who scored below 80.
```
mean(scores[scores<80])
```
```
[1] 68.25
```
c. 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.

numeric_vector <- 10:20
numeric_vector

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

character_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")
character_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)
single_vector <- c(a, b)
single_vector
```
```
[1] 1 2 3 4 5 6
```
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)
new_prices <- prices*0.9
new_prices
```
```
[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, …).
```
seq(5, 50, by = 5)
```
```
 [1]  5 10 15 20 25 30 35 40 45 50
```
Random Patterns: Simulate the results of rolling a 6-sided die 10 times.
```
sample(1:6, size = 10, replace = T)
```
```
 [1] 1 3 1 3 1 4 6 5 2 5
```
Character Vector Operations: 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 an 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 vector1 are less than or equal to the corresponding elements in vector2.
```
vector1 <- c(5, 10, 15)
vector2 <- c(10, 10, 20)
vector3 <- vector1 <= vector2
vector3
```
```
[1] TRUE TRUE TRUE
```