Exercise 1 - Vector

Author

Ervie Ferona Franchis

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 <- c("R is fun!")
```
Print the data type “char_var”.
```
print(char_var)
```
```
[1] "R is fun!"
```

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 = c("Mohammad Nasir Abdullah"),
  age = c(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”
“Grades”: “A”, “C”

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

   Name Age Grades
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 “vec_seq” that contains a sequence of number from 1 to 10.

vec_seq <- c(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 <- c(1:100)
sample(vec_rand, size=5, replace=5)

[1]  39  70  20 100  11

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)

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:
```
vec_bonus <- c(10,20,30,40,50)
vec_bonus
```
```
[1] 10 20 30 40 50
```

a. Extract all elements greater than 25.

vec_bonus[vec_bonus>25]

[1] 30 40 50

b. 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 <- (“Alice”, “Bob”, “Charlie”, ’Daisy”, “Eve”, “Frank”, “Grace”, “Henry”)

weights <- c(55,68,72,61,58,75,64,70)
weight_names <- c("Alice", "Bob", "Charlie", "Daisy", "Eve", "Frank", "Grace", "Henry")

a. Identify the names of individuals whose weight is greater than 65kg.

weight_names[weights>65]

[1] "Bob"     "Charlie" "Frank"   "Henry"

b. Find the average weight of individuals weighting less than or equal to 60kg.

mean(weights[weights<=60])

[1] 56.5

c. Extract the weights of individuals whose names start with the letter ‘A’ or ‘D’.

weights[substr(weight_names, 1, 1) %in% c("A","D")]

[1] 55 61

d. Determine the number of individuals whose weight is between 60kg and 70kg.

length(weights[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”)
```
temperatures <- c(22,25,19,21,18,24,23)

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

Identify the days when the temperature was below 20 Celsius.
```
days[temperatures<20]
```
```
[1] "Wednesday" "Friday"   
```

Calculate the difference between the highest and lowest temperatures of the week.
```
max(temperatures)-min(temperatures)
```
```
[1] 7
```

Determine the number of days when the temperature was between 20 Celsius and 24 Celsius (inclusive).
```
length(days[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”, “)
```
scores <- c(85,78,92,65,88,70,95,80,60,90)

students <- c("Anna", "Ben", "Cara", "Dan", "Ella", "Finn", "Grace", "Hank", "Ivy", "John")
```

Identify the student 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 if 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_vec <- c(10:20)
char_vec <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")

print(num_vec)

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

print(char_vec)

[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)

a
```
```
[1] 1 2 3
```
```
b
```
```
[1] 4 5 6
```
```
d <- c(a, b)
d
```
```
[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.
```
temperature <- c(15,18,20,22,19,17,16)
temperature
```
```
[1] 15 18 20 22 19 17 16
```
```
temperature[c(2,4,6)]
```
```
[1] 18 22 17
```
Omitting Elements: From the same temperatures vector, omit the temperature of the 5th day.
```
temperature[-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…)
```
vector_number <- seq(5,50, by = 5)
vector_number
```
```
 [1]  5 10 15 20 25 30 35 40 45 50
```
Random Patterns: Simulate the results of rolling a 6-sided dice 10 times.
```
sample(1:6, size = 10, replace = TRUE)
```
```
 [1] 3 3 3 3 1 3 3 2 3 6
```
Character Vector Operations: Given the vector fruits c(“apple”, “banana”, “cherry”), extract the first three letters of each fruits.
```
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 vector1 are less than or equal to the corresponding elements in vector2.
```
vectors1 <- c(5,10,15)
vectors2 <-c(10,10,20)

compares <- vectors1 <= vectors2
compares
```
```
[1] TRUE TRUE TRUE
```
```
vectors1[vectors1 <= vectors2]
```
```
[1]  5 10 15
```