num_var <- 42.5
num_var[1] 42.5Exercise 1 - Vector
Exercise 1
- Create a numeric variable “num_var” with the value “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 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 = 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 89Create a data frame “df_students” with the following columns
1) “Name” : “John” , “Pablo”
2) “Age” : “22” , “30”
3) “Grade” : “A” , “C”
df_students <- data.frame(
Name = c("John", "Pablo"),
Age = c(22,30),
Grade = factor(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 “vec_seq” that contains a sequence of number 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 to 100.
vec_rand <- sample(1:100, size = 5, replace = TRUE)
vec_rand[1] 100 66 23 27 43- 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:
1) Extract all elements greater than 25.
vec_bonus <- c(10,20,30,40,50) vec_bonus[vec_bonus>25][1] 30 40 502) Calculate the mean of the extracted elements.
vec_bonus <- c(10,20,30,40,50) mean(vec_bonus[vec_bonus>25])[1] 40Given the following vectors:
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.
weights <- c(55, 68, 72, 61, 58, 75, 64, 70) names <- c("Alice", "Bob", "Charlie", "Daisy", "Eve", "Frank", "Grace", "Henry") names[weights>65][1] "Bob" "Charlie" "Frank" "Henry"b) Find the average weight of individuals weighting less than or equal to 60kg.
weights <- c(55, 68, 72, 61, 58, 75, 64, 70) names <- c("Alice", "Bob", "Charlie", "Daisy", "Eve", "Frank", "Grace", "Henry") mean(weights[weights<=60])[1] 56.5c) Extract the weights of individuals whose names start with the letter ‘A’ or ‘D’.
weights <- c(55, 68, 72, 61, 58, 75, 64, 70) names <- c("Alice", "Bob", "Charlie", "Daisy", "Eve", "Frank", "Grace", "Henry") weights[substr(names, 1, 1) %in% c("A", "D")][1] 55 61d) Determine the number of individuals whose weight is between 60kg and 70kg (inclusive).
weights <- c(55, 68, 72, 61, 58, 75, 64, 70) names <- c("Alice", "Bob", "Charlie", "Daisy", "Eve", "Frank", "Grace", "Henry") length(weights[weights>=60 & weights<=70])[1] 4Hint :
You might find functions like mean() , substr() ,and length() useful.
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.
temperatures <- c(22, 25, 19, 21, 18, 24, 23) days <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday") days[temperatures < 20][1] "Wednesday" "Friday"b) Calculate the difference between the highest and lowest temperatures of the week.
temperatures <- c(22, 25, 19, 21, 18, 24, 23) days <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday") max(temperatures)-min(temperatures)[1] 7c) Determine the number of days when the temperature was between 20’C and 24’C (inclusive).
temperatures <- c(22, 25, 19, 21, 18, 24, 23) days <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday") length(days[temperatures>=20 & temperatures<=24])[1] 4Given 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”, “Jennie”)
a) Identify the students who scored above 90.
scores <- c(85, 78, 92, 65, 88, 70, 95, 80, 60, 90) students <- c("Anna", "Ben", "Cara", "Dan", "Ella", "Finn", "Grace", "Hank", "Ivy", "Jennie") students[scores>90][1] "Cara" "Grace"b) Find the average score of students who scored below 80.
scores <- c(85, 78, 92, 65, 88, 70, 95, 80, 60, 90) students <- c("Anna", "Ben", "Cara", "Dan", "Ella", "Finn", "Grace", "Hank", "Ivy", "Jennie") mean(scores[scores<80])[1] 68.25c) Extract the scores of students whose names start with a vowel (A, E, I, O, U).
scores <- c(85, 78, 92, 65, 88, 70, 95, 80, 60, 90) students <- c("Anna", "Ben", "Cara", "Dan", "Ella", "Finn", "Grace", "Hank", "Ivy", "Jennie") scores[substr(names,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 <- c(10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20) character_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday") numeric_vector[1] 10 11 12 13 14 15 16 17 18 19 20character_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_vector <- c(a,b) combine_vector[1] 1 2 3 4 5 6Element 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 17Omitting Elements: From the same temperatures vector, omit the temperature of the 5th day.
temperatures <- c(15, 18, 20, 22, 19, 17, 16) temperatures[-5][1] 15 18 20 22 17 16Vector 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) discount<- 0.9 new_prices <- prices * 0.9 new_prices[1] 9 18 27 36 45Patterned 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 50Random Patterns: Simulate the results of rolling a 6-sided die 10 times.
sample(1:6, size = 10, replace = TRUE)[1] 3 1 6 2 2 6 6 1 6 6Character 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 and less than 50.
ages <- c(25, 30, 35, 40, 45, 50) ages[ages>30 & ages<50][1] 35 40 45Relational Operations 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) compare <- vector1 <= vector2 compare[1] TRUE TRUE TRUE