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 of char_va.
class(char_var)[1] "character"- create a list “student_info” with the following elements:
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_student
df_student <- data.frame(
name = c( "John", "Pablo"),
age = c(22,30),
grade = c("A", "C")
)
df_student name age grade
1 John 22 A
2 Pablo 30 C- create a numeric vector “vec_num” with the value “5,10,15,20”.
vec_num <- c(5,10,15,20)
vec_num[1] 5 10 15 20- extract the second and third element 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
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, 5)
vec_rand[1] 59 95 18 78 40- create a character vector “vec_char” with the value “apple”, “banana”, “cherry”
vec_char <- c("apple", "banana", "cherry")
vec_char[1] "apple" "banana" "cherry"- use the substr() function to extract the first 3 character from each elements of “vec_char”.
substr(vec_char, 1,3)[1] "app" "ban" "che"- created a character vector “vec_colors” with the value of “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 the numeric vector “vec_bonus = c(10,20,30,40,50)”, write a code snippet to :
a) extract all elements greater than 25.
vec_bonus = c(10,20,30,40,50)
bonus_extract <- vec_bonus[vec_bonus > 25]
bonus_extract[1] 30 40 50b) calculate the mean of the extracted elements.
mean(bonus_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")a. identify the names of individuals whose weight is greater than 65kg.
names[weights > 65][1] "Bob" "Charlie" "Frank" "Henry" b. find the average weights of individuals weighting less than or equal to 60kg.
mean(weights[weights <= 60])[1] 56.5c.. extract the weights of individuals whose names starts with the letter ‘A’ or ‘D’.
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)
sum(weights >= 60 & weights <= 70)[1] 4given 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 temperatures 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] 7c) determine the number of days when the temperature was between 20C and 24C
sum(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")
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] 68.25c) 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 90TAKE HOME EXERCISE
create a numeric vector containing the numbers10 to 20 and a character vector containing the days of the week. Display both vectors.
num_vec <- 10:20 days_vec <- c("Monday", "Tuesday", "WedneSday", "Thursday", "Friday", "Saturday", "Sunday") num_vec[1] 10 11 12 13 14 15 16 17 18 19 20days_vec[1] "Monday" "Tuesday" "WedneSday" "Thursday" "Friday" "Saturday" [7] "Sunday"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)
c_vec <- c(a , b)
c_vec[1] 1 2 3 4 5 6- given the vector temperatures <- c(15, 18, 20, 22, 19, 17, 16), extract the temperatures for the 2nd, 4th, and 6th days.
::: {.cell}
```{.r .cell-code}
temperatures <- c(15, 18, 20, 22, 19, 17, 16)
extract_temps <- temperatures[c(2,4,6)]
extract_temps
```
::: {.cell-output .cell-output-stdout}
```
[1] 18 22 17
```
:::
:::from the same temperatures vectors, omit the temperature of the 5th day.
omitted_temps <- temperatures[-5] omitted_temps[1] 15 18 20 22 17 16given the vector prices <- c(10,20,30,40,50) calculate the new prices after 10% discount.
prices <- c(10,20,30,40,50) disc_prices <- prices * 0.90 disc_prices[1] 9 18 27 36 45created a vector that contains the numbers from 5 to 50, but only includes every 5th number (ie., 5,10,15)
pat_vec <- seq(from = 5,to = 50, by = 5)
pat_vec [1] 5 10 15 20 25 30 35 40 45 50simulate the results of rolling a 6- sides die 20 times.
dice_rolls <- sample(1:6 , size= 10, replace = TRUE) dice_rolls[1] 3 5 4 5 4 4 2 1 6 2given the vector fruits <- c(“apple” , “banana”, “cherry”) extract the first letters of each fruit
fruits <- c("apple" , "banana", "cherry")
first_three <- substr(fruits, start =1, stop = 3)
first_three[1] "app" "ban" "che"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) vec_ages <- ages[ages > 30 & ages < 50] vec_ages[1] 35 40 45given 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 coressponding elements in vector2.
vector1 <- c(5,10,15) vector2 <- c(10,10,20) cor_vec <- vector1 <= vector2 cor_vec[1] TRUE TRUE TRUE