1- Create a function named calculate_area_of_circle that takes one parameter, radius, representing the radius of a circle. The function should calculate and return the area using the formula:
area = đťś‹ Ă— radius^2
Test your function with at least two different radii and print the results.
calculate_area_of_circle <- function(radius) {
area <- pi * radius^2
return(area)
}
calculate_area_of_circle(3)## [1] 28.27433calculate_area_of_circle(5)## [1] 78.539822- Write a function called check_temperature that takes a single numeric input temp.
If temp > 30, print “Hot”
If temp >= 15 & temp <= 30, print “Warm”
Otherwise, print “Cold”
Test the function on three different values (e.g., 10, 20, 35).
check_tempature <- function(temp) {
if (temp > 30) {
print("Hot")
} else if (temp >= 15 & temp <= 30) {
print("Warm")
} else{
print("Cold")
}
}
check_tempature (10)## [1] "Cold"check_tempature (20)## [1] "Warm"check_tempature (35)## [1] "Hot"3- Write a function named sum_odd_numbers that uses a for loop to calculate the sum of all even numbers from 1 to n. The function should return the total sum.
Test the function with n = 9 and n = 39.
sum_even_numbers <- function(n) {
total <- 0
for (i in 1:n) {
if (i %% 2 == 0) {
total <- total + i
}
}
return(total)
}
sum_even_numbers(9)## [1] 20sum_even_numbers(39)## [1] 380For this section, use the built-in mtcars dataset (instead of airquality). Perform the following:
Check the structure of the dataset.
Display the summary statistics.
Check for any missing values.
Ask a simple question that can be answered with a basic function (e.g., What is the mean miles per gallon (mpg) across all cars?). Write the code and output the answer.
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ... summary(mtcars)## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000 colSums(is.na(mtcars))## mpg cyl disp hp drat wt qsec vs am gear carb
## 0 0 0 0 0 0 0 0 0 0 0 mean(mtcars$mpg)## [1] 20.09062The dataset includes variables such as mpg, horsepower, and weight. The summary statistic show the distribution of these variables. There are no missing values in the dataset. The average miles per gallon (mpg) is approximately 20.9