HW 5

Functions

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 = 𝜋 × radius^2

Test your function with at least two different radii and print the results.

calculate_area_of_circle <- function(r) {
  pi * r ^ 2
}
print(calculate_area_of_circle(1))

## [1] 3.141593

print(calculate_area_of_circle(2))

## [1] 12.56637

2- 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_temperature <- function(temp) {
  if(temp > 30) {
    print("Hot")
  } else if(temp >=15) {
    print("Warm")
  } else {
    print("Cold")
  }
}
check_temperature(10)

## [1] "Cold"

check_temperature(20)

## [1] "Warm"

check_temperature(35)

## [1] "Hot"

3- Write a function named sum_odd_numbers that uses a for loop to calculate the sum of all odd numbers from 1 to n. The function should return the total sum.

Test the function with n = 9 and n = 39.

sum_odd_numbers <- function(n) {
  odds <- seq(1, n, 2)
  result <- 0
  for(o in odds) {
    result <- result + o
  }
  result
}
sum_odd_numbers(9)

## [1] 25

sum_odd_numbers(39)

## [1] 400

EDA

For this section, use the built-in mtcars dataset. Perform the following:

Check the structure of the dataset.
Display the summary statistics.
Check for any missing values.

Write a question about the dataset and answer it using functions you learned in this class. Print the output of the answer.

Question: Is mpg correlated with qsec (quater mile time)?

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

print(summary(mtcars$mpg))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.40   15.43   19.20   20.09   22.80   33.90

print(summary(mtcars$qsec))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   14.50   16.89   17.71   17.85   18.90   22.90

plot(mtcars$mpg,mtcars$qsec)

print(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

cor_matrix <- cor(
  mtcars |>
    select(mpg,qsec), use = "complete.obs")
print(cor_matrix)

##           mpg     qsec
## mpg  1.000000 0.418684
## qsec 0.418684 1.000000

Yes, MPG is correlated with the quater mile time— more efficient cars are slower to complete the quarter mile, with an R = 0.4186.

HW 5

Rebecca Murphy

Functions

EDA