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(x) {
area <- (x^2)*pi
return(area)
}
calculate_area_of_circle(1)## [1] 3.141593calculate_area_of_circle(2)## [1] 12.566372- 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_temp <- function(x) {
if (x > 30) {
return("Hot")
}
else if (x >= 15 & x<= 30) {
return("Warm")
}
else {
return("Cold")
}
}
check_temp(10)## [1] "Cold"check_temp(20)## [1] "Warm"check_temp(30)## [1] "Warm"check_temp(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) {
total <- 0
for (i in 1:n) {
if (i %% 2 == 1) {
total <- total + i
}
}
return(total)
}
sum_odd_numbers(9)## [1] 25sum_odd_numbers(39)## [1] 400For 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.
data(mtcars)
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. :8.000colSums(is.na(mtcars))## mpg cyl disp hp drat wt qsec vs am gear carb
## 0 0 0 0 0 0 0 0 0 0 0How many cars with ___ cylinders weight over 3000 lbs?
library(tidyverse)
library(dplyr)
mtcarsfunc <- function(x) {
mtcars1 <- mtcars |>
filter(mtcars$cyl == x & mtcars$wt >= 3)
total <- pull(count(mtcars1))
return(total)
}
mtcarsfunc(4)## [1] 2mtcarsfunc(6)## [1] 4mtcarsfunc(8)## [1] 14