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(r) {
return(pi*r^2)
}
calculate_area_of_circle(8)## [1] 201.0619calculate_area_of_circle(30)## [1] 2827.4332- 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(x) {
if (x > 30) {
return("Hot")
} else if(x >= 15 & x <= 30) {
return("Warm")
} else {
return("Cold")
}
}
check_temperature(40)## [1] "Hot"check_temperature(25)## [1] "Warm"check_temperature(5)## [1] "Cold"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_odd_numbers <- function(n) {
total <- 0
for (i in 1:n) {
if (i %% 2 == 0) { #(i %% 2 == 1) to match function name
total <- total + i
}
}
return(total)
}
sum_odd_numbers(9)## [1] 20sum_odd_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 ...library(tidyverse)
summarize_by_cyl <- function(var_name) {
if (!(var_name %in% names(mtcars)) ||
!is.numeric(mtcars[[var_name]])) {
cat("Variable", var_name, "is invalid.\n")
return(NULL)
}
summary_stats <- mtcars |>
group_by(cyl) |>
summarise(
mean = round(mean(.data[[var_name]], na.rm = TRUE), 2),
median = round(median(.data[[var_name]], na.rm = TRUE), 2),
n_missing = sum(is.na(.data[[var_name]]))
)
return(summary_stats)
}or
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 0What is the mean displacement for each engine cylinder quantity and all cars?
summarize_by_cyl("disp")## # A tibble: 3 Ă— 4
## cyl mean median n_missing
## <dbl> <dbl> <dbl> <int>
## 1 4 105. 108 0
## 2 6 183. 168. 0
## 3 8 353. 350. 0mean(mtcars$disp)## [1] 230.7219