library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.3
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

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 = đťś‹ Ă— radius^2

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

# Function calculate_area_of_circle
calculate_area_of_circle <- function(radius) {
  return(pi * (radius^2))
}

calculate_area_of_circle(4)
## [1] 50.26548
calculate_area_of_circle(17)
## [1] 907.9203

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
check_temperature <- function(temp){
  
  #if condition
  if(temp > 30){
    return("Hot")
  }
  #else if condition
  else if(temp >= 15 && temp <=30){
    return("Warm")
  }
  #else condition
  else{
    return("Cold")
  }
  
}

check_temperature(46)
## [1] "Hot"
check_temperature(24)
## [1] "Warm"
check_temperature(9)
## [1] "Cold"
check_temperature(67)
## [1] "Hot"
check_temperature(5)
## [1] "Cold"
check_temperature(18)
## [1] "Warm"

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
sum_odd_numbers <- function(n){
  
  #total variable
  total <- 0
  
  #for-loop
  for (i in 1:n) {
    if(i %% 2 == 1){
      total<- total+i
    }
  }
  return(total)
}


sum_odd_numbers(9)
## [1] 25
sum_odd_numbers(39)
## [1] 400

EDA

For this section, use the built-in mtcars dataset (instead of airquality). Perform the following:

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

My Question: What’s the average horsepower(hp) for cars with 4,6, and 8 cylinders(cyl)?

# created a filtered data frame first so I could only work on cars cylinder and horsepower
my_df <- mtcars|>
  select(hp,cyl)
my_df
##                      hp cyl
## Mazda RX4           110   6
## Mazda RX4 Wag       110   6
## Datsun 710           93   4
## Hornet 4 Drive      110   6
## Hornet Sportabout   175   8
## Valiant             105   6
## Duster 360          245   8
## Merc 240D            62   4
## Merc 230             95   4
## Merc 280            123   6
## Merc 280C           123   6
## Merc 450SE          180   8
## Merc 450SL          180   8
## Merc 450SLC         180   8
## Cadillac Fleetwood  205   8
## Lincoln Continental 215   8
## Chrysler Imperial   230   8
## Fiat 128             66   4
## Honda Civic          52   4
## Toyota Corolla       65   4
## Toyota Corona        97   4
## Dodge Challenger    150   8
## AMC Javelin         150   8
## Camaro Z28          245   8
## Pontiac Firebird    175   8
## Fiat X1-9            66   4
## Porsche 914-2        91   4
## Lotus Europa        113   4
## Ford Pantera L      264   8
## Ferrari Dino        175   6
## Maserati Bora       335   8
## Volvo 142E          109   4
# created the a function called average_four_cyl_hp gets the mean horsepower depending on the cars cylinder
average_four_cyl_hp <- function(data,cyl_num) {
  
  four_cyl <- filter(data, cyl == cyl_num)
  avg_hp <- mean(four_cyl$hp)
  
  return(avg_hp)
} 

#only can pick 4,6, or 8 because those are the number in the data set
average_four_cyl_hp(my_df,4)
## [1] 82.63636
average_four_cyl_hp(my_df,6)
## [1] 122.2857
average_four_cyl_hp(my_df,8)
## [1] 209.2143
#made to check if my answer was correct for the function
check_answer <- my_df |>
  filter(cyl %in% c(8))|>
  summarise(mean(hp))

check_answer
##   mean(hp)
## 1 209.2143