HW 5
Muhammad Ahtisham
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 Γ radius^2
Test your function with at least two different radii and print the results.
calculate_area_of_circle <- function(r) {
return(3.14*r^2)
}
calculate_area_of_circle(20)## [1] 12562- Write a function called check_temperature that takes a single numeric input temp.
If temp > 30, print βHotβ
If temp >= 15 & temp <= 30, print βWarmβ
Otherwise, print βColdβ
Test the function on three different values (e.g., 10, 20, 35).
check_temperature <- function(temp) {
if(temp > 30)
return("Hot")
if(temp >= 15 & temp <= 30)
return("Warm")
else
return("Cold")
}
check_temperature(40)## [1] "Hot"check_temperature(19)## [1] "Warm"check_temperature(3)## [1] "Cold"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.Β 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] 400EDA
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: How do the means and medians of the mpg (Miles/Gallon) and wt (weight) compare with each other?
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 0mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2library(tidymodels)## ββ Attaching packages ββββββββββββββββββββββββββββββββββββββ tidymodels 1.4.1 ββ## β broom 1.0.9 β recipes 1.3.1
## β dials 1.4.2 β rsample 1.3.1
## β dplyr 1.1.4 β tailor 0.1.0
## β ggplot2 4.0.0 β tidyr 1.3.1
## β infer 1.0.9 β tune 2.0.0
## β modeldata 1.5.1 β workflows 1.3.0
## β parsnip 1.3.3 β workflowsets 1.1.1
## β purrr 1.1.0 β yardstick 1.3.2## Warning: package 'ggplot2' was built under R version 4.5.2## ββ Conflicts βββββββββββββββββββββββββββββββββββββββββ tidymodels_conflicts() ββ
## β purrr::discard() masks scales::discard()
## β dplyr::filter() masks stats::filter()
## β dplyr::lag() masks stats::lag()
## β recipes::step() masks stats::step()mtcars |>
summarise(mean_mpg = mean(mpg), median_mpg = median(mpg), mean_wt = mean(wt), median_wt = median(wt)) ## mean_mpg median_mpg mean_wt median_wt
## 1 20.09062 19.2 3.21725 3.325Answer: The mean and median for mpg (miles per gallon) is 20.09 and 19.2, respectively. The mean and median for wt (weight) is 3.22 and 3.33, respectively. The mean and median for each variable is relatively similar. This means that the graph of each of these variables would mostly be symmetrical, though for mpg, it might be a little right-skewed because the mean is greater than the median by almost 1 unit.
When comparing the means and medians to each other, we see that the mean and median for miles per gallon is higher than the mean and median for weight.
Quick plots I wanted to graph to check:
ggplot(mtcars, aes(x = mpg)) +
geom_density()
We can see that the graph is slightly right-skewed, but mostly symmetrical.
ggplot(mtcars, aes(x = wt)) +
geom_density()
We can see that the graph is mostly symmetrical, but it has some outliers towards the right. This is why itβs important to still graph your variables because you can derive hidden insights.