Install and load the necessary packages:
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
data("mtcars")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) {
if (r<0) {return ("Give me a non-negative value please....")}
area <- pi * r^2
return (area)
}
print (calculate_area_of_circle (5))## [1] 78.53982print (calculate_area_of_circle (10))## [1] 314.1593print (calculate_area_of_circle (0))## [1] 0print (calculate_area_of_circle (-10))## [1] "Give me a non-negative value please...."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 (t) {
case_when(t>30 ~ 'Hot', t>=15 & t<=30 ~ 'Warm', .default='Cold')
}
check_temperature (10)## [1] "Cold"check_temperature (20)## [1] "Warm"check_temperature (35)## [1] "Hot"check_temperature (-5)## [1] "Cold"# for practice, lets use if else
check_temperature2 <- function (t) {
if (t>30) {return ('Hot')}
if (t>=15 & t<=30) {return ('Warm')}
else {return ('Cold')}
}
check_temperature2 (10)## [1] "Cold"check_temperature2 (20)## [1] "Warm"check_temperature2 (35)## [1] "Hot"check_temperature2 (-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.
# for even numbers:
sum_even_numbers <- function (n) {
tot <- 0
upto <- n%/%2
for (i in 1:upto) {
tot <- tot + 2*i
}
return (tot)
}
sum_even_numbers(9)## [1] 20sum_even_numbers(39)## [1] 380# the following is for practice -
# using if
sum_even_numbers <- function (n) {
tot <- 0
for (i in 1:n) {
if (i %% 2 == 0) { tot <- tot + i}
}
return (tot)
}
sum_even_numbers(9)## [1] 20sum_even_numbers(10)## [1] 30sum_odd_numbers <- function (n) {
tot <- 0
for (i in 1:n) {
if (i %% 2 == 1) { tot <- tot + i}
}
return (tot)
}
sum_odd_numbers(9)## [1] 25sum_odd_numbers(10)## [1] 25# play using remainders
sum_odd_numbers <- function (n) {
tot <- 0
remainder <- case_when (n%%2==0 ~0, n%%2!=0 ~1)
upto <- n%/%2 + remainder
for (i in 1:upto) {
tot <- tot + 2*i -1
}
return (tot)
}
sum_odd_numbers(9)## [1] 25sum_odd_numbers(10)## [1] 25sum_odd_numbers(11)## [1] 36sum_odd_numbers(12)## [1] 36For 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.
mtcars # show the dataframe## 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 2str (mtcars) # show structure## '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) # get basic stats## 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)) # show count of missing values in each column -- 0## mpg cyl disp hp drat wt qsec vs am gear carb
## 0 0 0 0 0 0 0 0 0 0 0# question: what are the mean and median hp across all cars?
print ( mean (mtcars$hp) ) # dataset has no NAs, so na.rm=TRUE unnecessary## [1] 146.6875print ( median (mtcars$hp) ) # dataset has no NAs, so na.rm=TRUE unnecessary## [1] 123# or using summarize
mtcars |>
summarize ( hp_mean = mean (hp),
hp_median = median (hp))## hp_mean hp_median
## 1 146.6875 123Published at: https://rpubs.com/rmiranda/1357863