Q1. Create a numeric vector containing the first 15 positive integers. Find: Length of the vector, Sum of elements, Mean of elements
x = 1:15
len <- length(x)
total = 0
for (i in x){
total = total + i
}
mean_val = total/ len
len ## [1] 15total ## [1] 120mean_val## [1] 8Q2. Create a vector containing the squares of numbers from 1 to 10.
nums = 1:10
squares = nums * nums
squares## [1] 1 4 9 16 25 36 49 64 81 100Q3. Create a character vector of 5 programming languages and display: The first element & The last element
langs = c("R","CPP","Python", "Java","SQL")
first_lang = langs[1]
last_lang = langs[length(langs)]
first_lang## [1] "R"last_lang## [1] "SQL"Q4. Create a logical vector by checking whether numbers from 1 to 20 are even.
nums = 1:20
even_check = (nums %% 2) == 0
nums## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20even_check## [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE
## [13] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUEQ5. Create a numeric vector and determine: Minimum value, Maximum value, Range
x = c (12,45,7,33,19)
min_val = x[1]
max_val = x[1]
for (i in x){
if (i< min_val) min_val = i
if (i> max_val) max_val = i
}
range_val = max_val - min_val
min_val## [1] 7max_val## [1] 45range_val## [1] 38Q.6 - Create a vector of numbers from 10 to 50. Extract only the numbers greater than 30.
x = 10:50
x[x>30]## [1] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50Q7. Given the vector: x <- c(12, 5, 8, 20, 15, 3) Find all values that are: Greater than 10 & Less than 20
x <- c(12, 5, 8, 20, 15, 3)
x[x>10 & x< 20]## [1] 12 15Q8. Write a user-defined function square_cube() that: Takes a number as input, Returns its square and cube
square_cube = function(n){
square = n * n
cube = n*n*n
c(square= square, cube = cube)
}
square_cube(4)## square cube
## 16 64Q9. Write a function is_even() that checks whether a number is even or odd and prints an appropriate message.
is_even = function(n){
if (n%%2==0){
"even"
} else {
"odd"
}
}
is_even(1001)## [1] "odd"Q10. Create a function sum_n() that calculates the sum of integers from 1 to n. Use the function to find the sum of integers from 1 to 1,000.
sum_n = function(n){
total = 0
for (i in 1:n){
total = total +i
}
total
}
sum_n(1000)## [1] 500500Q11. Load the built-in dataset mtcars. Display: Number of rows, Number of columns, Column names
data(mtcars)
row = nrow(mtcars)
columns = ncol(mtcars)
names_cols = names(mtcars)
row## [1] 32columns## [1] 11names_cols## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1Q12. From mtcars, extract: Miles per gallon (mpg), Number of cylinders (cyl) Store them in a new data frame.
new_df = data.frame(
mpg = mtcars$mpg,
cyl = mtcars$cyl
)
new_df## mpg cyl
## 1 21.0 6
## 2 21.0 6
## 3 22.8 4
## 4 21.4 6
## 5 18.7 8
## 6 18.1 6
## 7 14.3 8
## 8 24.4 4
## 9 22.8 4
## 10 19.2 6
## 11 17.8 6
## 12 16.4 8
## 13 17.3 8
## 14 15.2 8
## 15 10.4 8
## 16 10.4 8
## 17 14.7 8
## 18 32.4 4
## 19 30.4 4
## 20 33.9 4
## 21 21.5 4
## 22 15.5 8
## 23 15.2 8
## 24 13.3 8
## 25 19.2 8
## 26 27.3 4
## 27 26.0 4
## 28 30.4 4
## 29 15.8 8
## 30 19.7 6
## 31 15.0 8
## 32 21.4 4Q13. Find the average mileage (mpg) for cars with: 4 cylinders, 6 cylinders, 8 cylinders
avg_mpg = function(cyl_val){
total = 0
count = 0
for (i in 1:nrow(mtcars)) {
if (mtcars$cyl[i] == cyl_val){
total = total + mtcars$mpg[i]
count = count +1
}
}
total/count
}
avg_mpg(4)## [1] 26.66364avg_mpg(6)## [1] 19.74286avg_mpg(8)## [1] 15.1#the average mileage (mpg) for cars with: 4 cylinders is 26.66, 6 cylinders is 19.74 and for 8 cylinders is 15.1Q14. Create a new column in mtcars that converts mileage from miles per gallon to kilometers per liter.
mtcars$km_per_liter = mtcars$mpg * 0.425
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## km_per_liter
## Mazda RX4 8.9250
## Mazda RX4 Wag 8.9250
## Datsun 710 9.6900
## Hornet 4 Drive 9.0950
## Hornet Sportabout 7.9475
## Valiant 7.6925Q15. Identify the car with: Highest mileage Lowest mileage
max_index = 1
min_index = 1
for (i in 1:nrow(mtcars)) {
if (mtcars$mpg[i] > mtcars$mpg[max_index]) max_index = i
if (mtcars$mpg[i] < mtcars$mpg[min_index]) min_index = i
}
mtcars[max_index,]## mpg cyl disp hp drat wt qsec vs am gear carb km_per_liter
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1 14.4075mtcars[min_index,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4
## km_per_liter
## Cadillac Fleetwood 4.42Q16. Using the mtcars dataset, find how many cars: Have mpg greater than 20 (ii) Have horsepower (hp) greater than 150
count_mpg = 0
count_hp = 0
for (i in 1:nrow(mtcars)){
if (mtcars$mpg[i] > 20) count_mpg = count_mpg + 1
if (mtcars$hp[i] > 150) count_hp = count_hp + 1
}
count_mpg## [1] 14count_hp## [1] 13Q17. Create a subset of mtcars that includes only cars with: Automatic transmission, mpg greater than 18
subset_mtcars = mtcars[mtcars$am==1 & mtcars$mpg > 18,]
subset_mtcars## mpg cyl disp hp drat wt qsec vs am gear carb km_per_liter
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 8.9250
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 8.9250
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 9.6900
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 13.7700
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 12.9200
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 14.4075
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 11.6025
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 11.0500
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 12.9200
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 8.3725
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 9.0950Q18. Rank cars based on mileage from highest to lowest and display the top 5.
mpg_vals = mtcars$mpg
ranked = order(-mpg_vals)
mtcars[ranked[1:5],] ## mpg cyl disp hp drat wt qsec vs am gear carb km_per_liter
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 14.4075
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 13.7700
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 12.9200
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 12.9200
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 11.6025Q19. Create a function summary_stats() that: Takes a numeric vector Returns mean, median, minimum, and maximum Apply this function to the mpg column of mtcars.
summary_stats = function(x) {
min_val = x[1]
max_val = x[1]
total = 0
for (i in x) {
if(i < min_val) min_val = i
if(i > max_val) max_val = i
total = total +i
}
mean_val = total/length(x)
sorted_x = sort(x)
n = length(x)
if (n %% 2 == 1) {
median_val = sorted_x[(n+1)/2]
} else {
median_val = (sorted_x[(n/2)] + sorted_x[n/2 + 1]) /2
}
c(mean = mean_val, median = median_val, min =min_val, max= max_val)
}
summary_stats(mtcars$mpg)## mean median min max
## 20.09062 19.20000 10.40000 33.90000summary(mtcars$mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90Q.20 Compute the percentage of cars that have: More than 6 cylinders