1.) Attach(mtcars)
2.) Sort by ascending MPG
3.) Sort by descending CYL
4.) Sort by both
attach(mtcars)
mtcars_df_mpg <- mtcars[order(mpg),]
mtcars_df_mpg## mpg cyl disp hp drat wt qsec vs am gear carb
## 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
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 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 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 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
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1mtcars_df_cyl <- mtcars[order(-cyl),]
mtcars_df_cyl ## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 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
## 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
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 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
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 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
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 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
## 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
## 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
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2mtcars_df_mpg_cyl <- mtcars[order(mpg,-cyl),]
mtcars_df_mpg_cyl ## mpg cyl disp hp drat wt qsec vs am gear carb
## 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
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 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 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 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
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1detach(mtcars)1.) Attach(mtcars)
2.) Find value of Chrysler Imperial HP
3.) Sort by ascending MPG
4.) Select cars whose HP is greater then Chrysler Imperial
attach(mtcars)
rownames(mtcars)## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710"
## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant"
## [7] "Duster 360" "Merc 240D" "Merc 230"
## [10] "Merc 280" "Merc 280C" "Merc 450SE"
## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood"
## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128"
## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona"
## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28"
## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2"
## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino"
## [31] "Maserati Bora" "Volvo 142E"CI_row <- mtcars[rownames(mtcars) == "Chrysler Imperial",]
CI_row## mpg cyl disp hp drat wt qsec vs am gear carb
## Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4CI_value <- CI_row$hp
CI_value## [1] 230mtcars_CI_mpg <- mtcars[order(mpg),]
mtcars_CIsubset <- mtcars_CI_mpg[(mtcars_CI_mpg$hp > CI_value),]
mtcars_CIsubset <- mtcars_CIsubset[complete.cases(mtcars_CIsubset),]
mtcars_CIsubset## mpg cyl disp hp drat wt qsec vs am gear carb
## Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4
## Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
## Maserati Bora 15.0 8 301 335 3.54 3.57 14.60 0 1 5 8
## Ford Pantera L 15.8 8 351 264 4.22 3.17 14.50 0 1 5 41.) Create a For Loop that calculates the squared values for 1 to 25.
q3_for_lst <- c()
for (i in seq(1, 25, by=1)) {
q3_for_lst[[i]] <- i*i
}
q3_for_matrix <- as.matrix(q3_for_lst)
print(q3_for_matrix)## [,1]
## [1,] 1
## [2,] 4
## [3,] 9
## [4,] 16
## [5,] 25
## [6,] 36
## [7,] 49
## [8,] 64
## [9,] 81
## [10,] 100
## [11,] 121
## [12,] 144
## [13,] 169
## [14,] 196
## [15,] 225
## [16,] 256
## [17,] 289
## [18,] 324
## [19,] 361
## [20,] 400
## [21,] 441
## [22,] 484
## [23,] 529
## [24,] 576
## [25,] 6251.) Create a For Loop that calculates the 50 elements from Fibonacci Series [1,1,2,3,5,8,13,21,….]
start.time <- Sys.time()
q4_for_lst <- c(1,2)
for (i in seq(3, 50, by=1)) {
q4_for_lst[[i]] <- q4_for_lst[i-2] + q4_for_lst[i-1]
}
q4_for_matrix <- as.matrix(q4_for_lst)
print(q4_for_matrix)## [,1]
## [1,] 1
## [2,] 2
## [3,] 3
## [4,] 5
## [5,] 8
## [6,] 13
## [7,] 21
## [8,] 34
## [9,] 55
## [10,] 89
## [11,] 144
## [12,] 233
## [13,] 377
## [14,] 610
## [15,] 987
## [16,] 1597
## [17,] 2584
## [18,] 4181
## [19,] 6765
## [20,] 10946
## [21,] 17711
## [22,] 28657
## [23,] 46368
## [24,] 75025
## [25,] 121393
## [26,] 196418
## [27,] 317811
## [28,] 514229
## [29,] 832040
## [30,] 1346269
## [31,] 2178309
## [32,] 3524578
## [33,] 5702887
## [34,] 9227465
## [35,] 14930352
## [36,] 24157817
## [37,] 39088169
## [38,] 63245986
## [39,] 102334155
## [40,] 165580141
## [41,] 267914296
## [42,] 433494437
## [43,] 701408733
## [44,] 1134903170
## [45,] 1836311903
## [46,] 2971215073
## [47,] 4807526976
## [48,] 7778742049
## [49,] 12586269025
## [50,] 20365011074end.time <- Sys.time()
time.taken <- end.time - start.time
time.taken## Time difference of 0.008983135 secsYou bought a house at price of $700K. If the interest to real estate market in the area increases and the estimated price of house goes above 750K, you may want to sell it. Otherwise, you will keep it. You will generate a random price between 600K and 800K for each quarter as a proxy for the interest to your house (The price can fluctuate between -100K to +100K around 700K after each quarter)
1.) Design a while loop and show how many quarters (loops) will it take to sell the house. Hint: There is no specific value for the answer since we generate random values with sample(x, size, replace = FALSE, prob = NULL) in each loop.
While Loop (while home price < $750K)
1 Generate random home price between parameters
2 Check if home price is over $750k
3 If yes, then break while loop sell and print message including the sale price and profit!
4 If no, continue in loop and start at 1
Home_Price <- 1
Sales_Quarters <-0
#Create the loop
while (Home_Price <= 750000){
Home_Price <- runif(1, min=600000, max=800000)
Sales_Quarters <- Sales_Quarters + 1
}
Home_Price <- paste('$',formatC(Home_Price, big.mark = ',', format = 'f', digits = 2L))Home_Price <- 1
Sales_Quarters <-0
#Create the loop
while (Home_Price <= 750000){
Home_Price <- sample(600000:800000, 1,replace = FALSE, prob = NULL)
Sales_Quarters <- Sales_Quarters + 1
}
Home_Price <- paste('$',formatC(Home_Price, big.mark = ',', format = 'f', digits = 2L))