Part 1

movielength <- c(94,109,110,123,125,108,92,106,84,119,110,140)
cat("Mean movies length (in min): \n", mean(movielength) , "minutes")
## Mean movies length (in min): 
##  110 minutes
paste0("The mean movie length is ", mean(movielength) , " minutes." )
## [1] "The mean movie length is 110 minutes."

Part 2

1

Recipes <- data.frame(
  Type = c("entree" , "appetizer" , "appetizer" , "entree" , "entree" , "appetizer" , "dessert" , "dessert" , "entree" ,"entree"),
  Ingredients = c(8,4,5,10,6,8,7,15,10,9),
  PrepTime = c(15,15,5,35,20,40,25,30,10,20),
  CookTime = c(30,15,20,55,25,10,120,25,45,60),
  Meat = c("yes", "yes", "yes", "no", "no", "yes", "no", "no","yes", "yes")
)
Recipes
##         Type Ingredients PrepTime CookTime Meat
## 1     entree           8       15       30  yes
## 2  appetizer           4       15       15  yes
## 3  appetizer           5        5       20  yes
## 4     entree          10       35       55   no
## 5     entree           6       20       25   no
## 6  appetizer           8       40       10  yes
## 7    dessert           7       25      120   no
## 8    dessert          15       30       25   no
## 9     entree          10       10       45  yes
## 10    entree           9       20       60  yes
Recipes2 <- data.frame(
  TotalTime = Recipes$PrepTime + Recipes$CookTime)
Recipes2
##    TotalTime
## 1         45
## 2         30
## 3         25
## 4         90
## 5         45
## 6         50
## 7        145
## 8         55
## 9         55
## 10        80
Recipes_combo <- cbind(Recipes, Recipes2)
Recipes_combo
##         Type Ingredients PrepTime CookTime Meat TotalTime
## 1     entree           8       15       30  yes        45
## 2  appetizer           4       15       15  yes        30
## 3  appetizer           5        5       20  yes        25
## 4     entree          10       35       55   no        90
## 5     entree           6       20       25   no        45
## 6  appetizer           8       40       10  yes        50
## 7    dessert           7       25      120   no       145
## 8    dessert          15       30       25   no        55
## 9     entree          10       10       45  yes        55
## 10    entree           9       20       60  yes        80
Recipe3 <- data.frame(
  Type = c("appetizer", "entree", "dessert", "appetizer"),
  Ingredients = c(3,15,8,5),
  PrepTime = c(10,35,45,10),
  CookTime = c(0,90,150,20),
  Meat = c("no", "no", "no", "yes")
)
Recipe3
##        Type Ingredients PrepTime CookTime Meat
## 1 appetizer           3       10        0   no
## 2    entree          15       35       90   no
## 3   dessert           8       45      150   no
## 4 appetizer           5       10       20  yes
Recipe4 <- data.frame(
  TotalTime = Recipe3$PrepTime + Recipe3$CookTime)
Recipe4
##   TotalTime
## 1        10
## 2       125
## 3       195
## 4        30
Recipes_combo2 <- cbind(Recipe3, Recipe4)
Recipes_combo2
##        Type Ingredients PrepTime CookTime Meat TotalTime
## 1 appetizer           3       10        0   no        10
## 2    entree          15       35       90   no       125
## 3   dessert           8       45      150   no       195
## 4 appetizer           5       10       20  yes        30
Recipe5 <- rbind(Recipes_combo, Recipes_combo2)
Recipe5
##         Type Ingredients PrepTime CookTime Meat TotalTime
## 1     entree           8       15       30  yes        45
## 2  appetizer           4       15       15  yes        30
## 3  appetizer           5        5       20  yes        25
## 4     entree          10       35       55   no        90
## 5     entree           6       20       25   no        45
## 6  appetizer           8       40       10  yes        50
## 7    dessert           7       25      120   no       145
## 8    dessert          15       30       25   no        55
## 9     entree          10       10       45  yes        55
## 10    entree           9       20       60  yes        80
## 11 appetizer           3       10        0   no        10
## 12    entree          15       35       90   no       125
## 13   dessert           8       45      150   no       195
## 14 appetizer           5       10       20  yes        30
(Recipe5[Recipe5$TotalTime < 60 , ])
##         Type Ingredients PrepTime CookTime Meat TotalTime
## 1     entree           8       15       30  yes        45
## 2  appetizer           4       15       15  yes        30
## 3  appetizer           5        5       20  yes        25
## 5     entree           6       20       25   no        45
## 6  appetizer           8       40       10  yes        50
## 8    dessert          15       30       25   no        55
## 9     entree          10       10       45  yes        55
## 11 appetizer           3       10        0   no        10
## 14 appetizer           5       10       20  yes        30

Part 3

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 ...
nrow(mtcars)
## [1] 32
head(mtcars, n = 9) 
##                    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
mtcars[ ,1]
##  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4
mean(mtcars$mpg)
## [1] 20.09062
median(mtcars$mpg)
## [1] 19.2
mtcars1 <-mtcars[ , -2:-3] 
(mtcars2 <- mtcars1[ , -3:-9])
##                      mpg  hp
## Mazda RX4           21.0 110
## Mazda RX4 Wag       21.0 110
## Datsun 710          22.8  93
## Hornet 4 Drive      21.4 110
## Hornet Sportabout   18.7 175
## Valiant             18.1 105
## Duster 360          14.3 245
## Merc 240D           24.4  62
## Merc 230            22.8  95
## Merc 280            19.2 123
## Merc 280C           17.8 123
## Merc 450SE          16.4 180
## Merc 450SL          17.3 180
## Merc 450SLC         15.2 180
## Cadillac Fleetwood  10.4 205
## Lincoln Continental 10.4 215
## Chrysler Imperial   14.7 230
## Fiat 128            32.4  66
## Honda Civic         30.4  52
## Toyota Corolla      33.9  65
## Toyota Corona       21.5  97
## Dodge Challenger    15.5 150
## AMC Javelin         15.2 150
## Camaro Z28          13.3 245
## Pontiac Firebird    19.2 175
## Fiat X1-9           27.3  66
## Porsche 914-2       26.0  91
## Lotus Europa        30.4 113
## Ford Pantera L      15.8 264
## Ferrari Dino        19.7 175
## Maserati Bora       15.0 335
## Volvo 142E          21.4 109
(mtcars[mtcars$hp >= 105, ])
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
(mtcars[mtcars$mpg < 20 | mtcars$mpg > 25 , ])
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
(mtcars[mtcars$mpg >= 22 & mtcars$hp < 95 , ])
##                 mpg cyl  disp hp drat    wt  qsec vs am gear carb
## 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
## 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
## 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