EXECUTE EACH SECTION INDIVIDUALLY IN YOUR OWN RStudio WINDOW

i.e. HIGHLIGHT ROWS AND HIT “RUN”

show the contents of a built in data frame

print(mtcars)
##                      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    2

just see how many rows it is

print(nrow(mtcars))
## [1] 32

adding columns

to add a vector, it needs to be the same length as the number of rows in the existing data frame

here, i just make a vector of car colors

set.seed just means that it will always use the same set of “random” choices

the assgnment line says “randomly pick from these three colors 32 times”

set.seed(1492)

car_colors = sample(c("blue", "red", "yellow"), nrow(mtcars), replace=TRUE)

print(car_colors)
##  [1] "blue"   "blue"   "blue"   "blue"   "blue"   "blue"   "yellow" "red"    "red"    "yellow" "red"    "red"   
## [13] "blue"   "red"    "red"    "yellow" "blue"   "red"    "red"    "yellow" "red"    "red"    "blue"   "red"   
## [25] "blue"   "yellow" "red"    "red"    "red"    "yellow" "blue"   "yellow"

Now, all we have to do to add it to the data frame is:

mtcars$sweet_color = car_colors

print(mtcars)
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb sweet_color
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4        blue
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4        blue
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1        blue
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1        blue
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2        blue
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1        blue
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4      yellow
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2         red
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2         red
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4      yellow
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4         red
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3         red
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3        blue
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3         red
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4         red
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4      yellow
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4        blue
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1         red
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2         red
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1      yellow
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1         red
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2         red
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2        blue
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4         red
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2        blue
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1      yellow
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2         red
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2         red
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4         red
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6      yellow
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8        blue
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2      yellow

You can add any type of vector

some_numbers = c(1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,0,1,2,3,4,5,6,6,7,8,9,10,1,2,3,4,5)

mtcars$moar_data = some_numbers

print(mtcars)
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb sweet_color moar_data
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4        blue         1
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4        blue         2
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1        blue         3
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1        blue         4
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2        blue         5
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1        blue         6
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4      yellow         7
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2         red         8
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2         red         9
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4      yellow         0
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4         red         1
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3         red         2
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3        blue         3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3         red         4
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4         red         5
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4      yellow         0
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4        blue         1
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1         red         2
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2         red         3
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1      yellow         4
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1         red         5
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2         red         6
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2        blue         6
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4         red         7
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2        blue         8
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1      yellow         9
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2         red        10
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2         red         1
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4         red         2
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6      yellow         3
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8        blue         4
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2      yellow         5