In this lab you can use the interactive console to explore but please record your commands here. Remember anything you type here can be “sent” to the console with Cmd-Enter (OS-X) or Cntr-Enter (Windows/Linux) (But only in side the {r} areas).

library(dplyr)
library(tidyverse)
library(jhur)

Part 1

  1. Check to see if you have the mtcars dataset
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
  1. What class is mtcars?
class(mtcars)
## [1] "data.frame"
  1. How many observations (rows) and variables (columns) are in the mtcars dataset?
dim(mtcars)
## [1] 32 11
nrow(mtcars)
## [1] 32
ncol(mtcars)
## [1] 11
glimpse(mtcars)
## Observations: 32
## Variables: 11
## $ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8…
## $ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8…
## $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 1…
## $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 18…
## $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92…
## $ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3…
## $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 1…
## $ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0…
## $ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0…
## $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3…
## $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2…
  1. Copy mtcars into an object called cars and rename mpg in cars to MPG. Use rename
cars = mtcars
cars = rename(cars, MPG = mpg)
head(cars)
##                    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
  1. Convert the column names of cars to all upper case. Use rename_all, and the toupper command (or colnames).
cars = rename_all(cars, toupper)
head(cars)
##                    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
cars = mtcars
cn = colnames(cars) # extract column names
cn = toupper(cn) # make them uppercase
colnames(cars) = cn # reassign
head(cars)
##                    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

Part 2

You can create a column called car using the rownames_to_column function.

cars = rownames_to_column(mtcars, var = "car")
  1. Subset the columns from cars that end in "p" and call it pvars, use ends_with().
pvars = select(cars, ends_with("p"))
  1. Create a subset of the data that only contains the columns: wt, qsec, and hp and assign this object to carsSub - what are the dimensions of this dataset? Use select() (and dim):
carsSub = select(mtcars, wt, qsec, hp)
dim(carsSub)
## [1] 32  3
  1. Convert the column names of carsSub to all upper case. Use rename_all(), and the toupper command (or colnames)
carsSub = rename_all(carsSub, toupper)

Part 3

  1. Subset the rows of cars that get more than 20 miles per gallon (mpg) of fuel efficiency - how many are there? Use filter()
cars_mpg = filter(cars, mpg > 18)
dim(cars_mpg)
## [1] 19 12
nrow(cars_mpg)
## [1] 19
# filter(cars, mpg > 20)

There are 19 cars. There are nrow(cars_mpg) cars.

cars %>% filter(mpg > 20) %>% nrow()
## [1] 14
filter(cars, mpg > 20) %>% nrow()
## [1] 14
  1. Subset the rows that get less than 16 miles per gallon (mpg) of fuel efficiency and have more than 100 horsepower (hp) - how many are there?
filter(cars, mpg < 16 & hp > 100)
##                    car  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 1           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## 2          Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## 3   Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## 4  Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## 5    Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## 6     Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## 7          AMC Javelin 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## 8           Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## 9       Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## 10       Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
nrow(filter(cars, mpg < 16 & hp > 100))
## [1] 10
nrow(filter(cars, mpg < 16, hp > 100))
## [1] 10
cars %>% filter(mpg < 16, hp > 100) %>% nrow()
## [1] 10

Part 4

  1. Create a subset from the cars data that only contains the columns: wt, qsec, and hp for only the cars with 8 cylinders and reassign this object to carsSub - what are the dimensions of this dataset?
carsSub = filter(cars, cyl == 8) 
carsSub = select(carsSub, wt, qsec, hp, car)
dim(carsSub)
## [1] 14  4
carsSub = cars %>% 
  filter(cyl == 8) %>% 
  select(wt, qsec, hp, car)
dim(carsSub)
## [1] 14  4
  1. Re-order the rows of carsSub by weight in increasing order. Use arrange()
carsSub = arrange(carsSub, wt)
  1. Create a new variable in carsSub called wt2, which is equal to wt^2, using mutate(). Use piping %>%:
carsSub %>% mutate(wt2 = wt^2)
##       wt  qsec  hp                 car      wt2
## 1  3.170 14.50 264      Ford Pantera L 10.04890
## 2  3.435 17.30 150         AMC Javelin 11.79922
## 3  3.440 17.02 175   Hornet Sportabout 11.83360
## 4  3.520 16.87 150    Dodge Challenger 12.39040
## 5  3.570 15.84 245          Duster 360 12.74490
## 6  3.570 14.60 335       Maserati Bora 12.74490
## 7  3.730 17.60 180          Merc 450SL 13.91290
## 8  3.780 18.00 180         Merc 450SLC 14.28840
## 9  3.840 15.41 245          Camaro Z28 14.74560
## 10 3.845 17.05 175    Pontiac Firebird 14.78403
## 11 4.070 17.40 180          Merc 450SE 16.56490
## 12 5.250 17.98 205  Cadillac Fleetwood 27.56250
## 13 5.345 17.42 230   Chrysler Imperial 28.56902
## 14 5.424 17.82 215 Lincoln Continental 29.41978
carsSub = carsSub %>% mutate(wt2 = wt^2)