Code

library("dplyr")
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
dat<-mtcars
head(dat)
##                    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
unique_cyl<-unique(dat$cyl)
print(unique_cyl)
## [1] 6 4 8
dat[dat$cyl==c(4,6),]->df
print(df)
##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 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
## Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## 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
## Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6

Explanation

I started my code by installing a package from tidyverse by using the function install.packages(‘tidyverse’). Once the installation was finished, I loaded and attached the packages by calling the library function. Subsequently, I stored the dataset “mtcars” into a data object referred to as “dat”, thereafter calling the head function in order to observe the first six rows of the data set. As the fourth step required me to observe the cyl variables in dat, I called the unique function and stored the data into another object named “unique_cyl”. I printed unique_cyl in order to ensure that the code was accurate, which was confirmed when 6,4,8 were printed out in the console. Following the fifth and final step, I sliced data sets 4 and 6 by deriving the cyl values and indicating the rows with the two numbers by writing “[dat$cyl==c(4,6),] (since this signifies that the computer will only derive data sets that have the cyl value of 4 and six.) After storing the spliced data sets into a new object named”df”, I printed it out to confirm the final answer.

head(df)
##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 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
## Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## 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