Question 1
1.Install the “dplyr” and “tidyverse” packages.
2. Load the “dplyr” and “tidyverse” packages.
3. We will use the “mtcars” dataset for this exercise. Take a look at the dataset with the “glimpse” command.

library(dplyr)
library(tidyverse)
glimpse(mtcars)
## Rows: 32
## Columns: 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, 16…
## $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180…
## $ 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, 18…
## $ 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,…

4.Print out the hp column using the select() function. Try using the pull() function instead of select to see what the difference is.

#select()
mtcars %>% select(hp)
#pull()
mtcars %>% pull(hp)
##  [1] 110 110  93 110 175 105 245  62  95 123 123 180 180 180 205 215 230  66  52
## [20]  65  97 150 150 245 175  66  91 113 264 175 335 109

The select() function will return a data frame with just the horsepower (hp) column. We would use this if we want to continue working within the data frame to add transformations or to keep column names, etc.
Using the pull() function will extract the hp column in the mtcars dataset as a vector. We might use this if we needed to work with the raw values to compute the mean or for graphing.

  1. Print out all but the hp column using the select() function.
mtcars %>% select(-hp)
  1. Print out the mpg, hp, vs, am, gear columns. Consider using the colon : symbol to simplify selection of consecutive columns.
mtcars %>% select(mpg, hp, vs:gear)
mtcars %>% select(1,2,8:10)
  1. Create the object mycars containing the columns mpg, hp columns but let the column names be miles_per_gallon, and horse_power respectively.
mycars = mtcars %>% select(miles_per_gallon = mpg, horse_power = hp)
  1. Create a new variable in the mycars data frame km_per_litre using the mutate() function.
mycars <- mycars %>% mutate(km_per_litre = 0.425*miles_per_gallon)
  1. Print out the row corresponding to the Lotus Europa car.
mycars %>% filter(row.names(mycars) == "Lotus Europa")

Question 2. Create a lecture video where you explain a topic in R that you find challenging by using the interactive “swirl”-package.
We can enter a swirl lesson by typing swirl() and ending a swirl lesson by typing bye() or using the esc key.

Question 3.
I have completed through chapter16.
LinkedInUnit1-10