To start off with, I will create an R script called dplyr_intro.R and set up my R environment. First, you should set your working directory to the ch7 project folder. Next, you should read the fuel economyhttps://catalog.data.gov/dataset/consumer-price-index-average-price-data dataset into a dataframe as follows:

setwd("path/to/your/project/folder")
vehicles<-read.csv("data/vehicles.csv")

The next step is to import the dplyr and tibble packages. In R, you can import a package using the library() function. The following lines import the dplyr package and the tibble package:

library('dplyr')
library('tibble')

I will start with the select() function. The select() function allows you to select a certain number of columns from a dataframe and returns another dataframe containing only those selected columns. As its first argument, the select() function takes a dataframe. The following arguments to the select() function after the first argument are the names of the columns to be selected from the original dataframe. The result is a dataframe containing only the selected columns.

With the fuel economy dataset, one use of the select() function might be to create a dataframe that just contains the product details for each car. In the following continuation of dplyr_intro.R, I use the select() function to select the make, model, and year columns from the vehicles dataframe: 

vehicles.product <- select(data,make,model,year)
print(Vehicles.product)

Running the previous lines should create a dataframe containing just the product information and produce a printout of the dataframe as follows:

You can make the printout a bit more elegant by converting the result to a tibble object as follows:

## the same thing as a tibble
vehicles.product.as.tibble <- as_tibble(
  select(vehicles,make,model,year)
)
print(vehicles.product.as.tibble)

Printing out the tibble version of the dataframe, as is done in the previous lines, will produce a much cleaner result, as follows:

Now that the product information for each of the data entries is available, it may be helpful to arrange the rows in a particular order. Arranging the order of the rows could be particularly helpful if the data will need to be viewed or processed manually.

The arrange() function arranges the rows in a particular order by column. The following code will arrange the vehicle product data first by make, then by model, and then by year:

vehicles.product.arranged <- as.tibble(
  arrange(vehicles.product,make,model,year)
)
print(vehicles.product.arranged)

The arranged version of the product data is organized in alphabetical order by make and model, and then by year, as follows:

## select the product info from the car
vehicles.product <- as_tibble(
  select(vehicles,make,model,year)
)

## arrange the rows of the vehicle product data
vehicles.product.arranged <- as.tibble(
  arrange(vehicles.product,make,model,year)
)