Flow of data manipulation in R using dplyr

We know that the main verbs for the dplyr package are -
1. select
2. filter
3. arrange
4. summarise
5. group_by
6. mutate

Let us take a simple question and try to find it's solution. While writing the code for this solution, I will demonstrate how R transforms our data. This step by step procedure will make it crystal clear on how to use these different functions together for powerful analyis.

Taking the in-built 'hflights' package for the data analysis.
Let's say we want to find the favourite or most frequented destination for each Carrier.

Code:

> b <- hflights %>%
       group_by(UniqueCarrier, Dest) %>%
 
       summarise(n = n()) %>%
 
       mutate(rank = rank(desc(n))) %>%
       filter(rank == 1)
> b
Source: local data frame [15 x 4]
Groups: UniqueCarrier
 
        UniqueCarrier Dest    n rank
1             AirTran  ATL 2029    1
2              Alaska  SEA  365    1
3            American  DFW 2105    1
4      American_Eagle  DFW 2424    1
5  Atlantic_Southeast  DTW  851    1
6         Continental  EWR 3924    1
7               Delta  ATL 2396    1
8          ExpressJet  CRP 3175    1
9            Frontier  DEN  837    1
10            JetBlue  JFK  695    1
11               Mesa  CLT   71    1
12            SkyWest  COS 1335    1
13          Southwest  DAL 8243    1
14         US_Airways  CLT 2212    1
15             United  SFO  643    1

Voila ! We have the list of Air Carriers and the most frequented destination by them. But, for a more intuitive understanding -

Let's split the code at each instance and see the kind of tables that R creates.

Code 1.1

b <- hflights %>%
      group_by(UniqueCarrier, Dest) %>%
      select(UniqueCarrier,Dest)
View(b)

Code 1.2

b <- hflights %>%
      group_by(UniqueCarrier, Dest) %>%
      summarise(n = n()) %>%
      mutate(rank = rank(desc(n)))
  View(b)

Code 1.3: Final Result

b <- hflights %>%
+       group_by(UniqueCarrier, Dest) %>%
+       summarise(n = n()) %>%
+       mutate(rank = rank(desc(n))) %>%
+       filter(rank == 1)
> b
Source: local data frame [15 x 4]
Groups: UniqueCarrier
 
        UniqueCarrier Dest    n rank
1             AirTran  ATL 2029    1
2              Alaska  SEA  365    1
3            American  DFW 2105    1
4      American_Eagle  DFW 2424    1
5  Atlantic_Southeast  DTW  851    1
6         Continental  EWR 3924    1
7               Delta  ATL 2396    1
8          ExpressJet  CRP 3175    1
9            Frontier  DEN  837    1
10            JetBlue  JFK  695    1
11               Mesa  CLT   71    1
12            SkyWest  COS 1335    1
13          Southwest  DAL 8243    1
14         US_Airways  CLT 2212    1
15             United  SFO  643    1