The pipe %>% can be used to perform operations sequentially without having to define intermediate objects.

Example

So far multiple dataframe operations were done independant from each other by storing inbetween results in variables:

library(dplyr)
library(dslabs)
data(murders)

murders <- mutate(murders, rate =  total / population * 100000, rank = rank(-rate))
my_states <- select(murders, state, rate, rank)

The pipe %>% permits us to perform both operations sequentially without having to define an intermediate variable. We therefore could have mutated and selected in the same line like this:

library(dplyr)
library(dslabs)
data(murders)

my_states <- murders %>% 
        mutate(rate =  total / population * 100000, rank = rank(-rate)) %>% 
        # TODO: Insert a filter function to filter on states from the Northeast or West region who have a murderrate lower than 1
        select(state, rate, rank)

Pitfall

Notice that mutate and select no longer have a data frame as the first argument. The first argument is assumed to be the result of the operation conducted right before the %>%.

Exercise

• Adjust the code from the example with pipes by inserting a functioncall into the pipeline. The pipeline should create a dataframe containing 3 columns: state, rate and rank for all states located in the Northeast or West region that have a murder rate lower than 1. The dataframe should be stored in my_states (as in the example code).