Assignment on Data Manipulation in R

Let us take a sample dataset and clean the data with the concepts that we learned in Dplyr and Tidyr package.

Problem:

Use Dplyr and Tidyr package to clean the NYC 2013 Flights dataset.

Solution:

For this assignment, we can use the data sets related to flights that departed from NYC in 2013.

Flights

Below is the code that I have used to clean the dataset:

	library(nycflights13)
	library(tidyr)
	library(dplyr)
	# Check the Dataset flights
	View(flights)
	#-----------------------------------------------------------------
	# Use Unite() to merge month, day and year columns into single column called fly_date
	clean_flights <- unite(flights,fly_date, month,day,year, sep = "-")
	#-----------------------------------------------------------------
	# Use arrange() to order data by origin and destination
	clean_flights <- arrange(clean_flights,origin, dest)
	#-----------------------------------------------------------------
	# Use mutate() to remove the columns which are not required
	clean_flights <- mutate(clean_flights,dep_delay = NULL,
	arr_delay= NULL,
	air_time = NULL,
	time_hour = NULL)
	# Use mutate() to add a new column "Total_Time"
	clean_flights <- mutate(clean_flights,total_time = hour* 60 + minute)
	# Use mutate() to add a new column "Speed"
	clean_flights <- mutate(clean_flights, speed = distance/total_time * 60)
	#-----------------------------------------------------------------
	# Use filter() to select flights from EWR to SFO which took less than 10 hours
	clean_flights <- filter(clean_flights,origin == "EWR", dest == "SFO", hour < 10)
	# Order Data in ascending order of Total_time
	clean_flights <- arrange(clean_flights,total_time)
	View(clean_flights)

by GitHub

Below is the output dataset:

Clean_Flights