Please, solve the question with an RStudio script. The flights data set seen in class is available in the package nycflights$13$ as a tibble. It contains on$-$time data for all flights that departed New York City via its three main airports throughout the whole year of $2013.$ The airport from which each flight departed is recorded in the origin column. Load the tidyverse suite of packages, as well as the nycflights$13$ package and the flights data therein. a$)$ Cancelled flights are defined as those for which no departure or arrival took place. Write code using pipe operators and filter$()$ to create a new data set flights$2$ which removes any rows with missing values $($i$.$e$.,$ NA entries$)$ for the dep$\_$delay or arr$\_$delay variables $($Hint: see $?$is$.$na and use the "and" operator & and the negation operator $!).$ b$)$ Define the new variable gain as the difference between the departure delay and the arrival delay, the new variable air$\_$hour as the air$\_$time variable re$-$expressed in hours, and the variable gain$\_$per$\_$hour as the gain per air$\_$hour. Write code using pipe operators and the functions mutate$()$ and select$()$ which creates a new tibble called flights$3$ containing only the gain$\_$per$\_$hour and origin variables. $($Hint: you may exploit the fact that mutate$()$ allows you to refer to variables you have just created$).$ c$)$ The ggplot$2$ code below plots a kernel density estimate of the gain$\_$per$\_$hour for each origin airport. However, it overplots all three estimated density curves on the one graph. Modify this code so that it $($i$)$ partitions$/$facets the graph into a separate panel for each origin and $($ii$)$ produces histograms with $50$ bins rather than density plots. ggplot$($flights$3,$ aes$($x $=$ gain$\_$per$\_$hour, fill $=$ origin$))+$ geom$\_$density$)$ Your final plot should look like this:EWR JFK LGA $30000-20000-$ count origin EWR JFK LGA $10000-0--200-100$ o $100-200-1006100-200-100$ o $100$ gain$\_$per$\_$hour NOTE: for parts $($b$)$ and $($c$)$ you may refer to the base R code below, which ultimately produces the same output flights. This code is provided for you to check your results only, do not use this code as a solution to parts $($b$)$ and $($c$).$ flights$2<-$ flights$[!$is$.$na$($flights$dep$\_$delay$)$ & lis.na$($flights arr$\_$delay$),]$ flights$2$$gain $<-$ flights$2$$dep$\_$delay $-$ flights$2$$arr$\_$delay flights$2$$hours $<-$ flights$2$$air$\_$time $/60$ flights$2$$gain$\_$per$\_$hour $<-$ flights$2$$gain $/$ flights$23$hours flights$3<-$ flights$2[,$c$("$gain$\_$per$\_$hour", "origin"$)]$