R/C2/Pipe-Operator/English

Title of the script: Pipe operator

Author: Varshit Dubey (CoE Pune) and Sudhakar Kumar (IIT Bombay)

Keywords: R, RStudio, dplyr package, ggplot2, summarise function, group_by function, pipe operator, boxplot, video tutorial

Visual Cue	Narration
Show slide Opening Slide	Welcome to this tutorial on Pipe Operator.
Show slide Learning Objective	In this tutorial, we will learn about: summarise and group_by functions Operations in summarise function Pipe operator
Show slide Pre-requisites https://spoken-tutorial.org/	To understand this tutorial, you should know, Basics of statistics Basics of ggplot2 and dplyr packages Data frames If not, please locate the relevant tutorials on R on this website.
Show slide System Specifications	This tutorial is recorded on Ubuntu Linux OS version 16.04 R version 3.4.4 RStudio version 1.1.463 Install R version 3.2.0 or higher.
Show slide Download Files	For this tutorial, we will use A data frame moviesData.csv A script file myPipe.R. Please download these files from the Code files link of this tutorial.
[Computer screen] Highlight moviesData.csv and myPipe.R in the folder pipeOps	I have downloaded and moved these files to pipeOps folder. This folder is located in myProject folder on my Desktop. I have also set pipeOps folder as my Working Directory.
Show slide summarise function	Now we learn about summarise function. summarise function reduces a data frame into a single row. It gives summaries like mean, median, etc. of the variables available in the data frame. We use summarise along with group_by function.
	Let us switch to RStudio.
Highlight myPipe.R in the Files window of RStudio	Open the script myPipe.R in RStudio.
Highlight the Source button	Run this script by clicking on the Source button.
Highlight movies in the Source window	movies data frame opens in the Source window.
Highlight the scroll bar in the Source window	In the movies dataframe, scroll from left to right. This will enable us to see the remaining objects of the movies data frame.
Highlight imdb_rating in the Source window	To know the mean of imdb_rating of all movies, we will use summarise function.
Highlight the script myPipe.R in the Source window	Click on the script myPipe.R
[RStudio] summarise(movies, mean(imdb_rating))	In the Source window, type the following command.
Highlight summarise in the Source window	Inside the summarise function, the first argument is a data frame to be summarised. Here, it is movies. The second argument is the information we need, that is the mean of imdb_rating.
Highlight the Run button in the Source window	Save the script and run the current line by pressing Ctrl+Enter keys simultaneously.
Highlight output in the Console window	The mean value is shown. One will argue that I can find the mean by using mean function along with dollar operator. What is the use of installing a whole package and using a complex function?
Highlight summarise in the Source window	Basically, we do not use summarise function for computing such things. This function is not useful unless we pair it with group underscore by function.
Show slide group_by() function	When we use group_by function, the data frame gets divided into different groups.
	Let us switch back to RStudio.
Highlight movies in the Source window	In the Source window, click on movies data frame.
Highlight the scroll bar in the Source window	In the movies data frame, scroll from right to left.
Highlight genre in the Source window	We will group the movies data frame based on the genre. For this, we will use group underscore by function.
Highlight the script myPipe.R in the Source window	Click on the script myPipe.R
[RStudio] groupMovies <- group_by(movies, genre)	In the Source window, type the following command.
Highlight Run button in the Source window	Run the current line.
Highlight groupMovies in the Environment window	A new data frame groupMovies is stored. Now, we will use summarise function on this data frame.
[RStudio] summarise(groupMovies, mean(imdb_rating))	In the Source window, type the following command.
Highlight Run button in the Source window	Run the current line.
	I will resize the Console window
Highlight output in the Console window. Point to the mean values in the console.	The mean values of all movies in different genres are displayed. Notice that, Documentary genre has the highest mean imdb_rating. And Comedy genre has the lowest mean imdb_rating.
	I will resize the Console window
Highlight movies in the Source window	In the Source window, click on movies data frame.
Highlight the scroll bar in the Source window	In the movies data frame, scroll from left to right.
Point to imdb_rating, genre and mpaa_rating in the movies dataframe.	Let us find the mean imdb_rating distribution for the movies of Drama genre. Also, we will group movies of Drama genre by mpaa_rating. For this, we will use filter, group_by, and summarise functions one by one.
Highlight the script myPipe.R in the Source window	Click on the script myPipe.R
[RStudio] dramaMov <- filter(movies, genre == "Drama") gr_dramaMov <- group_by(dramaMov, mpaa_rating) summarise(gr_dramaMov, mean(imdb_rating))	In the Source window, type the following commands.
Highlight filter(movies, genre == "Drama") in the Source window	First, we will extract the movies of Drama genre.
Highlight gr_dramaMov <- group_by(dramaMov, mpaa_rating) in the Source window	Then, we group these movies based on mpaa_rating.
Highlight summarise(gr_dramaMov, mean(imdb_rating)) in the Source window	Finally, we apply summarise function. This will calculate the mean of the filtered and grouped movies.
Highlight Run button in the Source window	Run the last three lines of code.
	I will resize the Console window
Highlight output in the Console window	The required mean values are printed on the console.
	I will resize the Console window again.
Highlight the last three lines of code in the Source window	In this code, we have to give names to each and every intermediate data frame. But there is an alternate method to write these statements using the pipe operator.
Show slide Pipe operator	The pipe operator is denoted as %>%. It prevents us from making unnecessary data frames. We can read the pipe as a series of imperative statements.
Show slide Example of pipe operator	If we have to find the cosine of sine of pi, we can write pi %>% sin() %>% cos()
	Let us switch to RStudio.
Highlight the last three lines of code in the Source window	We will learn how to do the same analysis by using the pipe operator.
[RStudio] movies %>% filter(genre=="Drama") %>% group_by(mpaa_rating) %>% summarise(mean(imdb_rating))	In the Source window, type the following command.
Highlight movies %>% filter(genre=="Drama") %>% group_by(mpaa_rating) %>% summarise(mean(imdb_rating)) in the Source window	Here three lines of code have been written as a series of statements. We can read this code as, Using the movies data frame, filter the movies of Drama genre Next, group the filtered movies by mpaa_rating Finally, summarise the mean of imdb_rating of the grouped data.
Highlight movies %>% filter(genre=="Drama") %>% group_by(mpaa_rating) %>% summarise(mean(imdb_rating)) in the Source window	This code is easier to read and write than the previous one. In the case of pipe operator, we don’t have to repeat the name of the data frame. Notice that we have written name of the data frame only once.
Highlight movies %>% filter(genre=="Drama") %>% group_by(mpaa_rating) %>% summarise(mean(imdb_rating)) in the Source window	Save the script and run the current line.
	I will resize the Console window.
Highlight output on the Console window	The required mean values are printed on the Console.
	I will resize the Console window again.
Highlight movies in the Source window	In the Source window, click on movies data frame.
Highlight the scroll bar in the Source window	In the Source window, scroll from left to right.
Highlight audience_score and critics_score in movies	Let us check what is the difference between critics_score and audience_scoreof all the movies. We will use a box plot for our study. By using the pipe operator, we can combine the functions of ggplot2 and dplyr package.
Highlight the script myPipe.R in the Source window	Click on the script myPipe.R
[RStudio] movies %>% mutate(diff = audience_score - critics_score) %>% ggplot(mapping = aes(x=genre, y=diff)) + geom_boxplot()	In the Source window, type the following command.
Highlight the Run button in the Source window	Save the script and run the current line.
Highlight Plots window	The required box plot appears in the Plots window.
Highlight Plots window	In the Plots window, click on the Zoom button to maximize the plot.
Highlight Plots window, highlight drama, horror, and mystery & suspense	Here you can see that for the genres Drama, Horror, and Mystery & Suspense movies, the median is close to zero. This means that the audience and critics opinions are very similar for these genres.
Highlight Plots window. highlight action & adventure and comedy	Whereas for Action & adventure and Comedy movies, the median is not close to zero. This means that the audience and critics opinions are different for these genres.
Highlight the close button in the Plot Zoom window	Close this plot.
Highlight movies in the Source window	Click on movies data frame.
Highlight the scroll bar in the Source window	In the Source window, scroll from right to left.
Highlight mpaa_rating and genre in movies	Let us check the number of movies in every category of mpaa_rating of each genre.
Highlight the script myPipe.R in the Source window	Click on the script myPipe.R
[RStudio] movies %>% group_by(genre, mpaa_rating) %>% summarise(num = n())	In the Source window, type the following command.
Highlight group_by in movies %>% group_by(genre, mpaa_rating) %>% summarise(num = n()) in the Source window	Notice that we have included both genre and mpaa_rating in group_by function. So, the analysis will be done on the data divided by these 2 variables.
Highlight summarise(num = n()) in movies %>% group_by(genre, mpaa_rating) %>% summarise(num = n()) in the Source window	Also, we used num = n(). The function n computes the number of times the event with specific condition has happened.
Highlight the Run button in the Source window	Run the current line.
	I will resize the Console window.
Highlight the output in the Console window	From the output, we can see that there are 22 Action and Adventure movies with mpaa_rating as R.
	Let us summarize what we have learnt.
Show slide Summary	In this tutorial, we have learnt about: summarise and group_by functions Operations in summarise function Pipe operator
Show slide Assignment	We now suggest an assignment. Use the built-in data set iris. Using the pipe operator, group the flowers by their species. Summarise the grouped data by the mean of Sepal.Length and Sepal.Width.
Show slide About the Spoken Tutorial Project	The video at the following link summarises the Spoken Tutorial project. Please download and watch it.
Show slide Spoken Tutorial Workshops	We conduct workshops using Spoken Tutorials and give certificates. Please contact us.
Show Slide Forum to answer questions	Please post your timed queries in this forum.
Show Slide Forum to answer questions	Please post your general queries in this forum.
Show Slide Textbook Companion	The FOSSEE team coordinates the TBC project. For more details, please visit these sites.
Show Slide Acknowledgment	The Spoken Tutorial project is funded by NMEICT, MHRD, Govt. of India
Show Slide Thank You	The script for this tutorial was contributed by Varshit Dubey (CoE Pune). This is Sudhakar Kumar from IIT Bombay signing off. Thanks for watching.

Contributors and Content Editors

Madhurig, Nancyvarkey, Sudhakarst