R/C2/Pipe-Operator/English - Revision history

Madhurig at 11:38, 23 September 2019

2019-09-23T11:38:08Z

Nancyvarkey at 07:26, 23 September 2019

2019-09-23T07:26:56Z

Nancyvarkey at 06:56, 20 September 2019

2019-09-20T06:56:22Z

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Sudhakarst at 11:30, 12 September 2019

2019-09-12T11:30:17Z

Sudhakarst at 11:29, 12 September 2019

2019-09-12T11:29:00Z

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Sudhakarst: Created page with "'''Title of the script''': Pipe operator '''Author''': Varshit Dubey (CoE Pune) and Sudhakar Kumar (IIT Bombay) '''Keywords''': R, RStudio, dplyr package, ggplot2, summaris..."

2019-09-12T11:26:29Z

Created page with "'''Title of the script''': Pipe operator '''Author''': Varshit Dubey (CoE Pune) and Sudhakar Kumar (IIT Bombay) '''Keywords''': R, RStudio, dplyr package, ggplot2, summaris..."

New page

'''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

{| border =1
|'''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/ 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''' window, 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''' dataframe, 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 '''audience_score''' and '''critics_score '''of all the movies.

We will use a '''boxplot''' 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
|| 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.
|-
|}

@@ Line 182: / Line 182: @@
 Point to the mean values in the console.
-|| The mean values of all '''movies''' in different '''genres''' are displayed.
+|| The mean values of all movies in different '''genres''' are displayed.
 Notice that, '''Documentary genre''' has the highest mean '''imdb_rating.'''
@@ Line 200: / Line 200: @@
 || 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. '''
+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.
@@ Line 328: / Line 328: @@
 |-
 || Highlight '''audience_score''' and '''critics_score''' in '''movies'''
-|| Let us check what is the difference between '''critics_score ''' and '''audience_score'''of all the movies.
+|| Let us check what is the difference between '''critics_score ''' and '''audience_score''' of 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.
+By using the '''pipe''' operator, we can combine the '''functions''' of '''ggplot2''' and '''dplyr packages'''.
 |-
 || Highlight the '''script myPipe.R''' in the '''Source''' window
@@ Line 371: / Line 371: @@
 |-
 || Highlight '''movies''' in the '''Source''' window
-|| Click on '''movies data frame. '''
+|| In the '''Source''' window, click on '''movies data frame. '''
 |-
 || Highlight the scroll bar in the '''Source''' window
@@ Line 399: / Line 399: @@
 '''summarise(num = n()) '''in the '''Source''' window
-|| Also, we used '''num = n()'''.
+|| Also, we have 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.
+|| '''Run''' the current line.
 |-
-||
+|| Resize the '''Console''' window.
 || I will resize the '''Console''' window.
 |-
@@ Line 430: / Line 430: @@
 Assignment
 || We now suggest an assignment.
-* Use the built-in data set '''iris'''. Using the '''pipe''' operator, group the flowers by their species.
+* Use the '''built-in data set iris'''.
 * Summarise the grouped data by the mean of '''Sepal.Length '''and '''Sepal.Width'''.

@@ Line 127: / Line 127: @@
 || 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'''.
+This '''function''' is not useful unless we pair it with '''group by function'''.
 |-
 || Show slide
@@ Line 176: / Line 176: @@
 || '''Run''' the current line.
 |-
-||
+|| Drag the Console window to resize.
 || I will resize the '''Console''' window
 |-
@@ Line 188: / Line 188: @@
 And '''Comedy genre''' has the lowest mean '''imdb_rating.'''
 |-
-||
+|| Drag the Console window to resize.
 || I will resize the '''Console''' window
 |-
@@ Line 236: / Line 236: @@
 || '''Run''' the last three lines of code.
 |-
-||
+|| Drag the Console window to resize.
 || I will resize the '''Console''' window
 |-
@@ Line 242: / Line 242: @@
 || The required mean values are printed on the '''console'''.
 |-
-||
+|| Drag the Console window to resize.
 || I will resize the '''Console''' window again.
 |-
@@ Line 252: / Line 252: @@
 || Show slide
-Pipe operator
+'''Pipe operator'''
 ||
 * The '''pipe '''operator is denoted as '''%>%'''.
@@ Line 261: / Line 261: @@
 || Show slide
-Example of pipe operator
+'''Example of pipe operator'''
 || If we have to find the '''cosine''' of '''sine''' of '''pi''', we can write
@@ Line 270: / Line 270: @@
 |-
 || 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.
+|| We will learn how to do the same analysis by using the '''pipe''' operator.
 |-
 || [RStudio]
@@ Line 312: / Line 312: @@
 || Save the '''script''' and run the current line.
 |-
-||
+|| Drag the Console window to resize.
 || I will resize the '''Console''' window.
 |-
@@ Line 318: / Line 318: @@
 || The required mean values are printed on the '''Console'''.
 |-
-||
+||Drag the Console window to resize.
 || I will resize the '''Console''' window again.
 |-
@@ Line 415: / Line 415: @@
 || Let us summarize what we have learnt.
 |-
-||
+|| Show slide
-−
 Show slide
-Summary
+'''Summary'''
 || In this tutorial, we have learnt about:
 * '''summarise''' and '''group_by functions'''

@@ Line 226: / Line 226: @@
 || Then, we group these movies based on '''mpaa_rating'''.
 |-
-|| Highlight '''summarise(gr_dramaMov, mean(imdb_rating)) '''in the Source window
+|| Highlight '''summarise(gr_dramaMov, mean(imdb_rating)) '''in the '''Source''' window
 || Finally, we apply '''summarise''' function.
@@ Line 283: / Line 283: @@
 '''group_by(mpaa_rating) %>% '''
-'''summarise(mean(imdb_rating)) '''in the Source window
+'''summarise(mean(imdb_rating)) '''in the '''Source''' window
 || Here three lines of code have been written as a series of statements.