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Revision as of 16:56, 12 September 2019

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’’’
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Opening Slide

Welcome to this tutorial on Pipe Operator.
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Learning Objective

In this tutorial, we will learn about:
  • summarise and group_by functions
  • Operations in summarise function
  • Pipe operator

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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.

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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.

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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.

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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.

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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.

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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.


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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.

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Summary

In this tutorial, we have learnt about:* summarise and group_by functions
  • Operations in summarise function
  • Pipe operator


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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.


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About the Spoken Tutorial Project

The video at the following link summarises the Spoken Tutorial project.

Please download and watch it.

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Spoken Tutorial Workshops

We conduct workshops using Spoken Tutorials and give certificates.


Please contact us.

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Forum to answer questions

Please post your timed queries in this forum.
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Forum to answer questions

Please post your general queries in this forum.
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Textbook Companion

The FOSSEE team coordinates the TBC project.

For more details, please visit these sites.

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Acknowledgment

The Spoken Tutorial project is funded by NMEICT, MHRD, Govt. of India
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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

Nancyvarkey, Sudhakarst