R/C2/Data-Manipulation-using-dplyr-Package/English

Title of the script: Data Manipulation using dplyr package

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

Keywords: R, RStudio, data manipulation, dplyr, filter, video tutorial

Visual Cue	Narration
Show slide Opening Slide	Welcome to this tutorial on Data manipulation using dplyr package.
Show slide Learning Objective	In this tutorial, we will learn about, Data manipulation dplyr package How to use filter and arrange functions
Show slide Pre-requisites	To understand this tutorial, you should know, Basics of Statistics Basics of ggplot2 package 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 myVis.R. Please download these files from the Code files link of this tutorial.
[Computer screen] Highlight moviesData.csv and myVis.R in the folder DataVis	I have downloaded and moved these files to DataVis folder. This folder is located in myProject folder on my Desktop. I have also set DataVis folder as my Working Directory.
Show slide Need for Data Manipulation	In real life, it is rare that we get the data in exactly the right form we need.
Show slide Need for Data Manipulation	Often we’ll need to create some new variables or summaries rename the variables reorder the observations in order to make the data a little easier to work with.
About dplyr package	We will learn how to achieve all this by using dplyr package.
Show slide About dplyr Package	dplyr is a package for data manipulation, written and maintained by Hadley Wickham. It comprises many functions that perform mostly used data manipulation operations.
	Let us switch to RStudio.
Highlight myVis.R in the Files window of RStudio	Open the script myVis.R in RStudio.
Highlight the Source button	Let us run this script by clicking on the Source button.
Highlight movies in the Source window	movies data frame opens in the Source window. This data frame will be used later in this tutorial.
Cursor on the interface.	Now, we will install dplyr package. Please make sure that you are connected to the Internet.
[RStudio] install.packages("dplyr")	In the Console window, type the following command and press Enter.
Highlight the red dot in the Console window	The installation of the package takes a few seconds. We will wait while the package is being installed.
Click at the top of the script myVis.R	To load this package, we will add the library at the top of the script.
Highlight the script myVis.R in the Source window	Click on the script myVis.R
[RStudio] library(dplyr) Press Ctrl+Enter keys.	At the top of the script, type library and dplyr in parentheses. Save the script and run this line by pressing Ctrl + Enter keys simultaneously.
Show slide Functions in dplyr package	Now we learn about some key functions in dplyr package: filter- to select cases based on their values. arrange - to reorder the cases. select - to select variables based on their names. mutate - to add new variables that are functions of existing variables.
Show slide Functions in dplyr package	summarise - to condense multiple values to a single value. All these functions can be combined with group underscore by function. It allows us to perform any operation by a group.
	Let us switch to RStudio.
Highlight movies in the Source window	In the Source window, click on movies.
Highlight the scroll bar in the Source window	In the Source window, scroll from left to right. This will enable us to see the remaining objects of movies data frame.
Highlight genre in the Source window	Suppose we want to filter the movies having genre as Comedy. For this, we will use the filter function.
Highlight the script myVis.R in the Source window	Click on the script myVis.R
[RStudio] moviesComedy <- filter(movies, genre == "Comedy")	In the Source window, type the following command.
Highlight filter in the Source window	Recall that, filter function in dplyr package allows us to select cases based on their values.
Highlight movies after filter in the Source window	Inside the filter function, the first argument is the name of the data frame which is movies.
Highlight genre == "Comedy" in the Source window	The second argument is the value by which we want to filter the movies data frame.
Highlight the Run button in the Source window	Save the script and run the current line.
Highlight moviesComedy in the Environment window	Resulting data frame is stored in an object called moviesComedy in the Environment window. Let us view the data frame moviesComedy to check whether it contains movies with genre as Comedy.
[RStudio] View(moviesComedy)	In the Source window, type the following command.
Highlight the Run button in the Source window	Run the current line.
Highlight moviesComedy in the Source window	moviesComedy data frame opens in the Source window.
Highlight genre in the Source window	All the movies having genre as Comedy have been filtered.
Highlight moviesComedy in the Source window	Let us close this data frame moviesComedy for now.
Highlight filter in the Source window	We can also use logical operators to combine two or more than two values.
Highlight movies in the Source window	In the Source window, click on movies.
Highlight genre in the Source window	Suppose we want to filter the movies with genre as either Comedy or Drama.
Highlight the script myVis.R in the Source window	Click on the script myVis.R
[RStudio] moviesComDr <- filter(movies, genre == "Comedy" | genre == "Drama") View(moviesComDr)	In the Source window, type the following commands.
Highlight filter in the Source widow	Here, we have two values by which we would like to filter movies data frame.
Highlight | in the Source window	For this, we have used a logical OR operator.
Highlight the Run button in the Source window	Run the last two lines of code.
Highlight moviesComDr in the Source window	moviesComDr opens in the Source window. The movies having genre as either Comedy or Drama have been filtered.
Highlight moviesComDr in the Source window	Let us close this data frame moviesComDr for now.
Highlight moviesComDr <- filter(movies, genre == "Comedy" | genre == "Drama") in the Source window	This filter function can also be written using the match operator.
[RStudio] moviesComDrP <- filter(movies, genre %in% c("Comedy", "Drama")) View(moviesComDrP)	In the Source window, type the following command.
Highlight %in% in the Source window	%in% is used for value matching.
[RStudio] help('%in%')	To know more about this operator, let us access the Help. In the Console window, type the following command and press Enter.
Highlight the Run button in the Source window	Run the last two lines of code.
Highlight moviesComDrP in the Source window	moviesComDrP opens in the Source window. The movies having genre as either Comedy or Drama have been filtered.
Highlight moviesComDrP in the Source window	Let us close this data frame moviesComDrP for now.
Highlight movies in the Source window	In the Source window, click on movies.
Highlight the scroll bar in the Source window	In the Source window, scroll from left to right.
Highlight genre and imdb_rating in the Source window	Let us now filter movies with genre as Comedy and imdb underscore rating greater than or equal to 7 point 5.
Highlight the script myVis.R in the Source window	Click on the script myVis.R
[RStudio] moviesComIm <- filter(movies, genre == "Comedy" & imdb_rating >= 7.5) View(moviesComIm)	In the Source window, type the following command.
Highlight genre == "Comedy" & imdb_rating >= 7.5 in the Source window	Here, we have used a logical AND operator to include both conditions.
Highlight the Run button in the Source window	Save the script and run the last two lines of code.
Highlight moviesComIm in the Source window	moviesComIm opens in the Source window. I will resize the Console window. There are seven movies with genre as Comedy and imdb underscore rating greater than or equal to 7 point 5.
Highlight moviesComIm in the Source window	Let us close this data frame moviesComIm for now.
Highlight movies in the Source window	In the Source window, click on movies.
Highlight imdb_rating in the Source window	Suppose, we want to arrange the movies in an ascending order of imdb underscore rating. For this, we will use the arrange function.
Highlight the script myVis.R in the Source window	Click on the script myVis.R
[RStudio] moviesImA <- arrange(movies, imdb_rating) View(moviesImA)	In the Source window, type the following command.
Highlight the Run button in the Source window	Run the last two lines of code.
Highlight moviesImA in the Source window	moviesImA opens in the Source window.
Highlight imdb_rating in the Source window	In the Source window, scroll from left to right and locate the imdb underscore rating column. The movies have been arranged in ascending order of imdb underscore rating.
Highlight imdb_rating in the Source window	Now, let us say we want to arrange the movies in descending order of imdb rating. For this, we use desc function.
Highlight moviesImA in the Source window	Let us close this data frame moviesImA for now.
[RStudio] moviesImD <- arrange(movies, desc(imdb_rating)) View(moviesImD)	In the Source window, type the following command.
Highlight the Run button in the Source window	Run the last two lines of code.
Highlight moviesImD in the Source window	moviesImD opens in the Source window.
Highlight imdb_rating in the Source window	In the Source window, scroll from left to right and locate the imdb underscore rating column. The movies have been arranged in descending order of imdb rating.
Highlight moviesImD in the Source window	Let us close this data frame moviesImD for now.
Highlight movies in the Source window	In the Source window, click on movies.
Highlight genre and imdb_rating in the Source window	Suppose we want to arrange the movies both by genre and imdb rating.
Highlight the script myVis.R in the Source window	Click on the script myVis.R
[RStudio] moviesGeIm <- arrange(movies, genre, imdb_rating) View(moviesGeIm)	In the Source window, type the following commands.
Highlight the Run button in the Source window	Run the last two lines of code.
Highlight moviesGeIm in the Source window	moviesGeIm opens in the Source window.
Highlight the scroll bar in the Source window	In the Source window, scroll from left to right. Movies have been arranged both by genre and imdb underscore rating.
	Let us summarize what we have learnt.
Show slide Summary	In this tutorial, we have learnt about: Data manipulation dplyr package How to use filter and arrange functions.
Show slide Assignment	We now suggest an assignment. Consider the built-in data set mtcars. Find the cars with hp greater than 100 and cyl equal to 3. Arrange the mtcars data set based on mpg variable.
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