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

Time	Narration
00:01	Welcome to this tutorial on Data manipulation using dplyr package.
00:08	In this tutorial, we will learn about,
00:12	Data manipulation
00:15	dplyr package
00:18	How to use filter and arrange functions
00:23	To understand this tutorial, you should know,
00:27	Basics of Statistics
00:30	Basics of ggplot2 package
00:34	Data frames
00:36	If not, please locate the relevant tutorials on R on this website.
00:43	This tutorial is recorded on,
00:46	Ubuntu Linux OS version 16.04
00:52	R version 3.4.4
00:56	RStudio version 1.1.463
01:02	Install R version 3.2.0 or higher.
01:08	For this tutorial, we will use
01:11	A data frame moviesData.csv
01: 16	And A script file myVis.R.
01:21	Please download these files from the Code files link of this tutorial.
01:28	I have downloaded and moved these files to DataVis folder.
01:34	This folder is located in myProject folder on my Desktop.
01:41	I have also set DataVis folder as my Working Directory.
01:47	In real life, it is rare that we get the data in exactly the right form we need.
01:55	Often we’ll need to
01:56	create some new variables or summaries
02:02	rename the variables
02:05	reorder the observations in order to make the data a little easier to work with.
02:12	We will learn how to achieve all this by using dplyr package.
02:19	dplyr is a package for data manipulation, written and maintained by Hadley Wickham.
02:27	It comprises many functions that perform mostly used data manipulation operations.
02:36	Let us switch to RStudio.
02:40	Open the script myVis.R in RStudio.
02:46	Let us run this script by clicking on the Source button.
02:52	movies data frame opens in the Source window.
02:57	This data frame will be used later in this tutorial.
03:02	Now, we will install dplyr package. Please make sure that you are connected to the Internet.
03:12	In the Console window, type the following command and press Enter.
03:19	The installation of the package takes a few seconds.
03:24	We will wait while the package is being installed.
03:29	To load this package, we will add the library at the top of the script.
03:36	Click on the script myVis.R
03:40	At the top of the script, type library and dplyr in parentheses.
03:49	Save the script and run this line by pressing Ctrl + Enter keys simultaneously.
03:59	Now we learn about some key functions in dplyr package:
04:05	filter- to select cases based on their values.
04:10	arrange - to reorder the cases.
04:14	select - to select variables based on their names.
04:19	mutate - to add new variables that are functions of existing variables.
04:26	summarise - to condense multiple values to a single value.
04:32	All these functions can be combined with group underscore by function.
04:39	It allows us to perform any operation by a group.
04:45	Let us switch to RStudio.
04:49	In the Source window, click on movies.
04:53	In the Source window, scroll from left to right.
04:58	This will enable us to see the remaining objects of movies data frame.
05:05	Suppose we want to filter the movies having genre as Comedy.
05:12	For this, we will use the filter function.
05:16	Click on the script myVis.R
05:21	In the Source window, type the following command.
05:26	Recall that, filter function in dplyr package allows us to select cases based on their values.
05:36	Inside the filter function, the first argument is the name of the data frame which is movies.
05:44	The second argument is the value by which we want to filter the movies data frame.
05:52	Save the script and run the current line.
05:57	Resulting data frame is stored in an object called moviesComedy in the Environment window.
06:06	Let us view the data frame moviesComedy to check whether it contains movies with genre as Comedy.
06:15	In the Source window, type the following command.
06:19	Run the current line.
06:22	moviesComedy data frame opens in the Source window.
06:28	All the movies having genre as Comedy have been filtered.
06:35	Let us close this data frame moviesComedy for now.
06:40	We can also use logical operators to combine two or more than two values.
06:48	In the Source window, click on movies.
06:52	Suppose we want to filter the movies with genre as either Comedy or Drama.
07:00	Click on the script myVis.R
07:05	In the Source window, type the following commands.
07:11	Here, we have two values by which we would like to filter movies data frame.
07:18	For this, we have used a logical OR operator.
07:23	Run the last two lines of code.
07:27	moviesComDr opens in the Source window.
07:32	The movies having genre as either Comedy or Drama have been filtered.
07:39	Let us close this data frame moviesComDr for now.
07:45	This filter function can also be written using the match operator.
07:52	In the Source window, type the following command.
07:57	%in% is used for value matching.
08:03	To know more about this operator, let us access the Help.
08:08	In the Console window, type the following command and press Enter.
08:17	Run the last two lines of code.
08:21	moviesComDrP opens in the Source window.
08:26	The movies having genre as either Comedy or Drama have been filtered.
08:33	Let us close this data frame moviesComDrP for now.
08:39	In the Source window, click on movies.
08:43	In the Source window, scroll from left to right.
08:48	Let us now filter movies with genre as Comedy and imdb underscore rating greater than or equal to 7 point 5.
09:01	Click on the script myVis.R
09:05	In the Source window, type the following command.
09:10	Here, we have used a logical AND operator to include both conditions.
09:17	Save the script and run the last two lines of code.
09:23	moviesComIm opens in the Source window.
09:28	I will resize the Console window.
09:32	There are seven movies with genre as Comedy and imdb underscore rating greater than or equal to 7 point 5.
09:43	Let us close this data frame moviesComIm for now.
09:49	In the Source window, click on movies.
09:53	Suppose, we want to arrange the movies in an ascending order of imdb underscore rating.
10:02	For this, we will use the arrange function.
10:06	Click on the script myVis.R
10:10	In the Source window, type the following command.
10:15	Run the last two lines of code.
10:19	moviesImA opens in the Source window.
10:24	In the Source window, scroll from left to right and locate the imdb underscore rating column.
10:35	The movies have been arranged in ascending order of imdb rating.
10:41	Now, let us say we want to arrange the movies in descending order of imdb rating.
10:48	For this, we use desc function.
10:54	Let us close this data frame moviesImA for now.
11:00	In the Source window, type the following command.
11:05	Run the last two lines of code.
11:09	moviesImD opens in the Source window.
11:14	In the Source window, scroll from left to right and locate the imdb underscore rating column.
11:24	The movies have been arranged in descending order of imdb rating.
11:30	Let us close this data frame moviesImD for now.
11:36	In the Source window, click on movies.
11:40	Suppose we want to arrange the movies both by genre and imdb rating.
11:48	Click on the script myVis.R
11:52	In the Source window, type the following commands.
11:56	Run the last two lines of code.
12:00	moviesGeIm opens in the Source window.
12:05	In the Source window, scroll from left to right.
12:09	Movies have been arranged both by genre and imdb rating.
12:15	Let us summarize what we have learnt.
12:19	In this tutorial, we have learnt about:
12:23	Data manipulation
12:26	dplyr package
12:28	How to use filter and arrange functions.
12:32	We now suggest an assignment.
12:36	Consider the built-in data set mtcars. Find the cars with hp greater than 100 and cyl equal to 3.
12:47	Arrange the mtcars data set based on mpg variable.
12:53	The video at the following link summarises the Spoken Tutorial project.
12:58	Please download and watch it.
13:02	We conduct workshops using Spoken Tutorials and give certificates.
13:08	Please contact us.
13:11	Please post your timed queries in this forum.
13:16	Please post your general queries in this forum.
13:21	The FOSSEE team coordinates the TBC project.
13:24	For more details, please visit these sites.
13:29	The Spoken Tutorial project is funded by , MHRD, Govt. of India
13:36	The script for this tutorial was contributed by Varshit Dubey (CoE Pune).
13:44	This is Sudhakar Kumar from IIT Bombay signing off. Thanks for watching.

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