R/C2/Indexing-and-Slicing-Data-Frames/English-timed

Time	Narration
00:01	Welcome to the spoken tutorial on Indexing and Slicing Data Frames.
00:08	In this tutorial, we will learn how to:
00:12	Extract rows or columns from a data frame
00:17	Create a subset from a data frame
00:21	Retrieve data using double square brackets
00:25	To understand this tutorial, you should have knowledge about
00:30	Data frames in R
00:33	R script in RStudio and
00:37	How to set Working directory in RStudio
00:41	If not, please locate the relevant tutorials on R on this website.
00:48	This tutorial is recorded on
00:52	Ubuntu Linux OS version 16.04
00:57	R version 3.2.3
01:01	RStudio version 1.1.456
01:07	Install R version 3.2.0 or higher.
01:14	For this tutorial, we will use the data frame CaptaincyData.csv and a script file mydataframe.R.
01:25	Download these files from the Code files link of this tutorial.
01:31	I have downloaded and moved these files to the folder myProject on my Desktop.
01:39	I have also set this folder as my Working Directory.
01:45	Let us switch to Rstudio.
01:48	Let us open the script mydataframe.R in RStudio.
01:55	For this, click on the script mydataframe.R.
02:01	Script mydataframe.R opens in Rstudio.
02:07	Here, we have declared a variable captaincy to store and read CaptaincyData.csv.
02:16	Also, View function is being used to see the contents of the file.
02:23	Remember, you may also use equal to sign in place of less than symbol followed by hyphen.
02:32	However, we recommend less than symbol followed by hyphen.
02:38	For this, there is a shortcut in RStudio.
02:42	Suppose we want to assign a value of 2 to a variable named testvar.
02:49	In the Console window, type testvar and then press Alt and -(hyphen) keys simultaneously.
02:58	Then type 2 and press Enter.
03:01	Let us get back to the Source window.
03:06	Run the script mydataframe.R by clicking on the Source button.
03:13	Now let us extract the contents of the third row of the captaincy data frame.
03:20	Click on script mydataframe.R
03:25	In the Source window, type captaincy
03:29	Type capt and press Enter to make the variable captaincy autocomplete. Now within square brackets 3 followed by comma. And press Enter.
03:47	Press Enter key at the end of every command.
03:51	Remember one of the most important features of RStudio include intelligent auto-completion of function names, packages, and R objects.
04:04	We use a comma within square brackets when we wish to extract a row.
04:11	Save the script and execute the current line by pressing Ctrl+Enter keys.
04:20	The third row of the captaincy data frame is seen in the Console window.
04:26	Now, let us run the same command without a comma.
04:31	In the Source window, type captaincy then within square brackets 3.
04:39	Save the script and run this line only, as shown earlier.
04:46	The contents of the third column of the data frame, are displayed on the Console window.
04:54	So, to extract a column, we shouldn’t use a comma within the square brackets.
05:01	When we extract data using row number or column number, it is known as numeric indexing.
05:09	In the Source window, click on the captaincy data frame.
05:14	Let us now extract the contents of second and third rows of the captaincy data frame.
05:22	To retrieve more than one row, we use a numeric index vector. Click on the script mydataframe.R
05:32	In the Source window, type the following command and press Enter.
05:38	c function is being used to concatenate the second and third rows.
05:45	Save the script and execute the current line.
05:51	The second and third rows of captaincy data frame are seen in the Console window.
05:58	In the Source window, click on the captaincy data frame.
06:03	Now, we’ll find who has played 25 matches from the played column of captaincy data frame.
06:12	Extracting this type of information is known as logical indexing.
06:19	Click on the script mydataframe.R
06:23	In the Source window, type captaincy
06:27	Within square brackets captaincy dollar sign played equal to equal to 25 comma. Press Enter.
06:38	Remember, dollar sign allows you to extract elements by name.
06:44	Please note that there is no space between the two equal to signs.
06:50	Save the script and execute the current line.
06:55	The details of captain Dravid are shown in the Console window.
07:01	In the Source window, click on the captaincy data frame.
07:06	Now let us learn how to get the values of any particular attribute for all the players.
07:14	We will fetch the names of all the captains.
07:18	For this, we need to know the values in the first column.
07:23	Click on the script mydataframe.R
07:28	In the Source window, type captaincy and within square brackets 1.
07:35	Please note that I have not used a comma inside the square brackets.
07:41	Save the script and execute the current line.
07:46	The names of the captains are seen in the Console window.
07:51	Usually we use column names instead of column numbers.
07:57	To know the names of the captains, type captaincy.
08:01	Within square brackets inside double quotes names
08:07	Save the script and execute the current line.
08:12	Names of the captains are shown in the Console window.
08:17	Extracting data by column names is known as name indexing.
08:23	Clear the Console window by clicking on the broom icon.
08:28	In the Source window, click on the captaincy data frame
08:33	Now let us view the names of the captains along with the number of matches they have won.
08:40	Click on the script mydataframe.R
08:44	In the Source window, type the following command and press Enter.
08:52	Here c function is used to concatenate names and won.
08:59	Observe that names and won have been written within double quotes.
09:06	Save the script and execute this line.
09:11	The names of captains and the number of matches won are seen in the Console window.
09:18	Now let us extract a subset from captaincy data frame.
09:24	We will create a subset of captains, who have won more than 30% of their matches.
09:32	This is called slicing a data frame.
09:36	In the Source window, click on the captaincy data frame.
09:41	Please note that there is a column named victory in the captaincy data frame.
09:48	For required subset of captains, their victory should be greater than 0.3.
09:55	For this subset, we shall only show the
09:58	names
10:00	number of matches played
10:02	number of matches won
10:05	Click on the script mydataframe.R
10:09	I will resize the Source window.
10:13	In the Source window, type the following command
10:18	Now, press Enter after the comma followed by 0 point 3.
10:25	You can press Enter after a comma for better visibility.
10:30	The select parameter is used to select the required columns, names, played, and won.
10:38	In the Source window, type print within parentheses subData .
10:45	Save the script and run these two lines.
10:51	I am resizing the Console window to see the output properly.
10:57	The subData is shown in the Console window.
11:01	In the Source window, click on the captaincy data frame.
11:07	Finally, let us learn how to extract a particular entry from some column of a data frame.
11:16	We will extract the third value in the fourth column of the captaincy data frame.
11:23	Click on the script mydataframe.R.
11:26	In the Source window, type captaincy within double square brackets 4 within single square brackets 3.
11:37	Save the script and execute the current line.
11:42	The expected value 14 is seen in the Console window.
11:48	For more information on indexing and slicing data frames, please refer to the Additional materials section on this website.
11:58	Let us summarize what we have learnt.
12:03	In this tutorial, we have learnt how to:
12:07	Extract rows or columns from a data frame
12:12	Create a subset from a data frame
12:16	Retrieve data using double square brackets
12:20	We now suggest an assignment.
12:24	Create a subset from captaincy data frame with the captains who have played more than(>) 20 matches and lost less than(<) 14 matches.
12:36	The video at the following link summarises the Spoken Tutorial project.
12:41	Please download and watch it.
12:44	We conduct workshops using Spoken Tutorials and give Certificates.
12:50	Please contact us.
12:53	Please post your timed queries in this forum.
12:57	Please post your general queries in this forum.
13:02	The FOSSEE team coordinates the TBC project.
13:06	For more details, please visit these sites.
13:10	The Spoken Tutorial project is funded by NMEICT, MHRD, Govt. of India
13:16	The script for this tutorial was contributed by Shaik Sameer (FOSSEE Fellow 2018).
13:24	This is Sudhakar Kumar from IIT Bombay signing off. Thanks for watching.

Contributors and Content Editors

Sakinashaikh