R/C2/Operations-on-Matrices-and-Data-Frames/English-timed

Time	Narration
00:01	Welcome to the spoken tutorial on Operations on Matrices and Data Frames.
00:08	In this tutorial, we will learn how to:
00:13	Perform operations on matrices
00:16	Add rows or columns to a data frame
00:20	To understand this tutorial, you should know
00:24	Data frames and Matrices in R
00:29	R script in RStudio
00:32	How to set working directory in RStudio
00:37	If not, please locate the relevant tutorials on R on this website.
00:44	This tutorial is recorded on
00:47	Ubuntu Linux OS version 16.04
00:53	R version 3.4.4
00:58	RStudio version 1.1.456
01:05	Install R version 3.2.0 or higher.
01:10	For this tutorial, we will use the data frame CaptaincyData.csv and the script file myMatrix.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 MatrixOps folder' in myProject folder on the Desktop.
01:39	I have also set MatrixOps folder as my Working directory.
01:46	Let us switch to Rstudio.
01:51	Open the script myMatrix.R in RStudio.
01:56	For this, click on the script myMatrix.R
02:02	Script myMatrix.R opens in Rstudio.
02:08	Recall that, we had created a matrix named matrixA.
02:14	This matrix was extracted as a subset from the captaincy data frame.
02:21	We will use matrixA here also.
02:25	Run this script myMatrix.R to load the values in the Workspace.
02:32	captaincy data frame opens in the Source window.
02:37	I will resize the Source window.
02:41	Now let us learn how to find the inverse of matrixA.
02:48	Click on the script myMatrix.R
02:53	In the Source window, type solve, within parentheses matrixA.
03:00	Press Enter. Press Enter at the end of every command.
03:07	Save the script and run this line.
03:12	The inverse of matrixA in shown in the Console window.
03:18	For more information on calculating inverse of a matrix in R, please refer to the Additional materials section on this website.
03:29	Now let us calculate the sum of all the elements in matrixA.
03:36	First, we shall use nested for loops for calculating this sum.
03:43	Also, we shall estimate the time taken to calculate the sum in this way.
03:50	Let us add a comment - Calculating sum using for loop in the script.
03:57	In the Source window, type # hash space Calculating sum using for loop.
04:05	Press Enter. To calculate the time taken, we record the present time.
04:12	In the Source window, type start Time with capital T.
04:18	Then press Alt and -(hyphen) keys simultaneously.
04:23	Next, type Sys dot time followed by parentheses.
04:30	Sys.time() is used to find the absolute date-time value in the current time.
04:38	Let us initialise a variable totalSum.
04:43	In the Source window, type totalSum
04:47	then press Alt and -(hyphen) keys simultaneously. Then type zero.
04:55	This variable will store the sum of elements in matrixA.
05:01	Now, we will create two for loops to iterate through the rows and columns of the matrixA.
05:10	In the Source window, type for in parentheses i space in space 1 colon 3 space
05:21	Now type opening curly bracket. RStudio automatically adds a closing curly bracket.
05:30	Press Enter just after the opening curly bracket and type the following command.
05:39	Press Enter after the closing curly bracket of outer loop for(i in 1:3).
05:48	In the Source window, type print totalSum in parentheses.
05:54	Now, we record the current time again.
05:59	Type endTime then press Alt and -(hyphen) keys simultaneously. Type Sys dot time parentheses.
06:10	Press Enter. Type endTime space minus sign space startTime to know the total time taken. And save the script.
06:23	I am resizing the Source window.
06:27	Run the block of code from the comment line Calculating sum using for loop to the end.
06:35	The totalSum is evaluated to be 237.
06:41	The time taken to calculate the sum of elements in matrixA using for loop is approximately 8 milliseconds.
06:51	However, it may vary from system to system.
06:56	Now, let us calculate the sum of all elements in matrixA using the sum function.
07:05	We shall estimate the time taken to calculate the sum in this way also.
07:12	In the script myMatrix.R, add a comment Calculating sum using inbuilt function.
07:21	In the Source window, type # hash space Calculating sum using inbuilt function.
07:30	Now, type the following commands.
07:35	Save the script and run the block of code from the comment line Calculating sum using inbuilt function to the end.
07:46	The time taken to calculate the sum of elements using sum function is 1.6 milliseconds.
07:56	Whereas, it took 8 milliseconds to calculate the same sum using for loop.
08:04	So, it is advisable to use inbuilt functions of R.
08:10	Let us learn how to calculate sum of each row and sum of each column.
08:18	In the Source window, type rowSums matrixA in parentheses.
08:26	Next, type colSums matrixA in parentheses.
08:32	Save the script and run these two lines to see the corresponding sums on the Console.
08:40	I am resizing the Console window to see the output properly.
08:47	Now let us learn how to add rows and columns to an existing data frame.
08:55	Click on the captaincy data frame.
08:59	Let us add a new row to the captaincy data frame.
09:04	Click on the script myMatrix.R
09:09	In the Source window, type the following command. Press Enter .
09:24	Press Enter after the comma for better visibility.
09:30	I am resizing the Source window
09:34	We have used rbind() function with the following arguments
09:40	name of the data frame to which we want to add the new row. Here, it is captaincy.
09:48	the row to be added as an argument to data.frame().
09:54	To data.frame() function, we provide the values according to the columns of the actual data frame.
10:03	names = “Kohli”
10:06	Y = 2016
10:10	played = 30
10:13	won = 20
10:17	lost = 9
10:20	victory = 20/30
10:24	In the Source window, type View within parentheses captaincy
10:30	Save the script and run the last two lines of code.
10:37	One new row with the details of Kohli is added in the captaincy data frame.
10:44	Now let us create a new column named defeat in the captaincy data frame.
10:52	Click on the script myMatrix.R.
10:57	In the Source window, type the following command.
11:03	Press Enter. Now we add defeat as a new column in captaincy data frame.
11:11	In the Source window, type the following command Press Enter.
11:17	We have used cbind() function with following two arguments:
11:23	name of the data frame to which we want to add the new column.
11:28	Here, it is captaincy. name of the column to be added. Here, it is defeat.
11:37	Now type View, captaincy in parentheses
11:42	Save the script and run the block of code starting from the declaration of defeat to the end.
11:52	defeat column is added in the captaincy data frame.
11:58	Let us summarize what we have learnt.
12:04	In this tutorial, we have learned how to:
12:07	Perform operation on matrices
12:11	Add rows or columns to a data frame
12:16	We now suggest an assignment.
12:20	Consider 2 vectors (9,10,11,12) and (13,14,15,16).
12:30	Create a 4 by 2 matrix from these two vectors.
12:35	Add another vector (17,18,19,20) as a column to the previous matrix.
12:45	For solutions, please refer to the Additional materials section on this website.
12:52	The video at the following link summarises the Spoken Tutorial project.
12:58	Please download and watch it.
13:01	We conduct workshops using Spoken Tutorials and give certificates. 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. For more details, please visit these sites.
13:30	The Spoken Tutorial project is funded by NMEICT, MHRD, Govt. of India
13:37	The script for this tutorial was contributed by Shaik Sameer (FOSSEE Fellow 2018).
13:46	This is Sudhakar Kumar from IIT Bombay signing off.
13:51	Thanks for watching.

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