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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 
00:27  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 datetime 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 c(9,10,11,12) and c(13,14,15,16). 
12:30  Create a 4 by 2 matrix from these two vectors. 
12:35  Add another vector c(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. 