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

Title of script: Operations on Matrices and Data Frames

Author: Shaik Sameer (IIIT Vadodara) and Sudhakar Kumar (IIT Bombay)

Keywords: R, RStudio, matrices, data frames, adding row, adding column, video tutorial

Visual Cue	Narration
Show slide Opening slide	Welcome to the spoken tutorial on Operations on Matrices and Data Frames.
Show slide Learning Objectives	In this tutorial, we will learn how to: Perform operations on matrices Add rows or columns to a data frame
Show slide Pre-requisites	To understand this tutorial, you should know Data frames and Matrices in R R script in RStudio How to set working directory in RStudio 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 1.1.456 Install R version 3.2.0 or higher.
Show slide Download Files	For this tutorial, we will use the data frame CaptaincyData.csv and the script file myMatrix.R. Please download these files from the Code files link of this tutorial.
[Computer screen] Highlight CaptaincyData.csv and myMatrix.R in the folder MatrixOps	I have downloaded and moved these files to MatrixOps folder in myProject folder on the Desktop. I have also set MatrixOps folder as my Working directory.
	Let us switch to RStudio.
Click myMatrix.R in RStudio Point to myMatrix.R in Rstudio	Open the script myMatrix.R in RStudio. For this, click on the script myMatrix.R Script myMatrix.R opens in Rstudio.
Highlight matrixA in the Source widow	Recall that, we had created a matrix named matrixA. This matrix was extracted as a subset from the captaincy data frame. We will use matrixA here also.
Highlight the Source button	Run this script myMatrix.R to load the values in the Workspace.
Highlight captaincy in the Source window	captaincy data frame opens in the Source window.
	I will resize the Source window.
Highlight matrixA in the Environment window	Now let us learn how to find the inverse of matrixA.
	Click on the script myMatrix.R
[RStudio] solve(matrixA)	In the Source window, type solve, within parentheses matrixA. Press Enter. Press Enter at the end of every command.
Press Ctrl + S >> Press Ctrl+Enter keys.	Save the script and run this line.
Highlight the output in the Console window	The inverse of matrixA in shown in the Console window.
	For more information on calculating inverse of a matrix in R, please refer to the Additional materials section on this website.
[RStudio] Highlight matrixA in the Environment window	Now let us calculate the sum of all the elements in matrixA. First, we shall use nested for loops for calculating this sum. Also, we shall estimate the time taken to calculate the sum in this way.
[RStudio] # Calculating sum using for loop	let us add a comment Calculating sum using for loop in the script. In the Source window, type # hash space Calculating sum using for loop. Press Enter.
[RStudio] startTime <- Sys.time()	To calculate the time taken, we record the present time. In the Source window, type start Time with capital T, then press Alt and -(hyphen) keys simultaneously. Next, type Sys dot time followed by parentheses.
Highlight Sys.time in the Source window	Sys.time() is used to find the absolute date-time value in the current time zone.
[RStudio] totalSum <- 0	Let us initialise a variable totalSum. In the Source window, type totalSum then press Alt and -(hyphen) keys simultaneously. Then type 0. This variable will store the sum of elements in matrixA.
[RStudio] for(i in 1:3) { }	Now, we will create two for loops to iterate through the rows and columns of the matrixA. In the Source window, type for in parentheses i space in space 1 colon 3 space Now type opening curly bracket. RStudio automatically adds a closing curly bracket.
[RStudio] for(j in 1:3) { totalSum <- totalSum + matrixA[i,j] }	Press Enter just after the opening curly bracket and type the following command.
[RStudio] print(totalSum)	Press Enter after the closing curly bracket of outer loop for(i in 1:3). In the Source window, type print totalSum in parentheses.
[RStudio] endTime <- Sys.time() endTime - startTime	Now, we record the current time again. Type endTime then press Alt and -(hyphen) keys simultaneously. Now type Sys dot time parentheses. Press Enter. Type endTime space minus sign space startTime to know the total time taken and save the script. I am resizing the Source window.
[RStudio] Highlight the sum in Console window	Run the block of code from the comment line Calculating sum using for loop to the end. The totalSum is evaluated to be 237.
[RStudio] Highlight the time taken in Console window	The time taken to calculate the sum of elements in matrixA using for loop is approximately 8 milliseconds. However, it may vary from system to system.
Highlight matrixA in the Source window	Now, let us calculate the sum of all elements in matrixA using the sum function.
	We shall estimate the time taken to calculate the sum in this way also.
[RStudio] # Calculating sum using inbuilt function	In the script myMatrix.R, add a comment Calculating sum using inbuilt function. In the Source window, type # hash space Calculating sum using inbuilt function.
[RStudio] startTime <- Sys.time() sum(matrixA) endTime <- Sys.time() endTime - startTime	Now, type the following commands.
[RStudio] Highlight the time taken in the Console window	Save the script and run the block of code from the comment line Calculating sum using inbuilt function to the end. The time taken to calculate the sum of elements using sum function is 1.6 milliseconds. Whereas, it took 8 milliseconds to calculate the same sum using for loop.
Cursor on the interface.	So, it is advisable to use inbuilt functions of R.
Cursor on the interface	Let us learn how to calculate sum of each row and sum of each column.
[RStudio] rowSums(matrixA) colSums(matrixA)	In the Source window, type rowSums matrixA in parentheses. Next, type colSums matrixA in parentheses. Save the script and run these two lines to see the corresponding sums on the Console.
	I am resizing the Console window to see the output properly.
	Now let us learn how to add rows and columns to an existing data frame.
	Click on the captaincy data frame.
Highlight captaincy data frame in the Source window	Let us add a new row to the captaincy data frame.
	Click on the script myMatrix.R
[RStudio] captaincy <- rbind(captaincy, data.frame(names="Kohli", Y = 2016, played = 30, won = 20, lost = 9, victory = 20/30))	In the Source window, type the following command. Press Enter after the comma for better visibility. .
	I am resizing the Source window
Highlight captaincy in the Source window	We have used rbind() function with the following arguments name of the data frame to which we want to add the new row. Here, it is captaincy. the row to be added as an argument to data.frame().
Highlight data.frame in the Source window	To data.frame() function, we provide the values according to the columns of the actual data frame. names = “Kohli” Y = 2016 played = 30 won = 20 lost = 9 victory = 20/30
[RStudio] View(captaincy) Highlight new row in the Source window	In the Source window, type View within parentheses captaincy Save the script and run the last two lines of code. One new row with the details of Kohli is added in the captaincy data frame.
[RStudio] defeat <- captaincy$lost / captaincy$played	Now let us create a new column named defeat from the captaincy data frame. Click on the script myMatrix.R. In the Source window, type the following command Press Enter.
captaincy <- cbind(captaincy, defeat)	Now we add defeat as a new column in captaincy data frame. In the Source window, type the following command Press Enter.
Highlight cbind in the Source window	We have used cbind() function with following two arguments: name of the data frame to which we want to add the new column. Here, it is captaincy. name of the column to be added. Here, it is defeat.
View(captaincy)	Now type View, captaincy in parentheses Save the script and run the block of code starting from the declaration of defeat to the end. One column of defeat is added in the captaincy data frame.
	Let us summarize what we have learnt.
Show slide Summary	In this tutorial, we have learned how to: Perform operation on matrices Add rows or columns to a data frame
Show slide Assignment	We now suggest an assignment. Consider 2 vectors c(9,10,11,12) and c(13,14,15,16). Create a 4 by 2 matrix from these two vectors. Add another vector c(17,18,19,20) as a column to the previous matrix. For solutions, please refer to the Additional materials section on this website.
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.
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Show Slide Textbook Companion	The FOSSEE team coordinates the TBC project. For more details, please visit these sites.
Show Slide Acknowledgement	The Spoken Tutorial project is funded by NMEICT, MHRD, Govt. of India
Show Slide Thank You	The script for this tutorial was contributed by Shaik Sameer (FOSSEE Fellow 2018). This is Sudhakar Kumar from IIT Bombay signing off. Thanks for watching.

Contributors and Content Editors

Nancyvarkey, Sudhakarst