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Basic MATLAB Commands for Beginners

MATLAB is a powerful programming language and an environment for numerical computation, visualisation, and algorithm development. It provides a flexible and interactive platform that allows users to perform various tasks, from simple calculations to complex data analysis, simulations, and modelling. In this blog, we will explore the power of MATLAB Commands for data analysis, simulations, modelling, image processing, & more. You can enhance your MATLAB skills with these essential Commands.  

Table of Contents  

1) Basic MATLAB Commands for working with the system and managing a session 

2) MATLAB Commands for input and output 

3) MATLAB Commands for vectors, matrices, and arrays  

4) MATLAB Commands for plotting 

5) MATLAB Commands for matrix 

6) Conclusion 

Basic MATLAB Commands for working with the system and managing a session  

These basic general purposes of MATLAB Commands are invaluable for managing your MATLAB session. It also helps in accessing documentation, clearing the Command window, saving and loading variables, navigating directories, and interacting with the underlying Operating System.   

When working with MATLAB, effectively managing your session can greatly enhance your workflow. Here are some essential Commands for session management. It also provides several Commands that allow you to interact with the underlying Operating System and perform system-related tasks.  

Understanding and utilising these Commands will help you streamline your workflow and improve your MATLAB skills.
 

Command 

Syntax 

Explanation 

help 

help functionName 

Displays information and documentation about a function 

clc 

clc 

Clears the Command window 

quit 

quit 

Terminates the MATLAB session 

save 

save fileName.mat variableName 

Saves variables or workspace to a file 

load 

load fileName.mat 

Loads variables or workspace from a file 

diary 

diary fileName.txt 

Logs all Commands and outputs to a text file 

cd 

cd folderPath 

Changes the current working directory 

dir 

dir 

Lists the contents of the current directory 

system 

system('Command') 

Executes a system Command from within MATLAB 

load 

load fileName.mat 

Loads previously saved variables and data from a file 

clear 

clear 

Removes all variables from the workspace 

clear var 

clear variableName 

Removes a specific variable from the workspace 

who 

who 

Lists all variables in the current workspace 

whos 

whos 

Provides detailed information about variables in the workspace 

workspace 

workspace 

Opens the workspace browser to view and modify variables 

isvarname 

isvarname('variableName') 

Checks if a string is a valid MATLAB variable name 

pwd 

pwd 

Returns the current working directory 

ls 

ls 

Lists files and directories in the current directory (UNIX-like) 

mkdir 

mkdir folderName 

Creates a new directory 

rmdir 

rmdir folderName 

Removes an empty directory 

delete 

delete fileName 

Deletes a file 

copyfile 

copyfile sourceFile destinationFolder 

Copies a file to a specified destination folder 

movefile 

movefile sourceFile destinationFolder 

Moves a file to a specified destination folder 

system 

system('Command') 

Executes a system Command from within MATLAB 

pause 

pause 

Pauses execution of a MATLAB program 

getenv 

getenv('variableName') 

Retrieves the value of an environment variable 

setenv 

setenv('variableName', 'value') 

Sets the value of an environment variable 

computer 

computer 

Returns the type of computer running MATLAB 

ispc 

ispc 

Checks if MATLAB is running on a Windows platform 

isunix 

isunix 

Checks if MATLAB is running on a UNIX-like platform 

ismac 

ismac 

Checks if MATLAB is running on a macOS platform 

 

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MATLAB Commands for input and output  

MATLAB provides several Commands for handling input and output operations, allowing you to interact with users, read and write data, and perform file operations. Here are some essential Commands for input and output in MATLAB, including specialized functions such as Matlab Convolation for advanced signal processing tasks. 

1) input  

variable = input('Prompt text: ');  

Stores it in a variable by prompting the user for input. 

2) disp  

disp('Text to display');  

Displays text or variables in the Command window.   

3) fprintf  

fprintf('Format string', variable1, variable2);  

Writes formatted data to a file or the Command window.   

4) fscanf  

variable = fscanf(fileID, 'Format specifier');  

Reads data from a file according to a specified format.   

5) fprintf  

fprintf(fileID, 'Format string', variable1, variable2);  

Writes formatted data to a file.   

6) fopen  

fileID = fopen('fileName', 'mode');  

Opens a file and returns a file identifier. 

7) fclose  

fclose(fileID);  

Closes an open file. 

8) fgetl  

line = fgetl(fileID);  

Reads a line of text from a file. 

9) fgets  

line = fgets(fileID);  

Reads a line of text from a file, including newline characters. 

9) fwrite  

fwrite(fileID, data, 'precision');  

Writes data to a file. 

10) feof  

feof(fileID)  

Checks if the end of a file has been reached. 

11) exist  

exist('fileName', 'file')  

Checks if a file or directory exists. 

12) mkdir  

mkdir('folderName')  

Creates a new directory.  

13) delete  

delete('fileName')  

Deletes a file or directory.

Examples for MATLAB Commands for input and output
 

Learn how to use MATLAB Commands effectively with MATLAB Masterclass. 

MATLAB Commands for vectors, matrices, and arrays  

MATLAB is renowned for its powerful capabilities in handling vectors, matrices, and arrays. These Commands allow you to efficiently create, manipulate, and perform operations on multidimensional data structures. Here are some essential MATLAB Commands for working with vectors, matrices, and arrays.  

These Commands enable you to create vectors, matrices, and matlab arrays of various sizes and initialise them with zeros, ones, random values, or identity matrices. You can also reshape, transpose, and tile matrices and calculate statistical properties such as minimum, maximum, sum, mean, and sort elements.  

You can manipulate and perform operations on vectors, matrices, and arrays by leveraging these MATLAB Commands, enabling efficient data analysis, simulations, modelling, and more within your MATLAB workflow.  

1) linspace  

linspace(start, end, num)   

Creates a row vector with equally spaced values between the start and endpoints. The 'num' parameter determines the number of values in the vector. 

2) zeros  

zeros(m, n)  

Creates a matrix or array filled with zeros of size m-by-n. It is useful for initialising matrices before storing or manipulating data. 

3) ones  

ones(m, n)  

Creates a matrix or array filled with ones of size m-by-n. It is commonly used for initialisation or generating matrices with specific dimensions. 

4) eye  

eye(n)  

Creates an identity matrix of size n-by-n. The identity matrix has ones on the main diagonal and zeros elsewhere. 

5) rand  

rand(m, n)  

Generates an m-by-n matrix or array filled with numbers ranging from 0 to 1 that are uniformly distributed. This Command is valuable for producing random data or initialising matrices with randomised values. 

6) Size  

size(matrix)  

Returns the size of a matrix or array in the form of [rows, columns]. It provides information about the dimensions of the given matrix or array. 

7) length  

length(vector)  

Returns the length of a vector, i.e., the number of elements in the vector. It is particularly useful for determining the size of a one-dimensional vector. 

8) reshape  

reshapedMatrix = reshape(originalMatrix, m, n)  

Reshapes a matrix or array into a new size specified by m and n. It rearranges the elements of the original matrix to fit the desired dimensions. 

9) transpose  

transposedMatrix = originalMatrix  

Transposes a matrix or array by interchanging its rows and columns. The transposed matrix is obtained by reflecting the original matrix along its main diagonal. 

10) repmat  

repeatedMatrix = repmat(originalMatrix, m, n)  

Creates a tiled copy of a matrix or array, repeating it m times in the row direction and n times in the column direction. It is useful for replicating matrices or arrays to match desired dimensions. 

11) min  

minimumValue = min(vector)  

Finds the minimum value in a vector. It returns the smallest element present in the given vector. 

12) max  

maximumValue = max(vector)  

Finds the maximum value in a vector. It returns the largest element present in the given vector. 

13) sum  

sum(vector)  

Calculates the sum of all elements in a vector. It returns the total sum of the values in the vector. 

14) mean  

mean(vector)  

Calculates the mean (average) of the elements in a vector. It returns the arithmetic mean of the values in the vector. 

15) sort  

sortedVector = sort(vector)  

Sorts the elements of a vector in ascending order. It rearranges the elements in the vector in a sorted manner. 

16) isvector  

isvector(matrix)  

Checks if a matrix or array is a vector. It returns true if the given matrix or array has only one dimension, indicating it is a vector. 

17) ismatrix  

ismatrix(array)  

Checks if an array is a matrix. It returns true if the given array has two dimensions, indicating it is a matrix.

MATLAB & SPSS Training
 

MATLAB Commands for plotting  

MATLAB projects offers a comprehensive set of plotting Commands that allow you to visualise and analyse data effectively. These Commands enable you to create various types of plots, customise their appearance, and add annotations. Here are some essential MATLAB plotting Commands.  

1) plot  

(plot(x, y))  

Creates a 2D line plot using the values in vectors x and y. It is widely used to visualise the relationship between two variables or to represent data trends. 

2) scatter  

(scatter(x, y))  

Creates a scatter plot using the values in vectors x and y. It is useful for visualising data point distribution and identifying patterns or clusters. 

3) bar  

(bar(x, y))  

Creates a bar plot using the values in vectors x and y. It is commonly used to compare different categories or groups and display their corresponding values. 

4) histogram  

(histogram(data))  

Creates a histogram plot to visualise the distribution of a dataset. It divides the data into bins and displays the frequency or count of values within each bin.  

5) pie  

(pie(values, labels))  

Creates a pie chart using the specified values and labels. It is useful for illustrating the proportions or percentages of different categories within a whole.  

6) boxplot  

(boxplot(data))  

Creates a box plot to display the distribution of data. It shows the median, quartiles, and any outliers or extreme values concisely and visually informatively. 

7) contour  

(contour(X, Y, Z))  

Creates a contour plot using the values in matrices X, Y, and Z. It represents 3D data on a 2D plane by displaying curves of constant values (contours) with varying heights. 

8) surf  

(surf(X, Y, Z))  

Creates a surface plot using the values in matrices X, Y, and Z. It visualises 3D data as a surface with varying heights, allowing you to analyse complex relationships and patterns.  

9) imagesc  

(imagesc(data))  

Creates a pseudocolour plot of a matrix or 2D array. It represents the values in the matrix using different colours, providing a visual representation of the data distribution. 

10) quiver  

(quiver(x, y, u, v))  

Creates a 2D vector plot using the components of vectors u and v at the locations given by x and y. It is useful for visualising vector fields and directional information. 

11) contourf  

(contourf(X, Y, Z))  

Creates a filled contour plot using the values in matrices X, Y, and Z. It is similar to the contour plot but fills the areas between contours with colours, enhancing the visual representation.  

12) polarplot  

(polarplot(theta, rho))  

Creates a polar coordinate plot using the angles in theta and the distances from the origin in rho. It is suitable for visualising data with circular or angular relationships.  

13) errorbar  

(errorbar(x, y, y_err))  

Creates an error bar plot using the values in vectors x, y, and y_err. It represents the uncertainty or error associated with each data point, providing a measure of variability.  

14) stem  

(stem(x, y))  

Creates a stem plot to visualise discrete data. It displays vertical lines (stems) at the x-axis positions given by x with corresponding values y, making it useful for representing sequences or event data.

An example for MATLAB Commands for plotting
 

MATLAB Commands for matrix 

MATLAB, with its robust set of capabilities and supported by various MATLAB operators, offers a variety of matrix commands that streamline matrix manipulation, operations, and linear algebra calculations. These commands enable users to manipulate matrices, extract elements or submatrices, calculate matrix properties, and solve linear systems efficiently. Here are some essential MATLAB matrix commands that contribute to the versatility of the software:

1) inv  

inverseMatrix = inv(matrix)  

Computes the inverse of a square matrix.   

2) det  

determinant = det(matrix)  

Calculates the determinant of a square matrix.   

3) rank  

matrixRank = rank(matrix)  

Determines the rank of a matrix.   

4) eig  

[eigenvalues, eigenvectors] = eig(matrix)  

Computes the eigenvectors and eigenvalues of a square matrix.   

5) trace  

matrixTrace = trace(matrix)  

Calculates the trace (sum of diagonal elements) of a matrix.   

6) diag  

diagonalMatrix = diag(vector)  

Creates a diagonal matrix using the elements of a vector.   

7) repmat  

repeatedMatrix = repmat(matrix, m, n)  

Creates a tiled copy of a matrix, repeating it m times in the row direction and n times in the column direction.   

8) flipud  

flippedMatrix = flipud(matrix)  

Flips the matrix upside down (reverses the order of rows).   

9) fliplr  

FlippedMatrix fliplr(matrix)  

Flips the matrix from left to right (reverses the order of columns).   

10) transpose  

transposedMatrix = matrix.  

Transposes a matrix, interchanging its rows and columns.   

11) repmat  

repeatedMatrix = repmat(matrix, m, n)  

Creates a tiled copy of a matrix, repeating it m times in the row direction and n times in the column direction.   

12) reshape  

reshapedMatrix = reshape(matrix, m, n)  

Reshapes a matrix into a new size specified by m and n.   

13) lu  

[L, U, P] = lu(matrix)  

Computes the LU factorisation of a matrix, where L is lower triangular, U is upper triangular, and P is the permutation matrix.   

14) qr  

[Q, R] = qr(matrix)  

Computes the QR factorisation of a matrix, where Q is orthogonal, and R is upper triangular.   

15) svd  

[U, S, V] = svd(matrix)  

Computes a matrix's singular value decomposition (SVD), S is a diagonal matrix, and U and V are orthogonal matrices.   

16) chol  

R = chol(matrix)  

Computes the Cholesky factorisation of a positive definite matrix, where R is upper triangular.   

17) pinv  

pseudoInverseMatrix = pinv(matrix)  

Computes the pseudo-inverse of a matrix using the Moore-Penrose inverse.   

18) Norm  

matrixNorm = norm(matrix)  

Computes the norm (magnitude) of a matrix.   

19) Solve  

solutionVector = matrix rightHandSideVector or solutionVector = inv(matrix) * rightHandSideVector  

It solves a linear system of equations using matrix inversion or the backslash operator.

An example of MATLAB Commands for matrix
 

Conclusion  

Advantages of MATLAB Commands are a powerful toolset for beginners to explore the features of MATLAB in data analysis, simulations, modelling, and image processing. By learning and mastering these essential Commands, users can efficiently manage sessions, handle input and output, manipulate vectors, matrices, arrays, and more. With these Commands, users can streamline their data analysis workflow, gain insights from datasets, and customise visualisations to meet their specific needs. 

Learn how to utilise the capabilities of SPSS with our SPSS Masterclass. 

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