MATLAB and R Programming (ME-705) - Unit 1 Short Notes

ME-705: MATLAB and R Programming - UNIT 1 Short Notes

Theme: Foundations, Syntax, and Basic Data Manipulation

1.0 Introduction & Environment Setup

1.1 Purpose and Application Domains

1.2 Installing and Launching

• MATLAB:

  • Requires license (institutional/personal).

  • Desktop Environment:

    • Command Window: Interactive command execution.

    • Workspace: Shows current variables.

    • Current Folder: File browser.

    • Editor: Write and run scripts (.m files).

• R:

  • Free, open-source. RStudio is the highly recommended IDE.

  • RStudio Panes:

    • Console: Interactive R commands.

    • Source: Script editor (.R files).

    • Environment/History: Variables and command history.

    • Files/Plots/Help: File navigation, plot viewer, documentation.

1.3 Getting Help and Documentation

[!TIP] Common Pitfall: In R, help.start() opens the main HTML help page. In MATLAB, doc without arguments opens the main documentation browser.

2.0 Basic Syntax, Arithmetic, and Variables

2.1 Command Line vs. Script/File Execution

• Interactive (Command Line/Console): Execute commands one-by-one. Good for testing.

• Script Execution: Write a sequence of commands in a file (.m for MATLAB, .R for R) and run the entire file. Essential for reproducibility.

2.2 Arithmetic and Logical Operators

• Arithmetic: +, -, *, / (element-wise), ^ (power), \ (left division in MATLAB).

• Relational: == (equal), ~= (MATLAB) / != (R) (not equal), >, <, >=, <=.

• Logical:

  • Element-wise: & (AND), | (OR), ~ (NOT) - operate on arrays element-by-element.

  • Short-circuit: && (AND), || (OR) - evaluate left to right, stop if outcome determined. Used in if conditions.

  • R Note: ! is used for NOT (e.g., !x).

2.3 Variables and Assignment

• Naming Rules: Must start with a letter, can contain letters, digits, underscores. Case-sensitive in both.

• Assignment Operators:

  • MATLAB: Only =. Example: x = 5;

  • R: = (common) or <- (traditional). Example: x <- 5 or x = 5.

• Inspecting/Clearing Variables:

  | Task | MATLAB | R | | :--- | :--- | :--- | | List variables | who (names only), whos (details) | ls() or objects() | | Clear variable(s) | clear x or clear all | rm(x) or rm(list=ls()) |

[!TIP] Critical Difference: MATLAB uses ~= for "not equal", R uses !=. MATLAB's && and || are scalar-only (for if), while & and | are array operators. In R, & and | are vectorized, && and || are scalar.

3.0 Core Data Structures & Objects

3.1 Vectors (1D Arrays)

3.2 Matrices & 2D Arrays

3.3 Lists and Cell Arrays (Heterogeneous)

3.4 Other Fundamental Types

• Scalars: Single values (numeric, integer, character/string, logical true/false or 1/0).

• R-Specific:

  • Factors: Categorical data. f = factor(c("low", "med", "high")). Stores levels efficiently.

  • Data Frames: Tabular data. Like a list of equal-length vectors (columns). df = data.frame(col1=1:3, col2=c("a","b","c")). Primary structure for statistical datasets. Subset with df$col1, df[,1], or df[["col1"]].

[!TIP] Key Distinction: MATLAB's fundamental array is the numeric matrix (all elements same type). R's fundamental structure is the vector (all elements same type). Data frames in R are lists of vectors.

4.0 Control Flow & Programmatic Logic

4.1 Conditional Statements

% MATLAB

if x > 0

    disp('Positive');

elseif x == 0

    disp('Zero');

else

    disp('Negative');

end

# R

if (x > 0) {

  print("Positive")

} else if (x == 0) {

  print("Zero")

} else {

  print("Negative")

}

[!CAUTION] Syntax Difference: MATLAB uses elseif (one word), R uses else if (two words). R requires parentheses around condition in if.

4.2 Loops

• for Loop (Iterating over values/indices):

  
  % MATLAB: Iterate over columns of matrix A
  
  for i = 1:size(A,2)
  
      col_sum = sum(A(:,i));
  
  end
  
  

  
  # R: Iterate over vector elements
  
  for (val in 1:5) {
  
      print(val^2)
  
  }
  
  

• while Loop (Condition-based):

  
  % MATLAB
  
  while abs(x - guess) > tolerance
  
      guess = guess / 2;
  
  end
  
  

  
  # R
  
  while (abs(x - guess) > tolerance) {
  
      guess <- guess / 2
  
  }
  
  

[!TIP] Golden Rule: Vectorize whenever possible. Both MATLAB and R are optimized for operations on entire vectors/matrices. Loops are often slower. Example: y = sin(x) is faster than for i=1:length(x), y(i)=sin(x(i)); end.

5.0 Basic Data Import/Export & Inspection

5.1 Reading Data from Files

5.2 Writing Data to Files

5.3 Inspecting Data Objects

6.0 Basic Visualization (Introduction)

6.1 Quick Plotting for Exploration

• MATLAB:

  
  x = 0:0.1:2*pi;
  
  y = sin(x);
  
  plot(x, y, 'b-'); % Blue solid line
  
  xlabel('x'); ylabel('sin(x)'); title('Sine Wave');
  
  scatter(x, y); % Scatter plot
  
  

• R (Base Graphics):

  
  x <- seq(0, 2*pi, by=0.1)
  
  y <- sin(x)
  
  plot(x, y, type='l', col='blue') # 'l' for line
  
  title("Sine Wave"); xlab("x"); ylab("sin(x)")
  
  hist(y) # Histogram
  
  boxplot(df$variable) # Boxplot
  
  

[!TIP] Philosophy: MATLAB's plot is function-based. R's base graphics are state-based (plot sets up a "device"). For publication-quality, R's ggplot2 (grammar of graphics) is dominant but has a steeper learning curve.

6.2 Saving Figures

Summary Comparison Table: Key Distinctions