Module 11: Debugging and Defensive Programming in R

GitHub Repository: 
AustinTCurtis/r-programming-assignments

We’re working with a function designed to flag rows in a numeric matrix that are outliers in every column according to the Tukey rule. However, the original code contains a deliberate bug, specifically in the line that uses the && operator.

Original (Buggy) Code:

# Helper function for Tukey's rule
tukey.outlier <- function(v) {
  qs  <- stats::quantile(v, c(0.25, 0.75), na.rm = TRUE)
  iqr <- diff(qs)
  (v < qs[1] - 1.5 * iqr) | (v > qs[2] + 1.5 * iqr)
}

# Original (buggy) function
tukey_multiple <- function(x) {
  outliers <- array(TRUE, dim = dim(x))
  for (j in 1:ncol(x)) {
    outliers[, j] <- outliers[, j] && tukey.outlier(x[, j])  # <-- BUG: '&&'
  }
  outlier.vec <- vector("logical", length = nrow(x))
  for (i in 1:nrow(x)) {
    outlier.vec[i] <- all(outliers[i, ])
  }
  return(outlier.vec)
}

Reproducing the Error:

set.seed(123)
test_mat <- matrix(rnorm(50), nrow = 10)
tukey_multiple(test_mat)

Error in outliers[, j] && tukey.outlier(x[, j]) : 
  'length = 10' in coercion to 'logical (1)'

Diagnosing the Bug:
- The error occurs because && only evaluates the first element of each logical vector and returns a single TRUE/FALSE.
- Since outliers [, j] is a vector of length 10, R attempts to assign a length-1 result to a longer object, triggering a mismatch error.
- Even if it didn’t throw an error, the logic would still be incorrect each column would collapse into one Boolean value.

Corrected Function:

corrected_tukey <- function(x) {
  # Defensive checks
  if (!is.matrix(x)) stop("`x` must be a matrix.")
  if (!is.numeric(x)) stop("`x` must be a numeric matrix (no character/factor columns).")
  if (ncol(x) == 0L || nrow(x) == 0L) stop("`x` must have at least 1 row and 1 column.")
  
  outliers <- array(TRUE, dim = dim(x))
  for (j in seq_len(ncol(x))) {
    outliers[, j] <- outliers[, j] & tukey.outlier(x[, j])  # <-- FIX: '&'
  }
  outlier.vec <- logical(nrow(x))
  for (i in seq_len(nrow(x))) {
    outlier.vec[i] <- all(outliers[i, ])
  }
  outlier.vec
}

Validating the Fix:

set.seed(123)
test_mat <- matrix(rnorm(50), nrow = 10)
corrected_tukey(test_mat)

Output:

[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE


Comments

Post a Comment

Popular posts from this blog

Module #4 Visualizing and Interpreting Hospital Patient Data

Image
Github Repository:   r-programming-assignments/Assignment_04.R at main · AustinTCurtis/r-programming-assignments CODE FROM R Studio: > # Define numeric vectors > Frequency <- c(0.6, 0.3, 0.4, 0.4, 0.2, 0.6, 0.3, 0.4, 0.9, 0.2) > BloodPressure <- c(103, 87, 32, 42, 59, 109, 78, 205, 135, 176) > > # Convert categorical strings to numeric codes > # First assessment: bad=1, good=0 > FirstAssess <- c(1, 1, 1, 1, 0, 0, 0, 0, NA, 1) > > # Second assessment: low=0, high=1 > SecondAssess <- c(0, 0, 1, 1, 0, 0, 1, 1, 1, 1) > > # Final decision: low=0, high=1 > FinalDecision <- c(0, 1, 0, 1, 0, 1, 0, 1, 1, 1) > > # Create data frame > df_hosp <- data.frame( + Frequency, BloodPressure, FirstAssess, + SecondAssess, FinalDecision, + stringsAsFactors = FALSE + ) > > # Inspect data > summary(df_hosp) Frequency BloodPressure FirstAssess SecondAssess FinalDecision Min. :0.20 Min. : 32.0...
Read more

Module # 2 Assignment Importing Data and Function Evaluation in R

Image
  Evaluate  myMean  Function Use the vector: assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) Consider this function: myMean <- function(assignment2) { return(sum(assignment) / length(someData)) } In RStudio, run  myMean(assignment2)  and record the output or error. After running the code in R Studio this is the error that occurred: > assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) > myMean <- function(assignment2) { + return(sum(assignment) / length(someData)) + } > assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22) > myMean <- function(assignment2) { + return(sum(assignment) / length(someData)) + } > myMean(assignment2) Show Traceback Rerun with Debug Error in myMean(assignment2) : object 'assignment' not found The function fails because of inconsistent variable names : You pass in "assignment2", but inside the function you try to use "assignment" and "someData...
Read more

Module 6: Matrix Operations and Construction

GitHub Repository:  r-programming-assignments/Assignment_06.R at main · AustinTCurtis/r-programming-assignments Part I — Matrix Addition & Subtraction In this section, I’m creating two matrices A and Busing the matrix() function. Both matrices have 2 columns. After defining them, I perform matrix addition (A+B)  and matrix subtraction (A-B)  These operations are performed elementwise, meaning R adds or subtracts each corresponding element from the same position in both matrices. R Code: # Define two 2x2 matrices A and B A <- matrix(c(2, 0, 1, 3), ncol = 2) B <- matrix(c(5, 2, 4, -1), ncol = 2) # Display A and B A B # Matrix addition A_plus_B <- A + B A_plus_B # Matrix subtraction A_minus_B <- A - B A_minus_B Part II — Creating a Diagonal Matrix Here, I use the diag() function to create a 4×4 diagonal matrix called D. A diagonal matrix has non-zero values only along its main diagonal (from top left to bottom right), with zeros everywhere else. I specify th...
Read more