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Mastering Debugging: A Step-by-Step Guide to Fixing Code Errors

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  Debugging is a crucial skill for programmers as it assists them in detecting and fixing errors or bugs in their code. In this detailed guide, we will take you through an example of debugging a code snippet and emphasize the essential tactics and tools needed to master the art of debugging. Understanding the Deliberate Bug Let's begin with the code snippet that contains a deliberate bug, which results in an error message during execution. Here's the faulty code, When this code is executed, it triggers an error message due to the unexpected placement of return(outlier.vec) within the for loop. Step 1: Identifying the Error To identify the error, we can use the traceback function, which prints the list of functions called before the error occurred. Running traceback("tukey_multiple") reveals the location of the error just before return(outlier.vec) . Step 2: Using Debugging Tools After locating the error, we implement the debug function to step through the code an...
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My intro to R programming

 Hey everyone! I just tested a function for calculating the mean using R studio, and I'm excited to share the results with you! Please refer to my   GitHub repository  for my work. After applying the function to my dataset (assignment), the mean value is 19.25.  I followed the instructions given by my professor Dr. Friedman and referred the book "The Art of R Programming" by Norman Matloff. Explanation: This function works by summing up all the values in the given dataset (sum(assignment)) and then dividing the sum by the total number of elements in the dataset (length(assignment)) , it calculates the average value of the dataset. The formula for mean is: Mean = Sum of Values / No. of Values In this case, the function successfully computes the mean of the 'assignment' dataset. Feel free to try it out with your own datasets or explore Matloff's book!                                 ...
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A Visual Analysis of Patient Data : Blood Pressure and Medical Assessments

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Hello all ✋   In this blog post, we'll be looking into a dataset obtained from a local hospital. This dataset contains blood pressure readings, evaluations by both general and external doctors, and the final decision regarding immediate care for patients. Our aim is to visually analyze the connection between blood pressure and medical assessments. To accomplish this, we'll be using side-by-side boxplots and histograms to identify patterns and trends within the data. To begin our analysis, we need to prepare the data and generate visualizations as in the URL given below. It will create side-by-side boxplots for general and external doctor assessments, along with a histogram showing the overall distribution of blood pressure values. The x-axis represents the assessment (bad or good), while the y-axis represents the blood pressure values.   Patient Health Assessment The boxplot displays: First Assessment (Bad vs. Good): ...
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