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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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Empowering RNA-Seq Analysis: Building and Utilizing Your Custom R Package

  Introduction In the vast realm of bioinformatics, understanding gene expression is crucial for unraveling the mysteries of biology and disease. One powerful tool in this pursuit is the R programming language, enriched by packages like DESeq2 for conducting differential gene expression analysis. However, navigating these tools can be complex for beginners. That's where our custom package, MyDataAnalytics, steps in to streamline the process. In this blog post, we'll walk you through the steps of conducting differential gene expression analysis using MyDataAnalytics, simplifying a task that can often seem daunting. Part 1: Creating Your Custom R Package Creating a custom R package involves several steps, from defining functions to structuring directories and documentation. Let's outline the process: Define Functions : Begin by defining functions that encapsulate specific tasks related to RNA-Seq analysis, such as creating DESeqDataSet objects, running DESeq analysis, filteri...
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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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