Week 2: Fixing the myMean Function and Understanding Flexibility in Function Inputs

Week 2: Fixing the `myMean` Function and Understanding Flexibility in Function Inputs

Introduction

This week, my assignment involved evaluating and debugging a custom function called myMean, which calculates the mean (average) of a dataset. While the concept behind the function was simple, there were a couple of key issues with the inputs that prevented it from working correctly. Here's a walkthrough of the problem, how I approached it, and the final solution.

The Problem

The original function I was asked to evaluate was defined as follows:

The dataset provided to test this function was:

At first glance, the function seemed like it should calculate the sum of the vector and divide it by the number of elements. However, there were two key issues:

Incorrect Input in sum(): The function was using sum(assignment), which referenced a variable not passed into the function.
Incorrect Input in length(): The function referenced length(someData), which again was not defined within the function.

The Solution

To fix this, I simply corrected the inputs to ensure the function would work with whatever data is passed in. I modified the function as follows:

This new version of the function allows flexibility in choosing any dataset when calling the function, making it more reusable. By changing the parameter to x, the function works with any input dataset passed to it, without hard-coding a specific variable name.

Verifying the Result

To ensure my solution was correct, I compared the result from my myMean function with R's built-in mean() function:

Both functions produced the same result, confirming that my fix worked correctly.

Progress in Learning R

In addition to fixing the myMean function, I've also made progress in my R programming journey, especially with data manipulation using the dplyr package. This week, I learned and applied some important functions like:

mutate(): To create or transform variables.
select(): To choose specific columns.
filter(): To subset the rows based on conditions.
summarize(): To aggregate and summarize data.

Using these functions, I analyzed data for a research project I'm working on. These tools made it easier to transform and interpret my dataset efficiently.

You can find the code and details of this analysis on my GitHub repository. I’m also attaching this week’s notes for reference, which include the functions I learned and applied.

Conclusion

This assignment was a great exercise in understanding how R functions handle inputs and the importance of flexibility when defining parameters. By ensuring the function references the correct input, it can work with any dataset passed to it. Using R's built-in mean() function was a simple but effective way to verify that my custom function was performing as expected.

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