Evaluating and Debugging an R Function

Import Instructions Review

For this assignment, I downloaded and reviewed the How to Import Data to RStudio document. This document serves as a data import cheat sheet and focuses on using functions from the readr package to bring data into R. Common functions discussed include read_csv(), read_tsv(), and read_delim() for importing tabular data with different delimiters. The guide also explains how R automatically guesses column data types, how to manually specify column types when needed, and how to diagnose parsing issues using the problems() function. Additionally, the document introduces tibbles as enhanced data frames and provides an overview of tidyr functions used to reshape and clean data into a tidy format.

Result / Error Message

When running the function, R returns the following error:


 

Explanation of the Error

The function fails because the variable names inside the function do not match the function’s argument. Although the argument is named assignment2, the function body attempts to use assignment and someData, which are not defined within the function. Because R evaluates variables within the function’s local scope, this results in an error.

Corrected myMean Function

Below is a corrected version of the function that properly uses the argument passed into it: 

 

This confirms that the corrected function successfully calculates and returns the mean of the vector.



 

 

Comments

Post a Comment

Popular posts from this blog

Module 5: Determinant and Inverse of Matrices in R

Image
 This assignment practiced creating matrices in R and computing a determinant and inverse (when possible).   Step 1: Creating Matrices A and B   I created the matrices using matrix() :    Matrix A contains the numbers 1 to 100 arranged into 10 rows. Matrix B contains the numbers 1 to 1000 arranged into 10 rows.    Step 2: Checking the Dimensions To check the size of each matrix, I used: Step 3: Finding the Determinant of A I used the det() function to find the determinant of A: This result shows that matrix A is singular , which means it does not have an inverse.    Step 4: Finding the Inverse of A I attempted to find the inverse of A using:   Result: R returned an error saying that the system is computationally singular. This confirms that A does not have an inverse because its determinant is zero.   Step 5: Testing Matrix B Since B is not a square matrix, I tested:     Result: Both commands returned errors. This...
Read more

Creating and Analyzing Data Frames in R

Image
Intro In Module 3, I worked with data frames in R to organize and analyze polling data from a fictional 2016 presidential election dataset. The purpose of this exercise was to practice creating vectors, combining them into a data frame, and performing basic comparisons across variables using R. The dataset consists of three variables: Name – the candidate’s name ABC – polling results from the ABC source CBS – polling results from the CBS source Each row in the data frame represents one candidate, and each column represents a variable. This structure makes it easy to compare values across different polling sources.   Results and Observations After creating the data frame, I compared the results from ABC and CBS. Some candidates showed higher support in CBS polls, while others performed better in ABC polls. This highlights how different polling sources can produce varying results even when measuring the same data. I also added calculated columns to examine the difference...
Read more

Module 10: Friedman R Package Proposal

     For this assignment, I created the initial structure for an R package called Friedman . The goal of this project was to learn how R packages are built and how they organize code, documentation, and metadata in a standardized way. Before this assignment, I didn’t fully understand how packages worked behind the scenes, so this helped me see how everything is connected. Purpose and Scope The purpose of the Friedman package is to make it easier to reuse code for data analysis and data mining tasks. In many assignments, I find myself writing similar code over and over again, especially for summarizing data or preparing results. This package is designed to group those repeated tasks into simple, reusable functions. The intended users are students and beginner R users who want a basic toolkit to help with analysis. Instead of rewriting code each time, they can use functions from the package to save time and stay organized. Key Functions Right now, the package includes one m...
Read more