Creating and Analyzing Data Frames in R

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 between the two sources and the average support for each candidate. This made it easier to rank candidates and observe overall trends.

Reflection

This module helped me better understand how data frames work in R and why they are one of the most important data structures for data analysis. Organizing data in a tabular format allows for easier comparisons, calculations, and interpretation of results. I feel more comfortable creating and modifying data frames after completing this assignment.

Conclusion

Overall, this assignment reinforced the importance of structured data and basic data manipulation in R. Learning how to create and analyze data frames will be useful in future statistical and data science projects.

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

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