Week 3 Blog: Playing with DataFrames—Correcting Errors and Analyzing Fictional Poll Results

Introduction

This week's assignment involved working with a fictional dataset representing poll results from the 2016 U.S. Presidential election. The dataset included seven candidates and their results from two polling sources: ABC and CBS. However, before diving into the analysis, I encountered a few errors in the initial dataset that I had to correct.

Initial Errors and Corrections

The data I was provided had some formatting issues that needed fixing before I could proceed. For example:

Quotation Marks: Some of the names had incorrect quotation marks (“ ” instead of "), which R couldn’t recognize. I fixed this by replacing all smart quotes with regular quotation marks (").
Commas: The ABC and CBS poll results had some missing commas. Without proper separation, R would misinterpret the values. I inserted the missing commas to correctly format the vectors.

Here’s how the corrected data looked:

Analyzing the Data

After cleaning the data, I created a data frame to store the candidate names and their respective poll results from ABC and CBS:

Next, I wrote code to determine the winners of each poll by identifying the candidates with the highest number of votes. Initially, I wrote a more manual approach, but after improving it with some help, I made the code more efficient.

Here’s the code I used to identify the winners of both polls:

Results

Using the corrected data and the improved code, I found the following results:

Donald won both polls decisively based on the fictional data provided.

Conclusion

This assignment helped me improve my data cleaning skills and practice handling issues in R such as formatting errors and missing separators. It also reinforced how to efficiently analyze data using functions like which.max() to determine the top performers in a dataset.

You can view the full code and project on my GitHub repository - (Named -assignment3).

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