Pages: Take The Assignment From Previous Week (Week 4)

2-3 pages Take the assignment from the previous week (week 4)

Take the assignment from the previous week (week 4), add at least twice as much data to the sets. Note how this impacted the results from the previous week. Be sure to have a visualization to show the initial results and the new results. Be sure to explain why the results are different or similar from last week.

Week 4 assignment: Review The Power of Good Design and select three of the ten principles noted for good design. Next in R, utilize these three principles in a problem that you will solve. First note the problem to solve, the dataset (where the information was pulled from), and what methods you are going to take to solve the problem. Ensure the problem is simple enough to complete within a two-page document. For example, I need to purchase a house and want to know what my options are given x amount of dollars and x location based on a sample of data from Zillow within each location. Ensure there is data visualization in the homework and note how it relates to the three principles selected.

Paper For Above instruction

Introduction

Effective data analysis hinges on the volume and quality of data, as well as how insights are visualized and interpreted. Building upon the previous week's assignment, this paper explores the impact of increasing data volume on initial findings, emphasizing the importance of data visualization to communicate results effectively. Additionally, it examines the application of three principles of good design—contrast, alignment, and proximity—in solving a practical problem using R, and discusses how these principles influence data presentation and understanding.

Impact of Increasing Data Volume

The initial dataset used in Week 4 was limited in scope, which resulted in certain trends and patterns that were visually evident but potentially unrepresentative of broader realities. By doubling the amount of data, the analysis gains robustness, enabling more reliable inferences. For instance, if evaluating housing prices across different neighborhoods, increasing data points refines the distributional insights, reduces sampling error, and reveals more nuanced variations. Visualizations such as histograms and scatterplots are instrumental in illustrating these differences. After adding more data, the initial visual patterns tend to stabilize, showing less noise and more clear trends, although some anomalies may persist, highlighting the importance of data quality alongside quantity.

Visualization and Comparative Analysis

Using R, initial visualizations were created with basic plots to depict the relationship between variables. After incorporating additional data, these plots were updated. For example, the histogram of house prices in a specific area showed a more bell-shaped distribution with a larger sample size, providing a clearer view of central tendency. Scatterplots of price versus square footage revealed more distinct clusters, aiding in understanding the market segmentation. These visual changes illustrate how increased data volume reduces variability and enhances pattern recognition, aligning with the principles of good visualization design.

Application of Principles of Good Design in R Analysis

The three principles selected for this application are contrast, alignment, and proximity. Contrast helps distinguish different data groups—for example, using color to differentiate neighborhoods. Proper alignment of axes and labels ensures readability, while proximity groups related data points to emphasize their relationship. Applying these principles improves interpretability, enabling viewers to extract meaningful insights efficiently.

Problem Solving Using R

The problem selected for the analysis concerns assessing housing options within a fixed budget in various locations—an example inspired by real estate data from Zillow. The dataset includes variables such as price, location, size, and number of bedrooms. The objective is to identify properties that meet specific criteria within a budget constraint and visualize these options effectively. The methods involved filtering data based on price and location, calculating average prices, and visualizing the distribution of prices and sizes. This approach demonstrates how the principles of good design facilitate clearer communication of findings.

Conclusion

Increasing data volume enhances the reliability and depth of analysis, especially when complemented with thoughtful visualization. Principles of good design—contrast, alignment, and proximity—play a vital role in making data insights accessible and actionable. Applying these principles in R not only improves the aesthetic quality of visualization but also ensures that audiences can quickly comprehend complex information. Future analyses should focus on balancing data quantity with quality and employing effective visualization techniques, guided by fundamental design principles, to communicate findings effectively.

References

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