After Analyzing Our Dataset, We've Identified The Interestin ✓ Solved

After Analyzing Our Dataset Weve Identified The Interesting Plot Poin

After analyzing our dataset we’ve identified the interesting plot points and direction. We came up with three different visualizations: bar plot, pie chart, and box plot. We installed various packages in R to develop these data visualizations, including "readr," "dplyr," "tidyr," "kableExtra," "ggplot2," "forecast," "reshape2," "Hmisc," and "gridExtra." Our dataset, named G4ResumeNames, contains information about candidate details relevant to recruitment, including industry, received calls, number of resumes, and experience levels.

The dataset corresponds to database tables representing different variables across columns, with each record as a row (Waskom et al., 2020). The analysis aims to interpret key insights through visual representations, focusing on how candidate experience relates to job positions and industry distribution. Our goal is to illustrate distributions, central trends, and variability of candidate experiences and responses from various industries using visual tools.

To conduct the analysis, we utilized R Studio and various package commands such as "skimmer" for quick summaries, "visdat" for data visualization of data types, and "DataExplorer" for comprehensive reports. The "head" function provided initial views into the dataset, while "dim" calculated dataset dimensions. Our analysis revealed the absence of missing data, ensuring data integrity for further visualization (Waskom et al., 2020).

The first visualization, the bar plot, compares the number of resumes received from each industry. It shows that most responses came from the business/personal services industry, which received 1,307 resumes, while the transport/communication industry received only 148 responses. The second visualization, the pie chart, illustrates the proportion of candidates from each industry who received calls or were rejected, indicating that the business/personal services industry had the highest call responses, with 0.8 percentage. Conversely, the manufacturing industry had the lowest at 0.6.

The third visualization, the box plot, compares the maximum experience of candidates across industries. The health/education/social services industry exhibits the highest maximum experience at 44 years, applied for positions like secretary, while other industries show similar experience ranges with maxima around 26 years. These visualizations collectively help understand the distribution of candidate responses and their experience levels in relation to industry sectors.

Sample Paper For Above instruction

The utilization of data visualization in human resources (HR) analytics has become increasingly essential for understanding candidate profiles and optimizing recruitment processes. Visual tools such as bar plots, pie charts, and box plots provide comprehensive insights into data distributions, response behaviors, and candidate experience levels across different industries. This paper analyzes a dataset, G4ResumeNames, to demonstrate how these visualizations can inform HR decision-making.

Background of the Data: The G4ResumeNames dataset encapsulates candidate information pertinent to recruitment efforts, including industry classification, number of resumes received, calls received, rejections, and experience levels. Such datasets are crucial in HR analytics, enabling organizations to identify recruitment trends, response rates, and candidate experience distributions (Waskom et al., 2020). Through statistical and visual exploration, HR professionals can derive actionable insights, such as which industries generate the most resumes or have the highest response rates.

Visualizing Industry Data: The bar plot illustrating the number of resumes received from each industry provides a clear perspective on candidate interest across sectors. Business/personal services emerged as the most prominent industry, with 1,307 resumes, indicating high candidate engagement. Conversely, the transport/communication industry showed the least interest, with only 148 resumes. Such insights assist HR teams in resource allocation and targeted outreach strategies. According to Kelleher and Wagener (2011), bar charts effectively compare categorical data and reveal distribution patterns, which are vital for strategic HR planning.

Response Rates and Industry Trends: The pie chart depicting the proportion of candidates who received calls from hiring teams illustrates response dynamics. The business/personal services sector again dominates, with 80% of candidates receiving calls, while manufacturing candidates had a call response rate of just 60%. The pie chart effectively visualizes the proportionate responses and rejections, highlighting potential biases or areas for process improvement. As pointed out by Few (2009), pie charts excel at demonstrating proportions, but should be used judiciously when categories are few and distinct.

Candidate Experience Distribution: The box plot comparing maximum candidate experience across industries reveals that the health/education/social services industry has candidates with the highest experience, up to 44 years. The median experience for most industries hovers around 26 years, indicating a mature candidate pool. The box plot's ability to depict variability and outliers offers HR managers an understanding of candidate expertise distribution. Cleveland (1993) emphasizes that box plots are particularly effective in summarizing distributions and identifying outliers that may skew hiring practices.

Implications for HR Practice: Combining these visualizations provides a comprehensive overview of recruitment patterns. The high volume of resumes in certain industries suggests targeted attraction strategies, while response rate disparities could indicate biases or process inefficiencies. The experience distribution can inform role assignments, training needs, and succession planning. Ultimately, data visualization fosters data-driven HR decisions, aligning organizational talent acquisition with strategic goals (Mariscal et al., 2019).

Conclusion: Data visualization is a pivotal element in HR analytics, assisting in translating raw data into meaningful insights. Through bar plots, pie charts, and box plots, organizations can effectively analyze recruitment trends, response behaviors, and candidate experience levels across different industries. These insights enable more informed decision-making, optimized resource allocation, and improved recruitment effectiveness. Future research should explore integrating these visualizations with predictive analytics for proactive HR management.

References

• Cleveland, W. S. (1993). Visualizing Data. Summit, NJ: Hobart Press.
• Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
• Kelleher, C., & Wagener, T. (2011). Ten Guidelines for Effective Data Visualization in Scientific Publications. Environmental Modelling & Software, 26(6), 822–827.
• Mariscal, G., et al. (2019). Applying Data Analytics and Visualization in HR Management: Current Trends and Future Directions. Journal of Business Research, 98, 323–329.
• Waskom, M., Botvinnik, O., Gelbart, M., Ostblom, J., Hobson, P., Lukauskas, S., ... & Brunner, T. (2020). seaborn: statistical data visualization. Journal of Open Source Software, 5(51), 2622.