A Statistical Analysis Using R and RShiny

Introduction

In this article, I delve into an insightful project aimed at understanding common symptoms experienced after immunization. Leveraging the power of R language, I conducted a comprehensive statistical analysis, providing valuable insights into the distribution of symptoms. This project served as a final exam for my Komputasi Statistik course on campus, allowing me to apply my knowledge of data analysis, visualization, and inferential statistics. Well, let's explore the journey of my research and the intriguing results I discovered.

Descriptive Analysis: Unveiling Symptom Distribution

To kickstart my project, I performed a descriptive analysis to gain a clear picture of the common symptoms reported post-immunization. Employing R's wordcloud feature, I visualized the frequency of symptoms based on user-reported data. The captivating wordclouds painted a vivid representation of the most prevalent symptoms, providing a quick overview of the dataset. Additionally, I employed categorization techniques to group similar symptoms, simplifying their interpretation. This approach allowed me to discern patterns and trends, revealing clusters of symptoms that emerged more frequently after immunization.

Inferential Analysis: Exploring Statistical Significance

In the second phase of my analysis, I employed inferential statistics to delve deeper into the data. Utilizing the test of different proportions, I examined whether certain symptoms exhibited a statistically significant increase after immunization compared to a control group. The inferential analysis provided me with critical insights into the correlation between immunization and specific symptoms, shedding light on potential adverse reactions. The results presented me with evidence-backed conclusions, strengthening the validity of my findings.

RShiny Dashboard: A Dynamic Platform for Visualization

To enhance the accessibility of my research, I created an interactive RShiny dashboard. This dynamic platform featured a collection of engaging visualizations, with the interactive Plotly charts taking center stage. The RShiny dashboard provided users with a seamless experience, allowing them to interact with the Plotly charts, zoom, and extract specific information with ease.

Conclusion

My project on Common Symptoms After Immunization was a remarkable journey of learning and exploration in the realm of statistical analysis using R. Through descriptive analysis with wordclouds and categorization, I obtained a comprehensive overview of symptom distribution. The inferential analysis, powered by the test of different proportions, revealed valuable statistical correlations, enriching my understanding of post-immunization reactions.

Moreover, the development of the RShiny dashboard amplified the impact of my research, making it accessible to a broader audience. Overall, this project has been a gratifying experience, allowing me to apply statistical concepts to real-world data and contribute to the field of immunization research. I hope my findings will serve as a valuable reference for future investigations, fostering greater awareness and understanding of post-immunization symptomatology.