In this project, I explored the relationship between various planetary attributes—solar radiation, atmospheric composition, and distance from the star—and the habitability of planets using a simulated dataset of 200 planets. I began by visualizing these attributes to identify potential patterns and relationships, followed by constructing a linear model to estimate their influence on habitability. Through this analysis, I aimed to understand how these factors interact and contribute to determining whether a planet might be habitable. Additionally, I assessed the model's residuals to ensure the errors were randomly distributed, reinforcing the reliability of the model. This work provided a foundational understanding of statistical learning methods in the context of planetary research, allowing for predictive insights and deeper exploration into the factors that influence planetary habitability.

The data.table package in R, I have gained a deeper appreciation for its efficiency and flexibility in handling large datasets. This experience has not only refined my data manipulation skills but also provided valuable insights into optimizing workflows for data analysis. Below, I outline my journey through the core functionalities of data.table and reflect on the practical applications and lessons learned. Creating and Subsetting data.table My exploration began with the basics of creating a data.table. Unlike the traditional data.frame, data.table offers enhanced performance and streamlined syntax. I constructed data.table structures using custom datasets, which allowed me to practice subsetting rows and columns with a variety of methods, including numeric indices, column names, and conditional logic. What stood out during this process was the difference in subsetting syntax between data.table, data.frame, and matrix. Understanding these nuances helped me appreciate the elegance and power of data.table in comparison to other data structures in R. This knowledge is crucial for ensuring code compatibility and leveraging the full potential of each data structure. Optimizing with Keys Next, I explored the use of keys in data.table to optimize data operations. By setting a key on a specific column, I could reorder the data.table, significantly speeding up search and join operations. Although this practice was essential in earlier versions of data.table, the introduction of newer features has rendered it less critical. Nonetheless, learning about keys provided historical context and highlighted the evolution of the package towards more user-friendly and efficient practices. Harnessing Secondary Indices One of the most exciting aspects of my journey was working with secondary indices. Unlike keys, secondary indices do not require sorting the entire table, which allows for quick subsetting on multiple columns without rekeying. This feature is particularly advantageous when dealing with large datasets that require frequent subsetting on different columns. I experimented with setindex to create secondary indices and used the on= syntax for efficient subsetting. This method not only streamlined my workflow but also demonstrated the power of data.table in reducing computational overhead and enhancing data exploration capabilities. Practical Applications To solidify my understanding, I applied these concepts to a personalized dataset containing product information, including columns for product, price, and in_stock status. Through practical application, I saw firsthand how data.table could simplify complex data operations and make them more intuitive. Setting keys and indices allowed me to perform fast subsetting and sorting, which would have been more cumbersome with traditional methods.

Dosen Pengampu Mata Kuliah: Dr. I Gede Nyoman Mindra Jaya, M.Si

UN World Data Forum Viz for Social Good