HTML (HyperText Markup Language) and JSON (JavaScript Object Notation) are important technologies for organizing, presenting, and exchanging information on the web. HTML provides the structure for displaying webpage content, while JSON organizes and exchanges data efficiently between applications. Both play important roles in data science and modern research.
HTML was introduced in the early 1990s to support the development of the World Wide Web, while JSON emerged in the early 2000s as a lightweight format for exchanging data. Today, both technologies are widely used in web applications, data management, and research, making them valuable tools for understanding how digital information is collected and processed.
This week’s introduction to HTML and JSON will help me better understand how digital information is structured, collected, and transformed for statistical analysis. By exploring R packages such as jsonlite, dplyr, and tidyr, I hope to strengthen my ability to import, clean, organize, and prepare data for further analysis. These skills will also support my ongoing academic research and help me connect programming concepts with statistical methods.
My approach is to begin by understanding the basic structures and differences between HTML and JSON, followed by practical applications using R. I will explore how HTML organizes webpage content and how JSON represents structured data through key-value pairs and arrays. Using R packages such as rvest and jsonlite, I will practice extracting and importing data, then apply dplyr and tidyr to clean, organize, and transform datasets. I will focus on small, manageable examples before applying these techniques to larger datasets. This step-by-step approach will help me connect programming concepts to statistical analysis and develop greater confidence in my data science skills.
Working with HTML and JSON presents several challenges, including understanding complex data structures, handling missing values, managing inconsistent formats, and resolving errors during data import and transformation. Overcoming these difficulties requires patience, practice, and a stronger programming foundation.
I still struggle with some of my previous assignments, mainly because I am still developing my programming skills. Although I have experience in statistical analysis, learning to apply programming languages and tools such as R, HTML, and JSON is challenging but valuable. I sometimes struggle with coding errors, importing datasets, and transforming data into appropriate formats. However, each challenge provides an opportunity to learn and improve. Through consistent practice, troubleshooting, and applying what I learn to my research, I am gradually becoming more comfortable with programming. I recognize that developing these skills takes time, but I remain motivated and committed to making progress. I am not there yet, but I am getting there.```