Article: Practical advantages of R language in Web Analytics and Data Science
This article provides a comprehensive introduction to leveraging Google Analytics as a powerful data source for data science projects. It explores the fundamentals of Google Analytics, including its implementation methods, limitations such as data sampling at high volumes, and the benefits of upgrading to GA360—while highlighting cost-effective alternatives. The piece delves into the R programming language and key libraries like RGA, emphasizing how R enables unsampled data retrieval via API calls, query partitioning, and seamless integration with tools like Power BI. Practical challenges in data collection are addressed, showcasing R’s role in circumventing sampling issues, merging custom reports, and preparing datasets for advanced analysis. Ideal for data scientists and marketers, this guide sets the stage for future explorations in exploratory data analysis (EDA) and predictive modeling using Tidyverse and Tidypredict in R.
