Data Aggregation

Aggregating data is essential for summarizing information. The dplyr package provides powerful functions for data aggregation:

1. group_by() and summarize(): Group data by one or more variables and calculate summary statistics.
2. count(): Count the frequency of unique values in a column.
3. pivot_longer() and pivot_wider(): Reshape data from wide to long and vice versa.

Organizing data is a fundamental step in data analysis and manipulation, and R Programming Language provides a powerful set of tools and techniques to help you efficiently structure and manage your data. Whether you’re working with small datasets or massive datasets, understanding how to organize your data effectively is crucial for data analysis, visualization, and modeling. In this article, we will explore various methods and holding for organizing data.