Mastering R: 100 Different Ways for Grouping and Aggregating Data

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Grouping and aggregating data in R involves organizing data into subsets based on one or more categorical variables (groups) and then applying summary functions to compute aggregate statistics within each group. This process is crucial for gaining insights into the distribution of data, understanding patterns within different categories, and performing analyses on specific subsets.

1. Base R with aggregate:

The aggregate function is a base R function that can be used to apply a function to a specified column, grouping by one or more factors.

aggregate(data$column, by=list(data$grouping_column), FUN=mean)

2. Base R with tapply:

tapply is a base R function that applies a function over subsets defined by a set of factors.

tapply(data$column, data$grouping_column, FUN=mean)

3. Base R with by:

The by function allows you to apply a function to data frame subsets defined by one or more factors.

by(data$column, data$grouping_column, mean)

4. Base R with split and lapply:

• Using split to create a list of data frames and then…