The Smart Chef

Aggregating data.table by values

Aggregating a data.table in R can be useful when you want to summarize your data and reduce it to a more manageable size.

By grouping your data based on one or more columns and aggregating it using functions like sum, mean, median, etc., you can get a sense of the overall trends and patterns in your data.

This can help you identify relationships and make decisions based on the aggregated results.

Here's a summary of some common use cases for data.table aggregations:

When using a data.table in R, you can aggregate by your selected variable(s) utilizing the syntax below.

If you are getting an error about an "unused argument" you may want to verify that your data is stored in a data.table!

Aggregate data.table by group of variable(s)

  

## Create a data.table
dt <- data.table(col1 = c("A", "B", "A", "C", "B"),
                 col2 = c(1, 2, 1, 2, 3),
                 col3 = c("x", "y", "x", "y", "z"))

## Return the count of values and call it "visits".  Group by "col1" and "col2".
dt[, .(visits = .N), by = c("col1", "col2")]

If you want a sum instead of a count, you can simply replace teh .N with sum(value). Here are some other values you could return to help describe your grouped data: