Mastering dplyr: How to Aggregate String Variables with summarise and across

Learn how to effectively use `dplyr` functions to aggregate string variables in R with this detailed guide on `summarise` and `across`.
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Mastering dplyr: How to Aggregate String Variables with summarise and across

In data manipulation using R, especially with dplyr, you may encounter a scenario where you need to aggregate string variables while also summing up numerical values. For example, you might have a data frame that contains recurring identifiers for different parties and associated values under certain columns, and you wish to combine those strings while also summarising numerical counts. This guide will walk you through how to effectively use the summarise and across functions in dplyr to achieve this data management task, especially after the deprecation of older functions like summarise_at and funs.

The Problem: Aggregating String Variables

Suppose you have a data frame df_input that consists of three columns: id, party, and winner. Each id may appear multiple times associated with different party names or winner values. The goal is to create a new data frame df_output that correctly aggregates party names into a single string for each unique id while summing the winner values. Previously, you might have used summarise_at and funs, but these approaches are now outdated.

Example Input Data

Here is the sample input data for reference:

[[See Video to Reveal this Text or Code Snippet]]

Expected Output Data

You want your output data frame df_output to look like this:

idpartywinner_sum1A12B13C14D,E25F,G,H36I17J18K19L110M1The Solution: Using summarise and across

Step 1: Basic Aggregation without across

First, you can aggregate without using across, since you’re performing separate operations on different columns. Here's how:

[[See Video to Reveal this Text or Code Snippet]]

This approach creates a new df_output that combines the party strings and computes the winner_sum per unique id.

Step 2: Using across for Multiple Variable Types

If you have multiple character and numeric columns, you can effectively loop over them by using across in a single summarise call. Here’s how:

[[See Video to Reveal this Text or Code Snippet]]

This snippet sums up all numeric columns (like winner) and concatenates string columns (like party) while ensuring that you get a clean output.

Additional Considerations

Selecting Columns by Prefix: If you have many columns that share a common prefix (e.g., all party-related columns), you can use starts_with("party") within across.

Different Column Names: If your columns have varied names, you can specify them directly in a vector, such as across(c(party, othercol), ...).

For example:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Aggregating string variables while summing numerical counts does not have to be complicated once you understand the functionality of dplyr functions, particularly summarise and across. By following the examples and steps provided, you can effectively manage your data to achieve the desired results. Happy coding!