Understanding the Regroup Function in R and Its Deprecation

The regroup function, a part of the dplyr package in R, has been deprecated in favor of its successor, group_by_. This change reflects the evolving nature of data manipulation packages in R, aimed at providing more efficient and robust methods for grouping data. In this article, we’ll delve into what the regroup function is used for, how it compares to group_by_, and discuss the implications of its deprecation.

What is Regroup Used For?

The regroup function is primarily used in conjunction with other dplyr verbs (such as mutate, filter, etc.) to group a data frame by one or more variables. When used with mutate, it allows you to specify multiple columns to be used for grouping. However, its syntax can sometimes lead to confusion due to its unique usage of the term “regroup” in this context.

For example:

# Grouping a dataframe
df %>% 
  filter(age > 18) %>% # Filter rows where age is greater than 18
    regroup(list(name, country)), # Regroup by name and country
    mutate(score = score * 10)

Here, regroup groups the data frame df by two variables, ’name’ and ‘country’, before applying the mutate verb to calculate a new column named “score”.

Understanding Group_by_

The recommended replacement for regroup is group_by_. While it shares similarities with its predecessor in terms of grouping functionality, group_by_ has undergone improvements that make it more efficient and easier to use.

# Example using group_by_
df %>% 
  filter(age > 18) %>% # Filter rows where age is greater than 18
    group_by_(name, country), # Group by name and country
    mutate(score = score * 10)

Comparison of Regroup and Group_by_

Both regroup and group_by_ are used to group data frames. However, the syntax and behavior might differ slightly:

• Syntax: The primary difference lies in how they’re invoked. While regroup uses a unique term for its purpose, group_by_ follows the more conventional use of grouping by variables.

Implications of Deprecation

The deprecation of regroup indicates that its developers believe it’s time to adopt group_by_. This change is part of an ongoing effort to simplify and improve dplyr functionality. The shift towards group_by_ offers several advantages, including:

• Improved Efficiency: Modern grouping operations in dplyr are optimized for performance, making them faster than their predecessor.

• Simplified Usage: The use of group_by_ is more straightforward than its predecessor, reducing confusion among users.

Writing a Custom Function

To maintain consistency with the evolving nature of R packages, it’s advisable to update your code to utilize group_by_ instead of regroup. Here’s an example:

# Update the custom function GrouperFunc to use group_by_
GrouperFunc <- function(df, ...) df %>% 
  group_by_(list(...)) # Use group_by_ for grouping

AirPlot <- function(departure, arrival, groupon){
    # Departure and arrival can be cities that are being entered.
    departCode <- AirportCode(departure)
    arriveCode <- AirportCode(arrival) # Call our earlier AirportCode function to get the airport ID 

    tempDB <- subset(flights, ORIGIN_AIRPORT_ID == departCode & DEST_AIRPORT_ID == arriveCode) # Only get flights for our flight path
    grouped <- GrouperFunc(tempDB, groupon) # Use group_by_ for grouping
    summaryDF <- summarize(grouped, mean = mean(ARR_DELAY)) # Call summarize from our grouped data frame

    finalBarPlot <- ggplot(summaryDF, aes_string(x=groupon, y='mean')) +
      geom_bar(color="black", width = 0.2, stat = 'identity') +
      guides(fill=FALSE)+
      xlab(groupon) + 
      ylab('Average Delay (minutes)')+
      ggtitle((paste('Flights from', departure, 'to', arrival)))

    return(finalBarPlot)
}
AirPlot('Dallas', 'Chicago', 'UNIQUE_CARRIER')

This updated version adheres to the latest best practices in dplyr package usage.

Last modified on 2024-03-01