The Great R Package Confusion: Why summarize Doesn’t Work with Group By in dplyr

In the world of data analysis, there are few things more frustrating than a seemingly simple operation that doesn’t work as expected. In this post, we’ll delve into the intricacies of loading packages and using functions from both plyr and dplyr, two popular R libraries for data manipulation.

Background: The Evolution of Data Manipulation in R

The landscape of data analysis in R has undergone significant changes over the years. In the early days, data analysts relied heavily on the base R package, which provided a range of built-in functions for data manipulation. However, as the popularity of R grew, so did the need for more specialized libraries.

Enter dplyr, a relatively new library that provides an efficient and flexible way to manipulate data. Launched in 2012, dplyr quickly gained popularity among data analysts due to its ease of use and performance benefits. However, as dplyr became widely adopted, another library, plyr, began to make a comeback.

The Case for Both dplyr and plyr

Both dplyr and plyr provide functions for data manipulation, but they differ in their approach. dplyr uses the pipe operator (%>%) to chain together multiple operations, while plyr relies on traditional function calls.

One of the key differences between the two libraries is their approach to grouping data. In dplyr, grouping is achieved using the group_by() function, which returns a grouped data frame that can be manipulated further. In contrast, plyr uses the .data argument within its functions to specify the data being operated on.

The Problem: Loading plyr After dplyr

When loading packages in R, it’s essential to consider the order in which they are loaded. In this case, we have a common problem where dplyr is loaded after plyr. This can lead to unexpected behavior, as mentioned in the original question.

The warning message that appears when loading plyr after dplyr indicates that there may be conflicts between the two libraries:

Attaching package: ‘plyr’

The following objects are masked from ‘package:dplyr’:

  arrange, desc, failwith, id, mutate, summarise, summarize

This warning message tells us that plyr has overridden functions from dplyr, which can lead to unexpected behavior.

Resolving the Issue: Loading Packages in the Correct Order

To resolve this issue, we need to load plyr before loading dplyr. This ensures that the correct library is used when calling functions like summarise().

There are three ways to load packages correctly:

1. Detach plyr: We can detach plyr before loading dplyr, as shown in the example:

library(plyr)
detach(package:plyr)

library(dplyr)
dfx %>% group_by(group, sex) %>% 
  summarise(mean = round(mean(age), 2), sd = round(sd(age), 2))

2. Restart R: Another option is to restart R and load dplyr first:

library(dplyr)
dfx %>% group_by(group, sex) %>% 
  summarise(mean = round(mean(age), 2), sd = round(sd(age), 2))

3. Explicitly call dplyr::summarise(): We can also explicitly call the summarise() function from dplyr using its namespace:

dfx %>% group_by(group, sex) %>% 
  dplyr::summarise(mean = round(mean(age), 2), sd = round(sd(age), 2))

Conclusion

In conclusion, loading packages in R can be a complex issue, especially when using functions from multiple libraries. By understanding the order in which packages are loaded and how to use their functions correctly, we can avoid unexpected behavior and ensure that our code runs smoothly.

Whether you choose to detach plyr before loading dplyr, restart R, or explicitly call the summarise() function using its namespace, there’s a solution that works for you.

Last modified on 2024-02-12