Grouping Output from naniar using dplyr: Understanding the Changes in R

In this article, we will explore how to group output from naniar using dplyr. We’ll delve into the changes made in the newer versions of R and how they affect our code. Specifically, we’ll focus on the warning messages related to group_by() and miss_var_summary(), as well as the error messages caused by the deprecation of certain functions.

Introduction

naniar is a popular package for summarizing and inspecting missing data in R datasets. It provides an easy-to-use interface for calculating various statistics, such as missing counts, percentages, and frequencies. One common use case for naniar is to group the output by different factors, which can be useful for exploratory data analysis or identifying patterns in the data.

In this article, we’ll start with a simple example of using naniar with dplyr to summarize missing values in a dataset. We’ll then discuss the changes made in newer versions of R that affect our code and provide solutions for resolving the warning messages and error caused by these changes.

Setting up the Environment

Before we dive into the code, let’s set up our environment using the reprex package:

library(naniar)
library(dplyr)

# Load a sample dataset (e.g., `oceanbuoys`)
data("oceanbuoys")

Original Code

Here’s an example of how we might use naniar with dplyr to group the output:

miss_trigger <- oceanbuoys %>% 
  group_by(Trigger_counter) %>% 
  miss_var_summary()

This code groups the output by Trigger_counter and calculates various statistics, such as missing counts and percentages.

Warning Messages

As we update R to newer versions, we start receiving warning messages related to group_by():

# Warning message:
#
#   `cols` is now required.
#
# Please use `cols = c(data)` 

The warning message indicates that the group_by() function requires an additional argument called cols, which specifies the columns to group by. However, in our original code, we haven’t explicitly specified the columns to group by.

Resolving Warning Messages

To resolve this warning message, we can add the cols argument to our group_by() call:

miss_trigger <- oceanbuoys %>% 
  group_by(Trigger_counter, year) %>% 
  miss_var_summary()

By adding year as a grouping column, we ensure that the output is grouped correctly.

Error Messages

After updating R to newer versions, we receive error messages caused by the deprecation of certain functions:

# Error in group_by_fun(data, .fun = miss_var_summary()) : 
  could not find function "group_by_fun"

The error message indicates that the group_by() function has been replaced with a new version called group_by_fun(). However, our code is still using the old group_by() syntax.

Resolving Error Messages

To resolve this error message, we need to update our code to use the new group_by() syntax:

miss_trigger <- oceanbuoys %>% 
  group_of Trigger_counter, year) %>% 
  miss_var_summary()

However, there’s a catch: the new group_by() function requires us to specify all the columns to group by explicitly. In our case, we need to add year as another grouping column.

The Correct Solution

After updating our code to use the correct syntax, we can run it successfully:

miss_trigger <- oceanbuoys %>% 
  group_by(Trigger_counter, year) %>% 
  miss_var_summary()

This code groups the output by Trigger_counter and year, providing us with a comprehensive summary of missing values in our dataset.

Conclusion

In this article, we explored how to group output from naniar using dplyr. We discussed the changes made in newer versions of R that affect our code and provided solutions for resolving warning messages and error caused by these changes. By following these steps, you should be able to update your code to use the correct syntax and run it successfully.

Last modified on 2023-12-11