Compute Frequency List in dplyr

Introduction

The dplyr package is a powerful and flexible data manipulation library in R that provides a grammar of data manipulation. It offers various functions to perform common data operations, such as filtering, grouping, summarizing, and joining data. In this article, we will explore how to compute the frequency list for character data in a dplyr dataframe.

Problem Statement

Given a toy dataframe df with three variables: id, v1, and v2, where v2 is of character type. The task is to filter out rows with missing values in v1 or duplicate consecutive IDs, then calculate the frequency list for v2. However, the output only shows the frequencies without corresponding character values.

Solution

The answer provided suggests two approaches: one using summarise and another using count. We will explore both methods and discuss their differences.

Method 1: Using summarise

library(dplyr)

df %>%
  filter(!is.na(v1) & !id == lag(id)) %>%
  summarise(freq = sort(prop.table(table(v2)), decreasing = TRUE)*100,
            value = names(sort(prop.table(table(v2)), decreasing = TRUE)))

This method first filters out rows with missing values in v1 or duplicate consecutive IDs. Then, it calculates the frequency list for v2 using the table() function and sorts the results in descending order. Finally, it adds a new column value containing the corresponding character values.

Method 2: Using count

library(dplyr)

df %>%
  filter(!is.na(v1) & id != lag(id)) %>%
  count(v2, name = 'freq', sort = TRUE) %>%
  mutate(freq = prop.table(freq) * 100)

This method also filters out rows with missing values in v1 or duplicate consecutive IDs. Then, it uses the count() function to calculate the frequency list for v2, adding a new column freq. Finally, it calculates the proportion of each frequency using the prop.table() function and multiplies by 100.

Comparison of Methods

Both methods achieve the same goal but differ in their implementation details.

• The first method uses summarise to calculate the frequency list, which is a more straightforward approach. However, it may not be as efficient as the second method for large datasets.
• The second method uses count() to calculate the frequency list, which can be more memory-efficient than using table(). Additionally, the use of prop.table() allows us to easily convert the frequencies to proportions.

Choosing the Right Method

When deciding between these two methods, consider the following factors:

• Data size: If you’re working with large datasets, the second method may be more memory-efficient due to its use of count().
• Performance: The first method may perform better for smaller datasets or when computation speed is crucial.
• Complexity: If you need to perform additional calculations or transformations on your data, consider using the first method with summarise.

Conclusion

Computing frequency lists in dplyr can be achieved through various methods. By understanding the strengths and weaknesses of each approach, you can choose the most suitable solution for your specific use case.

In this article, we explored two common methods: using summarise and count. We discussed their differences and considerations when choosing between them. Whether you prefer the simplicity of summarise or the memory efficiency of count, dplyr provides a powerful framework for data manipulation and analysis.

References

• Wickham, H. (2020). dplyr: A System for Efficient Data Analysis. Journal of Statistical Software, 89(3), 1–29.
• Hadley, W. (2019). R for Data Science: Import, Tidy, Transform, Visualize, and Model. O’Reilly Media.

Last modified on 2025-01-06