Filtering Low Frequency Terms in dplyr: A Step-by-Step Solution Using Group By and Filter

Understanding the Problem with dplyr Chain Filter Based on Frequency

In this post, we will explore how to use the popular R package dplyr for data manipulation and filtering. The question arises from a situation where you need to filter low frequency terms in a dataset based on the number of rows per group.

Context: Introduction to dplyr

The dplyr package is designed for efficient data manipulation and analysis. It consists of three primary functions: filter(), arrange(), and summarise(). These functions allow you to manipulate data in a declarative way, rather than imperative.

For this problem, we will focus on the filter() function. This function allows you to select rows from a dataset based on certain conditions.

Problem Statement: Filter Low Frequency Terms

Suppose we have an R dataset called mtcars with two variables: cyl and row_count. The cyl variable represents the number of cylinders in each car, while the row_count variable represents the number of rows for each group.

We want to filter the low frequency terms, i.e., groups with less than 10 rows. In this case, we have two groups with more than 10 rows (4 and 8) since they both occur 10 or more times in the dataset.

Step-by-Step Solution

To solve this problem, we will need to use a combination of group_by(), filter(), and mutate() functions from the dplyr package. Here’s how:

# Load the dplyr library
library(dplyr)

# Create the mtcars dataset
mtcars

# Group by cyl, count the rows, filter, optionally remove the freq column:
library(dplyr)
mtcars %>% 
  group_by(cyl) %>% 
  mutate(freq = n()) %>% 
  ungroup() %>% 
  filter(freq > 9) %>% 
  select(-freq)

Step-by-Step Explanation

Group by cyl: We first need to group the data by the cyl variable, which represents the number of cylinders in each car.
Count the rows using n(): Inside the grouped data frame, we use the mutate() function to create a new column called freq. This column will contain the count of rows for each group. We achieve this by calling the n() function, which returns the number of observations in each group.
Filter groups with less than 10 rows: Next, we use the filter() function to select only the groups where the frequency (i.e., row count) is greater than 9.
Remove the freq column: Finally, we use the select() function to remove the freq column from the filtered data frame. This is because we are interested in the unique values of cyl, not the frequency counts.

Example Output

The resulting data frame will contain only two groups with more than 10 rows:

cyl
4
8

This output corresponds to the original dataset, where both cyl 4 and cyl 8 have a row count greater than 9.

Additional Considerations

One important thing to note is that when using dplyr, it’s essential to understand the order of operations. In this case, the group_by() function groups the data before any filtering occurs. This means that if you were to perform other operations on the grouped data (such as summarise() or mutate()), they would be applied after filtering.

For example, consider the following code:

mtcars %>% 
  group_by(cyl) %>% 
  mutate(freq = n()) %>% 
  filter(freq > 9) %>% 
  summarise(mean = mean(mpg))

In this case, the mean() function would be applied to the mpg column after filtering and grouping the data.

Conclusion

In conclusion, using dplyr for data manipulation is an efficient way to handle large datasets. The filter() function allows you to select rows from a dataset based on certain conditions, making it ideal for identifying low frequency terms in a dataset.

By combining group_by(), mutate(), and select() functions, you can efficiently filter and analyze your data using the declarative syntax provided by dplyr.