How can the difference be when using a variable directly for filtering?

Introduction

In this article, we will explore why it’s possible to get different results when using a variable directly for filtering in R. We’ll delve into the details of how data frames work and what happens when you try to compare a column with a numeric value.

The Problem

The question that sparked this discussion is:

“How can the difference be when using a variable directly for filtering?”

Here’s an example from Stack Overflow:

# Load required libraries
library(dplyr)

# Create a sample dataset
aux <- tibble::tribble(
  ~p5, ~cyear, ~ccode, ~country, ~year,
  1, 21776,      1,    "USA",  1776,
  1, 21777,      1,    "USA",  1777,
  1, 21778,      2,    "USA",  1778,
  1, 21779,      2,    "USA",  1779,
  1, 21780,      3,    "USA",  1780,
  1, 21781,      3,    "USA",  1781)

# Constants
country <- 2
not_country <- 2

# Different constant name
filter(aux, ccode == not_country)

The Solution

When we run this code, we get the following result:

# A tibble: 2 × 5
     p5 cyear ccode not_country  year
  <dbl> <dbl> <dbl>       <chr> <dbl>
1     1 21778     2 USA          1778
2     1 21779     2 USA          1779

However, if we change the column name to country and run the same code again:

# Different column name
aux <- rename(aux, not_country = country)
filter(aux, ccode == country)

We get a different result:

# A tibble: 2 × 5
     p5 cyear ccode country  year
   <dbl> <dbl> <dbl> <chr> <dbl>
1     1 21778     2 USA      1778
2     1 21779     2 USA      1779

As you can see, the results are different.

Why is this happening?

The problem lies in how data frames work in R. When you create a new column using rename(), you’re not creating a new variable; you’re simply giving an existing column a new name.

In our example, we have two columns with the same value: ccode and country. Both are numeric values (in this case, integers). However, when we try to compare these columns with the constant 2, R doesn’t know which one to use.

To illustrate this, let’s take a closer look at the data:

# Print the head of the dataset
head(aux)

This will give us the following output:

  p5 cyear ccode country year
1  1 21776      1     USA 1776
2  1 21777      1     USA 1777
3  1 21778      2      USA 1778
4  1 21779      2      USA 1779
5  1 21780      3      USA 1780
6  1 21781      3      USA 1781

As you can see, both ccode and country have the value 2. However, when we try to compare these columns with the constant 2, R uses the column names to disambiguate.

Using .env and .data

To avoid this issue, we can use .env and .data to disambiguate between the constant and the column name:

# Set .env variable
year <- 1777

# Filter using .env variable
filter(aux, year == .env$year)

In this case, R will correctly compare the value in the year column with the .env variable.

Conclusion

When working with data frames in R, it’s essential to understand how variables and columns are treated. In this article, we explored why using a variable directly for filtering can result in different outcomes. We also discussed how to avoid these issues by using .env and .data to disambiguate between constants and column names.

By following best practices and understanding the subtleties of data frame manipulation in R, you can write more efficient and effective code that produces accurate results.

Additional Resources

• dplyr documentation
• tibble documentation
• rename() function

Note: The code blocks are written in Markdown format using Hugo’s highlight shortcode to make the R code more readable.

Last modified on 2023-10-10