Converting Values to Keys Based on a Key Table with dplyr and R

Converting Values to Keys Based on a Key Table with dplyr and R

In data analysis, it’s not uncommon to encounter datasets that require categorization or binning of values based on predefined rules. One common approach is to use a key table to map values from one domain to another. In this article, we’ll explore how to convert values to keys using the cut function in R, focusing on the popular dplyr package for data manipulation.

Background and Motivation

Suppose you have a dataset with scores that need to be mapped to levels based on a predefined key table. The key table contains two columns: score and level. For example:

score	level
2	a
3	b
4	c

Your goal is to assign levels to scores from the dataset data1 based on this key table.

Problem Statement

The question presents an equivalent scenario, where you have two datasets: data1 and data2. data1 contains scores with decimal values, while data2 has a corresponding column of levels. The task is to create a new column in data1 that maps the scores to their corresponding levels using the rules defined in data2.

Existing Solutions

The question provides an existing solution using the cut function in R, which bins data into specified intervals based on breaks. However, we’ll explore alternative approaches using dplyr, focusing on readability and maintainability.

Existing Solution Using cut

data1$result = cut(data1$score, breaks = c(-Inf, data2$score[-nrow(data2)], Inf), labels = data2$level)
data1
# # A tibble: 6 x 3
#      id score result
#   &lt;int&gt; &lt;dbl&gt; &lt;fct&gt; 
# 1     1   0.9 a     
# 2     2   1.9 a     
# 3     3   2.9 b     
# 4     4   3.9 c     
# 5     5   4.9 c     
# 6     6   5.9 c

In this solution, the cut function is used to bin the scores into intervals based on the levels in the key table. The breaks argument specifies the points where the bins should be created, and the labels argument maps these bins to the corresponding levels.

Alternative Approaches with dplyr

While the existing solution using cut works well for this particular problem, we can explore alternative approaches using dplyr. This will allow us to focus on data manipulation principles rather than relying solely on built-in functions like cut.

Using mutate and case_when

One way to achieve the same result using dplyr is by utilizing the mutate function to create a new column, followed by case_when for conditional logic.

library(dplyr)

# Create data1 with scores
data1 <- data_frame(id = 1:6, score = 1:6 - 0.1)

# Define the key table
data2 <- data_frame(score = c(2, 3, 4), level = c("a", "b", "c"))

# Merge data1 and data2 for comparison
merged_data <- inner_join(data1, data2, by.x = "score", by.y = "score")

# Create a new column with the corresponding levels using mutate and case_when
data1_result <- data1 %>%
  mutate(result = case_when(
    score == merged_data$score[1] ~ merged_data$level[1],
    score > merged_data$score[nrow(merged_data)] ~ merged_data$level[nrow(merged_data)],
    TRUE ~ ifelse(score < merged_data$score[1] | score > merged_data$score[nrow(merged_data)], "not assigned", "assigned")
  ))

# Display the result
data1_result

This approach involves merging data1 and data2 to create a temporary dataset, which is then used within the mutate function. The case_when statement checks for specific conditions to assign the corresponding levels.

Using case_map and inner_join

Another alternative uses case_map from the dplyr package, which provides a more concise way to perform conditional assignments.

library(dplyr)

# Create data1 with scores
data1 <- data_frame(id = 1:6, score = 1:6 - 0.1)

# Define the key table
data2 <- data_frame(score = c(2, 3, 4), level = c("a", "b", "c"))

# Use case_map to create a new column with levels using inner_join
data1_result <- data1 %>%
  inner_join(data2, by.x = "score", by.y = "score") %>%
  case_map(
    if_(score == data2$score[1], ~ level[1]),
    if_(score > data2$score[nrow(data2)], ~ level[nrow(data2)]),
    ~ ifelse(score < data2$score[1] | score > data2$score[nrow(data2)], "not assigned", "assigned")
  )

# Display the result
data1_result

In this approach, case_map is used to create a new column based on the conditions specified. The resulting solution has similar logic but with more concise syntax.

Conclusion

While both existing solutions using cut and alternative approaches using dplyr can achieve the same goal of mapping scores to levels based on a key table, the choice ultimately depends on personal preference and specific requirements for readability, maintainability, and performance. The case_map approach provides an attractive balance between conciseness and clarity.

Best Practices

When working with data manipulation in R using dplyr, it’s essential to:

Use clear and descriptive variable names.
Leverage functions like mutate, filter, inner_join, and left_join for efficient data processing.
Employ conditional logic using statements like ifelse or case_when.
Consider utilizing the case_map function for concise case assignments.

By following these best practices, you can create robust, readable, and maintainable code that effectively solves a wide range of data manipulation challenges.

Last modified on 2023-12-07