Lookup Table Based on Multiple Conditions in R

Introduction

In this article, we will explore a common problem in data analysis and visualization: creating a lookup table based on multiple conditions. The example is inspired by the Stack Overflow post “Lookup table based on multiple conditions in R” where a user asked how to create a new variable that indicates whether or not a patient’s performance on three different tasks is impaired, using a binary indicator.

Problem Statement

Given:

• A dataset patient_df with information about patients’ performance on three tasks.
• A lookup table cutoffs containing age- and education-based cutoff values to define a patient’s performance as impaired or not impaired on each task.

We need to create three new variables in patient_df that indicate whether or not a patient is impaired on each task with a binary indicator.

Solution Overview

To solve this problem, we will:

1. Convert the lookup table into long form for easier management.
2. Use fuzzy inner join to match data from patient_df with the transformed cutoffs in the lookup table based on both age and education ranges, while also matching task names exactly.
3. Calculate impaired status by comparing patient values with corresponding cutoff values.

Code

## Install required libraries
library(tidyverse)
library(fuzzyjoin)

## Transform cutoffs into long form
cutoffs_long <- cutoffs %>% 
  pivot_longer(cols = starts_with("task"), names_to = "task", values_to = "cutoff_value", names_pattern = "task(\\d+)")
cutoffs_long <- cutoffs_long %>% 
  mutate(age_low = age, age_high = age + 4)

## Transform patient_df into long form
patient_df <- patient_df %>% 
  pivot_longer(cols = starts_with("task"), names_to = "task", values_to = "patient_value", names_pattern = "(\\d+)")
patient_df <- patient_df %>% 
  mutate(education = ifelse(education < 16, "&lt;16", "&gt;=16"))

## Perform fuzzy inner join
impaired_df <- fuzzy_inner_join(cutoffs_long, patient_df, by = c("age" = "age_low", "age" = "age_high", "education", "task"), match_fun = list(`&gt;=` ,`&lt;=` ,`==`, `==`))

## Calculate impaired status
impaired_df <- impaired_df %>% 
  mutate(impaired = +(patient_value < cutoff_value))

## Output the result
impaired_df

Explanation

1. Convert lookup table into long form: The first step is to convert the lookup table cutoffs into a long format, which makes it easier to manage and perform joins with other datasets.
2. Use fuzzy inner join: We use the fuzzy_inner_join() function from the fuzzyjoin package to match data between patient_df and the transformed cutoffs in cutoffs_long. The match_fun = list(> ,<= ,==, ==) argument ensures that we are matching based on both age ranges and exact task names.
3. Calculate impaired status: After joining the data, we calculate the impaired status for each patient by comparing their values with the corresponding cutoff values.

Example Output

The resulting dataframe impaired_df contains the patient’s ID, age, education level, task name, patient value, and cutoff value. The impaired status is a binary indicator (1 for impaired, 0 for not impaired).

Last modified on 2024-04-13