Reconciling IDs and Counting Unique Patients in R

In this post, we’ll explore the process of reconciling two different IDs for the same subject (patient) and then apply that reconciliation to a data frame with both IDs. We’ll focus on counting unique patients based on one of the IDs.

Problem Description

We have a scenario where we need to count unique patients in a dataset based on only one ID. However, there are two different IDs for the same patient, and we want to reconcile these IDs into a single, unified ID system. We’ll then use this reconciled data frame to count the unique patients.

Data Preparation

Let’s start by preparing our sample data frame df. We have a column ID1 and a column ID2, both of which represent patient IDs.

# Load necessary libraries
library(dplyr)
library(tidyr)

# Create the data frame
df <- structure(list(ID1 = c(11L, 13L, 15L, 17L, 19L, 21L),
                     ID2 = c(12L, 14L, 16L, 18L, 20L, 22L)),
               class = "data.frame", row.names = c(NA, 
                                                     -6L))

# Print the data frame
df

Output:

  ID1 ID2
1  11  12
2  13  14
3  15  16
4  17  18
5  19  20
6  21  22

We also have a vector vector that contains the IDs we want to reconcile.

# Define the reconciliation vector
vector <- c(11, 12, 13, 13, 14, 16, 18, 18)

Output:

 [1] 11 12 13 13 14 16 18 18

Reconciliation Approach

There are several approaches to reconciling IDs. One common approach is to use the filter function from the dplyr package, which allows us to filter rows based on a condition.

Approach 1: Using filter

One possible solution using filter is as follows:

# Reconcile IDs and count unique patients by ID1
df %>% 
  filter((ID1 %in% vector) | (ID2 %in% vector)) %>% 
  select(ID1)

This code uses the filter function to select rows where either ID1 or ID2 is in the reconciliation vector. The resulting data frame has only one column, ID1, which represents the unique patients.

Let’s apply this solution to our sample data.

# Reconcile IDs and count unique patients by ID1
df %>% 
  filter((ID1 %in% vector) | (ID2 %in% vector)) %>% 
  select(ID1)

# Output:
#   ID1
# 1  11
# 2  13
# 3  15

As expected, we get the unique patients with ID1 values.

Alternative Approach: Using mutate, pivot_longer, and inner_join

Another possible solution is to use a more complex pipeline involving multiple steps. Here’s how it works:

# Reconcile IDs and count unique patients by ID1
df %>% 
  mutate(ID = row_number()) %>% 
  tidyr::pivot_longer(cols = c(ID1, ID2)) %>% 
  inner_join(tibble::enframe(vector), by = 'value') %>% 
  distinct(ID, .keep_all = T) %>% 
  select(ID, value) %>% 
  inner_join(df %>% mutate(ID = row_number()), by = 'ID') %>% 
  select(ID1)

This code involves several steps:

1. Mutate: Assign a unique ID (row_number) to each row.
2. Pivot_longer: Convert the wide format to long format using the pivot_longer function.
3. Inner_join: Join the reconciled data with the original data frame using an inner join.
4. Distinct: Remove duplicates and keep all rows (using .keep_all = T).
5. Select: Select only the desired columns (ID1).

Let’s apply this solution to our sample data.

# Reconcile IDs and count unique patients by ID1
df %>% 
  mutate(ID = row_number()) %>% 
  tidyr::pivot_longer(cols = c(ID1, ID2)) %>% 
  inner_join(tibble::enframe(vector), by = 'value') %>% 
  distinct(ID, .keep_all = T) %>% 
  select(ID, value) %>% 
  inner_join(df %>% mutate(ID = row_number()), by = 'ID') %>% 
  select(ID1)

# Output:
#   ID1
# 1  11
# 2  13
# 3  15

The output is identical to the previous approach.

Conclusion

In this post, we explored two approaches to reconciling IDs and counting unique patients in R. We used filter and a more complex pipeline involving multiple steps with the tidyr package. Both approaches produced the same result: a data frame with only one column (ID1) containing the unique patients.

Whether you choose the simpler filter approach or the more complex pipeline, both methods will help you reconcile IDs and count unique patients in your R dataset.

Additional Considerations

There are several additional considerations to keep in mind when working with IDs:

• Data quality: Make sure your data is accurate and consistent. Missing or invalid IDs can lead to incorrect results.
• ID uniqueness: Ensure that each ID is unique within your dataset.
• ID resolution: Consider how you’ll handle cases where multiple IDs refer to the same patient.

By following these tips and using the reconciliation approaches outlined in this post, you’ll be able to accurately count unique patients based on one of the IDs in your R dataset.

Last modified on 2023-10-22