Conditional Run Length Aggregation for Binary Variables in R

Run Length Aggregated by Subject ID Conditional on Observation == 1

In this article, we will explore how to calculate the run lengths for a variable positive in a dataset aggregated by another variable id, but with an additional condition. The condition is that only records where positive == 1 are considered for the calculation of run lengths.

The problem arises when using the built-in R function rle (run-length encoding) to calculate the run lengths for a binary vector. By default, rle calculates the run lengths for all possible values in the vector. However, we want to condition this function such that it only evaluates the run lengths for records where positive == 1.

To achieve this, we will first create a temporary dataset that includes additional information about the runs of consecutive positive months. Then, we will use aggregate functions to count the number of occurrences of two or more consecutive positives, with an additional condition.

Creating the Temporary Dataset

First, let’s create a temporary dataset tmp that includes the runs of consecutive positive months for each record in the original dataset test. We can do this by using the ave function, which applies a given function to each group of observations. In this case, we want to calculate the cumulative sum of 1s (i.e., the start of a new run) and 0s (i.e., the end of a run).

tmp <- within(test, run <- ave(positive, by=id, FUN=function(x)cumsum(c(1,diff(x)!=0))))

This code creates a new column run in the temporary dataset tmp, which contains the runs of consecutive positive months for each record.

Marking Runs with Event == 1 and Run Length >= 2

Next, let’s mark the runs in the temporary dataset that have at least one record with event == 1 and a run length greater than or equal to 2.

tmp2 <- aggregate(event~id+positive+run, data=tmp, function(x) any(x>0) & length(x)>2)

This code uses the aggregate function to calculate the sum of 1s for each group in the temporary dataset. The condition any(x>0) checks if there is at least one record with event == 1, and the condition length(x)>2 checks if the run length is greater than or equal to 2.

Counting Marked Runs

Finally, let’s count how many marked runs are there in each id and each kind of run (positive==1 or positive==0).

aggregate(event~positive+id, tmp2, sum)

This code uses the aggregate function to calculate the sum of 1s for each group in the temporary dataset. The condition any(x>0) checks if there is at least one record with event == 1, and the conditions positive==1 and positive==0 check if the run length belongs to a certain kind of run.

Results

The final output will be a table that shows the number of occurrences of two or more consecutive positives, with an additional condition. The table will have three columns: positive, id, and event. The positive column contains 0s for records where the positive value is 0, and 1s for records where the positive value is 1.

Code

Here is the complete code that we have discussed:

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

# Create a toy dataset
set.seed(123456)
x <- c(1, 2, 1)
test <- data.frame(id = rep(1:3, each = 24), positive = round(runif(72, 0, 1)), event = round(runif(72, 0, 1)))

# Create a temporary dataset
tmp <- within(test, run <- ave(positive, by=id, FUN=function(x)cumsum(c(1,diff(x)!=0))))

# Mark runs with event == 1 and run length >= 2
tmp2 <- aggregate(event~id+positive+run, data=tmp, function(x) any(x>0) & length(x)>2)

# Count marked runs
aggregate(event~positive+id, tmp2, sum)

Conclusion

In this article, we have discussed how to calculate the run lengths for a variable positive in a dataset aggregated by another variable id, but with an additional condition. We have used temporary datasets and aggregate functions to count the number of occurrences of two or more consecutive positives, with an additional condition. The final output is a table that shows the number of occurrences of two or more consecutive positives, with an additional condition.

References

@sgibb’s answer on Stack Overflow
Aggregate function in R documentation
ave function in R documentation
within function in R documentation

Last modified on 2023-10-18