Applying Factors to Different Subsets of a Larger Time Series Using a Custom Function in R

Applying Factors to Different Subsets of a Larger Time Series Using a Custom Function

In this article, we will explore how to apply factors to different subsets of a larger time series using a custom function in R. We will go through the process step by step, including creating a time series dataset, defining a custom function to apply the factor, and executing it on the dataset.

Introduction to Time Series Data

A time series is a sequence of data points measured at regular time intervals. In this article, we are working with a physiological variable with millisecond timestamps on multiple patients. The time series data has the following characteristics:

Millisecond timestamps for precise timing
Physiological variable (e.g., heart rate, blood pressure) recorded at regular intervals
Multiple patients with their own dataset

Creating a Time Series Dataset

We will start by creating a sample time series dataset using R. The seq function is used to generate a sequence of numbers representing the timestamps, and rnorm is used to generate random values for the physiological variable.

# Load required libraries
library(ggplot2)

# Create a time series dataset
TestPatient <- data.frame(
    Time = seq(as.Date("2011-12-22 12:00:00"), by = "sec", length.out = 100),
    Value = rnorm(100, 9, 3)
)

Defining the Custom Function

The next step is to define a custom function patientpositionslice that applies a factor to a subset of the time series data. The function takes four arguments:

patient: The original time series dataset
positiontype: The type of position (e.g., “Horizontal”, “Vertical”)
timestart: The start timestamp for the subset
timestop: The end timestamp for the subset

The function uses the ifelse function to apply the factor based on the condition format(patient$Time, "%Y-%m-%d %H:%M:%S") >= timestart & format(patient$Time, "%Y-%m-%d %H:%M:%S") < timestop. The result is a new column with the applied factor.

# Define the custom function
patientpositionslice <- function(patient, positiontype, timestart, timestop) {
    # Apply the factor using ifelse function
    new <- ifelse(
        format(patient$Time, "%Y-%m-%d %H:%M:%S") >= timestart & 
        format(patient$Time, "%Y-%m-%d %H:%M:%S") <  timestop , 
        positiontype, patient$position)
    # Update the patient dataset with the new column
    patient$position <- new
    return(patient)
}

Applying the Custom Function

We will apply the custom function to the original time series dataset. First, we create a new dataset TestPatientNew by applying the “Horizontal” factor from 12:00:05 to 12:00:10.

# Apply the custom function for the first position
TestPatientNew <- patientpositionslice(TestPatient, "Horizontal", as.POSIXct("2011-12-22 12:00:05"), as.POSIXct("2011-12-22 12:00:10"))

Next, we apply the “Vertical” factor from 12:00:15 to 12:00:20.

# Apply the custom function for the second position
TestPatientNew <- patientpositionslice(TestPatientNew, "Vertical", as.POSIXct("2011-12-22 12:00:15"), as.POSIXct("2011-12-22 12:00:20"))

Example Use Cases

Here are some example use cases for the custom function:

Applying multiple factors: You can apply multiple factors to different subsets of the time series data. For example, you can add a “Sitting” factor from 12:00:30 to 12:00:40.

# Apply additional factors
TestPatientNew <- patientpositionslice(TestPatientNew, "Sitting", as.POSIXct("2011-12-22 12:00:30"), as.POSIXct("2011-12-22 12:00:40"))

Using different data types: You can use different data types for the factor values. For example, you can use categorical variables or date/datetime objects.

# Define a custom data type
DataDate <- function(x) {
    ifelse(grepl("\\d{4}-\\d{2}-\\d{2}", x), as.Date(x), NA)
}

# Apply the custom function with a custom data type
TestPatientNew <- patientpositionslice(TestPatientNew, "Vertical", DataDate("2011-12-22 12:00:15"), DataDate("2011-12-22 12:00:20"))

Conclusion

In this article, we have explored how to apply factors to different subsets of a larger time series using a custom function in R. We defined the custom function patientpositionslice that applies a factor based on the condition and updated the original dataset with the new column. The example use cases demonstrate how to apply multiple factors and use different data types for the factor values.