compute_derivative_df: Compute Derivatives for Each Column in Data Frame

In Kneerav/biomechanics: Biomechanics Data Analysis

Compute Derivatives for Each Column in Data Frame

Description

This function computes the angular velocity (first derivative of angular position) for each column of data in a data frame, based on the specified time column. The derivative can be computed using either a smooth spline fit or numerical gradient.

Usage

compute_derivative_df(
  data,
  time_col = 1,
  method = c("spline", "gradient"),
  deriv = 1
)

Arguments

data	A data frame where the first column is the time values and other columns represent the data (e.g., angular positions).
time_col	A character or numeric value specifying the time column. If numeric, it represents the column position. If character, it represents the column name.
method	A character string specifying the method to compute the derivative. Options are "spline" for smooth spline fitting or "gradient" for numerical gradient using pracma::gradient. Default is "spline".
deriv	An integer specifying the order of the derivative. Default is 1 for the first derivative. Higher values (e.g., 2) will return higher derivatives (second derivative, etc.).

Value

A data frame with the same structure as the input, but with columns for angular velocities (or higher derivatives).

Examples

# Example data frame
data <- data.frame(time = seq(0, 10, by = 0.1),
                   position1 = sin(seq(0, 10, by = 0.1)),
                   position2 = cos(seq(0, 10, by = 0.1)))

# Compute derivatives using spline method (time column by name)
angular_velocities_spline <- compute_derivative_dataframe(data, time_col = "time", method = "spline", deriv = 1)

# Compute derivatives using gradient method (time column by position)
angular_velocities_gradient <- compute_derivative_dataframe(data, time_col = 1, method = "gradient", deriv = 1)

Kneerav/biomechanics documentation built on July 16, 2025, 4:51 p.m.