duet (version 0.1.1)

op_smooth_timeseries: Smooth Time Series Data with Various Methods

Description

This function applies different smoothing techniques to time series data for the selected columns (keypoints), including moving average, Kalman-Ziegler Adaptive (KZA), Savitzky-Golay filter, and Butterworth filter. It can optionally plot the smoothed data alongside the original data, with faceting based on the person and keypoint columns.

Value

A data frame with the smoothed time series data for the specified keypoints. If plot = TRUE, a plot is displayed comparing the original and smoothed data.

Arguments

data

A data frame containing the time series data. Must include person, time, and keypoints (e.g., x0, y0, etc.).

method

The smoothing method to use. Options are "zoo" (moving average), "kza" (Kalman-Ziegler Adaptive), "savitzky" (Savitzky-Golay filter), and "butterworth" (Butterworth filter). Default is "zoo".

kza_k

Window size for the KZA method. Default is 3.

kza_m

Number of iterations for the KZA method. Default is 2.

rollmean_width

Width of the moving average window for the zoo method. Default is 3.

sg_window

Window size for the Savitzky-Golay filter. Default is 5.

sg_order

Polynomial order for the Savitzky-Golay filter. Default is 3.

butter_order

Order of the Butterworth filter. Default is 3.

butter_cutoff

Cutoff frequency for the Butterworth filter. Default is 0.1.

side

Character string indicating which side of the data to smooth. Options are "left", "right", or "both". Default is "both".

plot

Logical, if TRUE, the function will generate a plot comparing the original and smoothed data. If FALSE, the function returns only the smoothed data frame without plotting. Default is TRUE.

keypoints

Vector of keypoint column names (e.g., x0, x1) to be smoothed and included in the plot. If NULL, all keypoints beginning with x or y will be smoothed and plotted. Default is NULL.

Examples

# Load example data from the package
data_path <- system.file("extdata/csv_data/dyad_1/A_body.csv", package = "duet")
data <- read.csv(data_path)

# Smooth the time series data using the Savitzky-Golay filter
smoothed_data <- op_smooth_timeseries(
  data = data,
  method = "savitzky",
  sg_window = 5,
  sg_order = 3,
  plot = TRUE,
  keypoints = c("x0", "y0") # Specify keypoints to smooth
)

# Print the smoothed data
print(smoothed_data)