process_timeseries.rmv_gaps | R Documentation |
Processes raw data in such a way that it can be directly inputted to the rythm_analysis_by_window function.
process_timeseries(df = NULL, sampling_rate = NULL, window_size_in_days = 3, window_step_in_days = 1,
movavg = TRUE, detrend_data = TRUE, butterworth = TRUE,
f_low = 1/4, f_high = 1/73, plot = TRUE,
smoothing_n = 4, datetime = NULL, values = NULL)
df |
A data.frame where the first column is a POSIXct object and the rest are independent measurement values. |
sampling_rate |
A character string indicating the sampling rate of the data. Examples: '30 minutes', '1 hour', '4 seconds', '100 days'. |
window_size_in_days |
a numeric indicating the width of the window size in day units. |
window_step_in_days |
a numeric indicating the amount of day by which to move the window in day units. |
movavg |
Logical. If TRUE (default) will smooth the measurement values useing a moving average. If FALSE measurement values won't be smoothed. |
detrend_data |
Logical. If TRUE (default) will detrend the data. If FALSE measurement values won't be detrended. If both, detrend_data and smooth_data are TRUE, the detrending will run over the smoothed data. |
butterworth |
Logical. If TRUE (default) will apply a buttwerworth filter to the measurement values using a moving average. If FALSE measurement values won't be filtered. |
datetime |
Optional if a data.frame is supplied. A POSIXct vector. |
values |
Optional if a data.frame is supplied. A vector of values from a mesurement. |
smoothing_n |
A numeric which indicated the amount of bins over which to run the smoothing average. Default = 4. |
order |
filter order. Default = 2. |
f_low |
Frequency for the low pass filter. Default = 1/4. |
f_high |
Frequency for the high pass filter. Default = 1/72. |
plot |
logical. If TRUE (default) plots the filtered data over the raw data. If FALSE, does not plot. |
A named list of data.frames containing the output of [circadiandynamics::butterworth], [find_gaps()], [makes_time_windows()], and [smooth_detrend_by_windows()] for each measurement value.
processed_data <- process_timeseries(df = raw_data, sampling_rate = "30 min")
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