# gapfill: Interpolate, subsample and fill gaps in time series by linear... In GPPFourier: Calculate Gross Primary Production (GPP) from O2 Time Series

## Description

Interpolate, subsample and fill gaps in time series by linear interpolation

Subsample and interpolate oversampled or undersampled time series to obtain equidistant data at a lower or higher sampling frequency. Gaps are filled with linear interpolation

## Usage

 ```1 2 3 4 5 6``` ```gapfill(x, dt = x[2, 1] - x[1, 1], units = c("auto", "secs", "mins", "hours", "days", "weeks"), type = "linear") subsample(x, dt, units = c("auto", "secs", "mins", "hours", "days", "weeks")) resample(x, dt, units = c("auto", "secs", "mins", "hours", "days", "weeks")) ```

## Arguments

 `x` Data frame with time in first column and time series data in second. `dt` Sampling interval. Default to time difference of first two observations `units` Optional: units of `dt`. Only necessary when time is of class `POSIXt` and `dt` is not of class `difftime`. `type` Type of interpolation. Only linear interpolation implemented

## Details

Subsampling, interpolating and gapfilling

An equidistant times series with sampling interval `dt` is created by linear interpolation. `gapfill` takes the time interval between the first and second observation as default value for `dt`. `subsample` and `resample` require a value of `dt`.

## Value

A data frame with time and interpolated time series.

## Author(s)

Tom Cox <tom.cox@uantwerp.be>

## Examples

 ```1 2``` ```plot(Hoernum, type="p", xlim=as.POSIXct(c("2008-08-01","2008-09-30"))) points(gapfill(Hoernum), type="p", pch=20, col="red", cex=0.2) ```

GPPFourier documentation built on Sept. 22, 2017, 5:06 p.m.