shorvalu {dataprep}

Interpolation with values to refer to within short periods

Description

Time gaps and available values are considered in NA interpolation by shorvalu. Thus, more reliable interpolation is realized with these constraints and the successive using of obsedele in the preceding outlier removal.

Usage

shorvalu(data, start, end, intervals = 30, units = 'mins')

Arguments

data

A data frame containing outliers. Its columns from start to end will be checked.

start

The column number of the first selected variable.

end

The column number of the last selected variable.

intervals

The time gap of dividing periods as groups, is 30 (minutes) by default. This confines the interpolation inside short periods so that each interpolation has observed value(s) to refer to within every half an hour.

units

Units in time intervals/differences. It can be one of "secs", "mins", "hours", "days", or "weeks". The default is 'mins'.

Details

It offers a robust interpolation method based on considering time gaps and available values.

Value

A data frame with missing values being replaced linearly within short periods and with values to refer to.

Author(s)

Chun-Sheng Liang <[e-mail]>

References

1. Example data is from https://smear.avaa.csc.fi/download. It includes particle number concentrations in SMEAR I Varrio forest.

2. Wickham, H., Francois, R., Henry, L. & Muller, K. 2017. dplyr: A Grammar of Data Manipulation. 0.7.4 ed. http://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr.

3. Wickham, H., Francois, R., Henry, L. & Muller, K. 2019. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr.

4. Zeileis, A. & Grothendieck, G. 2005. zoo: S3 infrastructure for regular and irregular time series. Journal of Statistical Software, 14(6):1-27.

5. Zeileis, A., Grothendieck, G. & Ryan, J.A. 2019. zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations). R package version 1.8-6. https://cran.r-project.org/web/packages/zoo/.

Examples

shorvalu(condextr(obsedele(data[1:250,c(1,4,17:19)],3,5,2,cores=2),
3,5,2,cores=2),3,5)