zoo: S3 Infrastructure for Regular and Irregular Time Series

By Achim Zeileis, G. Grothendieck. 2005

1,229 citations recorded by the source corpus.

zoo is an R package providing an S3 class with methods for indexed totally ordered observations, such as discrete irregular time series. Its key design goals are independence of a particular index/time/date class and consistency with base R and the "ts" class for regular time series. This paper describes how these are achieved within zoo and provides several illustrations of the available methods for "zoo" objects which include plotting, merging and binding, several mathematical operations, extracting and replacing data and index, coercion and NA handling. A subclass "zooreg" embeds regular time series into the "zoo" framework and thus bridges the gap between regular and irregular time series classes in R.

Subfields: Finance, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/372b4a9da410fcdf03864422030d984c1139a810

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Citations in the corpus

imputeTS: Time Series Missing Value Imputation in R (2017, neutral): “Most noteworthy being zoo (Zeileis and Grothendieck, 2005) and forecast (Hyndman, 2016).”
Continental-scale decreases in shorebird populations in Australia (2016, background): “2015), and data collation and manipulation (Zeileis and Grothendieck 2005; Venables 2013; Wickham and Francois 2014).”
ProQ3: Improved model quality assessments using Rosetta energy terms (2016, methodology): “R zoo package [31] was used to average values over varying window sizes.”
GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data (2019, methodology): “The R package signal is used for frequency ﬁ ltering (Ligges et al., 2014), R package zoo is used for calculating a rolling median, sum, and mean (Zeileis & Grothendieck, 2005), and R package data.table is used at some steps to ef ﬁ ciently handle large amounts of tabular data.”
Dates and Times Made Easy with lubridate (2011, background): “These include character strings, POSIXct, POSIXlt, Date, chron (James and Hornik 2010), timeDate (Wuertz and Chalabi 2010a), zoo (Zeileis and Grothendieck 2005), xts (Ryan and Ulrich 2010), its (Portfolio and Risk Advisory Group, Commerzbank Securities 2009), tis (Hallman 2010), timeSeries (Wuertz…”
ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages (2016, methodology): “The ggfortify package makes it much easier to visualize time series objects using ggplot2 and provides autoplot() and fortify() implementatons for ojects from many time series libraries such as zoo (Zeileis and Grothendieck, 2005), xts (Ryan and Ulrich, 2014), and timeSeries (Team et al., 2015).”
Warming advances top-down control and reduces producer biomass in a freshwater plankton community (2017, methodology): “Missing values were interpolated with the na.spline function from the zoo package (Zeileis and Grothendieck 2005).”
How to handle missing data: A comparison of different approaches (2015, neutral): “In the present study, we used the R-package (R Development Core Team, 2013) MICE (Multivariate Imputation by Chained Equations; Van Buuren & Groothuis-Oudshoorn, 2011; R package Plyr, Wickham, 2011; R package Zoo, Zeileis & Grothendieck, 2005).”
Electrical Power Fluctuations in a Network of DC/AC inverters in a Large PV Plant: relationship between correlation, distance and time scale (2013, methodology): “Software The methods described below have been implemented using the free software environment R [27] and several contributed packages, namely: zoo [28] for the time series, wmtsa [29] for the wavelet analysis, solaR [30] for the clearness index, sp [31], lattice and latticeExtra [32, 33] for displa”