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Occasionally R might not be the tool you want to use (hard to believe, but apparently that happens). Then you may need to export some data from R via a csv file. When the data is stored as a ts
object, the time index can easily get lost. So I wrote a little function to make this easier, using the tsibble
package to do most of the work in looking after the time index.
# Convert time series data to csv ts2csv <- function(x) { fname <- paste0(deparse(substitute(x)), ".csv") if (NCOL(x) == 1L) { # Univariate time series readr::write_csv( as.data.frame(tsibble::as_tsibble(x)), fname) } else { # Multivariate time series readr::write_csv( as.data.frame(tsibble::spread(tsibble::as_tsibble(x), key, value)), fname) } } library(fpp2) ts2csv(auscafe) # Univariate monthly data ts2csv(visnights) # Multivariate quarterly data ts2csv(elecdemand) # Multivariate half-hourly data
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