SeriesAggreg | R Documentation |

Conversion of time series to another time step (aggregation only) and regime computation.

Warning: on the aggregated outputs, the dates correspond to the beginning of the time step

(e.g. for daily time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2005-03-01 23:59)

(e.g. for monthly time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2005-03-31 23:59)

(e.g. for yearly time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2006-02-28 23:59)

```
## S3 method for class 'data.frame'
SeriesAggreg(x,
Format,
ConvertFun,
TimeFormat = NULL,
NewTimeFormat = NULL,
YearFirstMonth = 1,
TimeLag = 0,
...)
## S3 method for class 'list'
SeriesAggreg(x,
Format,
ConvertFun,
NewTimeFormat = NULL,
simplify = FALSE,
except = NULL,
recursive = TRUE,
...)
## S3 method for class 'InputsModel'
SeriesAggreg(x, Format, ...)
## S3 method for class 'OutputsModel'
SeriesAggreg(x, Format, ...)
```

`x` |
[InputsModel], [OutputsModel], [list] or [data.frame] containing the vector of dates ( |

`Format` |
[character] output time step format (i.e. yearly times series: |

`TimeFormat` |
(deprecated) [character] input time step format (i.e. |

`NewTimeFormat` |
(deprecated) [character] output time step format (i.e. |

`ConvertFun` |
[character] names of aggregation functions (e.g. for P[mm], T[degC], Q[mm]: |

`YearFirstMonth` |
(optional) [numeric] integer used when |

`TimeLag` |
(optional) [numeric] numeric indicating a time lag (in seconds) for the time series aggregation (especially useful to aggregate hourly time series into daily time series) |

`simplify` |
(optional) [boolean] if set to |

`except` |
(optional) [character] the name of the items to skip in the aggregation (default = |

`recursive` |
(optional) [boolean] if set to |

`...` |
Arguments passed to |

`SeriesAggreg.InputsModel`

and `SeriesAggreg.OutputsModel`

call `SeriesAggreg.list`

which itself calls `SeriesAggreg.data.frame`

.
So, all arguments passed to any `SeriesAggreg`

method will be passed to `SeriesAggreg.data.frame`

.

Argument `ConvertFun`

also supports quantile calculation by using the syntax "Q[nn]" with [nn] the requested percentile.
E.g. use "Q90" for calculating 90th percentile in the aggregation.
The formula used is: `quantile(x, probs = perc / 100, type = 8, na.rm = TRUE)`

.

As there are multiple ways to take into account missing values in aggregation functions, `NA`

s are not supported by `SeriesAggreg`

and it provides `NA`

values when `NA`

s are present in the `x`

input.

[POSIXct+numeric] data.frame containing a vector of aggregated dates (POSIXct) and time series values numeric)

Olivier Delaigue, David Dorchies

```
library(airGR)
## loading catchment data
data(L0123002)
## preparation of the initial time series data frame at the daily time step
TabSeries <- BasinObs[, c("DatesR", "P", "E", "T", "Qmm")]
## monthly time series
NewTabSeries <- SeriesAggreg(TabSeries,
Format = "%Y%m",
ConvertFun = c("sum", "sum", "mean", "sum"))
str(NewTabSeries)
## monthly regimes
NewTabSeries <- SeriesAggreg(TabSeries,
Format = "%m",
ConvertFun = c("sum", "sum", "mean", "sum"))
str(NewTabSeries)
## conversion of InputsModel
example("RunModel_GR2M")
## monthly regimes on OutputsModel object
SimulatedMonthlyRegime <- SeriesAggreg(OutputsModel, Format = "%m")
str(SimulatedMonthlyRegime)
```

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