Function to flexibly aggregate or expand data frames by different time periods, calculating vector-averaged wind direction where appropriate. The averaged periods can also take account of data capture rates.

## Usage

```
timeAverage(
mydata,
avg.time = "day",
data.thresh = 0,
statistic = "mean",
type = "default",
percentile = NA,
start.date = NA,
end.date = NA,
interval = NA,
vector.ws = FALSE,
fill = FALSE,
...
)
```

## Arguments

- mydata
A data frame containing a

`date`

field . Can be class`POSIXct`

or`Date`

.- avg.time
This defines the time period to average to. Can be “sec”, “min”, “hour”, “day”, “DSTday”, “week”, “month”, “quarter” or “year”. For much increased flexibility a number can precede these options followed by a space. For example, a timeAverage of 2 months would be

`period = "2 month"`

. In addition,`avg.time`

can equal “season”, in which case 3-month seasonal values are calculated with spring defined as March, April, May and so on.Note that

`avg.time`

can be*less*than the time interval of the original series, in which case the series is expanded to the new time interval. This is useful, for example, for calculating a 15-minute time series from an hourly one where an hourly value is repeated for each new 15-minute period. Note that when expanding data in this way it is necessary to ensure that the time interval of the original series is an exact multiple of`avg.time`

e.g. hour to 10 minutes, day to hour. Also, the input time series must have consistent time gaps between successive intervals so that`timeAverage`

can work out how much ‘padding’ to apply. To pad-out data in this way choose`fill = TRUE`

.- data.thresh
The data capture threshold to use (%). A value of zero means that all available data will be used in a particular period regardless if of the number of values available. Conversely, a value of 100 will mean that all data will need to be present for the average to be calculated, else it is recorded as

`NA`

. See also`interval`

,`start.date`

and`end.date`

to see whether it is advisable to set these other options.- statistic
The statistic to apply when aggregating the data; default is the mean. Can be one of “mean”, “max”, “min”, “median”, “frequency”, “sum”, “sd”, “percentile”. Note that “sd” is the standard deviation, “frequency” is the number (frequency) of valid records in the period and “data.cap” is the percentage data capture. “percentile” is the percentile level (%) between 0-100, which can be set using the “percentile” option --- see below. Not used if

`avg.time = "default"`

.- type
`type`

allows`timeAverage`

to be applied to cases where there are groups of data that need to be split and the function applied to each group. The most common example is data with multiple sites identified with a column representing site name e.g.`type = "site"`

. More generally,`type`

should be used where the date repeats for a particular grouping variable. However, if type is not supplied the data will still be averaged but the grouping variables (character or factor) will be dropped.- percentile
The percentile level in % used when

`statistic = "percentile"`

. The default is 95.- start.date
A string giving a start date to use. This is sometimes useful if a time series starts between obvious intervals. For example, for a 1-minute time series that starts “2009-11-29 12:07:00” that needs to be averaged up to 15-minute means, the intervals would be “2009-11-29 12:07:00”, “2009-11-29 12:22:00” etc. Often, however, it is better to round down to a more obvious start point e.g. “2009-11-29 12:00:00” such that the sequence is then “2009-11-29 12:00:00”, “2009-11-29 12:15:00” ...

`start.date`

is therefore used to force this type of sequence.- end.date
A string giving an end date to use. This is sometimes useful to make sure a time series extends to a known end point and is useful when

`data.thresh`

> 0 but the input time series does not extend up to the final full interval. For example, if a time series ends sometime in October but annual means are required with a data capture of >75% then it is necessary to extend the time series up until the end of the year. Input in the format yyyy-mm-dd HH:MM.- interval
The

`timeAverage`

function tries to determine the interval of the original time series (e.g. hourly) by calculating the most common interval between time steps. The interval is needed for calculations where the`data.thresh`

>0. For the vast majority of regular time series this works fine. However, for data with very poor data capture or irregular time series the automatic detection may not work. Also, for time series such as monthly time series where there is a variable difference in time between months users should specify the time interval explicitly e.g.`interval = "month"`

. Users can also supply a time interval to*force*on the time series. See`avg.time`

for the format.This option can sometimes be useful with

`start.date`

and`end.date`

to ensure full periods are considered e.g. a full year when`avg.time = "year"`

.- vector.ws
Should vector averaging be carried out on wind speed if available? The default is

`FALSE`

and scalar averages are calculated. Vector averaging of the wind speed is carried out on the u and v wind components. For example, consider the average of two hours where the wind direction and speed of the first hour is 0 degrees and 2m/s and 180 degrees and 2m/s for the second hour. The scalar average of the wind speed is simply the arithmetic average = 2m/s and the vector average is 0m/s. Vector-averaged wind speeds will always be lower than scalar-averaged values.- fill
When time series are expanded i.e. when a time interval is less than the original time series, data are ‘padded out’ with

`NA`

. To ‘pad-out’ the additional data with the first row in each original time interval, choose`fill = TRUE`

.- ...
Additional arguments for other functions calling

`timeAverage`

.

## Details

This function calculates time averages for a data frame. It also treats wind direction correctly through vector-averaging. For example, the average of 350 degrees and 10 degrees is either 0 or 360 - not 180. The calculations therefore average the wind components.

When a data capture threshold is set through `data.thresh`

it is
necessary for `timeAverage`

to know what the original time interval of
the input time series is. The function will try and calculate this interval
based on the most common time gap (and will print the assumed time gap to
the screen). This works fine most of the time but there are occasions where
it may not e.g. when very few data exist in a data frame or the data are
monthly (i.e. non-regular time interval between months). In this case the
user can explicitly specify the interval through `interval`

in the same
format as `avg.time`

e.g. `interval = "month"`

. It may also be
useful to set `start.date`

and `end.date`

if the time series do
not span the entire period of interest. For example, if a time series ended
in October and annual means are required, setting `end.date`

to the end
of the year will ensure that the whole period is covered and that
`data.thresh`

is correctly calculated. The same also goes for a time
series that starts later in the year where `start.date`

should be set
to the beginning of the year.

`timeAverage`

should be useful in many circumstances where it is
necessary to work with different time average data. For example, hourly air
pollution data and 15-minute meteorological data. To merge the two data sets
`timeAverage`

can be used to make the meteorological data 1-hour means
first. Alternatively, `timeAverage`

can be used to expand the hourly
data to 15 minute data - see example below.

For the research community `timeAverage`

should be useful for dealing
with outputs from instruments where there are a range of time periods used.

It is also very useful for plotting data using `timePlot`

.
Often the data are too dense to see patterns and setting different averaging
periods easily helps with interpretation.

## See also

See `timePlot`

that plots time series data and uses
`timeAverage`

to aggregate data where necessary.

## Examples

```
## daily average values
daily <- timeAverage(mydata, avg.time = "day")
## daily average values ensuring at least 75 % data capture
## i.e. at least 18 valid hours
if (FALSE) daily <- timeAverage(mydata, avg.time = "day", data.thresh = 75)
## 2-weekly averages
if (FALSE) fortnight <- timeAverage(mydata, avg.time = "2 week")
## make a 15-minute time series from an hourly one
if (FALSE) {
min15 <- timeAverage(mydata, avg.time = "15 min", fill = TRUE)
}
# average by grouping variable
if (FALSE) {
dat <- importAURN(c("kc1", "my1"), year = 2011:2013)
timeAverage(dat, avg.time = "year", type = "site")
# can also retain site code
timeAverage(dat, avg.time = "year", type = c("site", "code"))
# or just average all the data, dropping site/code
timeAverage(dat, avg.time = "year")
}
```