apply.daily | R Documentation |

Apply a specified function to each distinct period in a given time series object.

```
apply.daily(x, FUN, ...)
apply.weekly(x, FUN, ...)
apply.monthly(x, FUN, ...)
apply.quarterly(x, FUN, ...)
apply.yearly(x, FUN, ...)
```

`x` |
A time-series object coercible to xts. |

`FUN` |
A function to apply to each period. |

`...` |
Additional arguments to |

Simple mechanism to apply a function to non-overlapping time periods, e.g. weekly, monthly, etc. Different from rolling functions in that this will subset the data based on the specified time period (implicit in the call), and return a vector of values for each period in the original data.

Essentially a wrapper to the xts functions `endpoints()`

and
`period.apply()`

, mainly as a convenience.

A vector of results produced by `FUN`

, corresponding to the
appropriate periods.

When `FUN = mean`

the results will contain one column for every
column in the input, which is different from other math functions (e.g.
`median`

, `sum`

, `prod`

, `sd`

, etc.).

`FUN = mean`

works by column because the default method `stats::mean`

previously worked by column for matrices and data.frames. R Core changed the
behavior of `mean`

to always return one column in order to be consistent
with the other math functions. This broke some xts dependencies and
`mean.xts()`

was created to maintain the original behavior.

Using `FUN = mean`

will print a message that describes this inconsistency.
To avoid the message and confusion, use `FUN = colMeans`

to calculate means
by column and use `FUN = function(x) mean`

to calculate one mean for all the
data. Set `options(xts.message.period.apply.mean = FALSE)`

to suppress this
message.

Jeffrey A. Ryan

`endpoints()`

, `period.apply()`

, `to.monthly()`

```
xts.ts <- xts(rnorm(231),as.Date(13514:13744,origin="1970-01-01"))
start(xts.ts)
end(xts.ts)
apply.monthly(xts.ts,colMeans)
apply.monthly(xts.ts,function(x) var(x))
```

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