tibbletime (version 0.1.9)

as_period: Change tbl_time periodicity

Description

Convert a tbl_time object from daily to monthly, from minute data to hourly, and more. This allows the user to easily aggregate data to a less granular level by taking the value from either the beginning or end of the period.

Usage

as_period(
  .tbl_time,
  period = "year",
  start_date = NULL,
  side = "start",
  include_endpoints = FALSE,
  ...
)

Arguments

.tbl_time

A tbl_time object.

period

A character specification used for time-based grouping. The general format to use is "frequency period" where frequency is a number like 1 or 2, and period is an interval like weekly or yearly. There must be a space between the two.

Note that you can pass the specification in a flexible way:

• 1 Year: '1 year' / '1 Y'

This shorthand is available for year, quarter, month, day, hour, minute, second, millisecond and microsecond periodicities.

Additionally, you have the option of passing in a vector of dates to use as custom and more flexible boundaries.

start_date

Optional argument used to specify the start date for the first group. The default is to start at the closest period boundary below the minimum date in the supplied index.

side

Whether to return the date at the beginning or the end of the new period. By default, the "start" of the period. Use "end" to change to the end of the period.

include_endpoints

Whether to include the first or last data point in addition to the transformed data.

...

Not currently used.

Details

This function respects dplyr::group_by() groups.

The side argument is useful when you want to return data at, say, the end of a quarter, or the end of a month.

include_endpoints can be useful when calculating a change over time. In addition to changing to monthly dates, you often need the first data point as a baseline for the first calculation.

Examples

# Basic usage ---------------------------------------------------------------

# FB stock prices
data(FB)
FB <- as_tbl_time(FB, date)

# Aggregate FB to yearly data
as_period(FB, "year")

# Aggregate FB to every 2 years
as_period(FB, "2 years")

# Aggregate FB to yearly data, but use the last data point available
# in that period
as_period(FB, "year", side = "end")

# Aggregate FB to yearly data, end of period, and include the first
# endpoint
as_period(FB, "year", side = "end", include_endpoints = TRUE)

# Aggregate to weekly. Notice that it only uses the earliest day available
# in the data set at that periodicity. It will not set the date of the first
# row to 2013-01-01 because that date did not exist in the original data set.
as_period(FB, "weekly")

# FB is daily data, aggregate to minute?
# Not allowed for Date class indices, an error is thrown
# as_period(FB, "minute")

# Grouped usage -------------------------------------------------------------

# FANG contains Facebook, Amazon, Netflix and Google stock prices
data(FANG)
FANG <- as_tbl_time(FANG, date)

FANG <- dplyr::group_by(FANG, symbol)

# Respects groups
as_period(FANG, "year")

# Every 6 months, respecting groups
as_period(FANG, "6 months")

# Using start_date ----------------------------------------------------------


#### One method using start_date

# FB stock prices
data(FB)
FB <- as_tbl_time(FB, date)

# The Facebook series starts at 2013-01-02 so the 'every 2 day' counter
# starts at that date as well. Groups become (2013-01-02, 2013-01-03),
# (2013-01-04, 2013-01-05) and so on.
as_period(FB, "2 day")

# Specifying the `start_date = "2013-01-01"` might be preferable.
# Groups become (2013-01-01, 2013-01-02), (2013-01-03, 2013-01-04) and so on.
as_period(FB, "2 day", start_date = "2013-01-01")

#### Equivalent method using an index vector

# FB stock prices
data(FB)
FB <- as_tbl_time(FB, date)

custom_period <- create_series(
  time_formula = dplyr::first(FB$date) - 1 ~ dplyr::last(FB$date),
  period       = "2 day",
  class        = "Date",
  as_vector    = TRUE)

FB %>%
  as_tbl_time(date) %>%
  as_period(period = custom_period)

# Manually calculating returns at different periods -------------------------

data(FB)

# Annual Returns
# Convert to end of year periodicity, but include the endpoints to use as
# a reference for the first return calculation. Then calculate returns.
FB %>%
  as_tbl_time(date) %>%
  as_period("1 y", side = "end", include_endpoints = TRUE) %>%
  dplyr::mutate(yearly_return = adjusted / dplyr::lag(adjusted) - 1)