timetk (version 2.8.1)

filter_by_time: Filter (for Time-Series Data)

Description

The easiest way to filter time-based start/end ranges using shorthand timeseries notation. See filter_period() for applying filter expression by period (windows).

Usage

filter_by_time(.data, .date_var, .start_date = "start", .end_date = "end")

Arguments

.data

A tibble with a time-based column.

.date_var

A column containing date or date-time values to filter. If missing, attempts to auto-detect date column.

.start_date

The starting date for the filter sequence

.end_date

The ending date for the filter sequence

Details

Pure Time Series Filtering Flexibilty

The .start_date and .end_date parameters are designed with flexibility in mind.

Each side of the time_formula is specified as the character 'YYYY-MM-DD HH:MM:SS', but powerful shorthand is available. Some examples are:

• Year: .start_date = '2013', .end_date = '2015'

• Month: .start_date = '2013-01', .end_date = '2016-06'

• Day: .start_date = '2013-01-05', .end_date = '2016-06-04'

• Second: .start_date = '2013-01-05 10:22:15', .end_date = '2018-06-03 12:14:22'

• Variations: .start_date = '2013', .end_date = '2016-06'

Key Words: "start" and "end"

Use the keywords "start" and "end" as shorthand, instead of specifying the actual start and end values. Here are some examples:

• Start of the series to end of 2015: .start_date = 'start', .end_date = '2015'

• Start of 2014 to end of series: .start_date = '2014', .end_date = 'end'

Internal Calculations

All shorthand dates are expanded:

• The .start_date is expanded to be the first date in that period

• The .end_date side is expanded to be the last date in that period

This means that the following examples are equivalent (assuming your index is a POSIXct):

• .start_date = '2015' is equivalent to .start_date = '2015-01-01 + 00:00:00'

• .end_date = '2016' is equivalent to 2016-12-31 + 23:59:59'

References

• This function is based on the tibbletime::filter_time() function developed by Davis Vaughan.

See Also

Time-Based dplyr functions:

• summarise_by_time() - Easily summarise using a date column.

• mutate_by_time() - Simplifies applying mutations by time windows.

• pad_by_time() - Insert time series rows with regularly spaced timestamps

• filter_by_time() - Quickly filter using date ranges.

• filter_period() - Apply filtering expressions inside periods (windows)

• slice_period() - Apply slice inside periods (windows)

• condense_period() - Convert to a different periodicity

• between_time() - Range detection for date or date-time sequences.

• slidify() - Turn any function into a sliding (rolling) function

Examples

library(tidyverse)
library(tidyquant)
library(timetk)

# Filter values in January 1st through end of February, 2013
FANG %>%
    group_by(symbol) %>%
    filter_by_time(.start_date = "start", .end_date = "2013-02") %>%
    plot_time_series(date, adjusted, .facet_ncol = 2, .interactive = FALSE)