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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:

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)
#> .date_var is missing. Using: date