Filter (for Time-Series Data)Source:
The easiest way to filter time-based start/end ranges using shorthand timeseries notation.
filter_period() for applying filter expression by period (windows).
A tibble with a time-based column.
A column containing date or date-time values to filter. If missing, attempts to auto-detect date column.
The starting date for the filter sequence
The ending date for the filter sequence
Pure Time Series Filtering Flexibilty
.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:
.start_date = '2013', .end_date = '2015'
.start_date = '2013-01', .end_date = '2016-06'
.start_date = '2013-01-05', .end_date = '2016-06-04'
.start_date = '2013-01-05 10:22:15', .end_date = '2018-06-03 12:14:22'
.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'
All shorthand dates are expanded:
.start_dateis expanded to be the first date in that period
.end_dateside 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'
This function is based on the
tibbletime::filter_time()function developed by Davis Vaughan.
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
library(dplyr) 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