timetk (version 2.9.0)

filter_period: Apply filtering expressions inside periods (windows)

Description

Applies a dplyr filtering expression inside a time-based period (window). See filter_by_time() for filtering continuous ranges defined by start/end dates. filter_period() enables filtering expressions like:

• Filtering to the maximum value each month.

• Filtering the first date each month.

• Filtering all rows with value greater than a monthly average

Usage

filter_period(.data, ..., .date_var, .period = "1 day")

Value

A tibble or data.frame

Arguments

.data

A tbl object or data.frame

...

Filtering expression. Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

.date_var

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

.period

A period to filter within. Time units are grouped using lubridate::floor_date() or lubridate::ceiling_date().

The value can be:

• second

• minute

• hour

• day

• week

• month

• bimonth

• quarter

• season

• halfyear

• year

Arbitrary unique English abbreviations as in the lubridate::period() constructor are allowed:

• "1 year"

• "2 months"

• "30 seconds"

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

# Libraries
library(dplyr)

# Max value in each month
m4_daily %>%
    group_by(id) %>%
    filter_period(.period = "1 month", value == max(value))

# First date each month
m4_daily %>%
    group_by(id) %>%
    filter_period(.period = "1 month", date == first(date))

# All observations that are greater than a monthly average
m4_daily %>%
    group_by(id) %>%
    filter_period(.period = "1 month", value > mean(value))