Apply filtering expressions inside periods (windows)

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")

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"

Value

A tibble or data.frame

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))
#> .date_var is missing. Using: date
#> # A tibble: 350 × 3
#> # Groups:   id [4]
#>    id    date       value
#>    <fct> <date>     <dbl>
#>  1 D10   2014-07-03 2076.
#>  2 D10   2014-08-08 2028.
#>  3 D10   2014-09-30 2024.
#>  4 D10   2014-10-12 2155.
#>  5 D10   2014-11-13 2245.
#>  6 D10   2014-12-30 2345.
#>  7 D10   2015-01-09 2369.
#>  8 D10   2015-02-09 2341.
#>  9 D10   2015-03-31 2392.
#> 10 D10   2015-04-13 2500.
#> # ℹ 340 more rows

# First date each month
m4_daily %>%
    group_by(id) %>%
    filter_period(.period = "1 month", date == first(date))
#> .date_var is missing. Using: date
#> # A tibble: 323 × 3
#> # Groups:   id [4]
#>    id    date       value
#>    <fct> <date>     <dbl>
#>  1 D10   2014-07-03 2076.
#>  2 D10   2014-08-01 1923.
#>  3 D10   2014-09-01 1908.
#>  4 D10   2014-10-01 2049.
#>  5 D10   2014-11-01 2133.
#>  6 D10   2014-12-01 2244.
#>  7 D10   2015-01-01 2351 
#>  8 D10   2015-02-01 2286.
#>  9 D10   2015-03-01 2291.
#> 10 D10   2015-04-01 2396.
#> # ℹ 313 more rows

# All observations that are greater than a monthly average
m4_daily %>%
    group_by(id) %>%
    filter_period(.period = "1 month", value > mean(value))
#> .date_var is missing. Using: date
#> # A tibble: 4,880 × 3
#> # Groups:   id [4]
#>    id    date       value
#>    <fct> <date>     <dbl>
#>  1 D10   2014-07-03 2076.
#>  2 D10   2014-07-04 2073.
#>  3 D10   2014-07-05 2049.
#>  4 D10   2014-07-06 2049.
#>  5 D10   2014-07-07 2006.
#>  6 D10   2014-07-08 2018.
#>  7 D10   2014-07-09 2019.
#>  8 D10   2014-07-10 2007.
#>  9 D10   2014-07-11 2010 
#> 10 D10   2014-07-12 2002.
#> # ℹ 4,870 more rows