View source: R/dplyr-summarise_by_time.R
summarise_by_time | R Documentation |
summarise_by_time()
is a time-based variant of the popular dplyr::summarise()
function
that uses .date_var
to specify a date or date-time column and .by
to group the
calculation by groups like "5 seconds", "week", or "3 months".
summarise_by_time()
and summarize_by_time()
are synonyms.
summarise_by_time(
.data,
.date_var,
.by = "day",
...,
.type = c("floor", "ceiling", "round"),
.week_start = NULL
)
summarize_by_time(
.data,
.date_var,
.by = "day",
...,
.type = c("floor", "ceiling", "round"),
.week_start = NULL
)
.data |
A |
.date_var |
A column containing date or date-time values to summarize. If missing, attempts to auto-detect date column. |
.by |
A time unit to summarise by.
Time units are collapsed using The value can be:
Arbitrary unique English abbreviations as in the |
... |
Name-value pairs of summary functions. The name will be the name of the variable in the result. The value can be:
|
.type |
One of "floor", "ceiling", or "round. Defaults to "floor". See |
.week_start |
when unit is weeks, specify the reference day. 7 represents Sunday and 1 represents Monday. |
A tibble
or data.frame
Sum: sum()
Center: mean()
, median()
Spread: sd()
, var()
Range: min()
, max()
Count: dplyr::n()
, dplyr::n_distinct()
Position: dplyr::first()
, dplyr::last()
, dplyr::nth()
Correlation: cor()
, cov()
Time-Based dplyr functions:
summarise_by_time()
- Easily summarise using a date column.
mutate_by_time()
- Simplifies applying mutations by time windows.
filter_by_time()
- Quickly filter using date ranges.
filter_period()
- Apply filtering expressions inside periods (windows)
between_time()
- Range detection for date or date-time sequences.
pad_by_time()
- Insert time series rows with regularly spaced timestamps
condense_period()
- Convert to a different periodicity
slidify()
- Turn any function into a sliding (rolling) function
# Libraries
library(dplyr)
# First value in each month
m4_daily %>%
group_by(id) %>%
summarise_by_time(
.date_var = date,
.by = "month", # Setup for monthly aggregation
# Summarization
value = first(value)
)
# Last value in each month (day is first day of next month with ceiling option)
m4_daily %>%
group_by(id) %>%
summarise_by_time(
.by = "month",
value = last(value),
.type = "ceiling"
) %>%
# Shift to the last day of the month
mutate(date = date %-time% "1 day")
# Total each year (.by is set to "year" now)
m4_daily %>%
group_by(id) %>%
summarise_by_time(
.by = "year",
value = sum(value)
)
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