View source: R/aggregate_Datetime.R
aggregate_Datetime | R Documentation |
Condenses a dataset
by aggregating the data to a given (shorter) interval
unit
. aggregate_Datetime()
is opinionated in the sense that it sets
default handlers for each data type of numeric
, character
, logical
,
factor
, duration
, time
, and datetime
. These can be overwritten by the
user. Columns that do not fall into one of these categories need to be
handled individually by the user (...
argument) or will be removed during
aggregation. If no unit is specified the data will simply be aggregated to
the most common interval (dominant.epoch
), which is most often not an
aggregation but a rounding.)
aggregate_Datetime(
dataset,
unit = "dominant.epoch",
Datetime.colname = Datetime,
type = c("round", "floor", "ceiling"),
numeric.handler = mean,
character.handler = function(x) names(which.max(table(x, useNA = "ifany"))),
logical.handler = function(x) mean(x) >= 0.5,
factor.handler = function(x) factor(names(which.max(table(x, useNA = "ifany")))),
datetime.handler = mean,
duration.handler = function(x) lubridate::duration(mean(x)),
time.handler = function(x) hms::as_hms(mean(x)),
...
)
dataset |
A light logger dataset. Expects a |
unit |
Unit of binning. See |
Datetime.colname |
column name that contains the datetime. Defaults to
|
type |
One of |
numeric.handler , character.handler , logical.handler , factor.handler , datetime.handler , duration.handler , time.handler |
functions that handle the respective data types. The default handlers
calculate the |
... |
arguments given over to |
Summary values for type POSIXct
are calculated as the mean, which can be
nonsensical at times (e.g., the mean of Day1 18:00 and Day2 18:00, is Day2
6:00, which can be the desired result, but if the focus is on time, rather
then on datetime, it is recommended that values are converted to times via
hms::as_hms()
before applying the function (the mean of 18:00 and 18:00 is
still 18:00, not 6:00).
A tibble
with aggregated Datetime
data. Usually the number of
rows will be smaller than the input dataset
. If the handler arguments
capture all column types, the number of columns will be the same as in the
input dataset
.
#dominant epoch without aggregation
sample.data.environment %>%
dominant_epoch()
#dominant epoch with 5 minute aggregation
sample.data.environment %>%
aggregate_Datetime(unit = "5 mins") %>%
dominant_epoch()
#dominant epoch with 1 day aggregation
sample.data.environment %>%
aggregate_Datetime(unit = "1 day") %>%
dominant_epoch()
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