LightLogR (version 0.3.8)

aggregate_Date: Aggregate dates to a single day

Description

Condenses a dataset by aggregating the data to a single day per group, with a resolution of choice unit. aggregate_Date() is opinionated in the sense that it sets default handlers for each data type of numeric, character, logical, and factor. 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) in every group. aggregate_Date() is especially useful for summary plots that show an average day.

Usage

aggregate_Date(
  dataset,
  Datetime.colname = Datetime,
  unit = "none",
  type = c("round", "floor", "ceiling"),
  date.handler = stats::median,
  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")))),
  ...
)

Value

A tibble with aggregated Datetime data, at maximum one day per group. If the handler arguments capture all column types, the number of columns will be the same as in the input dataset.

Arguments

dataset

A light logger dataset. Expects a dataframe. If not imported by LightLogR, take care to choose a sensible variable for the Datetime.colname.

Datetime.colname

column name that contains the datetime. Defaults to "Datetime" which is automatically correct for data imported with LightLogR. Expects a symbol. Needs to be part of the dataset.

unit

Unit of binning. See lubridate::round_date() for examples. The default is "none", which will not aggregate the data at all, but is only recommended for regular data, as the condensation across different days will be performed by time. Another option is "dominant.epoch", which means everything will be aggregated to the most common interval. This is especially useful for slightly irregular data, but can be computationally expensive.

type

One of "round"(the default), "ceiling" or "floor". Setting chooses the relevant function from lubridate.

date.handler

A function that calculates the aggregated day for each group. By default, this is set to median.

numeric.handler, character.handler, logical.handler, factor.handler

functions that handle the respective data types. The default handlers calculate the mean for numeric and the mode for character, factor and logical types.

...

arguments given over to dplyr::summarize() to handle columns that do not fall into one of the categories above.

Details

aggregate_Date() splits the Datetime column into a Date.data and a Time.data column. It will create subgroups for each Time.data present in a group and aggregate each group into a single day, then remove the sub grouping.

Use the ... to create summary statistics for each group, e.g. maximum or minimum values for each time point group.

Performing aggregate_Datetime() with any unit and then aggregate_Date() with a unit of "none" is equivalent to just using aggregate_Date() with that unit directly (provided the other arguments are set the same between the functions). Disentangling the two functions can be useful to split the computational cost for very small instances of unit in large datasets. It can also be useful to apply different handlers when aggregating data to the desired unit of time, before further aggregation to a single day, as these handlers as well as ... are used twice if the unit is not set to "none".

Examples

library(ggplot2)
#gg_days without aggregation
sample.data.environment %>%
 gg_days()

#with daily aggregation
sample.data.environment %>%
 aggregate_Date() %>%
 gg_days()

#with daily aggregation and a different time aggregation
sample.data.environment %>%
 aggregate_Date(unit = "15 mins", type = "floor") %>%
 gg_days()

#adding further summary statistics about the range of MEDI
 sample.data.environment %>%
 aggregate_Date(unit = "15 mins", type = "floor",
                MEDI_max = max(MEDI),
                MEDI_min = min(MEDI)) %>%
 gg_days() +
 geom_ribbon(aes(ymin = MEDI_min, ymax = MEDI_max), alpha = 0.5)