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Statechange (sc) Timestamps to Intervals

Source: R/sc2interval.R
sc2interval.Rd

Takes an input of datetimes and Statechanges and creates a column with Intervals. If full = TRUE, it will also create intervals for the day prior to the first state change and after the last. If output.dataset = FALSE it will give a named vector, otherwise a tibble. The state change info requires a description or name of the state (like "sleep" or "wake", or "wear") that goes into effect at the given Datetime. Works for grouped data so that it does not mix up intervals between participants. Missing data should be explicit if at all possible. Also, the maximum allowed length of an interval can be set, so that implicit missing timestamps after a set period of times can be enforced.

Usage

sc2interval(
  dataset,
  Datetime.colname = Datetime,
  Statechange.colname = State,
  State.colname = State,
  Interval.colname = Interval,
  full = TRUE,
  starting.state = NA,
  output.dataset = TRUE,
  Datetime.keep = FALSE,
  length.restriction = 60 * 60 * 24
)

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. Must be of type POSIXct.

Statechange.colname, Interval.colname, State.colname

Column names that do contain the name/description of the state change and that will contain the Interval and State (which are also the default). Expects a symbol. The Statechange column needs do be part of the dataset.

full, starting.state

These arguments handle the state on the first day before the first state change and after the last state change on the last day. If full = TRUE(the default, expects a logical), it will create an interval on the first day from 00:00:00 up until the state change. This interval will be given the state specified in starting.state, which is NA by default, but can be any character scalar. It will further extend the interval for the last state change until the end of the last given day (more specifically until 00:00:00 the next day).

output.dataset

should the output be a data.frame (Default TRUE) or a vector with hms (FALSE) times? Expects a logical scalar.

Datetime.keep

If TRUE, the original Datetime column will be kept.

length.restriction

If the length between intervals is too great, the interval state can be set to NA, which effectively produces a gap in the data. This makes sense when intervals are implausibly wrong (e.g. someone slept for 50 hours), because when this data is combined with light logger data, e.g., through interval2state(), metrics and visualizations will remove the interval.

Value

One of

  • a data.frame object identical to dataset but with the interval instead of the datetime. The original Statechange column now indicates the State during the Interval.

  • a named vector with the intervals, where the names are the states

Examples

library(tibble)
library(lubridate)
library(dplyr)
sample <- tibble::tibble(Datetime = c("2023-08-15 6:00:00",
                                      "2023-08-15 23:00:00",
                                      "2023-08-16 6:00:00",
                                      "2023-08-16 22:00:00",
                                      "2023-08-17 6:30:00",
                                      "2023-08-18 1:00:00"),
                         State = rep(c("wake", "sleep"), 3),
                         Id = "Participant")
#intervals from sample
sc2interval(sample)
#> # A tibble: 7 × 3
#>   State Id          Interval                                        
#>   <chr> <chr>       <Interval>                                      
#> 1 NA    NA          2023-08-15 00:00:00 UTC--2023-08-15 06:00:00 UTC
#> 2 wake  Participant 2023-08-15 06:00:00 UTC--2023-08-15 23:00:00 UTC
#> 3 sleep Participant 2023-08-15 23:00:00 UTC--2023-08-16 06:00:00 UTC
#> 4 wake  Participant 2023-08-16 06:00:00 UTC--2023-08-16 22:00:00 UTC
#> 5 sleep Participant 2023-08-16 22:00:00 UTC--2023-08-17 06:30:00 UTC
#> 6 wake  Participant 2023-08-17 06:30:00 UTC--2023-08-18 01:00:00 UTC
#> 7 sleep Participant 2023-08-18 01:00:00 UTC--2023-08-19 00:00:00 UTC

#compare sample (y) and intervals (x)
sc2interval(sample) %>%
 mutate(Datetime = int_start(Interval)) %>%
 dplyr::left_join(sample, by = c("Id", "State"),
                  relationship = "many-to-many") %>%
 head()
#> # A tibble: 6 × 5
#>   State Id          Interval                                        
#>   <chr> <chr>       <Interval>                                      
#> 1 NA    NA          2023-08-15 00:00:00 UTC--2023-08-15 06:00:00 UTC
#> 2 wake  Participant 2023-08-15 06:00:00 UTC--2023-08-15 23:00:00 UTC
#> 3 wake  Participant 2023-08-15 06:00:00 UTC--2023-08-15 23:00:00 UTC
#> 4 wake  Participant 2023-08-15 06:00:00 UTC--2023-08-15 23:00:00 UTC
#> 5 sleep Participant 2023-08-15 23:00:00 UTC--2023-08-16 06:00:00 UTC
#> 6 sleep Participant 2023-08-15 23:00:00 UTC--2023-08-16 06:00:00 UTC
#> # ℹ 2 more variables: Datetime.x <dttm>, Datetime.y <chr>