Link gaps to (ESM) data

Gaps in mobile sensing data typically occur when the app is stopped by the operating system or the user. While small gaps may not pose problems with analyses, greater gaps may cause bias or skew your data. As a result, gap data should be considered in order to inspect and limit their influence. This function, analogous to link(), allows you to connect gaps to other data (usually ESM/EMA data) within a user-specified time range.

Usage

link_gaps(
  data,
  gaps,
  by = NULL,
  offset_before = 0,
  offset_after = 0,
  raw_data = FALSE
)

Arguments

data

A data frame or an extension to a data frame (e.g. a tibble). While gap data can be linked to any other type of data, ESM data is most commonly used.

gaps

A data frame (extension) containing the gap data. See identify_gaps() for retrieving gap data from an mpathsenser database. It should at least contain the columns from and to (both in a date-time format), as well as any specified columns in by.

by

A character vector indicating the variable(s) to match by, typically the participant IDs. If NULL, the default, *_join() will perform a natural join, using all variables in common across x and y. Therefore, all data will be mapped to each other based on the time stamps of x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly.

To join by different variables on x and y, use a named vector. For example, by = c('a' = 'b') will match x$a to y$b.

To join by multiple variables, use a vector with length > 1. For example, by = c('a', 'b') will match x$a to y$a and x$b to y$b. Use a named vector to match different variables in x and y. For example, by = c('a' = 'b', 'c' = 'd') will match x$a to y$b and x$c to y$d.

To perform a cross-join (when x and y have no variables in common), use by = character(). Note that the split argument will then be set to 1.

offset_before

The time before each measurement in x that denotes the period in which y is matched. Must be convertible to a period by lubridate::as.period().

offset_after

The time after each measurement in x that denotes the period in which y is matched. Must be convertible to a period by lubridate::as.period().

raw_data

Whether to include the raw data (i.e. the matched gap data) to the output as gap_data.

Value

The original data with an extra column duration indicating the gap during within the interval in seconds (if duration is TRUE), or an extra column called gap_data containing the gaps within the interval. The function ensures all durations and gap time stamps are within the range of the interval.

See also

bin_data() for linking two sets of intervals to each other; identify_gaps() for finding gaps in the sampling; add_gaps() for adding gaps to sensor data;

Examples

# Create some data
x <- data.frame(
  time = rep(seq.POSIXt(as.POSIXct("2021-11-14 13:00:00"), by = "1 hour", length.out = 3), 2),
  participant_id = c(rep("12345", 3), rep("23456", 3)),
  item_one = rep(c(40, 50, 60), 2)
)

# Create some gaps
gaps <- data.frame(
  from = as.POSIXct(c("2021-11-14 13:00:00", "2021-11-14 14:00:00")),
  to = as.POSIXct(c("2021-11-14 13:30:00", "2021-11-14 14:30:00")),
  participant_id = c("12345", "23456")
)

# Link the gaps to the data
link_gaps(x, gaps, by = "participant_id", offset_before = 0, offset_after = 1800)
#> # A tibble: 6 × 4
#>   time                participant_id item_one   gap
#>   <dttm>              <chr>             <dbl> <dbl>
#> 1 2021-11-14 13:00:00 12345                40  1800
#> 2 2021-11-14 14:00:00 12345                50     0
#> 3 2021-11-14 15:00:00 12345                60     0
#> 4 2021-11-14 13:00:00 23456                40     0
#> 5 2021-11-14 14:00:00 23456                50  1800
#> 6 2021-11-14 15:00:00 23456                60     0

# Link the gaps to the data and include the raw data
link_gaps(
  x,
  gaps,
  by = "participant_id",
  offset_before = 0,
  offset_after = 1800,
  raw_data = TRUE
)
#> # A tibble: 6 × 5
#>   time                participant_id item_one gap_data           gap
#>   <dttm>              <chr>             <dbl> <list>           <dbl>
#> 1 2021-11-14 13:00:00 12345                40 <tibble [1 × 3]>  1800
#> 2 2021-11-14 14:00:00 12345                50 <tibble [0 × 3]>     0
#> 3 2021-11-14 15:00:00 12345                60 <tibble [0 × 3]>     0
#> 4 2021-11-14 13:00:00 23456                40 <tibble [0 × 3]>     0
#> 5 2021-11-14 14:00:00 23456                50 <tibble [1 × 3]>  1800
#> 6 2021-11-14 15:00:00 23456                60 <tibble [0 × 3]>     0