Remove subjects by trajectory number

Specify a minimum number of trajectories that each subject must complete during a treatment, trial, or session.

Usage

rm_by_trajnum(
  obj_name,
  trajnum = 5,
  mirrored = FALSE,
  treatment1,
  treatment2,
  ...
)

Arguments

obj_name

The input viewr object; a tibble or data.frame with attribute pathviewr_steps that includes "viewr". Trajectories must be predefined (i.e. via separate_trajectories()).

trajnum

Minimum number of trajectories; must be numeric.

mirrored

Does the data have mirrored treatments? If so, arguments treatment1 and treatment2 must also be provided, indicating the names of two mirrored treatments, both of which must meet the trajectory threshold specified in trajnum. Default is FALSE.

treatment1

The first treatment or session during which the threshold must be met.

treatment2

A second treatment or session during which the threshold must be met.

...

Additional arguments passed to/from other pathviewr functions.

Value

A viewr object; a tibble or data.frame with attribute pathviewr_steps that includes "viewr" that now has fewer observations (rows) as a result of removal of subjects with too few trajectories according to the trajnum parameter.

Details

Depending on analysis needs, users may want to remove subjects that have not completed a certain number of trajectories during a treatment, trial, or session. If mirrored = FALSE, no treatment information is necessary and subjects will be removed based on total number of trajectories as specified in trajnum. If mirrored = TRUE, the treatment1 and treatment2 parameters will allow users to define during which treatments or sessions subjects must reach threshold as specified in the trajnum argument. For example, if mirrored = TRUE, setting treatment1 = "latA", treatment2 = "latB" and trajnum = 5 will remove subjects that have fewer than 5 trajectories during the "latA" treatment AND the "latB" treatment. treatment1 and treatment2 should be levels within a column named "treatment".

Author

Melissa S. Armstrong

Examples

library(pathviewr)

## Import the example Motive data included in the package
motive_data <-
  read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv",
                              package = 'pathviewr'))

## Clean, isolate, and label trajectories
motive_full <-
  motive_data %>%
  clean_viewr(desired_percent = 50,
              max_frame_gap = "autodetect",
              span = 0.95)
#> autodetect is an experimental feature -- please report issues.

##Remove subjects that have not completed at least 150 trajectories:
motive_rm_unmirrored <-
  motive_full %>%
  rm_by_trajnum(trajnum = 150)
#> Joining with `by = join_by(subject)`

## Add treatment information
motive_full$treatment <- c(rep("latA", 100),
                           rep("latB", 100),
                           rep("latA", 100),
                           rep("latB", 149))

## Remove subjects by that have not completed at least 10 trajectories in
## both treatments
motive_rm_mirrored <-
  motive_full %>%
  rm_by_trajnum(
    trajnum = 10,
    mirrored = TRUE,
    treatment1 = "latA",
    treatment2 = "latB"
  )
#> Joining with `by = join_by(subject)`