Clean missing data above an acceptable threshold — clean_missing

This function can be used to remove trials and participants who do not meet the threshold for a study. Note that there are two parameters for cleaning, one to remove trials above a threshold, the second to remove participants who drop more than a certain amount of trials.

clean_missing_data(
  data,
  pupil,
  trial_threshold = 1,
  subject_trial_threshold = 1
)

Arguments

data: your data of class PupillometryR
pupil: a column name denoting pupil size
trial_threshold: a proportion of missing data over which a trial can be considered lost
subject_trial_threshold: a proportion of missing trials over which a participant can be considered lost.

Value

A cleaned PupillometryR dataframe

Examples

data(pupil_data)
Sdata <- make_pupillometryr_data(data = pupil_data,
subject = ID,
trial = Trial,
time = Time,
condition = Type)
new_data <- downsample_time_data(data = Sdata,
pupil = LPupil,
timebin_size = 50,
option = 'mean')
#> Calculating mean pupil size in each timebin 
calculate_missing_data(data = new_data, pupil = LPupil)
#> # A tibble: 48 × 3
#>    ID    Trial Missing
#>    <chr> <fct>   <dbl>
#>  1 1     Easy1       0
#>  2 1     Hard1       0
#>  3 1     Easy2       0
#>  4 1     Hard2       0
#>  5 1     Easy3       0
#>  6 1     Hard3       0
#>  7 2     Easy1       0
#>  8 2     Hard1       0
#>  9 2     Easy2       0
#> 10 2     Hard2       0
#> # ℹ 38 more rows