Filter out when a participant has trained

Several LCBC projects have some memory training experimental procedure. For some papers, this might come in the way of answering hypotheses. This function helps filter out participants data after they have been exposed to training. Meaning data before training remain, data after do not.

filter_trainingexposed(data, predicate)

Arguments

data	The MOAS or a MOAS generated file.
predicate	a logical statement to identify rows of data under memory experimentation

Value

tibble

Examples

  dt <-  data.frame(
    CrossProject_ID = rep("1000000", 6),
    Site_Name = c("ousAvanto", "ousAvanto","ousAvanto",
                "ousSkyra", "ousSkyra", "ousSkyra"),
    Subject_Timepoint = c(1:3,3:5),
    Age = c(8, 10, 14, 14, 17, 20),
    stringsAsFactors = FALSE
  )
  filter_site(dt)
#> Keeping data from scanner with most data from double/triple scanned.
#> # A tibble: 5 x 4
#>   CrossProject_ID Site_Name Subject_Timepoint   Age
#>   <chr>           <chr>                 <int> <dbl>
#> 1 1000000         ousAvanto                 1     8
#> 2 1000000         ousAvanto                 2    10
#> 3 1000000         ousSkyra                  3    14
#> 4 1000000         ousSkyra                  4    17
#> 5 1000000         ousSkyra                  5    20