# Tests of rm_by_trajnum()
# pre-import tests
test_that("rm_by_trajnum() fails when data are missing or nonsense trajnum", {
expect_error(rm_by_trajnum(trajnum = "steve"))
expect_error(rm_by_trajnum())
})
# 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 the file.
#Recommended to use full pathviewr pipeline before rm_by_trajnum()
motive_full <-
motive_data %>%
clean_viewr(desired_percent = 50,
max_frame_gap = "autodetect",
span = 0.95)
#Remove subjects that have not completed at least 150 trajectories:
motive_rm_unmirrored <-
motive_full %>%
rm_by_trajnum(trajnum = 150)
#test unmirrored output
test_that("rm_by_trajnum() counts trajectories correctly", {
expect_equal(rm_by_trajnum(motive_full, trajnum = 150)[1, 25]$n, 223)
expect_equal(rm_by_trajnum(motive_full, trajnum = 150)[187, 25]$n, 223)
})
test_that("rm_by_trajnum() removes subjects w/n < trajnum", {
expect_equal(unique(rm_by_trajnum(motive_full, trajnum = 150)$subject),
"device02")
})
# Add treatment information
motive_full$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"
)
#test mirrored output
test_that("rm_by_trajnum() removes mirrored subjects w/n < trajnum", {
expect_equal(unique(rm_by_trajnum(motive_full, trajnum = 10, mirrored = TRUE,
treatment1 = "latA", treatment2 = "latB")$subject),
c("device02", "device03"))
})
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