tests/testthat/test_part1_with_allmetrics.R

In GGIR: Raw Accelerometer Data Analysis

library(GGIR)
context("g.part1")
test_that("Part 1 can run with all metrics", {
  skip_on_cran()
  #=======================
  # Part 1 with all metrics
  Ndays = 2
  # Using sf = 30 Hertz  because metric zc filters at 4 Hertz and brondcounts requires 30 Hertz
  create_test_acc_csv(Nmin = Ndays * 1440, sf = 30)
  fn = "123A_testaccfile.csv"
  metadatadir = paste0(getwd(), "/output_test")
  desiredtz = "Europe/London"
  dn = "output_test"
  if (file.exists(dn))  unlink(dn, recursive = TRUE)
  g.part1(datadir = fn, metadatadir = metadatadir, f0 = 1, f1 = 1, overwrite = TRUE, desiredtz = desiredtz,
          do.cal = FALSE, do.anglex = TRUE,
          # We are not doing all the metrics, because Travis-CI cannot allocate enough memory
          do.enmo = TRUE,do.lfenmo = TRUE,
          do.bfen = TRUE, do.hfenplus = TRUE,
          do.mad = TRUE, do.zcx = TRUE, #do.brondcounts = TRUE,
          windowsizes = c(15,3600,3600), do.parallel = FALSE,
          minimumFileSizeMB = 0, verbose = FALSE)
  
  rn = dir("output_test/meta/basic/",full.names = TRUE)
  load(rn[1])
  expect_equal(ncol(M$metashort), 9)
  expect_true(nrow(M$metashort) == 11280)
  expect_equal(mean(M$metashort$BFEN),  0.0458, tolerance = 4)
  expect_equal(mean(M$metashort$LFENMO),  0.0447, tolerance = 4)
  expect_equal(mean(M$metashort$HFENplus),  0.0914, tolerance = 4)
  expect_equal(mean(M$metashort$MAD),  0.0073, tolerance = 4)
  expect_equal(mean(M$metashort$anglex),  57.4683, tolerance = 4)
  expect_equal(mean(M$metashort$anglez),  0.3522, tolerance = 4)
  expect_equal(mean(M$metashort$ZCX),  14.94, tolerance = 2)
  # expect_equal(sum(M$metashort$BrondCount_x), 17690)
  # expect_equal(sum(M$metashort$BrondCount_y), 60971)
  # expect_equal(sum(M$metashort$BrondCount_z), 957584)
  if (file.exists(fn)) file.remove(fn)
})