tests/testthat/test-long_mv_workflow.R

In pcvr: Plant Phenotyping and Bayesian Statistics

test_that("reading mv github data as long works", {
  skip_on_cran()
  skip_if_offline(host = "r-project.org")
  #* test read.pcv
  mv <- read.pcv(paste0(
    "https://media.githubusercontent.com/media/joshqsumner/pcvrTestData/",
    "main/pcv4-multi-value-traits.csv"
  ), mode = "long")

  expect_equal(dim(mv), c(513720, 21))
  expect_equal(colnames(mv), c(
    "camera", "imgtype", "zoom", "exposure", "gain", "frame", "rotation",
    "lifter", "timestamp", "id", "barcode", "treatment", "velocity",
    "cartag", "measurementlabel", "other", "image", "sample", "trait",
    "value", "label"
  ))

  mv$genotype <- substr(mv$barcode, 3, 5)
  mv$genotype <- ifelse(mv$genotype == "002", "B73",
    ifelse(mv$genotype == "003", "W605S",
      ifelse(mv$genotype == "004", "MM", "Mo17")
    )
  )
  mv$fertilizer <- substr(mv$barcode, 8, 8)
  mv$fertilizer <- ifelse(mv$fertilizer == "A", "100",
    ifelse(mv$fertilizer == "B", "50", "0")
  )
  # test bw.time
  mv <- bw.time(mv, timeCol = "timestamp", group = "barcode", plot = FALSE)
  expect_equal(dim(mv), c(513720, 24))
  expect_equal(colnames(mv)[24], "DAS")

  # test bw.outliers

  mvNoOutliers <- suppressWarnings(bw.outliers(
    df = mv, phenotype = "hue_frequencies", naTo0 = FALSE, plot = TRUE,
    group = c("DAS", "genotype", "fertilizer"), cutoff = 3, plotgroup = c("barcode", "rotation")
  ))

  pct_removed <- nrow(mvNoOutliers$data) / nrow(mv)
  expect_equal(pct_removed, 0.93, tolerance = 0.015)
  expect_s3_class(mvNoOutliers$plot, "ggplot")

  mvNoOutliers <- suppressWarnings(bw.outliers(
    df = mv, phenotype = "hue_frequencies", naTo0 = FALSE, plot = FALSE, outlierMethod = "mahalanobis",
    group = c("DAS", "genotype", "fertilizer"), cutoff = 3, plotgroup = c("barcode", "rotation")
  ))

  pct_removed <- nrow(mvNoOutliers) / nrow(mv)
  expect_equal(pct_removed, 0.945, tolerance = 0.015)

  #* test joyplot
  joyplot <- pcv.joyplot(mv[mv$DAS == 18, ],
    index = "hue_frequencies",
    group = c("fertilizer", "genotype")
  )
  expect_s3_class(joyplot, "ggplot")

  #* test mv_ag
  mv_ag1 <- mv_ag(mv,
    group = c("DAS", "genotype", "fertilizer"),
    mvCols = "hue", n_per_group = 2
  )
  mv2 <- mv
  mv2$trait <- rep(c("hue_frequencies", "hue_other"), length.out = nrow(mv2))
  expect_error(
    mv_ag2 <- mv_ag(
      mv2,
      group = c("DAS", "genotype", "fertilizer"),
      mvCols = "hue",
      outRows = 3000
    )
  )
  expect_equal(dim(mv_ag1), c(42840, 6))

  #* test EMD
  images <- unique(mv$image)[1:10]
  emd <- pcv.emd(
    df = mv[mv$image %in% images, ], cols = "hue_frequencies", reorder = c("fertilizer", "genotype"),
    mat = FALSE, plot = TRUE, parallel = 1, raiseError = FALSE
  )
  expect_s3_class(emd$plot, "ggplot")
  expect_equal(dim(emd$data), c(110, 7))
  expect_equal(sum(emd$data$emd), 2080.817, tolerance = 0.01)

  #* test network
  net <- pcv.net(emd$data)
  expect_error(pcv.net(c(1, 2, 3)))
  expect_s3_class(net[[3]], "igraph")
  expect_equal(dim(net$nodes), c(8, 12))
  expect_equal(dim(net$edges), c(44, 11))
})