tests/testthat/test-filterProfile.r
In OskarHansson/strvalidator: Process Control and Validation of Forensic STR Kits
context("filterProfile")
################################################################################
# CHANGE LOG
# 22.03.2019: Changed deprecated 'matches' to 'expect_match'.
# 28.08.2016: Updated to work with improved filterProfile.
# 15.12.2015: Added test 10-12 to test new option 'exact'.
# 09.04.2015: Added test 09 to test new option 'invert'.
# 22.01.2014: Fixed test 2 in accordance with change in 'filterProfile'.
# 20.01.2014: All tests working.
#
# require(testthat)
# test_dir("inst/tests/")
# test_file("tests/testthat/test-filterProfile.r")
# test_dir("tests/testthat")
test_that("filterProfile", {
# Load test data.
data(set1)
data(ref1)
data(set2)
data(ref2)
data(set7)
data(ref7)
# Dataset with double notation (16/16 vs 16).
set1d <- slim(set1, fix = c("Sample.Name", "Marker"), stack = c("Allele", "Height"))
ref1d <- slim(ref1, fix = c("Sample.Name", "Marker"), stack = c("Allele"))
sampleName1 <- ref1d$Sample.Name
marker1 <- ref1d$Marker
allele1 <- ref1d$Allele
# Duplicate homozygous alleles.
allele1 <- c(allele1[1:19], allele1[19], allele1[20:33])
marker1 <- c(marker1[1:19], marker1[19], marker1[20:33])
sampleName1 <- c(sampleName1[1], sampleName1)
# Make new dataframe.
ref1d <- data.frame(Sample.Name = sampleName1, Marker = marker1, Allele = allele1, stringsAsFactors = FALSE)
# Dataset with missing marker.
set3 <- set2[set2$Marker != "TH01", ]
# Dataset with different case in sample name.
set4 <- set2
set4$Sample.Name[set4$Sample.Name == "SampleA02"] <- "sAmPLea02"
# Dataset with dirty' data.
set6 <- slim(set1,
fix = c("Sample.Name", "Marker"),
stack = c("Allele", "Height"),
keep.na = TRUE
)
ref6 <- slim(ref1,
fix = c("Sample.Name", "Marker"),
stack = c("Allele"),
keep.na = TRUE
)
# Extract a single samples.
set7 <- set7[set7$Sample.Name == "H01", ]
ref7 <- ref7[ref7$Sample.Name == "H", ]
# Dataset without Sample.Name column.
set8 <- slim(set1, fix = c("Sample.Name", "Marker"), stack = c("Allele", "Height"))
ref8 <- slim(ref1, fix = c("Sample.Name", "Marker"), stack = c("Allele"))
ref8 <- ref8[, c("Marker", "Allele")]
# Dataset testing exact matching.
# 'PC1' is in 'NOPC15'
set9 <- slim(set1, fix = c("Sample.Name", "Marker"), stack = c("Allele", "Height"))
set9 <- trim(data = set9, samples = "PC1|ladder")
set9[set9$Sample.Name == "Ladder", ]$Sample.Name <- "NOPC15"
ref9 <- suppressWarnings(guessProfile(data = set9, ol.rm = TRUE))
ref9$Height <- as.character(ref9$Height) # To enable comparison using 'all.equal'.
# TEST 01 -------------------------------------------------------------------
# Test that filtering of two 'clean' samples work.
# Analyse dataframe.
res <- filterProfile(
data = set2, ref = ref2,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
expect_true(any(grepl("Dye", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Dye)))
# Check result.
expect_that(ncol(res), equals(5))
expect_that(nrow(res), equals(31))
# TEST 02 -------------------------------------------------------------------
# Test that adding missing loci work.
# Analyse dataframe.
res <- filterProfile(
data = set3, ref = ref2,
add.missing.loci = TRUE,
keep.na = TRUE,
ignore.case = FALSE,
kit = "SGMPlus",
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_true(any(is.na(res$Allele)))
expect_true(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(6))
expect_that(nrow(res), equals(30))
# TEST 03 -------------------------------------------------------------------
# Test that keep NA loci work.
# Analyse dataframe.
res <- filterProfile(
data = set2, ref = ref2,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
kit = "SGMPlus",
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_true(any(is.na(res$Allele)))
expect_true(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(6))
expect_that(nrow(res), equals(32))
# TEST 04 -------------------------------------------------------------------
# Test that ignore case = FALSE work.
# Analyse dataframe.
res <- filterProfile(
data = set2, ref = ref2,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
kit = "SGMPlus",
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_true(any(is.na(res$Allele)))
expect_true(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(6))
expect_that(nrow(res), equals(32))
# TEST 05 -------------------------------------------------------------------
# Test that 'fat' data gives error.
# Test using 'fat' data.
expect_that(
filterProfile(
data = set1, ref = ref2,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
debug = FALSE
),
throws_error()
)
# Test using 'fat' ref.
expect_that(
filterProfile(
data = set2, ref = ref1,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
debug = FALSE
),
throws_error()
)
# Test using 'fat' data and ref.
expect_that(
filterProfile(
data = set1, ref = ref1,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
debug = FALSE
),
throws_error()
)
# TEST 06 -------------------------------------------------------------------
# Test that filtering of 'dirty' samples work.
# Analyse dataframe.
res <- filterProfile(
data = set6, ref = ref6,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(264))
# Check success of filtering.
expect_true(all(unique(res$Allele[res$Marker == "AMEL"]) == c("X", "Y")))
expect_true(all(unique(res$Allele[res$Marker == "D3S1358"]) == c("17", "18")))
expect_true(all(unique(res$Allele[res$Marker == "TH01"]) == c("6", "9.3")))
expect_true(all(unique(res$Allele[res$Marker == "D21S11"]) == c("29", "31.2")))
expect_true(all(unique(res$Allele[res$Marker == "D18S51"]) == c("16", "18")))
expect_true(all(unique(res$Allele[res$Marker == "D10S1248"]) == c("13", "15")))
expect_true(all(unique(res$Allele[res$Marker == "D1S1656"]) == c("12", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D2S1338"]) == c("22", "25")))
expect_true(all(unique(res$Allele[res$Marker == "D16S539"]) == c("9", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D22S1045"]) == c("16")))
expect_true(all(unique(res$Allele[res$Marker == "vWA"]) == c("16", "19")))
expect_true(all(unique(res$Allele[res$Marker == "D8S1179"]) == c("14", "15")))
expect_true(all(unique(res$Allele[res$Marker == "FGA"]) == c("20", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D2S441"]) == c("10", "14")))
expect_true(all(unique(res$Allele[res$Marker == "D12S391"]) == c("18", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D19S433"]) == c("13", "14")))
expect_true(all(unique(res$Allele[res$Marker == "SE33"]) == c("15", "16")))
# TEST 07 -------------------------------------------------------------------
# Test that filtering using double notation work (16/16 vs 16).
# Analyse dataframe.
res <- filterProfile(
data = set1d, ref = ref1d,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(264))
# Check success of filtering.
expect_true(all(unique(res$Allele[res$Marker == "AMEL"]) == c("X", "Y")))
expect_true(all(unique(res$Allele[res$Marker == "D3S1358"]) == c("17", "18")))
expect_true(all(unique(res$Allele[res$Marker == "TH01"]) == c("6", "9.3")))
expect_true(all(unique(res$Allele[res$Marker == "D21S11"]) == c("29", "31.2")))
expect_true(all(unique(res$Allele[res$Marker == "D18S51"]) == c("16", "18")))
expect_true(all(unique(res$Allele[res$Marker == "D10S1248"]) == c("13", "15")))
expect_true(all(unique(res$Allele[res$Marker == "D1S1656"]) == c("12", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D2S1338"]) == c("22", "25")))
expect_true(all(unique(res$Allele[res$Marker == "D16S539"]) == c("9", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D22S1045"]) == c("16")))
expect_true(all(unique(res$Allele[res$Marker == "vWA"]) == c("16", "19")))
expect_true(all(unique(res$Allele[res$Marker == "D8S1179"]) == c("14", "15")))
expect_true(all(unique(res$Allele[res$Marker == "FGA"]) == c("20", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D2S441"]) == c("10", "14")))
expect_true(all(unique(res$Allele[res$Marker == "D12S391"]) == c("18", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D19S433"]) == c("13", "14")))
expect_true(all(unique(res$Allele[res$Marker == "SE33"]) == c("15", "16")))
# TEST 08 -------------------------------------------------------------------
# Test that filtering of 'dirty' samples without Sample.Name work.
# Analyse dataframe.
res <- filterProfile(
data = set8, ref = ref8,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(270))
# Check that 'Ladder' is in result.
expect_true("Ladder" %in% res$Sample.Name)
# Check success of filtering.
expect_true(all(unique(res$Allele[res$Marker == "AMEL"]) == c("X", "Y")))
expect_true(all(unique(res$Allele[res$Marker == "D3S1358"]) == c("17", "18")))
expect_true(all(unique(res$Allele[res$Marker == "TH01"]) == c("6", "9.3")))
expect_true(all(unique(res$Allele[res$Marker == "D21S11"]) == c("29", "31.2")))
expect_true(all(unique(res$Allele[res$Marker == "D18S51"]) == c("16", "18")))
expect_true(all(unique(res$Allele[res$Marker == "D10S1248"]) == c("13", "15")))
expect_true(all(unique(res$Allele[res$Marker == "D1S1656"]) == c("12", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D2S1338"]) == c("22", "25")))
expect_true(all(unique(res$Allele[res$Marker == "D16S539"]) == c("9", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D22S1045"]) == c("16")))
expect_true(all(unique(res$Allele[res$Marker == "vWA"]) == c("16", "19")))
expect_true(all(unique(res$Allele[res$Marker == "D8S1179"]) == c("14", "15")))
expect_true(all(unique(res$Allele[res$Marker == "FGA"]) == c("20", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D2S441"]) == c("10", "14")))
expect_true(all(unique(res$Allele[res$Marker == "D12S391"]) == c("18", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D19S433"]) == c("13", "14")))
expect_true(all(unique(res$Allele[res$Marker == "SE33"]) == c("15", "16")))
# TEST 09 -------------------------------------------------------------------
# Test that invert filtering of 'dirty' samples work.
# Analyse dataframe.
res <- filterProfile(
data = set6, ref = ref6,
add.missing.loci = TRUE,
keep.na = TRUE,
ignore.case = FALSE,
invert = TRUE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_true(any(is.na(res$Allele)))
expect_true(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(240))
# Check success of filtering.
expect_false(all(unique(res$Allele[res$Marker == "AMEL"]) %in% c("X", "Y")))
expect_false(all(unique(res$Allele[res$Marker == "D3S1358"]) %in% c("17", "18")))
expect_false(all(unique(res$Allele[res$Marker == "TH01"]) %in% c("6", "9.3")))
expect_false(all(unique(res$Allele[res$Marker == "D21S11"]) %in% c("29", "31.2")))
expect_false(all(unique(res$Allele[res$Marker == "D18S51"]) %in% c("16", "18")))
expect_false(all(unique(res$Allele[res$Marker == "D10S1248"]) %in% c("13", "15")))
expect_false(all(unique(res$Allele[res$Marker == "D1S1656"]) %in% c("12", "13")))
expect_false(all(unique(res$Allele[res$Marker == "D2S1338"]) %in% c("22", "25")))
expect_false(all(unique(res$Allele[res$Marker == "D16S539"]) %in% c("9", "13")))
expect_false(all(unique(res$Allele[res$Marker == "D22S1045"]) %in% c("16")))
expect_false(all(unique(res$Allele[res$Marker == "vWA"]) %in% c("16", "19")))
expect_false(all(unique(res$Allele[res$Marker == "D8S1179"]) %in% c("14", "15")))
expect_false(all(unique(res$Allele[res$Marker == "FGA"]) %in% c("20", "23")))
expect_false(all(unique(res$Allele[res$Marker == "D2S441"]) %in% c("10", "14")))
expect_false(all(unique(res$Allele[res$Marker == "D12S391"]) %in% c("18", "23")))
expect_false(all(unique(res$Allele[res$Marker == "D19S433"]) %in% c("13", "14")))
expect_false(all(unique(res$Allele[res$Marker == "SE33"]) %in% c("15", "16")))
# TEST 10 -------------------------------------------------------------------
# Test that filtering generates incorrect profile in 'fast' mode.
# Analyse dataframe.
res <- filterProfile(
data = set9, ref = ref9,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
exact = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(71))
# Check if filtered profile equals reference.
expect_false(is.logical(all.equal(res[res$Sample.Name == "NOPC15", ],
ref9[ref9$Sample.Name == "NOPC15", ],
check.attributes = FALSE
)))
# TEST 11 -------------------------------------------------------------------
# Test that filtering generates correct profile in 'fast' mode.
# Analyse dataframe.
res <- filterProfile(
data = set9, ref = ref9,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
exact = TRUE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(66))
# Check if filtered profile equals reference.
expect_true(is.logical(all.equal(res[res$Sample.Name == "NOPC15", ],
ref9[ref9$Sample.Name == "NOPC15", ],
check.attributes = FALSE
)))
# TEST 12 -------------------------------------------------------------------
# Test that filtering generates incorrect profile in 'slow' mode.
# Analyse dataframe.
res <- filterProfile(
data = set9, ref = ref9,
add.missing.loci = TRUE,
keep.na = TRUE,
ignore.case = FALSE,
exact = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(105))
# Check if filtered profile equals reference.
expect_false(is.logical(all.equal(res[res$Sample.Name == "NOPC15", ],
ref9[ref9$Sample.Name == "NOPC15", ],
check.attributes = FALSE
)))
# TEST 12 -------------------------------------------------------------------
# Test that filtering generates correct profile in 'slow' mode.
# Analyse dataframe.
res <- filterProfile(
data = set9, ref = ref9,
add.missing.loci = TRUE,
keep.na = TRUE,
ignore.case = FALSE,
exact = TRUE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(66))
# Check if filtered profile equals reference.
expect_true(is.logical(all.equal(res[res$Sample.Name == "NOPC15", ],
ref9[ref9$Sample.Name == "NOPC15", ],
check.attributes = FALSE
)))
# TEST 13 -------------------------------------------------------------------
# Test that filtering of only quality sensors work.
# Analyse dataframe.
res <- filterProfile(
data = set7, ref = ref7, kit = "ESSplexSEQS",
add.missing.loci = FALSE,
keep.na = FALSE,
qs.rm = TRUE,
sex.rm = FALSE,
ignore.case = FALSE,
filter.allele = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true("Sample.Name" %in% names(res))
expect_true("Marker" %in% names(res))
expect_true("Dye" %in% names(res))
expect_true("Allele" %in% names(res))
expect_true("Size" %in% names(res))
expect_true("Height" %in% names(res))
expect_true("Data.Point" %in% names(res))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Dye)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Size)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Data.Point)))
# Check result.
expect_that(ncol(res), equals(7))
expect_that(nrow(res), equals(42))
# Check markers.
expect_false("QS1" %in% res$Marker)
expect_true("AM" %in% res$Marker)
expect_true("TH01" %in% res$Marker)
expect_true("D3S1358" %in% res$Marker)
expect_true("vWA" %in% res$Marker)
expect_true("D21S11" %in% res$Marker)
expect_false("QS2" %in% res$Marker)
expect_true("D16S539" %in% res$Marker)
expect_true("D1S1656" %in% res$Marker)
expect_true("D19S433" %in% res$Marker)
expect_true("SE33" %in% res$Marker)
expect_true("D10S1248" %in% res$Marker)
expect_true("D22S1045" %in% res$Marker)
expect_true("D12S391" %in% res$Marker)
expect_true("D8S1179" %in% res$Marker)
expect_true("D2S1338" %in% res$Marker)
expect_true("D2S441" %in% res$Marker)
expect_true("D18S51" %in% res$Marker)
expect_true("FGA" %in% res$Marker)
# Check success of filtering.
expect_false(all(c("Q") %in% res$Allele[res$Marker == "QS1"]))
expect_true(all(c("X", "Y") %in% res$Allele[res$Marker == "AM"]))
expect_true(all(c("6", "9.3") %in% res$Allele[res$Marker == "TH01"]))
expect_true(all(c("16", "17", "18") %in% res$Allele[res$Marker == "D3S1358"]))
expect_true(all(c("16", "18", "19") %in% res$Allele[res$Marker == "vWA"]))
expect_true(all(c("29", "31.2") %in% res$Allele[res$Marker == "D21S11"]))
expect_false(all(c("S") %in% res$Allele[res$Marker == "QS2"]))
expect_true(all(c("9", "10", "13") %in% res$Allele[res$Marker == "D16S539"]))
expect_true(all(c("12", "13", "14") %in% res$Allele[res$Marker == "D1S1656"]))
expect_true(all(c("13", "14") %in% res$Allele[res$Marker == "D19S433"]))
expect_true(all(c("15", "16") %in% res$Allele[res$Marker == "SE33"]))
expect_true(all(c("13", "15") %in% res$Allele[res$Marker == "D10S1248"]))
expect_true(all(c("13", "OL", "14", "16") %in% res$Allele[res$Marker == "D22S1045"]))
expect_true(all(c("18", "23") %in% res$Allele[res$Marker == "D12S391"]))
expect_true(all(c("14", "15") %in% res$Allele[res$Marker == "D8S1179"]))
expect_true(all(c("22", "25") %in% res$Allele[res$Marker == "D2S1338"]))
expect_true(all(c("OL", "10", "13.3", "14") %in% res$Allele[res$Marker == "D2S441"]))
expect_true(all(c("16", "18") %in% res$Allele[res$Marker == "D18S51"]))
expect_true(all(c("20", "23") %in% res$Allele[res$Marker == "FGA"]))
# TEST 14 -------------------------------------------------------------------
# Test that filtering of only sex markers work.
# Analyse dataframe.
res <- filterProfile(
data = set7, ref = ref7, kit = "ESSplexSEQS",
add.missing.loci = FALSE,
keep.na = FALSE,
qs.rm = FALSE,
sex.rm = TRUE,
ignore.case = FALSE,
filter.allele = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true("Sample.Name" %in% names(res))
expect_true("Marker" %in% names(res))
expect_true("Dye" %in% names(res))
expect_true("Allele" %in% names(res))
expect_true("Size" %in% names(res))
expect_true("Height" %in% names(res))
expect_true("Data.Point" %in% names(res))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Dye)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Size)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Data.Point)))
# Check markers.
expect_true("QS1" %in% res$Marker)
expect_false("AM" %in% res$Marker)
expect_true("TH01" %in% res$Marker)
expect_true("D3S1358" %in% res$Marker)
expect_true("vWA" %in% res$Marker)
expect_true("D21S11" %in% res$Marker)
expect_true("QS2" %in% res$Marker)
expect_true("D16S539" %in% res$Marker)
expect_true("D1S1656" %in% res$Marker)
expect_true("D19S433" %in% res$Marker)
expect_true("SE33" %in% res$Marker)
expect_true("D10S1248" %in% res$Marker)
expect_true("D22S1045" %in% res$Marker)
expect_true("D12S391" %in% res$Marker)
expect_true("D8S1179" %in% res$Marker)
expect_true("D2S1338" %in% res$Marker)
expect_true("D2S441" %in% res$Marker)
expect_true("D18S51" %in% res$Marker)
expect_true("FGA" %in% res$Marker)
# Check result.
expect_that(ncol(res), equals(7))
expect_that(nrow(res), equals(42))
# Check success of filtering.
expect_true(all(c("Q") %in% res$Allele[res$Marker == "QS1"]))
expect_false(all(c("X", "Y") %in% res$Allele[res$Marker == "AM"]))
expect_true(all(c("6", "9.3") %in% res$Allele[res$Marker == "TH01"]))
expect_true(all(c("16", "17", "18") %in% res$Allele[res$Marker == "D3S1358"]))
expect_true(all(c("16", "18", "19") %in% res$Allele[res$Marker == "vWA"]))
expect_true(all(c("29", "31.2") %in% res$Allele[res$Marker == "D21S11"]))
expect_true(all(c("S") %in% res$Allele[res$Marker == "QS2"]))
expect_true(all(c("9", "10", "13") %in% res$Allele[res$Marker == "D16S539"]))
expect_true(all(c("12", "13", "14") %in% res$Allele[res$Marker == "D1S1656"]))
expect_true(all(c("13", "14") %in% res$Allele[res$Marker == "D19S433"]))
expect_true(all(c("15", "16") %in% res$Allele[res$Marker == "SE33"]))
expect_true(all(c("13", "15") %in% res$Allele[res$Marker == "D10S1248"]))
expect_true(all(c("13", "OL", "14", "16") %in% res$Allele[res$Marker == "D22S1045"]))
expect_true(all(c("18", "23") %in% res$Allele[res$Marker == "D12S391"]))
expect_true(all(c("14", "15") %in% res$Allele[res$Marker == "D8S1179"]))
expect_true(all(c("22", "25") %in% res$Allele[res$Marker == "D2S1338"]))
expect_true(all(c("OL", "10", "13.3", "14") %in% res$Allele[res$Marker == "D2S441"]))
expect_true(all(c("16", "18") %in% res$Allele[res$Marker == "D18S51"]))
expect_true(all(c("20", "23") %in% res$Allele[res$Marker == "FGA"]))
# TEST 15 -------------------------------------------------------------------
# Test that filtering of sex markers and quality sensors work.
# Analyse dataframe.
res <- filterProfile(
data = set7, ref = ref7, kit = "ESSplexSEQS",
add.missing.loci = FALSE,
keep.na = FALSE,
qs.rm = TRUE,
sex.rm = TRUE,
ignore.case = FALSE,
filter.allele = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true("Sample.Name" %in% names(res))
expect_true("Marker" %in% names(res))
expect_true("Dye" %in% names(res))
expect_true("Allele" %in% names(res))
expect_true("Size" %in% names(res))
expect_true("Height" %in% names(res))
expect_true("Data.Point" %in% names(res))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Dye)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Size)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Data.Point)))
# Check markers.
expect_false("QS1" %in% res$Marker)
expect_false("AM" %in% res$Marker)
expect_true("TH01" %in% res$Marker)
expect_true("D3S1358" %in% res$Marker)
expect_true("vWA" %in% res$Marker)
expect_true("D21S11" %in% res$Marker)
expect_false("QS2" %in% res$Marker)
expect_true("D16S539" %in% res$Marker)
expect_true("D1S1656" %in% res$Marker)
expect_true("D19S433" %in% res$Marker)
expect_true("SE33" %in% res$Marker)
expect_true("D10S1248" %in% res$Marker)
expect_true("D22S1045" %in% res$Marker)
expect_true("D12S391" %in% res$Marker)
expect_true("D8S1179" %in% res$Marker)
expect_true("D2S1338" %in% res$Marker)
expect_true("D2S441" %in% res$Marker)
expect_true("D18S51" %in% res$Marker)
expect_true("FGA" %in% res$Marker)
# Check result.
expect_that(ncol(res), equals(7))
expect_that(nrow(res), equals(40))
# Check success of filtering.
expect_false(all(c("Q") %in% res$Allele[res$Marker == "QS1"]))
expect_false(all(c("X", "Y") %in% res$Allele[res$Marker == "AM"]))
expect_true(all(c("6", "9.3") %in% res$Allele[res$Marker == "TH01"]))
expect_true(all(c("16", "17", "18") %in% res$Allele[res$Marker == "D3S1358"]))
expect_true(all(c("16", "18", "19") %in% res$Allele[res$Marker == "vWA"]))
expect_true(all(c("29", "31.2") %in% res$Allele[res$Marker == "D21S11"]))
expect_false(all(c("S") %in% res$Allele[res$Marker == "QS2"]))
expect_true(all(c("9", "10", "13") %in% res$Allele[res$Marker == "D16S539"]))
expect_true(all(c("12", "13", "14") %in% res$Allele[res$Marker == "D1S1656"]))
expect_true(all(c("13", "14") %in% res$Allele[res$Marker == "D19S433"]))
expect_true(all(c("15", "16") %in% res$Allele[res$Marker == "SE33"]))
expect_true(all(c("13", "15") %in% res$Allele[res$Marker == "D10S1248"]))
expect_true(all(c("13", "OL", "14", "16") %in% res$Allele[res$Marker == "D22S1045"]))
expect_true(all(c("18", "23") %in% res$Allele[res$Marker == "D12S391"]))
expect_true(all(c("14", "15") %in% res$Allele[res$Marker == "D8S1179"]))
expect_true(all(c("22", "25") %in% res$Allele[res$Marker == "D2S1338"]))
expect_true(all(c("OL", "10", "13.3", "14") %in% res$Allele[res$Marker == "D2S441"]))
expect_true(all(c("16", "18") %in% res$Allele[res$Marker == "D18S51"]))
expect_true(all(c("20", "23") %in% res$Allele[res$Marker == "FGA"]))
# TEST 16 -------------------------------------------------------------------
# Test that filtering of sex markers, quality sensors, and known alleles work.
res <- filterProfile(
data = set7, ref = ref7, kit = "ESSplexSEQS",
add.missing.loci = FALSE,
keep.na = FALSE,
qs.rm = TRUE,
sex.rm = TRUE,
ignore.case = FALSE,
filter.allele = TRUE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true("Sample.Name" %in% names(res))
expect_true("Marker" %in% names(res))
expect_true("Dye" %in% names(res))
expect_true("Allele" %in% names(res))
expect_true("Size" %in% names(res))
expect_true("Height" %in% names(res))
expect_true("Data.Point" %in% names(res))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Dye)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Size)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Data.Point)))
# Check markers.
expect_false("QS1" %in% res$Marker)
expect_false("AM" %in% res$Marker)
expect_true("TH01" %in% res$Marker)
expect_true("D3S1358" %in% res$Marker)
expect_true("vWA" %in% res$Marker)
expect_true("D21S11" %in% res$Marker)
expect_false("QS2" %in% res$Marker)
expect_true("D16S539" %in% res$Marker)
expect_true("D1S1656" %in% res$Marker)
expect_true("D19S433" %in% res$Marker)
expect_true("SE33" %in% res$Marker)
expect_true("D10S1248" %in% res$Marker)
expect_true("D22S1045" %in% res$Marker)
expect_true("D12S391" %in% res$Marker)
expect_true("D8S1179" %in% res$Marker)
expect_true("D2S1338" %in% res$Marker)
expect_true("D2S441" %in% res$Marker)
expect_true("D18S51" %in% res$Marker)
expect_true("FGA" %in% res$Marker)
# Check result.
expect_that(ncol(res), equals(7))
expect_that(nrow(res), equals(31))
# Check success of filtering.
expect_false(all(c("Q") %in% res$Allele[res$Marker == "QS1"]))
expect_false(all(c("X", "Y") %in% res$Allele[res$Marker == "AM"]))
expect_true(all(c("6", "9.3") %in% res$Allele[res$Marker == "TH01"]))
expect_true(all(c("17", "18") %in% res$Allele[res$Marker == "D3S1358"]))
expect_true(all(c("16", "19") %in% res$Allele[res$Marker == "vWA"]))
expect_true(all(c("29", "31.2") %in% res$Allele[res$Marker == "D21S11"]))
expect_false(all(c("S") %in% res$Allele[res$Marker == "QS2"]))
expect_true(all(c("9", "13") %in% res$Allele[res$Marker == "D16S539"]))
expect_true(all(c("12", "13") %in% res$Allele[res$Marker == "D1S1656"]))
expect_true(all(c("13", "14") %in% res$Allele[res$Marker == "D19S433"]))
expect_true(all(c("15", "16") %in% res$Allele[res$Marker == "SE33"]))
expect_true(all(c("13", "15") %in% res$Allele[res$Marker == "D10S1248"]))
expect_true(all(c("16") %in% res$Allele[res$Marker == "D22S1045"]))
expect_true(all(c("18", "23") %in% res$Allele[res$Marker == "D12S391"]))
expect_true(all(c("14", "15") %in% res$Allele[res$Marker == "D8S1179"]))
expect_true(all(c("22", "25") %in% res$Allele[res$Marker == "D2S1338"]))
expect_true(all(c("10", "14") %in% res$Allele[res$Marker == "D2S441"]))
expect_true(all(c("16", "18") %in% res$Allele[res$Marker == "D18S51"]))
expect_true(all(c("20", "23") %in% res$Allele[res$Marker == "FGA"]))
})
OskarHansson/strvalidator documentation built on Jan. 25, 2025, 11:49 p.m.
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context("filterProfile")
################################################################################
# CHANGE LOG
# 22.03.2019: Changed deprecated 'matches' to 'expect_match'.
# 28.08.2016: Updated to work with improved filterProfile.
# 15.12.2015: Added test 10-12 to test new option 'exact'.
# 09.04.2015: Added test 09 to test new option 'invert'.
# 22.01.2014: Fixed test 2 in accordance with change in 'filterProfile'.
# 20.01.2014: All tests working.
#
# require(testthat)
# test_dir("inst/tests/")
# test_file("tests/testthat/test-filterProfile.r")
# test_dir("tests/testthat")
test_that("filterProfile", {
# Load test data.
data(set1)
data(ref1)
data(set2)
data(ref2)
data(set7)
data(ref7)
# Dataset with double notation (16/16 vs 16).
set1d <- slim(set1, fix = c("Sample.Name", "Marker"), stack = c("Allele", "Height"))
ref1d <- slim(ref1, fix = c("Sample.Name", "Marker"), stack = c("Allele"))
sampleName1 <- ref1d$Sample.Name
marker1 <- ref1d$Marker
allele1 <- ref1d$Allele
# Duplicate homozygous alleles.
allele1 <- c(allele1[1:19], allele1[19], allele1[20:33])
marker1 <- c(marker1[1:19], marker1[19], marker1[20:33])
sampleName1 <- c(sampleName1[1], sampleName1)
# Make new dataframe.
ref1d <- data.frame(Sample.Name = sampleName1, Marker = marker1, Allele = allele1, stringsAsFactors = FALSE)
# Dataset with missing marker.
set3 <- set2[set2$Marker != "TH01", ]
# Dataset with different case in sample name.
set4 <- set2
set4$Sample.Name[set4$Sample.Name == "SampleA02"] <- "sAmPLea02"
# Dataset with dirty' data.
set6 <- slim(set1,
fix = c("Sample.Name", "Marker"),
stack = c("Allele", "Height"),
keep.na = TRUE
)
ref6 <- slim(ref1,
fix = c("Sample.Name", "Marker"),
stack = c("Allele"),
keep.na = TRUE
)
# Extract a single samples.
set7 <- set7[set7$Sample.Name == "H01", ]
ref7 <- ref7[ref7$Sample.Name == "H", ]
# Dataset without Sample.Name column.
set8 <- slim(set1, fix = c("Sample.Name", "Marker"), stack = c("Allele", "Height"))
ref8 <- slim(ref1, fix = c("Sample.Name", "Marker"), stack = c("Allele"))
ref8 <- ref8[, c("Marker", "Allele")]
# Dataset testing exact matching.
# 'PC1' is in 'NOPC15'
set9 <- slim(set1, fix = c("Sample.Name", "Marker"), stack = c("Allele", "Height"))
set9 <- trim(data = set9, samples = "PC1|ladder")
set9[set9$Sample.Name == "Ladder", ]$Sample.Name <- "NOPC15"
ref9 <- suppressWarnings(guessProfile(data = set9, ol.rm = TRUE))
ref9$Height <- as.character(ref9$Height) # To enable comparison using 'all.equal'.
# TEST 01 -------------------------------------------------------------------
# Test that filtering of two 'clean' samples work.
# Analyse dataframe.
res <- filterProfile(
data = set2, ref = ref2,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
expect_true(any(grepl("Dye", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Dye)))
# Check result.
expect_that(ncol(res), equals(5))
expect_that(nrow(res), equals(31))
# TEST 02 -------------------------------------------------------------------
# Test that adding missing loci work.
# Analyse dataframe.
res <- filterProfile(
data = set3, ref = ref2,
add.missing.loci = TRUE,
keep.na = TRUE,
ignore.case = FALSE,
kit = "SGMPlus",
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_true(any(is.na(res$Allele)))
expect_true(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(6))
expect_that(nrow(res), equals(30))
# TEST 03 -------------------------------------------------------------------
# Test that keep NA loci work.
# Analyse dataframe.
res <- filterProfile(
data = set2, ref = ref2,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
kit = "SGMPlus",
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_true(any(is.na(res$Allele)))
expect_true(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(6))
expect_that(nrow(res), equals(32))
# TEST 04 -------------------------------------------------------------------
# Test that ignore case = FALSE work.
# Analyse dataframe.
res <- filterProfile(
data = set2, ref = ref2,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
kit = "SGMPlus",
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_true(any(is.na(res$Allele)))
expect_true(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(6))
expect_that(nrow(res), equals(32))
# TEST 05 -------------------------------------------------------------------
# Test that 'fat' data gives error.
# Test using 'fat' data.
expect_that(
filterProfile(
data = set1, ref = ref2,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
debug = FALSE
),
throws_error()
)
# Test using 'fat' ref.
expect_that(
filterProfile(
data = set2, ref = ref1,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
debug = FALSE
),
throws_error()
)
# Test using 'fat' data and ref.
expect_that(
filterProfile(
data = set1, ref = ref1,
add.missing.loci = FALSE,
keep.na = TRUE,
ignore.case = FALSE,
debug = FALSE
),
throws_error()
)
# TEST 06 -------------------------------------------------------------------
# Test that filtering of 'dirty' samples work.
# Analyse dataframe.
res <- filterProfile(
data = set6, ref = ref6,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(264))
# Check success of filtering.
expect_true(all(unique(res$Allele[res$Marker == "AMEL"]) == c("X", "Y")))
expect_true(all(unique(res$Allele[res$Marker == "D3S1358"]) == c("17", "18")))
expect_true(all(unique(res$Allele[res$Marker == "TH01"]) == c("6", "9.3")))
expect_true(all(unique(res$Allele[res$Marker == "D21S11"]) == c("29", "31.2")))
expect_true(all(unique(res$Allele[res$Marker == "D18S51"]) == c("16", "18")))
expect_true(all(unique(res$Allele[res$Marker == "D10S1248"]) == c("13", "15")))
expect_true(all(unique(res$Allele[res$Marker == "D1S1656"]) == c("12", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D2S1338"]) == c("22", "25")))
expect_true(all(unique(res$Allele[res$Marker == "D16S539"]) == c("9", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D22S1045"]) == c("16")))
expect_true(all(unique(res$Allele[res$Marker == "vWA"]) == c("16", "19")))
expect_true(all(unique(res$Allele[res$Marker == "D8S1179"]) == c("14", "15")))
expect_true(all(unique(res$Allele[res$Marker == "FGA"]) == c("20", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D2S441"]) == c("10", "14")))
expect_true(all(unique(res$Allele[res$Marker == "D12S391"]) == c("18", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D19S433"]) == c("13", "14")))
expect_true(all(unique(res$Allele[res$Marker == "SE33"]) == c("15", "16")))
# TEST 07 -------------------------------------------------------------------
# Test that filtering using double notation work (16/16 vs 16).
# Analyse dataframe.
res <- filterProfile(
data = set1d, ref = ref1d,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(264))
# Check success of filtering.
expect_true(all(unique(res$Allele[res$Marker == "AMEL"]) == c("X", "Y")))
expect_true(all(unique(res$Allele[res$Marker == "D3S1358"]) == c("17", "18")))
expect_true(all(unique(res$Allele[res$Marker == "TH01"]) == c("6", "9.3")))
expect_true(all(unique(res$Allele[res$Marker == "D21S11"]) == c("29", "31.2")))
expect_true(all(unique(res$Allele[res$Marker == "D18S51"]) == c("16", "18")))
expect_true(all(unique(res$Allele[res$Marker == "D10S1248"]) == c("13", "15")))
expect_true(all(unique(res$Allele[res$Marker == "D1S1656"]) == c("12", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D2S1338"]) == c("22", "25")))
expect_true(all(unique(res$Allele[res$Marker == "D16S539"]) == c("9", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D22S1045"]) == c("16")))
expect_true(all(unique(res$Allele[res$Marker == "vWA"]) == c("16", "19")))
expect_true(all(unique(res$Allele[res$Marker == "D8S1179"]) == c("14", "15")))
expect_true(all(unique(res$Allele[res$Marker == "FGA"]) == c("20", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D2S441"]) == c("10", "14")))
expect_true(all(unique(res$Allele[res$Marker == "D12S391"]) == c("18", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D19S433"]) == c("13", "14")))
expect_true(all(unique(res$Allele[res$Marker == "SE33"]) == c("15", "16")))
# TEST 08 -------------------------------------------------------------------
# Test that filtering of 'dirty' samples without Sample.Name work.
# Analyse dataframe.
res <- filterProfile(
data = set8, ref = ref8,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(270))
# Check that 'Ladder' is in result.
expect_true("Ladder" %in% res$Sample.Name)
# Check success of filtering.
expect_true(all(unique(res$Allele[res$Marker == "AMEL"]) == c("X", "Y")))
expect_true(all(unique(res$Allele[res$Marker == "D3S1358"]) == c("17", "18")))
expect_true(all(unique(res$Allele[res$Marker == "TH01"]) == c("6", "9.3")))
expect_true(all(unique(res$Allele[res$Marker == "D21S11"]) == c("29", "31.2")))
expect_true(all(unique(res$Allele[res$Marker == "D18S51"]) == c("16", "18")))
expect_true(all(unique(res$Allele[res$Marker == "D10S1248"]) == c("13", "15")))
expect_true(all(unique(res$Allele[res$Marker == "D1S1656"]) == c("12", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D2S1338"]) == c("22", "25")))
expect_true(all(unique(res$Allele[res$Marker == "D16S539"]) == c("9", "13")))
expect_true(all(unique(res$Allele[res$Marker == "D22S1045"]) == c("16")))
expect_true(all(unique(res$Allele[res$Marker == "vWA"]) == c("16", "19")))
expect_true(all(unique(res$Allele[res$Marker == "D8S1179"]) == c("14", "15")))
expect_true(all(unique(res$Allele[res$Marker == "FGA"]) == c("20", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D2S441"]) == c("10", "14")))
expect_true(all(unique(res$Allele[res$Marker == "D12S391"]) == c("18", "23")))
expect_true(all(unique(res$Allele[res$Marker == "D19S433"]) == c("13", "14")))
expect_true(all(unique(res$Allele[res$Marker == "SE33"]) == c("15", "16")))
# TEST 09 -------------------------------------------------------------------
# Test that invert filtering of 'dirty' samples work.
# Analyse dataframe.
res <- filterProfile(
data = set6, ref = ref6,
add.missing.loci = TRUE,
keep.na = TRUE,
ignore.case = FALSE,
invert = TRUE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_true(any(is.na(res$Allele)))
expect_true(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(240))
# Check success of filtering.
expect_false(all(unique(res$Allele[res$Marker == "AMEL"]) %in% c("X", "Y")))
expect_false(all(unique(res$Allele[res$Marker == "D3S1358"]) %in% c("17", "18")))
expect_false(all(unique(res$Allele[res$Marker == "TH01"]) %in% c("6", "9.3")))
expect_false(all(unique(res$Allele[res$Marker == "D21S11"]) %in% c("29", "31.2")))
expect_false(all(unique(res$Allele[res$Marker == "D18S51"]) %in% c("16", "18")))
expect_false(all(unique(res$Allele[res$Marker == "D10S1248"]) %in% c("13", "15")))
expect_false(all(unique(res$Allele[res$Marker == "D1S1656"]) %in% c("12", "13")))
expect_false(all(unique(res$Allele[res$Marker == "D2S1338"]) %in% c("22", "25")))
expect_false(all(unique(res$Allele[res$Marker == "D16S539"]) %in% c("9", "13")))
expect_false(all(unique(res$Allele[res$Marker == "D22S1045"]) %in% c("16")))
expect_false(all(unique(res$Allele[res$Marker == "vWA"]) %in% c("16", "19")))
expect_false(all(unique(res$Allele[res$Marker == "D8S1179"]) %in% c("14", "15")))
expect_false(all(unique(res$Allele[res$Marker == "FGA"]) %in% c("20", "23")))
expect_false(all(unique(res$Allele[res$Marker == "D2S441"]) %in% c("10", "14")))
expect_false(all(unique(res$Allele[res$Marker == "D12S391"]) %in% c("18", "23")))
expect_false(all(unique(res$Allele[res$Marker == "D19S433"]) %in% c("13", "14")))
expect_false(all(unique(res$Allele[res$Marker == "SE33"]) %in% c("15", "16")))
# TEST 10 -------------------------------------------------------------------
# Test that filtering generates incorrect profile in 'fast' mode.
# Analyse dataframe.
res <- filterProfile(
data = set9, ref = ref9,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
exact = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(71))
# Check if filtered profile equals reference.
expect_false(is.logical(all.equal(res[res$Sample.Name == "NOPC15", ],
ref9[ref9$Sample.Name == "NOPC15", ],
check.attributes = FALSE
)))
# TEST 11 -------------------------------------------------------------------
# Test that filtering generates correct profile in 'fast' mode.
# Analyse dataframe.
res <- filterProfile(
data = set9, ref = ref9,
add.missing.loci = FALSE,
keep.na = FALSE,
ignore.case = FALSE,
exact = TRUE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(66))
# Check if filtered profile equals reference.
expect_true(is.logical(all.equal(res[res$Sample.Name == "NOPC15", ],
ref9[ref9$Sample.Name == "NOPC15", ],
check.attributes = FALSE
)))
# TEST 12 -------------------------------------------------------------------
# Test that filtering generates incorrect profile in 'slow' mode.
# Analyse dataframe.
res <- filterProfile(
data = set9, ref = ref9,
add.missing.loci = TRUE,
keep.na = TRUE,
ignore.case = FALSE,
exact = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(105))
# Check if filtered profile equals reference.
expect_false(is.logical(all.equal(res[res$Sample.Name == "NOPC15", ],
ref9[ref9$Sample.Name == "NOPC15", ],
check.attributes = FALSE
)))
# TEST 12 -------------------------------------------------------------------
# Test that filtering generates correct profile in 'slow' mode.
# Analyse dataframe.
res <- filterProfile(
data = set9, ref = ref9,
add.missing.loci = TRUE,
keep.na = TRUE,
ignore.case = FALSE,
exact = TRUE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true(any(grepl("Sample.Name", names(res))))
expect_true(any(grepl("Marker", names(res))))
expect_true(any(grepl("Allele", names(res))))
expect_true(any(grepl("Height", names(res))))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Height)))
# Check result.
expect_that(ncol(res), equals(4))
expect_that(nrow(res), equals(66))
# Check if filtered profile equals reference.
expect_true(is.logical(all.equal(res[res$Sample.Name == "NOPC15", ],
ref9[ref9$Sample.Name == "NOPC15", ],
check.attributes = FALSE
)))
# TEST 13 -------------------------------------------------------------------
# Test that filtering of only quality sensors work.
# Analyse dataframe.
res <- filterProfile(
data = set7, ref = ref7, kit = "ESSplexSEQS",
add.missing.loci = FALSE,
keep.na = FALSE,
qs.rm = TRUE,
sex.rm = FALSE,
ignore.case = FALSE,
filter.allele = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true("Sample.Name" %in% names(res))
expect_true("Marker" %in% names(res))
expect_true("Dye" %in% names(res))
expect_true("Allele" %in% names(res))
expect_true("Size" %in% names(res))
expect_true("Height" %in% names(res))
expect_true("Data.Point" %in% names(res))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Dye)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Size)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Data.Point)))
# Check result.
expect_that(ncol(res), equals(7))
expect_that(nrow(res), equals(42))
# Check markers.
expect_false("QS1" %in% res$Marker)
expect_true("AM" %in% res$Marker)
expect_true("TH01" %in% res$Marker)
expect_true("D3S1358" %in% res$Marker)
expect_true("vWA" %in% res$Marker)
expect_true("D21S11" %in% res$Marker)
expect_false("QS2" %in% res$Marker)
expect_true("D16S539" %in% res$Marker)
expect_true("D1S1656" %in% res$Marker)
expect_true("D19S433" %in% res$Marker)
expect_true("SE33" %in% res$Marker)
expect_true("D10S1248" %in% res$Marker)
expect_true("D22S1045" %in% res$Marker)
expect_true("D12S391" %in% res$Marker)
expect_true("D8S1179" %in% res$Marker)
expect_true("D2S1338" %in% res$Marker)
expect_true("D2S441" %in% res$Marker)
expect_true("D18S51" %in% res$Marker)
expect_true("FGA" %in% res$Marker)
# Check success of filtering.
expect_false(all(c("Q") %in% res$Allele[res$Marker == "QS1"]))
expect_true(all(c("X", "Y") %in% res$Allele[res$Marker == "AM"]))
expect_true(all(c("6", "9.3") %in% res$Allele[res$Marker == "TH01"]))
expect_true(all(c("16", "17", "18") %in% res$Allele[res$Marker == "D3S1358"]))
expect_true(all(c("16", "18", "19") %in% res$Allele[res$Marker == "vWA"]))
expect_true(all(c("29", "31.2") %in% res$Allele[res$Marker == "D21S11"]))
expect_false(all(c("S") %in% res$Allele[res$Marker == "QS2"]))
expect_true(all(c("9", "10", "13") %in% res$Allele[res$Marker == "D16S539"]))
expect_true(all(c("12", "13", "14") %in% res$Allele[res$Marker == "D1S1656"]))
expect_true(all(c("13", "14") %in% res$Allele[res$Marker == "D19S433"]))
expect_true(all(c("15", "16") %in% res$Allele[res$Marker == "SE33"]))
expect_true(all(c("13", "15") %in% res$Allele[res$Marker == "D10S1248"]))
expect_true(all(c("13", "OL", "14", "16") %in% res$Allele[res$Marker == "D22S1045"]))
expect_true(all(c("18", "23") %in% res$Allele[res$Marker == "D12S391"]))
expect_true(all(c("14", "15") %in% res$Allele[res$Marker == "D8S1179"]))
expect_true(all(c("22", "25") %in% res$Allele[res$Marker == "D2S1338"]))
expect_true(all(c("OL", "10", "13.3", "14") %in% res$Allele[res$Marker == "D2S441"]))
expect_true(all(c("16", "18") %in% res$Allele[res$Marker == "D18S51"]))
expect_true(all(c("20", "23") %in% res$Allele[res$Marker == "FGA"]))
# TEST 14 -------------------------------------------------------------------
# Test that filtering of only sex markers work.
# Analyse dataframe.
res <- filterProfile(
data = set7, ref = ref7, kit = "ESSplexSEQS",
add.missing.loci = FALSE,
keep.na = FALSE,
qs.rm = FALSE,
sex.rm = TRUE,
ignore.case = FALSE,
filter.allele = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true("Sample.Name" %in% names(res))
expect_true("Marker" %in% names(res))
expect_true("Dye" %in% names(res))
expect_true("Allele" %in% names(res))
expect_true("Size" %in% names(res))
expect_true("Height" %in% names(res))
expect_true("Data.Point" %in% names(res))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Dye)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Size)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Data.Point)))
# Check markers.
expect_true("QS1" %in% res$Marker)
expect_false("AM" %in% res$Marker)
expect_true("TH01" %in% res$Marker)
expect_true("D3S1358" %in% res$Marker)
expect_true("vWA" %in% res$Marker)
expect_true("D21S11" %in% res$Marker)
expect_true("QS2" %in% res$Marker)
expect_true("D16S539" %in% res$Marker)
expect_true("D1S1656" %in% res$Marker)
expect_true("D19S433" %in% res$Marker)
expect_true("SE33" %in% res$Marker)
expect_true("D10S1248" %in% res$Marker)
expect_true("D22S1045" %in% res$Marker)
expect_true("D12S391" %in% res$Marker)
expect_true("D8S1179" %in% res$Marker)
expect_true("D2S1338" %in% res$Marker)
expect_true("D2S441" %in% res$Marker)
expect_true("D18S51" %in% res$Marker)
expect_true("FGA" %in% res$Marker)
# Check result.
expect_that(ncol(res), equals(7))
expect_that(nrow(res), equals(42))
# Check success of filtering.
expect_true(all(c("Q") %in% res$Allele[res$Marker == "QS1"]))
expect_false(all(c("X", "Y") %in% res$Allele[res$Marker == "AM"]))
expect_true(all(c("6", "9.3") %in% res$Allele[res$Marker == "TH01"]))
expect_true(all(c("16", "17", "18") %in% res$Allele[res$Marker == "D3S1358"]))
expect_true(all(c("16", "18", "19") %in% res$Allele[res$Marker == "vWA"]))
expect_true(all(c("29", "31.2") %in% res$Allele[res$Marker == "D21S11"]))
expect_true(all(c("S") %in% res$Allele[res$Marker == "QS2"]))
expect_true(all(c("9", "10", "13") %in% res$Allele[res$Marker == "D16S539"]))
expect_true(all(c("12", "13", "14") %in% res$Allele[res$Marker == "D1S1656"]))
expect_true(all(c("13", "14") %in% res$Allele[res$Marker == "D19S433"]))
expect_true(all(c("15", "16") %in% res$Allele[res$Marker == "SE33"]))
expect_true(all(c("13", "15") %in% res$Allele[res$Marker == "D10S1248"]))
expect_true(all(c("13", "OL", "14", "16") %in% res$Allele[res$Marker == "D22S1045"]))
expect_true(all(c("18", "23") %in% res$Allele[res$Marker == "D12S391"]))
expect_true(all(c("14", "15") %in% res$Allele[res$Marker == "D8S1179"]))
expect_true(all(c("22", "25") %in% res$Allele[res$Marker == "D2S1338"]))
expect_true(all(c("OL", "10", "13.3", "14") %in% res$Allele[res$Marker == "D2S441"]))
expect_true(all(c("16", "18") %in% res$Allele[res$Marker == "D18S51"]))
expect_true(all(c("20", "23") %in% res$Allele[res$Marker == "FGA"]))
# TEST 15 -------------------------------------------------------------------
# Test that filtering of sex markers and quality sensors work.
# Analyse dataframe.
res <- filterProfile(
data = set7, ref = ref7, kit = "ESSplexSEQS",
add.missing.loci = FALSE,
keep.na = FALSE,
qs.rm = TRUE,
sex.rm = TRUE,
ignore.case = FALSE,
filter.allele = FALSE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true("Sample.Name" %in% names(res))
expect_true("Marker" %in% names(res))
expect_true("Dye" %in% names(res))
expect_true("Allele" %in% names(res))
expect_true("Size" %in% names(res))
expect_true("Height" %in% names(res))
expect_true("Data.Point" %in% names(res))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Dye)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Size)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Data.Point)))
# Check markers.
expect_false("QS1" %in% res$Marker)
expect_false("AM" %in% res$Marker)
expect_true("TH01" %in% res$Marker)
expect_true("D3S1358" %in% res$Marker)
expect_true("vWA" %in% res$Marker)
expect_true("D21S11" %in% res$Marker)
expect_false("QS2" %in% res$Marker)
expect_true("D16S539" %in% res$Marker)
expect_true("D1S1656" %in% res$Marker)
expect_true("D19S433" %in% res$Marker)
expect_true("SE33" %in% res$Marker)
expect_true("D10S1248" %in% res$Marker)
expect_true("D22S1045" %in% res$Marker)
expect_true("D12S391" %in% res$Marker)
expect_true("D8S1179" %in% res$Marker)
expect_true("D2S1338" %in% res$Marker)
expect_true("D2S441" %in% res$Marker)
expect_true("D18S51" %in% res$Marker)
expect_true("FGA" %in% res$Marker)
# Check result.
expect_that(ncol(res), equals(7))
expect_that(nrow(res), equals(40))
# Check success of filtering.
expect_false(all(c("Q") %in% res$Allele[res$Marker == "QS1"]))
expect_false(all(c("X", "Y") %in% res$Allele[res$Marker == "AM"]))
expect_true(all(c("6", "9.3") %in% res$Allele[res$Marker == "TH01"]))
expect_true(all(c("16", "17", "18") %in% res$Allele[res$Marker == "D3S1358"]))
expect_true(all(c("16", "18", "19") %in% res$Allele[res$Marker == "vWA"]))
expect_true(all(c("29", "31.2") %in% res$Allele[res$Marker == "D21S11"]))
expect_false(all(c("S") %in% res$Allele[res$Marker == "QS2"]))
expect_true(all(c("9", "10", "13") %in% res$Allele[res$Marker == "D16S539"]))
expect_true(all(c("12", "13", "14") %in% res$Allele[res$Marker == "D1S1656"]))
expect_true(all(c("13", "14") %in% res$Allele[res$Marker == "D19S433"]))
expect_true(all(c("15", "16") %in% res$Allele[res$Marker == "SE33"]))
expect_true(all(c("13", "15") %in% res$Allele[res$Marker == "D10S1248"]))
expect_true(all(c("13", "OL", "14", "16") %in% res$Allele[res$Marker == "D22S1045"]))
expect_true(all(c("18", "23") %in% res$Allele[res$Marker == "D12S391"]))
expect_true(all(c("14", "15") %in% res$Allele[res$Marker == "D8S1179"]))
expect_true(all(c("22", "25") %in% res$Allele[res$Marker == "D2S1338"]))
expect_true(all(c("OL", "10", "13.3", "14") %in% res$Allele[res$Marker == "D2S441"]))
expect_true(all(c("16", "18") %in% res$Allele[res$Marker == "D18S51"]))
expect_true(all(c("20", "23") %in% res$Allele[res$Marker == "FGA"]))
# TEST 16 -------------------------------------------------------------------
# Test that filtering of sex markers, quality sensors, and known alleles work.
res <- filterProfile(
data = set7, ref = ref7, kit = "ESSplexSEQS",
add.missing.loci = FALSE,
keep.na = FALSE,
qs.rm = TRUE,
sex.rm = TRUE,
ignore.case = FALSE,
filter.allele = TRUE,
debug = FALSE
)
# Check return class.
expect_match(class(res), class(data.frame()))
# Check that expected columns exist.
expect_true("Sample.Name" %in% names(res))
expect_true("Marker" %in% names(res))
expect_true("Dye" %in% names(res))
expect_true("Allele" %in% names(res))
expect_true("Size" %in% names(res))
expect_true("Height" %in% names(res))
expect_true("Data.Point" %in% names(res))
# Check for NA's.
expect_false(any(is.na(res$Sample.Name)))
expect_false(any(is.na(res$Marker)))
expect_false(any(is.na(res$Dye)))
expect_false(any(is.na(res$Allele)))
expect_false(any(is.na(res$Size)))
expect_false(any(is.na(res$Height)))
expect_false(any(is.na(res$Data.Point)))
# Check markers.
expect_false("QS1" %in% res$Marker)
expect_false("AM" %in% res$Marker)
expect_true("TH01" %in% res$Marker)
expect_true("D3S1358" %in% res$Marker)
expect_true("vWA" %in% res$Marker)
expect_true("D21S11" %in% res$Marker)
expect_false("QS2" %in% res$Marker)
expect_true("D16S539" %in% res$Marker)
expect_true("D1S1656" %in% res$Marker)
expect_true("D19S433" %in% res$Marker)
expect_true("SE33" %in% res$Marker)
expect_true("D10S1248" %in% res$Marker)
expect_true("D22S1045" %in% res$Marker)
expect_true("D12S391" %in% res$Marker)
expect_true("D8S1179" %in% res$Marker)
expect_true("D2S1338" %in% res$Marker)
expect_true("D2S441" %in% res$Marker)
expect_true("D18S51" %in% res$Marker)
expect_true("FGA" %in% res$Marker)
# Check result.
expect_that(ncol(res), equals(7))
expect_that(nrow(res), equals(31))
# Check success of filtering.
expect_false(all(c("Q") %in% res$Allele[res$Marker == "QS1"]))
expect_false(all(c("X", "Y") %in% res$Allele[res$Marker == "AM"]))
expect_true(all(c("6", "9.3") %in% res$Allele[res$Marker == "TH01"]))
expect_true(all(c("17", "18") %in% res$Allele[res$Marker == "D3S1358"]))
expect_true(all(c("16", "19") %in% res$Allele[res$Marker == "vWA"]))
expect_true(all(c("29", "31.2") %in% res$Allele[res$Marker == "D21S11"]))
expect_false(all(c("S") %in% res$Allele[res$Marker == "QS2"]))
expect_true(all(c("9", "13") %in% res$Allele[res$Marker == "D16S539"]))
expect_true(all(c("12", "13") %in% res$Allele[res$Marker == "D1S1656"]))
expect_true(all(c("13", "14") %in% res$Allele[res$Marker == "D19S433"]))
expect_true(all(c("15", "16") %in% res$Allele[res$Marker == "SE33"]))
expect_true(all(c("13", "15") %in% res$Allele[res$Marker == "D10S1248"]))
expect_true(all(c("16") %in% res$Allele[res$Marker == "D22S1045"]))
expect_true(all(c("18", "23") %in% res$Allele[res$Marker == "D12S391"]))
expect_true(all(c("14", "15") %in% res$Allele[res$Marker == "D8S1179"]))
expect_true(all(c("22", "25") %in% res$Allele[res$Marker == "D2S1338"]))
expect_true(all(c("10", "14") %in% res$Allele[res$Marker == "D2S441"]))
expect_true(all(c("16", "18") %in% res$Allele[res$Marker == "D18S51"]))
expect_true(all(c("20", "23") %in% res$Allele[res$Marker == "FGA"]))
})
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