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Add grade test cases for eugly, hyper, and hypo
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Submodule iglu
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test_data = c(100, 120, 150, 200) # col 'gl' | ||
test_id = 'test 1' # col 'id' | ||
test_df <- data.frame(id = test_id, gl = test_data) | ||
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test_df <- data.frame(id = 'test 1', gl = c(100, 120, 150, 200)) | ||
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test_out <- test_df %>% | ||
dplyr::group_by(id) %>% | ||
dplyr::summarise( | ||
eA1C = (46.7+mean(gl, na.rm = TRUE) )/28.7 | ||
) | ||
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test_out$id = NULL | ||
dplyr::mutate( | ||
eA1C = (46.7 + sum(gl, na.rm = TRUE) / sum(!is.na(gl))) / 28.7 | ||
) %>% | ||
dplyr::ungroup() %>% | ||
dplyr::select(-id) | ||
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result = | ||
test_that("iglu::ea1c == base::ea1c", { | ||
testthat::test_that("iglu::ea1c", { | ||
expect_equal(iglu::ea1c(test_data), test_out , tolerance = 0.0001) | ||
}) | ||
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test_that("multiplication works", { | ||
expect_equal(2 * 2, 4) | ||
}) | ||
# Helper Function | ||
grade_formula_tester <- function(x){ | ||
grade = (425 * (log10(log10(x/18)) + 0.16)^2) | ||
grade <- pmin(grade, 50) | ||
return(grade) | ||
} | ||
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# Test Case: | ||
test_df <- data.frame(id = 'test 1', gl = c(100, 120, 150, 200, NA)) | ||
test1 = iglu::example_data_1_subject | ||
test2 = iglu::example_data_1_subject[1:300, ] | ||
test3 = iglu::example_data_5_subject | ||
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test_out = data.frame(GRADE = mean(grade_formula_tester(test_df$gl))) | ||
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out = iglu::grade(test3)$GRADE | ||
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result = | ||
testthat::test_that("iglu::grade", { | ||
expect_equal(iglu::grade(test1)$GRADE, mean(grade_formula_tester(test1$gl)), tolerance = 0.0001) | ||
expect_equal(iglu::grade(test2)$GRADE, mean(grade_formula_tester(test2$gl)), tolerance = 0.0001) | ||
expect_equal(out[1], mean(grade_formula_tester(test3[test3$id=="Subject 1", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[2], mean(grade_formula_tester(test3[test3$id=="Subject 2", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[3], mean(grade_formula_tester(test3[test3$id=="Subject 3", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[4], mean(grade_formula_tester(test3[test3$id=="Subject 4", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[5], mean(grade_formula_tester(test3[test3$id=="Subject 5", ]$gl)), tolerance = 0.0001) | ||
}) | ||
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# Helper Function | ||
grade_eugly_formula_tester <- function(gl, lower = 70, upper = 140){ | ||
grade = grade_formula_tester(gl) | ||
grade_eugly = sum(grade[gl >= lower & gl <= upper ], na.rm = TRUE) / | ||
sum(grade, na.rm = TRUE) * 100 | ||
return(grade_eugly) | ||
} | ||
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# Test Case: | ||
test_df <- data.frame(id = 'test 1', gl = c(100, 120, 150, 200, NA)) | ||
test1 = iglu::example_data_1_subject | ||
test2 = iglu::example_data_1_subject[1:300, ] | ||
test3 = iglu::example_data_5_subject | ||
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test_out = data.frame(GRADE_eugly = mean(grade_eugly_formula_tester(test_df$gl))) | ||
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out = iglu::grade_eugly(test3)$GRADE_eugly | ||
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outresult = | ||
testthat::test_that("iglu::grade_eugly", { | ||
expect_equal(iglu::grade_eugly(test_df$gl)$GRADE_eugly, test_out$GRADE_eugly, tolerance = 0.0001) | ||
expect_equal(iglu::grade_eugly(test1)$GRADE_eugly, mean(grade_eugly_formula_tester(test1$gl)), tolerance = 0.0001) | ||
expect_equal(iglu::grade_eugly(test2)$GRADE_eugly, mean(grade_eugly_formula_tester(test2$gl)), tolerance = 0.0001) | ||
expect_equal(out[1], mean(grade_eugly_formula_tester(test3[test3$id=="Subject 1", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[2], mean(grade_eugly_formula_tester(test3[test3$id=="Subject 2", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[3], mean(grade_eugly_formula_tester(test3[test3$id=="Subject 3", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[4], mean(grade_eugly_formula_tester(test3[test3$id=="Subject 4", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[5], mean(grade_eugly_formula_tester(test3[test3$id=="Subject 5", ]$gl)), tolerance = 0.0001) | ||
}) | ||
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# Helper Function | ||
grade_hyper_formula_tester <- function(gl, upper = 140){ | ||
grade = grade_formula_tester(gl) | ||
GRADE_hyper = sum(grade[gl > upper], na.rm = TRUE) / | ||
sum(grade, na.rm = TRUE) * 100 | ||
return(GRADE_hyper) | ||
} | ||
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# Test Case: | ||
test_df <- data.frame(id = 'test 1', gl = c(100, 120, 150, 200, NA)) | ||
test1 = iglu::example_data_1_subject | ||
test2 = iglu::example_data_1_subject[1:300, ] | ||
test3 = iglu::example_data_5_subject | ||
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test_out = data.frame(GRADE_hyper = mean(grade_hyper_formula_tester(test_df$gl))) | ||
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out = iglu::grade_hyper(test3)$GRADE_hyper | ||
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outresult = | ||
testthat::test_that("iglu::grade_hyper", { | ||
expect_equal(iglu::grade_hyper(test_df$gl)$GRADE_hyper, test_out$GRADE_hyper, tolerance = 0.0001) | ||
expect_equal(iglu::grade_hyper(test1)$GRADE_hyper, mean(grade_hyper_formula_tester(test1$gl)), tolerance = 0.0001) | ||
expect_equal(iglu::grade_hyper(test2)$GRADE_hyper, mean(grade_hyper_formula_tester(test2$gl)), tolerance = 0.0001) | ||
expect_equal(out[1], mean(grade_hyper_formula_tester(test3[test3$id=="Subject 1", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[2], mean(grade_hyper_formula_tester(test3[test3$id=="Subject 2", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[3], mean(grade_hyper_formula_tester(test3[test3$id=="Subject 3", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[4], mean(grade_hyper_formula_tester(test3[test3$id=="Subject 4", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[5], mean(grade_hyper_formula_tester(test3[test3$id=="Subject 5", ]$gl)), tolerance = 0.0001) | ||
}) | ||
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# Helper Function | ||
grade_hypo_formula_tester <- function(gl, lower = 80){ | ||
grade = grade_formula_tester(gl) | ||
GRADE_hypo = sum(grade[gl < lower], na.rm = TRUE) / | ||
sum(grade, na.rm = TRUE) * 100 | ||
return(GRADE_hypo) | ||
} | ||
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# Test Case: | ||
test_df <- data.frame(id = 'test 1', gl = c(100, 120, 150, 200, NA)) | ||
test1 = iglu::example_data_1_subject | ||
test2 = iglu::example_data_1_subject[1:300, ] | ||
test3 = iglu::example_data_5_subject | ||
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test_out = data.frame(GRADE_hyper = mean(grade_hypo_formula_tester(test_df$gl))) | ||
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out = iglu::grade_hypo(test3)$GRADE_hypo | ||
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outresult = | ||
testthat::test_that("iglu::grade_hypo", { | ||
expect_equal(iglu::grade_hypo(test_df$gl)$GRADE_hypo, test_out$GRADE_hyper, tolerance = 0.0001) | ||
expect_equal(iglu::grade_hypo(test1)$GRADE_hypo, mean(grade_hypo_formula_tester(test1$gl)), tolerance = 0.0001) | ||
expect_equal(iglu::grade_hypo(test2)$GRADE_hypo, mean(grade_hypo_formula_tester(test2$gl)), tolerance = 0.0001) | ||
expect_equal(out[1], mean(grade_hypo_formula_tester(test3[test3$id=="Subject 1", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[2], mean(grade_hypo_formula_tester(test3[test3$id=="Subject 2", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[3], mean(grade_hypo_formula_tester(test3[test3$id=="Subject 3", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[4], mean(grade_hypo_formula_tester(test3[test3$id=="Subject 4", ]$gl)), tolerance = 0.0001) | ||
expect_equal(out[5], mean(grade_hypo_formula_tester(test3[test3$id=="Subject 5", ]$gl)), tolerance = 0.0001) | ||
}) | ||
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