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basic testing of execution
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wibeasley committed Dec 24, 2021
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94 changes: 94 additions & 0 deletions tests/testthat/_snaps/execute-checks.md
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# execute-checks-biochemical

Code
result$ds_smell_result
Output
# A tibble: 12 x 13
check_name pass description priority active debug bound_lower bound_upper
<chr> <lgl> <chr> <int> <lgl> <lgl> <dbl> <dbl>
1 proportion~ FALSE Proportion f~ 1 TRUE FALSE 0.5 0.5
2 proportion~ FALSE Proportion m~ 1 TRUE FALSE 0.5 0.5
3 mean_age TRUE Mean age of ~ 1 TRUE FALSE 20 80
4 mean_serum~ FALSE Mean serum p~ 1 TRUE FALSE 32 39
5 mean_serum~ FALSE Mean serum c~ 1 TRUE FALSE 3 15
6 average_bm~ FALSE Average BMI ~ 1 TRUE FALSE 18 24
7 mean_serum~ TRUE Average Chol~ 1 TRUE FALSE 100 140
8 dialysis_a~ TRUE Normal range~ 1 TRUE FALSE 1.2 5
9 average_se~ TRUE Mean serum f~ 1 TRUE FALSE 501 1200
10 nutritiona~ TRUE Most patient~ 1 TRUE FALSE 0.85 0.99
11 definitive~ TRUE A 100 percen~ 1 TRUE FALSE 1 1
12 normalized~ FALSE Average Norm~ 1 TRUE FALSE 0 0.12
# ... with 5 more variables: bounds_template <chr>, value_template <chr>,
# equation <chr>, boundaries <chr>, value <dbl>

---

Code
as.data.frame(result$ds_smell_result)
Output
check_name pass
1 proportion_female_participants FALSE
2 proportion_male_participants FALSE
3 mean_age TRUE
4 mean_serum_prealbumin_level_at_baseline FALSE
5 mean_serum_creatinine_level_at_baseline FALSE
6 average_bmi_at_baseline FALSE
7 mean_serum_cholesterol_levels_at_baseline TRUE
8 dialysis_adequacy TRUE
9 average_serum_ferritin_levels_at_baseline TRUE
10 nutritional_counseling TRUE
11 definitive_diagnosis TRUE
12 normalized_protein_catabolic_rate FALSE
description
1 Proportion female participants is half the participants till study conclusion
2 Proportion male participants is half the participants till study conclusion
3 Mean age of participants is between 18 years to 20 years
4 Mean serum pre-albumin levels at baseline are between 31mg/dl and 39mg/dl
5 Mean serum creatinine levels at baseline are between 3mg/dl and 15mg/dl
6 Average BMI is between 18 and 24
7 Average Cholesterol levels range is between 100mg/dl-140mg/dl at baseline level
8 Normal range for Kt/V values are between 1.2 and 5
9 Mean serum ferritin levels are in the recommended range of > 500ng/dl-1200ng/dl
10 Most patients agreed to receiving nutritional counseling
11 A 100 percent of study participants were diagnosed with malnutrition associated with Chronic Renal Disease
12 Average Normalized Protein Catabolic Rate is < 1.2g/kg/day
priority active debug bound_lower bound_upper bounds_template value_template
1 1 TRUE FALSE 0.50 0.50 [%.2f, %.2f] %.4f
2 1 TRUE FALSE 0.50 0.50 [%.2f, %.2f] %.4f
3 1 TRUE FALSE 20.00 80.00 [%.2f, %.2f] %.4f
4 1 TRUE FALSE 32.00 39.00 [%.2f, %.2f] %.4f
5 1 TRUE FALSE 3.00 15.00 [%.2f, %.2f] %.4f
6 1 TRUE FALSE 18.00 24.00 [%.2f, %.2f] %.4f
7 1 TRUE FALSE 100.00 140.00 [%.2f, %.2f] %.4f
8 1 TRUE FALSE 1.20 5.00 [%.2f, %.2f] %.4f
9 1 TRUE FALSE 501.00 1200.00 [%.0f, %.0f] %.2f
10 1 TRUE FALSE 0.85 0.99 [%.2f, %.2f] %.4f
11 1 TRUE FALSE 1.00 1.00 [%.2f, %.2f] %.4f
12 1 TRUE FALSE 0.00 0.12 [%.2f, %.2f] %.4f
equation
1 function (d) {\n mean(d$sex == "female", na.rm=T)\n}\n
2 function (d) {\n mean(d$sex == "male", na.rm=T)\n}\n
3 function (d) {\n mean(d$age, na.rm=T)\n}\n
4 function (d) {\n mean(d$baseline_prealbumin_level, na.rm=T)\n}\n
5 function (d) {\n mean(d$baseline_creatinine_level, na.rm=T)\n}\n
6 function (d) {\n mean(d$baseline_bmi, na.rm=T)\n}\n
7 function (d) {\n mean(d$baseline_cholesterol, na.rm=T)\n}\n
8 function (d) {\n mean(d$completion_project_questionnaire_ktv_value, na.rm=T)\n}\n
9 function (d) {\n mean(d$baseline_ferritin_level, na.rm=T)\n}\n
10 function (d) {\n mean(d$nutritional_counseling, na.rm=T)\n}\n
11 function (d) {\n mean(d$differential_diagnoses_malnutrition, na.rm=T)\n}\n
12 function (d) {\n mean(d$baseline_normalized_protein_catabolic_rate, na.rm=T)\n}\n
boundaries value
1 [0.50, 0.50] 0.4444444
2 [0.50, 0.50] 0.5555556
3 [20.00, 80.00] 44.3888889
4 [32.00, 39.00] 23.6666667
5 [3.00, 15.00] 23.1687500
6 [18.00, 24.00] 24.8500000
7 [100.00, 140.00] 134.1176471
8 [1.20, 5.00] 1.9166667
9 [501, 1200] 589.1176471
10 [0.85, 0.99] 0.8888889
11 [1.00, 1.00] 1.0000000
12 [0.00, 0.12] 0.9312500

13 changes: 13 additions & 0 deletions tests/testthat/test-execute-checks.R
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library(testthat)

test_that("execute-checks-biochemical", {
path_data <- system.file("datasets/pt-event-biochemical.rds", package = "trawler")
path_checks <- system.file("checks/checks-biochemical.yml", package = "trawler")

ds_pt_event <- readr::read_rds(path_data)
checks <- load_checks(path_checks)
result <- execute_checks(ds_pt_event, checks$ds_smell)

expect_snapshot(result$ds_smell_result)
expect_snapshot(as.data.frame(result$ds_smell_result))
})

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