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Pair Types

pair-types.Rmd

The risk tables are created using the functions in the seg-shiny-1-3-3 repo. Through the multiple iterations of this application, different values have been used to establish these categories.

Objective

This vignette covers the the pair type table function and performs unit tests to ensure the output from the seg_pair_type_tbl() function match the output table in the application.

Load test data

The segtools package uses testthat, a common testing framework for performing unit tests. I’ll load a test data file used in the GitHub repo (VanderbiltComplete.csv) below and run it in the shiny app to generate the tables for comparison.

github_data_root <-
  "https://raw.githubusercontent.com/mjfrigaard/seg-shiny-data/master/Data/"
full_sample_repo <- base::paste0(github_data_root,
  "VanderbiltComplete.csv")
test_vand_comp_data <-
  vroom::vroom(file = full_sample_repo, delim = ",")
glimpse(test_vand_comp_data)
#> Rows: 9,891
#> Columns: 2
#> $ BGM <dbl> 121, 212, 161, 191, 189, 104, 293, 130, 261, 147, 83, 132, 146, 24…
#> $ REF <dbl> 127, 223, 166, 205, 210, 100, 296, 142, 231, 148, 81, 131, 155, 25…

Application (version 1.3.3) functions

The previous application functions are stored in the helpers.R file in the GitHub repo.

SEG Risk Variables

Create the risk_vars_tbl from seg_risk_vars():

risk_vars_tbl <- seg_risk_vars(df = test_vand_comp_data)
dplyr::glimpse(risk_vars_tbl)
#> Rows: 9,868
#> Columns: 19
#> $ BGM            <dbl> 121, 212, 161, 191, 189, 104, 293, 130, 261, 147, 83, 1…
#> $ REF            <dbl> 127, 223, 166, 205, 210, 100, 296, 142, 231, 148, 81, 1…
#> $ bgm_pair_cat   <chr> "BGM < REF", "BGM < REF", "BGM < REF", "BGM < REF", "BG…
#> $ ref_pair_2cat  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ included       <chr> "Total included in SEG Analysis", "Total included in SE…
#> $ RiskPairID     <dbl> 72849, 127636, 96928, 114997, 113800, 62605, 176390, 78…
#> $ RiskFactor     <dbl> 0.0025445, 0.0279900, 0.0000000, 0.2061100, 0.2086500, …
#> $ abs_risk       <dbl> 0.0025445, 0.0279900, 0.0000000, 0.2061100, 0.2086500, …
#> $ risk_cat       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ ABSLB          <dbl> -0.001, -0.001, -0.001, -0.001, -0.001, -0.001, -0.001,…
#> $ ABSUB          <dbl> 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, …
#> $ risk_cat_txt   <chr> "None", "None", "None", "None", "None", "None", "None",…
#> $ rel_diff       <dbl> -0.047244094, -0.049327354, -0.030120482, -0.068292683,…
#> $ abs_rel_diff   <dbl> 0.047244094, 0.049327354, 0.030120482, 0.068292683, 0.1…
#> $ sq_rel_diff    <dbl> 2.232004e-03, 2.433188e-03, 9.072434e-04, 4.663891e-03,…
#> $ iso_diff       <dbl> 4.7244094, 4.9327354, 3.0120482, 6.8292683, 10.0000000,…
#> $ iso_range      <chr> "<= 5% or 5 mg/dL", "<= 5% or 5 mg/dL", "<= 5% or 5 mg/…
#> $ risk_grade     <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", …
#> $ risk_grade_txt <chr> "0 - 0.5", "0 - 0.5", "0 - 0.5", "0 - 0.5", "0 - 0.5", …

Pair Type Table

The original pairtypeTable() function is stored in the application helpers.R file.

seg_pair_type_tbl()

I’ve re-written pairtypeTable() as seg_pair_type_tbl()

When I check seg_pair_type_tbl() on the test_vand_comp_data (VanderbiltComplete.csv) data, I see the following:

seg_pair_type_tbl(df = test_vand_comp_data)
Pair Type Count
Total 9891
BGM < REF 4710
BGM = REF 479
BGM > REF 4702
REF > 600: Excluded from SEG Analysis 23
Total included in SEG Analysis 9868

Application PairTypeTable

After uploading VanderbiltComplete.csv, the first table in the Summary Tables tab is the pairs table:

I’ve also stored these as app_pairs_tbl:

app_pairs_tbl = tibble::as_tibble(data.frame(
  stringsAsFactors = FALSE,
  check.names = FALSE,
  `Pair Type` = c(
    "Total",
    "BGM < REF",
    "BGM = REF",
    "BGM > REF",
    "REF > 600: Excluded from SEG Analysis",
    "Total included in SEG Analysis"
  ),
  Count = c(9891L, 4710L, 479L, 4702L, 23L, 9868L)
))
app_pairs_tbl
Pair Type Count
Total 9891
BGM < REF 4710
BGM = REF 479
BGM > REF 4702
REF > 600: Excluded from SEG Analysis 23
Total included in SEG Analysis 9868

Test

To test if the two tables are identical, I use testthat’s testthat::expect_equal() function, with object set to seg_pair_type_tbl(dat = test_vand_comp_data) and expected set to app_pairs_tbl.

testthat::test_that("Test seg_pair_type_tbl()", {
  testthat::expect_equal(
    # function table
    object = seg_pair_type_tbl(df = test_vand_comp_data), 
    # application table
    expected = app_pairs_tbl
  )
})
#> Test passed 🥇