ISO Range Table

library(segtools)
library(dplyr)
library(vroom)
library(janitor)
library(waldo)
library(diffobj)

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 ISO range table function and performs unit tests on the output table to ensure it matches the table in the output 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

Below are the three functions from the helpers.R file in the application 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", …

ISO Range Table

The ISORangeTable5 table is also created in the server function in the App.R file.

seg_iso_range_tbl()

I’ve re-written this below as seg_iso_range_tbl():

seg_iso_range_tbl(risk_vars_tbl)
#> # A tibble: 5 × 4
#>      ID `ISO range`             N Percent
#>   <int> <chr>               <int> <chr>  
#> 1     1 <= 5% or 5 mg/dL     5328 54%    
#> 2     2 > 5 - 10% or mg/dL   2842 28.8%  
#> 3     3 > 10 - 15% or mg/dL  1050 10.6%  
#> 4     4 > 15 - 20% mg/dL      340 3.4%   
#> 5     5 > 20% or 20 mg/dL     308 3.1%

Application ISORangeTable

Below is the output for the ISORangeTable in the application

I’ve re-created this as app_iso_range_tbl below:

app_iso_range_tbl <- tibble::tribble(
     ~ID,          ~`ISO range`,    ~N, ~Percent,
      1L,    "<= 5% or 5 mg/dL", 5328L,    "54%",
      2L,  "> 5 - 10% or mg/dL", 2842L,  "28.8%",
      3L, "> 10 - 15% or mg/dL", 1050L,  "10.6%",
      4L,    "> 15 - 20% mg/dL",  340L,   "3.4%",
      5L,   "> 20% or 20 mg/dL",  308L,   "3.1%"
     )
app_iso_range_tbl

ID	ISO range	N	Percent
1	<= 5% or 5 mg/dL	5328	54%
2	> 5 - 10% or mg/dL	2842	28.8%
3	> 10 - 15% or mg/dL	1050	10.6%
4	> 15 - 20% mg/dL	340	3.4%
5	> 20% or 20 mg/dL	308	3.1%

Test

Below we test the two ISORangeTable tables

testthat::test_that("Test risk grade table", {
testthat::expect_equal(
  # function table
  object = seg_iso_range_tbl(risk_vars_tbl),
  # application table
  expected = app_iso_range_tbl
  )
})
#> Test passed 🌈