Saliva analysis with CARWatch

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This workflow starts with complete Study Results and a long laboratory CSV. The laboratory file contains one row per participant and physical tube ID.

library(carwatch)

fixture <- system.file("extdata", "parity", "v1.0.0", package = "carwatch")
study_results <- read_study_results(file.path(fixture, "results.csv"))
saliva <- read_saliva(file.path(fixture, "saliva.csv"))

saliva
#> # A tibble: 2 × 3
#>   participant sample cortisol
#>   <chr>       <chr>     <dbl>
#> 1 VP01        tube-a        5
#> 2 VP01        tube-b        9

Merge laboratory values

Matching by sample uses the physical tube recorded by the app. This preserves the distinction between the tube planned for a position and the tube actually scanned there.

merged <- merge_saliva(study_results, saliva, match_on = "sample")
samples <- as_sample_events(merged)
samples[c(
  "participant", "day", "sample_position", "sample", "recorded_sample",
  "cortisol", "sample_compliant", "mismatch_corrected"
)]
#> # A tibble: 2 × 8
#>   participant day   sample_position sample recorded_sample cortisol
#>   <chr>       <chr>           <int> <chr>  <chr>              <dbl>
#> 1 VP01        D1                  1 tube-a tube-a                 5
#> 2 VP01        D1                  2 tube-b tube-b                 9
#> # ℹ 2 more variables: sample_compliant <lgl>, mismatch_corrected <lgl>

If a laboratory export identifies observations by day and sample position, use match_on = "position" and provide the day and sample_position columns instead.

Calculate response features

compute_features_from_carwatch() orders each curve by registered sample position and uses the actual minutes since awakening.

compute_features_from_carwatch(merged, saliva_type = "cortisol")
#> # A tibble: 1 × 14
#>   participant day   day_compliant expected_sample_count recorded_sample_count
#>   <chr>       <chr> <lgl>                         <int>                 <int>
#> 1 VP01        D1    TRUE                              2                     2
#> # ℹ 9 more variables: assessed_sample_count <int>,
#> #   compliant_sample_count <int>, non_compliant_samples <chr>,
#> #   cortisol_auc_g <dbl>, cortisol_auc_i <dbl>, cortisol_ini_val <dbl>,
#> #   cortisol_max_val <dbl>, cortisol_max_inc <dbl>, cortisol_slope12 <dbl>

Inspect timing and measurements

plot_sampling_timeline(merged, participant = "VP01", day = "D1")

plot_compliance_overview(merged)

plot_timing_deviation(merged)

plot_saliva_curve(merged, value = "cortisol", ci = NULL)

These plots return ordinary ggplot2 objects, so themes, labels, and export settings can be adjusted with the normal ggplot2 workflow.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.