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Pivot a pft_result to long form

Source: R/pft_result.R
pft_long.Rd

Reshapes a wide pft_interpret() / pft_spirometry() / pft_volumes() / pft_diffusion() output (one row per patient, one column per measure × statistic) into long form (one row per (patient, measure, year) with columns for each statistic). This is the natural shape for dplyr / ggplot2 faceting, cohort modelling, and broom-style downstream workflows.

Usage

pft_long(x, ...)

Arguments

x

A data frame; typically a pft_result but any data frame with <measure>_pred[_<year>] columns works. Named x (rather than data) to match the S3 first-argument convention shared with print.pft_result, plot.pft_result, and the other pft_result methods.

...

Currently unused; reserved for forward compatibility.

Value

A tibble with columns .patient (integer row position), measure, year (character; NA for non-suffixed outputs), pred, lln, uln, measured, zscore, pctpred, and severity. Missing statistics fill with NA of the appropriate type.

Details

Discovery is keyed off <measure>_pred columns; the four-digit GLI year is extracted from the column suffix and recorded in the year column. Spirometry outputs from pft_spirometry() / pft_interpret() always carry a year suffix (fev1_pred_2012, fev1_pred_2022, ...) and produce a populated year; lung-volume (Hall 2021) and diffusion (GLI 2017) outputs are unsuffixed and produce year = NA until a competing standard ships and the same suffixing convention is adopted there. Columns whose suffix does not match a recognised statistic are ignored, so id / demographic columns are dropped (use the .patient integer to join back).

See also

pft_interpret() to produce the wide-form input.

Examples

patient <- data.frame(
  sex = c("M","F"), age = c(45, 60), height = c(178, 165),
  race = "Caucasian",
  fev1_measured = c(2.5, 1.8), fvc_measured = c(3.8, 2.4)
)
result <- pft_interpret(patient)
pft_long(result)
#> # A tibble: 26 × 10
#>    .patient measure year   pred   lln   uln measured zscore pctpred severity
#>       <int> <chr>   <chr> <dbl> <dbl> <dbl>    <dbl>  <dbl>   <dbl> <chr>   
#>  1        1 fev1    2022  3.87  2.94  4.75       2.5  -2.39    64.6 mild    
#>  2        2 fev1    2022  2.47  1.77  3.12       1.8  -1.58    73.0 normal  
#>  3        1 fvc     2022  4.81  3.68  5.95       3.8  -1.47    79.1 normal  
#>  4        2 fvc     2022  3.10  2.25  3.97       2.4  -1.36    77.4 normal  
#>  5        1 fev1fvc 2022  0.803 0.696 0.891     NA    NA       NA   NA      
#>  6        2 fev1fvc 2022  0.797 0.676 0.894     NA    NA       NA   NA      
#>  7        1 frc     NA    3.39  2.32  4.75      NA    NA       NA   NA      
#>  8        2 frc     NA    2.82  2.00  3.85      NA    NA       NA   NA      
#>  9        1 tlc     NA    7.21  5.84  8.61      NA    NA       NA   NA      
#> 10        2 tlc     NA    5.28  4.27  6.41      NA    NA       NA   NA      
#> # ℹ 16 more rows