Run prep_visits(), clean_weight(), calc_adg(), calc_adfi() and
calc_fcr() in a row with the same QC settings.
run_pipeline(
data,
station = NULL,
qc = qc_config(),
breeds = NULL,
verbose = 1,
cores = 1
)Raw station data (see prep_visits()).
"nedap", "fire" or NULL to detect it.
QC settings from qc_config().
Optional breed table passed to add_breed() (columns
responder and breed).
0 (silent), 1 (summary, default) or 2 (details).
Number of CPU cores for the per-pig computations. Default 1.
An object of class ppt_pipeline: a list with visits,
weight, adg, adfi and fcr, plus removed (every pig removed,
with the step and the reason). Printing it shows a summary box. With
verbose >= 1 the steps are numbered with their run time, the box is
shown at the end and the result is returned invisibly.
res <- run_pipeline(fire, qc = qc_config(stage_range = c(60, 120)), verbose = 0)
res
#> ╭ Results FIRE ───────────────────────────────────────────────────────────╮
#> │ Pigs 26 in the data · 25 with FCR (21 ok, 4 low confidence) · 1 removed │
#> │ ADG 1207 ± 106 g/d │
#> │ ADFI 3249 ± 250 g/d │
#> │ FCR 2.70 ± 0.16 │
#> │ │
#> │ Check │
#> │ 25822 (101): weight records cover 5.1 kg of 70-80 kg │
#> │ 35858 (102): weight records cover 4.4 kg of 100-110 kg │
#> │ 35892 (102): weight data flagged │
#> │ 35897 (102): weight data flagged │
#> │ 35888 (101): removed, test period 9 d < 35 d │
#> │ │
#> │ Next: plot_adg(), plot_adfi(), write_report(), write_results() │
#> ╰───────────────────────────────────────────────────────────────────────────╯
res$fcr$summary[group == "all"]
#> group trait n min max median
#> <char> <char> <int> <num> <num> <num>
#> 1: all adg 25 939.7806629 1455.9297415 1205.5702281
#> 2: all adfi 25 2591.1968792 3875.9180223 3220.8402264
#> 3: all fcr 25 2.3545315 3.0172000 2.6729084
#> 4: all gf 25 0.3314331 0.4247129 0.3741243
#> 5: all stage_days 25 40.4287257 63.3513721 48.9403502
#> 6: all start_weight 25 59.9999805 60.0000044 59.9999959
#> 7: all end_weight 25 119.9999801 120.0000042 119.9999944
#> mean sd cv
#> <num> <num> <num>
#> 1: 1207.2469699 1.058301e+02 8.766236e-02
#> 2: 3249.3079139 2.503801e+02 7.705644e-02
#> 3: 2.6982479 1.577920e-01 5.847943e-02
#> 4: 0.3718191 2.159314e-02 5.807431e-02
#> 5: 49.6475333 4.745273e+00 9.557922e-02
#> 6: 59.9999954 5.704224e-06 9.507041e-08
#> 7: 119.9999944 4.646078e-06 3.871732e-08