Calculate absorbance means with optional outlier removal
Source:R/qp_calc_abs_mean.R
qp_calc_abs_mean.RdCalculate absorbance means with optional outlier removal
Arguments
- x
A
data.frameorlistcontaining adata.framenamedqp. See details.- ignore_outliers
Which sample types should have outliers ignored from their mean calculations? If
.is_outliercolumn is supplied, this argument is ignored.
Details
Input data.frame must contain the following columns:
sample_type. Character. Must contain values either "standard" or "unknown"index. Numeric. Denotes sample number..abs. Numeric. Contains absorbance values.If a boolean
.is_outlieris supplied, that will be used instead.
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
abs <- expand.grid(
sample_type = c("standard", "unknown"),
index = 1:7,
rep = 1:3
) |>
dplyr::arrange(sample_type, index, rep)
abs$.abs <- abs(rnorm(nrow(abs), mean = abs$index))
# Selecting different subsets for outlier removal
qp_calc_abs_mean(abs, "none")
#> sample_type index rep .abs .is_outlier .mean
#> 1 standard 1 1 0.4000435 NA 1.030875
#> 2 standard 1 2 1.2553171 NA 1.030875
#> 3 standard 1 3 1.4372636 NA 1.030875
#> 4 standard 2 1 1.9944287 NA 2.588131
#> 5 standard 2 2 2.6215527 NA 2.588131
#> 6 standard 2 3 3.1484116 NA 2.588131
#> 7 standard 3 1 1.1781823 NA 2.228886
#> 8 standard 3 2 2.7526747 NA 2.228886
#> 9 standard 3 3 2.7558004 NA 2.228886
#> 10 standard 4 1 3.7172946 NA 3.930859
#> 11 standard 4 2 3.4463006 NA 3.930859
#> 12 standard 4 3 4.6289820 NA 3.930859
#> 13 standard 5 1 7.0650249 NA 5.315487
#> 14 standard 5 2 3.3690106 NA 5.315487
#> 15 standard 5 3 5.5124269 NA 5.315487
#> 16 standard 6 1 4.1369885 NA 5.187458
#> 17 standard 6 2 5.4779875 NA 5.187458
#> 18 standard 6 3 5.9473981 NA 5.187458
#> 19 standard 7 1 7.5429963 NA 7.032359
#> 20 standard 7 2 6.0859252 NA 7.032359
#> 21 standard 7 3 7.4681544 NA 7.032359
#> 22 unknown 1 1 1.3629513 NA 1.135090
#> 23 unknown 1 2 0.3045435 NA 1.135090
#> 24 unknown 1 3 1.7377763 NA 1.135090
#> 25 unknown 2 1 3.8885049 NA 2.285071
#> 26 unknown 2 2 1.9025549 NA 2.285071
#> 27 unknown 2 3 1.0641526 NA 2.285071
#> 28 unknown 3 1 2.9840497 NA 2.214954
#> 29 unknown 3 2 2.1732110 NA 2.214954
#> 30 unknown 3 3 1.4876003 NA 2.214954
#> 31 unknown 4 1 4.9353632 NA 4.451846
#> 32 unknown 4 2 4.1764886 NA 4.451846
#> 33 unknown 4 3 4.2436855 NA 4.451846
#> 34 unknown 5 1 6.6235489 NA 5.533863
#> 35 unknown 5 2 5.1120381 NA 5.533863
#> 36 unknown 5 3 4.8660030 NA 5.533863
#> 37 unknown 6 1 4.0899125 NA 5.165743
#> 38 unknown 6 2 5.7207628 NA 5.165743
#> 39 unknown 6 3 5.6865540 NA 5.165743
#> 40 unknown 7 1 8.0673079 NA 7.166073
#> 41 unknown 7 2 7.0700349 NA 7.166073
#> 42 unknown 7 3 6.3608767 NA 7.166073
qp_calc_abs_mean(abs, "standards")
#> sample_type index rep .abs .is_outlier .mean
#> 1 standard 1 1 0.4000435 TRUE 1.346290
#> 2 standard 1 2 1.2553171 FALSE 1.346290
#> 3 standard 1 3 1.4372636 FALSE 1.346290
#> 4 standard 2 1 1.9944287 FALSE 2.588131
#> 5 standard 2 2 2.6215527 FALSE 2.588131
#> 6 standard 2 3 3.1484116 FALSE 2.588131
#> 7 standard 3 1 1.1781823 TRUE 2.754238
#> 8 standard 3 2 2.7526747 FALSE 2.754238
#> 9 standard 3 3 2.7558004 FALSE 2.754238
#> 10 standard 4 1 3.7172946 FALSE 3.581798
#> 11 standard 4 2 3.4463006 FALSE 3.581798
#> 12 standard 4 3 4.6289820 TRUE 3.581798
#> 13 standard 5 1 7.0650249 FALSE 5.315487
#> 14 standard 5 2 3.3690106 FALSE 5.315487
#> 15 standard 5 3 5.5124269 FALSE 5.315487
#> 16 standard 6 1 4.1369885 TRUE 5.712693
#> 17 standard 6 2 5.4779875 FALSE 5.712693
#> 18 standard 6 3 5.9473981 FALSE 5.712693
#> 19 standard 7 1 7.5429963 FALSE 7.505575
#> 20 standard 7 2 6.0859252 TRUE 7.505575
#> 21 standard 7 3 7.4681544 FALSE 7.505575
#> 22 unknown 1 1 1.3629513 NA 1.135090
#> 23 unknown 1 2 0.3045435 NA 1.135090
#> 24 unknown 1 3 1.7377763 NA 1.135090
#> 25 unknown 2 1 3.8885049 NA 2.285071
#> 26 unknown 2 2 1.9025549 NA 2.285071
#> 27 unknown 2 3 1.0641526 NA 2.285071
#> 28 unknown 3 1 2.9840497 NA 2.214954
#> 29 unknown 3 2 2.1732110 NA 2.214954
#> 30 unknown 3 3 1.4876003 NA 2.214954
#> 31 unknown 4 1 4.9353632 NA 4.451846
#> 32 unknown 4 2 4.1764886 NA 4.451846
#> 33 unknown 4 3 4.2436855 NA 4.451846
#> 34 unknown 5 1 6.6235489 NA 5.533863
#> 35 unknown 5 2 5.1120381 NA 5.533863
#> 36 unknown 5 3 4.8660030 NA 5.533863
#> 37 unknown 6 1 4.0899125 NA 5.165743
#> 38 unknown 6 2 5.7207628 NA 5.165743
#> 39 unknown 6 3 5.6865540 NA 5.165743
#> 40 unknown 7 1 8.0673079 NA 7.166073
#> 41 unknown 7 2 7.0700349 NA 7.166073
#> 42 unknown 7 3 6.3608767 NA 7.166073
qp_calc_abs_mean(abs, "samples")
#> sample_type index rep .abs .is_outlier .mean
#> 1 standard 1 1 0.4000435 NA 1.030875
#> 2 standard 1 2 1.2553171 NA 1.030875
#> 3 standard 1 3 1.4372636 NA 1.030875
#> 4 standard 2 1 1.9944287 NA 2.588131
#> 5 standard 2 2 2.6215527 NA 2.588131
#> 6 standard 2 3 3.1484116 NA 2.588131
#> 7 standard 3 1 1.1781823 NA 2.228886
#> 8 standard 3 2 2.7526747 NA 2.228886
#> 9 standard 3 3 2.7558004 NA 2.228886
#> 10 standard 4 1 3.7172946 NA 3.930859
#> 11 standard 4 2 3.4463006 NA 3.930859
#> 12 standard 4 3 4.6289820 NA 3.930859
#> 13 standard 5 1 7.0650249 NA 5.315487
#> 14 standard 5 2 3.3690106 NA 5.315487
#> 15 standard 5 3 5.5124269 NA 5.315487
#> 16 standard 6 1 4.1369885 NA 5.187458
#> 17 standard 6 2 5.4779875 NA 5.187458
#> 18 standard 6 3 5.9473981 NA 5.187458
#> 19 standard 7 1 7.5429963 NA 7.032359
#> 20 standard 7 2 6.0859252 NA 7.032359
#> 21 standard 7 3 7.4681544 NA 7.032359
#> 22 unknown 1 1 1.3629513 FALSE 1.550364
#> 23 unknown 1 2 0.3045435 TRUE 1.550364
#> 24 unknown 1 3 1.7377763 FALSE 1.550364
#> 25 unknown 2 1 3.8885049 TRUE 1.483354
#> 26 unknown 2 2 1.9025549 FALSE 1.483354
#> 27 unknown 2 3 1.0641526 FALSE 1.483354
#> 28 unknown 3 1 2.9840497 FALSE 2.214954
#> 29 unknown 3 2 2.1732110 FALSE 2.214954
#> 30 unknown 3 3 1.4876003 FALSE 2.214954
#> 31 unknown 4 1 4.9353632 TRUE 4.210087
#> 32 unknown 4 2 4.1764886 FALSE 4.210087
#> 33 unknown 4 3 4.2436855 FALSE 4.210087
#> 34 unknown 5 1 6.6235489 TRUE 4.989021
#> 35 unknown 5 2 5.1120381 FALSE 4.989021
#> 36 unknown 5 3 4.8660030 FALSE 4.989021
#> 37 unknown 6 1 4.0899125 TRUE 5.703658
#> 38 unknown 6 2 5.7207628 FALSE 5.703658
#> 39 unknown 6 3 5.6865540 FALSE 5.703658
#> 40 unknown 7 1 8.0673079 FALSE 7.166073
#> 41 unknown 7 2 7.0700349 FALSE 7.166073
#> 42 unknown 7 3 6.3608767 FALSE 7.166073
qp_calc_abs_mean(abs, "all")
#> sample_type index rep .abs .is_outlier .mean
#> 1 standard 1 1 0.4000435 TRUE 1.346290
#> 2 standard 1 2 1.2553171 FALSE 1.346290
#> 3 standard 1 3 1.4372636 FALSE 1.346290
#> 4 standard 2 1 1.9944287 FALSE 2.588131
#> 5 standard 2 2 2.6215527 FALSE 2.588131
#> 6 standard 2 3 3.1484116 FALSE 2.588131
#> 7 standard 3 1 1.1781823 TRUE 2.754238
#> 8 standard 3 2 2.7526747 FALSE 2.754238
#> 9 standard 3 3 2.7558004 FALSE 2.754238
#> 10 standard 4 1 3.7172946 FALSE 3.581798
#> 11 standard 4 2 3.4463006 FALSE 3.581798
#> 12 standard 4 3 4.6289820 TRUE 3.581798
#> 13 standard 5 1 7.0650249 FALSE 5.315487
#> 14 standard 5 2 3.3690106 FALSE 5.315487
#> 15 standard 5 3 5.5124269 FALSE 5.315487
#> 16 standard 6 1 4.1369885 TRUE 5.712693
#> 17 standard 6 2 5.4779875 FALSE 5.712693
#> 18 standard 6 3 5.9473981 FALSE 5.712693
#> 19 standard 7 1 7.5429963 FALSE 7.505575
#> 20 standard 7 2 6.0859252 TRUE 7.505575
#> 21 standard 7 3 7.4681544 FALSE 7.505575
#> 22 unknown 1 1 1.3629513 FALSE 1.550364
#> 23 unknown 1 2 0.3045435 TRUE 1.550364
#> 24 unknown 1 3 1.7377763 FALSE 1.550364
#> 25 unknown 2 1 3.8885049 TRUE 1.483354
#> 26 unknown 2 2 1.9025549 FALSE 1.483354
#> 27 unknown 2 3 1.0641526 FALSE 1.483354
#> 28 unknown 3 1 2.9840497 FALSE 2.214954
#> 29 unknown 3 2 2.1732110 FALSE 2.214954
#> 30 unknown 3 3 1.4876003 FALSE 2.214954
#> 31 unknown 4 1 4.9353632 TRUE 4.210087
#> 32 unknown 4 2 4.1764886 FALSE 4.210087
#> 33 unknown 4 3 4.2436855 FALSE 4.210087
#> 34 unknown 5 1 6.6235489 TRUE 4.989021
#> 35 unknown 5 2 5.1120381 FALSE 4.989021
#> 36 unknown 5 3 4.8660030 FALSE 4.989021
#> 37 unknown 6 1 4.0899125 TRUE 5.703658
#> 38 unknown 6 2 5.7207628 FALSE 5.703658
#> 39 unknown 6 3 5.6865540 FALSE 5.703658
#> 40 unknown 7 1 8.0673079 FALSE 7.166073
#> 41 unknown 7 2 7.0700349 FALSE 7.166073
#> 42 unknown 7 3 6.3608767 FALSE 7.166073
# If an `.is_outlier` column is provided, that will be used instead:
abs$.is_outlier <- rep(c(TRUE, FALSE), length.out = nrow(abs))
qp_calc_abs_mean(abs)
#> Data has `.is_outlier` column, using that
#> sample_type index rep .abs .is_outlier .mean
#> 1 standard 1 1 0.4000435 TRUE 1.255317
#> 2 standard 1 2 1.2553171 FALSE 1.255317
#> 3 standard 1 3 1.4372636 TRUE 1.255317
#> 4 standard 2 1 1.9944287 FALSE 2.571420
#> 5 standard 2 2 2.6215527 TRUE 2.571420
#> 6 standard 2 3 3.1484116 FALSE 2.571420
#> 7 standard 3 1 1.1781823 TRUE 2.752675
#> 8 standard 3 2 2.7526747 FALSE 2.752675
#> 9 standard 3 3 2.7558004 TRUE 2.752675
#> 10 standard 4 1 3.7172946 FALSE 4.173138
#> 11 standard 4 2 3.4463006 TRUE 4.173138
#> 12 standard 4 3 4.6289820 FALSE 4.173138
#> 13 standard 5 1 7.0650249 TRUE 3.369011
#> 14 standard 5 2 3.3690106 FALSE 3.369011
#> 15 standard 5 3 5.5124269 TRUE 3.369011
#> 16 standard 6 1 4.1369885 FALSE 5.042193
#> 17 standard 6 2 5.4779875 TRUE 5.042193
#> 18 standard 6 3 5.9473981 FALSE 5.042193
#> 19 standard 7 1 7.5429963 TRUE 6.085925
#> 20 standard 7 2 6.0859252 FALSE 6.085925
#> 21 standard 7 3 7.4681544 TRUE 6.085925
#> 22 unknown 1 1 1.3629513 FALSE 1.550364
#> 23 unknown 1 2 0.3045435 TRUE 1.550364
#> 24 unknown 1 3 1.7377763 FALSE 1.550364
#> 25 unknown 2 1 3.8885049 TRUE 1.902555
#> 26 unknown 2 2 1.9025549 FALSE 1.902555
#> 27 unknown 2 3 1.0641526 TRUE 1.902555
#> 28 unknown 3 1 2.9840497 FALSE 2.235825
#> 29 unknown 3 2 2.1732110 TRUE 2.235825
#> 30 unknown 3 3 1.4876003 FALSE 2.235825
#> 31 unknown 4 1 4.9353632 TRUE 4.176489
#> 32 unknown 4 2 4.1764886 FALSE 4.176489
#> 33 unknown 4 3 4.2436855 TRUE 4.176489
#> 34 unknown 5 1 6.6235489 FALSE 5.744776
#> 35 unknown 5 2 5.1120381 TRUE 5.744776
#> 36 unknown 5 3 4.8660030 FALSE 5.744776
#> 37 unknown 6 1 4.0899125 TRUE 5.720763
#> 38 unknown 6 2 5.7207628 FALSE 5.720763
#> 39 unknown 6 3 5.6865540 TRUE 5.720763
#> 40 unknown 7 1 8.0673079 FALSE 7.214092
#> 41 unknown 7 2 7.0700349 TRUE 7.214092
#> 42 unknown 7 3 6.3608767 FALSE 7.214092