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@@ -85,31 +85,31 @@ suspicious.
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sir <- random_sir(10)
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sir
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#> Class <sir>
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#> [1] S S I S I S I S I R
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#> [1] S R I R R R R I I I
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mean_amr_distance(sir)
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#> [1] -0.3162278 -0.3162278 -0.3162278 -0.3162278 -0.3162278 -0.3162278
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#> [7] -0.3162278 -0.3162278 -0.3162278 2.8460499
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#> [1] -0.9486833 0.9486833 -0.9486833 0.9486833 0.9486833 0.9486833
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#> [7] 0.9486833 -0.9486833 -0.9486833 -0.9486833
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mic <- random_mic(10)
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mic
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#> Class <mic>
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#> [1] <=0.0001 0.0002 0.5 0.0005 0.004 0.001 0.016 0.0002
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#> [9] 0.032 1
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#> [1] 0.032 0.064 0.125 0.5 0.016 0.008 <=0.0005 0.032
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#> [9] 0.5 1
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mean_amr_distance(mic)
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#> [1] -1.13933320 -0.92906618 1.44436742 -0.65110831 -0.02030726 -0.44084129
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#> [7] 0.40022677 -0.92906618 0.61049379 1.65463443
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#> [1] -0.20876566 0.09541835 0.38919449 0.99756251 -0.51294967 -0.81713368
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#> [7] -2.03386972 -0.20876566 0.99756251 1.30174652
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# equal to the Z-score of their log2:
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(log2(mic) - mean(log2(mic))) / sd(log2(mic))
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#> [1] -1.13933320 -0.92906618 1.44436742 -0.65110831 -0.02030726 -0.44084129
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#> [7] 0.40022677 -0.92906618 0.61049379 1.65463443
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#> [1] -0.20876566 0.09541835 0.38919449 0.99756251 -0.51294967 -0.81713368
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#> [7] -2.03386972 -0.20876566 0.99756251 1.30174652
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disk <- random_disk(10)
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disk
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#> Class <disk>
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#> [1] 49 30 42 50 16 27 12 13 28 32
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#> [1] 48 45 48 40 44 9 39 49 39 29
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mean_amr_distance(disk)
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#> [1] 1.37726856 0.00721083 0.87251045 1.44937686 -1.00230539 -0.20911407
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#> [7] -1.29073860 -1.21863029 -0.13700577 0.15142743
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#> [1] 0.74202711 0.49468474 0.74202711 0.08244746 0.41223728 -2.47342369
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#> [7] 0.00000000 0.82447456 0.00000000 -0.82447456
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y <- data.frame(
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id = LETTERS[1:10],
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@@ -120,35 +120,35 @@ y <- data.frame(
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)
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y
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#> id amox cipr gent tobr
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#> 1 A I 32 4 8
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#> 2 B R 32 4 8
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#> 3 C I 30 8 8
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#> 4 D S 16 4 8
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#> 5 E S 33 4 16
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#> 6 F R 32 4 8
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#> 7 G I 29 8 8
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#> 8 H I 31 8 16
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#> 9 I I 30 4 16
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#> 10 J R 20 8 8
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#> 1 A R 26 4 4
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#> 2 B R 28 8 1
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#> 3 C R 31 8 1
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#> 4 D R 30 4 2
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#> 5 E I 32 8 2
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#> 6 F S 28 32 2
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#> 7 G S 33 <=2 >=8
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#> 8 H I 24 <=2 4
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#> 9 I S 20 <=2 1
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#> 10 J R 19 16 2
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mean_amr_distance(y)
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#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent", and
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#> "tobr"
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#> [1] -0.351732344 0.165816825 0.045278388 -1.048629829 0.209372918
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#> [6] 0.165816825 0.001722296 0.606383651 0.078704640 0.127266630
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#> [1] 0.31430391 0.09942875 0.25436185 0.26948082 0.08306514 0.24576214
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#> [7] 0.26823618 -0.44796371 -1.15734233 0.07066724
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y$amr_distance <- mean_amr_distance(y, is.mic(y))
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#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr"
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y[order(y$amr_distance), ]
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#> id amox cipr gent tobr amr_distance
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#> 1 A I 32 4 8 -0.6978278
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#> 2 B R 32 4 8 -0.6978278
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#> 4 D S 16 4 8 -0.6978278
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#> 6 F R 32 4 8 -0.6978278
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#> 3 C I 30 8 8 0.2704180
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#> 7 G I 29 8 8 0.2704180
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#> 10 J R 20 8 8 0.2704180
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#> 5 E S 33 4 16 0.3372705
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#> 9 I I 30 4 16 0.3372705
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#> 8 H I 31 8 16 1.3055163
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#> 9 I S 20 <=2 1 -1.1069930
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#> 2 B R 28 8 1 -0.3684440
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#> 3 C R 31 8 1 -0.3684440
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#> 4 D R 30 4 2 -0.2349174
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#> 8 H I 24 <=2 4 -0.1013907
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#> 5 E I 32 8 2 0.1343571
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#> 1 A R 26 4 4 0.2678838
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#> 7 G S 33 <=2 >=8 0.4014105
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#> 10 J R 19 16 2 0.5036316
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#> 6 F S 28 32 2 0.8729061
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if (require("dplyr")) {
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y %>%
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@@ -160,17 +160,17 @@ if (require("dplyr")) {
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}
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#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent", and
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#> "tobr"
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#> id amox cipr gent tobr amr_distance check_id_C
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#> 1 C I 30 8 8 0.045278388 0.00000000
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#> 2 I I 30 4 16 0.078704640 0.03342625
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#> 3 G I 29 8 8 0.001722296 0.04355609
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#> 4 J R 20 8 8 0.127266630 0.08198824
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#> 5 B R 32 4 8 0.165816825 0.12053844
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#> 6 F R 32 4 8 0.165816825 0.12053844
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#> 7 E S 33 4 16 0.209372918 0.16409453
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#> 8 A I 32 4 8 -0.351732344 0.39701073
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#> 9 H I 31 8 16 0.606383651 0.56110526
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#> 10 D S 16 4 8 -1.048629829 1.09390822
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#> id amox cipr gent tobr amr_distance check_id_C
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#> 1 C R 31 8 1 0.25436185 0.000000000
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#> 2 F S 28 32 2 0.24576214 0.008599711
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#> 3 G S 33 <=2 >=8 0.26823618 0.013874329
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#> 4 D R 30 4 2 0.26948082 0.015118966
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#> 5 A R 26 4 4 0.31430391 0.059942062
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#> 6 B R 28 8 1 0.09942875 0.154933106
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#> 7 E I 32 8 2 0.08306514 0.171296709
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#> 8 J R 19 16 2 0.07066724 0.183694618
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#> 9 H I 24 <=2 4 -0.44796371 0.702325561
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#> 10 I S 20 <=2 1 -1.15734233 1.411704178
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if (require("dplyr")) {
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# support for groups
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example_isolates %>%
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