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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] R I R I I S R S I I
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#> [1] S R S R I I R R R S
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mean_amr_distance(sir)
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#> [1] 1.449138 -0.621059 1.449138 -0.621059 -0.621059 -0.621059 1.449138
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#> [8] -0.621059 -0.621059 -0.621059
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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] 0.25 0.5 8 2 0.004 4 1 0.001
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#> [9] 0.004 <=0.0005
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#> [1] 0.5 <=0.0001 0.25 0.0005 0.001 0.0002 8 <=0.0001
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#> [9] <=0.0001 0.004
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mean_amr_distance(mic)
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#> [1] 0.2626139 0.4520471 1.2097801 0.8309136 -0.8675039 1.0203468
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#> [7] 0.6414804 -1.2463704 -0.8675039 -1.4358036
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#> [1] 1.18999891 -0.86809178 1.02250714 -0.47918793 -0.31169616 -0.70060001
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#> [7] 1.85996599 -0.86809178 -0.86809178 0.02328738
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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] 0.2626139 0.4520471 1.2097801 0.8309136 -0.8675039 1.0203468
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#> [7] 0.6414804 -1.2463704 -0.8675039 -1.4358036
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#> [1] 1.18999891 -0.86809178 1.02250714 -0.47918793 -0.31169616 -0.70060001
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#> [7] 1.85996599 -0.86809178 -0.86809178 0.02328738
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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 38 33 31 17 42 43 46 37 46
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#> [1] 20 45 31 26 23 38 44 41 20 49
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mean_amr_distance(disk)
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#> [1] 1.14152462 -0.02113934 -0.54962296 -0.76101641 -2.24077054 0.40164755
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#> [7] 0.50734427 0.82443445 -0.12683607 0.82443445
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#> [1] -1.2414143 1.0239402 -0.2446583 -0.6977292 -0.9695717 0.3896410
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#> [7] 0.9333261 0.6614835 -1.2414143 1.3863970
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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 S 28 >=2 8
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#> 2 B R 33 1 8
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#> 3 C R 32 1 8
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#> 4 D I 25 1 16
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#> 5 E I 19 0.5 16
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#> 6 F S 23 0.5 8
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#> 7 G R 27 0.5 16
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#> 8 H S 29 0.5 8
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#> 9 I R 32 1 8
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#> 10 J R 32 0.5 16
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#> 1 A R 33 16 <=1
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#> 2 B S 32 <=8 32
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#> 3 C S 27 16 32
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#> 4 D I 33 <=8 8
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#> 5 E I 22 <=8 2
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#> 6 F R 32 <=8 4
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#> 7 G S 31 16 8
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#> 8 H I 31 <=8 <=1
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#> 9 I R 28 16 2
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#> 10 J S 22 16 4
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mean_amr_distance(y)
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#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
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#> and "tobr"
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#> [1] 0.06974787 0.45859464 0.40418392 0.03309016 -0.65092262 -0.91740267
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#> [7] 0.25870477 -0.59093836 0.40418392 0.53075837
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#> [1] 0.52677313 0.16501937 0.34372779 -0.05155946 -0.97765805 0.26901032
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#> [7] 0.30452889 -0.58337098 0.36899264 -0.36546366
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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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#> 6 F S 23 0.5 8 -0.8163565
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#> 8 H S 29 0.5 8 -0.8163565
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#> 2 B R 33 1 8 -0.1012596
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#> 3 C R 32 1 8 -0.1012596
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#> 9 I R 32 1 8 -0.1012596
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#> 5 E I 19 0.5 16 0.1518893
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#> 7 G R 27 0.5 16 0.1518893
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#> 10 J R 32 0.5 16 0.1518893
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#> 1 A S 28 >=2 8 0.6138374
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#> 4 D I 25 1 16 0.8669863
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#> 8 H I 31 <=8 <=1 -1.0808937
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#> 5 E I 22 <=8 2 -0.8051882
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#> 6 F R 32 <=8 4 -0.5294827
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#> 4 D I 33 <=8 8 -0.2537773
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#> 1 A R 33 16 <=1 -0.1322104
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#> 9 I R 28 16 2 0.1434951
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#> 2 B S 32 <=8 32 0.2976337
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#> 10 J S 22 16 4 0.4192006
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#> 7 G S 31 16 8 0.6949060
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#> 3 C S 27 16 32 1.2463170
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if (require("dplyr")) {
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y %>%
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@@ -161,16 +161,16 @@ if (require("dplyr")) {
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#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
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#> and "tobr"
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#> id amox cipr gent tobr amr_distance check_id_C
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#> 1 C R 32 1 8 0.40418392 0.00000000
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#> 2 I R 32 1 8 0.40418392 0.00000000
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#> 3 B R 33 1 8 0.45859464 0.05441072
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#> 4 J R 32 0.5 16 0.53075837 0.12657445
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#> 5 G R 27 0.5 16 0.25870477 0.14547915
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#> 6 A S 28 >=2 8 0.06974787 0.33443605
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#> 7 D I 25 1 16 0.03309016 0.37109376
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#> 8 H S 29 0.5 8 -0.59093836 0.99512228
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#> 9 E I 19 0.5 16 -0.65092262 1.05510655
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#> 10 F S 23 0.5 8 -0.91740267 1.32158659
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#> 1 C S 27 16 32 0.34372779 0.00000000
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#> 2 I R 28 16 2 0.36899264 0.02526485
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#> 3 G S 31 16 8 0.30452889 0.03919890
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#> 4 F R 32 <=8 4 0.26901032 0.07471748
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#> 5 B S 32 <=8 32 0.16501937 0.17870842
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#> 6 A R 33 16 <=1 0.52677313 0.18304534
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#> 7 D I 33 <=8 8 -0.05155946 0.39528726
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#> 8 J S 22 16 4 -0.36546366 0.70919145
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#> 9 H I 31 <=8 <=1 -0.58337098 0.92709877
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#> 10 E I 22 <=8 2 -0.97765805 1.32138584
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if (require("dplyr")) {
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# support for groups
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example_isolates %>%
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