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@@ -85,30 +85,30 @@ 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] I I R I R S S S I S
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#> [1] R R I R I I S R S I
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mean_amr_distance(sir)
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#> [1] -0.4743416 -0.4743416 1.8973666 -0.4743416 1.8973666 -0.4743416
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#> [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416
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#> [1] 1.1618950 1.1618950 -0.7745967 1.1618950 -0.7745967 -0.7745967
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#> [7] -0.7745967 1.1618950 -0.7745967 -0.7745967
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mic <- random_mic(10)
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mic
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#> Class 'mic'
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#> [1] 0.004 2 0.002 0.0001 0.004 0.002 >=4 0.0002 0.032 0.004
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#> [1] 0.032 0.5 1 >=8 4 0.016 >=8 1 0.004 0.008
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mean_amr_distance(mic)
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#> [1] -0.2047915 1.5799751 -0.4038557 -1.2641969 -0.2047915 -0.4038557
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#> [7] 1.7790393 -1.0651327 0.3924011 -0.2047915
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#> [1] -0.7311752 0.2104422 0.4478776 1.1601837 0.9227483 -0.9686106
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#> [7] 1.1601837 0.4478776 -1.4434813 -1.2060459
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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] -0.2047915 1.5799751 -0.4038557 -1.2641969 -0.2047915 -0.4038557
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#> [7] 1.7790393 -1.0651327 0.3924011 -0.2047915
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#> [1] -0.7311752 0.2104422 0.4478776 1.1601837 0.9227483 -0.9686106
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#> [7] 1.1601837 0.4478776 -1.4434813 -1.2060459
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disk <- random_disk(10)
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disk
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#> Class 'disk'
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#> [1] 43 12 28 32 22 31 35 25 43 35
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#> [1] 50 49 38 33 31 17 42 43 46 37
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mean_amr_distance(disk)
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#> [1] 1.30998909 -1.96498364 -0.27467513 0.14790199 -0.90854082 0.04225771
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#> [7] 0.46483484 -0.59160798 1.30998909 0.46483484
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#> [1] 1.15131286 1.05032051 -0.06059541 -0.56555720 -0.76754191 -2.18143490
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#> [7] 0.34337401 0.44436637 0.74734344 -0.16158777
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y <- data.frame(
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id = LETTERS[1:10],
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@@ -119,35 +119,35 @@ 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 31 2 >=16
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#> 2 B S 27 <=1 8
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#> 3 C R 25 2 4
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#> 4 D R 25 <=1 2
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#> 5 E I 31 <=1 2
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#> 6 F S 32 <=1 8
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#> 7 G I 29 2 2
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#> 8 H S 18 <=1 4
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#> 9 I S 28 <=1 4
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#> 10 J R 17 <=1 2
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#> 1 A I 27 >=2 8
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#> 2 B S 28 1 8
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#> 3 C R 33 1 8
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#> 4 D R 32 1 16
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#> 5 E I 25 0.5 16
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#> 6 F I 19 0.5 8
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#> 7 G S 23 0.5 16
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#> 8 H R 27 0.5 8
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#> 9 I S 29 1 8
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#> 10 J R 32 0.5 16
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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] 0.90606144 -0.03989270 0.66241774 -0.09230226 -0.32300020 0.19914999
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#> [7] 0.09893189 -0.70734036 -0.22925499 -0.47477055
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#> [1] 0.08471751 -0.21572693 0.55391610 0.98093500 -0.26046456 -1.08721162
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#> [7] -0.37467261 -0.14625651 -0.15862291 0.62338653
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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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#> 4 D R 25 <=1 2 -0.7848712
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#> 5 E I 31 <=1 2 -0.7848712
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#> 10 J R 17 <=1 2 -0.7848712
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#> 8 H S 18 <=1 4 -0.3105295
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#> 9 I S 28 <=1 4 -0.3105295
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#> 2 B S 27 <=1 8 0.1638121
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#> 6 F S 32 <=1 8 0.1638121
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#> 7 G I 29 2 2 0.2502272
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#> 3 C R 25 2 4 0.7245688
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#> 1 A S 31 2 >=16 1.6732521
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#> 6 F I 19 0.5 8 -0.8163565
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#> 8 H R 27 0.5 8 -0.8163565
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#> 2 B S 28 1 8 -0.1012596
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#> 3 C R 33 1 8 -0.1012596
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#> 9 I S 29 1 8 -0.1012596
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#> 5 E I 25 0.5 16 0.1518893
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#> 7 G S 23 0.5 16 0.1518893
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#> 10 J R 32 0.5 16 0.1518893
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#> 1 A I 27 >=2 8 0.6138374
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#> 4 D R 32 1 16 0.8669863
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if (require("dplyr")) {
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y %>%
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@@ -160,16 +160,16 @@ 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 25 2 4 0.66241774 0.0000000
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#> 2 A S 31 2 >=16 0.90606144 0.2436437
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#> 3 F S 32 <=1 8 0.19914999 0.4632678
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#> 4 G I 29 2 2 0.09893189 0.5634858
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#> 5 B S 27 <=1 8 -0.03989270 0.7023104
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#> 6 D R 25 <=1 2 -0.09230226 0.7547200
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#> 7 I S 28 <=1 4 -0.22925499 0.8916727
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#> 8 E I 31 <=1 2 -0.32300020 0.9854179
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#> 9 J R 17 <=1 2 -0.47477055 1.1371883
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#> 10 H S 18 <=1 4 -0.70734036 1.3697581
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#> 1 C R 33 1 8 0.55391610 0.00000000
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#> 2 J R 32 0.5 16 0.62338653 0.06947042
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#> 3 D R 32 1 16 0.98093500 0.42701889
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#> 4 A I 27 >=2 8 0.08471751 0.46919859
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#> 5 H R 27 0.5 8 -0.14625651 0.70017262
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#> 6 I S 29 1 8 -0.15862291 0.71253901
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#> 7 B S 28 1 8 -0.21572693 0.76964304
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#> 8 E I 25 0.5 16 -0.26046456 0.81438066
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#> 9 G S 23 0.5 16 -0.37467261 0.92858871
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#> 10 F I 19 0.5 8 -1.08721162 1.64112773
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if (require("dplyr")) {
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# support for groups
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example_isolates %>%
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