x <- random_mic(10)
x
#> Class 'mic'
-#> [1] 0.125 16 0.001 0.002 2 64 0.5 32 0.025 16
+#> [1] 1 128 0.025 0.002 2 8 0.025 2 128 0.5
mean_amr_distance(x)
-#> [1] -0.37342995 0.82860681 -1.56959121 -1.39787167 0.31344820 1.17204589
-#> [7] -0.02999087 1.00032635 -0.77215037 0.82860681
+#> [1] 0.03073988 1.36755493 -0.98560777 -1.68148803 0.22171346 0.60366062
+#> [7] -0.98560777 0.22171346 1.36755493 -0.16023370
y <- data.frame(
id = LETTERS[1:10],
@@ -213,38 +213,38 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A 32 0.025 4 0.5
-#> 2 B 4 0.25 4 1
-#> 3 C >=64 >=1 8 0.5
-#> 4 D 16 0.5 2 1
-#> 5 E 32 0.5 2 0.5
-#> 6 F >=64 >=1 16 8
-#> 7 G >=64 0.0625 16 4
-#> 8 H 32 >=1 4 0.25
-#> 9 I >=64 0.0625 16 8
-#> 10 J >=64 0.025 4 1
+#> id amox cipr gent tobr
+#> 1 A 4 >=4 1 4
+#> 2 B 16 >=4 2 4
+#> 3 C 16 0.5 2 2
+#> 4 D 16 >=4 4 <=0.5
+#> 5 E 16 0.125 1 1
+#> 6 F 32 0.25 4 4
+#> 7 G 4 <=0.0625 1 2
+#> 8 H 64 0.5 4 4
+#> 9 I 32 2 4 <=0.5
+#> 10 J <=2 0.5 4 2
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> "id" and "tobr"
#> Warning: NAs introduced by coercion
-#> [1] -0.6576279 -0.7207257 0.3548800 -0.4303788 -0.3775652 1.1329490
-#> [7] 0.5359322 -0.1930342 0.6774164 -0.3218457
+#> [1] -0.08036866 0.51076165 -0.04318986 0.15636345 -0.75415022 0.48217462
+#> [7] -0.97945442 0.75675773 0.20085706 -0.24975135
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and
#> "tobr"
y[order(y$amr_distance), ]
-#> id amox cipr gent tobr amr_distance
-#> 2 B 4 0.25 4 1 -0.7207257
-#> 1 A 32 0.025 4 0.5 -0.6576279
-#> 4 D 16 0.5 2 1 -0.4303788
-#> 5 E 32 0.5 2 0.5 -0.3775652
-#> 10 J >=64 0.025 4 1 -0.3218457
-#> 8 H 32 >=1 4 0.25 -0.1930342
-#> 3 C >=64 >=1 8 0.5 0.3548800
-#> 7 G >=64 0.0625 16 4 0.5359322
-#> 9 I >=64 0.0625 16 8 0.6774164
-#> 6 F >=64 >=1 16 8 1.1329490
+#> id amox cipr gent tobr amr_distance
+#> 7 G 4 <=0.0625 1 2 -0.97945442
+#> 5 E 16 0.125 1 1 -0.75415022
+#> 10 J <=2 0.5 4 2 -0.24975135
+#> 1 A 4 >=4 1 4 -0.08036866
+#> 3 C 16 0.5 2 2 -0.04318986
+#> 4 D 16 >=4 4 <=0.5 0.15636345
+#> 9 I 32 2 4 <=0.5 0.20085706
+#> 6 F 32 0.25 4 4 0.48217462
+#> 2 B 16 >=4 2 4 0.51076165
+#> 8 H 64 0.5 4 4 0.75675773
if (require("dplyr")) {
y %>%
@@ -256,17 +256,17 @@
}
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and
#> "tobr"
-#> id amox cipr gent tobr amr_distance check_id_C
-#> 1 C >=64 >=1 8 0.5 0.3548800 0.0000000
-#> 2 G >=64 0.0625 16 4 0.5359322 0.1810522
-#> 3 I >=64 0.0625 16 8 0.6774164 0.3225364
-#> 4 H 32 >=1 4 0.25 -0.1930342 0.5479142
-#> 5 J >=64 0.025 4 1 -0.3218457 0.6767257
-#> 6 E 32 0.5 2 0.5 -0.3775652 0.7324451
-#> 7 F >=64 >=1 16 8 1.1329490 0.7780690
-#> 8 D 16 0.5 2 1 -0.4303788 0.7852588
-#> 9 A 32 0.025 4 0.5 -0.6576279 1.0125079
-#> 10 B 4 0.25 4 1 -0.7207257 1.0756057
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C 16 0.5 2 2 -0.04318986 0.0000000
+#> 2 A 4 >=4 1 4 -0.08036866 0.0371788
+#> 3 D 16 >=4 4 <=0.5 0.15636345 0.1995533
+#> 4 J <=2 0.5 4 2 -0.24975135 0.2065615
+#> 5 I 32 2 4 <=0.5 0.20085706 0.2440469
+#> 6 F 32 0.25 4 4 0.48217462 0.5253645
+#> 7 B 16 >=4 2 4 0.51076165 0.5539515
+#> 8 E 16 0.125 1 1 -0.75415022 0.7109604
+#> 9 H 64 0.5 4 4 0.75675773 0.7999476
+#> 10 G 4 <=0.0625 1 2 -0.97945442 0.9362646
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 52864b9c6..716adcc22 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 1.8.2.9067
+ 1.8.2.9068