x <- random_mic(10)
x
#> Class 'mic'
-#> [1] 0.025 0.005 0.01 4 4 8 1 >=16 0.001 0.125
+#> [1] <=0.001 0.0625 0.01 0.005 32 0.01 4 0.025 2
+#> [10] 32
mean_amr_distance(x)
-#> [1] -0.6319520 -1.0956588 -0.8959511 0.8302930 0.8302930 1.0300007
-#> [7] 0.4308777 1.2297083 -1.5593656 -0.1682452
+#> [1] -1.3194635 -0.2327999 -0.7143765 -0.8965258 1.4065439 -0.7143765
+#> [7] 0.8600960 -0.4735882 0.6779467 1.4065439
y <- data.frame(
id = LETTERS[1:10],
@@ -213,38 +214,38 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A 1 1 1 4
-#> 2 B 1 2 8 2
-#> 3 C 1 0.5 8 1
-#> 4 D 1 1 16 2
-#> 5 E 4 0.5 4 16
-#> 6 F 4 1 1 16
-#> 7 G 16 <=0.125 16 8
-#> 8 H 16 0.25 8 8
-#> 9 I 4 1 8 8
-#> 10 J 4 1 1 16
+#> id amox cipr gent tobr
+#> 1 A 16 1 4 2
+#> 2 B 4 0.25 1 8
+#> 3 C >=32 0.125 4 <=0.5
+#> 4 D >=32 0.5 <=0.5 <=0.5
+#> 5 E 4 0.125 1 8
+#> 6 F 4 0.5 >=8 4
+#> 7 G <=1 0.5 1 8
+#> 8 H 16 1 2 1
+#> 9 I 16 0.0625 >=8 <=0.5
+#> 10 J 4 0.125 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] -0.55258772 -0.05081832 -0.65116115 -0.10941895 0.20887247 0.11308804
-#> [7] 0.23423260 0.29283323 0.40187176 0.11308804
+#> [1] 0.61790612 -0.10920284 -0.05949768 -0.23827191 -0.28797707 0.45880700
+#> [7] -0.23661482 0.29204478 -0.21259079 -0.22460280
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
-#> 3 C 1 0.5 8 1 -0.65116115
-#> 1 A 1 1 1 4 -0.55258772
-#> 4 D 1 1 16 2 -0.10941895
-#> 2 B 1 2 8 2 -0.05081832
-#> 6 F 4 1 1 16 0.11308804
-#> 10 J 4 1 1 16 0.11308804
-#> 5 E 4 0.5 4 16 0.20887247
-#> 7 G 16 <=0.125 16 8 0.23423260
-#> 8 H 16 0.25 8 8 0.29283323
-#> 9 I 4 1 8 8 0.40187176
+#> id amox cipr gent tobr amr_distance
+#> 5 E 4 0.125 1 8 -0.28797707
+#> 4 D >=32 0.5 <=0.5 <=0.5 -0.23827191
+#> 7 G <=1 0.5 1 8 -0.23661482
+#> 10 J 4 0.125 4 2 -0.22460280
+#> 9 I 16 0.0625 >=8 <=0.5 -0.21259079
+#> 2 B 4 0.25 1 8 -0.10920284
+#> 3 C >=32 0.125 4 <=0.5 -0.05949768
+#> 8 H 16 1 2 1 0.29204478
+#> 6 F 4 0.5 >=8 4 0.45880700
+#> 1 A 16 1 4 2 0.61790612
if (require("dplyr")) {
y %>%
@@ -256,17 +257,17 @@
}
#> 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 1 0.5 8 1 -0.65116115 0.00000000
-#> 2 A 1 1 1 4 -0.55258772 0.09857343
-#> 3 D 1 1 16 2 -0.10941895 0.54174220
-#> 4 B 1 2 8 2 -0.05081832 0.60034283
-#> 5 F 4 1 1 16 0.11308804 0.76424920
-#> 6 J 4 1 1 16 0.11308804 0.76424920
-#> 7 E 4 0.5 4 16 0.20887247 0.86003362
-#> 8 G 16 <=0.125 16 8 0.23423260 0.88539375
-#> 9 H 16 0.25 8 8 0.29283323 0.94399438
-#> 10 I 4 1 8 8 0.40187176 1.05303291
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C >=32 0.125 4 <=0.5 -0.05949768 0.00000000
+#> 2 B 4 0.25 1 8 -0.10920284 0.04970516
+#> 3 I 16 0.0625 >=8 <=0.5 -0.21259079 0.15309311
+#> 4 J 4 0.125 4 2 -0.22460280 0.16510512
+#> 5 G <=1 0.5 1 8 -0.23661482 0.17711714
+#> 6 D >=32 0.5 <=0.5 <=0.5 -0.23827191 0.17877424
+#> 7 E 4 0.125 1 8 -0.28797707 0.22847939
+#> 8 H 16 1 2 1 0.29204478 0.35154246
+#> 9 F 4 0.5 >=8 4 0.45880700 0.51830468
+#> 10 A 16 1 4 2 0.61790612 0.67740380
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 1b67a91a..2beb8e8e 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -1,5 +1,5 @@
-Data Set with 5,910 Common Microorganism Codes — microorganisms.codes • AMR (for R)Data Set with 5,910 Common Microorganism Codes — microorganisms.codes • AMR (for R)
@@ -10,7 +10,7 @@
AMR (for R)
- 1.8.2.9065
+ 1.8.2.9066