diff --git a/404.html b/404.html
index e2e0087f..253640e1 100644
--- a/404.html
+++ b/404.html
@@ -36,7 +36,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 428b80fb..132b724e 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/AMR.html b/articles/AMR.html
index 8a4e529c..537ceedc 100644
--- a/articles/AMR.html
+++ b/articles/AMR.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index a1c5a161..ad6c0056 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/MDR.html b/articles/MDR.html
index 8f72c4d5..f49c61c0 100644
--- a/articles/MDR.html
+++ b/articles/MDR.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -402,18 +402,18 @@ names or codes, this would have worked exactly the same way:
head ( my_TB_data )
#> rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-#> 1 I I R I S S
-#> 2 R R S I S S
-#> 3 R S S S S R
-#> 4 S I S S I S
-#> 5 S I R R R R
-#> 6 R R I R R S
+#> 1 I I I R I R
+#> 2 R I S I S R
+#> 3 I I S S S R
+#> 4 R I I S I R
+#> 5 S R R R S S
+#> 6 R I I R R R
#> kanamycin
-#> 1 R
-#> 2 R
-#> 3 I
-#> 4 I
-#> 5 R
+#> 1 S
+#> 2 S
+#> 3 S
+#> 4 S
+#> 5 S
#> 6 R
We can now add the interpretation of MDR-TB to our data set. You can
use:
@@ -455,40 +455,40 @@ Unique: 5
1
Mono-resistant
-3172
-63.44%
-3172
-63.44%
+3255
+65.10%
+3255
+65.10%
2
Negative
-1056
-21.12%
-4228
-84.56%
+938
+18.76%
+4193
+83.86%
3
Multi-drug-resistant
-423
-8.46%
-4651
-93.02%
+436
+8.72%
+4629
+92.58%
4
Poly-resistant
-251
-5.02%
-4902
-98.04%
+274
+5.48%
+4903
+98.06%
5
Extensively drug-resistant
-98
-1.96%
+97
+1.94%
5000
100.00%
diff --git a/articles/PCA.html b/articles/PCA.html
index 38705eff..6738d2c8 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/WHONET.html b/articles/WHONET.html
index 118d133d..038d3700 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/datasets.html b/articles/datasets.html
index 26b527fb..d988a2c3 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/index.html b/articles/index.html
index 50bee29d..5b70de4a 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/other_pkg.html b/articles/other_pkg.html
index 1c0e094c..0ef5da68 100644
--- a/articles/other_pkg.html
+++ b/articles/other_pkg.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html
index 10812204..81826954 100644
--- a/articles/resistance_predict.html
+++ b/articles/resistance_predict.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html
index e8c90808..0f6fa897 100644
--- a/articles/welcome_to_AMR.html
+++ b/articles/welcome_to_AMR.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/authors.html b/authors.html
index b3da1f3d..b5cced69 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/index.html b/index.html
index 2a9851ce..f90f996c 100644
--- a/index.html
+++ b/index.html
@@ -42,7 +42,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/news/index.html b/news/index.html
index aa023c85..331d931a 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -159,9 +159,9 @@
-
AMR 2.0.0.9030
+
AMR 2.0.0.9031
-
New
+
New
Clinical breakpoints and intrinsic resistance of EUCAST 2023 and CLSI 2023 have been added for as.sir()
. EUCAST 2023 (v13.0) is now the new default guideline for all MIC and disks diffusion interpretations
The EUCAST dosage guideline of v13.0 has been added to the dosage
data set
ECOFF: the clinical_breakpoints
data set now contains epidemiological cut-off (ECOFF) values. These ECOFFs can be used for MIC/disk interpretation using as.sir(..., breakpoint_type = "ECOFF")
, which is an important new addition for veterinary microbiology.
@@ -176,7 +176,7 @@
Added microbial codes for Gram-negative/positive anaerobic bacteria
-
Changed
+
Changed
Updated algorithm of as.mo()
by giving more weight to fungi
mo_rank()
now returns NA
for ‘unknown’ microorganisms (B_ANAER
, B_ANAER-NEG
, B_ANAER-POS
, B_GRAMN
, B_GRAMP
, F_FUNGUS
, F_YEAST
, and UNKNOWN
)
diff --git a/pkgdown.yml b/pkgdown.yml
index 8cc6d1f6..5f5676fe 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
other_pkg: other_pkg.html
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
-last_built: 2023-07-10T14:49Z
+last_built: 2023-07-10T15:06Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index 6ee8c5e5..1ea6e96d 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 3d1e4546..a9d02e32 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/AMR.html b/reference/AMR.html
index 703f1a4d..812be29c 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -24,7 +24,7 @@ The AMR package is available in English, Chinese, Czech, Danish, Dutch, Finnish,
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index b1aade18..456a14f7 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/WHONET.html b/reference/WHONET.html
index 9d7d9984..a9490a14 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index 7de8b7a6..4fc82828 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/ab_property.html b/reference/ab_property.html
index 3f8f0c70..d7aedf2e 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index 5b9348ea..8c05affc 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index d2bcb2b3..4f037316 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/age.html b/reference/age.html
index f359a659..3d49c3ed 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -222,16 +222,16 @@
df
#> birth_date age age_exact age_at_y2k
-#> 1 1931-12-21 91 91.55068 68
-#> 2 1942-05-14 81 81.15616 57
-#> 3 1958-04-02 65 65.27123 41
-#> 4 1947-07-06 76 76.01096 52
-#> 5 1953-12-17 69 69.56164 46
-#> 6 1988-05-23 35 35.13151 11
-#> 7 1987-03-04 36 36.35068 12
-#> 8 1945-08-19 77 77.89041 54
-#> 9 1959-03-20 64 64.30685 40
-#> 10 1957-04-12 66 66.24384 42
+#> 1 1965-08-31 57 57.85753 34
+#> 2 1959-01-07 64 64.50411 40
+#> 3 1935-12-20 87 87.55342 64
+#> 4 1943-06-23 80 80.04658 56
+#> 5 1987-01-10 36 36.49589 12
+#> 6 1998-12-26 24 24.53699 1
+#> 7 1992-10-01 30 30.77260 7
+#> 8 1984-10-15 38 38.73425 15
+#> 9 1936-11-01 86 86.68767 63
+#> 10 1978-02-10 45 45.41096 21
On this page
diff --git a/reference/age_groups.html b/reference/age_groups.html
index 04126785..818998b6 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index bf4b2836..21098cc3 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index 37a5f2d6..b6bb0513 100644
--- a/reference/antibiotic_class_selectors.html
+++ b/reference/antibiotic_class_selectors.html
@@ -12,7 +12,7 @@ In short, if you have a column name that resembles an antimicrobial drug, it wil
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -627,10 +627,10 @@ In short, if you have a column name that resembles an antimicrobial drug, it wil
#> kefzol
#> <sir>
#> 1 R
-#> 2 R
+#> 2 I
#> 3 I
#> 4 R
-#> 5 I
+#> 5 S
if ( require ( "dplyr" ) ) {
# get AMR for all aminoglycosides e.g., per ward:
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index 4a99b04d..7963a80d 100644
--- a/reference/antibiotics.html
+++ b/reference/antibiotics.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/as.ab.html b/reference/as.ab.html
index b2e404c4..ebe6233f 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/as.av.html b/reference/as.av.html
index 516732e1..fce7f3c2 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/as.disk.html b/reference/as.disk.html
index f7c4d875..0cf7c688 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/as.mic-1.png b/reference/as.mic-1.png
index 228b623c..24b5977f 100644
Binary files a/reference/as.mic-1.png and b/reference/as.mic-1.png differ
diff --git a/reference/as.mic-2.png b/reference/as.mic-2.png
index bec5faca..228b623c 100644
Binary files a/reference/as.mic-2.png and b/reference/as.mic-2.png differ
diff --git a/reference/as.mic-3.png b/reference/as.mic-3.png
index 2031e69f..bec5faca 100644
Binary files a/reference/as.mic-3.png and b/reference/as.mic-3.png differ
diff --git a/reference/as.mic-4.png b/reference/as.mic-4.png
index 0454041d..2031e69f 100644
Binary files a/reference/as.mic-4.png and b/reference/as.mic-4.png differ
diff --git a/reference/as.mic-5.png b/reference/as.mic-5.png
new file mode 100644
index 00000000..0454041d
Binary files /dev/null and b/reference/as.mic-5.png differ
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 83250d41..6f34ac29 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -291,22 +291,22 @@
# plot MIC values, see ?plot
plot ( mic_data )
-#> Error in plot_colours_subtitle_guideline(x = x, mo = mo, ab = ab, guideline = guideline, colours_SIR = colours_SIR, fn = as.mic, language = language, method = "MIC", include_PKPD = include_PKPD, breakpoint_type = breakpoint_type, ...): object 'sir_history' not found
-plot ( mic_data , mo = "E. coli" , ab = "cipro" )
+plot ( mic_data , mo = "E. coli" , ab = "cipro" )
+
if ( require ( "ggplot2" ) ) {
autoplot ( mic_data , mo = "E. coli" , ab = "cipro" )
}
-
+
if ( require ( "ggplot2" ) ) {
autoplot ( mic_data , mo = "E. coli" , ab = "cipro" , language = "nl" ) # Dutch
}
-
+
if ( require ( "ggplot2" ) ) {
autoplot ( mic_data , mo = "E. coli" , ab = "cipro" , language = "uk" ) # Ukrainian
}
-
+
On this page
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 5e1649b6..a77f7678 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/as.sir.html b/reference/as.sir.html
index b0baba1d..206f9de6 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -14,7 +14,7 @@ All breakpoints used for interpretation are publicly available in the clinical_b
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -556,14 +556,14 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
#> # A tibble: 8 × 13
#> datetime index ab_input ab_guideline mo_input mo_guideline
#> <dttm> <int> <chr> <ab> <chr> <mo>
-#> 1 2023-07-10 14:50:29 1 CIP CIP Escherichia … UNKNOWN
-#> 2 2023-07-10 14:50:29 1 AMP AMP Escherichia … UNKNOWN
-#> 3 2023-07-10 14:50:21 1 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
-#> 4 2023-07-10 14:50:21 2 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
-#> 5 2023-07-10 14:50:21 3 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
-#> 6 2023-07-10 14:50:21 4 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
-#> 7 2023-07-10 14:50:21 1 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
-#> 8 2023-07-10 14:50:20 1 ampicillin AMP Strep pneu B_STRPT_PNMN
+#> 1 2023-07-10 15:07:05 1 CIP CIP Escherichia … UNKNOWN
+#> 2 2023-07-10 15:07:05 1 AMP AMP Escherichia … UNKNOWN
+#> 3 2023-07-10 15:06:57 1 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
+#> 4 2023-07-10 15:06:57 2 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
+#> 5 2023-07-10 15:06:57 3 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
+#> 6 2023-07-10 15:06:57 4 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
+#> 7 2023-07-10 15:06:57 1 AMX AMX B_STRPT_PNMN B_STRPT_PNMN
+#> 8 2023-07-10 15:06:56 1 ampicillin AMP Strep pneu B_STRPT_PNMN
#> # ℹ 7 more variables: guideline <chr>, ref_table <chr>, uti <lgl>,
#> # method <chr>, input <dbl>, outcome <sir>, breakpoint_S_R <chr>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index 0e7c5a1b..dc57e8a9 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index 0d10070f..58dbb2ee 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/av_property.html b/reference/av_property.html
index dc6842f5..3cc330e5 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/availability.html b/reference/availability.html
index fec8d430..48631d93 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index b3f173ba..da11b28b 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index 63715ac0..d8042174 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/count.html b/reference/count.html
index ac469b01..b24cacc8 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -12,7 +12,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index b474c07e..05d96cfb 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/dosage.html b/reference/dosage.html
index 1ae4a61a..afcc69ba 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index 80d31b87..c47802c0 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 6da3b25f..d54470e4 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index d1f7d904..52b3c289 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index f327b198..96d6cb9b 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -12,7 +12,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/g.test.html b/reference/g.test.html
index 8e25b9c8..ef429487 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/get_episode.html b/reference/get_episode.html
index 16a1d5cf..22e80e66 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -263,28 +263,28 @@
df <- example_isolates [ sample ( seq_len ( 2000 ) , size = 100 ) , ]
get_episode ( df $ date , episode_days = 60 ) # indices
-#> [1] 1 50 26 12 10 18 50 42 43 21 34 19 13 25 44 35 45 16 17 3 25 20 3 17 45
-#> [26] 6 10 28 42 9 47 1 11 27 18 45 17 14 6 22 31 42 41 2 23 38 16 46 29 8
-#> [51] 40 48 7 49 20 34 16 7 36 38 48 2 49 14 32 14 27 10 37 24 18 23 4 46 8
-#> [76] 36 6 7 32 22 28 49 16 47 38 21 30 27 6 22 14 24 48 33 21 5 48 39 16 15
+#> [1] 43 14 7 8 29 4 16 40 5 32 9 43 27 7 20 13 28 1 3 23 38 24 42 2 26
+#> [26] 44 21 36 11 35 25 31 6 17 27 28 19 46 35 14 6 48 47 47 29 32 16 15 46 45
+#> [51] 32 1 17 29 15 10 45 43 20 22 49 3 44 27 23 13 24 10 21 34 16 44 6 12 5
+#> [76] 12 37 5 14 46 13 49 33 41 44 21 39 22 4 47 17 36 48 39 22 30 37 34 18 44
is_new_episode ( df $ date , episode_days = 60 ) # TRUE/FALSE
-#> [1] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
-#> [13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
-#> [25] FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
-#> [37] FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
-#> [49] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
-#> [61] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
-#> [73] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
-#> [85] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
-#> [97] FALSE TRUE FALSE TRUE
+#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
+#> [13] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
+#> [25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
+#> [37] TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
+#> [49] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
+#> [61] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
+#> [73] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
+#> [85] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
+#> [97] FALSE FALSE TRUE FALSE
# filter on results from the third 60-day episode only, using base R
df [ which ( get_episode ( df $ date , 60 ) == 3 ) , ]
#> # A tibble: 2 × 46
-#> date patient age gender ward mo PEN OXA FLC AMX
-#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2002-06-04 082413 78 M ICU B_STRPT_PNMN S NA NA S
-#> 2 2002-06-23 798871 82 M Clinical B_ENTRC_FCLS NA NA NA NA
+#> date patient age gender ward mo PEN OXA FLC AMX
+#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
+#> 1 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA
+#> 2 2003-06-11 E35356 71 F ICU B_STPHY_CONS R NA R R
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
@@ -318,19 +318,19 @@
arrange ( patient , condition , date )
}
#> # A tibble: 100 × 4
-#> # Groups: patient, condition [95]
+#> # Groups: patient, condition [99]
#> patient date condition new_episode
#> <chr> <date> <chr> <lgl>
-#> 1 008268 2007-02-20 B TRUE
-#> 2 051150 2007-01-02 B TRUE
-#> 3 067927 2002-01-07 A TRUE
-#> 4 074321 2015-09-20 A TRUE
-#> 5 078381 2014-07-17 B TRUE
-#> 6 078381 2014-08-17 B FALSE
-#> 7 082413 2002-06-04 C TRUE
-#> 8 0C0688 2014-09-05 B TRUE
-#> 9 0E2483 2007-05-29 A TRUE
-#> 10 15D386 2004-08-01 B TRUE
+#> 1 022060 2004-05-04 A TRUE
+#> 2 022060 2004-05-04 C TRUE
+#> 3 080086 2007-10-26 B TRUE
+#> 4 082622 2014-02-08 C TRUE
+#> 5 092034 2006-06-12 C TRUE
+#> 6 0D7D34 2011-03-16 C TRUE
+#> 7 0E2483 2007-05-29 A TRUE
+#> 8 0E2483 2007-05-29 C TRUE
+#> 9 126334 2009-11-26 B TRUE
+#> 10 155435 2014-06-18 C TRUE
#> # ℹ 90 more rows
if ( require ( "dplyr" ) ) {
@@ -344,19 +344,19 @@
arrange ( patient , ward , date )
}
#> # A tibble: 100 × 5
-#> # Groups: ward, patient [94]
-#> ward date patient new_index new_logical
-#> <chr> <date> <chr> <int> <lgl>
-#> 1 ICU 2007-02-20 008268 1 TRUE
-#> 2 ICU 2007-01-02 051150 1 TRUE
-#> 3 ICU 2002-01-07 067927 1 TRUE
-#> 4 ICU 2015-09-20 074321 1 TRUE
-#> 5 ICU 2014-07-17 078381 1 TRUE
-#> 6 ICU 2014-08-17 078381 1 FALSE
-#> 7 ICU 2002-06-04 082413 1 TRUE
-#> 8 Clinical 2014-09-05 0C0688 1 TRUE
-#> 9 Clinical 2007-05-29 0E2483 1 TRUE
-#> 10 ICU 2004-08-01 15D386 1 TRUE
+#> # Groups: ward, patient [93]
+#> ward date patient new_index new_logical
+#> <chr> <date> <chr> <int> <lgl>
+#> 1 ICU 2004-05-04 022060 1 TRUE
+#> 2 ICU 2004-05-04 022060 1 FALSE
+#> 3 Clinical 2007-10-26 080086 1 TRUE
+#> 4 ICU 2014-02-08 082622 1 TRUE
+#> 5 ICU 2006-06-12 092034 1 TRUE
+#> 6 ICU 2011-03-16 0D7D34 1 TRUE
+#> 7 Clinical 2007-05-29 0E2483 1 TRUE
+#> 8 Clinical 2007-05-29 0E2483 1 FALSE
+#> 9 Outpatient 2009-11-26 126334 1 TRUE
+#> 10 Clinical 2014-06-18 155435 1 TRUE
#> # ℹ 90 more rows
if ( require ( "dplyr" ) ) {
@@ -372,9 +372,9 @@
#> # A tibble: 3 × 5
#> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30
#> <chr> <int> <int> <int> <int>
-#> 1 Clinical 52 13 36 46
-#> 2 ICU 38 12 28 35
-#> 3 Outpatient 4 3 4 4
+#> 1 Clinical 66 13 38 47
+#> 2 ICU 24 9 20 24
+#> 3 Outpatient 3 3 3 3
# grouping on patients and microorganisms leads to the same
# results as first_isolate() when using 'episode-based':
@@ -403,19 +403,19 @@
select ( group_vars ( . ) , flag_episode )
}
#> # A tibble: 100 × 4
-#> # Groups: patient, mo, ward [97]
-#> patient mo ward flag_episode
-#> <chr> <mo> <chr> <lgl>
-#> 1 067927 B_STPHY_EPDR ICU TRUE
-#> 2 A85702 B_CTRBC_KOSR ICU TRUE
-#> 3 949877 B_STRPT_PNMN Clinical TRUE
-#> 4 D43733 B_ESCHR_COLI ICU TRUE
-#> 5 BB8157 B_STPHY_CONS Clinical TRUE
-#> 6 59C7F2 B_STRPT_PNMN Clinical TRUE
-#> 7 693199 B_STRPT_PNMN Clinical TRUE
-#> 8 A76045 B_ENTRC_FACM ICU TRUE
-#> 9 559068 B_STPHY_CPTS Clinical TRUE
-#> 10 533225 B_ESCHR_COLI Clinical TRUE
+#> # Groups: patient, mo, ward [98]
+#> patient mo ward flag_episode
+#> <chr> <mo> <chr> <lgl>
+#> 1 F61180 B_ESCHR_COLI ICU TRUE
+#> 2 54890C B_ESCHR_COLI Clinical TRUE
+#> 3 869231 B_KLBSL_PNMN Clinical TRUE
+#> 4 D52219 B_STRPT_GRPA ICU TRUE
+#> 5 690B42 B_ESCHR_COLI Clinical TRUE
+#> 6 914520 B_STPHY_AURS Clinical TRUE
+#> 7 C89738 B_STRPT_MITS Clinical TRUE
+#> 8 D8D632 B_ESCHR_COLI ICU TRUE
+#> 9 F35553 B_ENTRBC_CLOC ICU TRUE
+#> 10 905108 B_STRPT_PNMN Clinical TRUE
#> # ℹ 90 more rows
# }
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 3955e619..f89b9f07 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 38b67a4e..6a79561a 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 5e50eb08..6027ca8c 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/index.html b/reference/index.html
index d8b0de5a..d0dac86a 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 8900f80e..0033bfda 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index d6e2d5c4..0426f333 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/join.html b/reference/join.html
index a383d5b2..97b9be73 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index db5ccd51..8d91bcbe 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index a18488a8..2a4e666d 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -199,9 +199,9 @@
Examples
kurtosis ( rnorm ( 10000 ) )
-#> [1] 2.993275
+#> [1] 3.03876
kurtosis ( rnorm ( 10000 ) , excess = TRUE )
-#> [1] 0.005991136
+#> [1] -0.05207763
On this page
diff --git a/reference/like.html b/reference/like.html
index e2ad961d..e1d57d7d 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/mdro.html b/reference/mdro.html
index 55401126..71cca1d8 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 1e20b387..efe0fcca 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index addf0622..14c1b176 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index 8e68492b..b3e2450d 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.groups.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 825ae83e..67fa4059 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index b5f580d3..7b287bda 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/mo_property.html b/reference/mo_property.html
index ee3a33c1..3ce2fef6 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/mo_source.html b/reference/mo_source.html
index ed7909f8..10334075 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/pca.html b/reference/pca.html
index 8402ddf7..c9dc15c5 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/plot.html b/reference/plot.html
index 6e461344..bc7ba0b9 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/proportion.html b/reference/proportion.html
index 1ea4d1a0..3b6e37aa 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -12,7 +12,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/random.html b/reference/random.html
index aa3df0b8..2a75a89a 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 134f1182..601894b8 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/reference/skewness.html b/reference/skewness.html
index 1352e7f0..abd6ff96 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -12,7 +12,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
@@ -198,7 +198,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
Examples
skewness ( runif ( 1000 ) )
-#> [1] -0.01314082
+#> [1] -0.0683066
On this page
diff --git a/reference/translate.html b/reference/translate.html
index 6fee5b1c..68d1bd23 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9030
+ 2.0.0.9031
diff --git a/search.json b/search.json
index b3314374..370f8b6c 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 11 Dec 2022. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\" mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\" mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Microorganism translation was uncertain for four microorganisms. Run #> mo_uncertainties() to review these uncertainties, or use #> add_custom_microorganisms() to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #> (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #> (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #> (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #> cloacae (0.571), Enterobacter cloacae complex (0.571), and Ehrlichia #> canis (0.567) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella #> pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis #> (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500), #> Kingella potus (0.400), Kosakonia pseudosacchari (0.361), Kaistella #> palustris (0.333), Kocuria palustris (0.333), and Kocuria pelophila #> (0.333) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), Salmonella #> Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness #> (0.587), Selenomonas artemidis (0.571), Salmonella choleraesuis #> arizonae (0.562), Streptococcus anginosus anginosus (0.561), and #> Salmonella Abaetetuba (0.548) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Serratia #> proteamaculans quinovora (0.545), Streptococcus pseudoporcinus (0.536), #> Staphylococcus pseudintermedius (0.532), Serratia proteamaculans #> proteamaculans (0.526), Salmonella Portanigra (0.524), Sphingomonas #> paucimobilis (0.520), Streptococcus pluranimalium (0.519), #> Streptococcus constellatus pharyngis (0.514), and Salmonella Pakistan #> (0.500) #> #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>% mutate_at(vars(AMX:GEN), as.sir) # method 2, let the AMR package determine the eligible columns our_data <- our_data %>% mutate_if(is_sir_eligible, as.sir) # result: our_data #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 88% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 626 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,626 'phenotype-based' first isolates (87.6% within scope and #> 87.5% of total where a microbial ID was available) our_data_1st <- our_data %>% filter(first == TRUE) our_data_1st <- our_data %>% filter_first_isolate() our_data_1st #> # A tibble: 2,626 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE #> 3 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 4 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 5 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE #> 6 J8 A 2016-06-14 B_ESCHR_COLI R S S S TRUE #> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,616 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #> patient_id hospital date #> Length:2626 Length:2626 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-14 #> Mode :character Mode :character Median :2015-06-05 #> Mean :2015-06-15 #> 3rd Qu.:2017-08-23 #> Max. :2020-01-01 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %R :43.2% (n=1134) %R :36.1% (n=947) #> Unique:4 %SI :56.8% (n=1492) %SI :63.9% (n=1679) #> #1 :B_ESCHR_COLI - %S :41.1% (n=1080) - %S :52.7% (n=1383) #> #2 :B_STPHY_AURS - %I :15.7% (n=412) - %I :11.3% (n=296) #> #3 :B_STRPT_PNMN #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %R :42.0% (n=1102) %R :37.0% (n=971) TRUE:2626 #> %SI :58.0% (n=1524) %SI :63.0% (n=1655) #> - %S :51.9% (n=1362) - %S :59.9% (n=1574) #> - %I : 6.2% (n=162) - %I : 3.1% (n=81) #> glimpse(our_data_1st) #> Rows: 2,626 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"J8\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, S, S, R, R, R, S, S, S, S, R, S, S, R, R, R, R, I, S,… #> $ AMC I, I, I, S, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, R,… #> $ CIP S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,… # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id hospital date bacteria AMX AMC CIP #> 260 3 1808 4 3 3 3 #> GEN first #> 3 1"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1518 #> 2 Staphylococcus aureus 730 #> 3 Streptococcus pneumoniae 426 #> 4 Klebsiella pneumoniae 326 our_data_1st %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1250 #> 2 Staphylococcus aureus 661 #> 3 Streptococcus pneumoniae 399 #> 4 Klebsiella pneumoniae 316"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>% select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,626 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2015-12-10 S #> 4 2015-03-02 S #> 5 2018-03-31 S #> 6 2016-06-14 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,616 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,626 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI S I #> 4 B_ESCHR_COLI S S #> 5 B_STPHY_AURS R S #> 6 B_ESCHR_COLI R S #> 7 B_ESCHR_COLI R S #> 8 B_ESCHR_COLI S S #> 9 B_STPHY_AURS S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,616 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,626 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI S I S S #> 4 B_ESCHR_COLI S S S S #> 5 B_STPHY_AURS R S R S #> 6 B_ESCHR_COLI R S S S #> 7 B_ESCHR_COLI R S S S #> 8 B_ESCHR_COLI S S S S #> 9 B_STPHY_AURS S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,616 more rows # filtering using AB selectors is also possible: our_data_1st %>% filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 971 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 961 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 6 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 7 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 8 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 9 C5 A 2015-08-30 B_KLBSL_PNMN R R S R TRUE #> 10 W9 A 2013-10-02 B_ESCHR_COLI R R S S TRUE #> # ℹ 461 more rows # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 6 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 7 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 8 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 9 C5 A 2015-08-30 B_KLBSL_PNMN R R S R TRUE #> 10 W9 A 2013-10-02 B_ESCHR_COLI R R S S TRUE #> # ℹ 461 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , …"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create WISCA, must state combination therapy antibiotics argument (similar Combination Antibiogram), define syndromic group syndromic_group argument (similar Syndromic Antibiogram) cases predefined based clinical demographic characteristics (e.g., endocarditis 75+ females). next example simplification without clinical characteristics, just gives idea WISCA can created:","code":"wisca <- antibiogram(example_isolates, antibiotics = c(\"AMC\", \"AMC+CIP\", \"TZP\", \"TZP+TOB\"), mo_transform = \"gramstain\", minimum = 10, # this should be >= 30, but now just as example syndromic_group = ifelse(example_isolates$age >= 65 & example_isolates$gender == \"M\", \"WISCA Group 1\", \"WISCA Group 2\")) wisca"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(wisca)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: Author: Dr. Matthijs Berends, 26th Feb 2023","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4318355 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.343 #> 2 B 0.569 #> 3 C 0.375"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.1, 2016). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame( mo = c( \"Klebsiella\", \"Escherichia\" ), ampicillin = \"S\" ) oops #> mo ampicillin #> 1 Klebsiella S #> 2 Escherichia S eucast_rules(oops, info = FALSE) #> mo ampicillin #> 1 Klebsiella R #> 2 Escherichia S mo_is_intrinsic_resistant( c(\"Klebsiella\", \"Escherichia\"), \"ampicillin\" ) #> [1] TRUE FALSE mo_is_intrinsic_resistant( \"Klebsiella\", c(\"ampicillin\", \"kanamycin\") ) #> [1] TRUE FALSE data <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline( CIP == \"R\" & age > 60 ~ \"Elderly Type A\", ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #> 1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #> 2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #> 3. Otherwise: Negative #> #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #> Negative Elderly Type A Elderly Type B #> 1070 198 732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>% mdro() %>% freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame( rifampicin = random_sir(5000), isoniazid = random_sir(5000), gatifloxacin = random_sir(5000), ethambutol = random_sir(5000), pyrazinamide = random_sir(5000), moxifloxacin = random_sir(5000), kanamycin = random_sir(5000) ) my_TB_data <- data.frame( RIF = random_sir(5000), INH = random_sir(5000), GAT = random_sir(5000), ETH = random_sir(5000), PZA = random_sir(5000), MFX = random_sir(5000), KAN = random_sir(5000) ) head(my_TB_data) #> rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1 I I R I S S #> 2 R R S I S S #> 3 R S S S S R #> 4 S I S S I S #> 5 S I R R R R #> 6 R R I R R S #> kanamycin #> 1 R #> 2 R #> 3 I #> 4 I #> 5 R #> 6 R mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) #> ℹ No column found as input for col_mo, assuming all rows contain #> Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) #> Rows: 2,000 #> Columns: 46 #> $ date 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>% group_by( order = mo_order(mo), # group on anything, like order genus = mo_genus(mo) ) %>% # and genus as we do here summarise_if(is.sir, resistance) %>% # then get resistance of all drugs select( order, genus, AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT ) # and select only relevant columns head(resistance_data) #> # A tibble: 6 × 10 #> # Groups: order [5] #> order genus AMC CXM CTX CAZ GEN TOB TMP SXT #> #> 1 (unknown order) (unknown ge… NA NA NA NA NA NA NA NA #> 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA #> 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA #> 4 Campylobacterales Campylobact… NA NA NA NA NA NA NA NA #> 5 Caryophanales Gemella NA NA NA NA NA NA NA NA #> 6 Caryophanales Listeria NA NA NA NA NA NA NA NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #> \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" #> Importance of components: #> PC1 PC2 PC3 PC4 PC5 PC6 PC7 #> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups: can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) + ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 3: S < < R Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>% # get microbial ID based on given organism mutate(mo = as.mo(Organism)) %>% # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `sir` class mutate_at(vars(AMP_ND10:CIP_EE), as.sir) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_sir() function:","code":"data %>% group_by(Country) %>% select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>% ggplot_sir(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 52 169 rows 23 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, oxygen_tolerance, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence, snomed. data set R available microorganisms, load AMR package. last updated 8 July 2023 15:30:05 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.7 MB) Download Microsoft Excel workbook (5.2 MB) Download Apache Feather file (5.5 MB) Download Apache Parquet file (2.6 MB) Download SAS data (SAS) file (50.9 MB) Download SAS transport (XPT) file (48.6 MB) Download IBM SPSS Statistics data file (17.8 MB) Download Stata DTA file (48.6 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. file structures compression techniques inefficient. Advice? Use R instead. ’s free much better many ways. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org December 11th, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotics-antibiotic-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antibiotics: Antibiotic (+Antifungal) Drugs","title":"Data sets for download / own use","text":"data set 483 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antibiotics, load AMR package. last updated 22 February 2023 13:38:57 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (39 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (66 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (97 kB) Download SAS data (SAS) file (1.9 MB) Download SAS transport (XPT) file (1.4 MB) Download IBM SPSS Statistics data file (0.3 MB) Download Stata DTA file (0.4 MB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antibiotics: Antibiotic (+Antifungal) Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antivirals, load AMR package. last updated 13 November 2022 07:46:10 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (16 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (15 kB) Download Apache Parquet file (13 kB) Download SAS data (SAS) file (84 kB) Download SAS transport (XPT) file (68 kB) Download IBM SPSS Statistics data file (30 kB) Download Stata DTA file (73 kB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir","dir":"Articles","previous_headings":"","what":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","title":"Data sets for download / own use","text":"data set 28 885 rows 12 columns, containing following column names:guideline, type, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti. data set R available clinical_breakpoints, load AMR package. last updated 10 July 2023 11:41:52 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (59 kB) Download tab-separated text file (3.2 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (87 kB) Download SAS data (SAS) file (3.6 MB) Download SAS transport (XPT) file (7.7 MB) Download IBM SPSS Statistics data file (4.4 MB) Download Stata DTA file (7.6 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2011-2023) EUCAST (2011-2023).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 134 634 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 16 December 2022 15:10:43 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (78 kB) Download tab-separated text file (5.1 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (0.2 MB) Download SAS data (SAS) file (9.8 MB) Download SAS transport (XPT) file (9.5 MB) Download IBM SPSS Statistics data file (7.4 MB) Download Stata DTA file (9.5 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 503 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt, eucast_version. data set R available dosage, load AMR package. last updated 22 June 2023 13:10:59 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (43 kB) Download Microsoft Excel workbook (25 kB) Download Apache Feather file (21 kB) Download Apache Parquet file (9 kB) Download SAS data (SAS) file (92 kB) Download SAS transport (XPT) file (0.1 MB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : (), ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021), ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2 000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP, RIF. data set R available example_isolates, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3 000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP, GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-groups-species-groups-and-microbiological-complexes","dir":"Articles","previous_headings":"","what":"microorganisms.groups: Species Groups and Microbiological Complexes","title":"Data sets for download / own use","text":"data set 444 rows 4 columns, containing following column names:mo_group, mo, mo_group_name, mo_name. data set R available microorganisms.groups, load AMR package. last updated 8 July 2023 15:30:05 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (42 kB) Download Microsoft Excel workbook (18 kB) Download Apache Feather file (17 kB) Download Apache Parquet file (12 kB) (unavailable SAS data (SAS) file) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (54 kB) Download Stata DTA file (68 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-8","dir":"Articles","previous_headings":"microorganisms.groups: Species Groups and Microbiological Complexes","what":"Source","title":"Data sets for download / own use","text":"data set contains species groups microbiological complexes, used clinical_breakpoints data set.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-codes-common-laboratory-codes","dir":"Articles","previous_headings":"","what":"microorganisms.codes: Common Laboratory Codes","title":"Data sets for download / own use","text":"data set 4 957 rows 2 columns, containing following column names:code mo. data set R available microorganisms.codes, load AMR package. last updated 8 July 2023 15:30:05 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (22 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (91 kB) Download Apache Feather file (85 kB) Download Apache Parquet file (57 kB) (unavailable SAS data (SAS) file) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (0.1 MB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-9","dir":"Articles","previous_headings":"microorganisms.codes: Common Laboratory Codes","what":"Source","title":"Data sets for download / own use","text":"data set contains commonly used codes microorganisms, laboratory systems WHONET.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions: fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_sir_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\") # or: example_isolates %>% resistance_predict( col_ab = \"TZP\", model = \"binomial\" ) # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>% resistance_predict( col_ab = \"TZP\", model = \"binomial\" ) predict_TZP #> # A tibble: 32 × 7 #> year value se_min se_max observations observed estimated #> *