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AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/AMR_intro.png b/AMR_intro.png
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+
+
+
+Antimicrobial Resistance (AMR) Data Analysis in R Generates antibiograms – traditional, combined, syndromic, and even WISCA Provides the full microbiological taxonomy and data on all antimicrobial drugs Applies all recent CLSI and EUCAST clinical breakpoints for MICs and disk zones Corrects for duplicate isolates, calculates and predicts AMR per antibiotic class Integrates with WHONET , ATC, EARS-Net , PubChem, LOINC and SNOMED CT Works on Windows, macOS and Linux with all versions of R since R-3.0 and is completely dependency-free , highly suitable for places with limited resources https://msberends.github.io/AMRhttps://doi.org/10.18637/jss.v104.i03
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 7773d4db..6705ebd8 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/articles/AMR.html b/articles/AMR.html
index d73c5a06..cd7daf8c 100644
--- a/articles/AMR.html
+++ b/articles/AMR.html
@@ -39,7 +39,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
@@ -205,7 +205,7 @@
website update since they are based on randomly created values and the
page was written in R
Markdown . However, the methodology remains unchanged. This page was
-generated on 23 April 2024.
+generated on 24 April 2024.
Introduction
@@ -261,21 +261,21 @@ make the structure of your data generally look like this:
-2024-04-23
+2024-04-24
abcd
Escherichia coli
S
S
-2024-04-23
+2024-04-24
abcd
Escherichia coli
S
R
-2024-04-23
+2024-04-24
efgh
Escherichia coli
R
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index 9e27c8f5..ae954ed0 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -39,7 +39,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/articles/MDR.html b/articles/MDR.html
index 13968a56..d8bf6bed 100644
--- a/articles/MDR.html
+++ b/articles/MDR.html
@@ -39,7 +39,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/articles/PCA.html b/articles/PCA.html
index b68f1ada..bf8a9e32 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -39,7 +39,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/articles/WHONET.html b/articles/WHONET.html
index 449f9abb..59276735 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -39,7 +39,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/articles/datasets.html b/articles/datasets.html
index b4c7f797..f37fd6cf 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -39,7 +39,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
@@ -194,7 +194,7 @@
-
AMR 2.1.1.9022
+
AMR 2.1.1.9025
(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support!)
-
A New Milestone: One Health Support (= Human + Veterinary + Environmental)
+
A New Milestone: One Health Support (= Human + Veterinary + Environmental)
This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the University of Prince Edward Island , Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.
-
Breaking
+
Breaking
Removed all functions and references that used the deprecated rsi
class, which were all replaced with their sir
equivalents over a year ago
-
New
+
New
One Health implementation
Function as.sir()
now has extensive support for animal breakpoints from CLSI. Use breakpoint_type = "animal"
and set the host
argument to a variable that contains animal species names.
The clinical_breakpoints
data set contains all these breakpoints, and can be downloaded on our download page .
@@ -185,7 +185,7 @@
Function mo_group_members()
to retrieve the member microorganisms. For example, mo_group_members("Strep group C")
returns a vector of all microorganisms that are in that group.
-
Changed
+
Changed
For MICs:
Added as valid levels: 4096, 6 powers of 0.0625, and 5 powers of 192 (192, 384, 576, 768, 960)
Added new argument keep_operators
to as.mic()
. This can be "all"
(default), "none"
, or "edges"
. This argument is also available in the new limit_mic_range()
and scale_*_mic()
functions.
@@ -197,9 +197,12 @@
Greatly improved vctrs
integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as dplyr
’s bind_rows()
, rowwise()
and c_across()
are now supported for e.g. columns of class mic
. Despite this, this AMR
package is still zero-dependent on any other package, including dplyr
and vctrs
.
Updated all ATC codes from WHOCC
Updated all antibiotic DDDs from WHOCC
+Fix for using a manual value for mo_transform
in antibiogram()
+
+Fix for mapping ‘high level’ antibiotics in as.ab()
(amphotericin B-high, gentamicin-high, kanamycin-high, streptomycin-high, tobramycin-high)
-
Other
+
Other
Added Jordan Stull, Matthew Saab, and Javier Sanchez as contributors, to thank them for their valuable input
diff --git a/pkgdown.yml b/pkgdown.yml
index 25643a68..ad43d496 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
-last_built: 2024-04-23T08:59Z
+last_built: 2024-04-24T08:04Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index a1cf26f9..195a882e 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/AMR.html b/reference/AMR.html
index c0732afb..f1451718 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)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index 6a4e9f53..2e4927f0 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/WHONET.html b/reference/WHONET.html
index aa464447..0a51419d 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index 51aafb38..b0b655d6 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/ab_property.html b/reference/ab_property.html
index ecaa96ba..1be6b0b7 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index e8d86a6a..b9b4b372 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index 12b71099..4d386cb5 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/age.html b/reference/age.html
index 9c5bd07c..e97f5c53 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
@@ -222,16 +222,16 @@
df
#> birth_date age age_exact age_at_y2k
-#> 1 1965-12-05 58 58.38251 34
-#> 2 1980-03-01 44 44.14481 19
-#> 3 1949-11-01 74 74.47541 50
-#> 4 1947-02-14 77 77.18852 52
-#> 5 1940-02-19 84 84.17486 59
-#> 6 1988-01-10 36 36.28415 11
-#> 7 1997-08-27 26 26.65574 2
-#> 8 1978-01-26 46 46.24044 21
-#> 9 1972-06-17 51 51.84973 27
-#> 10 1986-08-10 37 37.70219 13
+#> 1 1965-12-05 58 58.38525 34
+#> 2 1980-03-01 44 44.14754 19
+#> 3 1949-11-01 74 74.47814 50
+#> 4 1947-02-14 77 77.19126 52
+#> 5 1940-02-19 84 84.17760 59
+#> 6 1988-01-10 36 36.28689 11
+#> 7 1997-08-27 26 26.65847 2
+#> 8 1978-01-26 46 46.24317 21
+#> 9 1972-06-17 51 51.85246 27
+#> 10 1986-08-10 37 37.70492 13
On this page
diff --git a/reference/age_groups.html b/reference/age_groups.html
index 8aae04ea..8fb768a4 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index 8f3ca14d..68b857ff 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index ad3e586d..59c0d006 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)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index 51683625..f8285d47 100644
--- a/reference/antibiotics.html
+++ b/reference/antibiotics.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/as.ab.html b/reference/as.ab.html
index 909b605f..9e77323d 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/as.av.html b/reference/as.av.html
index 0f73fc26..d2287a51 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/as.disk.html b/reference/as.disk.html
index b24df063..e385d859 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 15fc7e19..8f5968d7 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 98f34092..27a217e3 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/as.sir.html b/reference/as.sir.html
index 87342fb5..f68e224e 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -24,7 +24,7 @@ All breakpoints used for interpretation are publicly available in the clinical_b
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
@@ -592,19 +592,19 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
#> # A tibble: 13 × 14
#> datetime index ab_user mo_user ab mo input
#> <dttm> <int> <chr> <chr> <ab> <mo> <dbl>
-#> 1 2024-04-23 08:59:51 1 TOB Escherich… TOB B_[ORD]_ENTRBCTR 16
-#> 2 2024-04-23 08:59:51 1 GEN Escherich… GEN B_[ORD]_ENTRBCTR 18
-#> 3 2024-04-23 08:59:51 1 CIP Escherich… CIP B_[ORD]_ENTRBCTR 0.256
-#> 4 2024-04-23 08:59:51 1 AMP Escherich… AMP B_[ORD]_ENTRBCTR 8
-#> 5 2024-04-23 08:59:46 1 AMX B_STRPT_P… AMX B_STRPT_PNMN 0.01
-#> 6 2024-04-23 08:59:46 2 AMX B_STRPT_P… AMX B_STRPT_PNMN 2
-#> 7 2024-04-23 08:59:46 3 AMX B_STRPT_P… AMX B_STRPT_PNMN 4
-#> 8 2024-04-23 08:59:46 4 AMX B_STRPT_P… AMX B_STRPT_PNMN 8
-#> 9 2024-04-23 08:59:46 1 AMX B_STRPT_P… AMX B_STRPT_PNMN 2
-#> 10 2024-04-23 08:59:45 1 TOB Escherich… TOB B_[ORD]_ENTRBCTR 16
-#> 11 2024-04-23 08:59:45 1 GEN Escherich… GEN B_[ORD]_ENTRBCTR 18
-#> 12 2024-04-23 08:59:45 1 AMP Escherich… AMP B_[ORD]_ENTRBCTR 20
-#> 13 2024-04-23 08:59:45 1 ampicillin Strep pneu AMP B_STRPT_PNMN 18
+#> 1 2024-04-24 08:04:50 1 TOB Escherich… TOB B_[ORD]_ENTRBCTR 16
+#> 2 2024-04-24 08:04:49 1 GEN Escherich… GEN B_[ORD]_ENTRBCTR 18
+#> 3 2024-04-24 08:04:49 1 CIP Escherich… CIP B_[ORD]_ENTRBCTR 0.256
+#> 4 2024-04-24 08:04:49 1 AMP Escherich… AMP B_[ORD]_ENTRBCTR 8
+#> 5 2024-04-24 08:04:44 1 AMX B_STRPT_P… AMX B_STRPT_PNMN 0.01
+#> 6 2024-04-24 08:04:44 2 AMX B_STRPT_P… AMX B_STRPT_PNMN 2
+#> 7 2024-04-24 08:04:44 3 AMX B_STRPT_P… AMX B_STRPT_PNMN 4
+#> 8 2024-04-24 08:04:44 4 AMX B_STRPT_P… AMX B_STRPT_PNMN 8
+#> 9 2024-04-24 08:04:44 1 AMX B_STRPT_P… AMX B_STRPT_PNMN 2
+#> 10 2024-04-24 08:04:44 1 TOB Escherich… TOB B_[ORD]_ENTRBCTR 16
+#> 11 2024-04-24 08:04:43 1 GEN Escherich… GEN B_[ORD]_ENTRBCTR 18
+#> 12 2024-04-24 08:04:43 1 AMP Escherich… AMP B_[ORD]_ENTRBCTR 20
+#> 13 2024-04-24 08:04:43 1 ampicillin Strep pneu AMP B_STRPT_PNMN 18
#> # ℹ 7 more variables: outcome <sir>, method <chr>, breakpoint_S_R <chr>,
#> # guideline <chr>, host <chr>, ref_table <chr>, uti <lgl>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index ad0028ee..d55978d1 100644
--- a/reference/atc_online.html
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AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index f525b602..aef3e30b 100644
--- a/reference/av_from_text.html
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@@ -10,7 +10,7 @@
AMR (for R)
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+ 2.1.1.9025
diff --git a/reference/av_property.html b/reference/av_property.html
index 9bb8d2f5..39c3a4f8 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
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AMR (for R)
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+ 2.1.1.9025
diff --git a/reference/availability.html b/reference/availability.html
index fe27d659..75416b18 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index 450b74d0..cffe1031 100644
--- a/reference/bug_drug_combinations.html
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AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index 7578239d..158582b0 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -16,7 +16,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."> AMR (for R)
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+ 2.1.1.9025
diff --git a/reference/count.html b/reference/count.html
index e45e3a6a..fb98c9f2 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)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index 57e78535..c96d770e 100644
--- a/reference/custom_eucast_rules.html
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AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/dosage.html b/reference/dosage.html
index 216b20c9..070adc88 100644
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AMR (for R)
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+ 2.1.1.9025
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index 479fa29c..b8e7fb89 100644
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@@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
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diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 17f7369a..1e36cd32 100644
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AMR (for R)
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+ 2.1.1.9025
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 32dd87c4..232b9040 100644
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AMR (for R)
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diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 31486846..6258232b 100644
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AMR (for R)
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diff --git a/reference/g.test.html b/reference/g.test.html
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AMR (for R)
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diff --git a/reference/get_episode.html b/reference/get_episode.html
index 63181125..f6ea68d3 100644
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AMR (for R)
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+ 2.1.1.9025
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
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index 9867c37f..33966218 100644
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diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 781397f2..b30c399e 100644
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diff --git a/reference/index.html b/reference/index.html
index 006d485f..0fcbf5c2 100644
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diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index efabcf75..49302f2a 100644
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+ 2.1.1.9025
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 3695f15c..a117899d 100644
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+ 2.1.1.9025
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diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index a3e0b68f..50990a3b 100644
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+ 2.1.1.9025
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 4f871334..61cad461 100644
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+ 2.1.1.9025
diff --git a/reference/like.html b/reference/like.html
index 13461e7f..3bc07c1d 100644
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+ 2.1.1.9025
diff --git a/reference/mdro.html b/reference/mdro.html
index ce677611..8da9f358 100644
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+ 2.1.1.9025
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 79e8316f..a242b0c9 100644
--- a/reference/mean_amr_distance.html
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- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 00bb8edd..c4e1adf5 100644
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diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
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diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 14d74ae6..111456ce 100644
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+ 2.1.1.9025
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 44a946d3..1ae4ea10 100644
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@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/mo_property.html b/reference/mo_property.html
index 88698411..b99c4862 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -10,7 +10,7 @@
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- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/mo_source.html b/reference/mo_source.html
index eb75e7fa..f9b20545 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)
- 2.1.1.9022
+ 2.1.1.9025
diff --git a/reference/pca.html b/reference/pca.html
index b8fb8edd..028ae7ae 100644
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+ 2.1.1.9025
diff --git a/reference/plot.html b/reference/plot.html
index b90f48bf..469cedb3 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -12,7 +12,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
AMR (for R)
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+ 2.1.1.9025
diff --git a/reference/proportion.html b/reference/proportion.html
index 0e2a26da..be407892 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -12,7 +12,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
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+ 2.1.1.9025
diff --git a/reference/random.html b/reference/random.html
index 87ba5373..fac9511a 100644
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diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
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--- a/reference/resistance_predict.html
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+ 2.1.1.9025
diff --git a/reference/skewness.html b/reference/skewness.html
index 71aa70cb..ec566930 100644
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@@ -12,7 +12,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
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+ 2.1.1.9025
diff --git a/reference/translate.html b/reference/translate.html
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- 2.1.1.9022
+ 2.1.1.9025
diff --git a/search.json b/search.json
index 438b4c19..13619816 100644
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+++ b/search.json
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-[{"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: 90% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 712 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 2,712 'phenotype-based' first isolates (90.4% 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: 2,712 × 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 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 7 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 8 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 9 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> 10 Z1 A 2014-09-05 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,702 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:2712 Length:2712 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-05-03 #> Mode :character Mode :character Median :2015-06-16 #> Mean :2015-06-21 #> 3rd Qu.:2017-08-24 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %R :42.9% (n=1163) %R :36.1% (n=978) #> Unique:4 %SI :57.1% (n=1549) %SI :63.9% (n=1734) #> #1 :B_ESCHR_COLI - %S :41.0% (n=1112) - %S :52.0% (n=1409) #> #2 :B_STPHY_AURS - %I :16.1% (n=437) - %I :12.0% (n=325) #> #3 :B_STRPT_PNMN #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %R :42.0% (n=1138) %R :37.3% (n=1011) TRUE:2712 #> %SI :58.0% (n=1574) %SI :62.7% (n=1701) #> - %S :51.5% (n=1396) - %S :59.6% (n=1616) #> - %I : 6.6% (n=178) - %I : 3.1% (n=85) #> glimpse(our_data_1st) #> Rows: 2,712 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\", \"Z1\"… #> $ 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, S, S, S, S, R, S, S, S, R, R, R, R, S, R,… #> $ AMC I, I, I, S, S, S, S, S, S, S, S, S, I, S, S, S, S, R, S, S,… #> $ 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, R, S, 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 1852 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 1319 #> 2 Staphylococcus aureus 676 #> 3 Streptococcus pneumoniae 400 #> 4 Klebsiella pneumoniae 317"},{"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: 2,712 × 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 2015-10-25 S #> 7 2019-06-19 S #> 8 2015-04-27 S #> 9 2011-06-21 S #> 10 2014-09-05 S #> # ℹ 2,702 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,712 × 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 S S #> 8 B_STPHY_AURS S S #> 9 B_ESCHR_COLI S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,702 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,712 × 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 S S S S #> 8 B_STPHY_AURS S S S S #> 9 B_ESCHR_COLI S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,702 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: 1,011 × 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 1,001 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 483 × 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 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 473 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: 483 × 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 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 473 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] 0.4288348 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.342 #> 2 B 0.564 #> 3 C 0.372"},{"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: 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 R S S S S #> 2 S S I R R S #> 3 R I I I R I #> 4 I S S S S S #> 5 I I I S I S #> 6 R S R S I I #> kanamycin #> 1 R #> 2 I #> 3 S #> 4 I #> 5 I #> 6 I 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 1.232e-16 #> 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 52 171 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 14 July 2023 08:49:06 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 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 (2023). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org January 8th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microorganism’, 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 24 February 2024 14:16:52 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (44 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (73 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (0.1 MB) Download SAS transport (XPT) file (1.5 MB) Download IBM SPSS Statistics data file (0.4 MB) Download Stata DTA file (0.5 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 20 October 2023 12:51:48 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (17 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (13 kB) Download SAS transport (XPT) file (73 kB) Download IBM SPSS Statistics data file (32 kB) Download Stata DTA file (78 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 29 883 rows 13 columns, containing following column names:guideline, type, host, 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 24 February 2024 14:16:52 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (61 kB) Download tab-separated text file (2.5 MB) Download Microsoft Excel workbook (1.4 MB) Download Apache Feather file (1.3 MB) Download Apache Parquet file (91 kB) Download SAS transport (XPT) file (8.2 MB) Download IBM SPSS Statistics data file (4.9 MB) Download Stata DTA file (8.1 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). Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate development WHONET software. CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET AMR package. NOTE: AMR package (WHONET software well) contains internal methods apply guidelines, rather complex. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","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 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 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 521 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 14 July 2023 08:49:06 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (49 kB) Download Microsoft Excel workbook (20 kB) Download Apache Feather file (19 kB) Download Apache Parquet file (13 kB) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (81 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) 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: 33 × 7 #> year value se_min se_max observations observed estimated #> * #> 1 2002 0.2 NA NA 15 0.2 0.0562 #> 2 2003 0.0625 NA NA 32 0.0625 0.0616 #> 3 2004 0.0854 NA NA 82 0.0854 0.0676 #> 4 2005 0.05 NA NA 60 0.05 0.0741 #> 5 2006 0.0508 NA NA 59 0.0508 0.0812 #> 6 2007 0.121 NA NA 66 0.121 0.0889 #> 7 2008 0.0417 NA NA 72 0.0417 0.0972 #> 8 2009 0.0164 NA NA 61 0.0164 0.106 #> 9 2010 0.0566 NA NA 53 0.0566 0.116 #> 10 2011 0.183 NA NA 93 0.183 0.127 #> # ℹ 23 more rows plot(predict_TZP) ggplot_sir_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_sir_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous: Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate: model also available object, attribute:","code":"example_isolates %>% filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>% resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>% ggplot_sir_predict() example_isolates %>% filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>% resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>% ggplot_sir_predict() model <- attributes(predict_TZP)$model summary(model)$family #> #> Family: binomial #> Link function: logit summary(model)$coefficients #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 #> year 0.09883005 0.02295317 4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Casper J. Albers. Thesis advisor. Peter Dutey-Magni. Contributor. Judith M. Fonville. Contributor. Alex W. Friedrich. Thesis advisor. Corinna Glasner. Thesis advisor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Annick Lenglet. Contributor. Christian F. Luz. Contributor. Bart C. Meijer. Contributor. Dmytro Mykhailenko. Contributor. Anton Mymrikov. Contributor. Andrew P. Norgan. Contributor. Sofia Ny. Contributor. Matthew Saab. Contributor. Jonas Salm. Contributor. Javier Sanchez. Contributor. Rogier P. Schade. Contributor. Bhanu N. M. Sinha. Thesis advisor. Jason Stull. Contributor. Anthony Underwood. Contributor. Anita Williams. Contributor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, journal = {Journal of Statistical Software}, year = {2022}, volume = {104}, number = {3}, pages = {1--31}, doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Provides --one solution AMR data analysis One Health approach Generates antibiograms - traditional, combined, syndromic, even WISCA Provides full microbiological taxonomy data antimicrobial drugs Applies recent CLSI EUCAST clinical veterinary breakpoints MICs disk zones Corrects duplicate isolates, calculates predicts AMR per antibiotic class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC SNOMED CT Works Windows, macOS Linux versions R since R-3.0 completely dependency-free, highly suitable places limited resources https://msberends.github.io/AMR https://doi.org/10.18637/jss.v104.i03","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 distinct microbial species (updated December 2022) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-over-175-countries-translated-into-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, translated into 20 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. help contributors corners world, AMR package available English, Czech, Chinese, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"One powerful functions package, aside calculating plotting AMR, selecting filtering based antibiotic columns. can done using -called antibiotic class selectors work base R, dplyr data.table: defined row filter Gram-negative bacteria intrinsic resistance cefotaxime (mo_is_gram_negative() mo_is_intrinsic_resistant()) column selection two antibiotic groups (aminoglycosides() carbapenems()), reference data microorganisms antibiotics AMR package make sure get meant: base R equivalent : base R code work version R since April 2013 (R-3.0). Moreover, code works identically data.table package, starting :","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr) example_isolates %>% mutate(bacteria = mo_fullname()) %>% # filtering functions for microorganisms: filter(mo_is_gram_negative(), mo_is_intrinsic_resistant(ab = \"cefotax\")) %>% # antibiotic selectors: select(bacteria, aminoglycosides(), carbapenems()) library(AMR) example_isolates$bacteria <- mo_fullname(example_isolates$mo) example_isolates[which(mo_is_gram_negative() & mo_is_intrinsic_resistant(ab = \"cefotax\")), c(\"bacteria\", aminoglycosides(), carbapenems())] example_isolates <- data.table::as.data.table(example_isolates)"},{"path":"https://msberends.github.io/AMR/index.html","id":"generating-antibiograms","dir":"","previous_headings":"Practical examples","what":"Generating antibiograms","title":"Antimicrobial Resistance Data Analysis","text":"AMR package supports generating traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). used inside R Markdown Quarto, table printed right output format automatically (markdown, LaTeX, HTML, etc.). combination antibiograms, clear combined antibiotics yield higher empiric coverage: Like many functions package, antibiogram() comes support 20 languages often detected automatically based system language:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), mo_transform = \"gramstain\") antibiogram(example_isolates, antibiotics = c(\"cipro\", \"tobra\", \"genta\"), # any arbitrary name or code will work mo_transform = \"gramstain\", ab_transform = \"name\", language = \"uk\") # Ukrainian"},{"path":"https://msberends.github.io/AMR/index.html","id":"calculating-resistance-per-group","dir":"","previous_headings":"Practical examples","what":"Calculating resistance per group","title":"Antimicrobial Resistance Data Analysis","text":"manual approach, can use resistance susceptibility() function: use antibiotic class selectors select series antibiotic columns:","code":"example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance() for gentamicin and tobramycin # and get their 95% confidence intervals using sir_confidence_interval(): summarise(across(c(GEN, TOB), list(total_R = resistance, conf_int = function(x) sir_confidence_interval(x, collapse = \"-\")))) library(AMR) library(dplyr) out <- example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance(), over all aminoglycosides and polymyxins: summarise(across(c(aminoglycosides(), polymyxins()), resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (manual) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antibiotic (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters SIR (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed GitHub two ways: Manually, using: Automatically, using rOpenSci R-universe platform, adding R-universe address list repositories (‘repos’): , can install update AMR package like official release (e.g., using install.packages(\"AMR\") RStudio via Tools > Check Package Updates…).","code":"install.packages(\"remotes\") # if you haven't already remotes::install_github(\"msberends/AMR\") options(repos = c(getOption(\"repos\"), msberends = \"https://msberends.r-universe.dev\"))"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible following non-profit organisations initiatives:","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Options for the AMR package — AMR-options","title":"Options for the AMR package — AMR-options","text":"overview package-specific options() can set AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"options","dir":"Reference","previous_headings":"","what":"Options","title":"Options for the AMR package — AMR-options","text":"AMR_breakpoint_type character use .sir(), indicate breakpoint type use. must either \"ECOFF\", \"animal\", \"human\". AMR_cleaning_regex regular expression (case-insensitive) use .mo() mo_* functions, clean user input. default outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". AMR_custom_ab file location RDS file, use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo file location RDS file, use custom microorganisms package. explained add_custom_microorganisms(). AMR_eucastrules character set default types rules eucast_rules() function, must one : \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). AMR_guideline character set default guideline interpreting MIC values disk diffusion diameters .sir(). Can guideline name (e.g., \"CLSI\") name year (e.g. \"CLSI 2019\"). default latest implemented EUCAST guideline, currently \"EUCAST 2023\". Supported guideline currently EUCAST (2011-2023) CLSI (2011-2023). AMR_ignore_pattern regular expression ignore (.e., make NA) match given .mo() mo_* functions. AMR_include_PKPD logical use .sir(), indicate PK/PD clinical breakpoints must applied last resort - default TRUE. AMR_include_screening logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_locale character set language AMR package, can one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). default current system language (supported, English otherwise). AMR_mo_source file location manual code list used .mo() mo_* functions. explained set_mo_source().","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"saving-settings-between-sessions","dir":"Reference","previous_headings":"","what":"Saving Settings Between Sessions","title":"Options for the AMR package — AMR-options","text":"Settings R saved globally thus lost R exited. can save options .Rprofile file, user-specific file. can edit using: file, can set options ... ...add Portuguese language support antibiotics, allow PK/PD rules interpreting MIC values .sir().","code":"utils::file.edit(\"~/.Rprofile\") options(AMR_locale = \"pt\") options(AMR_include_PKPD = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"share-options-within-team","dir":"Reference","previous_headings":"","what":"Share Options Within Team","title":"Options for the AMR package — AMR-options","text":"global approach, e.g. within (data) team, save options file remote file location, shared network drive, user read file automatically start-. work way: Save plain text file e.g. \"X:/team_folder/R_options.R\" fill preferred settings. user, open .Rprofile file using utils::file.edit(\"~/.Rprofile\") put : Reload R/RStudio check settings getOption(), e.g. getOption(\"AMR_locale\") set value. Now team settings configured one place, can maintained .","code":"source(\"X:/team_folder/R_options.R\")"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); doi:10.18637/jss.v104.i03 ) formed basis two PhD theses (doi:10.33612/diss.177417131 doi:10.33612/diss.192486375 ). installing package, R knows ~52 000 microorganisms (updated January 2024) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences public University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"The AMR Package — AMR","text":"cite AMR publications use: Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). \"AMR: R Package Working Antimicrobial Resistance Data.\" Journal Statistical Software, 104(3), 1-31. doi:10.18637/jss.v104.i03 BibTeX entry LaTeX users :","code":"@Article{, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, journal = {Journal of Statistical Software}, year = {2022}, volume = {104}, number = {3}, pages = {1--31}, doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"The AMR Package — AMR","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The AMR Package — AMR","text":"Maintainer: Matthijs S. Berends m.s.berends@umcg.nl (ORCID) Authors: Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Casper J. Albers (ORCID) [thesis advisor] Peter Dutey-Magni (ORCID) [contributor] Judith M. Fonville [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Christian F. Luz (ORCID) [contributor] Bart C. Meijer [contributor] Dmytro Mykhailenko [contributor] Anton Mymrikov [contributor] Andrew P. Norgan (ORCID) [contributor] Sofia Ny (ORCID) [contributor] Matthew Saab [contributor] Jonas Salm [contributor] Javier Sanchez (ORCID) [contributor] Rogier P. Schade [contributor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Jason Stull (ORCID) [contributor] Anthony Underwood (ORCID) [contributor] Anita Williams (ORCID) [contributor]","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class 'ab' #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\" ab_tradenames(\"flucloxacillin\") #> [1] \"culpen\" \"floxacillin\" \"floxacillin sodium\" #> [4] \"floxapen\" \"floxapen sodium salt\" \"fluclox\" #> [7] \"flucloxacilina\" \"flucloxacillin\" \"flucloxacilline\" #> [10] \"flucloxacillinum\" \"fluorochloroxacillin\" \"staphylex\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antibiotic results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"tibble 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 28 different antibiotics. can lookup abbreviations antibiotics data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antibiotic class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> # A tibble: 500 × 53 #> `Identification number` `Specimen number` Organism Country Laboratory #> #> 1 fe41d7bafa 1748 SPN Belgium National … #> 2 91f175ec37 1767 eco The Netherlands National … #> 3 cc4015056e 1343 eco The Netherlands National … #> 4 e864b692f5 1894 MAP Denmark National … #> 5 3d051fe345 1739 PVU Belgium National … #> 6 c80762a08d 1846 103 The Netherlands National … #> 7 8022d3727c 1628 103 Denmark National … #> 8 f3dc5f553d 1493 eco The Netherlands National … #> 9 15add38f6c 1847 eco France National … #> 10 fd41248def 1458 eco Germany National … #> # ℹ 490 more rows #> # ℹ 48 more variables: `Last name` , `First name` , Sex