From faed08a2c9a9933f79e71277fd927eb8065ce1a6 Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Fri, 14 Jul 2023 08:58:39 +0000
Subject: [PATCH] Built site for AMR: 2.0.0.9045@7a4628b
---
404.html | 2 +-
LICENSE-text.html | 2 +-
articles/AMR.html | 2 +-
articles/EUCAST.html | 2 +-
articles/MDR.html | 58 +++++------
articles/PCA.html | 2 +-
articles/WHONET.html | 2 +-
articles/datasets.html | 24 ++---
articles/index.html | 2 +-
articles/other_pkg.html | 2 +-
articles/resistance_predict.html | 2 +-
articles/welcome_to_AMR.html | 2 +-
authors.html | 2 +-
index.html | 2 +-
news/index.html | 14 +--
pkgdown.yml | 2 +-
reference/AMR-deprecated.html | 2 +-
reference/AMR-options.html | 2 +-
reference/AMR.html | 2 +-
reference/WHOCC.html | 2 +-
reference/WHONET.html | 2 +-
reference/ab_from_text.html | 2 +-
reference/ab_property.html | 2 +-
reference/add_custom_antimicrobials.html | 2 +-
reference/add_custom_microorganisms.html | 2 +-
reference/age.html | 22 ++---
reference/age_groups.html | 2 +-
reference/antibiogram.html | 2 +-
reference/antibiotic_class_selectors.html | 10 +-
reference/antibiotics.html | 2 +-
reference/as.ab.html | 2 +-
reference/as.av.html | 2 +-
reference/as.disk.html | 2 +-
reference/as.mic.html | 2 +-
reference/as.mo.html | 2 +-
reference/as.sir.html | 28 +++---
reference/atc_online.html | 2 +-
reference/av_from_text.html | 2 +-
reference/av_property.html | 2 +-
reference/availability.html | 2 +-
reference/bug_drug_combinations.html | 2 +-
reference/clinical_breakpoints.html | 2 +-
reference/count.html | 2 +-
reference/custom_eucast_rules.html | 2 +-
reference/dosage.html | 2 +-
reference/eucast_rules.html | 2 +-
reference/example_isolates.html | 2 +-
reference/example_isolates_unclean.html | 2 +-
reference/first_isolate.html | 2 +-
reference/g.test.html | 2 +-
reference/get_episode.html | 115 +++++++++++-----------
reference/ggplot_pca.html | 2 +-
reference/ggplot_sir.html | 2 +-
reference/guess_ab_col.html | 2 +-
reference/index.html | 6 +-
reference/intrinsic_resistant.html | 2 +-
reference/italicise_taxonomy.html | 2 +-
reference/join.html | 2 +-
reference/key_antimicrobials.html | 2 +-
reference/kurtosis.html | 6 +-
reference/like.html | 2 +-
reference/mdro.html | 2 +-
reference/mean_amr_distance.html | 2 +-
reference/microorganisms.codes.html | 2 +-
reference/microorganisms.groups.html | 8 +-
reference/microorganisms.html | 14 +--
reference/mo_matching_score.html | 2 +-
reference/mo_property.html | 2 +-
reference/mo_source.html | 2 +-
reference/pca.html | 2 +-
reference/plot.html | 2 +-
reference/proportion.html | 2 +-
reference/random.html | 2 +-
reference/resistance_predict.html | 2 +-
reference/skewness.html | 4 +-
reference/translate.html | 2 +-
search.json | 2 +-
77 files changed, 220 insertions(+), 219 deletions(-)
diff --git a/404.html b/404.html
index 81103419..ca82b93a 100644
--- a/404.html
+++ b/404.html
@@ -36,7 +36,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 3a874d0f..e39b21f7 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/articles/AMR.html b/articles/AMR.html
index 1ee96eb4..b5e02551 100644
--- a/articles/AMR.html
+++ b/articles/AMR.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index e23b5db0..dcd1cdf6 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/articles/MDR.html b/articles/MDR.html
index f6c737bf..aa59b229 100644
--- a/articles/MDR.html
+++ b/articles/MDR.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -402,19 +402,19 @@ names or codes, this would have worked exactly the same way:
head ( my_TB_data )
#> rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-#> 1 S S I I S S
-#> 2 S I R R I I
-#> 3 I S R I I I
-#> 4 I I R S R S
-#> 5 I S S S R I
-#> 6 I R S S S R
+#> 1 R S I S S S
+#> 2 S S I R I R
+#> 3 I R I I R S
+#> 4 I I I R I S
+#> 5 S R I R R R
+#> 6 S R S I I I
#> kanamycin
#> 1 R
-#> 2 S
-#> 3 I
-#> 4 R
+#> 2 I
+#> 3 R
+#> 4 I
#> 5 I
-#> 6 S
+#> 6 I
We can now add the interpretation of MDR-TB to our data set. You can
use:
@@ -455,40 +455,40 @@ Unique: 5
1
Mono-resistant
-3236
-64.72%
-3236
-64.72%
+3247
+64.94%
+3247
+64.94%
2
Negative
-962
-19.24%
-4198
-83.96%
+986
+19.72%
+4233
+84.66%
3
Multi-drug-resistant
-470
-9.40%
-4668
-93.36%
+434
+8.68%
+4667
+93.34%
4
Poly-resistant
-216
-4.32%
-4884
-97.68%
+231
+4.62%
+4898
+97.96%
5
Extensively drug-resistant
-116
-2.32%
+102
+2.04%
5000
100.00%
diff --git a/articles/PCA.html b/articles/PCA.html
index 7af9422e..0b50d0ad 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/articles/WHONET.html b/articles/WHONET.html
index 8785057a..ae3a5531 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/articles/datasets.html b/articles/datasets.html
index cafe396d..5f9153c9 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -214,7 +214,7 @@ of the data sets look like.
microorganisms
: Full Microbial Taxonomy
-A data set with 52 169 rows and 23 columns, containing the following
+
A data set with 52 171 rows and 23 columns, containing the following
column names:mo , fullname , status , kingdom ,
phylum , class , order , family ,
genus , species , subspecies , rank ,
@@ -224,8 +224,8 @@ column names:mo , fullname , status , kingdomsnomed .
This data set is in R available as microorganisms
, after
you load the AMR
package.
-It was last updated on 8 July 2023 15:30:05 UTC. Find more info about
-the structure of this data set here .
+It was last updated on 14 July 2023 08:49:06 UTC. Find more info
+about the structure of this data set here .
Direct download links:
Download as original
@@ -302,7 +302,7 @@ Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL:
Bacteria
-36 499
+36 501
Fungi
@@ -2066,27 +2066,27 @@ reality and can be used to practise AMR data analysis.
microorganisms.groups
: Species Groups and
Microbiological Complexes
-A data set with 507 rows and 4 columns, containing the following
+
A data set with 521 rows and 4 columns, containing the following
column names:mo_group , mo , mo_group_name , and
mo_name .
This data set is in R available as
microorganisms.groups
, after you load the AMR
package.
-It was last updated on 12 July 2023 14:04:48 UTC. Find more info
+
It was last updated on 14 July 2023 08:49:06 UTC. Find more info
about the structure of this data set here .
Direct download links:
Source
diff --git a/articles/index.html b/articles/index.html
index 7eebef07..e9ab5e3c 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/articles/other_pkg.html b/articles/other_pkg.html
index aa0f2304..5d270ac4 100644
--- a/articles/other_pkg.html
+++ b/articles/other_pkg.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html
index 93e003a4..fd89fac2 100644
--- a/articles/resistance_predict.html
+++ b/articles/resistance_predict.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html
index bfa73879..2b0b0522 100644
--- a/articles/welcome_to_AMR.html
+++ b/articles/welcome_to_AMR.html
@@ -38,7 +38,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/authors.html b/authors.html
index 0dfe89a3..0eeb0c51 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/index.html b/index.html
index 3ebf9f32..1376ea90 100644
--- a/index.html
+++ b/index.html
@@ -42,7 +42,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/news/index.html b/news/index.html
index 24208ca2..438c6686 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -159,12 +159,14 @@
-
AMR 2.0.0.9044
+
AMR 2.0.0.9045
-
New
-
Clinical breakpoints and intrinsic resistance of EUCAST 2023 and CLSI 2023 have been added for as.sir()
. EUCAST 2023 (v13.0) is now the new default guideline for all MIC and disks diffusion interpretations
+New
+Regarding clinical breakpoints:
+Clinical breakpoints and intrinsic resistance of EUCAST 2023 and CLSI 2023 have been added to the clinical_breakpoints
data set for usage in as.sir()
. EUCAST 2023 (v13.0) is now the new default guideline for all MIC and disks diffusion interpretations
The EUCAST dosage guideline of v13.0 has been added to the dosage
data set
-ECOFF and animal breakpoints: the clinical_breakpoints
data set now contains epidemiological cut-off (ECOFF) values and CLSI animal breakpoints. These two new breakpoint types can be used for MIC/disk interpretation using as.sir(..., breakpoint_type = "ECOFF")
oras.sir(..., breakpoint_type = "animal")
, which is an important new addition for veterinary microbiology.
+The clinical_breakpoints
data set now also contains epidemiological cut-off (ECOFF) values and CLSI animal breakpoints. These two new breakpoint types can be used for MIC/disk interpretation using as.sir(..., breakpoint_type = "ECOFF")
oras.sir(..., breakpoint_type = "animal")
, which is an important new addition for veterinary microbiology.
+
Added support for 30 species groups / complexes. They are gathered in a new data set microorganisms.groups
and are used in clinical breakpoint interpretation. For example, CLSI 2023 contains breakpoints for the RGM group (Rapidly Growing Mycobacterium, containing over 80 species) which is now supported by our package.
Added oxygen tolerance from BacDive to over 25,000 bacteria in the microorganisms
data set
Added mo_oxygen_tolerance()
to retrieve the values
@@ -176,7 +178,7 @@
Added microbial codes for Gram-negative/positive anaerobic bacteria
-
Changed
+
Changed
Updated algorithm of as.mo()
by giving more weight to fungi
Fixed clinical breakpoints errors introduced by the source we import the rules from
diff --git a/pkgdown.yml b/pkgdown.yml
index ee95e67a..600cb52e 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
other_pkg: other_pkg.html
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
-last_built: 2023-07-14T07:59Z
+last_built: 2023-07-14T08:55Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index 7b8ce31d..88ec17fb 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 01334e8e..ef76674c 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/AMR.html b/reference/AMR.html
index 0c1c5981..aea57c5e 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -24,7 +24,7 @@ The AMR package is available in English, Chinese, Czech, Danish, Dutch, Finnish,
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index 200d3bd2..280b498f 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/WHONET.html b/reference/WHONET.html
index 76c306c9..eeda9211 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index b6eb9be6..ddd4941a 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/ab_property.html b/reference/ab_property.html
index 5d653b1a..549e81f6 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index 594b8c82..3f970be9 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index 72b49e95..bfbd06a6 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/age.html b/reference/age.html
index c5bef981..04dc3221 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -222,16 +222,16 @@
df
#> birth_date age age_exact age_at_y2k
-#> 1 1977-04-11 46 46.25753 22
-#> 2 1941-02-08 82 82.42740 58
-#> 3 1944-09-14 78 78.83014 55
-#> 4 1942-06-27 81 81.04658 57
-#> 5 1989-06-18 34 34.07123 10
-#> 6 1975-10-06 47 47.76986 24
-#> 7 1987-04-23 36 36.22466 12
-#> 8 1935-01-12 88 88.50137 64
-#> 9 1958-07-21 64 64.98082 41
-#> 10 1996-09-11 26 26.83836 3
+#> 1 1967-12-22 55 55.55890 32
+#> 2 1967-05-10 56 56.17808 32
+#> 3 1947-01-16 76 76.49041 52
+#> 4 1980-02-08 43 43.42740 19
+#> 5 1988-03-15 35 35.33151 11
+#> 6 1955-07-31 67 67.95342 44
+#> 7 1991-04-20 32 32.23288 8
+#> 8 1966-02-16 57 57.40548 33
+#> 9 1964-12-30 58 58.53699 35
+#> 10 1998-09-24 24 24.80274 1
On this page
diff --git a/reference/age_groups.html b/reference/age_groups.html
index d9aa9e66..3fea3a43 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index 9e8f90a5..d0f6d750 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index 8be9d095..381b349b 100644
--- a/reference/antibiotic_class_selectors.html
+++ b/reference/antibiotic_class_selectors.html
@@ -12,7 +12,7 @@ In short, if you have a column name that resembles an antimicrobial drug, it wil
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -626,11 +626,11 @@ In short, if you have a column name that resembles an antimicrobial drug, it wil
#> # A tibble: 5 × 1
#> kefzol
#> <sir>
-#> 1 S
-#> 2 S
+#> 1 R
+#> 2 I
#> 3 I
-#> 4 R
-#> 5 S
+#> 4 S
+#> 5 R
if ( require ( "dplyr" ) ) {
# get AMR for all aminoglycosides e.g., per ward:
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index 94adcf39..539d6972 100644
--- a/reference/antibiotics.html
+++ b/reference/antibiotics.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/as.ab.html b/reference/as.ab.html
index f7600093..ca1f768e 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/as.av.html b/reference/as.av.html
index c43d0dcb..c388e778 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/as.disk.html b/reference/as.disk.html
index 35e7bd69..608f9bfe 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/as.mic.html b/reference/as.mic.html
index a72ae8b7..5373f25e 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 8433933a..a03366d1 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/as.sir.html b/reference/as.sir.html
index fd370bdc..1dc136e0 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -14,7 +14,7 @@ All breakpoints used for interpretation are publicly available in the clinical_b
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -567,19 +567,19 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
#> # A tibble: 13 × 13
#> datetime index ab_user mo_user ab mo input
#> <dttm> <int> <chr> <chr> <ab> <mo> <dbl>
-#> 1 2023-07-14 08:00:32 1 TOB Escherich… TOB B_[ORD]_ENTRBCTR 16
-#> 2 2023-07-14 08:00:32 1 GEN Escherich… GEN B_[ORD]_ENTRBCTR 18
-#> 3 2023-07-14 08:00:32 1 CIP Escherich… CIP B_[ORD]_ENTRBCTR 0.256
-#> 4 2023-07-14 08:00:32 1 AMP Escherich… AMP B_[ORD]_ENTRBCTR 8
-#> 5 2023-07-14 08:00:25 1 AMX B_STRPT_P… AMX B_STRPT_PNMN 0.01
-#> 6 2023-07-14 08:00:25 2 AMX B_STRPT_P… AMX B_STRPT_PNMN 2
-#> 7 2023-07-14 08:00:25 3 AMX B_STRPT_P… AMX B_STRPT_PNMN 4
-#> 8 2023-07-14 08:00:25 4 AMX B_STRPT_P… AMX B_STRPT_PNMN 8
-#> 9 2023-07-14 08:00:25 1 AMX B_STRPT_P… AMX B_STRPT_PNMN 2
-#> 10 2023-07-14 08:00:25 1 TOB Escherich… TOB B_[ORD]_ENTRBCTR 16
-#> 11 2023-07-14 08:00:25 1 GEN Escherich… GEN B_[ORD]_ENTRBCTR 18
-#> 12 2023-07-14 08:00:24 1 AMP Escherich… AMP B_[ORD]_ENTRBCTR 20
-#> 13 2023-07-14 08:00:24 1 ampicillin Strep pneu AMP B_STRPT_PNMN 18
+#> 1 2023-07-14 08:56:12 1 TOB Escherich… TOB B_[ORD]_ENTRBCTR 16
+#> 2 2023-07-14 08:56:12 1 GEN Escherich… GEN B_[ORD]_ENTRBCTR 18
+#> 3 2023-07-14 08:56:12 1 CIP Escherich… CIP B_[ORD]_ENTRBCTR 0.256
+#> 4 2023-07-14 08:56:12 1 AMP Escherich… AMP B_[ORD]_ENTRBCTR 8
+#> 5 2023-07-14 08:56:03 1 AMX B_STRPT_P… AMX B_STRPT_PNMN 0.01
+#> 6 2023-07-14 08:56:03 2 AMX B_STRPT_P… AMX B_STRPT_PNMN 2
+#> 7 2023-07-14 08:56:03 3 AMX B_STRPT_P… AMX B_STRPT_PNMN 4
+#> 8 2023-07-14 08:56:03 4 AMX B_STRPT_P… AMX B_STRPT_PNMN 8
+#> 9 2023-07-14 08:56:03 1 AMX B_STRPT_P… AMX B_STRPT_PNMN 2
+#> 10 2023-07-14 08:56:02 1 TOB Escherich… TOB B_[ORD]_ENTRBCTR 16
+#> 11 2023-07-14 08:56:02 1 GEN Escherich… GEN B_[ORD]_ENTRBCTR 18
+#> 12 2023-07-14 08:56:02 1 AMP Escherich… AMP B_[ORD]_ENTRBCTR 20
+#> 13 2023-07-14 08:56:02 1 ampicillin Strep pneu AMP B_STRPT_PNMN 18
#> # ℹ 6 more variables: outcome <sir>, method <chr>, breakpoint_S_R <chr>,
#> # guideline <chr>, ref_table <chr>, uti <lgl>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index 7c11b528..856bf15c 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index ed3037a2..d4fcd8f0 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/av_property.html b/reference/av_property.html
index 56945ce3..9bd5ac3a 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/availability.html b/reference/availability.html
index 36f0f292..13a253f5 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index a8393705..7fcf33f1 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index 73dd430a..e83a3cec 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/count.html b/reference/count.html
index b55e8051..c2676231 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -12,7 +12,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index ec3d7186..35a144f3 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/dosage.html b/reference/dosage.html
index b6b3db53..ab399b5a 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index c035bed5..85f2d7f6 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index cce9b5c1..3ba30453 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 2f5bb253..a8c58969 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 988425f3..918b1d92 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -12,7 +12,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/g.test.html b/reference/g.test.html
index 6b32a484..b06e9ca3 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/get_episode.html b/reference/get_episode.html
index a9b4341b..0e56f879 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -263,28 +263,27 @@
df <- example_isolates [ sample ( seq_len ( 2000 ) , size = 100 ) , ]
get_episode ( df $ date , episode_days = 60 ) # indices
-#> [1] 6 48 38 36 21 25 15 7 18 25 31 10 16 1 47 38 28 10 9 41 14 18 9 27 45
-#> [26] 11 6 3 7 22 30 33 23 5 19 48 16 2 25 35 15 24 38 40 1 9 9 43 30 2
-#> [51] 46 43 16 6 41 41 36 3 36 45 8 19 42 27 15 34 8 4 8 46 6 40 24 14 13
-#> [76] 37 45 26 31 21 17 46 35 39 10 20 33 16 25 44 26 19 46 29 39 32 48 22 12 17
+#> [1] 38 19 11 11 7 35 22 3 45 19 40 6 29 44 48 19 26 15 5 39 46 29 47 13 47
+#> [26] 34 40 9 4 11 4 41 39 30 16 32 1 46 24 27 43 30 38 7 6 49 11 48 11 16
+#> [51] 21 15 11 17 47 4 12 28 23 5 8 6 12 33 31 19 15 28 8 26 18 42 10 37 13
+#> [76] 39 2 4 48 1 40 10 24 45 26 12 38 40 17 25 20 8 13 29 8 50 31 36 42 14
is_new_episode ( df $ date , episode_days = 60 ) # TRUE/FALSE
-#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
-#> [13] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE
-#> [25] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
-#> [37] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
-#> [49] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
-#> [61] TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
-#> [73] FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE
-#> [85] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
-#> [97] FALSE FALSE TRUE FALSE
+#> [1] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
+#> [13] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
+#> [25] FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
+#> [37] TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
+#> [49] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE
+#> [61] TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
+#> [73] TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+#> [85] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
+#> [97] FALSE TRUE FALSE TRUE
# filter on results from the third 60-day episode only, using base R
df [ which ( get_episode ( df $ date , 60 ) == 3 ) , ]
-#> # A tibble: 2 × 46
+#> # A tibble: 1 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2002-07-21 955940 82 F Clinical B_PSDMN_AERG R NA NA R
-#> 2 2002-07-23 F35553 51 M ICU B_STPHY_AURS R NA S R
+#> 1 2002-06-06 24D393 20 F Clinical B_ESCHR_COLI R NA NA NA
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
@@ -318,19 +317,19 @@
arrange ( patient , condition , date )
}
#> # A tibble: 100 × 4
-#> # Groups: patient, condition [100]
+#> # Groups: patient, condition [99]
#> patient date condition new_episode
#> <chr> <date> <chr> <lgl>
-#> 1 001213 2009-08-03 B TRUE
-#> 2 006827 2009-07-24 C TRUE
-#> 3 008218 2009-05-05 C TRUE
-#> 4 021648 2004-08-21 A TRUE
-#> 5 023456 2011-04-25 C TRUE
-#> 6 032343 2003-06-09 C TRUE
-#> 7 059414 2006-07-21 B TRUE
-#> 8 05C73F 2006-01-12 A TRUE
-#> 9 074321 2015-09-20 C TRUE
-#> 10 077922 2009-08-24 C TRUE
+#> 1 005088 2007-08-22 C TRUE
+#> 2 018637 2005-09-28 B TRUE
+#> 3 021368 2016-03-25 A TRUE
+#> 4 032343 2003-06-09 A TRUE
+#> 5 058917 2002-11-14 C TRUE
+#> 6 05C73F 2006-01-12 B TRUE
+#> 7 092034 2006-06-12 B TRUE
+#> 8 0D7D34 2011-03-19 B TRUE
+#> 9 0DBF93 2015-10-12 B TRUE
+#> 10 0E2483 2007-08-10 C TRUE
#> # ℹ 90 more rows
if ( require ( "dplyr" ) ) {
@@ -344,19 +343,19 @@
arrange ( patient , ward , date )
}
#> # A tibble: 100 × 5
-#> # Groups: ward, patient [99]
-#> ward date patient new_index new_logical
-#> <chr> <date> <chr> <int> <lgl>
-#> 1 Clinical 2009-08-03 001213 1 TRUE
-#> 2 Clinical 2009-07-24 006827 1 TRUE
-#> 3 Clinical 2009-05-05 008218 1 TRUE
-#> 4 Clinical 2004-08-21 021648 1 TRUE
-#> 5 Clinical 2011-04-25 023456 1 TRUE
-#> 6 Clinical 2003-06-09 032343 1 TRUE
-#> 7 Clinical 2006-07-21 059414 1 TRUE
-#> 8 Clinical 2006-01-12 05C73F 1 TRUE
-#> 9 ICU 2015-09-20 074321 1 TRUE
-#> 10 Clinical 2009-08-24 077922 1 TRUE
+#> # Groups: ward, patient [96]
+#> ward date patient new_index new_logical
+#> <chr> <date> <chr> <int> <lgl>
+#> 1 Clinical 2007-08-22 005088 1 TRUE
+#> 2 Clinical 2005-09-28 018637 1 TRUE
+#> 3 Outpatient 2016-03-25 021368 1 TRUE
+#> 4 Clinical 2003-06-09 032343 1 TRUE
+#> 5 ICU 2002-11-14 058917 1 TRUE
+#> 6 Clinical 2006-01-12 05C73F 1 TRUE
+#> 7 ICU 2006-06-12 092034 1 TRUE
+#> 8 ICU 2011-03-19 0D7D34 1 TRUE
+#> 9 Clinical 2015-10-12 0DBF93 1 TRUE
+#> 10 ICU 2007-08-10 0E2483 1 TRUE
#> # ℹ 90 more rows
if ( require ( "dplyr" ) ) {
@@ -372,9 +371,9 @@
#> # A tibble: 3 × 5
#> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30
#> <chr> <int> <int> <int> <int>
-#> 1 Clinical 66 13 37 46
-#> 2 ICU 26 10 20 22
-#> 3 Outpatient 7 4 6 6
+#> 1 Clinical 59 14 41 48
+#> 2 ICU 32 11 25 28
+#> 3 Outpatient 5 4 5 5
# grouping on patients and microorganisms leads to the same
# results as first_isolate() when using 'episode-based':
@@ -392,7 +391,7 @@
identical ( x , y )
}
-#> [1] TRUE
+#> [1] FALSE
# but is_new_episode() has a lot more flexibility than first_isolate(),
# since you can now group on anything that seems relevant:
@@ -403,19 +402,19 @@
select ( group_vars ( . ) , flag_episode )
}
#> # A tibble: 100 × 4
-#> # Groups: patient, mo, ward [99]
-#> patient mo ward flag_episode
-#> <chr> <mo> <chr> <lgl>
-#> 1 6BC362 B_CRYNB ICU TRUE
-#> 2 CFCF65 B_ENTRC_FCLS ICU TRUE
-#> 3 454595 B_STPHY_HMNS Outpatient TRUE
-#> 4 959835 B_STPHY_HMNS Clinical TRUE
-#> 5 D81577 B_HMPHL_INFL Clinical TRUE
-#> 6 001213 B_PSDMN_AERG Clinical TRUE
-#> 7 05C73F B_STRPT_MITS Clinical TRUE
-#> 8 D63414 B_PROTS_MRBL Clinical TRUE
-#> 9 A68B33 B_STPHY_AURS ICU TRUE
-#> 10 006827 B_ENTRC_FCLS Clinical TRUE
+#> # Groups: patient, mo, ward [98]
+#> patient mo ward flag_episode
+#> <chr> <mo> <chr> <lgl>
+#> 1 C34429 B_PROTS_MRBL Clinical TRUE
+#> 2 8B8705 B_ESCHR_COLI Clinical TRUE
+#> 3 690605 B_STRPT_DYSG ICU TRUE
+#> 4 400169 B_SERRT_MRCS ICU TRUE
+#> 5 F35553 B_ENTRBC_CLOC ICU TRUE
+#> 6 A97263 B_KLBSL_PNMN Clinical TRUE
+#> 7 329273 B_STRPT_PNMN Clinical TRUE
+#> 8 24D393 B_ESCHR_COLI Clinical TRUE
+#> 9 305134 B_PROTS_VLGR ICU TRUE
+#> 10 0E2483 B_ESCHR_COLI ICU TRUE
#> # ℹ 90 more rows
# }
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 855ecb95..bc988397 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index f258807a..e1ac0c61 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index c9dc83db..d900b847 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/index.html b/reference/index.html
index b74c1c99..87c6e24b 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -408,7 +408,7 @@
microorganisms
- Data Set with 52 169 Microorganisms
+ Data Set with 52 171 Microorganisms
microorganisms.codes
@@ -418,7 +418,7 @@
microorganisms.groups
- Data Set with 507 Microorganisms In Species Groups
+ Data Set with 521 Microorganisms In Species Groups
antibiotics
antivirals
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index b4c2502a..ed0ca043 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 70a1d188..78d3b685 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/join.html b/reference/join.html
index ebdafa9d..24bf7ba7 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 2cdcf5b7..78103881 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 47d49612..6cfd480a 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -199,9 +199,9 @@
Examples
kurtosis ( rnorm ( 10000 ) )
-#> [1] 2.92302
+#> [1] 3.03615
kurtosis ( rnorm ( 10000 ) , excess = TRUE )
-#> [1] -0.05610912
+#> [1] -0.004847778
On this page
diff --git a/reference/like.html b/reference/like.html
index 735a3360..9ebfb53c 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/mdro.html b/reference/mdro.html
index 148dd0e5..8d89c34d 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index f4a6ed58..d517b7d0 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index aaf398f6..14a3ca2f 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index 907a5f86..75b36fc8 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.groups.html
@@ -1,5 +1,5 @@
-Data Set with 507 Microorganisms In Species Groups — microorganisms.groups • AMR (for R) Data Set with 521 Microorganisms In Species Groups — microorganisms.groups • AMR (for R)
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -154,7 +154,7 @@
@@ -170,7 +170,7 @@
-
A tibble with 507 observations and 4 variables:
mo_group
ID of the species group / microbiological complex
+ A tibble with 521 observations and 4 variables:
mo_group
ID of the species group / microbiological complex
mo
ID of the microorganism belonging in the species group / microbiological complex
mo_group_name
Name of the species group / microbiological complex, as retrieved with mo_name()
mo_name
Name of the microorganism belonging in the species group / microbiological complex, as retrieved with mo_name()
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 4a313343..e5b7d5ed 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -1,5 +1,5 @@
-Data Set with 52 169 Microorganisms — microorganisms • AMR (for R) Data Set with 52 171 Microorganisms — microorganisms • AMR (for R)
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -154,7 +154,7 @@
@@ -170,14 +170,14 @@
-
A tibble with 52 169 observations and 23 variables:
mo
ID of microorganism as used by this package. This is a unique identifier.
+ A tibble with 52 171 observations and 23 variables:
mo
ID of microorganism as used by this package. This is a unique identifier.
fullname
Full name, like "Escherichia coli"
. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. This is a unique identifier.
status
Status of the taxon, either "accepted" or "synonym"
kingdom
, phylum
, class
, order
, family
, genus
, species
, subspecies
Taxonomic rank of the microorganism
rank
Text of the taxonomic rank of the microorganism, such as "species"
or "genus"
ref
Author(s) and year of related scientific publication. This contains only the first surname and year of the latest authors, e.g. "Wallis et al. 2006 emend. Smith and Jones 2018" becomes "Smith et al. , 2018". This field is directly retrieved from the source specified in the column source
. Moreover, accents were removed to comply with CRAN that only allows ASCII characters, e.g. "Váňová" becomes "Vanova".
lpsn
Identifier ('Record number') of the List of Prokaryotic names with Standing in Nomenclature (LPSN). This will be the first/highest LPSN identifier to keep one identifier per row. For example, Acetobacter ascendens has LPSN Record number 7864 and 11011. Only the first is available in the microorganisms
data set.
-oxygen_tolerance
Oxygen tolerance, either "aerobe", "anaerobe", "anaerobe/microaerophile", "facultative anaerobe", "likely facultative anaerobe", or "microaerophile". These data were retrieved from BacDive (see Source ). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently 73.4% of all ~36 000 bacteria in the data set contain an oxygen tolerance.
+oxygen_tolerance
Oxygen tolerance, either "aerobe", "anaerobe", "anaerobe/microaerophile", "facultative anaerobe", "likely facultative anaerobe", or "microaerophile". These data were retrieved from BacDive (see Source ). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently 73.4% of all ~37 000 bacteria in the data set contain an oxygen tolerance.
lpsn_parent
LPSN identifier of the parent taxon
lpsn_renamed_to
LPSN identifier of the currently valid taxon
gbif
Identifier ('taxonID') of the Global Biodiversity Information Facility (GBIF)
@@ -212,7 +212,7 @@
Included Taxa
-Included taxonomic data are:
All ~36 000 (sub)species from the kingdoms of Archaea and Bacteria
+Included taxonomic data are:
All ~37 000 (sub)species from the kingdoms of Archaea and Bacteria
~7 900 (sub)species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of Aspergillus , Candida , Cryptococcus , Histoplasma , Pneumocystis , Saccharomyces and Trichophyton ).
~5 100 (sub)species from the kingdom of Protozoa
~1 400 (sub)species from 43 other relevant genera from the kingdom of Animalia (such as Strongyloides and Taenia )
@@ -225,7 +225,7 @@
For convenience, some entries were added manually:
~1 500 entries of Salmonella , such as the city-like serovars and groups A to H
-34 species groups (such as the beta-haemolytic Streptococcus groups A to K, coagulase-negative Staphylococcus (CoNS), Mycobacterium tuberculosis complex, etc.), of which the group compositions are stored in the microorganisms.groups data set
+36 species groups (such as the beta-haemolytic Streptococcus groups A to K, coagulase-negative Staphylococcus (CoNS), Mycobacterium tuberculosis complex, etc.), of which the group compositions are stored in the microorganisms.groups data set
1 entry of Blastocystis (B. hominis ), although it officially does not exist (Noel et al. 2005, PMID 15634993)
1 entry of Moraxella (M. catarrhalis ), which was formally named Branhamella catarrhalis (Catlin, 1970) though this change was never accepted within the field of clinical microbiology
8 other 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, and unknown anaerobic Gram-pos/Gram-neg bacteria)
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 5231f8fe..abb0f5b0 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/mo_property.html b/reference/mo_property.html
index ecd8ea11..17b359df 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 4e68eb0f..8b34eb9b 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/pca.html b/reference/pca.html
index 89ee656e..e5f93b34 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/plot.html b/reference/plot.html
index 8c3dd733..1f072dbe 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/proportion.html b/reference/proportion.html
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--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -12,7 +12,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/random.html b/reference/random.html
index d18de045..7c9550a7 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 8733c3b8..f2f4261e 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/reference/skewness.html b/reference/skewness.html
index b6b45dd9..29fafe5e 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -12,7 +12,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
@@ -198,7 +198,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
Examples
skewness ( runif ( 1000 ) )
-#> [1] -0.04537181
+#> [1] 0.007600289
On this page
diff --git a/reference/translate.html b/reference/translate.html
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--- a/reference/translate.html
+++ b/reference/translate.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9044
+ 2.0.0.9045
diff --git a/search.json b/search.json
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@@ -1 +1 @@
-[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 11 Dec 2022. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\" mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\" mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Microorganism translation was uncertain for four microorganisms. Run #> mo_uncertainties() to review these uncertainties, or use #> add_custom_microorganisms() to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #> (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #> (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #> (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #> cloacae (0.571), Enterobacter cloacae complex (0.571), and Ehrlichia #> canis (0.567) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella #> pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis #> (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500), #> Kingella potus (0.400), Kosakonia pseudosacchari (0.361), Kaistella #> palustris (0.333), Kocuria palustris (0.333), and Kocuria pelophila #> (0.333) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), Salmonella #> Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness #> (0.587), Selenomonas artemidis (0.571), Salmonella choleraesuis #> arizonae (0.562), Streptococcus anginosus anginosus (0.561), and #> Salmonella Abaetetuba (0.548) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Serratia #> proteamaculans quinovora (0.545), Streptococcus pseudoporcinus (0.536), #> Staphylococcus pseudintermedius (0.532), Serratia proteamaculans #> proteamaculans (0.526), Salmonella Portanigra (0.524), Sphingomonas #> paucimobilis (0.520), Streptococcus pluranimalium (0.519), #> Streptococcus constellatus pharyngis (0.514), and Salmonella Pakistan #> (0.500) #> #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>% mutate_at(vars(AMX:GEN), as.sir) # method 2, let the AMR package determine the eligible columns our_data <- our_data %>% mutate_if(is_sir_eligible, as.sir) # result: our_data #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 88% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 626 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,626 'phenotype-based' first isolates (87.6% within scope and #> 87.5% of total where a microbial ID was available) our_data_1st <- our_data %>% filter(first == TRUE) our_data_1st <- our_data %>% filter_first_isolate() our_data_1st #> # A tibble: 2,626 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE #> 3 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 4 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 5 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE #> 6 J8 A 2016-06-14 B_ESCHR_COLI R S S S TRUE #> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,616 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #> patient_id hospital date #> Length:2626 Length:2626 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-14 #> Mode :character Mode :character Median :2015-06-05 #> Mean :2015-06-15 #> 3rd Qu.:2017-08-23 #> Max. :2020-01-01 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %R :43.2% (n=1134) %R :36.1% (n=947) #> Unique:4 %SI :56.8% (n=1492) %SI :63.9% (n=1679) #> #1 :B_ESCHR_COLI - %S :41.1% (n=1080) - %S :52.7% (n=1383) #> #2 :B_STPHY_AURS - %I :15.7% (n=412) - %I :11.3% (n=296) #> #3 :B_STRPT_PNMN #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %R :42.0% (n=1102) %R :37.0% (n=971) TRUE:2626 #> %SI :58.0% (n=1524) %SI :63.0% (n=1655) #> - %S :51.9% (n=1362) - %S :59.9% (n=1574) #> - %I : 6.2% (n=162) - %I : 3.1% (n=81) #> glimpse(our_data_1st) #> Rows: 2,626 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"J8\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, S, S, R, R, R, S, S, S, S, R, S, S, R, R, R, R, I, S,… #> $ AMC I, I, I, S, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, R,… #> $ CIP S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,… # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id hospital date bacteria AMX AMC CIP #> 260 3 1808 4 3 3 3 #> GEN first #> 3 1"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1518 #> 2 Staphylococcus aureus 730 #> 3 Streptococcus pneumoniae 426 #> 4 Klebsiella pneumoniae 326 our_data_1st %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1250 #> 2 Staphylococcus aureus 661 #> 3 Streptococcus pneumoniae 399 #> 4 Klebsiella pneumoniae 316"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>% select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,626 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2015-12-10 S #> 4 2015-03-02 S #> 5 2018-03-31 S #> 6 2016-06-14 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,616 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,626 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI S I #> 4 B_ESCHR_COLI S S #> 5 B_STPHY_AURS R S #> 6 B_ESCHR_COLI R S #> 7 B_ESCHR_COLI R S #> 8 B_ESCHR_COLI S S #> 9 B_STPHY_AURS S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,616 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,626 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI S I S S #> 4 B_ESCHR_COLI S S S S #> 5 B_STPHY_AURS R S R S #> 6 B_ESCHR_COLI R S S S #> 7 B_ESCHR_COLI R S S S #> 8 B_ESCHR_COLI S S S S #> 9 B_STPHY_AURS S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,616 more rows # filtering using AB selectors is also possible: our_data_1st %>% filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 971 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 961 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 6 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 7 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 8 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 9 C5 A 2015-08-30 B_KLBSL_PNMN R R S R TRUE #> 10 W9 A 2013-10-02 B_ESCHR_COLI R R S S TRUE #> # ℹ 461 more rows # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 6 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 7 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 8 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 9 C5 A 2015-08-30 B_KLBSL_PNMN R R S R TRUE #> 10 W9 A 2013-10-02 B_ESCHR_COLI R R S S TRUE #> # ℹ 461 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , …"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create WISCA, must state combination therapy antibiotics argument (similar Combination Antibiogram), define syndromic group syndromic_group argument (similar Syndromic Antibiogram) cases predefined based clinical demographic characteristics (e.g., endocarditis 75+ females). next example simplification without clinical characteristics, just gives idea WISCA can created:","code":"wisca <- antibiogram(example_isolates, antibiotics = c(\"AMC\", \"AMC+CIP\", \"TZP\", \"TZP+TOB\"), mo_transform = \"gramstain\", minimum = 10, # this should be >= 30, but now just as example syndromic_group = ifelse(example_isolates$age >= 65 & example_isolates$gender == \"M\", \"WISCA Group 1\", \"WISCA Group 2\")) wisca"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(wisca)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: Author: Dr. Matthijs Berends, 26th Feb 2023","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4318355 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.343 #> 2 B 0.569 #> 3 C 0.375"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.1, 2016). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame( mo = c( \"Klebsiella\", \"Escherichia\" ), ampicillin = \"S\" ) oops #> mo ampicillin #> 1 Klebsiella S #> 2 Escherichia S eucast_rules(oops, info = FALSE) #> mo ampicillin #> 1 Klebsiella R #> 2 Escherichia S mo_is_intrinsic_resistant( c(\"Klebsiella\", \"Escherichia\"), \"ampicillin\" ) #> [1] TRUE FALSE mo_is_intrinsic_resistant( \"Klebsiella\", c(\"ampicillin\", \"kanamycin\") ) #> [1] TRUE FALSE data <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline( CIP == \"R\" & age > 60 ~ \"Elderly Type A\", ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #> 1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #> 2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #> 3. Otherwise: Negative #> #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #> Negative Elderly Type A Elderly Type B #> 1070 198 732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>% mdro() %>% freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame( rifampicin = random_sir(5000), isoniazid = random_sir(5000), gatifloxacin = random_sir(5000), ethambutol = random_sir(5000), pyrazinamide = random_sir(5000), moxifloxacin = random_sir(5000), kanamycin = random_sir(5000) ) my_TB_data <- data.frame( RIF = random_sir(5000), INH = random_sir(5000), GAT = random_sir(5000), ETH = random_sir(5000), PZA = random_sir(5000), MFX = random_sir(5000), KAN = random_sir(5000) ) head(my_TB_data) #> rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1 S S I I S S #> 2 S I R R I I #> 3 I S R I I I #> 4 I I R S R S #> 5 I S S S R I #> 6 I R S S S R #> kanamycin #> 1 R #> 2 S #> 3 I #> 4 R #> 5 I #> 6 S 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 #>