@@ -1315,14 +1315,14 @@ values for Klebsiella pneumoniae and ciprofloxacin:
#> MIC SIR
#> <mic> <sir>
#> 1 <= 0.0001 S
-#> 2 0.0160 S
-#> 3 >= 8.0000 R
-#> 4 0.0320 S
-#> 5 0.0080 S
-#> 6 64.0000 R
-#> 7 0.0080 S
-#> 8 0.1250 S
-#> 9 0.0320 S
+#> 2 0.0160 S
+#> 3 >= 8.0000 R
+#> 4 0.0320 S
+#> 5 0.0080 S
+#> 6 64.0000 R
+#> 7 0.0080 S
+#> 8 0.1250 S
+#> 9 0.0320 S
#> 10 0.0002 S
#> # ℹ 90 more rows
This allows direct interpretation according to EUCAST or CLSI
diff --git a/articles/AMR_for_Python.html b/articles/AMR_for_Python.html
index dabc6e894..4a8a179a3 100644
--- a/articles/AMR_for_Python.html
+++ b/articles/AMR_for_Python.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/articles/AMR_with_tidymodels.html b/articles/AMR_with_tidymodels.html
index 84d08ca91..1e01f3d8d 100644
--- a/articles/AMR_with_tidymodels.html
+++ b/articles/AMR_with_tidymodels.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -147,16 +147,16 @@ package.
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
@@ -179,9 +179,9 @@ package.
mo = as.factor ( mo_gramstain ( mo ) ) ) %>%
# drop NAs - the ones without a Gramstain (fungi, etc.)
drop_na ( )
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
-#> ℹ For `betalactams()` using columns ' PEN ' (benzylpenicillin), ' OXA '
+#> ℹ For `betalactams()` using columns ' PEN ' (benzylpenicillin), ' OXA '
#> (oxacillin), ' FLC ' (flucloxacillin), ' AMX ' (amoxicillin), ' AMC '
#> (amoxicillin/clavulanic acid), ' AMP ' (ampicillin), ' TZP '
#> (piperacillin/tazobactam), ' CZO ' (cefazolin), ' FEP ' (cefepime), ' CXM '
@@ -227,9 +227,9 @@ we have with step_corr()
, the necessary parameters can be
estimated from a training set using prep()
:
prep ( resistance_recipe )
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
-#> ℹ For `betalactams()` using columns ' PEN ' (benzylpenicillin), ' OXA '
+#> ℹ For `betalactams()` using columns ' PEN ' (benzylpenicillin), ' OXA '
#> (oxacillin), ' FLC ' (flucloxacillin), ' AMX ' (amoxicillin), ' AMC '
#> (amoxicillin/clavulanic acid), ' AMP ' (ampicillin), ' TZP '
#> (piperacillin/tazobactam), ' CZO ' (cefazolin), ' FEP ' (cefepime), ' CXM '
@@ -494,7 +494,7 @@ into a structured time-series format.
.names = "res_{.col}" ) ,
.groups = "drop" ) %>%
filter ( ! is.na ( res_AMX ) & ! is.na ( res_AMC ) & ! is.na ( res_CIP ) ) # Drop missing values
-#> ℹ Using column ' mo ' as input for `col_mo` .
+#> ℹ Using column ' mo ' as input for `col_mo` .
data_time
#> # A tibble: 32 × 5
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index aa959c6cc..611e9a7cf 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -140,7 +140,7 @@ guideline:
#> # A tibble: 2 × 2
#> mo ampicillin
#> <chr> <sir>
-#> 1 Klebsiella pneumoniae R
+#> 1 Klebsiella pneumoniae R
#> 2 Escherichia coli S
A more convenient function is
mo_is_intrinsic_resistant()
that uses the same guideline,
diff --git a/articles/PCA.html b/articles/PCA.html
index 08c1df420..0295ca341 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/articles/WHONET.html b/articles/WHONET.html
index c80bd12f6..2f6d95c8f 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/articles/WISCA.html b/articles/WISCA.html
index cd8bfd6d7..1c7676199 100644
--- a/articles/WISCA.html
+++ b/articles/WISCA.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -253,16 +253,16 @@ I (intermediate [CLSI], or susceptible, increased exposure
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
diff --git a/articles/datasets.html b/articles/datasets.html
index 7a3f464f6..35eb2e1bc 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -80,7 +80,7 @@
-
AMR 3.0.0.9033
+
AMR 3.0.0.9034
This is a bugfix release following the release of v3.0.0 in June 2025.
-
Changed
+
Changed
Fixed bugs introduced by ggplot2
v4.0.0 (#236 )
Fixed a bug in antibiogram()
for when no antimicrobials are set
Fixed a bug in antibiogram()
to allow column names containing the +
character (#222 )
@@ -61,8 +61,9 @@
Fixed a bug in as.sir()
to allow any tidyselect language (#220 )
Fixed a bug in as.sir()
to pick right breakpoint when uti = FALSE
(#216 )
Fixed a bug in ggplot_sir()
when using combine_SI = FALSE
(#213 )
-Fixed a bug the antimicrobials
data set to remove statins (#229 )
Fixed a bug in mdro()
to make sure all genes specified in arguments are acknowledged
+Fixed a bug the antimicrobials
data set to remove statins (#229 )
+Fixed a bug the microorganisms
data set for MycoBank IDs and synonyms (#233 )
Fixed ATC J01CR05 to map to piperacillin/tazobactam rather than piperacillin/sulbactam (#230 )
Fixed skimmers (skimr
package) of class ab
, sir
, and disk
(#234 )
Fixed all plotting to contain a separate colour for SDD (susceptible dose-dependent) (#223 )
diff --git a/pkgdown.yml b/pkgdown.yml
index cbf40a800..a97a93563 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -10,7 +10,7 @@ articles:
PCA: PCA.html
WHONET: WHONET.html
WISCA: WISCA.html
-last_built: 2025-09-15T07:15Z
+last_built: 2025-09-18T13:04Z
urls:
reference: https://amr-for-r.org/reference
article: https://amr-for-r.org/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index 5dd1a651f..beda520dd 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 1e6f36206..bd78cf581 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/AMR.html b/reference/AMR.html
index 09a81721d..501773ea0 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -21,7 +21,7 @@ The AMR package is available in English, Arabic, Bengali, Chinese, Czech, Danish
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index 545d99ae0..c35b805f6 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/WHONET.html b/reference/WHONET.html
index d3f1566de..f164f2a89 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index b9c28880c..12bb951a2 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/ab_property.html b/reference/ab_property.html
index 553149c7c..d9cc65431 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index d13e9a491..47d9e4005 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -111,7 +111,7 @@
group = "Test Group"
)
)
-#> ℹ Added one record to the internal `antimicrobials` data set.
+#> ℹ Added one record to the internal `antimicrobials` data set.
# "testab" is now a new antibiotic:
as.ab ( "testab" )
@@ -180,7 +180,7 @@
group = "Beta-lactams/penicillins"
)
)
-#> ℹ Added one record to the internal `antimicrobials` data set.
+#> ℹ Added one record to the internal `antimicrobials` data set.
ab_atc ( "Co-fluampicil" )
#> [1] "J01CR50"
ab_name ( "J01CR50" )
@@ -197,7 +197,7 @@
#> random_column coflu ampicillin
#> 1 some value S R
x [ , betalactams ( ) ]
-#> ℹ For `betalactams()` using columns ' coflu ' (co-fluampicil) and
+#> ℹ For `betalactams()` using columns ' coflu ' (co-fluampicil) and
#> ' ampicillin '
#> coflu ampicillin
#> 1 S R
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index 413b7c87a..869707bb7 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -109,7 +109,7 @@
species = "asburiae/cloacae"
)
)
-#> ℹ Added Enterobacter asburiae/cloacae to the internal `microorganisms` data
+#> ℹ Added Enterobacter asburiae/cloacae to the internal `microorganisms` data
#> set.
# E. asburiae/cloacae is now a new microorganism:
@@ -204,7 +204,7 @@
SPECIES = "SPECIES"
)
)
-#> ℹ Added Bacteroides/Parabacteroides to the internal `microorganisms` data
+#> ℹ Added Bacteroides/Parabacteroides to the internal `microorganisms` data
#> set.
mo_name ( "BACTEROIDES / PARABACTEROIDES" )
#> [1] "Bacteroides/Parabacteroides"
@@ -225,7 +225,7 @@
)
)
#> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the
-#> internal `microorganisms` data set.
+#> internal `microorganisms` data set.
mo_name ( c ( "C. freundii complex" , "C. braakii complex" ) )
#> [1] "Citrobacter freundii complex" "Citrobacter braakii complex"
mo_species ( c ( "C. freundii complex" , "C. braakii complex" ) )
diff --git a/reference/age.html b/reference/age.html
index b1c04decb..b60fc6fc1 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -112,16 +112,16 @@
df
#> birth_date age age_exact age_at_y2k
-#> 1 1980-02-27 45 45.54795 19
-#> 2 1953-07-26 72 72.13973 46
-#> 3 1949-09-02 76 76.03562 50
-#> 4 1986-08-03 39 39.11781 13
-#> 5 1932-11-19 92 92.82192 67
-#> 6 1949-03-30 76 76.46301 50
-#> 7 1996-06-23 29 29.23014 3
-#> 8 1963-09-16 61 61.99726 36
-#> 9 1952-05-16 73 73.33425 47
-#> 10 1952-11-14 72 72.83562 47
+#> 1 1980-02-27 45 45.55616 19
+#> 2 1953-07-26 72 72.14795 46
+#> 3 1949-09-02 76 76.04384 50
+#> 4 1986-08-03 39 39.12603 13
+#> 5 1932-11-19 92 92.83014 67
+#> 6 1949-03-30 76 76.47123 50
+#> 7 1996-06-23 29 29.23836 3
+#> 8 1963-09-16 62 62.00548 36
+#> 9 1952-05-16 73 73.34247 47
+#> 10 1952-11-14 72 72.84384 47
On this page
diff --git a/reference/age_groups.html b/reference/age_groups.html
index eec885730..4c912902d 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index 384a25df9..f2091f8ff 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -384,16 +384,16 @@ Adhering to previously described approaches (see Source) and especially the Baye
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
@@ -408,9 +408,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
antibiogram ( example_isolates ,
antimicrobials = c ( aminoglycosides ( ) , carbapenems ( ) )
)
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> # An Antibiogram: 10 × 7
#> # Type: Non-WISCA with 95% CI
#> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin
@@ -433,7 +433,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
ab_transform = "atc" ,
mo_transform = "gramstain"
)
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
#> # An Antibiogram: 2 × 5
#> # Type: Non-WISCA with 95% CI
@@ -449,7 +449,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
ab_transform = "name" ,
mo_transform = "name"
)
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> # An Antibiogram: 5 × 3
#> # Type: Non-WISCA with 95% CI
#> Pathogen Imipenem Meropenem
@@ -487,7 +487,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
antimicrobials = ureidopenicillins ( ) + c ( "" , "GEN" , "tobra" ) ,
mo_transform = "gramstain"
)
-#> ℹ For `ureidopenicillins()` using column ' TZP ' (piperacillin/tazobactam)
+#> ℹ For `ureidopenicillins()` using column ' TZP ' (piperacillin/tazobactam)
#> # An Antibiogram: 2 × 4
#> # Type: Non-WISCA with 95% CI
#> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³
@@ -524,9 +524,9 @@ Adhering to previously described approaches (see Source) and especially the Baye
antimicrobials = c ( aminoglycosides ( ) , carbapenems ( ) ) ,
syndromic_group = "ward"
)
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> # An Antibiogram: 14 × 8
#> # Type: Non-WISCA with 95% CI
#> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem
@@ -551,7 +551,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
# now define a data set with only E. coli
ex1 <- example_isolates [ which ( mo_genus ( ) == "Escherichia" ) , ]
-#> ℹ Using column ' mo ' as input for `mo_genus()`
+#> ℹ Using column ' mo ' as input for `mo_genus()`
# with a custom language, though this will be determined automatically
# (i.e., this table will be in Spanish on Spanish systems)
@@ -563,7 +563,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
) ,
language = "es"
)
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
#> # An Antibiogram: 2 × 5
#> # Type: Non-WISCA with 95% CI
@@ -603,7 +603,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
syndromic_group = "ward" ,
wisca = TRUE
)
-#> ℹ For `ureidopenicillins()` using column ' TZP ' (piperacillin/tazobactam)
+#> ℹ For `ureidopenicillins()` using column ' TZP ' (piperacillin/tazobactam)
# in an Rmd file, you would just need to return `ureido` in a chunk,
# but to be explicit here:
diff --git a/reference/antimicrobial_selectors.html b/reference/antimicrobial_selectors.html
index f24855a58..32bcb82bd 100644
--- a/reference/antimicrobial_selectors.html
+++ b/reference/antimicrobial_selectors.html
@@ -17,7 +17,7 @@ my_data_with_all_these_columns %>%
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -263,16 +263,16 @@ my_data_with_all_these_columns %>%
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
@@ -284,10 +284,10 @@ my_data_with_all_these_columns %>%
# you can use the selectors separately to retrieve all possible antimicrobials:
carbapenems ( )
-#> ℹ in `carbapenems()` : Imipenem/EDTA ( `IPE` ) and meropenem/nacubactam
-#> ( `MNC` ) are not included since `only_treatable = TRUE` .
-#> ℹ This 'ab' vector was retrieved using `carbapenems()` , which should
-#> normally be used inside a `dplyr` verb or `data.frame` call, e.g.:
+#> ℹ in `carbapenems()` : Imipenem/EDTA ( `IPE` ) and meropenem/nacubactam
+#> ( `MNC` ) are not included since `only_treatable = TRUE` .
+#> ℹ This 'ab' vector was retrieved using `carbapenems()` , which should
+#> normally be used inside a `dplyr` verb or `data.frame` call, e.g.:
#> • your_data %>% select(carbapenems())
#> • your_data %>% select(column_a, column_b, carbapenems())
#> • your_data %>% filter(any(carbapenems() == "R"))
@@ -392,7 +392,7 @@ my_data_with_all_these_columns %>%
# select columns 'IPM' (imipenem) and 'MEM' (meropenem)
example_isolates [ , carbapenems ( ) ]
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> # A tibble: 2,000 × 2
#> IPM MEM
#> <sir> <sir>
@@ -410,7 +410,7 @@ my_data_with_all_these_columns %>%
# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
example_isolates [ , c ( "mo" , aminoglycosides ( ) ) ]
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
#> # A tibble: 2,000 × 5
#> mo GEN TOB AMK KAN
@@ -429,7 +429,7 @@ my_data_with_all_these_columns %>%
# select only antimicrobials with DDDs for oral treatment
example_isolates [ , administrable_per_os ( ) ]
-#> ℹ For `administrable_per_os()` using columns ' OXA ' (oxacillin), ' FLC '
+#> ℹ For `administrable_per_os()` using columns ' OXA ' (oxacillin), ' FLC '
#> (flucloxacillin), ' AMX ' (amoxicillin), ' AMC ' (amoxicillin/clavulanic acid),
#> ' AMP ' (ampicillin), ' CXM ' (cefuroxime), ' KAN ' (kanamycin), ' TMP '
#> (trimethoprim), ' NIT ' (nitrofurantoin), ' FOS ' (fosfomycin), ' LNZ '
@@ -441,15 +441,15 @@ my_data_with_all_these_columns %>%
#> # A tibble: 2,000 × 23
#> OXA FLC AMX AMC AMP CXM KAN TMP NIT FOS LNZ CIP MFX
#> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir>
-#> 1 NA NA NA I NA I NA R NA NA R NA NA
-#> 2 NA NA NA I NA I NA R NA NA R NA NA
-#> 3 NA R NA NA NA R NA S NA NA NA NA NA
-#> 4 NA R NA NA NA R NA S NA NA NA NA NA
-#> 5 NA R NA NA NA R NA R NA NA NA NA NA
-#> 6 NA R NA NA NA R NA R NA NA NA NA NA
-#> 7 NA S R S R S NA R NA NA NA NA NA
-#> 8 NA S R S R S NA R NA NA NA NA NA
-#> 9 NA R NA NA NA R NA S NA NA NA S NA
+#> 1 NA NA NA I NA I NA R NA NA R NA NA
+#> 2 NA NA NA I NA I NA R NA NA R NA NA
+#> 3 NA R NA NA NA R NA S NA NA NA NA NA
+#> 4 NA R NA NA NA R NA S NA NA NA NA NA
+#> 5 NA R NA NA NA R NA R NA NA NA NA NA
+#> 6 NA R NA NA NA R NA R NA NA NA NA NA
+#> 7 NA S R S R S NA R NA NA NA NA NA
+#> 8 NA S R S R S NA R NA NA NA NA NA
+#> 9 NA R NA NA NA R NA S NA NA NA S NA
#> 10 NA S NA NA NA S NA S NA NA NA S NA
#> # ℹ 1,990 more rows
#> # ℹ 10 more variables: VAN <sir>, TCY <sir>, DOX <sir>, ERY <sir>, CLI <sir>,
@@ -457,20 +457,20 @@ my_data_with_all_these_columns %>%
# filter using any() or all()
example_isolates [ any ( carbapenems ( ) == "R" ) , ]
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> # A tibble: 55 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2004-06-09 529296 69 M ICU B_ ENTRC_ FACM NA NA NA NA
#> 2 2004-06-09 529296 69 M ICU B_ ENTRC_ FACM NA NA NA NA
-#> 3 2004-11-03 D65308 80 F ICU B_ STNTR_ MLTP R NA NA R
+#> 3 2004-11-03 D65308 80 F ICU B_ STNTR_ MLTP R NA NA R
#> 4 2005-04-21 452212 82 F ICU B_ ENTRC NA NA NA NA
#> 5 2005-04-22 452212 82 F ICU B_ ENTRC NA NA NA NA
#> 6 2005-04-22 452212 82 F ICU B_ ENTRC_ FACM NA NA NA NA
#> 7 2007-02-21 8BBC46 61 F Clinical B_ ENTRC_ FACM NA NA NA NA
#> 8 2007-12-15 401043 72 M Clinical B_ ENTRC_ FACM NA NA NA NA
-#> 9 2008-01-22 1710B8 82 M Clinical B_ PROTS_ MRBL R NA NA NA
-#> 10 2008-01-22 1710B8 82 M Clinical B_ PROTS_ MRBL R NA NA NA
+#> 9 2008-01-22 1710B8 82 M Clinical B_ PROTS_ MRBL R NA NA NA
+#> 10 2008-01-22 1710B8 82 M Clinical B_ PROTS_ MRBL R NA NA NA
#> # ℹ 45 more rows
#> # 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>,
@@ -479,20 +479,20 @@ my_data_with_all_these_columns %>%
#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
subset ( example_isolates , any ( carbapenems ( ) == "R" ) )
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> # A tibble: 55 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2004-06-09 529296 69 M ICU B_ ENTRC_ FACM NA NA NA NA
#> 2 2004-06-09 529296 69 M ICU B_ ENTRC_ FACM NA NA NA NA
-#> 3 2004-11-03 D65308 80 F ICU B_ STNTR_ MLTP R NA NA R
+#> 3 2004-11-03 D65308 80 F ICU B_ STNTR_ MLTP R NA NA R
#> 4 2005-04-21 452212 82 F ICU B_ ENTRC NA NA NA NA
#> 5 2005-04-22 452212 82 F ICU B_ ENTRC NA NA NA NA
#> 6 2005-04-22 452212 82 F ICU B_ ENTRC_ FACM NA NA NA NA
#> 7 2007-02-21 8BBC46 61 F Clinical B_ ENTRC_ FACM NA NA NA NA
#> 8 2007-12-15 401043 72 M Clinical B_ ENTRC_ FACM NA NA NA NA
-#> 9 2008-01-22 1710B8 82 M Clinical B_ PROTS_ MRBL R NA NA NA
-#> 10 2008-01-22 1710B8 82 M Clinical B_ PROTS_ MRBL R NA NA NA
+#> 9 2008-01-22 1710B8 82 M Clinical B_ PROTS_ MRBL R NA NA NA
+#> 10 2008-01-22 1710B8 82 M Clinical B_ PROTS_ MRBL R NA NA NA
#> # ℹ 45 more rows
#> # 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>,
@@ -503,21 +503,21 @@ my_data_with_all_these_columns %>%
# filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
example_isolates [ any ( carbapenems ( ) ) , ]
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> ℹ Filtering any of columns ' IPM ' and ' MEM ' to contain value "S", "I" or "R"
#> # A tibble: 962 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
-#> 2 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
-#> 3 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
-#> 4 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
-#> 5 2002-02-05 067927 45 F ICU B_ SERRT_ MRCS R NA NA R
-#> 6 2002-02-05 067927 45 F ICU B_ SERRT_ MRCS R NA NA R
-#> 7 2002-02-05 067927 45 F ICU B_ SERRT_ MRCS R NA NA R
-#> 8 2002-02-27 066895 85 F Clinical B_ KLBSL_ PNMN R NA NA R
-#> 9 2002-02-27 066895 85 F Clinical B_ KLBSL_ PNMN R NA NA R
-#> 10 2002-03-08 4FC193 69 M Clinical B_ ESCHR_ COLI R NA NA R
+#> 1 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
+#> 2 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
+#> 3 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
+#> 4 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
+#> 5 2002-02-05 067927 45 F ICU B_ SERRT_ MRCS R NA NA R
+#> 6 2002-02-05 067927 45 F ICU B_ SERRT_ MRCS R NA NA R
+#> 7 2002-02-05 067927 45 F ICU B_ SERRT_ MRCS R NA NA R
+#> 8 2002-02-27 066895 85 F Clinical B_ KLBSL_ PNMN R NA NA R
+#> 9 2002-02-27 066895 85 F Clinical B_ KLBSL_ PNMN R NA NA R
+#> 10 2002-03-08 4FC193 69 M Clinical B_ ESCHR_ COLI R NA NA R
#> # ℹ 952 more rows
#> # 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>,
@@ -526,7 +526,7 @@ my_data_with_all_these_columns %>%
#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
example_isolates [ all ( carbapenems ( ) ) , ]
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> ℹ Filtering all of columns ' IPM ' and ' MEM ' to contain value "S", "I" or "R"
#> # A tibble: 756 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
@@ -536,11 +536,11 @@ my_data_with_all_these_columns %>%
#> 3 2003-04-08 114570 74 M ICU B_ STRPT_ GRPA S NA S S
#> 4 2003-04-08 114570 74 M ICU B_ STRPT_ GRPA S NA S S
#> 5 2003-08-14 F71508 0 F Clinic… B_ STRPT_ GRPB S NA S S
-#> 6 2003-10-16 650870 63 F ICU B_ ESCHR_ COLI R NA NA R
-#> 7 2003-10-20 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
-#> 8 2003-10-20 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
-#> 9 2003-11-04 2FC253 87 F ICU B_ ESCHR_ COLI R NA NA NA
-#> 10 2003-11-04 2FC253 87 F ICU B_ ESCHR_ COLI R NA NA NA
+#> 6 2003-10-16 650870 63 F ICU B_ ESCHR_ COLI R NA NA R
+#> 7 2003-10-20 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
+#> 8 2003-10-20 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
+#> 9 2003-11-04 2FC253 87 F ICU B_ ESCHR_ COLI R NA NA NA
+#> 10 2003-11-04 2FC253 87 F ICU B_ ESCHR_ COLI R NA NA NA
#> # ℹ 746 more rows
#> # 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>,
@@ -551,22 +551,22 @@ my_data_with_all_these_columns %>%
# filter with multiple antimicrobial selectors using c()
example_isolates [ all ( c ( carbapenems ( ) , aminoglycosides ( ) ) == "R" ) , ]
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
#> # A tibble: 26 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2004-11-03 D65308 80 F ICU B_ STNTR_ MLTP R NA NA R
+#> 1 2004-11-03 D65308 80 F ICU B_ STNTR_ MLTP R NA NA R
#> 2 2005-04-22 452212 82 F ICU B_ ENTRC_ FACM NA NA NA NA
#> 3 2007-02-21 8BBC46 61 F Clinical B_ ENTRC_ FACM NA NA NA NA
#> 4 2007-12-15 401043 72 M Clinical B_ ENTRC_ FACM NA NA NA NA
-#> 5 2008-12-06 501361 43 F Clinical B_ STNTR_ MLTP R NA NA R
+#> 5 2008-12-06 501361 43 F Clinical B_ STNTR_ MLTP R NA NA R
#> 6 2011-05-09 207325 82 F ICU B_ ENTRC_ FACM NA NA NA NA
-#> 7 2012-03-12 582258 80 M ICU B_ STPHY_ CONS R R R R
-#> 8 2012-05-19 C25552 89 F Outpati… B_ STPHY_ CONS R R R R
-#> 9 2012-07-17 F05015 83 M ICU B_ STPHY_ CONS R R R R
-#> 10 2012-07-20 404299 66 F Clinical B_ STPHY_ CONS R R R R
+#> 7 2012-03-12 582258 80 M ICU B_ STPHY_ CONS R R R R
+#> 8 2012-05-19 C25552 89 F Outpati… B_ STPHY_ CONS R R R R
+#> 9 2012-07-17 F05015 83 M ICU B_ STPHY_ CONS R R R R
+#> 10 2012-07-20 404299 66 F Clinical B_ STPHY_ CONS R R R R
#> # ℹ 16 more rows
#> # 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>,
@@ -577,8 +577,8 @@ my_data_with_all_these_columns %>%
# filter + select in one go: get penicillins in carbapenem-resistant strains
example_isolates [ any ( carbapenems ( ) == "R" ) , penicillins ( ) ]
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
-#> ℹ For `penicillins()` using columns ' PEN ' (benzylpenicillin), ' OXA '
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `penicillins()` using columns ' PEN ' (benzylpenicillin), ' OXA '
#> (oxacillin), ' FLC ' (flucloxacillin), ' AMX ' (amoxicillin), ' AMC '
#> (amoxicillin/clavulanic acid), ' AMP ' (ampicillin), and ' TZP '
#> (piperacillin/tazobactam)
@@ -587,14 +587,14 @@ my_data_with_all_these_columns %>%
#> <sir> <sir> <sir> <sir> <sir> <sir> <sir>
#> 1 NA NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA NA
-#> 3 R NA NA R R R R
-#> 4 NA NA NA NA NA NA R
-#> 5 NA NA NA NA NA NA R
-#> 6 NA NA NA NA NA NA R
-#> 7 NA NA NA NA NA NA R
-#> 8 NA NA NA NA NA NA R
-#> 9 R NA NA NA S NA S
-#> 10 R NA NA NA S NA S
+#> 3 R NA NA R R R R
+#> 4 NA NA NA NA NA NA R
+#> 5 NA NA NA NA NA NA R
+#> 6 NA NA NA NA NA NA R
+#> 7 NA NA NA NA NA NA R
+#> 8 NA NA NA NA NA NA R
+#> 9 R NA NA NA S NA S
+#> 10 R NA NA NA S NA S
#> # ℹ 45 more rows
# You can combine selectors with '&' to be more specific. For example,
@@ -603,11 +603,11 @@ my_data_with_all_these_columns %>%
# drugs are both omitted since benzylpenicillin is not administrable per os
# and erythromycin is not a penicillin:
example_isolates [ , penicillins ( ) & administrable_per_os ( ) ]
-#> ℹ For `penicillins()` using columns ' PEN ' (benzylpenicillin), ' OXA '
+#> ℹ For `penicillins()` using columns ' PEN ' (benzylpenicillin), ' OXA '
#> (oxacillin), ' FLC ' (flucloxacillin), ' AMX ' (amoxicillin), ' AMC '
#> (amoxicillin/clavulanic acid), ' AMP ' (ampicillin), and ' TZP '
#> (piperacillin/tazobactam)
-#> ℹ For `administrable_per_os()` using columns ' OXA ' (oxacillin), ' FLC '
+#> ℹ For `administrable_per_os()` using columns ' OXA ' (oxacillin), ' FLC '
#> (flucloxacillin), ' AMX ' (amoxicillin), ' AMC ' (amoxicillin/clavulanic acid),
#> ' AMP ' (ampicillin), ' CXM ' (cefuroxime), ' KAN ' (kanamycin), ' TMP '
#> (trimethoprim), ' NIT ' (nitrofurantoin), ' FOS ' (fosfomycin), ' LNZ '
@@ -621,13 +621,13 @@ my_data_with_all_these_columns %>%
#> <sir> <sir> <sir> <sir> <sir>
#> 1 NA NA NA I NA
#> 2 NA NA NA I NA
-#> 3 NA R NA NA NA
-#> 4 NA R NA NA NA
-#> 5 NA R NA NA NA
-#> 6 NA R NA NA NA
-#> 7 NA S R S R
-#> 8 NA S R S R
-#> 9 NA R NA NA NA
+#> 3 NA R NA NA NA
+#> 4 NA R NA NA NA
+#> 5 NA R NA NA NA
+#> 6 NA R NA NA NA
+#> 7 NA S R S R
+#> 8 NA S R S R
+#> 9 NA R NA NA NA
#> 10 NA S NA NA NA
#> # ℹ 1,990 more rows
@@ -635,7 +635,7 @@ my_data_with_all_these_columns %>%
# very flexible. For instance, to select antimicrobials with an oral DDD
# of at least 1 gram:
example_isolates [ , amr_selector ( oral_ddd > 1 & oral_units == "g" ) ]
-#> ℹ For `amr_selector(oral_ddd > 1 & oral_units == "g")` using columns ' OXA '
+#> ℹ For `amr_selector(oral_ddd > 1 & oral_units == "g")` using columns ' OXA '
#> (oxacillin), ' FLC ' (flucloxacillin), ' AMX ' (amoxicillin), ' AMC '
#> (amoxicillin/clavulanic acid), ' AMP ' (ampicillin), ' KAN ' (kanamycin), ' FOS '
#> (fosfomycin), ' LNZ ' (linezolid), ' VAN ' (vancomycin), ' ERY ' (erythromycin),
@@ -643,15 +643,15 @@ my_data_with_all_these_columns %>%
#> # A tibble: 2,000 × 13
#> OXA FLC AMX AMC AMP KAN FOS LNZ VAN ERY CLI MTR CHL
#> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir>
-#> 1 NA NA NA I NA NA NA R R R R NA NA
-#> 2 NA NA NA I NA NA NA R R R R NA NA
-#> 3 NA R NA NA NA NA NA NA S R NA NA NA
-#> 4 NA R NA NA NA NA NA NA S R NA NA NA
-#> 5 NA R NA NA NA NA NA NA S R NA NA NA
-#> 6 NA R NA NA NA NA NA NA S R R NA NA
-#> 7 NA S R S R NA NA NA S S NA NA NA
-#> 8 NA S R S R NA NA NA S S NA NA NA
-#> 9 NA R NA NA NA NA NA NA S R NA NA NA
+#> 1 NA NA NA I NA NA NA R R R R NA NA
+#> 2 NA NA NA I NA NA NA R R R R NA NA
+#> 3 NA R NA NA NA NA NA NA S R NA NA NA
+#> 4 NA R NA NA NA NA NA NA S R NA NA NA
+#> 5 NA R NA NA NA NA NA NA S R NA NA NA
+#> 6 NA R NA NA NA NA NA NA S R R NA NA
+#> 7 NA S R S R NA NA NA S S NA NA NA
+#> 8 NA S R S R NA NA NA S S NA NA NA
+#> 9 NA R NA NA NA NA NA NA S R NA NA NA
#> 10 NA S NA NA NA NA NA NA S S NA NA NA
#> # ℹ 1,990 more rows
@@ -676,17 +676,17 @@ my_data_with_all_these_columns %>%
#> The following objects are masked from ‘package:AMR’:
#>
#> %like%, like
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> Warning: It should never be needed to print an antimicrobial selector class. Are you
-#> using data.table? Then add the argument `with = FALSE` , see our examples at
-#> `?amr_selector` .
+#> using data.table? Then add the argument `with = FALSE` , see our examples at
+#> `?amr_selector` .
#> Class 'amr_selector'
#> [1] IPM MEM
if ( require ( "data.table" ) ) {
# so `with = FALSE` is required
dt [ , carbapenems ( ) , with = FALSE ]
}
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> IPM MEM
#> <sir> <sir>
#> 1: <NA> <NA>
@@ -705,7 +705,7 @@ my_data_with_all_these_columns %>%
if ( require ( "data.table" ) ) {
dt [ , c ( "mo" , aminoglycosides ( ) ) ]
}
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
#> mo GEN TOB AMK KAN
#> <mo> <sir> <sir> <sir> <sir>
@@ -723,8 +723,8 @@ my_data_with_all_these_columns %>%
if ( require ( "data.table" ) ) {
dt [ , c ( carbapenems ( ) , aminoglycosides ( ) ) ]
}
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
#> IPM MEM GEN TOB AMK KAN
#> <sir> <sir> <sir> <sir> <sir> <sir>
@@ -744,7 +744,7 @@ my_data_with_all_these_columns %>%
if ( require ( "data.table" ) ) {
dt [ any ( carbapenems ( ) == "S" ) , ]
}
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> date patient age gender ward mo PEN OXA FLC
#> <Date> <char> <num> <char> <char> <mo> <sir> <sir> <sir>
#> 1: 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R <NA> <NA>
@@ -813,8 +813,8 @@ my_data_with_all_these_columns %>%
if ( require ( "data.table" ) ) {
dt [ any ( carbapenems ( ) == "S" ) , penicillins ( ) , with = FALSE ]
}
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
-#> ℹ For `penicillins()` using columns ' PEN ' (benzylpenicillin), ' OXA '
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `penicillins()` using columns ' PEN ' (benzylpenicillin), ' OXA '
#> (oxacillin), ' FLC ' (flucloxacillin), ' AMX ' (amoxicillin), ' AMC '
#> (amoxicillin/clavulanic acid), ' AMP ' (ampicillin), and ' TZP '
#> (piperacillin/tazobactam)
diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html
index 105d0b4df..3969e61f1 100644
--- a/reference/antimicrobials.html
+++ b/reference/antimicrobials.html
@@ -9,7 +9,7 @@ The antibiotics data set has been renamed to antimicrobials. The old name will b
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/as.ab.html b/reference/as.ab.html
index 786ceadca..ea6c3cfc8 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -188,16 +188,16 @@
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo J01CE01 J01CF04 J01CF05
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir>
-#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA
-#> 2 2002-01-03 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA
-#> 3 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R
-#> 4 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R
-#> 5 2002-01-13 067927 45 F ICU B_ STPHY_ EPDR R NA R
-#> 6 2002-01-13 067927 45 F ICU B_ STPHY_ EPDR R NA R
-#> 7 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S
-#> 8 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S
-#> 9 2002-01-16 067927 45 F ICU B_ STPHY_ EPDR R NA R
-#> 10 2002-01-17 858515 79 F ICU B_ STPHY_ EPDR R NA S
+#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA
+#> 2 2002-01-03 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA
+#> 3 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R
+#> 4 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R
+#> 5 2002-01-13 067927 45 F ICU B_ STPHY_ EPDR R NA R
+#> 6 2002-01-13 067927 45 F ICU B_ STPHY_ EPDR R NA R
+#> 7 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S
+#> 8 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S
+#> 9 2002-01-16 067927 45 F ICU B_ STPHY_ EPDR R NA R
+#> 10 2002-01-17 858515 79 F ICU B_ STPHY_ EPDR R NA S
#> # ℹ 1,990 more rows
#> # ℹ 37 more variables: J01CA04 <sir>, J01CR02 <sir>, J01CA01 <sir>,
#> # J01CR05 <sir>, J01DB04 <sir>, J01DE01 <sir>, J01DC02 <sir>, J01DC01 <sir>,
diff --git a/reference/as.av.html b/reference/as.av.html
index e8a291809..3f0ed88fc 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/as.disk.html b/reference/as.disk.html
index 87e102987..9acdc8ab3 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 0a2400078..2380a8da9 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 5c0b430b7..144628aca 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/as.sir.html b/reference/as.sir.html
index e1fee3cf3..0e63b3ada 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -9,7 +9,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -342,16 +342,16 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
@@ -415,10 +415,10 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
#> # A tibble: 4 × 18
#> datetime index method ab_given mo_given host_given input_given
#> <dttm> <int> <chr> <chr> <chr> <chr> <chr>
-#> 1 2025-09-15 07:16:09 1 MIC amoxicillin Escherich… human 8
-#> 2 2025-09-15 07:16:09 1 MIC cipro Escherich… human 0.256
-#> 3 2025-09-15 07:16:10 1 DISK tobra Escherich… human 16
-#> 4 2025-09-15 07:16:10 1 DISK genta Escherich… human 18
+#> 1 2025-09-18 13:05:32 1 MIC amoxicillin Escherich… human 8
+#> 2 2025-09-18 13:05:33 1 MIC cipro Escherich… human 0.256
+#> 3 2025-09-18 13:05:33 1 DISK tobra Escherich… human 16
+#> 4 2025-09-18 13:05:33 1 DISK genta Escherich… human 18
#> # ℹ 11 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>,
#> # outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
#> # breakpoint_S_R <chr>, site <chr>
@@ -427,7 +427,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
# using parallel computing, which is available in base R:
as.sir ( df_wide , parallel = TRUE , info = TRUE )
#> ℹ Returning previously coerced values for various antimicrobials. Run
-#> `ab_reset_session()` to reset this. This note will be shown once per
+#> `ab_reset_session()` to reset this. This note will be shown once per
#> session.
#>
#> Running in parallel mode using 3 out of 4 cores, on columns ' amoxicillin ',
@@ -435,7 +435,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
#> DONE
#>
#>
-#> ℹ Run `sir_interpretation_history()` to retrieve a logbook with all details
+#> ℹ Run `sir_interpretation_history()` to retrieve a logbook with all details
#> of the breakpoint interpretations.
#> microorganism amoxicillin cipro tobra genta ERY
#> 1 Escherichia coli S I S S R
@@ -548,7 +548,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
df_wide %>%
mutate_at ( vars ( cipro : genta ) , as.sir , mo = "E. coli" , uti = TRUE )
}
-#> ℹ For `aminopenicillins()` using column ' amoxicillin '
+#> ℹ For `aminopenicillins()` using column ' amoxicillin '
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `across(...)`.
#> Caused by warning:
@@ -619,7 +619,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
# For CLEANING existing SIR values -------------------------------------
as.sir ( c ( "S" , "SDD" , "I" , "R" , "NI" , "A" , "B" , "C" ) )
-#> Warning: in `as.sir()` : 3 results in index '20' truncated (38%) that were invalid
+#> Warning: in `as.sir()` : 3 results in index '20' truncated (38%) that were invalid
#> antimicrobial interpretations: "A", "B", and "C"
#> Class 'sir'
#> [1] S SDD I R NI <NA> <NA> <NA>
@@ -661,16 +661,16 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
diff --git a/reference/atc_online.html b/reference/atc_online.html
index c784220ed..dbcbbefe3 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -135,10 +135,10 @@
atc_online_property ( "J01CA04" , property = "groups" ) # search hierarchical groups of amoxicillin
}
#> Loading required namespace: rvest
-#> ℹ in `atc_online_property()` : no properties found for ATC QG51AA03. Please
+#> ℹ in `atc_online_property()` : no properties found for ATC QG51AA03. Please
#> check
#> https://atcddd.fhi.no/atcvet/atcvet_index/?code=QG51AA03&showdescription=no.
-#> ℹ in `atc_online_property()` : no properties found for ATC QJ01CA04. Please
+#> ℹ in `atc_online_property()` : no properties found for ATC QJ01CA04. Please
#> check
#> https://atcddd.fhi.no/atcvet/atcvet_index/?code=QJ01CA04&showdescription=no.
#> [1] "ANTIINFECTIVES FOR SYSTEMIC USE"
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index 325af3bbc..f470df1c1 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/av_property.html b/reference/av_property.html
index c70ba5d58..a66844843 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/availability.html b/reference/availability.html
index f12d89613..70333f291 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index 6d97839ed..9054ce65c 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -142,16 +142,16 @@
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index 14583bd8b..e0227ebf4 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."> AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/count.html b/reference/count.html
index 45f5da473..176290e6c 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -243,7 +243,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
group_by ( ward ) %>%
count_df ( translate = FALSE )
}
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
#> # A tibble: 12 × 4
#> ward antibiotic interpretation value
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index 0f597438f..8fed93fd4 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -195,10 +195,10 @@
x
#> A set of custom EUCAST rules:
#>
-#> 1. If AMC is R and genus is "Klebsiella" then set to R :
+#> 1. If AMC is R and genus is "Klebsiella" then set to R :
#> amoxicillin (AMX), ampicillin (AMP)
#>
-#> 2. If AMC is I and genus is "Klebsiella" then set to I :
+#> 2. If AMC is I and genus is "Klebsiella" then set to I :
#> amoxicillin (AMX), ampicillin (AMP)
# run the custom rule set (verbose = TRUE will return a logbook instead of the data set):
@@ -229,13 +229,13 @@
x2
#> A set of custom EUCAST rules:
#>
-#> 1. If AMC is R and genus is "Klebsiella" then set to R :
+#> 1. If AMC is R and genus is "Klebsiella" then set to R :
#> amoxicillin (AMX), ampicillin (AMP)
#>
-#> 2. If AMC is I and genus is "Klebsiella" then set to I :
+#> 2. If AMC is I and genus is "Klebsiella" then set to I :
#> amoxicillin (AMX), ampicillin (AMP)
#>
-#> 3. If TZP is R then set to R :
+#> 3. If TZP is R then set to R :
#> biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM),
#> imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM),
#> meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN),
diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html
index 6ef6e9454..4899d8981 100644
--- a/reference/custom_mdro_guideline.html
+++ b/reference/custom_mdro_guideline.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -188,8 +188,8 @@
)
x
#> 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
+#> 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 .
@@ -233,8 +233,8 @@
)
my_guideline
#> A set of custom MDRO rules:
-#> 1. If AMX is R then: Custom MDRO 1
-#> 2. If all of cephalosporins_2nd() is R then: Custom MDRO 2
+#> 1. If AMX is R then: Custom MDRO 1
+#> 2. If all of cephalosporins_2nd() is R then: Custom MDRO 2
#> 3. Otherwise: Negative
#>
#> Unmatched rows will return NA .
@@ -249,10 +249,10 @@
#> it seems to be lenampicillin (LEN)
#> ℹ Column ' vanB ' is SIR eligible (despite only having empty values), since
#> it seems to be metronidazole (MTR)
-#> ℹ For `cephalosporins_2nd()` using columns ' CXM ' (cefuroxime) and ' FOX '
+#> ℹ For `cephalosporins_2nd()` using columns ' CXM ' (cefuroxime) and ' FOX '
#> (cefoxitin)
-#> ℹ Assuming a filter on all 2 cephalosporins_2nd. Wrap around `all()` or
-#> `any()` to prevent this note.
+#> ℹ Assuming a filter on all 2 cephalosporins_2nd. Wrap around `all()` or
+#> `any()` to prevent this note.
table ( out )
#> out
#> Negative Custom MDRO 1 Custom MDRO 2
diff --git a/reference/dosage.html b/reference/dosage.html
index b8a5d09bd..91fba11b4 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index a9a68f9e5..7c3943561 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -215,7 +215,7 @@ Leclercq et al. EUCAST expert rules in antimicrobial susceptibility test
# apply EUCAST rules: some results wil be changed
b <- eucast_rules ( a , overwrite = TRUE )
-#> Warning: in `eucast_rules()` : not all columns with antimicrobial results are of
+#> Warning: in `eucast_rules()` : not all columns with antimicrobial results are of
#> class 'sir'. Transform them on beforehand, with e.g.:
#> - a %>% as.sir(CXM:AMX)
#> - a %>% mutate_if(is_sir_eligible, as.sir)
@@ -233,7 +233,7 @@ Leclercq et al. EUCAST expert rules in antimicrobial susceptibility test
# do not apply EUCAST rules, but rather get a data.frame
# containing all details about the transformations:
c <- eucast_rules ( a , overwrite = TRUE , verbose = TRUE )
-#> Warning: in `eucast_rules()` : not all columns with antimicrobial results are of
+#> Warning: in `eucast_rules()` : not all columns with antimicrobial results are of
#> class 'sir'. Transform them on beforehand, with e.g.:
#> - a %>% as.sir(CXM:AMX)
#> - a %>% mutate_if(is_sir_eligible, as.sir)
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index a04d838aa..e9ab2b30a 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -83,16 +83,16 @@
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 4fca91ea8..e8248bc48 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
index 5c4605a48..7ab6c77c3 100644
--- a/reference/export_ncbi_biosample.html
+++ b/reference/export_ncbi_biosample.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 42add6395..2f6fff816 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -9,7 +9,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -177,34 +177,18 @@
According to previously-mentioned sources, there are different methods (algorithms) to select first isolates with increasing reliability: isolate-based, patient-based, episode-based and phenotype-based. All methods select on a combination of the taxonomic genus and species (not subspecies).
-All mentioned methods are covered in the first_isolate()
function:
Method Function to apply Isolate-based first_isolate(x, method = "isolate-based")
(= all isolates) Patient-based first_isolate(x, method = "patient-based")
(= first isolate per patient) Episode-based first_isolate(x, method = "episode-based")
, or:(= first isolate per episode) - 7-Day interval from initial isolate - first_isolate(x, method = "e", episode_days = 7)
- 30-Day interval from initial isolate - first_isolate(x, method = "e", episode_days = 30)
Phenotype-based first_isolate(x, method = "phenotype-based")
, or:(= first isolate per phenotype) - Major difference in any antimicrobial result - first_isolate(x, type = "points")
- Any difference in key antimicrobial results - first_isolate(x, type = "keyantimicrobials")
-
-
-
Isolate-based
-
-
+
All mentioned methods are covered in the first_isolate()
function:
Method Function to apply Isolate-based first_isolate(x, method = "isolate-based")
(= all isolates) Patient-based first_isolate(x, method = "patient-based")
(= first isolate per patient) Episode-based first_isolate(x, method = "episode-based")
, or:(= first isolate per episode) - 7-Day interval from initial isolate - first_isolate(x, method = "e", episode_days = 7)
- 30-Day interval from initial isolate - first_isolate(x, method = "e", episode_days = 30)
Phenotype-based first_isolate(x, method = "phenotype-based")
, or:(= first isolate per phenotype) - Major difference in any antimicrobial result - first_isolate(x, type = "points")
- Any difference in key antimicrobial results - first_isolate(x, type = "keyantimicrobials")
Isolate-based
+
Minimum variables required: Microorganism identifier
This method does not require any selection, as all isolates should be included. It does, however, respect all arguments set in the first_isolate()
function. For example, the default setting for include_unknown
(FALSE
) will omit selection of rows without a microbial ID.
-
-
-
-
Patient-based
-
-
-
To include every genus-species combination per patient once, set the episode_days
to Inf
. This method makes sure that no duplicate isolates are selected from the same patient. This method is preferred to e.g. identify the first MRSA finding of each patient to determine the incidence. Conversely, in a large longitudinal data set, this could mean that isolates are excluded that were found years after the initial isolate.
-
-
-
-
Episode-based
-
-
-
To include every genus-species combination per patient episode once, set the episode_days
to a sensible number of days. Depending on the type of analysis, this could be 14, 30, 60 or 365. Short episodes are common for analysing specific hospital or ward data or ICU cases, long episodes are common for analysing regional and national data.
+
Patient-based
+
Minimum variables required: Microorganism identifier, Patient identifier
+
This method includes every genus-species combination per patient once. This method makes sure that no duplicate isolates are selected from the same patient. This method is preferred to e.g. identify the first MRSA finding of each patient to determine the incidence. Conversely, in a large longitudinal data set, this could mean that isolates are excluded that were found years after the initial isolate.
+
Episode-based
+
Minimum variables required: Microorganism identifier, Patient identifier, Date
+
To include every genus-species combination per patient episode once, set the episode_days
to a sensible number of days. Depending on the type of analysis, this could be e.g., 14, 30, 60 or 365. Short episodes are common for analysing specific hospital or ward data or ICU cases, long episodes are common for analysing regional and national data.
This is the most common method to correct for duplicate isolates. Patients are categorised into episodes based on their ID and dates (e.g., the date of specimen receipt or laboratory result). While this is a common method, it does not take into account antimicrobial test results. This means that e.g. a methicillin-resistant Staphylococcus aureus (MRSA) isolate cannot be differentiated from a wildtype Staphylococcus aureus isolate.
-
-
-
-
Phenotype-based
-
-
+
Phenotype-based
+
Minimum variables required: Microorganism identifier, Patient identifier, Date, Antimicrobial test results
This is a more reliable method, since it also weighs the antibiogram (antimicrobial test results) yielding so-called 'first weighted isolates'. There are two different methods to weigh the antibiogram:
Using type = "points"
and argument points_threshold
(default)
This method weighs all antimicrobial drugs available in the data set. Any difference from I to S or R (or vice versa) counts as 0.5
points, a difference from S to R (or vice versa) counts as 1
point. When the sum of points exceeds points_threshold
, which defaults to 2
, an isolate will be selected as a first weighted isolate.
All antimicrobials are internally selected using the all_antimicrobials()
function. The output of this function does not need to be passed to the first_isolate()
function.
@@ -227,26 +211,26 @@
example_isolates [ first_isolate ( info = TRUE ) , ]
#> ℹ Determining first isolates using an episode length of 365 days
-#> ℹ Using column ' date ' as input for `col_date` .
-#> ℹ Using column ' patient ' as input for `col_patient_id` .
+#> ℹ Using column ' date ' as input for `col_date` .
+#> ℹ Using column ' patient ' as input for `col_patient_id` .
#> ℹ Basing inclusion on all antimicrobial results, using a points threshold
#> of 2
#> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column ' mo ')
-#> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a
-#> microbial ID was available)
+#> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a
+#> microbial ID was available)
#> # A tibble: 1,387 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA NA
-#> 2 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
-#> 3 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
-#> 4 2002-01-16 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
-#> 5 2002-01-17 858515 79 F ICU B_ STPHY_ EPDR R NA S NA
-#> 6 2002-01-17 495616 67 M Clinical B_ STPHY_ EPDR R NA S NA
-#> 7 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
-#> 8 2002-01-21 462081 75 F Clinical B_ CTRBC_ FRND R NA NA R
-#> 9 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
-#> 10 2002-02-03 481442 76 M ICU B_ STPHY_ CONS R NA S NA
+#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA NA
+#> 2 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
+#> 3 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
+#> 4 2002-01-16 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
+#> 5 2002-01-17 858515 79 F ICU B_ STPHY_ EPDR R NA S NA
+#> 6 2002-01-17 495616 67 M Clinical B_ STPHY_ EPDR R NA S NA
+#> 7 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
+#> 8 2002-01-21 462081 75 F Clinical B_ CTRBC_ FRND R NA NA R
+#> 9 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
+#> 10 2002-02-03 481442 76 M ICU B_ STPHY_ CONS R NA S NA
#> # ℹ 1,377 more rows
#> # 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>,
@@ -257,20 +241,20 @@
# \donttest{
# get all first Gram-negatives
example_isolates [ which ( first_isolate ( info = FALSE ) & mo_is_gram_negative ( ) ) , ]
-#> ℹ Using column ' mo ' as input for `mo_is_gram_negative()`
+#> ℹ Using column ' mo ' as input for `mo_is_gram_negative()`
#> # A tibble: 441 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA NA
-#> 2 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
-#> 3 2002-01-21 462081 75 F Clinical B_ CTRBC_ FRND R NA NA R
-#> 4 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
-#> 5 2002-02-05 067927 45 F ICU B_ SERRT_ MRCS R NA NA R
-#> 6 2002-02-27 066895 85 F Clinical B_ KLBSL_ PNMN R NA NA R
-#> 7 2002-03-08 4FC193 69 M Clinical B_ ESCHR_ COLI R NA NA R
-#> 8 2002-03-16 4FC193 69 M Clinical B_ PSDMN_ AERG R NA NA R
-#> 9 2002-04-01 496896 46 F ICU B_ ESCHR_ COLI R NA NA NA
-#> 10 2002-04-23 EE2510 69 F ICU B_ ESCHR_ COLI R NA NA NA
+#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA NA
+#> 2 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
+#> 3 2002-01-21 462081 75 F Clinical B_ CTRBC_ FRND R NA NA R
+#> 4 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
+#> 5 2002-02-05 067927 45 F ICU B_ SERRT_ MRCS R NA NA R
+#> 6 2002-02-27 066895 85 F Clinical B_ KLBSL_ PNMN R NA NA R
+#> 7 2002-03-08 4FC193 69 M Clinical B_ ESCHR_ COLI R NA NA R
+#> 8 2002-03-16 4FC193 69 M Clinical B_ PSDMN_ AERG R NA NA R
+#> 9 2002-04-01 496896 46 F ICU B_ ESCHR_ COLI R NA NA NA
+#> 10 2002-04-23 EE2510 69 F ICU B_ ESCHR_ COLI R NA NA NA
#> # ℹ 431 more rows
#> # 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>,
@@ -288,21 +272,21 @@
#> ℹ Basing inclusion on all antimicrobial results, using a points threshold
#> of 2
#> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column ' mo ')
-#> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a
-#> microbial ID was available)
+#> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a
+#> microbial ID was available)
#> # A tibble: 1,387 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA NA
-#> 2 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
-#> 3 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
-#> 4 2002-01-16 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
-#> 5 2002-01-17 858515 79 F ICU B_ STPHY_ EPDR R NA S NA
-#> 6 2002-01-17 495616 67 M Clinical B_ STPHY_ EPDR R NA S NA
-#> 7 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
-#> 8 2002-01-21 462081 75 F Clinical B_ CTRBC_ FRND R NA NA R
-#> 9 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
-#> 10 2002-02-03 481442 76 M ICU B_ STPHY_ CONS R NA S NA
+#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA NA
+#> 2 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
+#> 3 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
+#> 4 2002-01-16 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
+#> 5 2002-01-17 858515 79 F ICU B_ STPHY_ EPDR R NA S NA
+#> 6 2002-01-17 495616 67 M Clinical B_ STPHY_ EPDR R NA S NA
+#> 7 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
+#> 8 2002-01-21 462081 75 F Clinical B_ CTRBC_ FRND R NA NA R
+#> 9 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
+#> 10 2002-02-03 481442 76 M ICU B_ STPHY_ CONS R NA S NA
#> # ℹ 1,377 more rows
#> # 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>,
@@ -318,16 +302,16 @@
#> # A tibble: 1,387 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA NA
-#> 2 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
-#> 3 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
-#> 4 2002-01-16 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
-#> 5 2002-01-17 858515 79 F ICU B_ STPHY_ EPDR R NA S NA
-#> 6 2002-01-17 495616 67 M Clinical B_ STPHY_ EPDR R NA S NA
-#> 7 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
-#> 8 2002-01-21 462081 75 F Clinical B_ CTRBC_ FRND R NA NA R
-#> 9 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
-#> 10 2002-02-03 481442 76 M ICU B_ STPHY_ CONS R NA S NA
+#> 1 2002-01-02 A77334 65 F Clinical B_ ESCHR_ COLI R NA NA NA
+#> 2 2002-01-07 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
+#> 3 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
+#> 4 2002-01-16 067927 45 F ICU B_ STPHY_ EPDR R NA R NA
+#> 5 2002-01-17 858515 79 F ICU B_ STPHY_ EPDR R NA S NA
+#> 6 2002-01-17 495616 67 M Clinical B_ STPHY_ EPDR R NA S NA
+#> 7 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
+#> 8 2002-01-21 462081 75 F Clinical B_ CTRBC_ FRND R NA NA R
+#> 9 2002-01-22 F35553 50 M ICU B_ PROTS_ MRBL R NA NA NA
+#> 10 2002-02-03 481442 76 M ICU B_ STPHY_ CONS R NA S NA
#> # ℹ 1,377 more rows
#> # 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>,
@@ -348,18 +332,18 @@
#>
#> Group: ward = "Clinical"
#> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column ' mo ')
-#> => Found 865 'phenotype-based' first isolates (70.1% of total where a
-#> microbial ID was available)
+#> => Found 865 'phenotype-based' first isolates (70.1% of total where a
+#> microbial ID was available)
#>
#> Group: ward = "ICU"
#> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column ' mo ')
-#> => Found 452 'phenotype-based' first isolates (70.0% of total where a
-#> microbial ID was available)
+#> => Found 452 'phenotype-based' first isolates (70.0% of total where a
+#> microbial ID was available)
#>
#> Group: ward = "Outpatient"
#> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column ' mo ')
-#> => Found 99 'phenotype-based' first isolates (82.5% of total where a
-#> microbial ID was available)
+#> => Found 99 'phenotype-based' first isolates (82.5% of total where a
+#> microbial ID was available)
#> # A tibble: 2,000 × 5
#> # Groups: ward [3]
#> ward date patient mo first
diff --git a/reference/g.test.html b/reference/g.test.html
index 7aa6f0f68..64ea064a1 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/get_episode.html b/reference/get_episode.html
index b986fd659..7c0145343 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -174,8 +174,8 @@
#> # A tibble: 2 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 1 2002-07-23 F35553 51 M ICU B_ STPHY_ AURS R NA S R
-#> 2 2002-07-23 F35553 51 M ICU B_ STPHY_ AURS R NA S R
+#> 1 2002-07-23 F35553 51 M ICU B_ STPHY_ AURS R NA S R
+#> 2 2002-07-23 F35553 51 M ICU B_ STPHY_ AURS R NA S R
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index d3a87c4ba..e1859fb18 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -224,7 +224,7 @@
#> ℹ In group 5: `order = "Lactobacillales"` `genus = "Enterococcus"`.
#> Caused by warning:
#> ! Introducing NA: only 14 results available for PEN in group: order =
-#> "Lactobacillales", genus = "Enterococcus" (`minimum` = 30).
+#> "Lactobacillales", genus = "Enterococcus" (`minimum` = 30).
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings.
#> ℹ Columns selected for PCA: " AMC ", " CAZ ", " CTX ", " CXM ", " GEN ", " SXT ",
#> " TMP ", and " TOB ". Total observations available: 7.
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 1074fbc7e..5f15a4a62 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -285,7 +285,7 @@
datalabels = FALSE
)
}
-#> ℹ Using column ' mo ' as input for `mo_is_gram_negative()`
+#> ℹ Using column ' mo ' as input for `mo_is_gram_negative()`
# }
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index db06d8bc9..0e668f337 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/index.html b/reference/index.html
index 07899329c..0872d1d0c 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 7f49cc2b9..78e1bc314 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 310c51162..8f29b50c2 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/join.html b/reference/join.html
index 747a962c4..8e47f2a74 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -154,7 +154,7 @@
left_join_microorganisms ( ) %>%
colnames ( )
}
-#> Joining, by = "mo"
+#> Joining, by = "mo"
#> [1] "date" "patient" "age"
#> [4] "gender" "ward" "mo"
#> [7] "PEN" "OXA" "FLC"
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 7ba99a4b5..202cf5570 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 4990f7707..8ea7a86fd 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/like.html b/reference/like.html
index 6d985575e..b850df0d1 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -131,7 +131,7 @@
# \donttest{
# get isolates whose name start with 'Entero' (case-insensitive)
example_isolates [ which ( mo_name ( ) %like% "^entero" ) , ]
-#> ℹ Using column ' mo ' as input for `mo_name()`
+#> ℹ Using column ' mo ' as input for `mo_name()`
#> # A tibble: 106 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
@@ -141,10 +141,10 @@
#> 4 2002-06-23 798871 82 M Clinic… B_ ENTRC_ FCLS NA NA NA NA
#> 5 2003-04-20 6BC362 62 M ICU B_ ENTRC NA NA NA NA
#> 6 2003-04-21 6BC362 62 M ICU B_ ENTRC NA NA NA NA
-#> 7 2003-08-13 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
+#> 7 2003-08-13 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
#> 8 2003-08-13 F35553 52 M ICU B_ ENTRC_ FCLS NA NA NA NA
#> 9 2003-09-05 F35553 52 M ICU B_ ENTRC NA NA NA NA
-#> 10 2003-09-05 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
+#> 10 2003-09-05 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
#> # ℹ 96 more rows
#> # 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>,
@@ -157,7 +157,7 @@
example_isolates %>%
filter ( mo_name ( ) %like% "^ent" )
}
-#> ℹ Using column ' mo ' as input for `mo_name()`
+#> ℹ Using column ' mo ' as input for `mo_name()`
#> # A tibble: 106 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
@@ -167,10 +167,10 @@
#> 4 2002-06-23 798871 82 M Clinic… B_ ENTRC_ FCLS NA NA NA NA
#> 5 2003-04-20 6BC362 62 M ICU B_ ENTRC NA NA NA NA
#> 6 2003-04-21 6BC362 62 M ICU B_ ENTRC NA NA NA NA
-#> 7 2003-08-13 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
+#> 7 2003-08-13 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
#> 8 2003-08-13 F35553 52 M ICU B_ ENTRC_ FCLS NA NA NA NA
#> 9 2003-09-05 F35553 52 M ICU B_ ENTRC NA NA NA NA
-#> 10 2003-09-05 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
+#> 10 2003-09-05 F35553 52 M ICU B_ ENTRBC_ CLOC R NA NA R
#> # ℹ 96 more rows
#> # 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>,
diff --git a/reference/mdro.html b/reference/mdro.html
index 7ef417453..f4eed42bb 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -205,8 +205,8 @@ Ordered facto
Examples
out <- mdro ( example_isolates )
-#> Warning: in `mdro()` : NA introduced for isolates where the available percentage of
-#> antimicrobial classes was below 50% (set with `pct_required_classes` )
+#> Warning: in `mdro()` : NA introduced for isolates where the available percentage of
+#> antimicrobial classes was below 50% (set with `pct_required_classes` )
str ( out )
#> Ord.factor w/ 4 levels "Negative"<"Multi-drug-resistant (MDR)"<..: NA NA 1 1 1 1 NA NA 1 1 ...
table ( out )
@@ -232,8 +232,8 @@ Ordered facto
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `MDRO = mdro()`.
#> Caused by warning:
-#> ! in `mdro()` : NA introduced for isolates where the available percentage of
-#> antimicrobial classes was below 50% (set with `pct_required_classes` )
+#> ! in `mdro()` : NA introduced for isolates where the available percentage of
+#> antimicrobial classes was below 50% (set with `pct_required_classes` )
#> # A tibble: 3 × 2
#> MDRO n
#> <ord> <int>
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index f0b602623..b76258663 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -204,9 +204,9 @@
mutate ( dist = mean_amr_distance ( . ) ) %>%
arrange ( mo , dist )
}
-#> ℹ Using column ' mo ' as input for `mo_genus()`
-#> ℹ Using column ' mo ' as input for `mo_species()`
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ Using column ' mo ' as input for `mo_genus()`
+#> ℹ Using column ' mo ' as input for `mo_species()`
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> ℹ Calculating mean AMR distance based on columns "TCY", "IPM", and "MEM"
#> # A tibble: 63 × 5
#> # Groups: mo [4]
@@ -216,12 +216,12 @@
#> 2 B_ ENTRC_ AVIM S S NA 0
#> 3 B_ ENTRC_ CSSL NA S NA NA
#> 4 B_ ENTRC_ FACM S S NA -2.66
-#> 5 B_ ENTRC_ FACM S R R -0.423
-#> 6 B_ ENTRC_ FACM S R R -0.423
-#> 7 B_ ENTRC_ FACM NA R R 0.224
-#> 8 B_ ENTRC_ FACM NA R R 0.224
-#> 9 B_ ENTRC_ FACM NA R R 0.224
-#> 10 B_ ENTRC_ FACM NA R R 0.224
+#> 5 B_ ENTRC_ FACM S R R -0.423
+#> 6 B_ ENTRC_ FACM S R R -0.423
+#> 7 B_ ENTRC_ FACM NA R R 0.224
+#> 8 B_ ENTRC_ FACM NA R R 0.224
+#> 9 B_ ENTRC_ FACM NA R R 0.224
+#> 10 B_ ENTRC_ FACM NA R R 0.224
#> # ℹ 53 more rows
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index a13537dba..b06bab859 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index 6bb51bb27..4e2b041d0 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.groups.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 7c3a6f9cf..8ee10b38d 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -74,7 +74,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
lpsn
Identifier ('Record number') of 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. This is a unique identifier , though available for only ~33 000 records.
lpsn_parent
LPSN identifier of the parent taxon
lpsn_renamed_to
LPSN identifier of the currently valid taxon
-mycobank
Identifier ('MycoBank #') of MycoBank. This is a unique identifier , though available for only ~18 000 records.
+mycobank
Identifier ('MycoBank #') of MycoBank. This is a unique identifier , though available for only ~19 000 records.
mycobank_parent
MycoBank identifier of the parent taxon
mycobank_renamed_to
MycoBank identifier of the currently valid taxon
gbif
Identifier ('taxonID') of Global Biodiversity Information Facility (GBIF). This is a unique identifier , though available for only ~49 000 records.
@@ -118,7 +118,7 @@ Public Health Information Network Vocabulary Access and Distribution System (PHI
~28 000 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 ).
~8 100 (sub)species from the kingdom of Protozoa
~1 600 (sub)species from 39 other relevant genera from the kingdom of Animalia (such as Strongyloides and Taenia )
-All ~22 000 previously accepted names of all included (sub)species (these were taxonomically renamed)
+All ~26 000 previously accepted names of all included (sub)species (these were taxonomically renamed)
The complete taxonomic tree of all included (sub)species: from kingdom to subspecies
The identifier of the parent taxons
The year and first author of the related scientific publication
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 5f55d1651..8fb617d91 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -120,8 +120,8 @@
#> [1] B_ESCHR_COLI
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` .
-#> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000
+#> taxonomic name, and the pathogenicity in humans. See `?mo_matching_score` .
+#> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000
#>
#> --------------------------------------------------------------------------------
#> "E. coli" -> Escherichia coli (B_ESCHR_COLI, 0.688 )
@@ -133,8 +133,8 @@
#> dissolvens ( 0.565 )
#>
#> 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.
+#> `print(mo_uncertainties(), n = ...)` to view more entries, or save
+#> `mo_uncertainties()` to an object.
mo_matching_score (
x = "E. coli" ,
diff --git a/reference/mo_property.html b/reference/mo_property.html
index b7fa7c430..4eeb89af9 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -367,9 +367,9 @@
mo_mycobank ( "Candida krusei" )
#> [1] "337013"
mo_mycobank ( "Candida krusei" , keep_synonyms = TRUE )
-#> Warning: Function `as.mo()` returned one old taxonomic name. Use `as.mo(...,
-#> keep_synonyms = FALSE)` to clean the input to currently accepted taxonomic
-#> names, or set the R option `AMR_keep_synonyms` to `FALSE` . This warning
+#> Warning: Function `as.mo()` returned one old taxonomic name. Use `as.mo(...,
+#> keep_synonyms = FALSE)` to clean the input to currently accepted taxonomic
+#> names, or set the R option `AMR_keep_synonyms` to `FALSE` . This warning
#> will be shown once per session.
#> [1] "268707"
@@ -462,8 +462,8 @@
filter ( mo_is_gram_positive ( ) ) %>%
count ( mo_genus ( ) , sort = TRUE )
}
-#> ℹ Using column ' mo ' as input for `mo_is_gram_positive()`
-#> ℹ Using column ' mo ' as input for `mo_genus()`
+#> ℹ Using column ' mo ' as input for `mo_is_gram_positive()`
+#> ℹ Using column ' mo ' as input for `mo_genus()`
#> # A tibble: 18 × 2
#> `mo_genus()` n
#> <chr> <int>
@@ -490,8 +490,8 @@
filter ( mo_is_intrinsic_resistant ( ab = "vanco" ) ) %>%
count ( mo_genus ( ) , sort = TRUE )
}
-#> ℹ Using column ' mo ' as input for `mo_is_intrinsic_resistant()`
-#> ℹ Using column ' mo ' as input for `mo_genus()`
+#> ℹ Using column ' mo ' as input for `mo_is_intrinsic_resistant()`
+#> ℹ Using column ' mo ' as input for `mo_genus()`
#> # A tibble: 19 × 2
#> `mo_genus()` n
#> <chr> <int>
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 46a5573d6..c40c858ca 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/pca.html b/reference/pca.html
index 171dab92c..414d39fd4 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -152,7 +152,7 @@
#> ℹ In group 5: `order = "Lactobacillales"` `genus = "Enterococcus"`.
#> Caused by warning:
#> ! Introducing NA: only 14 results available for PEN in group: order =
-#> "Lactobacillales", genus = "Enterococcus" (`minimum` = 30).
+#> "Lactobacillales", genus = "Enterococcus" (`minimum` = 30).
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings.
#> ℹ Columns selected for PCA: " AMC ", " CAZ ", " CTX ", " CXM ", " GEN ", " SXT ",
#> " TMP ", and " TOB ". Total observations available: 7.
diff --git a/reference/plot.html b/reference/plot.html
index b073b488a..83093e0d8 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/proportion.html b/reference/proportion.html
index ca66d106c..78ee3d900 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -210,16 +210,16 @@ resistance() should be used to calculate resistance, susceptibility() should be
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
@@ -306,16 +306,16 @@ resistance() should be used to calculate resistance, susceptibility() should be
resistance
)
}
-#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
+#> ℹ For `aminoglycosides()` using columns ' GEN ' (gentamicin), ' TOB '
#> (tobramycin), ' AMK ' (amikacin), and ' KAN ' (kanamycin)
-#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
+#> ℹ For `carbapenems()` using columns ' IPM ' (imipenem) and ' MEM ' (meropenem)
#> Warning: There was 1 warning in `summarise()`.
#> ℹ In argument: `KAN = (function (..., minimum = 30, as_percent = FALSE,
#> only_all_tested = FALSE) ...`.
#> ℹ In group 3: `ward = "Outpatient"`.
#> Caused by warning:
#> ! Introducing NA: only 23 results available for KAN in group: ward =
-#> "Outpatient" (`minimum` = 30).
+#> "Outpatient" (`minimum` = 30).
#> # A tibble: 3 × 7
#> ward GEN TOB AMK KAN IPM MEM
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
diff --git a/reference/random.html b/reference/random.html
index 4617a839c..e7d396e4f 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index c22de97ed..3cc247fd8 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -9,7 +9,7 @@ NOTE: These functions are deprecated and will be removed in a future version. Us
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -179,8 +179,8 @@ NOTE: These functions are deprecated and will be removed in a future version. Us
year_min = 2010 ,
model = "binomial"
)
-#> Warning: The `resistance_predict()` function is deprecated and will be removed in a
-#> future version, see `?AMR-deprecated` . Use the tidymodels framework
+#> Warning: The `resistance_predict()` function is deprecated and will be removed in a
+#> future version, see `?AMR-deprecated` . Use the tidymodels framework
#> instead, for which we have written a basic and short introduction on our
#> website: https://amr-for-r.org/articles/AMR_with_tidymodels.html
#> This warning will be shown once per session.
diff --git a/reference/skewness.html b/reference/skewness.html
index 94c9d57ee..2fb125f9c 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html
index 136633f94..a6a63b5f4 100644
--- a/reference/top_n_microorganisms.html
+++ b/reference/top_n_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
@@ -105,14 +105,14 @@
#> # A tibble: 1,015 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
-#> 4 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
-#> 5 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
-#> 6 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
-#> 7 2002-02-03 481442 76 M ICU B_ STPHY_ CONS R NA S NA
-#> 8 2002-02-14 067927 45 F ICU B_ STPHY_ CONS R NA R NA
+#> 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 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
+#> 4 2002-01-14 462729 78 M Clinical B_ STPHY_ AURS R NA S R
+#> 5 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
+#> 6 2002-01-19 738003 71 M Clinical B_ ESCHR_ COLI R NA NA NA
+#> 7 2002-02-03 481442 76 M ICU B_ STPHY_ CONS R NA S NA
+#> 8 2002-02-14 067927 45 F ICU B_ STPHY_ CONS R NA R NA
#> 9 2002-02-14 067927 45 F ICU B_ STPHY_ CONS S NA S NA
#> 10 2002-02-21 A56499 64 M Clinical B_ STPHY_ CONS S NA S NA
#> # ℹ 1,005 more rows
@@ -130,16 +130,16 @@
#> # A tibble: 1,742 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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 1,732 more rows
#> # 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>,
diff --git a/reference/translate.html b/reference/translate.html
index 51122a264..c0c358cc1 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.0.9033
+ 3.0.0.9034
diff --git a/search.json b/search.json
index 2fe381268..80f1af6b6 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"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://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"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://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"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://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"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 24 Jun 2024. 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 Retrieved values from the `microorganisms.codes` data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> 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`. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #> dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), 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), #> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis #> (0.363), and Kosakonia pseudosacchari (0.361) #> -------------------------------------------------------------------------------- #> \"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), #> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #> Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #> (0.533), Staphylococcus pseudintermedius (0.532), Serratia #> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #> periodonticum (0.519) #> #> 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://amr-for-r.org/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"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://amr-for-r.org/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"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: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 724 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 2,724 'phenotype-based' first isolates (90.8% 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: 2,724 × 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 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R 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 2,714 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"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:2724 Length:2724 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-07 #> Mode :character Mode :character Median :2015-06-03 #> Mean :2015-06-09 #> 3rd Qu.:2017-08-11 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :41.6% (n=1133) %S :52.6% (n=1432) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :16.4% (n=446) %I :12.2% (n=333) #> #2 :B_STPHY_AURS %R :42.0% (n=1145) %R :35.2% (n=959) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :52.5% (n=1431) %S :61.0% (n=1661) TRUE:2724 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.5% (n=176) %I : 3.0% (n=82) #> %R :41.0% (n=1117) %R :36.0% (n=981) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,724 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, S, R, 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 1854 4 3 3 3 #> GEN first #> 3 1"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"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 1321 #> 2 Staphylococcus aureus 682 #> 3 Streptococcus pneumoniae 402 #> 4 Klebsiella pneumoniae 319"},{"path":"https://amr-for-r.org/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":"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: 2,724 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2014-09-19 S #> 4 2015-12-10 S #> 5 2015-03-02 S #> 6 2018-03-31 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,724 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI R S #> 4 B_ESCHR_COLI S I #> 5 B_ESCHR_COLI S S #> 6 B_STPHY_AURS 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 2,714 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,724 × 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 R S S S #> 4 B_ESCHR_COLI S I S S #> 5 B_ESCHR_COLI S S S S #> 6 B_STPHY_AURS R S R 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 2,714 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: 981 × 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 971 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 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 452 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: 462 × 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 452 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"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://amr-for-r.org/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"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, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, Vietnamese. 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://amr-for-r.org/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"Conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL) combined_ab"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"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://amr-for-r.org/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":"Conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10) # Recommended threshold: ≥30 example_isolates %>% top_n_microorganisms(n = 10) %>% group_by(age_group = age_groups(age, c(25, 50, 75)), gender) %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"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(combined_ab)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"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:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4203377 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.340 #> 2 B 0.551 #> 3 C 0.370"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"Conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 <=0.0001 S #> 2 0.0160 S #> 3 >=8.0000 R #> 4 0.0320 S #> 5 0.0080 S #> 6 64.0000 R #> 7 0.0080 S #> 8 0.1250 S #> 9 0.0320 S #> 10 0.0002 S #> # ℹ 90 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"Conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25) ggplot(my_data, aes(x = group, y = MIC, colour = SIR)) + geom_jitter(width = 0.2, size = 2) + geom_boxplot(fill = NA, colour = \"grey40\") + scale_y_mic() + scale_colour_sir() + labs(title = \"MIC Distribution and SIR Interpretation\", x = \"Sample Groups\", y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-1-using-antimicrobial-selectors","dir":"Articles","previous_headings":"","what":"Example 1: Using Antimicrobial Selectors","title":"AMR with tidymodels","text":"leveraging power tidymodels AMR package, ’ll build reproducible machine learning workflow predict Gramstain microorganism two important antibiotic classes: aminoglycosides beta-lactams.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Prepare data: Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) # Your data could look like this: 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 , … # Select relevant columns for prediction data <- example_isolates %>% # select AB results dynamically select(mo, aminoglycosides(), betalactams()) %>% # replace NAs with NI (not-interpretable) mutate(across(where(is.sir), ~replace_na(.x, \"NI\")), # make factors of SIR columns across(where(is.sir), as.integer), # get Gramstain of microorganisms mo = as.factor(mo_gramstain(mo))) %>% # drop NAs - the ones without a Gramstain (fungi, etc.) drop_na() #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `betalactams()` using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antimicrobial selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>% step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `betalactams()` using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #> #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>% set_engine(\"glm\") # Use the Generalised Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organises entire modelling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>% add_recipe(resistance_recipe) %>% # Add the preprocessing recipe add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #> #> • step_corr() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antimicrobial selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram stain 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split) # Testing set # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>% fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>% predict(testing_data) # Generate predictions probabilities <- fitted_workflow %>% predict(testing_data, type = \"prob\") # Generate probabilities predictions <- predictions %>% bind_cols(probabilities) %>% bind_cols(testing_data) # Combine with true labels predictions #> # A tibble: 394 × 24 #> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB #> #> 1 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5 #> 2 Gram-positive 3.17e- 8 1.000e+ 0 Gram-p… 5 1 #> 3 Gram-negative 9.99e- 1 1.42 e- 3 Gram-n… 5 5 #> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5 #> 5 Gram-negative 9.46e- 1 5.42 e- 2 Gram-n… 5 5 #> 6 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5 #> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5 #> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4 #> 9 Gram-negative 1 e+ 0 2.22 e-16 Gram-n… 1 1 #> 10 Gram-positive 6.05e-11 1.000e+ 0 Gram-p… 4 4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK , KAN , PEN , OXA , FLC , #> # AMX , AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX