-| 2025-09-15 |
+2025-09-18 |
efgh |
Escherichia coli |
R |
@@ -263,18 +263,18 @@ user input can be used:
Now we can thus clean our data:
our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE)
-#> ℹ Retrieved values from the `microorganisms.codes` data set for "ESCCOL",
+#> 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.
Apparently, there was some uncertainty about the translation to
taxonomic codes. Let’s check this:
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)
@@ -289,9 +289,9 @@ taxonomic codes. Let’s check this:
#> 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)
+#> (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
@@ -303,16 +303,16 @@ taxonomic codes. Let’s check this:
#> --------------------------------------------------------------------------------
#> "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)
+#> 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.
That’s all good.
@@ -342,14 +342,14 @@ dplyr:
#> # A tibble: 3,000 × 8
#> patient_id hospital date bacteria AMX AMC CIP GEN
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir>
-#> 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
+#> 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
+#> 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
@@ -400,13 +400,13 @@ the methods on the first_isolate
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 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)
+#> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a
+#> microbial ID was available)
So only 91% is suitable for resistance analysis! We can now filter on
it with the filter() function, also from the
dplyr package:
@@ -424,13 +424,13 @@ like:
#> # A tibble: 2,724 × 9
#> patient_id hospital date bacteria AMX AMC CIP GEN first
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
-#> 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
+#> 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
+#> 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
@@ -523,7 +523,7 @@ in:
our_data_1st %>%
select(date, aminoglycosides())
-#> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
+#> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
#> # A tibble: 2,724 × 2
#> date GEN
#> <date> <sir>
@@ -541,18 +541,18 @@ in:
our_data_1st %>%
select(bacteria, betalactams())
-#> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC'
+#> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC'
#> (amoxicillin/clavulanic acid)
#> # A tibble: 2,724 × 3
#> bacteria AMX AMC
#> <mo> <sir> <sir>
-#> 1 B_ESCHR_COLI R I
-#> 2 B_KLBSL_PNMN R I
-#> 3 B_ESCHR_COLI R S
+#> 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
+#> 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
@@ -563,13 +563,13 @@ in:
#> # A tibble: 2,724 × 5
#> bacteria AMX AMC CIP GEN
#> <mo> <sir> <sir> <sir> <sir>
-#> 1 B_ESCHR_COLI R I S S
-#> 2 B_KLBSL_PNMN R I S S
-#> 3 B_ESCHR_COLI R S S S
+#> 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
+#> 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
@@ -578,58 +578,58 @@ in:
# filtering using AB selectors is also possible:
our_data_1st %>%
filter(any(aminoglycosides() == "R"))
-#> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
+#> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
#> # A tibble: 981 × 9
#> patient_id hospital date bacteria AMX AMC CIP GEN first
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
-#> 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
+#> 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'
+#> 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
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
-#> 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
+#> 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'
+#> 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
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
-#> 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
+#> 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
@@ -670,16 +670,16 @@ like:
#> # 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>,
@@ -697,9 +697,9 @@ previously mentioned antibiotic class selectors:
antibiogram(example_isolates,
antibiotics = 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)
@@ -828,7 +828,7 @@ language to be Spanish using the language argument:
antibiotics = aminoglycosides(),
ab_transform = "name",
language = "es")
-#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
@@ -954,9 +954,9 @@ argument must be used. This can be any column in the data, or e.g. an
antibiogram(example_isolates,
antibiotics = 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)
@@ -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
#> # 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
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