-| 2025-11-24 |
+2025-12-15 |
efgh |
Escherichia coli |
R |
@@ -405,7 +405,7 @@ the methods on the first_isolate
#> 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
+#> => Found 2,730 'phenotype-based' first isolates (91.0% 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
@@ -417,11 +417,11 @@ it with the
our_data_1st <- our_data %>%
filter_first_isolate()
-So we end up with 2 724 isolates for analysis. Now our data looks
+
So we end up with 2 730 isolates for analysis. Now our data looks
like:
our_data_1st
-#> # A tibble: 2,724 × 9
+#> # A tibble: 2,730 × 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
@@ -434,7 +434,7 @@ like:
#> 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
Time for the analysis.
@@ -447,29 +447,29 @@ impression, as it comes with support for the new mo and
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
+#> Length:2730 Length:2730 Min. :2011-01-01
+#> Class :character Class :character 1st Qu.:2013-04-06
+#> Mode :character Mode :character Median :2015-06-04
#> Mean :2015-06-09
-#> 3rd Qu.:2017-08-11
+#> 3rd Qu.:2017-08-14
#> Max. :2019-12-27
#> bacteria AMX AMC
#> Class :mo Class:sir Class:sir
-#> <NA> :0 %S :41.6% (n=1133) %S :52.6% (n=1432)
+#> <NA> :0 %S :40.1% (n=1071) %S :51.1% (n=1354)
#> 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)
+#> #1 :B_ESCHR_COLI %I :17.0% (n=453) %I :12.7% (n=335)
+#> #2 :B_STPHY_AURS %R :42.9% (n=1147) %R :36.2% (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
+#> %S :52.2% (n=1426) %S :60.7% (n=1656) TRUE:2730
#> %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)
+#> %I : 6.5% (n=178) %I : 3.0% (n=83)
+#> %R :41.2% (n=1126) %R :36.3% (n=991)
#> %NI : 0.0% (n=0) %NI : 0.0% (n=0)
glimpse(our_data_1st)
-#> Rows: 2,724
+#> Rows: 2,730
#> Columns: 9
#> $ patient_id <chr> "J3", "R7", "P3", "P10", "B7", "W3", "M3", "J3", "G6", "P4"…
#> $ hospital <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…
@@ -484,7 +484,7 @@ impression, as it comes with support for the new mo and
# 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
+#> 260 3 1854 4 4 4 3
#> GEN first
#> 3 1
@@ -509,9 +509,9 @@ microorganisms:
#> # A tibble: 4 × 2
#> `mo_name(bacteria)` n
#> <chr> <int>
-#> 1 Escherichia coli 1321
-#> 2 Staphylococcus aureus 682
-#> 3 Streptococcus pneumoniae 402
+#> 1 Escherichia coli 1326
+#> 2 Staphylococcus aureus 684
+#> 3 Streptococcus pneumoniae 401
#> 4 Klebsiella pneumoniae 319
@@ -524,7 +524,7 @@ in:
our_data_1st %>%
select(date, aminoglycosides())
#> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
-#> # A tibble: 2,724 × 2
+#> # A tibble: 2,730 × 2
#> date GEN
#> <date> <sir>
#> 1 2012-11-21 S
@@ -537,13 +537,13 @@ in:
#> 8 2019-06-19 S
#> 9 2015-04-27 S
#> 10 2011-06-21 S
-#> # ℹ 2,714 more rows
+#> # ℹ 2,720 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
+#> # A tibble: 2,730 × 3
#> bacteria AMX AMC
#> <mo> <sir> <sir>
#> 1 B_ESCHR_COLI R I
@@ -556,11 +556,11 @@ in:
#> 8 B_ESCHR_COLI S S
#> 9 B_STPHY_AURS S S
#> 10 B_ESCHR_COLI S S
-#> # ℹ 2,714 more rows
+#> # ℹ 2,720 more rows
our_data_1st %>%
select(bacteria, where(is.sir))
-#> # A tibble: 2,724 × 5
+#> # A tibble: 2,730 × 5
#> bacteria AMX AMC CIP GEN
#> <mo> <sir> <sir> <sir> <sir>
#> 1 B_ESCHR_COLI R I S S
@@ -573,13 +573,13 @@ in:
#> 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
+#> # ℹ 2,720 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
+#> # A tibble: 991 × 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
@@ -592,13 +592,13 @@ in:
#> 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
+#> # ℹ 981 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
+#> # A tibble: 461 × 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
@@ -611,13 +611,13 @@ in:
#> 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
+#> # ℹ 451 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
+#> # A tibble: 461 × 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
@@ -630,7 +630,7 @@ in:
#> 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
Generate antibiograms
@@ -1283,7 +1283,7 @@ I (proportion_SI(), equa
own:
+#> [1] 0.4294272
Or can be used in conjunction with group_by() and
summarise(), both from the dplyr package:
@@ -1293,8 +1293,8 @@ own:
#> # A tibble: 3 × 2
#> hospital amoxicillin
#> <chr> <dbl>
-#> 1 A 0.340
-#> 2 B 0.551
+#> 1 A 0.341
+#> 2 B 0.586
#> 3 C 0.370
diff --git a/articles/AMR.md b/articles/AMR.md
index 516ecd23f..041b17c84 100644
--- a/articles/AMR.md
+++ b/articles/AMR.md
@@ -3,7 +3,7 @@
**Note:** values on this page will change with every website update
since they are based on randomly created values and the page was written
in [R Markdown](https://rmarkdown.rstudio.com/). However, the
-methodology remains unchanged. This page was generated on 24 November
+methodology remains unchanged. This page was generated on 15 December
2025.
## Introduction
@@ -52,9 +52,9 @@ structure of your data generally look like this:
| date | patient_id | mo | AMX | CIP |
|:----------:|:----------:|:----------------:|:---:|:---:|
-| 2025-11-24 | abcd | Escherichia coli | S | S |
-| 2025-11-24 | abcd | Escherichia coli | S | R |
-| 2025-11-24 | efgh | Escherichia coli | R | S |
+| 2025-12-15 | abcd | Escherichia coli | S | S |
+| 2025-12-15 | abcd | Escherichia coli | S | R |
+| 2025-12-15 | efgh | Escherichia coli | R | S |
### Needed R packages
@@ -322,7 +322,7 @@ our_data <- our_data %>%
#> 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
+#> => Found 2,730 'phenotype-based' first isolates (91.0% of total where a
#> microbial ID was available)
```
@@ -342,11 +342,11 @@ our_data_1st <- our_data %>%
filter_first_isolate()
```
-So we end up with 2 724 isolates for analysis. Now our data looks like:
+So we end up with 2 730 isolates for analysis. Now our data looks like:
``` r
our_data_1st
-#> # A tibble: 2,724 × 9
+#> # A tibble: 2,730 × 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
@@ -359,7 +359,7 @@ our_data_1st
#> 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
+#> # ℹ 2,720 more rows
```
Time for the analysis.
@@ -373,29 +373,29 @@ and `sir` classes that we now have in our data set:
``` r
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
+#> Length:2730 Length:2730 Min. :2011-01-01
+#> Class :character Class :character 1st Qu.:2013-04-06
+#> Mode :character Mode :character Median :2015-06-04
#> Mean :2015-06-09
-#> 3rd Qu.:2017-08-11
+#> 3rd Qu.:2017-08-14
#> Max. :2019-12-27
#> bacteria AMX AMC
#> Class :mo Class:sir Class:sir
-#> :0 %S :41.6% (n=1133) %S :52.6% (n=1432)
+#> :0 %S :40.1% (n=1071) %S :51.1% (n=1354)
#> 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)
+#> #1 :B_ESCHR_COLI %I :17.0% (n=453) %I :12.7% (n=335)
+#> #2 :B_STPHY_AURS %R :42.9% (n=1147) %R :36.2% (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
+#> %S :52.2% (n=1426) %S :60.7% (n=1656) TRUE:2730
#> %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)
+#> %I : 6.5% (n=178) %I : 3.0% (n=83)
+#> %R :41.2% (n=1126) %R :36.3% (n=991)
#> %NI : 0.0% (n=0) %NI : 0.0% (n=0)
glimpse(our_data_1st)
-#> Rows: 2,724
+#> Rows: 2,730
#> 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",…
@@ -410,7 +410,7 @@ glimpse(our_data_1st)
# 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
+#> 260 3 1854 4 4 4 3
#> GEN first
#> 3 1
```
@@ -437,9 +437,9 @@ our_data_1st %>%
#> # A tibble: 4 × 2
#> `mo_name(bacteria)` n
#>
-#> 1 Escherichia coli 1321
-#> 2 Staphylococcus aureus 682
-#> 3 Streptococcus pneumoniae 402
+#> 1 Escherichia coli 1326
+#> 2 Staphylococcus aureus 684
+#> 3 Streptococcus pneumoniae 401
#> 4 Klebsiella pneumoniae 319
```
@@ -453,7 +453,7 @@ in:
our_data_1st %>%
select(date, aminoglycosides())
#> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
-#> # A tibble: 2,724 × 2
+#> # A tibble: 2,730 × 2
#> date GEN
#>
#> 1 2012-11-21 S
@@ -466,13 +466,13 @@ our_data_1st %>%
#> 8 2019-06-19 S
#> 9 2015-04-27 S
#> 10 2011-06-21 S
-#> # ℹ 2,714 more rows
+#> # ℹ 2,720 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
+#> # A tibble: 2,730 × 3
#> bacteria AMX AMC
#>
#> 1 B_ESCHR_COLI R I
@@ -485,11 +485,11 @@ our_data_1st %>%
#> 8 B_ESCHR_COLI S S
#> 9 B_STPHY_AURS S S
#> 10 B_ESCHR_COLI S S
-#> # ℹ 2,714 more rows
+#> # ℹ 2,720 more rows
our_data_1st %>%
select(bacteria, where(is.sir))
-#> # A tibble: 2,724 × 5
+#> # A tibble: 2,730 × 5
#> bacteria AMX AMC CIP GEN
#>
#> 1 B_ESCHR_COLI R I S S
@@ -502,13 +502,13 @@ our_data_1st %>%
#> 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
+#> # ℹ 2,720 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
+#> # A tibble: 991 × 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
@@ -521,13 +521,13 @@ our_data_1st %>%
#> 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
+#> # ℹ 981 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
+#> # A tibble: 461 × 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
@@ -540,13 +540,13 @@ our_data_1st %>%
#> 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
+#> # ℹ 451 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
+#> # A tibble: 461 × 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
@@ -559,7 +559,7 @@ our_data_1st[all(betalactams() == "R"), ]
#> 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
+#> # ℹ 451 more rows
```
### Generate antibiograms
@@ -843,7 +843,7 @@ These functions can be used on their own:
``` r
our_data_1st %>% resistance(AMX)
-#> [1] 0.4203377
+#> [1] 0.4294272
```
Or can be used in conjunction with
@@ -858,8 +858,8 @@ our_data_1st %>%
#> # A tibble: 3 × 2
#> hospital amoxicillin
#>
-#> 1 A 0.340
-#> 2 B 0.551
+#> 1 A 0.341
+#> 2 B 0.586
#> 3 C 0.370
```
diff --git a/articles/AMR_for_Python.html b/articles/AMR_for_Python.html
index 65d08935e..d19f090a3 100644
--- a/articles/AMR_for_Python.html
+++ b/articles/AMR_for_Python.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9003
+ 3.0.1.9004