P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2026-03-30 06:55:56 +02:00
Code Releases Activity

Built site for AMR@3.0.1.9040: 9c95aa4

Browse Source
This commit is contained in:
github-actions
2026-03-24 12:34:17 +00:00
parent 330f1a9dfe
commit d55d073ae9
109 changed files with 762 additions and 777 deletions
Download Patch File Download Diff File Expand all files Collapse all files

56
articles/AMR.md
View File

@@ -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 22 March 2026.
methodology remains unchanged. This page was generated on 24 March 2026.
## Introduction
@@ -51,9 +51,9 @@ structure of your data generally look like this:
| date | patient_id | mo | AMX | CIP |
|:----------:|:----------:|:----------------:|:---:|:---:|
| 2026-03-22 | abcd | Escherichia coli | S | S |
| 2026-03-22 | abcd | Escherichia coli | S | R |
| 2026-03-22 | efgh | Escherichia coli | R | S |
| 2026-03-24 | abcd | Escherichia coli | S | S |
| 2026-03-24 | abcd | Escherichia coli | S | R |
| 2026-03-24 | efgh | Escherichia coli | R | S |
### Needed R packages
@@ -169,8 +169,8 @@ 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()` to review these uncertainties, or use
#> `add_custom_microorganisms()` to add custom entries.
```
Apparently, there was some uncertainty about the translation to
@@ -179,7 +179,7 @@ taxonomic codes. Let’s check this:
``` r
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()`.
#> 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)
@@ -212,8 +212,8 @@ mo_uncertainties()
#> Streptococcus gallolyticus pasteurianus (0.526), Salmonella Portanigra (0.524),
#> and Streptococcus periodonticum (0.519)
#> ℹ 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.
```
That’s all good.
@@ -311,11 +311,11 @@ The outcome of the function can easily be added to our data:
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 Column 'first' is SIR eligible (despite only having empty values), since it
#> ℹ Using column bacteria as input for `col_mo`.
#> ℹ Column first is SIR eligible (despite only having empty values), since it
#> seems to be cefozopran (ZOP)
#> ℹ Using column 'date' as input for `col_date`.
#> ℹ Using column 'patient_id' as input for `col_patient_id`.
#> 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)
@@ -447,7 +447,7 @@ in:
``` r
our_data_1st %>%
select(date, aminoglycosides())
#> ℹ For `?aminoglycosides()` using column GEN
#> ℹ For `aminoglycosides()` using column GEN
#> (gentamicin)
#> # A tibble: 2,724 × 2
#> date GEN
@@ -466,7 +466,7 @@ our_data_1st %>%
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
@@ -503,7 +503,7 @@ our_data_1st %>%
# filtering using AB selectors is also possible:
our_data_1st %>%
filter(any(aminoglycosides() == "R"))
#> ℹ For `?aminoglycosides()` using column GEN
#> ℹ For `aminoglycosides()` using column GEN
#> (gentamicin)
#> # A tibble: 981 × 9
#> patient_id hospital date bacteria AMX AMC CIP GEN first
@@ -522,7 +522,7 @@ our_data_1st %>%
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
@@ -541,7 +541,7 @@ our_data_1st %>%
# 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
@@ -624,9 +624,9 @@ antibiotic class selectors:
``` r
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)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
```
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
@@ -663,8 +663,8 @@ antibiogram(example_isolates,
antibiotics = aminoglycosides(),
ab_transform = "name",
language = "es")
#> ℹ For `?aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin),
#> AMK (amikacin), and KAN (kanamycin)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
```
| Patógeno | Amikacina | Gentamicina | Kanamicina | Tobramicina |
@@ -707,9 +707,9 @@ on certain columns:
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)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
```
| Syndromic Group | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
@@ -840,9 +840,9 @@ These functions can be used on their own:
``` r
our_data_1st %>% resistance(AMX)
#> ℹ `?resistance()` assumes the EUCAST guideline and thus considers the 'I'
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
#> category susceptible. Set the `guideline` argument or the `AMR_guideline`
#> option to either "CLSI" or "EUCAST", see AMR-options.
#> option to either "CLSI" or "EUCAST", see `?AMR-options`.
#> ℹ This message will be shown once per session.
#> [1] 0.4203377
```