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# Data Set with 2 000 Example Isolates
A data set containing 2 000 microbial isolates with their full
antibiograms. This data set contains randomised fictitious data, but
reflects reality and can be used to practise AMR data analysis. For
examples, please read [the tutorial on our
website](https://amr-for-r.org/articles/AMR.html).
## Usage
``` r
example_isolates
```
## Format
A [tibble](https://tibble.tidyverse.org/reference/tibble.html) with 2
000 observations and 46 variables:
- `date`
Date of receipt at the laboratory
- `patient`
ID of the patient
- `age`
Age of the patient
- `gender`
Gender of the patient, either "F" or "M"
- `ward`
Ward type where the patient was admitted, either "Clinical", "ICU", or
"Outpatient"
- `mo`
ID of microorganism created with
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md), see also the
[microorganisms](https://amr-for-r.org/reference/microorganisms.md)
data set
- `PEN:RIF`
40 different antimicrobials with class
[`sir`](https://amr-for-r.org/reference/as.sir.md) (see
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md)); these column
names occur in the
[antimicrobials](https://amr-for-r.org/reference/antimicrobials.md)
data set and can be translated with
[`set_ab_names()`](https://amr-for-r.org/reference/ab_property.md) or
[`ab_name()`](https://amr-for-r.org/reference/ab_property.md)
## Download Our Reference Data
All reference data sets in the AMR package - including information on
microorganisms, antimicrobials, and clinical breakpoints - are freely
available for download in multiple formats: R, MS Excel, Apache Feather,
Apache Parquet, SPSS, and Stata.
For maximum compatibility, we also provide machine-readable,
tab-separated plain text files suitable for use in any software,
including laboratory information systems.
Visit [our website for direct download
links](https://amr-for-r.org/articles/datasets.html), or explore the
actual files in [our GitHub
repository](https://github.com/msberends/AMR/tree/main/data-raw/datasets).
## Examples
``` r
example_isolates
#> # 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
#> # 1,990 more rows
#> # 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>,
#> # FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
```