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93 lines
3.5 KiB
Markdown
93 lines
3.5 KiB
Markdown
# Data Set with 2 000 Example Isolates
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A data set containing 2 000 microbial isolates with their full
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antibiograms. This data set contains randomised fictitious data, but
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reflects reality and can be used to practise AMR data analysis. For
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examples, please read [the tutorial on our
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website](https://amr-for-r.org/articles/AMR.html).
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## Usage
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``` r
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example_isolates
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```
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## Format
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A [tibble](https://tibble.tidyverse.org/reference/tibble.html) with 2
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000 observations and 46 variables:
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- `date`
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Date of receipt at the laboratory
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- `patient`
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ID of the patient
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- `age`
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Age of the patient
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- `gender`
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Gender of the patient, either "F" or "M"
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- `ward`
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Ward type where the patient was admitted, either "Clinical", "ICU", or
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"Outpatient"
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- `mo`
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ID of microorganism created with
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[`as.mo()`](https://amr-for-r.org/reference/as.mo.md), see also the
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[microorganisms](https://amr-for-r.org/reference/microorganisms.md)
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data set
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- `PEN:RIF`
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40 different antimicrobials with class
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[`sir`](https://amr-for-r.org/reference/as.sir.md) (see
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[`as.sir()`](https://amr-for-r.org/reference/as.sir.md)); these column
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names occur in the
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[antimicrobials](https://amr-for-r.org/reference/antimicrobials.md)
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data set and can be translated with
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[`set_ab_names()`](https://amr-for-r.org/reference/ab_property.md) or
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[`ab_name()`](https://amr-for-r.org/reference/ab_property.md)
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## Download Our Reference Data
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All reference data sets in the AMR package - including information on
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microorganisms, antimicrobials, and clinical breakpoints - are freely
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available for download in multiple formats: R, MS Excel, Apache Feather,
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Apache Parquet, SPSS, and Stata.
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For maximum compatibility, we also provide machine-readable,
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tab-separated plain text files suitable for use in any software,
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including laboratory information systems.
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Visit [our website for direct download
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links](https://amr-for-r.org/articles/datasets.html), or explore the
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actual files in [our GitHub
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repository](https://github.com/msberends/AMR/tree/main/data-raw/datasets).
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## Examples
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``` r
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example_isolates
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#> # A tibble: 2,000 × 46
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#> date patient age gender ward mo PEN OXA FLC AMX
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#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
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#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
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#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
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#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
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#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
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#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
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#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
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#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
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#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
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#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA
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#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA
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#> # ℹ 1,990 more rows
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#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
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#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
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#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
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#> # FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
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#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
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#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
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```
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