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dr. M.S. (Matthijs) Berends 2019-02-22 01:42:35 +01:00
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README.md
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% AMR (for R)
# `AMR` (for R)
<img src="man/figures/logo.png" align="right" height="120px" />
### Not a developer? Then please visit our website [https://msberends.gitlab.io/AMR](https://msberends.gitlab.io/AMR) to read about this package.
@ -58,7 +59,7 @@ Development Test | Result | Reference
--- | :---: | ---
All functions checked on Linux | [![pipeline status](https://gitlab.com/msberends/AMR/badges/master/pipeline.svg)](https://gitlab.com/msberends/AMR/commits/master) | GitLab CI [[ref 1]](https://gitlab.com/msberends/AMR)
All functions checked on Windows | [![AppVeyor_Build](https://ci.appveyor.com/api/projects/status/gitlab/msberends/AMR?branch=master&svg=true)](https://ci.appveyor.com/project/msberends/amr-svxon) | Appveyor Systems Inc. [[ref 2]](https://ci.appveyor.com/project/msberends/amr-svxon)
Percentage of syntax lines checked | [![Code_Coverage](https://codecov.io/gl/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gl/msberends/AMR) | Codecov LLC [[ref 3]](https://codecov.io/gl/msberends/AMR)
Percentage of syntax lines checked | [![Code_Coverage](https://codecov.io/gl/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gl/msberends/AMR) [![Code_Coverage](https://gitlab.com/msberends/AMR/badges/master/coverage.svg)](https://codecov.io/gl/msberends/AMR) | Codecov LLC [[ref 3]](https://codecov.io/gl/msberends/AMR)
If so, try it with:
```r

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index.md
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@ -130,7 +130,7 @@ The `AMR` package basically does four important things:
1. It **cleanses existing data** by providing new *classes* for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use artificial intelligence to guess results that you would expect:
* Use `as.mo()` to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" or "esccol" and tries to find expected results using artificial intelligence (AI) on the included ITIS data set, consisting of almost 20,000 microorganisms. It is *very* fast, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
* Use `as.mo()` to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" or "esccol" and tries to find expected results using artificial intelligence on the included Catalogue of Life data set, consisting of almost 60,000 microorganisms. It only takes milliseconds to find results, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
* Use `as.rsi()` to transform values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
* Use `as.mic()` to cleanse your MIC values. It produces a so-called factor (called *ordinal* in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
* Use `as.atc()` to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantin", "nitro" all return the ATC code of Nitrofurantoine.
@ -141,7 +141,7 @@ The `AMR` package basically does four important things:
* Use `first_isolate()` to identify the first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute).
* You can also identify first *weighted* isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
* Use `mdro()` (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
* The [data set `microorganisms`](./reference/microorganisms.html) contains the complete taxonomic tree of almost 20,000 microorganisms (bacteria, fungi/yeasts and protozoa). Furthermore, the colloquial name and Gram stain are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus()`, `mo_family()`, `mo_gramstain()` or even `mo_phylum()`. As they use `as.mo()` internally, they also use artificial intelligence. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
* The [data set `microorganisms`](./reference/microorganisms.html) contains the complete taxonomic tree of almost 60,000 microorganisms. Furthermore, the colloquial name and Gram stain are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus()`, `mo_family()`, `mo_gramstain()` or even `mo_phylum()`. As they use `as.mo()` internally, they also use artificial intelligence. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
* The [data set `antibiotics`](./reference/antibiotics.html) contains almost 500 antimicrobial drugs with their ATC code, EARS-Net code, common LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains hundreds of trade names. Use functions like `atc_name()` and `atc_tradenames()` to look up values. The `atc_*` functions use `as.atc()` internally so they support AI to guess your expected result. For example, `atc_name("Fluclox")`, `atc_name("Floxapen")` and `atc_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
3. It **analyses the data** with convenient functions that use well-known methods.