AMR/README.md

# `AMR`
### An [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR).

[![logo_rug](man/figures/logo_rug.png)](https://www.rug.nl)[![logo_umcg](man/figures/logo_umcg.png)](https://www.umcg.nl)

This R package was created for academic research by PhD students of the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the Medical Microbiology & Infection Prevention (MMBI) department of the [University Medical Center Groningen (UMCG)](https://www.umcg.nl). See [Authors](#authors).

## Why this package?
This R package contains functions to make **microbiological, epidemiological data analysis easier**. It allows the use of some new classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).

With `AMR` you can also:
* Create frequency tables with the `freq` function
* Conduct AMR analysis with the `rsi` function, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`) to calculate the resistance percentages (and even co-resistance) of different antibiotic columns of a table
* Predict antimicrobial resistance for the nextcoming years with the `rsi_predict` function
* Apply [EUCAST rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) with the `EUCAST_rules` function
* Identify first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute) with the `first_isolate` function
* Translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`) with the `abname` function

With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).

This package contains an example data set `septic_patients`, consisting of 2000 isolates from anonymised septic patients between 2001 and 2017.

## How to get it?
This package is available on CRAN and also here on GitHub.

### From CRAN (recommended)
[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/AMR)](http://cran.r-project.org/package=AMR)
  
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> In [RStudio](http://www.rstudio.com) (recommended):
  - Click on `Tools` and then `Install Packages...`
  - Type in `AMR` and press <kbd>Install</kbd>

- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> In R directly:
  - `install.packages("AMR")`

- <img src="https://exploratory.io/favicon.ico" alt="Exploratory favicon" height="20px"> In [Exploratory.io](https://exploratory.io):
  - (Exploratory.io costs $40/month but the somewhat limited Community Plan is free for students and teachers, [click here to enroll](https://exploratory.io/plan?plan=Community))
  - Start the software and log in
  - Click on your username at the right hand side top
  - Click on `R Packages`
  - Click on the `Install` tab
  - Type in `AMR` and press <kbd>Install</kbd>
  - Once it’s installed it will show up in the `User Packages` section under the `Packages` tab.

### From GitHub (latest development version)
[![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
[![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)
[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg)](https://github.com/msberends/AMR/commits/master)
[![Code_Coverage](https://codecov.io/gh/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gh/msberends/AMR)

```r
install.packages("devtools")
devtools::install_github("msberends/AMR")
```

## How to use it?
```r
# Call it with:
library(AMR)

# For a list of functions:
help(package = "AMR")
```
### Overwrite/force resistance based on EUCAST rules
This is also called *interpretive reading*.
```r
before <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
                                "ENCFAE",  # Enterococcus faecalis
                                "ESCCOL",  # Escherichia coli
                                "KLEPNE",  # Klebsiella pneumoniae
                                "PSEAER"), # Pseudomonas aeruginosa
                     vanc = "-",           # Vancomycin
                     amox = "-",           # Amoxicillin
                     coli = "-",           # Colistin
                     cfta = "-",           # Ceftazidime
                     cfur = "-",           # Cefuroxime
                     stringsAsFactors = FALSE)
before
#   bactid vanc amox coli cfta cfur
# 1 STAAUR    -    -    -    -    -
# 2 ENCFAE    -    -    -    -    -
# 3 ESCCOL    -    -    -    -    -
# 4 KLEPNE    -    -    -    -    -
# 5 PSEAER    -    -    -    -    -

# Now apply those rules; just need a column with bacteria ID's and antibiotic results:
after <- EUCAST_rules(before)
after
#   bactid vanc amox coli cfta cfur
# 1 STAAUR    -    -    R    R    -
# 2 ENCFAE    -    -    R    R    R
# 3 ESCCOL    R    -    -    -    -
# 4 KLEPNE    R    R    -    -    -
# 5 PSEAER    R    R    -    -    R
```

### Frequency tables
Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`).
```r
## Factors sort on item by default:

freq(septic_patients$hospital_id)
# Class:     factor
# Length:    2000 (of which NA: 0 = 0.0%)
# Unique:    5
# 
# Item    Count   Percent   Cum. Count   Cum. Percent   (Factor Level)
# -----  ------  --------  -----------  -------------  ---------------
# A         233     11.7%          233          11.7%                1
# B         583     29.1%          816          40.8%                2
# C         221     11.1%         1037          51.8%                3
# D         650     32.5%         1687          84.4%                4
# E         313     15.7%         2000         100.0%                5


## This can be changed with the `sort.count` parameter:

freq(septic_patients$hospital_id, sort.count = TRUE)
# Class:     factor
# Length:    2000 (of which NA: 0 = 0.0%)
# Unique:    5
# 
# Item    Count   Percent   Cum. Count   Cum. Percent   (Factor Level)
# -----  ------  --------  -----------  -------------  ---------------
# D         650     32.5%          650          32.5%                4
# B         583     29.1%         1233          61.7%                2
# E         313     15.7%         1546          77.3%                5
# A         233     11.7%         1779          88.9%                1
# C         221     11.1%         2000         100.0%                3


## Other types, like numbers or dates, sort on count by default:

> freq(septic_patients$date)
# Class:     Date
# Length:    2000 (of which NA: 0 = 0.0%)
# Unique:    1662
# 
# Oldest:    2 January 2001
# Newest:    18 October 2017 (+6133)
# 
# Item          Count   Percent   Cum. Count   Cum. Percent
# -----------  ------  --------  -----------  -------------
# 2008-12-24        5      0.2%            5           0.2%
# 2010-12-10        4      0.2%            9           0.4%
# 2011-03-03        4      0.2%           13           0.6%
# 2013-06-24        4      0.2%           17           0.8%
# 2017-09-01        4      0.2%           21           1.1%
# 2002-09-02        3      0.2%           24           1.2%
# 2003-10-14        3      0.2%           27           1.4%
# 2004-06-25        3      0.2%           30           1.5%
# 2004-06-27        3      0.2%           33           1.7%
# 2004-10-29        3      0.2%           36           1.8%
# 2005-09-27        3      0.2%           39           2.0%
# 2006-08-01        3      0.2%           42           2.1%
# 2006-10-10        3      0.2%           45           2.2%
# 2007-11-16        3      0.2%           48           2.4%
# 2008-03-09        3      0.2%           51           2.5%
# ... and 1647 more (n = 1949; 97.5%). Use `nmax` to show more rows.


## For numeric values, some extra descriptive statistics will be calculated:

> freq(runif(n = 10, min = 1, max = 5))
# Class:     numeric
# Length:    10 (of which NA: 0 = 0.0%)
# Unique:    10
#   
# Mean:      3
# Std. dev.: 0.93 (CV: 0.31)
# Five-Num:  1.1  |  2.3  |  3.1  |  3.8  |  4.0 (CQV: 0.25)
# Outliers:  0
# 
#      Item   Count   Percent   Cum. Count   Cum. Percent
# ---------  ------  --------  -----------  -------------
#  1.132033       1     10.0%            1          10.0%
#  2.226903       1     10.0%            2          20.0%
#  2.280779       1     10.0%            3          30.0%
#  2.640898       1     10.0%            4          40.0%
#  2.913462       1     10.0%            5          50.0%
#  3.364201       1     10.0%            6          60.0%
#  3.771975       1     10.0%            7          70.0%
#  3.802861       1     10.0%            8          80.0%
#  3.803547       1     10.0%            9          90.0%
#  3.985691       1     10.0%           10         100.0%
# 
# Warning message:
# All observations are unique. 
```
Learn more about this function with:
```r
?freq
```

### New classes
This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`). 
Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.

```r
# Transform values to new classes
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
```
These functions also try to coerce valid values.

Quick overviews when just printing objects:
```r
mic_data
# Class 'mic': 7 isolates
# 
# <NA>  0
# 
# <=0.128       1       8      16    >=32
#       1       1       2       2       1

rsi_data
# Class 'rsi': 880 isolates
# 
# <NA>:       0 
# Sum of S:   474 
# Sum of IR:  406 
# - Sum of R: 370 
# - Sum of I: 36 
# 
#   %S  %IR   %I   %R 
# 53.9 46.1  4.1 42.0 
```

A plot of `rsi_data`:
```r
plot(rsi_data)
```

![example1](man/figures/rsi_example.png)

A plot of `mic_data` (defaults to bar plot):
```r
plot(mic_data)
```

![example2](man/figures/mic_example.png)

Other epidemiological functions:

```r
# Determine key antibiotic based on bacteria ID
key_antibiotics(...)

# Selection of first isolates of any patient
first_isolate(...)

# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
rsi(...)
# Predict resistance levels of antibiotics
rsi_predict(...)

# Get name of antibiotic by ATC code
abname(...)
abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
```

### Databases included in package
Datasets to work with antibiotics and bacteria properties.
```r
# Dataset with 2000 random blood culture isolates from anonymised
# septic patients between 2001 and 2017 in 5 Dutch hospitals
septic_patients   # A tibble: 4,000 x 47

# Dataset with ATC antibiotics codes, official names, trade names 
# and DDD's (oral and parenteral)
antibiotics       # A tibble: 420 x 18

# Dataset with bacteria codes and properties like gram stain and 
# aerobic/anaerobic
microorganisms    # A tibble: 2,453 x 12
```


## Authors

  - [Berends MS](https://github.com/msberends)<sup>1,2</sup>, PhD Student
  - [Luz CF](https://github.com/ceefluz)<sup>1</sup>, PhD Student
  - [Hassing EEA](https://github.com/erwinhassing)<sup>2</sup>, Data Analyst (contributor)
  
<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

<sup>2</sup> Certe Medical Diagnostics & Advice, Groningen, the Netherlands

[![logo_umcg](man/figures/logo_umcg.png)](https://www.umcg.nl)[![logo_certe](man/figures/logo_certe.png)](https://www.certe.nl)[![logo_eh1h](man/figures/logo_eh1h.png)](http://www.eurhealth-1health.eu)[![logo_interreg](man/figures/logo_interreg.png)](http://www.eurhealth-1health.eu)

## Copyright
[![License](https://img.shields.io/github/license/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/blob/master/LICENSE)

This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:

- May be used for commercial purposes

- May be used for private purposes

- May **not** be used for patent purposes

- May be modified, although:

  - Modifications **must** be released under the same license when distributing the package
  - Changes made to the code **must** be documented

- May be distributed, although:

  - Source code **must** be made available when the package is distributed
  - A copy of the license and copyright notice **must** be included with the package.

- Comes with a LIMITATION of liability

- Comes with NO warranty