Function reference • AMR (for R)

Preparing data: microorganisms These functions are meant to get taxonomically valid properties of microorganisms from any input. Use `mo_source()` to teach this package how to translate your own codes to valid microorganism codes.
`as.mo()` `is.mo()` `mo_failures()` `mo_uncertainties()` `mo_renamed()`	Transform Input to a Microorganism Code
`mo_name()` `mo_fullname()` `mo_shortname()` `mo_subspecies()` `mo_species()` `mo_genus()` `mo_family()` `mo_order()` `mo_class()` `mo_phylum()` `mo_kingdom()` `mo_domain()` `mo_type()` `mo_gramstain()` `mo_is_gram_negative()` `mo_is_gram_positive()` `mo_is_yeast()` `mo_is_intrinsic_resistant()` `mo_snomed()` `mo_ref()` `mo_authors()` `mo_year()` `mo_lpsn()` `mo_rank()` `mo_taxonomy()` `mo_synonyms()` `mo_info()` `mo_url()` `mo_property()`	Get Properties of a Microorganism
`set_mo_source()` `get_mo_source()`	User-Defined Reference Data Set for Microorganisms
Preparing data: antibiotics Use these functions to get valid properties of antibiotics from any input or to clean your input. You can even retrieve drug names and doses from clinical text records, using `ab_from_text()`.
`as.ab()` `is.ab()`	Transform Input to an Antibiotic ID
`ab_name()` `ab_cid()` `ab_synonyms()` `ab_tradenames()` `ab_group()` `ab_atc()` `ab_atc_group1()` `ab_atc_group2()` `ab_loinc()` `ab_ddd()` `ab_ddd_units()` `ab_info()` `ab_url()` `ab_property()` `set_ab_names()`	Get Properties of an Antibiotic
`ab_from_text()`	Retrieve Antimicrobial Drug Names and Doses from Clinical Text
`atc_online_property()` `atc_online_groups()` `atc_online_ddd()` `atc_online_ddd_units()`	Get ATC Properties from WHOCC Website
Preparing data: antimicrobial resistance With `as.mic()` and `as.disk()` you can transform your raw input to valid MIC or disk diffusion values. Use `as.rsi()` for cleaning raw data to let it only contain “R”, “I” and “S”, or to interpret MIC or disk diffusion values as R/SI based on the lastest EUCAST and CLSI guidelines. Afterwards, you can extend antibiotic interpretations by applying EUCAST rules with `eucast_rules()`.
`as.rsi()` `NA_rsi_` `is.rsi()` `is.rsi.eligible()`	Interpret MIC and Disk Values, or Clean Raw R/SI Data
`as.mic()` `NA_mic_` `is.mic()` `droplevels(<mic>)`	Transform Input to Minimum Inhibitory Concentrations (MIC)
`as.disk()` `NA_disk_` `is.disk()`	Transform Input to Disk Diffusion Diameters
`eucast_rules()` `eucast_dosage()`	Apply EUCAST Rules
`custom_eucast_rules()`	Define Custom EUCAST Rules
Analysing data: antimicrobial resistance Use these function for the analysis part. You can use `susceptibility()` or `resistance()` on any antibiotic column. Be sure to first select the isolates that are appropiate for analysis, by using `first_isolate()` or `is_new_episode()`. You can also filter your data on certain resistance in certain antibiotic classes (`carbapenems()`, `aminoglycosides()`), or determine multi-drug resistant microorganisms (MDRO, `mdro()`).
`resistance()` `susceptibility()` `proportion_R()` `proportion_IR()` `proportion_I()` `proportion_SI()` `proportion_S()` `proportion_df()` `rsi_df()`	Calculate Microbial Resistance
`count_resistant()` `count_susceptible()` `count_R()` `count_IR()` `count_I()` `count_SI()` `count_S()` `count_all()` `n_rsi()` `count_df()`	Count Available Isolates
`get_episode()` `is_new_episode()`	Determine (New) Episodes for Patients
`first_isolate()` `filter_first_isolate()`	Determine First Isolates
`key_antimicrobials()` `all_antimicrobials()` `antimicrobials_equal()`	(Key) Antimicrobials for First Weighted Isolates
`mdro()` `custom_mdro_guideline()` `brmo()` `mrgn()` `mdr_tb()` `mdr_cmi2012()` `eucast_exceptional_phenotypes()`	Determine Multidrug-Resistant Organisms (MDRO)
`plot(<mic>)` `autoplot(<mic>)` `fortify(<mic>)` `plot(<disk>)` `autoplot(<disk>)` `fortify(<disk>)` `plot(<rsi>)` `autoplot(<rsi>)` `fortify(<rsi>)`	Plotting for Classes `rsi`, `mic` and `disk`
`ggplot_rsi()` `geom_rsi()` `facet_rsi()` `scale_y_percent()` `scale_rsi_colours()` `theme_rsi()` `labels_rsi_count()`	AMR Plots with `ggplot2`
`bug_drug_combinations()` `format(<bug_drug_combinations>)`	Determine Bug-Drug Combinations
`ab_class()` `ab_selector()` `aminoglycosides()` `aminopenicillins()` `antifungals()` `antimycobacterials()` `betalactams()` `carbapenems()` `cephalosporins()` `cephalosporins_1st()` `cephalosporins_2nd()` `cephalosporins_3rd()` `cephalosporins_4th()` `cephalosporins_5th()` `fluoroquinolones()` `glycopeptides()` `lincosamides()` `lipoglycopeptides()` `macrolides()` `oxazolidinones()` `penicillins()` `polymyxins()` `streptogramins()` `quinolones()` `tetracyclines()` `trimethoprims()` `ureidopenicillins()` `administrable_per_os()` `administrable_iv()` `not_intrinsic_resistant()`	Antibiotic Selectors
`resistance_predict()` `rsi_predict()` `plot(<resistance_predict>)` `ggplot_rsi_predict()` `autoplot(<resistance_predict>)`	Predict Antimicrobial Resistance
`guess_ab_col()`	Guess Antibiotic Column
Background information on included data Some pages about our package and its external sources. Be sure to read our How To’s for more information about how to work with functions in this package.
`AMR`	The `AMR` Package
`example_isolates`	Data Set with 2,000 Example Isolates
`microorganisms`	Data Set with 70,764 Microorganisms
`microorganisms.codes`	Data Set with 5,604 Common Microorganism Codes
`microorganisms.old`	Data Set with Previously Accepted Taxonomic Names
`antibiotics` `antivirals`	Data Sets with 566 Antimicrobial Drugs
`intrinsic_resistant`	Data Set with Bacterial Intrinsic Resistance
`dosage`	Data Set with Treatment Dosages as Defined by EUCAST
`catalogue_of_life`	The Catalogue of Life
`catalogue_of_life_version()`	Version info of included Catalogue of Life
`WHOCC`	WHOCC: WHO Collaborating Centre for Drug Statistics Methodology
`lifecycle`	Lifecycles of Functions in the `AMR` Package
`example_isolates_unclean`	Data Set with Unclean Data
`rsi_translation`	Data Set for R/SI Interpretation
`WHONET`	Data Set with 500 Isolates - WHONET Example
Other: miscellaneous functions These functions are mostly for internal use, but some of them may also be suitable for your analysis. Especially the ‘like’ function can be useful: `if (x %like% y) {...}`.
`age_groups()`	Split Ages into Age Groups
`age()`	Age in Years of Individuals
`availability()`	Check Availability of Columns
`get_AMR_locale()`	Translate Strings from AMR Package
`ggplot_pca()`	PCA Biplot with `ggplot2`
`italicise_taxonomy()` `italicize_taxonomy()`	Italicise Taxonomic Families, Genera, Species, Subspecies
`inner_join_microorganisms()` `left_join_microorganisms()` `right_join_microorganisms()` `full_join_microorganisms()` `semi_join_microorganisms()` `anti_join_microorganisms()`	Join microorganisms to a Data Set
`like()` `%like%` `%unlike%` `%like_case%` `%unlike_case%`	Vectorised Pattern Matching with Keyboard Shortcut
`mo_matching_score()`	Calculate the Matching Score for Microorganisms
`pca()`	Principal Component Analysis (for AMR)
`random_mic()` `random_disk()` `random_rsi()`	Random MIC Values/Disk Zones/RSI Generation
Other: statistical tests Some statistical tests or methods are not part of base R and were added to this package for convenience.
`g.test()`	G-test for Count Data
`kurtosis()`	Kurtosis of the Sample
`skewness()`	Skewness of the Sample
Other: deprecated functions These functions are deprecated, meaning that they will still work but show a warning with every use and will be removed in a future version.
`AMR-deprecated`	Deprecated Functions

Reference

Preparing data: microorganisms

Preparing data: antibiotics

Preparing data: antimicrobial resistance

Analysing data: antimicrobial resistance

Background information on included data

Other: miscellaneous functions

Other: statistical tests

Other: deprecated functions