Function reference • AMR (for R)

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
`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
`microorganisms`	Data set with 67,151 microorganisms
`antibiotics` `antivirals`	Data sets with 557 antimicrobials
`intrinsic_resistant`	Data set with bacterial intrinsic resistance
`example_isolates`	Data set with 2,000 example isolates
`example_isolates_unclean`	Data set with unclean data
`rsi_translation`	Data set for R/SI interpretation
`microorganisms.codes`	Data set with 5,583 common microorganism codes
`microorganisms.old`	Data set with previously accepted taxonomic names
`WHONET`	Data set with 500 isolates - WHONET example
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 ID
`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_snomed()` `mo_ref()` `mo_authors()` `mo_year()` `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_atc()` `ab_cid()` `ab_synonyms()` `ab_tradenames()` `ab_group()` `ab_atc_group1()` `ab_atc_group2()` `ab_loinc()` `ab_ddd()` `ab_info()` `ab_url()` `ab_property()`	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()`	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()` `is.rsi()` `is.rsi.eligible()`	Interpret MIC and disk values, or clean raw R/SI data
`as.mic()` `is.mic()`	Transform input to minimum inhibitory concentrations (MIC)
`as.disk()` `is.disk()`	Transform input to disk diffusion diameters
`eucast_rules()`	Apply EUCAST rules
`plot(<mic>)` `barplot(<mic>)` `plot(<rsi>)` `barplot(<rsi>)`	Plotting for classes `rsi` and `disk`
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()`. You can also filter your data on certain resistance in certain antibiotic classes (`filter_ab_class()`), 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
`first_isolate()` `filter_first_isolate()` `filter_first_weighted_isolate()`	Determine first (weighted) isolates
`key_antibiotics()` `key_antibiotics_equal()`	Key antibiotics for first weighted isolates
`mdro()` `brmo()` `mrgn()` `mdr_tb()` `mdr_cmi2012()` `eucast_exceptional_phenotypes()`	Determine multidrug-resistant organisms (MDRO)
`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()` `aminoglycosides()` `carbapenems()` `cephalosporins()` `cephalosporins_1st()` `cephalosporins_2nd()` `cephalosporins_3rd()` `cephalosporins_4th()` `cephalosporins_5th()` `fluoroquinolones()` `glycopeptides()` `macrolides()` `penicillins()` `tetracyclines()`	Antibiotic class selectors
`filter_ab_class()` `filter_aminoglycosides()` `filter_carbapenems()` `filter_cephalosporins()` `filter_1st_cephalosporins()` `filter_2nd_cephalosporins()` `filter_3rd_cephalosporins()` `filter_4th_cephalosporins()` `filter_5th_cephalosporins()` `filter_fluoroquinolones()` `filter_glycopeptides()` `filter_macrolides()` `filter_penicillins()` `filter_tetracyclines()`	Filter isolates on result in antimicrobial class
`resistance_predict()` `rsi_predict()` `plot(<resistance_predict>)` `ggplot_rsi_predict()`	Predict antimicrobial resistance
`guess_ab_col()`	Guess antibiotic column
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_locale()`	Translate strings from AMR package
`ggplot_pca()`	PCA biplot with `ggplot2`
`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%` `%like_case%`	Pattern Matching
`mo_matching_score()`	Calculate the matching score for microorganisms
`pca()`	Principal Component Analysis (for AMR)
Other: statistical tests Some statistical tests or methods are not part of base R and are added to this package for convenience.
`g.test()`	G-test for Count Data
`kurtosis()`	Kurtosis of the sample
`skewness()`	Skewness of the sample
`p_symbol()`	Symbol of a p-value

Reference

Background information on included data

Preparing data: microorganisms

Preparing data: antibiotics

Preparing data: antimicrobial resistance

Analysing data: antimicrobial resistance

Other: miscellaneous functions

Other: statistical tests