Package index
Getting started
An overview of the package, its options for users and teams, and its 28 supported languages.
Cleaning data
Transform raw input of any kind into valid microorganisms with as.mo(), antimicrobials with as.ab(), and MIC values and disk diffusion zones with as.mic() and as.disk(), and retrieve their properties.
- Transform Arbitrary Input to Valid Microbial Taxonomy
as.mo()is.mo()mo_uncertainties()mo_renamed()mo_failures()mo_reset_session()mo_cleaning_regex()NA_mo_- Get Properties of a Microorganism
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_status()mo_pathogenicity()mo_gramstain()mo_is_gram_negative()mo_is_gram_positive()mo_is_yeast()mo_is_intrinsic_resistant()mo_oxygen_tolerance()mo_is_anaerobic()mo_morphology()mo_snomed()mo_ref()mo_authors()mo_year()mo_lpsn()mo_mycobank()mo_gbif()mo_rank()mo_taxonomy()mo_synonyms()mo_current()mo_group_members()mo_info()mo_url()mo_property()
Interpreting results as SIR
Interpret MIC values and disk diffusion zones as SIR with as.sir(), according to EUCAST or CLSI, for every guideline year from 2011 to 2026, and apply interpretive rules. as.sir() also cleans existing SIR data.
Analysing data
Select first isolates with first_isolate(), generate traditional, combination, syndromic, and WISCA antibiograms with antibiogram() and wisca(), calculate resistance, and determine multidrug-resistant organisms.
- (Key) Antimicrobials for First Weighted Isolates
key_antimicrobials()all_antimicrobials()antimicrobials_equal()- Antimicrobial Selectors
aminoglycosides()aminopenicillins()antifungals()antimycobacterials()betalactams()betalactams_with_inhibitor()carbapenems()cephalosporins()cephalosporins_1st()cephalosporins_2nd()cephalosporins_3rd()cephalosporins_4th()cephalosporins_5th()fluoroquinolones()glycopeptides()ionophores()isoxazolylpenicillins()lincosamides()lipoglycopeptides()macrolides()monobactams()nitrofurans()oxazolidinones()penicillins()peptides()phenicols()phosphonics()polymyxins()quinolones()rifamycins()spiropyrimidinetriones()streptogramins()sulfonamides()tetracyclines()trimethoprims()ureidopenicillins()amr_class()amr_selector()administrable_per_os()administrable_iv()not_intrinsic_resistant()
Visualising data
Plot MIC values, disk zones, and SIR results in base R or ggplot2, with scales and colours tailored to AMR data.
- Plotting Helpers for AMR Data Analysis
scale_x_mic()scale_y_mic()scale_colour_mic()scale_fill_mic()scale_x_sir()scale_colour_sir()scale_fill_sir()plot(<mic>)autoplot(<mic>)plot(<disk>)autoplot(<disk>)plot(<sir>)autoplot(<sir>)facet_sir()scale_y_percent()scale_sir_colours()theme_sir()
Modelling
Use AMR data types in tidymodels pipelines, and perform principal component analysis or distance calculations on resistance profiles.
Reference data
The data sets underlying this package, all of which can also be downloaded in other formats.
Utilities
Helper functions for pattern matching with %like%, ages, joining and italicising taxonomy, generating random test results, and data export.
- Vectorised Pattern Matching with Keyboard Shortcut
like()`%like%``%unlike%``%like_case%``%unlike_case%`- Join microorganisms to a Data Set
inner_join_microorganisms()left_join_microorganisms()right_join_microorganisms()full_join_microorganisms()semi_join_microorganisms()anti_join_microorganisms()