Function reference
Preparing data: microorganisms
These functions are meant to get taxonomically valid properties of microorganisms from any input, but also properties derived from taxonomy, such as the Gram stain (mo_gramstain()) , or mo_is_yeast(). Use mo_source() to teach this package how to translate your own codes to valid microorganisms, and use `add_custom_microorganisms() to add your own custom microorganisms to this package.
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as.mo()is.mo()mo_uncertainties()mo_renamed()mo_failures()mo_reset_session()mo_cleaning_regex() - Transform Input to a Microorganism Code
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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_snomed()mo_ref()mo_authors()mo_year()mo_lpsn()mo_gbif()mo_rank()mo_taxonomy()mo_synonyms()mo_current()mo_info()mo_url()mo_property() - Get Properties of a Microorganism
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add_custom_microorganisms()clear_custom_microorganisms() - Add Custom Microorganisms
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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().
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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
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ab_from_text() - Retrieve Antimicrobial Drug Names and Doses from Clinical Text
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atc_online_property()atc_online_groups()atc_online_ddd()atc_online_ddd_units() - Get ATC Properties from WHOCC Website
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add_custom_antimicrobials()clear_custom_antimicrobials() - Add Custom Antimicrobials
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.sir() for cleaning raw data to let it only contain “R”, “I” and “S”, or to interpret MIC or disk diffusion values as SIR based on the lastest EUCAST and CLSI guidelines. Afterwards, you can extend antibiotic interpretations by applying EUCAST rules with eucast_rules().
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as.sir()NA_sir_is.sir()is_sir_eligible()sir_interpretation_history() - Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data
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as.mic()NA_mic_is.mic()droplevels(<mic>) - Transform Input to Minimum Inhibitory Concentrations (MIC)
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eucast_rules()eucast_dosage() - Apply EUCAST Rules
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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()).
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resistance()susceptibility()sir_confidence_interval()proportion_R()proportion_IR()proportion_I()proportion_SI()proportion_S()proportion_df()sir_df() - Calculate Microbial Resistance
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count_resistant()count_susceptible()count_R()count_IR()count_I()count_SI()count_S()count_all()n_sir()count_df() - Count Available Isolates
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get_episode()is_new_episode() - Determine (New) Episodes for Patients
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first_isolate()filter_first_isolate() - Determine First Isolates
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key_antimicrobials()all_antimicrobials()antimicrobials_equal() - (Key) Antimicrobials for First Weighted Isolates
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mdro()custom_mdro_guideline()brmo()mrgn()mdr_tb()mdr_cmi2012()eucast_exceptional_phenotypes() - Determine Multidrug-Resistant Organisms (MDRO)
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plot(<mic>)autoplot(<mic>)fortify(<mic>)plot(<disk>)autoplot(<disk>)fortify(<disk>)plot(<sir>)autoplot(<sir>)fortify(<sir>) - Plotting for Classes
sir,micanddisk
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ggplot_sir()geom_sir()facet_sir()scale_y_percent()scale_sir_colours()theme_sir()labels_sir_count() - AMR Plots with
ggplot2
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bug_drug_combinations()format(<bug_drug_combinations>) - Determine Bug-Drug Combinations
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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
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mean_amr_distance()amr_distance_from_row() - Calculate the Mean AMR Distance
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resistance_predict()sir_predict()plot(<resistance_predict>)ggplot_sir_predict()autoplot(<resistance_predict>) - Predict Antimicrobial Resistance
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guess_ab_col() - Guess Antibiotic Column
Other: AMR-specific options
The AMR package is customisable, by providing settings that can be set per user or per team. For example, the default interpretation guideline can be changed from EUCAST to CLSI, or a supported language can be set for the whole team (system-language independent) for antibiotic names in a foreign language.
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AMR-options - Options for the AMR package
Other: antiviral drugs
This package also provides extensive support for antiviral agents, even though it is not the primary scope of this package. Working with data containing information about antiviral drugs was never easier. Use these functions to get valid properties of antiviral drugs from any input or to clean your input. You can even retrieve drug names and doses from clinical text records, using av_from_text().
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av_name()av_cid()av_synonyms()av_tradenames()av_group()av_atc()av_loinc()av_ddd()av_ddd_units()av_info()av_url()av_property() - Get Properties of an Antiviral Drug
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av_from_text() - Retrieve Antiviral Drug Names and Doses from Clinical Text
Other: 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.
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AMRAMR-package - The
AMRPackage
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example_isolates - Data Set with 2,000 Example Isolates
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microorganisms - Data Set with 52,141 Microorganisms
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microorganisms.codes - Data Set with 5,910 Common Microorganism Codes
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antibioticsantivirals - Data Sets with 603 Antimicrobial Drugs
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intrinsic_resistant - Data Set with Bacterial Intrinsic Resistance
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dosage - Data Set with Treatment Dosages as Defined by EUCAST
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WHOCC - WHOCC: WHO Collaborating Centre for Drug Statistics Methodology
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example_isolates_unclean - Data Set with Unclean Data
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clinical_breakpoints - Data Set with Clinical Breakpoints for SIR Interpretation
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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) {...}.
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age_groups() - Split Ages into Age Groups
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age() - Age in Years of Individuals
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availability() - Check Availability of Columns
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get_AMR_locale()set_AMR_locale()reset_AMR_locale()translate_AMR() - Translate Strings from the AMR Package
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ggplot_pca() - PCA Biplot with
ggplot2
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italicise_taxonomy()italicize_taxonomy() - Italicise Taxonomic Families, Genera, Species, Subspecies
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inner_join_microorganisms()left_join_microorganisms()right_join_microorganisms()full_join_microorganisms()semi_join_microorganisms()anti_join_microorganisms() - Join microorganisms to a Data Set
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like()`%like%``%unlike%``%like_case%``%unlike_case%` - Vectorised Pattern Matching with Keyboard Shortcut
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mo_matching_score() - Calculate the Matching Score for Microorganisms
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pca() - Principal Component Analysis (for AMR)
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random_mic()random_disk()random_sir() - Random MIC Values/Disk Zones/SIR Generation
Other: statistical tests
Some statistical tests or methods are not part of base R and were added to this package for convenience.