Package index • AMR (for R)

Introduction to the package

Please find the introduction to (and some general information about) our package here.

AMR-package AMR: The AMR Package

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.

as.mo() is.mo() mo_uncertainties() mo_renamed() mo_failures() mo_reset_session() mo_cleaning_regex(): Transform Arbitrary Input to Valid Microbial Taxonomy

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_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(): Get Properties of a Microorganism

add_custom_microorganisms() clear_custom_microorganisms(): Add Custom Microorganisms

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

add_custom_antimicrobials() clear_custom_antimicrobials(): Add Custom Antimicrobials

Preparing data: antimicrobial results

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().

as.sir() NA_sir_ is.sir() is_sir_eligible() sir_interpretation_history(): Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data

as.mic() is.mic() NA_mic_ rescale_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

Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. With antibiogram(), you can generate a traditional, combined, syndromic, or weighted-incidence syndromic combination antibiogram(WISCA). This function also comes with support for R Markdown and Quarto. 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()).

antibiogram() plot(<antibiogram>) autoplot(<antibiogram>) knit_print(<antibiogram>): Generate Traditional, Combination, Syndromic, or WISCA Antibiograms

resistance() susceptibility() sir_confidence_interval() proportion_R() proportion_IR() proportion_I() proportion_SI() proportion_S() proportion_df() sir_df(): Calculate Antimicrobial Resistance

count_resistant() count_susceptible() count_S() count_SI() count_I() count_IR() count_R() count_all() n_sir() count_df(): Count Available Isolates

get_episode() is_new_episode(): Determine Clinical or Epidemic Episodes

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)

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() nitrofurans() oxazolidinones() penicillins() polymyxins() quinolones() rifamycins() streptogramins() tetracyclines() trimethoprims() ureidopenicillins() administrable_per_os() administrable_iv() not_intrinsic_resistant(): Antibiotic Selectors

mean_amr_distance() amr_distance_from_row(): Calculate the Mean AMR Distance

resistance_predict() sir_predict() plot(<resistance_predict>) ggplot_sir_predict() autoplot(<resistance_predict>): Predict Antimicrobial Resistance

guess_ab_col(): Guess Antibiotic Column

Plotting data

Use these functions for the plotting part. The scale_*_mic() functions extend the ggplot2 package to allow plotting of MIC values, even within a manually set range. If using plot() (base R) or autoplot() (ggplot2) on MIC values or disk diffusion values, the user can set the interpretation guideline to give the bars the right SIR colours. The ggplot_sir() function is a short wrapper for users not much accustomed to ggplot2 yet. The ggplot_pca() function is a specific function to plot so-called biplots for PCA (principal component analysis).

scale_x_mic() scale_y_mic() scale_colour_mic() scale_fill_mic() plot(<mic>) autoplot(<mic>) fortify(<mic>) plot(<disk>) autoplot(<disk>) fortify(<disk>) plot(<sir>) autoplot(<sir>) fortify(<sir>): Plotting for Classes sir, mic and disk

ggplot_sir() geom_sir() facet_sir() scale_y_percent() scale_sir_colours() theme_sir() labels_sir_count(): AMR Plots with ggplot2

ggplot_pca(): PCA Biplot with ggplot2

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.

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().

as.av() is.av(): Transform Input to an Antiviral Drug ID

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

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.

microorganisms: Data Set with 78 678 Taxonomic Records of Microorganisms

antibiotics antivirals: Data Sets with 605 Antimicrobial Drugs

clinical_breakpoints: Data Set with Clinical Breakpoints for SIR Interpretation

example_isolates: Data Set with 2 000 Example Isolates

microorganisms.codes: Data Set with 4 971 Common Microorganism Codes

microorganisms.groups: Data Set with 521 Microorganisms In Species Groups

intrinsic_resistant: Data Set with Bacterial Intrinsic Resistance

dosage: Data Set with Treatment Dosages as Defined by EUCAST

WHOCC: WHOCC: WHO Collaborating Centre for Drug Statistics Methodology

example_isolates_unclean: Data Set with Unclean Data

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

export_ncbi_biosample(): Export Data Set as NCBI BioSample Antibiogram

availability(): Check Availability of Columns

get_AMR_locale() set_AMR_locale() reset_AMR_locale() translate_AMR(): Translate Strings from the AMR Package

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_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.

g.test(): G-test for Count Data

kurtosis(): Kurtosis of the Sample

skewness(): Skewness of the Sample