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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.

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().

Preparing data

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 Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA)
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() oxazolidinones() penicillins() polymyxins() streptogramins() quinolones() 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).

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.

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.

example_isolates
Data Set with 2 000 Example Isolates
microorganisms
Data Set with 52 171 Microorganisms
microorganisms.codes
Data Set with 4 957 Common Microorganism Codes
microorganisms.groups
Data Set with 521 Microorganisms In Species Groups
antibiotics antivirals
Data Sets with 603 Antimicrobial Drugs
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
clinical_breakpoints
Data Set with Clinical Breakpoints for SIR 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() 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