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How to
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Conduct AMR analysis
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Predict antimicrobial resistance
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Data sets for download / own use
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Conduct principal component analysis for AMR
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Determine multi-drug resistance (MDR)
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Work with WHONET data
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Import data from SPSS/SAS/Stata
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Apply EUCAST rules
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Get properties of a microorganism
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Get properties of an antibiotic
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Other: benchmarks
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Manual
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Source Code
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Survey
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< / header >
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< h1 > Reference< / h1 >
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< col class = "title" / >
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< h2 id = "section-background-information-on-included-data" class = "hasAnchor" > < a href = "#section-background-information-on-included-data" class = "anchor" > < / a > Background information on included data< / h2 >
< p class = "section-desc" > < p > Some pages about our package and its external sources. Be sure to read our < a href = "./../articles/index.html" > How To’ < / a > for more information about how to work with functions in this package.< / p > < / p >
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< / th >
< / tr >
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< / tbody > < tbody >
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< tr >
< td >
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< p > < code > < a href = "AMR.html" > AMR< / a > < / code > < / p >
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< / td >
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< td > < p > The < code > AMR< / code > Package< / p > < / td >
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< / tr > < tr >
< td >
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< p > < code > < a href = "catalogue_of_life.html" > catalogue_of_life< / a > < / code > < / p >
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< / td >
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< td > < p > The Catalogue of Life< / p > < / td >
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< / tr > < tr >
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< td >
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< p > < code > < a href = "catalogue_of_life_version.html" > catalogue_of_life_version()< / a > < / code > < / p >
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< / td >
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< td > < p > Version info of included Catalogue of Life< / p > < / td >
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< / tr > < tr >
< td >
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< p > < code > < a href = "WHOCC.html" > WHOCC< / a > < / code > < / p >
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< / td >
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< td > < p > WHOCC: WHO Collaborating Centre for Drug Statistics Methodology< / p > < / td >
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< / tr > < tr >
< td >
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< p > < code > < a href = "lifecycle.html" > lifecycle< / a > < / code > < / p >
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< / td >
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< td > < p > Lifecycles of functions in the < code > AMR< / code > package< / p > < / td >
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< / tr > < tr >
< td >
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< p > < code > < a href = "microorganisms.html" > microorganisms< / a > < / code > < / p >
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< / td >
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< td > < p > Data set with 67,151 microorganisms< / p > < / td >
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< / tr > < tr >
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< td >
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< p > < code > < a href = "antibiotics.html" > antibiotics< / a > < / code > < code > < a href = "antibiotics.html" > antivirals< / a > < / code > < / p >
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< / td >
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< td > < p > Data sets with 557 antimicrobials< / p > < / td >
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< / tr > < tr >
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< td >
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< p > < code > < a href = "intrinsic_resistant.html" > intrinsic_resistant< / a > < / code > < / p >
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< / td >
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< td > < p > Data set with bacterial intrinsic resistance< / p > < / td >
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< / tr > < tr >
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< td >
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< p > < code > < a href = "example_isolates.html" > example_isolates< / a > < / code > < / p >
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< / td >
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< td > < p > Data set with 2,000 example isolates< / p > < / td >
< / tr > < tr >
< td >
< p > < code > < a href = "example_isolates_unclean.html" > example_isolates_unclean< / a > < / code > < / p >
< / td >
< td > < p > Data set with unclean data< / p > < / td >
< / tr > < tr >
< td >
< p > < code > < a href = "rsi_translation.html" > rsi_translation< / a > < / code > < / p >
< / td >
< td > < p > Data set for R/SI interpretation< / p > < / td >
< / tr > < tr >
< td >
< p > < code > < a href = "microorganisms.codes.html" > microorganisms.codes< / a > < / code > < / p >
< / td >
< td > < p > Data set with 5,583 common microorganism codes< / p > < / td >
< / tr > < tr >
< td >
< p > < code > < a href = "microorganisms.old.html" > microorganisms.old< / a > < / code > < / p >
< / td >
< td > < p > Data set with previously accepted taxonomic names< / p > < / td >
< / tr > < tr >
< td >
< p > < code > < a href = "WHONET.html" > WHONET< / a > < / code > < / p >
< / td >
< td > < p > Data set with 500 isolates - WHONET example< / p > < / td >
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< / tr >
< / tbody > < tbody >
< tr >
< th colspan = "2" >
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< h2 id = "section-preparing-data-microorganisms" class = "hasAnchor" > < a href = "#section-preparing-data-microorganisms" class = "anchor" > < / a > Preparing data: microorganisms< / h2 >
< p class = "section-desc" > < p > These functions are meant to get taxonomically valid properties of microorganisms from any input. Use < code > < a href = "../reference/mo_source.html" > mo_source()< / a > < / code > to teach this package how to translate your own codes to valid microorganism codes.< / p > < / p >
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< / th >
< / tr >
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< / tbody > < tbody >
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< tr >
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< td >
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< p > < code > < a href = "as.mo.html" > as.mo()< / a > < / code > < code > < a href = "as.mo.html" > is.mo()< / a > < / code > < code > < a href = "as.mo.html" > mo_failures()< / a > < / code > < code > < a href = "as.mo.html" > mo_uncertainties()< / a > < / code > < code > < a href = "as.mo.html" > mo_renamed()< / a > < / code > < / p >
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< / td >
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< td > < p > Transform input to a microorganism ID< / p > < / td >
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< / tr > < tr >
< td >
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< p > < code > < a href = "mo_property.html" > mo_name()< / a > < / code > < code > < a href = "mo_property.html" > mo_fullname()< / a > < / code > < code > < a href = "mo_property.html" > mo_shortname()< / a > < / code > < code > < a href = "mo_property.html" > mo_subspecies()< / a > < / code > < code > < a href = "mo_property.html" > mo_species()< / a > < / code > < code > < a href = "mo_property.html" > mo_genus()< / a > < / code > < code > < a href = "mo_property.html" > mo_family()< / a > < / code > < code > < a href = "mo_property.html" > mo_order()< / a > < / code > < code > < a href = "mo_property.html" > mo_class()< / a > < / code > < code > < a href = "mo_property.html" > mo_phylum()< / a > < / code > < code > < a href = "mo_property.html" > mo_kingdom()< / a > < / code > < code > < a href = "mo_property.html" > mo_domain()< / a > < / code > < code > < a href = "mo_property.html" > mo_type()< / a > < / code > < code > < a href = "mo_property.html" > mo_gramstain()< / a > < / code > < code > < a href = "mo_property.html" > is_gram_negative()< / a > < / code > < code > < a href = "mo_property.html" > is_gram_positive()< / a > < / code > < code > < a href = "mo_property.html" > mo_snomed()< / a > < / code > < code > < a href = "mo_property.html" > mo_ref()< / a > < / code > < code > < a href = "mo_property.html" > mo_authors()< / a > < / code > < code > < a href = "mo_property.html" > mo_year()< / a > < / code > < code > < a href = "mo_property.html" > mo_rank()< / a > < / code > < code > < a href = "mo_property.html" > mo_taxonomy()< / a > < / code > < code > < a href = "mo_property.html" > mo_synonyms()< / a > < / code > < code > < a href = "mo_property.html" > mo_info()< / a > < / code > < code > < a href = "mo_property.html" > mo_url()< / a > < / code > < code > < a href = "mo_property.html" > mo_property()< / a > < / code > < / p >
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< / td >
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< td > < p > Get properties of a microorganism< / p > < / td >
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< / tr > < tr >
< td >
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< p > < code > < a href = "mo_source.html" > set_mo_source()< / a > < / code > < code > < a href = "mo_source.html" > get_mo_source()< / a > < / code > < / p >
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< / td >
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< td > < p > User-defined reference data set for microorganisms< / p > < / td >
< / tr >
< / tbody > < tbody >
< tr >
< th colspan = "2" >
< h2 id = "section-preparing-data-antibiotics" class = "hasAnchor" > < a href = "#section-preparing-data-antibiotics" class = "anchor" > < / a > Preparing data: antibiotics< / h2 >
< p class = "section-desc" > < p > 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 < code > < a href = "../reference/ab_from_text.html" > ab_from_text()< / a > < / code > .< / p > < / p >
< / th >
< / tr >
< / tbody > < tbody >
< tr >
2018-12-23 21:26:21 +01:00
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "as.ab.html" > as.ab()< / a > < / code > < code > < a href = "as.ab.html" > is.ab()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
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< td > < p > Transform input to an antibiotic ID< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "ab_property.html" > ab_name()< / a > < / code > < code > < a href = "ab_property.html" > ab_atc()< / a > < / code > < code > < a href = "ab_property.html" > ab_cid()< / a > < / code > < code > < a href = "ab_property.html" > ab_synonyms()< / a > < / code > < code > < a href = "ab_property.html" > ab_tradenames()< / a > < / code > < code > < a href = "ab_property.html" > ab_group()< / a > < / code > < code > < a href = "ab_property.html" > ab_atc_group1()< / a > < / code > < code > < a href = "ab_property.html" > ab_atc_group2()< / a > < / code > < code > < a href = "ab_property.html" > ab_loinc()< / a > < / code > < code > < a href = "ab_property.html" > ab_ddd()< / a > < / code > < code > < a href = "ab_property.html" > ab_info()< / a > < / code > < code > < a href = "ab_property.html" > ab_url()< / a > < / code > < code > < a href = "ab_property.html" > ab_property()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Get properties of an antibiotic< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "ab_from_text.html" > ab_from_text()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Retrieve antimicrobial drug names and doses from clinical text< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "atc_online.html" > atc_online_property()< / a > < / code > < code > < a href = "atc_online.html" > atc_online_groups()< / a > < / code > < code > < a href = "atc_online.html" > atc_online_ddd()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Get ATC properties from WHOCC website< / p > < / td >
< / tr >
< / tbody > < tbody >
< tr >
< th colspan = "2" >
< h2 id = "section-preparing-data-antimicrobial-resistance" class = "hasAnchor" > < a href = "#section-preparing-data-antimicrobial-resistance" class = "anchor" > < / a > Preparing data: antimicrobial resistance< / h2 >
2020-10-08 11:16:03 +02:00
< p class = "section-desc" > < p > With < code > < a href = "../reference/as.mic.html" > as.mic()< / a > < / code > and < code > < a href = "../reference/as.disk.html" > as.disk()< / a > < / code > you can transform your raw input to valid MIC or disk diffusion values. Use < code > < a href = "../reference/as.rsi.html" > as.rsi()< / a > < / code > 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 < a href = "https://www.eucast.org/expert_rules_and_intrinsic_resistance/" > EUCAST rules< / a > with < code > < a href = "../reference/eucast_rules.html" > eucast_rules()< / a > < / code > .< / p > < / p >
2020-09-03 12:31:48 +02:00
< / th >
< / tr >
< / tbody > < tbody >
< tr >
< td >
< p > < code > < a href = "as.rsi.html" > as.rsi()< / a > < / code > < code > < a href = "as.rsi.html" > is.rsi()< / a > < / code > < code > < a href = "as.rsi.html" > is.rsi.eligible()< / a > < / code > < / p >
< / td >
2020-09-25 14:44:50 +02:00
< td > < p > Interpret MIC and disk values, or clean raw R/SI data< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "as.mic.html" > as.mic()< / a > < / code > < code > < a href = "as.mic.html" > is.mic()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > Transform input to minimum inhibitory concentrations (MIC)< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "as.disk.html" > as.disk()< / a > < / code > < code > < a href = "as.disk.html" > is.disk()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Transform input to disk diffusion diameters< / p > < / td >
2019-05-10 16:44:59 +02:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "eucast_rules.html" > eucast_rules()< / a > < / code > < / p >
2019-05-10 16:44:59 +02:00
< / td >
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< td > < p > Apply EUCAST rules< / p > < / td >
2020-09-12 08:49:01 +02:00
< / tr > < tr >
< td >
< p > < code > < a href = "plot.html" > plot(< i > < mic> < / i > )< / a > < / code > < code > < a href = "plot.html" > barplot(< i > < mic> < / i > )< / a > < / code > < code > < a href = "plot.html" > plot(< i > < rsi> < / i > )< / a > < / code > < code > < a href = "plot.html" > barplot(< i > < rsi> < / i > )< / a > < / code > < / p >
< / td >
< td > < p > Plotting for classes < code > rsi< / code > and < code > disk< / code > < / p > < / td >
2018-12-29 22:24:19 +01:00
< / tr >
< / tbody > < tbody >
< tr >
< th colspan = "2" >
2020-09-03 12:31:48 +02:00
< h2 id = "section-analysing-data-antimicrobial-resistance" class = "hasAnchor" > < a href = "#section-analysing-data-antimicrobial-resistance" class = "anchor" > < / a > Analysing data: antimicrobial resistance< / h2 >
< p class = "section-desc" > < p > Use these function for the analysis part. You can use < code > < a href = "../reference/proportion.html" > susceptibility()< / a > < / code > or < code > < a href = "../reference/proportion.html" > resistance()< / a > < / code > on any antibiotic column. Be sure to first select the isolates that are appropiate for analysis, by using < code > < a href = "../reference/first_isolate.html" > first_isolate()< / a > < / code > . You can also filter your data on certain resistance in certain antibiotic classes (< code > < a href = "../reference/filter_ab_class.html" > filter_ab_class()< / a > < / code > ), or determine multi-drug resistant microorganisms (MDRO, < code > < a href = "../reference/mdro.html" > mdro()< / a > < / code > ).< / p > < / p >
2018-12-29 22:24:19 +01:00
< / th >
< / tr >
2020-04-13 21:09:56 +02:00
< / tbody > < tbody >
2018-12-29 22:24:19 +01:00
< tr >
2018-12-23 21:26:21 +01:00
2020-03-07 21:48:21 +01:00
< td >
< p > < code > < a href = "proportion.html" > resistance()< / a > < / code > < code > < a href = "proportion.html" > susceptibility()< / a > < / code > < code > < a href = "proportion.html" > proportion_R()< / a > < / code > < code > < a href = "proportion.html" > proportion_IR()< / a > < / code > < code > < a href = "proportion.html" > proportion_I()< / a > < / code > < code > < a href = "proportion.html" > proportion_SI()< / a > < / code > < code > < a href = "proportion.html" > proportion_S()< / a > < / code > < code > < a href = "proportion.html" > proportion_df()< / a > < / code > < code > < a href = "proportion.html" > rsi_df()< / a > < / code > < / p >
< / td >
< td > < p > Calculate microbial resistance< / p > < / td >
< / tr > < tr >
< td >
< p > < code > < a href = "count.html" > count_resistant()< / a > < / code > < code > < a href = "count.html" > count_susceptible()< / a > < / code > < code > < a href = "count.html" > count_R()< / a > < / code > < code > < a href = "count.html" > count_IR()< / a > < / code > < code > < a href = "count.html" > count_I()< / a > < / code > < code > < a href = "count.html" > count_SI()< / a > < / code > < code > < a href = "count.html" > count_S()< / a > < / code > < code > < a href = "count.html" > count_all()< / a > < / code > < code > < a href = "count.html" > n_rsi()< / a > < / code > < code > < a href = "count.html" > count_df()< / a > < / code > < / p >
< / td >
< td > < p > Count available isolates< / p > < / td >
< / tr > < tr >
2019-02-08 16:06:54 +01:00
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "first_isolate.html" > first_isolate()< / a > < / code > < code > < a href = "first_isolate.html" > filter_first_isolate()< / a > < / code > < code > < a href = "first_isolate.html" > filter_first_weighted_isolate()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Determine first (weighted) isolates< / p > < / td >
2020-03-07 21:48:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "key_antibiotics.html" > key_antibiotics()< / a > < / code > < code > < a href = "key_antibiotics.html" > key_antibiotics_equal()< / a > < / code > < / p >
2020-03-07 21:48:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Key antibiotics for first < em > weighted< / em > isolates< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
2020-06-17 15:14:37 +02:00
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "mdro.html" > mdro()< / a > < / code > < code > < a href = "mdro.html" > brmo()< / a > < / code > < code > < a href = "mdro.html" > mrgn()< / a > < / code > < code > < a href = "mdro.html" > mdr_tb()< / a > < / code > < code > < a href = "mdro.html" > mdr_cmi2012()< / a > < / code > < code > < a href = "mdro.html" > eucast_exceptional_phenotypes()< / a > < / code > < / p >
2020-06-17 15:14:37 +02:00
< / td >
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< td > < p > Determine multidrug-resistant organisms (MDRO)< / p > < / td >
2020-06-17 15:14:37 +02:00
< / tr > < tr >
2019-03-06 14:39:02 +01:00
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "ggplot_rsi.html" > ggplot_rsi()< / a > < / code > < code > < a href = "ggplot_rsi.html" > geom_rsi()< / a > < / code > < code > < a href = "ggplot_rsi.html" > facet_rsi()< / a > < / code > < code > < a href = "ggplot_rsi.html" > scale_y_percent()< / a > < / code > < code > < a href = "ggplot_rsi.html" > scale_rsi_colours()< / a > < / code > < code > < a href = "ggplot_rsi.html" > theme_rsi()< / a > < / code > < code > < a href = "ggplot_rsi.html" > labels_rsi_count()< / a > < / code > < / p >
2019-03-06 14:39:02 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > AMR plots with < code > ggplot2< / code > < / p > < / td >
2019-03-06 14:39:02 +01:00
< / tr > < tr >
2018-12-23 21:26:21 +01:00
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "bug_drug_combinations.html" > bug_drug_combinations()< / a > < / code > < code > < a href = "bug_drug_combinations.html" > format(< i > < bug_drug_combinations> < / i > )< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Determine bug-drug combinations< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "antibiotic_class_selectors.html" > ab_class()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > aminoglycosides()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > carbapenems()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > cephalosporins()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > cephalosporins_1st()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > cephalosporins_2nd()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > cephalosporins_3rd()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > cephalosporins_4th()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > cephalosporins_5th()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > fluoroquinolones()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > glycopeptides()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > macrolides()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > penicillins()< / a > < / code > < code > < a href = "antibiotic_class_selectors.html" > tetracyclines()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Antibiotic class selectors< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "filter_ab_class.html" > filter_ab_class()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_aminoglycosides()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_carbapenems()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_cephalosporins()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_1st_cephalosporins()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_2nd_cephalosporins()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_3rd_cephalosporins()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_4th_cephalosporins()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_5th_cephalosporins()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_fluoroquinolones()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_glycopeptides()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_macrolides()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_penicillins()< / a > < / code > < code > < a href = "filter_ab_class.html" > filter_tetracyclines()< / a > < / code > < / p >
2019-11-18 12:10:47 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Filter isolates on result in antimicrobial class< / p > < / td >
2019-11-18 12:10:47 +01:00
< / tr > < tr >
2018-12-23 21:26:21 +01:00
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "resistance_predict.html" > resistance_predict()< / a > < / code > < code > < a href = "resistance_predict.html" > rsi_predict()< / a > < / code > < code > < a href = "resistance_predict.html" > plot(< i > < resistance_predict> < / i > )< / a > < / code > < code > < a href = "resistance_predict.html" > ggplot_rsi_predict()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Predict antimicrobial resistance< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
< td >
2020-09-03 12:31:48 +02:00
< p > < code > < a href = "guess_ab_col.html" > guess_ab_col()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-03 12:31:48 +02:00
< td > < p > Guess antibiotic column< / p > < / td >
2018-12-29 22:24:19 +01:00
< / tr >
< / tbody > < tbody >
< tr >
< th colspan = "2" >
2020-09-03 12:31:48 +02:00
< h2 id = "section-other-miscellaneous-functions" class = "hasAnchor" > < a href = "#section-other-miscellaneous-functions" class = "anchor" > < / a > Other: miscellaneous functions< / h2 >
< p class = "section-desc" > < p > These functions are mostly for internal use, but some of them may also be suitable for your analysis. Especially the ‘ ’ < code > if (x %like% y) {...}< / code > .< / p > < / p >
2018-12-29 22:24:19 +01:00
< / th >
< / tr >
2020-04-13 21:09:56 +02:00
< / tbody > < tbody >
2018-12-29 22:24:19 +01:00
< tr >
2018-12-23 21:26:21 +01:00
2020-03-07 21:48:21 +01:00
< td >
2020-09-18 16:05:53 +02:00
< p > < code > < a href = "age_groups.html" > age_groups()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > Split ages into age groups< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
2020-08-15 12:54:47 +02:00
< td >
2020-09-18 16:05:53 +02:00
< p > < code > < a href = "age.html" > age()< / a > < / code > < / p >
2020-08-15 12:54:47 +02:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > Age in years of individuals< / p > < / td >
2020-08-15 12:54:47 +02:00
< / tr > < tr >
2018-12-23 21:26:21 +01:00
< td >
2020-09-18 16:05:53 +02:00
< p > < code > < a href = "availability.html" > availability()< / a > < / code > < / p >
2018-12-23 21:26:21 +01:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > Check availability of columns< / p > < / td >
2019-11-18 12:10:47 +01:00
< / tr > < tr >
2020-02-21 21:13:38 +01:00
< td >
2020-09-18 16:05:53 +02:00
< p > < code > < a href = "translate.html" > get_locale()< / a > < / code > < / p >
2020-02-21 21:13:38 +01:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > Translate strings from AMR package< / p > < / td >
2020-02-21 21:13:38 +01:00
< / tr > < tr >
2019-11-18 12:10:47 +01:00
< td >
2020-09-18 16:05:53 +02:00
< p > < code > < a href = "ggplot_pca.html" > ggplot_pca()< / a > < / code > < / p >
2019-11-18 12:10:47 +01:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > PCA biplot with < code > ggplot2< / code > < / p > < / td >
2019-11-18 12:10:47 +01:00
< / tr > < tr >
< td >
2020-09-18 16:05:53 +02:00
< p > < code > < a href = "join.html" > inner_join_microorganisms()< / a > < / code > < code > < a href = "join.html" > left_join_microorganisms()< / a > < / code > < code > < a href = "join.html" > right_join_microorganisms()< / a > < / code > < code > < a href = "join.html" > full_join_microorganisms()< / a > < / code > < code > < a href = "join.html" > semi_join_microorganisms()< / a > < / code > < code > < a href = "join.html" > anti_join_microorganisms()< / a > < / code > < / p >
2019-11-18 12:10:47 +01:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > Join microorganisms to a data set< / p > < / td >
2019-11-18 12:10:47 +01:00
< / tr > < tr >
< td >
2020-09-18 16:05:53 +02:00
< p > < code > < a href = "like.html" > like()< / a > < / code > < code > < a href = "like.html" > `%like%`< / a > < / code > < code > < a href = "like.html" > `%like_case%`< / a > < / code > < / p >
2019-11-18 12:10:47 +01:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > Pattern Matching< / p > < / td >
2018-12-23 21:26:21 +01:00
< / tr > < tr >
2020-09-19 12:57:51 +02:00
< td >
< p > < code > < a href = "mo_matching_score.html" > mo_matching_score()< / a > < / code > < / p >
< / td >
< td > < p > Calculate the matching score for microorganisms< / p > < / td >
< / tr > < tr >
2019-01-29 20:20:09 +01:00
< td >
2020-09-18 16:05:53 +02:00
< p > < code > < a href = "pca.html" > pca()< / a > < / code > < / p >
2019-01-29 20:20:09 +01:00
< / td >
2020-09-18 16:05:53 +02:00
< td > < p > Principal Component Analysis (for AMR)< / p > < / td >
2019-11-18 12:10:47 +01:00
< / tr >
< / tbody > < tbody >
< tr >
< th colspan = "2" >
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< h2 id = "section-other-statistical-tests" class = "hasAnchor" > < a href = "#section-other-statistical-tests" class = "anchor" > < / a > Other: statistical tests< / h2 >
< p class = "section-desc" > < p > Some statistical tests or methods are not part of base R and are added to this package for convenience.< / p > < / p >
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< p > < code > < a href = "g.test.html" > g.test()< / a > < / code > < / p >
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< td > < p > < em > G< / em > -test for Count Data< / p > < / td >
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< p > < code > < a href = "kurtosis.html" > kurtosis()< / a > < / code > < / p >
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< td > < p > Kurtosis of the sample< / p > < / td >
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< p > < code > < a href = "skewness.html" > skewness()< / a > < / code > < / p >
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< td > < p > Skewness of the sample< / p > < / td >
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< p > < code > < a href = "AMR-deprecated.html" > p_symbol()< / a > < / code > < / p >
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< td > < p > Deprecated functions< / p > < / td >
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< p > Developed by < a href = 'https://www.rug.nl/staff/m.s.berends/' > Matthijs S. Berends< / a > , < a href = 'https://www.rug.nl/staff/c.f.luz/' > Christian F. Luz< / a > , < a href = 'https://www.rug.nl/staff/a.w.friedrich/' > Alexander W. Friedrich< / a > , < a href = 'https://www.rug.nl/staff/b.sinha/' > Bhanu N. M. Sinha< / a > , < a href = 'https://www.rug.nl/staff/c.j.albers/' > Casper J. Albers< / a > , < a href = 'https://www.rug.nl/staff/c.glasner/' > Corinna Glasner< / a > .< / p >
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< p > Site built with < a href = "https://pkgdown.r-lib.org/" > pkgdown< / a > 1.6.1.< / p >
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