[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations RSI values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables.","code":"library(dplyr) library(ggplot2) library(AMR) library(cleaner) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\", \"cleaner\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"creation-of-data","dir":"Articles","previous_headings":"","what":"Creation of data","title":"How to conduct AMR data analysis","text":"create fake example data use analysis. AMR data analysis, need least: patient ID, name code microorganism, date antimicrobial results (antibiogram). also include specimen type (e.g. filter blood urine), ward type (e.g. filter ICUs). additional columns (like hospital name, patients gender even [well-defined] clinical properties) can comparative analysis, tutorial demonstrate .","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"patients","dir":"Articles","previous_headings":"Creation of data","what":"Patients","title":"How to conduct AMR data analysis","text":"start patients, need unique list patients. LETTERS object available R - ’s vector 26 characters: Z. patients object just created now vector length 260, values (patient IDs) varying A1 Z10. Now also set gender patients, putting ID gender table: first 135 patient IDs now male, 125 female.","code":"patients <- unlist(lapply(LETTERS, paste0, 1:10)) patients_table <- data.frame( patient_id = patients, gender = c( rep(\"M\", 135), rep(\"F\", 125) ) )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dates","dir":"Articles","previous_headings":"Creation of data","what":"Dates","title":"How to conduct AMR data analysis","text":"Let’s pretend data consists blood cultures isolates 1 January 2010 1 January 2018. dates object now contains days date range.","code":"dates <- seq(as.Date(\"2010-01-01\"), as.Date(\"2018-01-01\"), by = \"day\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"microorganisms","dir":"Articles","previous_headings":"Creation of data > Dates","what":"Microorganisms","title":"How to conduct AMR data analysis","text":"tutorial, uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Klebsiella pneumoniae:","code":"bacteria <- c( \"Escherichia coli\", \"Staphylococcus aureus\", \"Streptococcus pneumoniae\", \"Klebsiella pneumoniae\" )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"put-everything-together","dir":"Articles","previous_headings":"Creation of data","what":"Put everything together","title":"How to conduct AMR data analysis","text":"Using sample() function, can randomly select items objects defined earlier. let fake data reflect reality bit, also approximately define probabilities bacteria antibiotic results, using random_rsi() function. Using left_join() function dplyr package, can ‘map’ gender patient ID using patients_table object created earlier: resulting data set contains 20,000 blood culture isolates. head() function can preview first 6 rows data set: Now, let’s start cleaning analysis!","code":"sample_size <- 20000 data <- data.frame( date = sample(dates, size = sample_size, replace = TRUE), patient_id = sample(patients, size = sample_size, replace = TRUE), hospital = sample(c( \"Hospital A\", \"Hospital B\", \"Hospital C\", \"Hospital D\" ), size = sample_size, replace = TRUE, prob = c(0.30, 0.35, 0.15, 0.20) ), bacteria = sample(bacteria, size = sample_size, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10) ), AMX = random_rsi(sample_size, prob_RSI = c(0.35, 0.60, 0.05)), AMC = random_rsi(sample_size, prob_RSI = c(0.15, 0.75, 0.10)), CIP = random_rsi(sample_size, prob_RSI = c(0.20, 0.80, 0.00)), GEN = random_rsi(sample_size, prob_RSI = c(0.08, 0.92, 0.00)) ) data <- data %>% left_join(patients_table) head(data)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"cleaning-the-data","dir":"Articles","previous_headings":"","what":"Cleaning the data","title":"How to conduct AMR data analysis","text":"also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. example, gender variable: Frequency table Class: character Length: 20,000 Available: 20,000 (100%, NA: 0 = 0%) Unique: 2 Shortest: 1 Longest: 1 , can draw least two conclusions immediately. data scientists perspective, data looks clean: values M F. researchers perspective: slightly men. Nothing didn’t already know. data already quite clean, still need transform variables. bacteria column now consists text, want add variables based microbial IDs later . , transform column valid IDs. mutate() function dplyr package makes really easy: also want transform antibiotics, real life data don’t know really clean. .rsi() function ensures reliability reproducibility kind variables. .rsi.eligible() can check columns probably columns R/SI test results. Using mutate() across(), can apply transformation formal class: Finally, apply EUCAST rules antimicrobial results. Europe, medical microbiological laboratories already apply rules. package features latest insights intrinsic resistance exceptional phenotypes. Moreover, eucast_rules() function can also apply additional rules, like forcing ampicillin = R amoxicillin/clavulanic acid = R. amoxicillin (column AMX) amoxicillin/clavulanic acid (column AMC) data generated randomly, rows undoubtedly contain AMX = S AMC = R, technically impossible. eucast_rules() fixes :","code":"data %>% freq(gender) data <- data %>% mutate(bacteria = as.mo(bacteria)) is.rsi.eligible(data) # [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE colnames(data)[is.rsi.eligible(data)] # [1] \"AMX\" \"AMC\" \"CIP\" \"GEN\" data <- data %>% mutate(across(where(is.rsi.eligible), as.rsi)) data <- eucast_rules(data, col_mo = \"bacteria\", rules = \"all\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"adding-new-variables","dir":"Articles","previous_headings":"","what":"Adding new variables","title":"How to conduct AMR data analysis","text":"Now microbial ID, can add taxonomic properties:","code":"data <- data %>% mutate( gramstain = mo_gramstain(bacteria), genus = mo_genus(bacteria), species = mo_species(bacteria) )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Adding new variables","what":"First isolates","title":"How to conduct AMR data analysis","text":"also need know isolates can actually use analysis. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. method also takes account antimicrobial susceptibility test results using all_microbials(). Read methods first_isolate() page. outcome function can easily added data: 53.2% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 10,638 isolates analysis. Now data looks like: Time analysis!","code":"data <- data %>% mutate(first = first_isolate(info = TRUE)) # Determining first isolates using an episode length of 365 days # ℹ Using column 'bacteria' as input for col_mo. # ℹ Using column 'date' as input for col_date. # ℹ Using column 'patient_id' as input for col_patient_id. # Basing inclusion on all antimicrobial results, using a points threshold of # 2 # Including isolates from ICU. # => Found 10,638 'phenotype-based' first isolates (53.2% of total where a # microbial ID was available) data_1st <- data %>% filter(first == TRUE) data_1st <- data %>% filter_first_isolate() # Including isolates from ICU. head(data_1st)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"might want start getting idea data distributed. ’s important start, also decides continue analysis. Although package contains convenient function make frequency tables, exploratory data analysis (EDA) primary scope package. Use package like DataExplorer , read free online book Exploratory Data Analysis R Roger D. Peng.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dispersion-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Dispersion of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table freq() function. created genus species column earlier based microbial ID. paste(), can concatenate together. freq() function can used like base R language intended: can used like dplyr way, easier readable: Frequency table Class: character Length: 10,638 Available: 10,638 (100%, NA: 0 = 0%) Unique: 4 Shortest: 16 Longest: 24","code":"freq(paste(data_1st$genus, data_1st$species)) data_1st %>% freq(genus, species)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"overview-of-different-bugdrug-combinations","dir":"Articles","previous_headings":"Analysing the data","what":"Overview of different bug/drug combinations","title":"How to conduct AMR data analysis","text":"Using tidyverse selections, can also select filter columns based antibiotic class : want get quick glance number isolates different bug/drug combinations, can use bug_drug_combinations() function: give crude numbers data. calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"data_1st %>% filter(any(aminoglycosides() == \"R\")) # ℹ For aminoglycosides() using column 'GEN' (gentamicin) data_1st %>% bug_drug_combinations() %>% head() # show first 6 rows data_1st %>% select(bacteria, aminoglycosides()) %>% bug_drug_combinations() # ℹ For aminoglycosides() using column 'GEN' (gentamicin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: course convenient know number isolates responsible percentages. purpose n_rsi() can used, works exactly like n_distinct() dplyr package. counts isolates available every group (.e. values S, R): functions can also used get proportion multiple antibiotics, calculate empiric susceptibility combination therapies easily: curious resistance within certain antibiotic classes, use antibiotic class selector penicillins(), automatically include columns AMX AMC data: make transition next part, let’s see differences previously calculated combination therapies plotted:","code":"data_1st %>% resistance(AMX) # [1] 0.5423952 data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) data_1st %>% group_by(hospital) %>% summarise( amoxicillin = resistance(AMX), available = n_rsi(AMX) ) data_1st %>% group_by(genus) %>% summarise( amoxiclav = susceptibility(AMC), gentamicin = susceptibility(GEN), amoxiclav_genta = susceptibility(AMC, GEN) ) data_1st %>% # group by hospital group_by(hospital) %>% # / -> select all penicillins in the data for calculation # | / -> use resistance() for all peni's per hospital # | | / -> print as percentages summarise(across(penicillins(), resistance, as_percent = TRUE)) %>% # format the antibiotic column names, using so-called snake case, # so 'Amoxicillin/clavulanic acid' becomes 'amoxicillin_clavulanic_acid' rename_with(set_ab_names, penicillins()) data_1st %>% group_by(genus) %>% summarise( \"1. Amoxi/clav\" = susceptibility(AMC), \"2. Gentamicin\" = susceptibility(GEN), \"3. Amoxi/clav + genta\" = susceptibility(AMC, GEN) ) %>% # pivot_longer() from the tidyr package \"lengthens\" data: tidyr::pivot_longer(-genus, names_to = \"antibiotic\") %>% ggplot(aes( x = genus, y = value, fill = antibiotic )) + geom_col(position = \"dodge2\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plots","dir":"Articles","previous_headings":"Analysing the data","what":"Plots","title":"How to conduct AMR data analysis","text":"show results plots, R users nowadays use ggplot2 package. package lets create plots layers. can read website. quick example look like syntaxes: AMR package contains functions extend ggplot2 package, example geom_rsi(). automatically transforms data count_df() proportion_df() show results stacked bars. simplest shortest example: Omit translate_ab = FALSE antibiotic codes (AMX, AMC, CIP, GEN) translated official names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin). group e.g. genus column add additional functions package, can create : simplify , also created ggplot_rsi() function, combines almost functions:","code":"ggplot( data = a_data_set, mapping = aes( x = year, y = value ) ) + geom_col() + labs( title = \"A title\", subtitle = \"A subtitle\", x = \"My X axis\", y = \"My Y axis\" ) # or as short as: ggplot(a_data_set) + geom_bar(aes(year)) ggplot(data_1st) + geom_rsi(translate_ab = FALSE) # group the data on `genus` ggplot(data_1st %>% group_by(genus)) + # create bars with genus on x axis # it looks for variables with class `rsi`, # of which we have 4 (earlier created with `as.rsi`) geom_rsi(x = \"genus\") + # split plots on antibiotic facet_rsi(facet = \"antibiotic\") + # set colours to the R/SI interpretations (colour-blind friendly) scale_rsi_colours() + # show percentages on y axis scale_y_percent(breaks = 0:4 * 25) + # turn 90 degrees, to make it bars instead of columns coord_flip() + # add labels labs( title = \"Resistance per genus and antibiotic\", subtitle = \"(this is fake data)\" ) + # and print genus in italic to follow our convention # (is now y axis because we turned the plot) theme(axis.text.y = element_text(face = \"italic\")) data_1st %>% group_by(genus) %>% ggplot_rsi( x = \"genus\", facet = \"antibiotic\", breaks = 0:4 * 25, datalabels = FALSE ) + coord_flip()"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data > Plots","what":"Plotting MIC and disk diffusion values","title":"How to conduct AMR data analysis","text":"AMR package also extends plot() ggplot2::autoplot() functions plotting minimum inhibitory concentrations (MIC, created .mic()) disk diffusion diameters (created .disk()). random_mic() random_disk() functions, can generate sampled values new data types (S3 classes) : also specific, generating MICs likely found E. coli ciprofloxacin: plot() autoplot() function, can define microorganism antimicrobial agent way. add interpretation values according chosen guidelines (defaults latest EUCAST guideline). Default colours colour-blind friendly, maintaining convention e.g. ‘susceptible’ green ‘resistant’ red: disk diffusion values, much difference plotting: using ggplot2 package, now choosing latest implemented CLSI guideline (notice EUCAST-specific term “Susceptible, incr. exp.” changed “Intermediate”):","code":"mic_values <- random_mic(size = 100) mic_values # Class 'mic' # [1] 128 32 0.01 <=0.001 0.125 0.125 64 0.025 8 # [10] 8 32 8 0.002 1 0.125 0.125 0.002 0.01 # [19] 128 1 <=0.001 8 0.005 0.01 0.002 64 1 # [28] 0.125 0.5 256 256 2 1 0.025 4 8 # [37] 0.025 1 0.5 <=0.001 0.0625 0.002 64 0.002 0.005 # [46] 256 0.01 0.0625 <=0.001 0.025 2 1 256 1 # [55] 2 32 0.25 32 64 0.5 <=0.001 0.005 0.5 # [64] 0.5 32 0.25 4 16 0.025 0.5 <=0.001 16 # [73] 2 0.0625 128 4 2 2 0.005 256 0.125 # [82] 0.002 256 8 <=0.001 0.0625 64 <=0.001 0.002 1 # [91] 64 16 0.025 16 0.025 0.002 1 0.25 32 # [100] 64 # base R: plot(mic_values) # ggplot2: autoplot(mic_values) mic_values <- random_mic(size = 100, mo = \"E. coli\", ab = \"cipro\") # base R: plot(mic_values, mo = \"E. coli\", ab = \"cipro\") # ggplot2: autoplot(mic_values, mo = \"E. coli\", ab = \"cipro\") disk_values <- random_disk(size = 100, mo = \"E. coli\", ab = \"cipro\") disk_values # Class 'disk' # [1] 27 25 21 24 18 18 19 26 23 28 19 21 28 28 31 28 22 26 17 29 19 27 21 24 23 # [26] 20 26 18 18 18 17 28 31 23 23 22 29 29 23 20 21 20 30 22 26 26 31 19 18 25 # [51] 19 22 23 26 21 30 21 27 18 27 21 26 21 30 31 21 31 18 18 18 21 30 27 29 19 # [76] 26 29 25 19 25 20 31 20 24 18 26 28 21 28 18 25 31 17 23 28 21 25 30 26 23 # base R: plot(disk_values, mo = \"E. coli\", ab = \"cipro\") autoplot( disk_values, mo = \"E. coli\", ab = \"cipro\", guideline = \"CLSI\" )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"independence-test","dir":"Articles","previous_headings":"Analysing the data","what":"Independence test","title":"How to conduct AMR data analysis","text":"next example uses example_isolates data set. data set included package contains 2,000 microbial isolates full antibiograms. reflects reality can used practise AMR data analysis. compare resistance amoxicillin/clavulanic acid (column FOS) ICU clinical wards. input fisher.test() can retrieved transformation like : can apply test now : can seen, p value practically zero (0.0000002263247), means amoxicillin/clavulanic acid resistance found isolates patients ICUs clinical wards really different.","code":"# use package 'tidyr' to pivot data: library(tidyr) check_FOS <- example_isolates %>% filter(ward %in% c(\"ICU\", \"Clinical\")) %>% # filter on only these wards select(ward, AMC) %>% # select the wards and amoxi/clav group_by(ward) %>% # group on the wards count_df(combine_SI = TRUE) %>% # count all isolates per group (ward) pivot_wider( names_from = ward, # transform output so \"ICU\" and \"Clinical\" are columns values_from = value ) %>% select(ICU, Clinical) %>% # and only select these columns as.matrix() # transform to a good old matrix for fisher.test() check_FOS # ICU Clinical # [1,] 396 942 # [2,] 184 240 # do Fisher's Exact Test fisher.test(check_FOS) # # Fisher's Exact Test for Count Data # # data: check_FOS # p-value = 2.263e-07 # alternative hypothesis: true odds ratio is not equal to 1 # 95 percent confidence interval: # 0.435261 0.691614 # sample estimates: # odds ratio # 0.5485079"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.3, 2021). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame( mo = c( \"Klebsiella\", \"Escherichia\" ), ampicillin = \"S\" ) oops # mo ampicillin # 1 Klebsiella S # 2 Escherichia S eucast_rules(oops, info = FALSE) # mo ampicillin # 1 Klebsiella R # 2 Escherichia S mo_is_intrinsic_resistant( c(\"Klebsiella\", \"Escherichia\"), \"ampicillin\" ) # [1] TRUE FALSE mo_is_intrinsic_resistant( \"Klebsiella\", c(\"ampicillin\", \"kanamycin\") ) # [1] TRUE FALSE data <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline( CIP == \"R\" & age > 60 ~ \"Elderly Type A\", ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom # A set of custom MDRO rules: # 1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A # 2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B # 3. Otherwise: Negative # # Unmatched rows will return NA. # Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) # x # Negative Elderly Type A Elderly Type B # 1070 198 732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>% mdro() %>% freq() # show frequency table of the result # Warning: in mdro(): NA introduced for isolates where the available percentage of # antimicrobial classes was below 50% (set with pct_required_classes) # random_rsi() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame( rifampicin = random_rsi(5000), isoniazid = random_rsi(5000), gatifloxacin = random_rsi(5000), ethambutol = random_rsi(5000), pyrazinamide = random_rsi(5000), moxifloxacin = random_rsi(5000), kanamycin = random_rsi(5000) ) my_TB_data <- data.frame( RIF = random_rsi(5000), INH = random_rsi(5000), GAT = random_rsi(5000), ETH = random_rsi(5000), PZA = random_rsi(5000), MFX = random_rsi(5000), KAN = random_rsi(5000) ) head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin # 1 R R R S R S # 2 R I I S S I # 3 S S S S S S # 4 I I R R R R # 5 R S S I R S # 6 S R S R R I # kanamycin # 1 S # 2 I # 3 R # 4 S # 5 I # 6 S mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) # ℹ No column found as input for col_mo, assuming all rows contain # Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) # Rows: 2,000 # Columns: 46 # $ date 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… # $ patient \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… # $ age 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… # $ gender \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… # $ ward \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… # $ mo \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… # $ PEN R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… # $ OXA NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FLC NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… # $ AMX NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… # $ AMC I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… # $ AMP NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… # $ TZP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CZO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… # $ FEP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CXM I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… # $ FOX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… # $ CTX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ CAZ NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … # $ CRO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ GEN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TOB NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… # $ AMK NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ KAN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TMP R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … # $ SXT R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… # $ NIT NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… # $ FOS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ LNZ R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… # $ CIP NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… # $ MFX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ VAN R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … # $ TEC R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… # $ TCY R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … # $ TGC NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… # $ DOX NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… # $ ERY R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… # $ CLI R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… # $ AZM R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… # $ IPM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ MEM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ MTR NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CHL NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ COL NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … # $ MUP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ RIF R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>% group_by( order = mo_order(mo), # group on anything, like order genus = mo_genus(mo) ) %>% # and genus as we do here summarise_if(is.rsi, resistance) %>% # then get resistance of all drugs select( order, genus, AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT ) # and select only relevant columns head(resistance_data) # # A tibble: 6 × 10 # # Groups: order [5] # order genus AMC CXM CTX CAZ GEN TOB TMP SXT # # 1 (unknown order) (unknown ge… NA NA NA NA NA NA NA NA # 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA # 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA # 4 Campylobacterales Campylobact… NA NA NA NA NA NA NA NA # 5 Caryophanales Gemella NA NA NA NA NA NA NA NA # 6 Caryophanales Listeria NA NA NA NA NA NA NA NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) # ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" # and \"TOB\". Total observations available: 7. summary(pca_result) # Groups (n=4, named as 'order'): # [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" # Importance of components: # PC1 PC2 PC3 PC4 PC5 PC6 PC7 # Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 # Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 # Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 # Groups (n=4, named as 'order'): # [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups: can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) + ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss-sas-stata","dir":"Articles","previous_headings":"","what":"SPSS / SAS / Stata","title":"How to import data from SPSS / SAS / Stata","text":"SPSS (Statistical Package Social Sciences) probably well-known software package statistical analysis. SPSS easier learn R, SPSS click menu run parts analysis. user-friendliness, taught universities particularly useful students new statistics. experience, guess pretty much (bio)medical students know time graduate. SAS Stata comparable statistical packages popular big industries.","code":""},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"compared-to-r","dir":"Articles","previous_headings":"","what":"Compared to R","title":"How to import data from SPSS / SAS / Stata","text":"said, SPSS easier learn R. SPSS, SAS Stata come major downsides comparing R: R highly modular. official R network (CRAN) features 16,000 packages time writing, AMR package one . packages peer-reviewed publication. Aside official channel, also developers choose submit CRAN, rather keep public repository, like GitHub. may even lot 14,000 packages . Bottom line , can really extend ask somebody . Take example AMR package. Among things, adds reliable reference data R help data cleaning analysis. SPSS, SAS Stata never know valid MIC value Gram stain E. coli . species Klebiella resistant amoxicillin Floxapen® trade name flucloxacillin. facts properties often needed clean existing data, inconvenient software package without reliable reference data. See demonstration. R extremely flexible. write syntax , can anything want. flexibility transforming, arranging, grouping summarising data, drawing plots, endless - SPSS, SAS Stata bound algorithms format styles. may bit flexible, can probably never create specific publication-ready plot without using (paid) software. sometimes write syntaxes SPSS run complete analysis ‘automate’ work, lot less time R. notice writing syntaxes R lot nifty clever SPSS. Still, working statistical package, knowledge (statistically) willing accomplish. R can easily automated. last years, R Markdown really made interesting development. R Markdown, can easily produce reports, whether format Word, PowerPoint, website, PDF document just raw data Excel. even allows use reference file containing layout style (e.g. fonts colours) organisation. use lot generate weekly monthly reports automatically. Just write code enjoy automatically updated reports interval like. even professional environment, create Shiny apps: live manipulation data using custom made website. webdesign knowledge needed (JavaScript, CSS, HTML) almost zero. R huge community. Many R users just ask questions websites like StackOverflow.com, largest online community programmers. time writing, 475,076 R-related questions already asked platform (covers questions answers programming language). experience, questions answered within couple minutes. R understands data type, including SPSS/SAS/Stata. ’s vice versa ’m afraid. can import data source R. example SPSS, SAS Stata (link), Minitab, Epi Info EpiData (link), Excel (link), flat files like CSV, TXT TSV (link), directly databases datawarehouses anywhere world (link). can even scrape websites download tables live internet (link) get results API call transform data one command (link). best part - can export R data formats well. can import SPSS file, analysis neatly R export resulting tables Excel files sharing. R completely free open-source. strings attached. created maintained volunteers believe (data) science open publicly available everybody. SPSS, SAS Stata quite expensive. IBM SPSS Staticstics comes subscriptions nowadays, varying USD 1,300 USD 8,500 per user per year. SAS Analytics Pro costs around USD 10,000 per computer. Stata also business model subscription fees, varying USD 600 USD 2,800 per computer per year, lower prices come limitation number variables can work . still offer benefits R. working midsized small company, can save tens thousands dollars using R instead e.g. SPSS - gaining even functions flexibility. R enthousiasts can much PR want (like ), nobody officially associated affiliated R. really free. R (nowadays) preferred analysis software academic papers. present, R among world powerful statistical languages, generally popular science (Bollmann et al., 2017). reasons, number references R analysis method academic papers rising continuously even surpassed SPSS academic use (Muenchen, 2014). believe thing SPSS , always great user interface easy learn use. Back developed , little competition, let alone R. R didn’t even professional user interface last decade (called RStudio, see ). people used R nineties 2010 almost completely incomparable R used now. language restyled completely volunteers dedicated professionals field data science. SPSS great nothing else compete. now 2022, don’t see reason SPSS better use R. demonstrate first point:","code":"# not all values are valid MIC values: as.mic(0.125) # Class 'mic' # [1] 0.125 as.mic(\"testvalue\") # Class 'mic' # [1] # the Gram stain is available for all bacteria: mo_gramstain(\"E. coli\") # [1] \"Gram-negative\" # Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST: klebsiella_test <- data.frame( mo = \"klebsiella\", amox = \"S\", stringsAsFactors = FALSE ) klebsiella_test # (our original data) # mo amox # 1 klebsiella S eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) # mo amox # 1 klebsiella R # hundreds of trade names can be translated to a name, trade name or an ATC code: ab_name(\"floxapen\") # [1] \"Flucloxacillin\" ab_tradenames(\"floxapen\") # [1] \"culpen\" \"floxacillin\" \"floxacillin sodium\" # [4] \"floxapen\" \"floxapen sodium salt\" \"fluclox\" # [7] \"flucloxacilina\" \"flucloxacillin\" \"flucloxacilline\" # [10] \"flucloxacillinum\" \"fluorochloroxacillin\" \"staphylex\" ab_atc(\"floxapen\") # [1] \"J01CF05\""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"rstudio","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"RStudio","title":"How to import data from SPSS / SAS / Stata","text":"work R, probably best option use RStudio. open-source free desktop environment allows run R code, also supports project management, version management, package management convenient import menus work data sources. can also install RStudio Server private corporate server, brings nothing less complete RStudio software website (home work). import data file, just click Import Dataset Environment tab: additional packages needed, RStudio ask installed beforehand. window opens, can define options (parameters) used import ’re ready go: want named variables imported factors resembles SPSS , use as_factor(). difference :","code":"SPSS_data # # A tibble: 4,203 x 4 # v001 sex status statusage # # 1 10002 1 1 76.6 # 2 10004 0 1 59.1 # 3 10005 1 1 54.5 # 4 10006 1 1 54.1 # 5 10007 1 1 57.7 # 6 10008 1 1 62.8 # 7 10010 0 1 63.7 # 8 10011 1 1 73.1 # 9 10017 1 1 56.7 # 10 10018 0 1 66.6 # # ... with 4,193 more rows as_factor(SPSS_data) # # A tibble: 4,203 x 4 # v001 sex status statusage # # 1 10002 Male alive 76.6 # 2 10004 Female alive 59.1 # 3 10005 Male alive 54.5 # 4 10006 Male alive 54.1 # 5 10007 Male alive 57.7 # 6 10008 Male alive 62.8 # 7 10010 Female alive 63.7 # 8 10011 Male alive 73.1 # 9 10017 Male alive 56.7 # 10 10018 Female alive 66.6 # # ... with 4,193 more rows"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"base-r","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"Base R","title":"How to import data from SPSS / SAS / Stata","text":"import data SPSS, SAS Stata, can use great haven package : can now import files follows:","code":"# download and install the latest version: install.packages(\"haven\") # load the package you just installed: library(haven)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SPSS","title":"How to import data from SPSS / SAS / Stata","text":"read files SPSS R: forget as_factor(), mentioned . export R objects SPSS file format:","code":"# read any SPSS file based on file extension (best way): read_spss(file = \"path/to/file\") # read .sav or .zsav file: read_sav(file = \"path/to/file\") # read .por file: read_por(file = \"path/to/file\") # save as .sav file: write_sav(data = yourdata, path = \"path/to/file\") # save as compressed .zsav file: write_sav(data = yourdata, path = \"path/to/file\", compress = TRUE)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"sas","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SAS","title":"How to import data from SPSS / SAS / Stata","text":"read files SAS R: export R objects SAS file format:","code":"# read .sas7bdat + .sas7bcat files: read_sas(data_file = \"path/to/file\", catalog_file = NULL) # read SAS transport files (version 5 and version 8): read_xpt(file = \"path/to/file\") # save as regular SAS file: write_sas(data = yourdata, path = \"path/to/file\") # the SAS transport format is an open format # (required for submission of the data to the FDA) write_xpt(data = yourdata, path = \"path/to/file\", version = 8)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"stata","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"Stata","title":"How to import data from SPSS / SAS / Stata","text":"read files Stata R: export R objects Stata file format:","code":"# read .dta file: read_stata(file = \"/path/to/file\") # works exactly the same: read_dta(file = \"/path/to/file\") # save as .dta file, Stata version 14: # (supports Stata v8 until v15 at the time of writing) write_dta(data = yourdata, path = \"/path/to/file\", version = 14)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .rsi() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > rsi (numeric) Length: 500 Levels: 3: S < < R Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>% # get microbial ID based on given organism mutate(mo = as.mo(Organism)) %>% # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `rsi` class mutate_at(vars(AMP_ND10:CIP_EE), as.rsi) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_rsi() function:","code":"data %>% group_by(Country) %>% select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>% ggplot_rsi(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 52,141 rows 22 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence snomed. data set R available microorganisms, load AMR package. last updated 20 December 2022 15:14:04 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.3 MB) Download Microsoft Excel workbook (5 MB) Download Apache Feather file (5.4 MB) Download Apache Parquet file (2.6 MB) Download SAS data file (50.9 MB) Download IBM SPSS Statistics data file (16.9 MB) Download Stata DTA file (47.1 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. Advice? Use R instead. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotics-antibiotic-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antibiotics: Antibiotic (+Antifungal) Drugs","title":"Data sets for download / own use","text":"data set 483 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units loinc. data set R available antibiotics, load AMR package. last updated 30 October 2022 20:05:46 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (39 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (66 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (97 kB) Download SAS data file (1.9 MB) Download IBM SPSS Statistics data file (0.3 MB) Download Stata DTA file (0.4 MB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antibiotics: Antibiotic (+Antifungal) Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units loinc. data set R available antivirals, load AMR package. last updated 13 November 2022 07:46:10 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (16 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (15 kB) Download Apache Parquet file (13 kB) Download SAS data file (84 kB) Download IBM SPSS Statistics data file (30 kB) Download Stata DTA file (73 kB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"rsi_translation-interpretation-from-mic-values-disk-diameters-to-rsi","dir":"Articles","previous_headings":"","what":"rsi_translation: Interpretation from MIC values / disk diameters to R/SI","title":"Data sets for download / own use","text":"data set 18,308 rows 11 columns, containing following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R uti. data set R available rsi_translation, load AMR package. last updated 29 October 2022 17:01:23 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (42 kB) Download tab-separated text file (1.9 MB) Download Microsoft Excel workbook (0.8 MB) Download Apache Feather file (0.7 MB) Download Apache Parquet file (87 kB) Download SAS data file (3.6 MB) Download IBM SPSS Statistics data file (2.3 MB) Download Stata DTA file (3.4 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"rsi_translation: Interpretation from MIC values / disk diameters to R/SI","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2013-2022) EUCAST (2013-2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 134,634 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 16 December 2022 15:10:43 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (78 kB) Download tab-separated text file (5.1 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (0.2 MB) Download SAS data file (9.8 MB) Download IBM SPSS Statistics data file (7.4 MB) Download Stata DTA file (9.5 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 336 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt eucast_version. data set R available dosage, load AMR package. last updated 14 November 2022 14:20:39 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (29 kB) Download Microsoft Excel workbook (19 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (8 kB) Download SAS data file (92 kB) Download IBM SPSS Statistics data file (43 kB) Download Stata DTA file (82 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021) ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2,000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP RIF. data set R available example_isolates, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3,000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions: fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_rsi_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\") # or: example_isolates %>% resistance_predict( col_ab = \"TZP\", model = \"binomial\" ) # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>% resistance_predict( col_ab = \"TZP\", model = \"binomial\" ) predict_TZP # # A tibble: 31 × 7 # year value se_min se_max observations observed estimated # * # 1 2002 0.2 NA NA 15 0.2 0.0562 # 2 2003 0.0625 NA NA 32 0.0625 0.0616 # 3 2004 0.0854 NA NA 82 0.0854 0.0676 # 4 2005 0.05 NA NA 60 0.05 0.0741 # 5 2006 0.0508 NA NA 59 0.0508 0.0812 # 6 2007 0.121 NA NA 66 0.121 0.0889 # 7 2008 0.0417 NA NA 72 0.0417 0.0972 # 8 2009 0.0164 NA NA 61 0.0164 0.106 # 9 2010 0.0566 NA NA 53 0.0566 0.116 # 10 2011 0.183 NA NA 93 0.183 0.127 # # … with 21 more rows plot(predict_TZP) ggplot_rsi_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_rsi_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous: Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate: model also available object, attribute:","code":"example_isolates %>% filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>% resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>% ggplot_rsi_predict() example_isolates %>% filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>% resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>% ggplot_rsi_predict() model <- attributes(predict_TZP)$model summary(model)$family # # Family: binomial # Link function: logit summary(model)$coefficients # Estimate Std. Error z value Pr(>|z|) # (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 # year 0.09883005 0.02295317 4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Christian F. Luz. Author, contributor. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Casper J. Albers. Thesis advisor. Peter Dutey-Magni. Contributor. Judith M. Fonville. Contributor. Alex W. Friedrich. Thesis advisor. Corinna Glasner. Thesis advisor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Annick Lenglet. Contributor. Bart C. Meijer. Contributor. Dmytro Mykhailenko. Contributor. Anton Mymrikov. Contributor. Sofia Ny. Contributor. Jonas Salm. Contributor. Rogier P. Schade. Contributor. Bhanu N. M. Sinha. Thesis advisor. Anthony Underwood. Contributor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, journal = {Journal of Statistical Software}, year = {2022}, volume = {104}, number = {3}, pages = {1--31}, doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Works Windows, macOS Linux versions R since R-3.0 Provides full microbiological taxonomy data antimicrobial drugs Applies recent CLSI EUCAST clinical breakpoints MICs disk zones Corrects duplicate isolates, calculates predicts AMR per antibiotic class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC SNOMED CT Completely dependency-free, highly suitable places limited resources https://msberends.github.io/AMR https://doi.org/10.18637/jss.v104.i03","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 distinct microbial species (updated December 2022) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid R/SI MIC values. integral breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen, actively durably maintained two public healthcare organisations Netherlands.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-175-countries-translated-to-16-languages","dir":"","previous_headings":"Introduction","what":"Used in 175 countries, translated to 16 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. AMR package available English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish, Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"defined row filter Gram-negative bacteria intrinsic resistance cefotaxime (mo_is_gram_negative() mo_is_intrinsic_resistant()) column selection two antibiotic groups (aminoglycosides() carbapenems()), reference data microorganisms antibiotics AMR package make sure get meant: base R equivalent : base R snippet work version R since April 2013 (R-3.0).","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr) example_isolates %>% mutate(bacteria = mo_fullname()) %>% filter(mo_is_gram_negative(), mo_is_intrinsic_resistant(ab = \"cefotax\")) %>% select(bacteria, aminoglycosides(), carbapenems()) example_isolates$bacteria <- mo_fullname(example_isolates$mo) example_isolates[which(mo_is_gram_negative() & mo_is_intrinsic_resistant(ab = \"cefotax\")), c(\"bacteria\", aminoglycosides(), carbapenems())]"},{"path":"https://msberends.github.io/AMR/index.html","id":"calculating-resistance-per-group","dir":"","previous_headings":"Practical examples","what":"Calculating resistance per group","title":"Antimicrobial Resistance Data Analysis","text":"","code":"library(AMR) library(dplyr) out <- example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance(), over all aminoglycosides # and polymyxins: summarise(across(c(aminoglycosides(), polymyxins()), resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (manual) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline last 10 years (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antibiotic (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters R/SI (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed GitHub two ways: Manually, using: Automatically, using rOpenSci R-universe platform, adding R-universe address list repositories (‘repos’): , can install update AMR package like official release (e.g., using install.packages(\"AMR\") RStudio via Tools > Check Package Updates…).","code":"install.packages(\"remotes\") # if you haven't already remotes::install_github(\"msberends/AMR\") options(repos = c(getOption(\"repos\"), msberends = \"https://msberends.r-universe.dev\"))"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible following non-profit organisations initiatives:","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"functions -called 'Deprecated'. removed future release. Using functions give warning name function replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR free, open-source independent R package simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible antimicrobial resistance data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. work published Journal Statistical Software (Volume 104(3); doi:10.18637/jss.v104.i03 ) formed basis two PhD theses (doi:10.33612/diss.177417131 doi:10.33612/diss.192486375 ). installing package, R knows ~52,000 distinct microbial species ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-NET, LOINC SNOMED CT), knows valid R/SI MIC values. supports data format, including WHONET/EARS-Net data. package fully independent R package works Windows, macOS Linux versions R since R-3.0.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice University Medical Center Groningen. R package actively maintained free software; can freely use distribute personal commercial (patent) purposes terms GNU General Public License version 2.0 (GPL-2), published Free Software Foundation. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline last 10 years Retrieving antimicrobial drug names, doses forms administration clinical health care records Determining first isolates used AMR data analysis Calculating antimicrobial resistance Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) Calculating (empirical) susceptibility mono therapy combination therapies Predicting future antimicrobial resistance using regression models Getting properties microorganism (Gram stain, species, genus family) Getting properties antibiotic (name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) Plotting antimicrobial resistance Applying EUCAST expert rules Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code Machine reading EUCAST CLSI guidelines 2011-2020 translate MIC values disk diffusion diameters R/SI Principal component analysis AMR","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"The AMR Package — AMR","text":"cite AMR publications use: Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). \"AMR: R Package Working Antimicrobial Resistance Data.\" Journal Statistical Software, 104(3), 1-31. doi:10.18637/jss.v104.i03 . BibTeX entry LaTeX users :","code":"@Article{, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, journal = {Journal of Statistical Software}, year = {2022}, volume = {104}, number = {3}, pages = {1--31}, doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"The AMR Package — AMR","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The AMR Package — AMR","text":"Maintainer: Matthijs S. Berends m.berends@certe.nl (ORCID) Authors: Christian F. Luz (ORCID) [contributor] Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Casper J. Albers (ORCID) [thesis advisor] Peter Dutey-Magni (ORCID) [contributor] Judith M. Fonville [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Bart C. Meijer [contributor] Dmytro Mykhailenko [contributor] Anton Mymrikov [contributor] Sofia Ny (ORCID) [contributor] Jonas Salm [contributor] Rogier P. Schade [contributor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Anthony Underwood (ORCID) [contributor]","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class 'ab' #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\" ab_tradenames(\"flucloxacillin\") #> [1] \"culpen\" \"floxacillin\" \"floxacillin sodium\" #> [4] \"floxapen\" \"floxapen sodium salt\" \"fluclox\" #> [7] \"flucloxacilina\" \"flucloxacillin\" \"flucloxacilline\" #> [10] \"flucloxacillinum\" \"fluorochloroxacillin\" \"staphylex\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antibiotic results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"tibble 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 28 different antibiotics. can lookup abbreviations antibiotics data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antibiotic class, using .rsi().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> # A tibble: 500 × 53 #> Identif…¹ Speci…² Organ…³ Country Labor…⁴ Last …⁵ First…⁶ Sex Age Age c…⁷ #> #> 1 fe41d7ba… 1748 SPN Belgium Nation… Abel B. F 68 55-74 #> 2 91f175ec… 1767 eco The Ne… Nation… Delacr… F. M 89 75+ #> 3 cc401505… 1343 eco The Ne… Nation… Steens… F. M 85 75+ #> 4 e864b692… 1894 MAP Denmark Nation… Beyers… L. M 62 55-74 #> 5 3d051fe3… 1739 PVU Belgium Nation… Hummel W. M 86 75+ #> 6 c80762a0… 1846 103 The Ne… Nation… Eikenb… J. F 53 25-54 #> 7 8022d372… 1628 103 Denmark Nation… Leclerc S. F 77 75+ #> 8 f3dc5f55… 1493 eco The Ne… Nation… Delacr… W. M 53 25-54 #> 9 15add38f… 1847 eco France Nation… Van La… S. F 63 55-74 #> 10 fd41248d… 1458 eco Germany Nation… Moulin O. F 75 75+ #> # … with 490 more rows, 43 more variables: `Date of admission` , #> # `Specimen date` , `Specimen type` , #> # `Specimen type (Numeric)` , Reason , `Isolate number` , #> # `Organism type` , Serotype , `Beta-lactamase` , ESBL , #> # Carbapenemase , `MRSA screening test` , #> # `Inducible clindamycin resistance` , Comment , #> # `Date of data entry` , AMP_ND10 , AMC_ED20 , …"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text( text, type = c(\"drug\", \"dose\", \"administration\"), collapse = NULL, translate_ab = FALSE, thorough_search = NULL, info = interactive(), ... )"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antibiotics data set translate antibiotic abbreviations , using ab_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list, character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line, # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tid\") #> [[1]] #> Class 'ab' #> [1] AMX #> ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class 'ab' #> [1] AMX CIP #> ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #> ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\" #> ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\" if (require(\"dplyr\")) { tibble(clinical_text = c( \"given 400mg cipro and 500 mg amox\", \"started on doxy iv today\" )) %>% mutate( abx_codes = ab_from_text(clinical_text), abx_doses = ab_from_text(clinical_text, type = \"doses\"), abx_admin = ab_from_text(clinical_text, type = \"admin\"), abx_coll = ab_from_text(clinical_text, collapse = \"|\"), abx_coll_names = ab_from_text(clinical_text, collapse = \"|\", translate_ab = \"name\" ), abx_coll_doses = ab_from_text(clinical_text, type = \"doses\", collapse = \"|\" ), abx_coll_admin = ab_from_text(clinical_text, type = \"admin\", collapse = \"|\" ) ) } #> Loading required package: dplyr #> #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #> clinical_text abx_c…¹ abx_d…² abx_a…³ abx_c…⁴ abx_c…⁵ abx_c…⁶ abx_c…⁷ #> #> 1 given 400mg cipro and… CIP|AMX Ciprof… 400|500 NA #> 2 started on doxy iv to… DOX Doxycy… NA iv #> # … with abbreviated variable names ¹​abx_codes, ²​abx_doses, ³​abx_admin, #> # ⁴​abx_coll, ⁵​abx_coll_names, ⁶​abx_coll_doses, ⁷​abx_coll_admin # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antibiotics data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...) ab_cid(x, ...) ab_synonyms(x, ...) ab_tradenames(x, ...) ab_group(x, language = get_AMR_locale(), ...) ab_atc(x, only_first = FALSE, ...) ab_atc_group1(x, language = get_AMR_locale(), ...) ab_atc_group2(x, language = get_AMR_locale(), ...) ab_loinc(x, ...) ab_ddd(x, administration = \"oral\", ...) ab_ddd_units(x, administration = \"oral\", ...) ab_info(x, language = get_AMR_locale(), ...) ab_url(x, open = FALSE, ...) ab_property(x, property = \"name\", language = get_AMR_locale(), ...) set_ab_names( data, ..., property = \"name\", language = get_AMR_locale(), snake_case = NULL )"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic drug code .ab() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: variables select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antibiotics data set: vector_or(colnames(antibiotics), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\") #> [1] \"Amoxicillin\" ab_atc(\"AMX\") #> [1] \"J01CA04\" ab_cid(\"AMX\") #> [1] 33613 ab_synonyms(\"AMX\") #> [1] \"actimoxi\" \"amoclen\" \"amolin\" #> [4] \"amopen\" \"amopenixin\" \"amoxibiotic\" #> [7] \"amoxicaps\" \"amoxicilina\" \"amoxicillin\" #> [10] \"amoxicillin hydrate\" \"amoxicilline\" \"amoxicillinum\" #> [13] \"amoxiden\" \"amoxil\" \"amoxivet\" #> [16] \"amoxy\" \"amoxycillin\" \"amoxyke\" #> [19] \"anemolin\" \"aspenil\" \"atoksilin\" #> [22] \"biomox\" \"bristamox\" \"cemoxin\" #> [25] \"clamoxyl\" \"damoxy\" \"delacillin\" #> [28] \"demoksil\" \"dispermox\" \"efpenix\" #> [31] \"flemoxin\" \"hiconcil\" \"histocillin\" #> [34] \"hydroxyampicillin\" \"ibiamox\" \"imacillin\" #> [37] \"lamoxy\" \"largopen\" \"metafarma capsules\" #> [40] \"metifarma capsules\" \"moksilin\" \"moxacin\" #> [43] \"moxatag\" \"ospamox\" \"pamoxicillin\" #> [46] \"piramox\" \"promoxil\" \"remoxil\" #> [49] \"robamox\" \"sawamox pm\" \"tolodina\" #> [52] \"topramoxin\" \"unicillin\" \"utimox\" #> [55] \"vetramox\" ab_tradenames(\"AMX\") #> [1] \"actimoxi\" \"amoclen\" \"amolin\" #> [4] \"amopen\" \"amopenixin\" \"amoxibiotic\" #> [7] \"amoxicaps\" \"amoxicilina\" \"amoxicillin\" #> [10] \"amoxicillin hydrate\" \"amoxicilline\" \"amoxicillinum\" #> [13] \"amoxiden\" \"amoxil\" \"amoxivet\" #> [16] \"amoxy\" \"amoxycillin\" \"amoxyke\" #> [19] \"anemolin\" \"aspenil\" \"atoksilin\" #> [22] \"biomox\" \"bristamox\" \"cemoxin\" #> [25] \"clamoxyl\" \"damoxy\" \"delacillin\" #> [28] \"demoksil\" \"dispermox\" \"efpenix\" #> [31] \"flemoxin\" \"hiconcil\" \"histocillin\" #> [34] \"hydroxyampicillin\" \"ibiamox\" \"imacillin\" #> [37] \"lamoxy\" \"largopen\" \"metafarma capsules\" #> [40] \"metifarma capsules\" \"moksilin\" \"moxacin\" #> [43] \"moxatag\" \"ospamox\" \"pamoxicillin\" #> [46] \"piramox\" \"promoxil\" \"remoxil\" #> [49] \"robamox\" \"sawamox pm\" \"tolodina\" #> [52] \"topramoxin\" \"unicillin\" \"utimox\" #> [55] \"vetramox\" ab_group(\"AMX\") #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\") #> Amoxicillin #> \"https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no\" # smart lowercase tranformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\" ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\" # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\" ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #> #> $cid #> [1] 33613 #> #> $name #> [1] \"Amoxicillin\" #> #> $group #> [1] \"Beta-lactams/penicillins\" #> #> $atc #> [1] \"J01CA04\" #> #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #> #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #> #> $tradenames #> [1] \"actimoxi\" \"amoclen\" \"amolin\" #> [4] \"amopen\" \"amopenixin\" \"amoxibiotic\" #> [7] \"amoxicaps\" \"amoxicilina\" \"amoxicillin\" #> [10] \"amoxicillin hydrate\" \"amoxicilline\" \"amoxicillinum\" #> [13] \"amoxiden\" \"amoxil\" \"amoxivet\" #> [16] \"amoxy\" \"amoxycillin\" \"amoxyke\" #> [19] \"anemolin\" \"aspenil\" \"atoksilin\" #> [22] \"biomox\" \"bristamox\" \"cemoxin\" #> [25] \"clamoxyl\" \"damoxy\" \"delacillin\" #> [28] \"demoksil\" \"dispermox\" \"efpenix\" #> [31] \"flemoxin\" \"hiconcil\" \"histocillin\" #> [34] \"hydroxyampicillin\" \"ibiamox\" \"imacillin\" #> [37] \"lamoxy\" \"largopen\" \"metafarma capsules\" #> [40] \"metifarma capsules\" \"moksilin\" \"moxacin\" #> [43] \"moxatag\" \"ospamox\" \"pamoxicillin\" #> [46] \"piramox\" \"promoxil\" \"remoxil\" #> [49] \"robamox\" \"sawamox pm\" \"tolodina\" #> [52] \"topramoxin\" \"unicillin\" \"utimox\" #> [55] \"vetramox\" #> #> $loinc #> [1] \"16365-9\" \"25274-2\" \"3344-9\" \"80133-2\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #> [1] \"21066-6\" \"3355-5\" \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\" # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\" # use set_ab_names() for renaming columns colnames(example_isolates) #> [1] \"date\" \"patient\" \"age\" \"gender\" \"ward\" \"mo\" \"PEN\" #> [8] \"OXA\" \"FLC\" \"AMX\" \"AMC\" \"AMP\" \"TZP\" \"CZO\" #> [15] \"FEP\" \"CXM\" \"FOX\" \"CTX\" \"CAZ\" \"CRO\" \"GEN\" #> [22] \"TOB\" \"AMK\" \"KAN\" \"TMP\" \"SXT\" \"NIT\" \"FOS\" #> [29] \"LNZ\" \"CIP\" \"MFX\" \"VAN\" \"TEC\" \"TCY\" \"TGC\" #> [36] \"DOX\" \"ERY\" \"CLI\" \"AZM\" \"IPM\" \"MEM\" \"MTR\" #> [43] \"CHL\" \"COL\" \"MUP\" \"RIF\" colnames(set_ab_names(example_isolates)) #> [1] \"date\" \"patient\" #> [3] \"age\" \"gender\" #> [5] \"ward\" \"mo\" #> [7] \"benzylpenicillin\" \"oxacillin\" #> [9] \"flucloxacillin\" \"amoxicillin\" #> [11] \"amoxicillin_clavulanic_acid\" \"ampicillin\" #> [13] \"piperacillin_tazobactam\" \"cefazolin\" #> [15] \"cefepime\" \"cefuroxime\" #> [17] \"cefoxitin\" \"cefotaxime\" #> [19] \"ceftazidime\" \"ceftriaxone\" #> [21] \"gentamicin\" \"tobramycin\" #> [23] \"amikacin\" \"kanamycin\" #> [25] \"trimethoprim\" \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" #> [31] \"moxifloxacin\" \"vancomycin\" #> [33] \"teicoplanin\" \"tetracycline\" #> [35] \"tigecycline\" \"doxycycline\" #> [37] \"erythromycin\" \"clindamycin\" #> [39] \"azithromycin\" \"imipenem\" #> [41] \"meropenem\" \"metronidazole\" #> [43] \"chloramphenicol\" \"colistin\" #> [45] \"mupirocin\" \"rifampicin\" colnames(set_ab_names(example_isolates, NIT:VAN)) #> [1] \"date\" \"patient\" \"age\" \"gender\" #> [5] \"ward\" \"mo\" \"PEN\" \"OXA\" #> [9] \"FLC\" \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" \"CXM\" #> [17] \"FOX\" \"CTX\" \"CAZ\" \"CRO\" #> [21] \"GEN\" \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" \"moxifloxacin\" \"vancomycin\" #> [33] \"TEC\" \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" \"IPM\" #> [41] \"MEM\" \"MTR\" \"CHL\" \"COL\" #> [45] \"MUP\" \"RIF\" # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% set_ab_names() %>% head() # this does the same: example_isolates %>% rename_with(set_ab_names) %>% head() # set_ab_names() works with any AB property: example_isolates %>% set_ab_names(property = \"atc\") %>% head() example_isolates %>% set_ab_names(where(is.rsi)) %>% colnames() example_isolates %>% set_ab_names(NIT:VAN) %>% colnames() } #> [1] \"date\" \"patient\" \"age\" \"gender\" #> [5] \"ward\" \"mo\" \"PEN\" \"OXA\" #> [9] \"FLC\" \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" \"CXM\" #> [17] \"FOX\" \"CTX\" \"CAZ\" \"CRO\" #> [21] \"GEN\" \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"nitrofurantoin\" \"fosfomycin\" #> [29] \"linezolid\" \"ciprofloxacin\" \"moxifloxacin\" \"vancomycin\" #> [33] \"TEC\" \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" \"IPM\" #> [41] \"MEM\" \"MTR\" \"CHL\" \"COL\" #> [45] \"MUP\" \"RIF\" # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials to This Package — add_custom_antimicrobials","title":"Add Custom Antimicrobials to This Package — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug codes AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials to This Package — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x) clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials to This Package — add_custom_antimicrobials","text":"x data.frame resembling antibiotics data set, least containing columns \"ab\" \"name\"","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Antimicrobials to This Package — add_custom_antimicrobials","text":"Due R works, add_custom_antimicrobials() function run every R session - added antimicrobials stored sessions thus lost R exited. possible save antimicrobial additions .Rprofile file circumvent , although requires load AMR package every start-: Use clear_custom_antimicrobials() clear previously added antimicrobials.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\") # Add custom antibiotic drug codes: library(AMR) add_custom_antimicrobials( data.frame(ab = \"TESTAB\", name = \"Test Antibiotic\", group = \"Test Group\") )"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Antimicrobials to This Package — add_custom_antimicrobials","text":"","code":"# \\donttest{ # returns NA and throws a warning (which is now suppressed): suppressWarnings( as.ab(\"testab\") ) #> Class 'ab' #> [1] # now add a custom entry - it will be considered by as.ab() and # all ab_*() functions add_custom_antimicrobials( data.frame( ab = \"TESTAB\", name = \"Test Antibiotic\", # you can add any property present in the # 'antibiotics' data set, such as 'group': group = \"Test Group\" ) ) #> ℹ Added one record to the internal antibiotics data set. # \"testab\" is now a new antibiotic: as.ab(\"testab\") #> Class 'ab' #> [1] TESTAB ab_name(\"testab\") #> [1] \"Test Antibiotic\" ab_group(\"testab\") #> [1] \"Test Group\" ab_info(\"testab\") #> $ab #> [1] \"TESTAB\" #> #> $cid #> [1] NA #> #> $name #> [1] \"Test Antibiotic\" #> #> $group #> [1] \"Test Group\" #> #> $atc #> [1] NA #> #> $atc_group1 #> [1] NA #> #> $atc_group2 #> [1] NA #> #> $tradenames #> [1] NA #> #> $loinc #> [1] NA #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] NA #> #> $ddd$oral$units #> [1] NA #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] NA #> #> $ddd$iv$units #> [1] NA #> #> #> # Add Co-fluampicil, which is one of the many J01CR50 codes, see # https://www.whocc.no/ddd/list_of_ddds_combined_products/ add_custom_antimicrobials( data.frame( ab = \"COFLU\", name = \"Co-fluampicil\", atc = \"J01CR50\", group = \"Beta-lactams/penicillines\" ) ) #> ℹ Added one record to the internal antibiotics data set. ab_atc(\"Co-fluampicil\") #> [1] \"J01CR50\" ab_name(\"J01CR50\") #> [1] \"Co-fluampicil\" # even antibiotic selectors work x <- data.frame( random_column = \"some value\", coflu = as.rsi(\"S\"), ampicillin = as.rsi(\"R\") ) x #> random_column coflu ampicillin #> 1 some value S R x[, betalactams()] #> ℹ For betalactams() using columns 'coflu' (co-fluampicil) and #> 'ampicillin' #> coflu ampicillin #> 1 S R # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (defaults today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000) # add ages df$age <- age(df$birth_date) # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE) # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\") df #> birth_date age age_exact age_at_y2k #> 1 1984-08-19 38 38.33699 15 #> 2 1983-11-11 39 39.10685 16 #> 3 1948-12-24 73 73.98904 51 #> 4 1993-06-16 29 29.51233 6 #> 5 1971-11-03 51 51.12877 28 #> 6 1946-01-19 76 76.91781 53 #> 7 1949-03-27 73 73.73425 50 #> 8 1966-05-07 56 56.62192 33 #> 9 1956-08-07 66 66.36986 43 #> 10 1961-02-25 61 61.81644 38"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x , defaults age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21) # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+ 0-49 50+ 50+ 0-49 0-49 #> Levels: 0-49 < 50+ # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19 0-19 0-19 50+ 20-49 50+ 50+ 20-49 20-49 #> Levels: 0-19 < 20-49 < 50+ # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9 0-9 10-19 50-59 30-39 70-79 100+ 40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9 0-9 10-19 50-59 30-39 70-79 100+ 40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4 5-9 15-19 50-54 30-34 75-79 100+ 40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4 5-9 15-19 50-54 30-34 75-79 100+ 40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3 6-12 13-17 18+ 18+ 18+ 18+ 18+ 18+ #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3 6-12 13-17 18+ 18+ 18+ 18+ 18+ 18+ #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\") && require(\"ggplot2\")) { example_isolates %>% filter_first_isolate() %>% filter(mo == as.mo(\"Escherichia coli\")) %>% group_by(age_group = age_groups(age)) %>% select(age_group, CIP) %>% ggplot_rsi( x = \"age_group\", minimum = 0, x.title = \"Age Group\", title = \"Ciprofloxacin resistance per age group\" ) } #> Loading required package: ggplot2 #> Including isolates from ICU. # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antibiotic Selectors — antibiotic_class_selectors","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions allow filtering rows selecting columns based antibiotic test results specific antibiotic class group, without need define columns antibiotic abbreviations. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"CZO\" \"J01DB04\" picked cephalosporins().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"ab_class(ab_class, only_rsi_columns = FALSE, only_treatable = TRUE, ...) ab_selector(filter, only_rsi_columns = FALSE, only_treatable = TRUE, ...) aminoglycosides(only_rsi_columns = FALSE, only_treatable = TRUE, ...) aminopenicillins(only_rsi_columns = FALSE, ...) antifungals(only_rsi_columns = FALSE, ...) antimycobacterials(only_rsi_columns = FALSE, ...) betalactams(only_rsi_columns = FALSE, only_treatable = TRUE, ...) carbapenems(only_rsi_columns = FALSE, only_treatable = TRUE, ...) cephalosporins(only_rsi_columns = FALSE, ...) cephalosporins_1st(only_rsi_columns = FALSE, ...) cephalosporins_2nd(only_rsi_columns = FALSE, ...) cephalosporins_3rd(only_rsi_columns = FALSE, ...) cephalosporins_4th(only_rsi_columns = FALSE, ...) cephalosporins_5th(only_rsi_columns = FALSE, ...) fluoroquinolones(only_rsi_columns = FALSE, ...) glycopeptides(only_rsi_columns = FALSE, ...) lincosamides(only_rsi_columns = FALSE, ...) lipoglycopeptides(only_rsi_columns = FALSE, ...) macrolides(only_rsi_columns = FALSE, ...) oxazolidinones(only_rsi_columns = FALSE, ...) penicillins(only_rsi_columns = FALSE, ...) polymyxins(only_rsi_columns = FALSE, only_treatable = TRUE, ...) streptogramins(only_rsi_columns = FALSE, ...) quinolones(only_rsi_columns = FALSE, ...) tetracyclines(only_rsi_columns = FALSE, ...) trimethoprims(only_rsi_columns = FALSE, ...) ureidopenicillins(only_rsi_columns = FALSE, ...) administrable_per_os(only_rsi_columns = FALSE, ...) administrable_iv(only_rsi_columns = FALSE, ...) not_intrinsic_resistant( only_rsi_columns = FALSE, col_mo = NULL, version_expertrules = 3.3, ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"ab_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antibiotics data set searched (case-insensitive) value. only_rsi_columns logical indicate whether columns class rsi must selected (defaults FALSE), see .rsi() only_treatable logical indicate whether antimicrobial drugs excluded laboratory tests (defaults TRUE), gentamicin-high (GEH) imipenem/EDTA (IPE) ... ignored, place allow future extensions filter expression evaluated antibiotics data set, name %like% \"trim\" col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"(internally) character vector column names, additional class \"ab_selector\"","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions can used data set calls selecting columns filtering rows. heavily inspired Tidyverse selection helpers everything(), also work base R dplyr verbs. Nonetheless, convenient use dplyr functions select(), filter() summarise(), see Examples. columns data functions called searched known antibiotic names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antibiotics data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. ab_class() function can used filter/select manually defined antibiotic class. searches results antibiotics data set within columns group, atc_group1 atc_group2. ab_selector() function can used internally filter antibiotics data set results, see Examples. allows filtering (part ) certain name, /group name even minimum DDDs oral treatment. function yields highest flexibility, also least user-friendly, since requires hard-coded filter set. administrable_per_os() administrable_iv() functions also rely antibiotics data set - antibiotic columns matched DDD (defined daily dose) resp. oral IV treatment available antibiotics data set. not_intrinsic_resistant() function can used select antibiotic columns pose intrinsic resistance microorganisms data set. example, data set contains microorganism codes names E. coli K. pneumoniae contains column \"vancomycin\", column removed (rather, unselected) using function. currently applies 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"full-list-of-supported-antibiotic-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antibiotic) classes","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"aminoglycosides() can select: amikacin (AMK), amikacin/fosfomycin (AKF), amphotericin B-high (AMH), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB) tobramycin-high (TOH) aminopenicillins() can select: amoxicillin (AMX) ampicillin (AMP) antifungals() can select: amphotericin B (AMB), anidulafungin (ANI), butoconazole (), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), flucytosine (FCT), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), oteseconazole (OTE), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC) voriconazole (VOR) antimycobacterials() can select: 4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), protionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), simvastatin/fenofibrate (SMF), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone (TAT), thioacetazone/isoniazid (THI1), tiocarlide (TCR) viomycin (VIO) betalactams() can select: amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) carbapenems() can select: biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA) tebipenem (TBP) cephalosporins() can select: cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), latamoxef (LTM) loracarbef (LOR) cephalosporins_1st() can select: cefacetrile (CAC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefroxadine (CRD), ceftezole (CTL) cephradine (CED) cephalosporins_2nd() can select: cefaclor (CEC), cefamandole (MAN), cefmetazole (CMZ), cefonicid (CID), ceforanide (CND), cefotetan (CTT), cefotiam (CTF), cefoxitin (FOX), cefoxitin screening (FOX1), cefprozil (CPR), cefuroxime (CXM), cefuroxime axetil (CXA) loracarbef (LOR) cephalosporins_3rd() can select: cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefetamet (CAT), cefetamet pivoxil (CPI), cefixime (CFM), cefmenoxime (CMX), cefodizime (DIZ), cefoperazone (CFP), cefoperazone/sulbactam (CSL), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotiam hexetil (CHE), cefovecin (FOV), cefpimizole (CFZ), cefpiramide (CPM), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefsulodin (CFS), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB) latamoxef (LTM) cephalosporins_4th() can select: cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetecol (CCL), cefoselis (CSE), cefozopran (ZOP), cefpirome (CPO) cefquinome (CEQ) cephalosporins_5th() can select: ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftobiprole (BPR), ceftobiprole medocaril (CFM1) ceftolozane/tazobactam (CZT) fluoroquinolones() can select: besifloxacin (BES), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nifuroquine (NIF), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) glycopeptides() can select: avoparcin (AVO), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN) vancomycin-macromethod (VAM) lincosamides() can select: acetylmidecamycin (ACM), acetylspiramycin (ASP), clindamycin (CLI), gamithromycin (GAM), kitasamycin (KIT), lincomycin (LIN), meleumycin (MEL), nafithromycin (ZWK), pirlimycin (PRL), primycin (PRM), solithromycin (SOL), tildipirosin (TIP), tilmicosin (TIL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) lipoglycopeptides() can select: dalbavancin (DAL), oritavancin (ORI) telavancin (TLV) macrolides() can select: acetylmidecamycin (ACM), acetylspiramycin (ASP), azithromycin (AZM), clarithromycin (CLR), dirithromycin (DIR), erythromycin (ERY), flurithromycin (FLR1), gamithromycin (GAM), josamycin (JOS), kitasamycin (KIT), meleumycin (MEL), midecamycin (MID), miocamycin (MCM), nafithromycin (ZWK), oleandomycin (OLE), pirlimycin (PRL), primycin (PRM), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) oxazolidinones() can select: cadazolid (CDZ), cycloserine (CYC), linezolid (LNZ), tedizolid (TZD) thiacetazone (THA) penicillins() can select: amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), carbenicillin (CRB), carindacillin (CRN), cefepime/nacubactam (FNC), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), epicillin (EPC), flucloxacillin (FLC), hetacillin (HET), lenampicillin (LEN), mecillinam (MEC), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) polymyxins() can select: colistin (COL), polymyxin B (PLB) polymyxin B/polysorbate 80 (POP) quinolones() can select: besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), flumequine (FLM), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pipemidic acid (PPA), piromidic acid (PIR), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rosoxacin (ROS), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) streptogramins() can select: pristinamycin (PRI) quinupristin/dalfopristin (QDA) tetracyclines() can select: cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), sarecycline (SRC), tetracycline (TCY) tigecycline (TGC) trimethoprims() can select: brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP) trimethoprim/sulfamethoxazole (SXT) ureidopenicillins() can select: azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP) piperacillin/tazobactam (TZP)","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF # base R ------------------------------------------------------------------ # select columns 'IPM' (imipenem) and 'MEM' (meropenem) example_isolates[, carbapenems()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #> IPM MEM #> #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> 7 NA NA #> 8 NA NA #> 9 NA NA #> 10 NA NA #> # … with 1,990 more rows # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #> mo GEN TOB AMK KAN #> #> 1 B_ESCHR_COLI NA NA NA NA #> 2 B_ESCHR_COLI NA NA NA NA #> 3 B_STPHY_EPDR NA NA NA NA #> 4 B_STPHY_EPDR NA NA NA NA #> 5 B_STPHY_EPDR NA NA NA NA #> 6 B_STPHY_EPDR NA NA NA NA #> 7 B_STPHY_AURS NA S NA NA #> 8 B_STPHY_AURS NA S NA NA #> 9 B_STPHY_EPDR NA NA NA NA #> 10 B_STPHY_EPDR NA NA NA NA #> # … with 1,990 more rows # select only antibiotic columns with DDDs for oral treatment example_isolates[, administrable_per_os()] #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #> (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #> 'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #> (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #> (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #> (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #> (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #> (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #> (rifampicin) #> # A tibble: 2,000 × 23 #> OXA FLC AMX AMC AMP CXM KAN TMP NIT FOS LNZ CIP MFX #> #> 1 NA NA NA I NA I NA R NA NA R NA NA #> 2 NA NA NA I NA I NA R NA NA R NA NA #> 3 NA R NA NA NA R NA S NA NA NA NA NA #> 4 NA R NA NA NA R NA S NA NA NA NA NA #> 5 NA R NA NA NA R NA R NA NA NA NA NA #> 6 NA R NA NA NA R NA R NA NA NA NA NA #> 7 NA S R S R S NA R NA NA NA NA NA #> 8 NA S R S R S NA R NA NA NA NA NA #> 9 NA R NA NA NA R NA S NA NA NA S NA #> 10 NA S NA NA NA S NA S NA NA NA S NA #> # … with 1,990 more rows, and 10 more variables: VAN , TCY , #> # DOX , ERY , CLI , AZM , MTR , CHL , #> # COL , RIF # filter using any() or all() example_isolates[any(carbapenems() == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 2 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 3 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 4 2005-04-21 452212 82 F ICU B_ENTRC NA NA NA NA #> 5 2005-04-22 452212 82 F ICU B_ENTRC NA NA NA NA #> 6 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 8 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 9 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> 10 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> # … with 45 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF subset(example_isolates, any(carbapenems() == \"R\")) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 2 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> 3 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 4 2005-04-21 452212 82 F ICU B_ENTRC NA NA NA NA #> 5 2005-04-22 452212 82 F ICU B_ENTRC NA NA NA NA #> 6 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 8 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 9 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> 10 2008-01-22 1710B8 82 M Clinical B_PROTS_MRBL R NA NA NA #> # … with 45 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): example_isolates[any(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"R\", \"S\" or \"I\" #> # A tibble: 962 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 6 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 7 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 8 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 9 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 10 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R #> # … with 952 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF example_isolates[all(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"R\", \"S\" or \"I\" #> # A tibble: 756 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-04-14 F30196 73 M Outpat… B_STRPT_GRPB S NA S S #> 2 2003-04-08 114570 74 M ICU B_STRPT_PYGN S NA S S #> 3 2003-04-08 114570 74 M ICU B_STRPT_GRPA S NA S S #> 4 2003-04-08 114570 74 M ICU B_STRPT_GRPA S NA S S #> 5 2003-08-14 F71508 0 F Clinic… B_STRPT_GRPB S NA S S #> 6 2003-10-16 650870 63 F ICU B_ESCHR_COLI R NA NA R #> 7 2003-10-20 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-10-20 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 9 2003-11-04 2FC253 87 F ICU B_ESCHR_COLI R NA NA NA #> 10 2003-11-04 2FC253 87 F ICU B_ESCHR_COLI R NA NA NA #> # … with 746 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF # filter with multiple antibiotic selectors using c() example_isolates[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 26 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2004-11-03 D65308 80 F ICU B_STNTR_MLTP R NA NA R #> 2 2005-04-22 452212 82 F ICU B_ENTRC_FACM NA NA NA NA #> 3 2007-02-21 8BBC46 61 F Clinical B_ENTRC_FACM NA NA NA NA #> 4 2007-12-15 401043 72 M Clinical B_ENTRC_FACM NA NA NA NA #> 5 2008-12-06 501361 43 F Clinical B_STNTR_MLTP R NA NA R #> 6 2011-05-09 207325 82 F ICU B_ENTRC_FACM NA NA NA NA #> 7 2012-03-12 582258 80 M ICU B_STPHY_CONS R R R R #> 8 2012-05-19 C25552 89 F Outpati… B_STPHY_CONS R R R R #> 9 2012-07-17 F05015 83 M ICU B_STPHY_CONS R R R R #> 10 2012-07-20 404299 66 F Clinical B_STPHY_CONS R R R R #> # … with 16 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF # filter + select in one go: get penicillins in carbapenems-resistant strains example_isolates[any(carbapenems() == \"R\"), penicillins()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #> (piperacillin/tazobactam) #> # A tibble: 55 × 7 #> PEN OXA FLC AMX AMC AMP TZP #> #> 1 NA NA NA NA NA NA NA #> 2 NA NA NA NA NA NA NA #> 3 R NA NA R R R R #> 4 NA NA NA NA NA NA R #> 5 NA NA NA NA NA NA R #> 6 NA NA NA NA NA NA R #> 7 NA NA NA NA NA NA R #> 8 NA NA NA NA NA NA R #> 9 R NA NA NA S NA S #> 10 R NA NA NA S NA S #> # … with 45 more rows # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #> (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #> (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #> 'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #> (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #> (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #> (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #> (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #> (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #> (rifampicin) #> # A tibble: 2,000 × 5 #> OXA FLC AMX AMC AMP #> #> 1 NA NA NA I NA #> 2 NA NA NA I NA #> 3 NA R NA NA NA #> 4 NA R NA NA NA #> 5 NA R NA NA NA #> 6 NA R NA NA NA #> 7 NA S R S R #> 8 NA S R S R #> 9 NA R NA NA NA #> 10 NA S NA NA NA #> # … with 1,990 more rows # ab_selector() applies a filter in the `antibiotics` data set and is thus very # flexible. For instance, to select antibiotic columns with an oral DDD of at # least 1 gram: example_isolates[, ab_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For ab_selector(oral_ddd > 1 & oral_units == \"g\") using columns 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'KAN' (kanamycin), 'FOS' #> (fosfomycin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'ERY' (erythromycin), #> 'CLI' (clindamycin), 'MTR' (metronidazole) and 'CHL' (chloramphenicol) #> # A tibble: 2,000 × 13 #> OXA FLC AMX AMC AMP KAN FOS LNZ VAN ERY CLI MTR CHL #> #> 1 NA NA NA I NA NA NA R R R R NA NA #> 2 NA NA NA I NA NA NA R R R R NA NA #> 3 NA R NA NA NA NA NA NA S R NA NA NA #> 4 NA R NA NA NA NA NA NA S R NA NA NA #> 5 NA R NA NA NA NA NA NA S R NA NA NA #> 6 NA R NA NA NA NA NA NA S R R NA NA #> 7 NA S R S R NA NA NA S S NA NA NA #> 8 NA S R S R NA NA NA S S NA NA NA #> 9 NA R NA NA NA NA NA NA S R NA NA NA #> 10 NA S NA NA NA NA NA NA S S NA NA NA #> # … with 1,990 more rows # dplyr ------------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) { # get AMR for all aminoglycosides e.g., per ward: example_isolates %>% group_by(ward) %>% summarise(across(aminoglycosides(), resistance)) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> Warning: Introducing NA: only 23 results available for KAN in group: ward = #> \"Outpatient\" (minimum = 30). #> # A tibble: 3 × 5 #> ward GEN TOB AMK KAN #> #> 1 Clinical 0.229 0.315 0.626 1 #> 2 ICU 0.290 0.400 0.662 1 #> 3 Outpatient 0.2 0.368 0.605 NA if (require(\"dplyr\")) { # You can combine selectors with '&' to be more specific: example_isolates %>% select(penicillins() & administrable_per_os()) } #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #> (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #> (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #> 'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #> (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #> (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #> (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #> (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #> (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #> (rifampicin) #> # A tibble: 2,000 × 5 #> OXA FLC AMX AMC AMP #> #> 1 NA NA NA I NA #> 2 NA NA NA I NA #> 3 NA R NA NA NA #> 4 NA R NA NA NA #> 5 NA R NA NA NA #> 6 NA R NA NA NA #> 7 NA S R S R #> 8 NA S R S R #> 9 NA R NA NA NA #> 10 NA S NA NA NA #> # … with 1,990 more rows if (require(\"dplyr\")) { # get AMR for only drugs that matter - no intrinsic resistance: example_isolates %>% filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>% group_by(ward) %>% summarise(across(not_intrinsic_resistant(), resistance)) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ For not_intrinsic_resistant() removing columns 'PEN' #> (benzylpenicillin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'TEC' #> (teicoplanin), 'ERY' (erythromycin), 'CLI' (clindamycin), 'AZM' #> (azithromycin) and 'RIF' (rifampicin) #> Warning: Introducing NA: no results available for OXA in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 25 results available for AMX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for AMC in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for AMP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 24 results available for TZP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CZO in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 10 results available for CZO in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 20 results available for FEP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CXM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 22 results available for FOX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for CTX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CAZ in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for CRO in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for GEN in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TOB in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for AMK in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 22 results available for TMP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for SXT in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for NIT in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for FOS in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CIP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for MFX in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 4 results available for MFX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for TCY in group: ward = #> \"Clinical\" (minimum = 30). #> Warning: Introducing NA: no results available for TCY in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for TCY in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 13 results available for TGC in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 2 results available for TGC in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 24 results available for IPM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: ward = #> \"Clinical\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"Outpatient\" #> (minimum = 30). #> # A tibble: 3 × 33 #> ward OXA FLC AMX AMC AMP TZP CZO FEP CXM FOX #> #> 1 Clin… NA NA 0.606 0.121 0.606 0.0504 0.0656 0.0159 0.0622 0.0648 #> 2 ICU NA NA 0.535 0.172 0.535 0.119 NA 0.0722 0.0828 0.0992 #> 3 Outp… NA NA NA NA NA NA NA NA NA NA #> # … with 22 more variables: CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , CIP , MFX , TCY , TGC , DOX , #> # IPM , MEM , MTR , CHL , COL , MUP if (require(\"dplyr\")) { # get susceptibility for antibiotics whose name contains \"trim\": example_isolates %>% filter(first_isolate()) %>% group_by(ward) %>% summarise(across(ab_selector(name %like% \"trim\"), susceptibility)) } #> Including isolates from ICU. #> ℹ For ab_selector(name %like% \"trim\") using columns 'TMP' (trimethoprim) #> and 'SXT' (trimethoprim/sulfamethoxazole) #> # A tibble: 3 × 3 #> ward TMP SXT #> #> 1 Clinical 0.627 0.808 #> 2 ICU 0.549 0.778 #> 3 Outpatient 0.667 0.821 if (require(\"dplyr\")) { # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem): example_isolates %>% select(carbapenems()) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #> IPM MEM #> #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> 7 NA NA #> 8 NA NA #> 9 NA NA #> 10 NA NA #> # … with 1,990 more rows if (require(\"dplyr\")) { # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB': example_isolates %>% select(mo, aminoglycosides()) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #> mo GEN TOB AMK KAN #> #> 1 B_ESCHR_COLI NA NA NA NA #> 2 B_ESCHR_COLI NA NA NA NA #> 3 B_STPHY_EPDR NA NA NA NA #> 4 B_STPHY_EPDR NA NA NA NA #> 5 B_STPHY_EPDR NA NA NA NA #> 6 B_STPHY_EPDR NA NA NA NA #> 7 B_STPHY_AURS NA S NA NA #> 8 B_STPHY_AURS NA S NA NA #> 9 B_STPHY_EPDR NA NA NA NA #> 10 B_STPHY_EPDR NA NA NA NA #> # … with 1,990 more rows if (require(\"dplyr\")) { # any() and all() work in dplyr's filter() too: example_isolates %>% filter( any(aminoglycosides() == \"R\"), all(cephalosporins_2nd() == \"R\") ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ For cephalosporins_2nd() using columns 'CXM' (cefuroxime) and 'FOX' #> (cefoxitin) #> # A tibble: 112 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinical B_ENTRC_FACM NA NA NA NA #> 2 2002-03-16 4FC193 69 M Clinical B_PSDMN_AERG R NA NA R #> 3 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinical B_ENTRC_FCLS NA NA NA NA #> 5 2002-06-23 798871 82 M Clinical B_ENTRC_FCLS NA NA NA NA #> 6 2002-07-21 955940 82 F Clinical B_PSDMN_AERG R NA NA R #> 7 2002-07-21 955940 82 F Clinical B_PSDMN_AERG R NA NA R #> 8 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 10 2004-06-09 529296 69 M ICU B_ENTRC_FACM NA NA NA NA #> # … with 102 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF if (require(\"dplyr\")) { # also works with c(): example_isolates %>% filter(any(c(carbapenems(), aminoglycosides()) == \"R\")) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 531 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinical B_ENTRC_FACM NA NA NA NA #> 2 2002-03-16 4FC193 69 M Clinical B_PSDMN_AERG R NA NA R #> 3 2002-03-17 B30560 78 M Clinical B_STPHY_CONS R NA R R #> 4 2002-04-04 E61143 67 M Clinical B_STRPT_SNGN S NA NA S #> 5 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 6 2002-04-14 F30196 73 M Outpati… B_STRPT_GRPB S NA S S #> 7 2002-05-07 D91570 83 M Clinical B_STPHY_CONS R NA R R #> 8 2002-05-07 D91570 83 M Clinical B_STPHY_CONS R NA R R #> 9 2002-05-14 077552 86 F Clinical B_STRPT_PNMN S NA NA S #> 10 2002-05-14 077552 86 F Clinical B_STRPT_PNMN S NA NA S #> # … with 521 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF if (require(\"dplyr\")) { # not setting any/all will automatically apply all(): example_isolates %>% filter(aminoglycosides() == \"R\") } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ Assuming a filter on all 4 aminoglycosides. Wrap around all() or #> any() to prevent this note. #> # A tibble: 427 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinical B_ENTRC_FACM NA NA NA NA #> 2 2002-03-17 B30560 78 M Clinical B_STPHY_CONS R NA R R #> 3 2002-04-04 E61143 67 M Clinical B_STRPT_SNGN S NA NA S #> 4 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 5 2002-04-14 F30196 73 M Outpati… B_STRPT_GRPB S NA S S #> 6 2002-05-07 D91570 83 M Clinical B_STPHY_CONS R NA R R #> 7 2002-05-07 D91570 83 M Clinical B_STPHY_CONS R NA R R #> 8 2002-05-14 077552 86 F Clinical B_STRPT_PNMN S NA NA S #> 9 2002-05-14 077552 86 F Clinical B_STRPT_PNMN S NA NA S #> 10 2002-05-16 D25302 65 F ICU B_STRPT_ANGN S NA NA S #> # … with 417 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF if (require(\"dplyr\")) { # this will select columns 'mo' and all antimycobacterial drugs ('RIF'): example_isolates %>% select(mo, ab_class(\"mycobact\")) } #> Error in select(., mo, ab_class(\"mycobact\")): Problem while evaluating `ab_class(\"mycobact\")`. if (require(\"dplyr\")) { # get bug/drug combinations for only glycopeptides in Gram-positives: example_isolates %>% filter(mo_is_gram_positive()) %>% select(mo, glycopeptides()) %>% bug_drug_combinations() %>% format() } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ For glycopeptides() using columns 'VAN' (vancomycin) and 'TEC' #> (teicoplanin) #> # A tibble: 2 × 8 #> Group Drug CoNS E. fa…¹ S. au…² S. ep…³ S. ho…⁴ S. pn…⁵ #> #> 1 \"Glycopeptides\" Teicoplanin (TE… \"\" \"\" \" 0.0%… \"64.1%… \" 6.8%… \"\" #> 2 \"\" Vancomycin (VAN… \" 0.… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… #> # … with abbreviated variable names ¹​`E. faecalis`, ²​`S. aureus`, #> # ³​`S. epidermidis`, ⁴​`S. hominis`, ⁵​`S. pneumoniae` if (require(\"dplyr\")) { data.frame( some_column = \"some_value\", J01CA01 = \"S\" ) %>% # ATC code of ampicillin select(penicillins()) # only the 'J01CA01' column will be selected } #> ℹ For penicillins() using column 'J01CA01' (ampicillin) #> J01CA01 #> 1 S if (require(\"dplyr\")) { # with recent versions of dplyr this is all equal: x <- example_isolates[carbapenems() == \"R\", ] y <- example_isolates %>% filter(carbapenems() == \"R\") z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == \"R\")) identical(x, y) && identical(y, z) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #> prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #> prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 603 Antimicrobial Drugs — antibiotics","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Two data sets containing antibiotics/antimycotics antivirals. Use .ab() one ab_* functions retrieve values antibiotics data set. Three identifiers included data set: antibiotic ID (ab, primarily used package) defined WHONET/EARS-Net, ATC code (atc) defined , Compound ID (cid) found PubChem. properties data set derived one codes. Note drugs multiple ATC codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics antivirals"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"object class tbl_df (inherits tbl, data.frame) 120 rows 11 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antibiotics-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antibiotics data set: a tibble with 483 observations and 14 variables:","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available cid Compound ID found PubChem name Official name used WHONET/EARS-Net group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antibiotic susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 174 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 146 drugs iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug. Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antivirals-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a tibble with 120 observations and 11 variables:","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"av Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name used WHONET/EARS-Net atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://www.whocc./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.73 (8 August, 2022). Accessed https://loinc.org 30 October, 2022. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived PubChem Compound ID (column cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Like data sets package, data sets publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics #> # A tibble: 483 × 14 #> ab cid name group atc atc_g…¹ atc_g…² abbre…³ synon…⁴ oral_…⁵ #> #> 1 AMA 4649 4-aminosal… Anti… Drugs … Aminos… 12 #> 2 ACM 6450012 Acetylmide… Macr… NA NA NA #> 3 ASP 49787020 Acetylspir… Macr… NA NA NA #> 4 ALS 8954 Aldesulfon… Othe… Drugs … Drugs … 0.33 #> 5 AMK 37768 Amikacin Amin… Aminog… Other … NA #> 6 AKF NA Amikacin/f… Amin… NA NA NA #> 7 AMX 33613 Amoxicillin Beta… Beta-l… Penici… 1.5 #> 8 AMC 23665637 Amoxicilli… Beta… Beta-l… Combin… 1.5 #> 9 AXS 465441 Amoxicilli… Beta… NA NA NA #> 10 AMB 5280965 Amphoteric… Anti… Antimy… Antibi… 40 #> # … with 473 more rows, 4 more variables: oral_units , iv_ddd , #> # iv_units , loinc , and abbreviated variable names ¹​atc_group1, #> # ²​atc_group2, ³​abbreviations, ⁴​synonyms, ⁵​oral_ddd antivirals #> # A tibble: 120 × 11 #> av name atc cid atc_g…¹ synon…² oral_…³ oral_…⁴ iv_ddd iv_un…⁵ loinc #> #> 1 ABA Abac… J05A… 4.41e5 Nucleo… 0.6 g NA NA #> 2 ACI Acic… J05A… 1.35e8 Nucleo… 4 g 4 g #> 3 ADD Adef… J05A… 6.09e4 Nucleo… 10 mg NA NA #> 4 AME Amen… J05A… 1.14e7 Other … 0.4 g NA NA #> 5 AMP Ampr… J05A… 6.50e4 Protea… 1.2 g NA NA #> 6 ASU Asun… J05A… 1.61e7 Antivi… 0.2 g NA NA #> 7 ATA Ataz… J05A… 1.48e5 Protea… 0.3 g NA NA #> 8 ATA+… Ataz… J05A… 8.66e7 Antivi… NA NA NA NA #> 9 ATA+… Ataz… J05A… 2.51e7 Antivi… 0.3 g NA NA #> 10 BAM Balo… J05A… 1.24e8 Other … 40 mg NA NA #> # … with 110 more rows, and abbreviated variable names ¹​atc_group, ²​synonyms, #> # ³​oral_ddd, ⁴​oral_units, ⁵​iv_units"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antibiotic drug code one antibiotics. data set antibiotics searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, info = interactive(), ...) is.ab(x)"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic drug code name can retrieved single input value. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antibiotics data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems. can add manual codes considered .ab() ab_* functions, see add_custom_antimicrobials().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class 'ab' #> [1] ERY as.ab(\"J 01 FA 01\") #> Class 'ab' #> [1] ERY as.ab(\"Erythromycin\") #> Class 'ab' #> [1] ERY as.ab(\"eryt\") #> Class 'ab' #> [1] ERY as.ab(\" eryt 123\") #> Class 'ab' #> [1] ERY as.ab(\"ERYT\") #> Class 'ab' #> [1] ERY as.ab(\"ERY\") #> Class 'ab' #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class 'ab' #> [1] ERY as.ab(\"Erythrocin\") # trade name #> Class 'ab' #> [1] ERY as.ab(\"Romycin\") # trade name #> Class 'ab' #> [1] ERY # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\" # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\") #> [1] \"Erythromycin\" ab_name(\"eryt\") #> [1] \"Erythromycin\" # \\donttest{ if (require(\"dplyr\")) { # you can quickly rename 'rsi' columns using set_ab_names() with dplyr: example_isolates %>% set_ab_names(where(is.rsi), property = \"atc\") } #> # A tibble: 2,000 × 46 #> date patient age gender ward mo J01CE01 J01CF04 J01CF05 #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S #> # … with 1,990 more rows, and 37 more variables: J01CA04 , J01CR02 , #> # J01CA01 , J01CR05 , J01DB04 , J01DE01 , J01DC02 , #> # J01DC01 , J01DD01 , J01DD02 , J01DD04 , J01GB03 , #> # J01GB01 , J01GB06 , J01GB04 , J01EA01 , J01EE01 , #> # J01XE01 , J01XX01 , J01XX08 , J01MA02 , J01MA14 , #> # J01XA01 , J01XA02 , J01AA07 , J01AA12 , J01AA02 , #> # J01FA01 , J01FF01 , J01FA10 , J01DH51 , … # }"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antiviral Drug ID — as.av","title":"Transform Input to an Antiviral Drug ID — as.av","text":"Use function determine antiviral drug code one antiviral drugs. data set antivirals searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"as.av(x, flag_multiple_results = TRUE, info = interactive(), ...) is.av(x)"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antiviral Drug ID — as.av","text":"x character vector determine antiviral drug ID flag_multiple_results logical indicate whether note printed console probably one antiviral drug code name can retrieved single input value. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antiviral Drug ID — as.av","text":"character vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antiviral Drug ID — as.av","text":"entries antivirals data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .av() can correct different forms misspelling: Wrong spelling drug names (\"acyclovir\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"aycclovir\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use av_* functions get properties based returned antiviral drug ID, see Examples. Note: .av() av_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antiviral Drug ID — as.av","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antiviral Drug ID — as.av","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antiviral Drug ID — as.av","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"# these examples all return \"ACI\", the ID of aciclovir: as.av(\"J05AB01\") #> Class 'av' #> [1] ACI as.av(\"J 05 AB 01\") #> Class 'av' #> [1] ACI as.av(\"Aciclovir\") #> Class 'av' #> [1] ACI as.av(\"aciclo\") #> Class 'av' #> [1] ACI as.av(\" aciclo 123\") #> Class 'av' #> [1] ACI as.av(\"ACICL\") #> Class 'av' #> [1] ACI as.av(\"ACI\") #> Class 'av' #> [1] ACI as.av(\"Virorax\") # trade name #> Class 'av' #> [1] ACI as.av(\"Zovirax\") # trade name #> Class 'av' #> [1] ACI as.av(\"acyklofir\") # severe spelling error, yet works #> Class 'av' #> [1] ACI # use av_* functions to get a specific properties (see ?av_property); # they use as.av() internally: av_name(\"J05AB01\") #> [1] \"Aciclovir\" av_name(\"acicl\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 6 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE) NA_disk_ is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values RSI values .rsi(). supports guidelines EUCAST CLSI. Disk diffusion growth zone sizes must 6 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 6-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame( microorganism = \"Escherichia coli\", AMP = 20, CIP = 14, GEN = 18, TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of 5 variables: #> $ microorganism: chr \"Escherichia coli\" #> $ AMP : 'disk' int 20 #> $ CIP : 'disk' int 14 #> $ GEN : 'disk' int 18 #> $ TOB : 'disk' int 16 # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) { df %>% mutate(across(AMP:TOB, as.disk)) } #> microorganism AMP CIP GEN TOB #> 1 Escherichia coli 20 14 18 16 # } # interpret disk values, see ?as.rsi as.rsi( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #> EUCAST 2022... #> OK. #> Class 'rsi' #> [1] R # interpret whole data set, pretend to be all from urinary tract infections: as.rsi(df, uti = TRUE) #> => Interpreting disk diffusion zones of column 'AMP' (ampicillin) according #> to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of column 'CIP' (ciprofloxacin) #> according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #> to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #> to EUCAST 2022... #> OK. #> microorganism AMP CIP GEN TOB #> 1 Escherichia coli S R S S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE) NA_mic_ is.mic(x) # S3 method for mic droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed .mic logical indicate whether mic class kept, defaults FALSE ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts decimal numbers. Bare mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values RSI values, use .rsi() MIC values. supports guidelines EUCAST (2013-2022) CLSI (2013-2022). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers: makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.: following generic functions implemented MIC class: !, !=, %%, %/%, &, *, +, -, /, <, <=, ==, >, >=, ^, |, abs(), acos(), acosh(), (), (), asin(), asinh(), atan(), atanh(), ceiling(), cos(), cosh(), cospi(), cummax(), cummin(), cumprod(), cumsum(), digamma(), exp(), expm1(), floor(), gamma(), lgamma(), log(), log1p(), log2(), log10(), max(), mean(), min(), prod(), range(), round(), sign(), signif(), sin(), sinh(), sinpi(), sqrt(), sum(), tan(), tanh(), tanpi(), trigamma() trunc(). functions stats package also implemented: median(), quantile(), mad(), IQR(), fivenum(). Also, boxplot.stats() supported. Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_.","code":"x <- random_mic(10) x #> Class 'mic' #> [1] 16 1 8 8 64 >=128 0.0625 32 32 16 is.factor(x) #> [1] TRUE x[1] * 2 #> [1] 32 median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16 8 8 64 >=128 32 32 16 df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #> x hospital #> 1 16 A #> 5 64 A #> 6 >=128 A #> 8 32 A #> 9 32 A #> 10 16 A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32 1 1 1 8 <=0.128 8 16 16 is.mic(mic_data) #> [1] TRUE # this can also coerce combined MIC/RSI values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002 # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1] 0.128 1.000 8.000 16.000 32.000 quantile(mic_data) #> 0% 25% 50% 75% 100% #> 0.128 1.000 8.000 16.000 32.000 all(mic_data < 512) #> [1] TRUE # interpret MIC values as.rsi( x = as.mic(2), mo = as.mo(\"Streptococcus pneumoniae\"), ab = \"AMX\", guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #> 2022... #> * NOTE * #> • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #> pneumoniae - assuming body site 'Meningitis'. #> Class 'rsi' #> [1] R as.rsi( x = as.mic(c(0.01, 2, 4, 8)), mo = as.mo(\"Streptococcus pneumoniae\"), ab = \"AMX\", guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #> 2022... #> * NOTE * #> • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #> pneumoniae - assuming body site 'Meningitis'. #> Class 'rsi' #> [1] S R R R # plot MIC values, see ?plot plot(mic_data) plot(mic_data, mo = \"E. coli\", ab = \"cipro\") if (require(\"ggplot2\")) { autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") } if (require(\"ggplot2\")) { autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch } if (require(\"ggplot2\")) { autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\") # Ukrainian }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to a Microorganism Code — as.mo","title":"Transform Input to a Microorganism Code — as.mo","text":"Use function determine valid microorganism code (mo). Determination done using intelligent rules complete taxonomic kingdoms Animalia, Archaea, Bacteria Protozoa, microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"as.mo( x, Becker = FALSE, Lancefield = FALSE, minimum_matching_score = NULL, keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), reference_df = get_mo_source(), ignore_pattern = getOption(\"AMR_ignore_pattern\", NULL), remove_from_input = mo_cleaning_regex(), language = get_AMR_locale(), info = interactive(), ... ) is.mo(x) mo_uncertainties() mo_renamed() mo_failures() mo_reset_session() mo_cleaning_regex()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to a Microorganism Code — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (see Source). excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (see Source). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. minimum_matching_score numeric value set lower limit MO matching score. left blank, determined automatically based character length x, taxonomic kingdom human pathogenicity. keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). remove_from_input regular expression (case-insensitive) clean input x. Everything matched x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced, defaults TRUE interactive mode ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to a Microorganism Code — as.mo","text":"character vector additional class mo","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to a Microorganism Code — as.mo","text":"microorganism (MO) code package (class: mo) human readable typically looks like examples: Values coerced considered 'unknown' returned MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. lead effect e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first. algorithm uses data List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (see microorganisms).","code":"Code Full name --------------- -------------------------------------- B_KLBSL Klebsiella B_KLBSL_PNMN Klebsiella pneumoniae B_KLBSL_PNMN_RHNS Klebsiella pneumoniae rhinoscleromatis | | | | | | | | | | | \\---> subspecies, a 3-5 letter acronym | | \\----> species, a 3-6 letter acronym | \\----> genus, a 4-8 letter acronym \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria), F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, remove_from_input argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value remove_from_input outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"coercion rules consider prevalence microorganisms humans grouped three groups, available prevalence columns microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c( \"sau\", # WHONET code \"stau\", \"STAU\", \"staaur\", \"S. aureus\", \"S aureus\", \"Sthafilokkockus aureus\", # handles incorrect spelling \"Staphylococcus aureus (MRSA)\", \"MRSA\", # Methicillin Resistant S. aureus \"VISA\", # Vancomycin Intermediate S. aureus \"VRSA\", # Vancomycin Resistant S. aureus 115329001 # SNOMED CT code )) #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS # Dyslexia is no problem - these all work: as.mo(c( \"Ureaplasma urealyticum\", \"Ureaplasma urealyticus\", \"Ureaplasmium urealytica\", \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #> 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #> will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing R/SI values. transforms input new class rsi, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"","code":"as.rsi(x, ...) NA_rsi_ is.rsi(x) is.rsi.eligible(x, threshold = 0.05) # S3 method for mic as.rsi( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::rsi_translation, ... ) # S3 method for disk as.rsi( x, mo = NULL, ab = deparse(substitute(x)), guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, add_intrinsic_resistance = FALSE, reference_data = AMR::rsi_translation, ... ) # S3 method for data.frame as.rsi( x, ..., col_mo = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL, conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE, reference_data = AMR::rsi_translation ) rsi_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"object class rsi (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .rsi() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline rsi_translation data set), can set option AMR_guideline. Supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .rsi() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults rsi_translation data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure rsi_translation data set (column names column types). Please note guideline argument ignored reference_data manually set. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"Ordered factor new class rsi","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"NA_rsi_ missing value new rsi class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":".rsi() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values R/SI interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, R/SI interpretation can done easily either: Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, R/SI interpretation can done easily either: interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .rsi(your_data). points 2, 3 4: Use rsi_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .rsi() call.","code":"your_data %>% mutate_if(is.mic, as.rsi) your_data %>% mutate(across(where(is.mic), as.rsi)) your_data %>% mutate_if(is.disk, as.rsi) your_data %>% mutate(across(where(is.disk), as.rsi))"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline AMR_guideline option (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\") options(AMR_guideline = \"CLSI 2018\") options(AMR_guideline = \"EUCAST 2020\") # or to reset: options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"using .rsi(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"repository package contains machine-readable version guidelines. CSV file consisting 18,308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"function .rsi() detects input contains class rsi. input data.frame, iterates columns returns logical vector. function .rsi.eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF summary(example_isolates) # see all R/SI results at a glance #> date patient age gender #> Min. :2002-01-02 Length:2000 Min. : 0.00 Length:2000 #> 1st Qu.:2005-07-31 Class :character 1st Qu.:63.00 Class :character #> Median :2009-07-31 Mode :character Median :74.00 Mode :character #> Mean :2009-11-20 Mean :70.69 #> 3rd Qu.:2014-05-30 3rd Qu.:82.00 #> Max. :2017-12-28 Max. :97.00 #> ward mo PEN #> Length:2000 Class :mo Class:rsi #> Class :character :0 %R :73.7% (n=1201) #> Mode :character Unique:90 %SI :26.3% (n=428) #> #1 :B_ESCHR_COLI - %S :25.6% (n=417) #> #2 :B_STPHY_CONS - %I : 0.7% (n=11) #> #3 :B_STPHY_AURS #> OXA FLC AMX #> Class:rsi Class:rsi Class:rsi #> %R :31.2% (n=114) %R :29.5% (n=278) %R :59.6% (n=804) #> %SI :68.8% (n=251) %SI :70.5% (n=665) %SI :40.4% (n=546) #> - %S :68.8% (n=251) - %S :70.5% (n=665) - %S :40.2% (n=543) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.2% (n=3) #> #> AMC AMP TZP #> Class:rsi Class:rsi Class:rsi #> %R :23.7% (n=446) %R :59.6% (n=804) %R :12.6% (n=126) #> %SI :76.3% (n=1433) %SI :40.4% (n=546) %SI :87.4% (n=875) #> - %S :71.4% (n=1342) - %S :40.2% (n=543) - %S :86.1% (n=862) #> - %I : 4.8% (n=91) - %I : 0.2% (n=3) - %I : 1.3% (n=13) #> #> CZO FEP CXM #> Class:rsi Class:rsi Class:rsi #> %R :44.6% (n=199) %R :14.2% (n=103) %R :26.3% (n=470) #> %SI :55.4% (n=247) %SI :85.8% (n=621) %SI :73.7% (n=1319) #> - %S :54.9% (n=245) - %S :85.6% (n=620) - %S :72.5% (n=1297) #> - %I : 0.4% (n=2) - %I : 0.1% (n=1) - %I : 1.2% (n=22) #> #> FOX CTX CAZ #> Class:rsi Class:rsi Class:rsi #> %R :27.4% (n=224) %R :15.5% (n=146) %R :66.5% (n=1204) #> %SI :72.6% (n=594) %SI :84.5% (n=797) %SI :33.5% (n=607) #> - %S :71.6% (n=586) - %S :84.4% (n=796) - %S :33.5% (n=607) #> - %I : 1.0% (n=8) - %I : 0.1% (n=1) - %I : 0.0% (n=0) #> #> CRO GEN TOB #> Class:rsi Class:rsi Class:rsi #> %R :15.5% (n=146) %R :24.6% (n=456) %R :34.4% (n=465) #> %SI :84.5% (n=797) %SI :75.4% (n=1399) %SI :65.6% (n=886) #> - %S :84.4% (n=796) - %S :74.0% (n=1372) - %S :65.1% (n=879) #> - %I : 0.1% (n=1) - %I : 1.5% (n=27) - %I : 0.5% (n=7) #> #> AMK KAN TMP #> Class:rsi Class:rsi Class:rsi #> %R :63.7% (n=441) %R :100.0% (n=471) %R :38.1% (n=571) #> %SI :36.3% (n=251) %SI : 0.0% (n=0) %SI :61.9% (n=928) #> - %S :36.3% (n=251) - %S : 0.0% (n=0) - %S :61.2% (n=918) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.7% (n=10) #> #> SXT NIT FOS #> Class:rsi Class:rsi Class:rsi #> %R :20.5% (n=361) %R :17.1% (n=127) %R :42.2% (n=148) #> %SI :79.5% (n=1398) %SI :82.9% (n=616) %SI :57.8% (n=203) #> - %S :79.1% (n=1392) - %S :76.0% (n=565) - %S :57.8% (n=203) #> - %I : 0.3% (n=6) - %I : 6.9% (n=51) - %I : 0.0% (n=0) #> #> LNZ CIP MFX #> Class:rsi Class:rsi Class:rsi #> %R :69.3% (n=709) %R :16.2% (n=228) %R :33.6% (n=71) #> %SI :30.7% (n=314) %SI :83.8% (n=1181) %SI :66.4% (n=140) #> - %S :30.7% (n=314) - %S :78.9% (n=1112) - %S :64.5% (n=136) #> - %I : 0.0% (n=0) - %I : 4.9% (n=69) - %I : 1.9% (n=4) #> #> VAN TEC TCY #> Class:rsi Class:rsi Class:rsi #> %R :38.3% (n=712) %R :75.7% (n=739) %R :29.8% (n=357) #> %SI :61.7% (n=1149) %SI :24.3% (n=237) %SI :70.3% (n=843) #> - %S :61.7% (n=1149) - %S :24.3% (n=237) - %S :68.3% (n=820) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 1.9% (n=23) #> #> TGC DOX ERY #> Class:rsi Class:rsi Class:rsi #> %R :12.7% (n=101) %R :27.7% (n=315) %R :57.2% (n=1084) #> %SI :87.3% (n=697) %SI :72.3% (n=821) %SI :42.8% (n=810) #> - %S :87.3% (n=697) - %S :71.7% (n=814) - %S :42.3% (n=801) #> - %I : 0.0% (n=0) - %I : 0.6% (n=7) - %I : 0.5% (n=9) #> #> CLI AZM IPM #> Class:rsi Class:rsi Class:rsi #> %R :61.2% (n=930) %R :57.2% (n=1084) %R : 6.2% (n=55) #> %SI :38.8% (n=590) %SI :42.8% (n=810) %SI :93.8% (n=834) #> - %S :38.6% (n=586) - %S :42.3% (n=801) - %S :92.7% (n=824) #> - %I : 0.3% (n=4) - %I : 0.5% (n=9) - %I : 1.1% (n=10) #> #> MEM MTR CHL #> Class:rsi Class:rsi Class:rsi #> %R : 5.9% (n=49) %R :14.7% (n=5) %R :21.4% (n=33) #> %SI :94.1% (n=780) %SI :85.3% (n=29) %SI :78.6% (n=121) #> - %S :94.1% (n=780) - %S :85.3% (n=29) - %S :78.6% (n=121) #> - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0) #> #> COL MUP RIF #> Class:rsi Class:rsi Class:rsi #> %R :81.2% (n=1331) %R : 5.9% (n=16) %R :69.6% (n=698) #> %SI :18.8% (n=309) %SI :94.1% (n=254) %SI :30.4% (n=305) #> - %S :18.8% (n=309) - %S :93.0% (n=251) - %S :30.2% (n=303) #> - %I : 0.0% (n=0) - %I : 1.1% (n=3) - %I : 0.2% (n=2) #> # For INTERPRETING disk diffusion and MIC values ----------------------- # a whole data set, even with combined MIC values and disk zones df <- data.frame( microorganism = \"Escherichia coli\", AMP = as.mic(8), CIP = as.mic(0.256), GEN = as.disk(18), TOB = as.disk(16), ERY = \"R\" ) as.rsi(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #> to EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #> to EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Assigning class 'rsi' to already clean column 'ERY' (erythromycin)... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # return a 'logbook' about the results: rsi_interpretation_history() #> # A tibble: 50 × 12 #> datetime index ab_input ab_gu…¹ mo_in…² mo_guideline guide…³ #> #> 1 2022-12-20 15:19:13 1 ampicillin AMP Strep … B_STRPT_PNMN EUCAST… #> 2 2022-12-20 15:19:14 1 AMP AMP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 3 2022-12-20 15:19:14 1 CIP CIP Escher… B_[ORD]_ENTRBCTR EUCAST… #> 4 2022-12-20 15:19:15 1 GEN GEN Escher… B_[ORD]_ENTRBCTR EUCAST… #> 5 2022-12-20 15:19:15 1 TOB TOB Escher… B_[ORD]_ENTRBCTR EUCAST… #> 6 2022-12-20 15:19:16 1 AMX AMX B_STRP… B_STRPT_PNMN EUCAST… #> 7 2022-12-20 15:19:16 1 AMX AMX B_STRP… B_STRPT_PNMN EUCAST… #> 8 2022-12-20 15:19:16 2 AMX AMX B_STRP… B_STRPT_PNMN EUCAST… #> 9 2022-12-20 15:19:16 3 AMX AMX B_STRP… B_STRPT_PNMN EUCAST… #> 10 2022-12-20 15:19:16 4 AMX AMX B_STRP… B_STRPT_PNMN EUCAST… #> # … with 40 more rows, 5 more variables: ref_table , method , #> # input , outcome , breakpoint_S_R , and abbreviated variable #> # names ¹​ab_guideline, ²​mo_input, ³​guideline # for single values as.rsi( x = as.mic(2), mo = as.mo(\"S. pneumoniae\"), ab = \"AMP\", guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> * NOTE * #> • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #> pneumoniae - assuming body site 'Non-meningitis'. #> Class 'rsi' #> [1] R as.rsi( x = as.disk(18), mo = \"Strep pneu\", # `mo` will be coerced with as.mo() ab = \"ampicillin\", # and `ab` with as.ab() guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #> EUCAST 2022... #> OK. #> Class 'rsi' #> [1] R # \\donttest{ # the dplyr way if (require(\"dplyr\")) { df %>% mutate_if(is.mic, as.rsi) df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi) df %>% mutate(across(where(is.mic), as.rsi)) df %>% mutate_at(vars(AMP:TOB), as.rsi) df %>% mutate(across(AMP:TOB, as.rsi)) df %>% mutate_at(vars(AMP:TOB), as.rsi, mo = .$microorganism) # to include information about urinary tract infections (UTI) data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), from_the_bladder = c(TRUE, FALSE) ) %>% as.rsi(uti = \"from_the_bladder\") data.frame( mo = \"E. coli\", NIT = c(\"<= 2\", 32), specimen = c(\"urine\", \"blood\") ) %>% as.rsi() # automatically determines urine isolates df %>% mutate_at(vars(AMP:TOB), as.rsi, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #> 'microorganism' according to EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #> 'microorganism' according to EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #> 'microorganism' according to EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> * NOTE * #> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #> Escherichia coli - assuming non-UTI. Use argument uti to set which #> isolates are from urine. See ?as.rsi. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.rsi(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.rsi. #> * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #> infection. #> Use as.rsi(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #> EUCAST 2022... #> Warning: in as.rsi(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.rsi. #> * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #> 2022... #> OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #> 2022... #> OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #> EUCAST 2022... #> OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #> EUCAST 2022... #> OK. #> microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli S I S S R # For CLEANING existing R/SI values ------------------------------------ as.rsi(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.rsi(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\" and \"C\" #> Class 'rsi' #> [1] S I R as.rsi(\"<= 0.002; S\") # will return \"S\" #> Class 'rsi' #> [1] S rsi_data <- as.rsi(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.rsi(rsi_data) #> [1] TRUE plot(rsi_data) # for percentages barplot(rsi_data) # for frequencies # the dplyr way if (require(\"dplyr\")) { example_isolates %>% mutate_at(vars(PEN:RIF), as.rsi) # same: example_isolates %>% as.rsi(PEN:RIF) # fastest way to transform all columns with already valid AMR results to class `rsi`: example_isolates %>% mutate_if(is.rsi.eligible, as.rsi) # since dplyr 1.0.0, this can also be: # example_isolates %>% # mutate(across(where(is.rsi.eligible), as.rsi)) } #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property( atc_code, property, administration = \"O\", url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\", url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" ) atc_online_groups(atc_code, ...) atc_online_ddd(atc_code, ...) atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) { # oral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"O\") atc_online_ddd(ab_atc(\"amox\")) # parenteral DDD (Defined Daily Dose) of amoxicillin atc_online_property(\"J01CA04\", \"DDD\", \"P\") atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\" #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\" #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\" # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text( text, type = c(\"drug\", \"dose\", \"administration\"), collapse = NULL, translate_av = FALSE, thorough_search = NULL, info = interactive(), ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list, character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #> av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...) av_cid(x, ...) av_synonyms(x, ...) av_tradenames(x, ...) av_group(x, language = get_AMR_locale(), ...) av_atc(x, ...) av_loinc(x, ...) av_ddd(x, administration = \"oral\", ...) av_ddd_units(x, administration = \"oral\", ...) av_info(x, language = get_AMR_locale(), ...) av_url(x, open = FALSE, ...) av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_tradenames(\"ACI\") #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #> Aciclovir #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\" # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\" \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\" \"valaciclovir\" # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\" av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #> #> $cid #> [1] 135398513 #> #> $name #> [1] \"Aciclovir\" #> #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #> #> $atc #> [1] \"J05AB01\" #> #> $tradenames #> [1] \"acicloftal\" \"aciclovier\" \"aciclovirum\" #> [4] \"activir\" \"acyclofoam\" \"acycloguanosine\" #> [7] \"acyclovir\" \"acyclovir lauriad\" \"avaclyr\" #> [10] \"cargosil\" \"cyclovir\" \"genvir\" #> [13] \"gerpevir\" \"hascovir\" \"maynar\" #> [16] \"novirus\" \"poviral\" \"sitavig\" #> [19] \"sitavir\" \"vipral\" \"viropump\" #> [22] \"virorax\" \"zovirax\" \"zyclir\" #> #> $loinc #> [1] \"\" #> #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #> #> $ddd$oral$units #> [1] \"g\" #> #> #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #> #> $ddd$iv$units #> [1] \"g\" #> #> #> # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #> count available visual_availabilty resistant visual_resistance #> date 2000 100.0% |####################| #> patient 2000 100.0% |####################| #> age 2000 100.0% |####################| #> gender 2000 100.0% |####################| #> ward 2000 100.0% |####################| #> mo 2000 100.0% |####################| #> PEN 1629 81.5% |################----| 73.7% |##############------| #> OXA 365 18.3% |###-----------------| 31.2% |######--------------| #> FLC 943 47.2% |#########-----------| 29.5% |#####---------------| #> AMX 1350 67.5% |#############-------| 59.6% |###########---------| #> AMC 1879 94.0% |##################--| 23.7% |####----------------| #> AMP 1350 67.5% |#############-------| 59.6% |###########---------| #> TZP 1001 50.0% |##########----------| 12.6% |##------------------| #> CZO 446 22.3% |####----------------| 44.6% |########------------| #> FEP 724 36.2% |#######-------------| 14.2% |##------------------| #> CXM 1789 89.5% |#################---| 26.3% |#####---------------| #> FOX 818 40.9% |########------------| 27.4% |#####---------------| #> CTX 943 47.2% |#########-----------| 15.5% |###-----------------| #> CAZ 1811 90.6% |##################--| 66.5% |#############-------| #> CRO 943 47.2% |#########-----------| 15.5% |###-----------------| #> GEN 1855 92.8% |##################--| 24.6% |####----------------| #> TOB 1351 67.6% |#############-------| 34.4% |######--------------| #> AMK 692 34.6% |######--------------| 63.7% |############--------| #> KAN 471 23.6% |####----------------| 100.0% |####################| #> TMP 1499 75.0% |###############-----| 38.1% |#######-------------| #> SXT 1759 88.0% |#################---| 20.5% |####----------------| #> NIT 743 37.2% |#######-------------| 17.1% |###-----------------| #> FOS 351 17.6% |###-----------------| 42.2% |########------------| #> LNZ 1023 51.2% |##########----------| 69.3% |#############-------| #> CIP 1409 70.5% |#############-------| 16.2% |###-----------------| #> MFX 211 10.6% |##------------------| 33.6% |######--------------| #> VAN 1861 93.1% |##################--| 38.3% |#######-------------| #> TEC 976 48.8% |#########-----------| 75.7% |###############-----| #> TCY 1200 60.0% |###########---------| 29.8% |#####---------------| #> TGC 798 39.9% |########------------| 12.7% |##------------------| #> DOX 1136 56.8% |###########---------| 27.7% |#####---------------| #> ERY 1894 94.7% |##################--| 57.2% |###########---------| #> CLI 1520 76.0% |###############-----| 61.2% |############--------| #> AZM 1894 94.7% |##################--| 57.2% |###########---------| #> IPM 889 44.5% |########------------| 6.2% |#-------------------| #> MEM 829 41.5% |########------------| 5.9% |#-------------------| #> MTR 34 1.7% |--------------------| 14.7% |##------------------| #> CHL 154 7.7% |#-------------------| 21.4% |####----------------| #> COL 1640 82.0% |################----| 81.2% |################----| #> MUP 270 13.5% |##------------------| 5.9% |#-------------------| #> RIF 1003 50.2% |##########----------| 69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% filter(mo == as.mo(\"Escherichia coli\")) %>% select_if(is.rsi) %>% availability() } #> count available visual_availabilty resistant visual_resistance #> PEN 467 100.0% |######################| 100.0% |######################| #> OXA 0 0.0% |----------------------| #> FLC 0 0.0% |----------------------| #> AMX 392 83.9% |##################----| 50.0% |###########-----------| #> AMC 467 100.0% |######################| 13.1% |##--------------------| #> AMP 392 83.9% |##################----| 50.0% |###########-----------| #> TZP 416 89.1% |###################---| 5.5% |#---------------------| #> CZO 82 17.6% |###-------------------| 2.4% |----------------------| #> FEP 317 67.9% |##############--------| 2.8% |----------------------| #> CXM 465 99.6% |######################| 5.4% |#---------------------| #> FOX 377 80.7% |#################-----| 6.9% |#---------------------| #> CTX 459 98.3% |#####################-| 2.4% |----------------------| #> CAZ 460 98.5% |#####################-| 2.4% |----------------------| #> CRO 459 98.3% |#####################-| 2.4% |----------------------| #> GEN 460 98.5% |#####################-| 2.0% |----------------------| #> TOB 462 98.9% |#####################-| 2.6% |----------------------| #> AMK 171 36.6% |########--------------| 0.0% |----------------------| #> KAN 0 0.0% |----------------------| #> TMP 396 84.8% |##################----| 39.1% |########--------------| #> SXT 465 99.6% |######################| 31.6% |######----------------| #> NIT 458 98.1% |#####################-| 2.8% |----------------------| #> FOS 61 13.1% |##--------------------| 0.0% |----------------------| #> LNZ 467 100.0% |######################| 100.0% |######################| #> CIP 456 97.6% |#####################-| 12.5% |##--------------------| #> MFX 57 12.2% |##--------------------| 100.0% |######################| #> VAN 467 100.0% |######################| 100.0% |######################| #> TEC 467 100.0% |######################| 100.0% |######################| #> TCY 3 0.6% |----------------------| 66.7% |##############--------| #> TGC 68 14.6% |###-------------------| 0.0% |----------------------| #> DOX 0 0.0% |----------------------| #> ERY 467 100.0% |######################| 100.0% |######################| #> CLI 467 100.0% |######################| 100.0% |######################| #> AZM 467 100.0% |######################| 100.0% |######################| #> IPM 422 90.4% |###################---| 0.0% |----------------------| #> MEM 418 89.5% |###################---| 0.0% |----------------------| #> MTR 2 0.4% |----------------------| 0.0% |----------------------| #> CHL 0 0.0% |----------------------| #> COL 240 51.4% |###########-----------| 0.0% |----------------------| #> MUP 0 0.0% |----------------------| #> RIF 467 100.0% |######################| 100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...) # S3 method for bug_drug_combinations format( x, translate_ab = \"name (ab, atc)\", language = get_AMR_locale(), minimum = 30, combine_SI = TRUE, add_ab_group = TRUE, remove_intrinsic_resistant = FALSE, decimal.mark = getOption(\"OutDec\"), big.mark = ifelse(decimal.mark == \",\", \".\", \",\"), ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R, defaults TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric decimal point. big.mark character; empty used mark every big.interval decimals (hence big) decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #> mo ab S I R total #> #> 1 (unknown species) PEN 14 0 1 15 #> 2 (unknown species) OXA 0 0 1 1 #> 3 (unknown species) FLC 0 0 0 0 #> 4 (unknown species) AMX 15 0 1 16 #> 5 (unknown species) AMC 15 0 0 15 #> 6 (unknown species) AMP 15 0 1 16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #> Group Drug CoNS E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #> #> 1 \"Aminogl… Amik… \"100… \" 0.0… \"100.0… \"\" \"\" \"\" \"\" \"100.0… #> 2 \"\" Gent… \" 13… \" 2.0… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 0.9… \" 21.5… #> 3 \"\" Kana… \"100… \"\" \"100.0… \"\" \"100.0… \"\" \"\" \"100.0… #> 4 \"\" Tobr… \" 78… \" 2.6… \"100.0… \" 10.3… \" 0.0… \" 5.9… \" 2.3… \" 49.4… #> 5 \"Ampheni… Chlo… \"\" \"\" \"\" \"\" \"100.0… \"\" \" 0.0… \" 3.1… #> 6 \"Antimyc… Rifa… \"\" \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 0.0… \" 2.7… #> 7 \"Beta-la… Amox… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 8 \"\" Amox… \" 42… \" 13.1… \"\" \" 10.3… \"100.0… \" 2.8… \" 0.4… \" 54.5… #> 9 \"\" Ampi… \" 93… \" 50.0… \"\" \"100.0… \"100.0… \"\" \" 93.9… \" 98.9… #> 10 \"\" Benz… \" 77… \"100.0… \"\" \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` , #> # `S. pneumoniae` , and abbreviated variable names ¹​`E. coli`, #> # ²​`E. faecalis`, ³​`K. pneumoniae`, ⁴​`P. aeruginosa`, ⁵​`P. mirabilis`, #> # ⁶​`S. aureus`, ⁷​`S. epidermidis` # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates, FUN = mo_gramstain ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 Gram-negative PEN 8 0 717 725 #> 2 Gram-negative OXA 6 0 0 6 #> 3 Gram-negative FLC 6 0 0 6 #> 4 Gram-negative AMX 226 0 405 631 #> 5 Gram-negative AMC 463 89 174 726 #> 6 Gram-negative AMP 226 0 405 631 #> 7 Gram-negative TZP 554 11 76 641 #> 8 Gram-negative CZO 94 2 110 206 #> 9 Gram-negative FEP 470 1 14 485 #> 10 Gram-negative CXM 539 22 142 703 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. bug_drug_combinations(example_isolates, FUN = function(x) { ifelse(x == as.mo(\"Escherichia coli\"), \"E. coli\", \"Others\" ) } ) #> # A tibble: 80 × 6 #> mo ab S I R total #> * #> 1 E. coli PEN 0 0 467 467 #> 2 E. coli OXA 0 0 0 0 #> 3 E. coli FLC 0 0 0 0 #> 4 E. coli AMX 196 0 196 392 #> 5 E. coli AMC 332 74 61 467 #> 6 E. coli AMP 196 0 196 392 #> 7 E. coli TZP 388 5 23 416 #> 8 E. coli CZO 79 1 2 82 #> 9 E. coli FEP 308 0 9 317 #> 10 E. coli CXM 425 15 25 465 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE) count_susceptible(..., only_all_tested = FALSE) count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_S(..., only_all_tested = FALSE) count_all(..., only_all_tested = FALSE) n_rsi(..., only_all_tested = FALSE) count_df( data, translate_ab = \"name\", language = get_AMR_locale(), combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .rsi() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class rsi (see .rsi()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_rsi() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data rsi class (created .rsi()) counts number S's, 's R's. also supports grouped variables. function rsi_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI: Please note , combination therapies, only_all_tested = TRUE applies : , combination therapies, only_all_tested = FALSE applies : Using only_all_tested impact using one antibiotic input.","code":"-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B include as include as include as include as numerator denominator numerator denominator -------- -------- ---------- ----------- ---------- ----------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- count_S() + count_I() + count_R() = count_all() proportion_S() + proportion_I() + proportion_R() = 1 count_S() + count_I() + count_R() >= count_all() proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350 # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804 # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_rsi(example_isolates$AMX) #> [1] 1350 # n_rsi() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX) #> [1] 546 # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% group_by(ward) %>% summarise( R = count_R(CIP), I = count_I(CIP), S = count_S(CIP), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_rsi(CIP), # same - analogous to n_distinct total = n() ) # NOT the number of tested isolates! # Number of available isolates for a whole antibiotic class # (i.e., in this data set columns GEN, TOB, AMK, KAN) example_isolates %>% group_by(ward) %>% summarise(across(aminoglycosides(), n_rsi)) # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. # Please mind that `susceptibility()` calculates percentages right away instead. example_isolates %>% count_susceptible(AMC) # 1433 example_isolates %>% count_all(AMC) # 1879 example_isolates %>% count_susceptible(GEN) # 1399 example_isolates %>% count_all(GEN) # 1855 example_isolates %>% count_susceptible(AMC, GEN) # 1764 example_isolates %>% count_all(AMC, GEN) # 1936 # Get number of S+I vs. R immediately of selected columns example_isolates %>% select(AMX, CIP) %>% count_df(translate = FALSE) # It also supports grouping variables example_isolates %>% select(ward, AMX, CIP) %>% group_by(ward) %>% count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #> ward antibiotic interpretation value #> * #> 1 Clinical AMX SI 357 #> 2 Clinical AMX R 487 #> 3 Clinical CIP SI 741 #> 4 Clinical CIP R 128 #> 5 ICU AMX SI 158 #> 6 ICU AMX R 270 #> 7 ICU CIP SI 362 #> 8 ICU CIP R 85 #> 9 Outpatient AMX SI 31 #> 10 Outpatient AMX R 47 #> 11 Outpatient CIP SI 78 #> 12 Outpatient CIP R 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde: two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work: rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\", TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #> 1. If TZP is \"S\" then set to S : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If TZP is \"R\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"), TZP = as.rsi(\"R\"), ampi = as.rsi(\"S\"), cipro = as.rsi(\"S\")) df #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R S S eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R R S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception variables used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\", TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\") eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #> mo TZP ampi cipro #> 1 Escherichia coli R S S #> 2 Klebsiella pneumoniae R R S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules( AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates, rules = \"custom\", custom_rules = x, info = FALSE, verbose = TRUE ) #> # A tibble: 8 × 9 #> row col mo_fullname old new rule rule_…¹ rule_…² rule_…³ #> #> 1 33 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 2 33 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 3 34 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 4 34 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 5 531 AMP Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 6 531 AMX Klebsiella pneumoniae R I \"report… Custom… Custom… Object… #> 7 1485 AMP Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> 8 1485 AMX Klebsiella oxytoca R I \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹​rule_group, ²​rule_name, ³​rule_source # combine rule sets x2 <- c( x, custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #> #> 1. If AMC is \"R\" and genus is \"Klebsiella\" then set to R : #> amoxicillin (AMX), ampicillin (AMP) #> #> 2. If AMC is \"I\" and genus is \"Klebsiella\" then set to I : #> amoxicillin (AMX), ampicillin (AMP) #> #> 3. If TZP is \"R\" then set to R : #> biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #> imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #> meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #> razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #> ab name type dose dose_…¹ admin…² notes origi…³ eucas…⁴ #> #> 1 AMK Amikacin stan… 25-3… 1 iv \"\" 25-30 … 12 #> 2 AMX Amoxicillin high… 2 g 6 iv \"\" 2 g x … 12 #> 3 AMX Amoxicillin stan… 1 g 3 iv \"\" 1 g x … 12 #> 4 AMX Amoxicillin high… 0.75… 3 oral \"\" 0.75-1… 12 #> 5 AMX Amoxicillin stan… 0.5 g 3 oral \"\" 0.5 g … 12 #> 6 AMX Amoxicillin unco… 0.5 g 3 oral \"\" 0.5 g … 12 #> 7 AMC Amoxicillin/clavulani… high… 2 g … 3 iv \"\" (2 g a… 12 #> 8 AMC Amoxicillin/clavulani… stan… 1 g … 3 iv \"\" (1 g a… 12 #> 9 AMC Amoxicillin/clavulani… high… 0.87… 3 oral \"\" (0.875… 12 #> 10 AMC Amoxicillin/clavulani… stan… 0.5 … 3 oral \"\" (0.5 g… 12 #> # … with 326 more rows, and abbreviated variable names ¹​dose_times, #> # ²​administration, ³​original_txt, ⁴​eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules( x, col_mo = NULL, info = interactive(), rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")), verbose = FALSE, version_breakpoints = 12, version_expertrules = 3.3, ampc_cephalosporin_resistance = NA, only_rsi_columns = FALSE, custom_rules = NULL, ... ) eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value, e.g. using options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\". EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_rsi_columns logical indicate whether antibiotic columns must detected transformed class rsi (see .rsi()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values RSI values. Use .rsi() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv. Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\", AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\") eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylpenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) head(a) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - - - - - S S #> 2 Enterococcus faecalis - - - - - S S #> 3 Escherichia coli - - - - - S S #> 4 Klebsiella pneumoniae - - - - - S S #> 5 Pseudomonas aeruginosa - - - - - S S # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'rsi'. Transform them on beforehand, with e.g.: #> - a %>% as.rsi(CXM:AMX) #> - a %>% mutate_if(is.rsi.eligible, as.rsi) #> - a %>% mutate(across(where(is.rsi.eligible), as.rsi)) head(b) #> mo VAN AMX COL CAZ CXM PEN FOX #> 1 Staphylococcus aureus - S R S S S S #> 2 Enterococcus faecalis - - R R R S R #> 3 Escherichia coli R - - - - R S #> 4 Klebsiella pneumoniae R R - - - R S #> 5 Pseudomonas aeruginosa R R - - R R R # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'rsi'. Transform them on beforehand, with e.g.: #> - a %>% as.rsi(CXM:AMX) #> - a %>% mutate_if(is.rsi.eligible, as.rsi) #> - a %>% mutate(across(where(is.rsi.eligible), as.rsi)) head(c) #> row col mo_fullname old new rule rule_group #> 1 1 AMX Staphylococcus aureus - S Breakpoints #> 2 1 CXM Staphylococcus aureus - S Breakpoints #> 3 1 CAZ Staphylococcus aureus R S Expert Rules #> 4 1 CAZ Staphylococcus aureus - R Expert Rules #> 5 1 COL Staphylococcus aureus - R Expert Rules #> 6 2 CAZ Enterococcus faecalis - R Expert Rules #> rule_name #> 1 Staphylococcus #> 2 Staphylococcus #> 3 Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #> rule_source #> 1 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2 'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # } # Dosage guidelines: eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #> Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #> ab name standard_dosage high_dosage #> #> 1 TOB Tobramycin 6-7 mg/kg x 1 iv NA #> 2 GEN Gentamicin 6-7 mg/kg x 1 iv NA #> 3 CIP Ciprofloxacin 0.4 g x 2 iv 0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"tibble 2,000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\" \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class rsi (see .rsi()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # … with 1,990 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF "},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3,000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"tibble 3,000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antibiotics transformed .rsi()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Unclean Data — example_isolates_unclean","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # … with 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate( x = NULL, col_date = NULL, col_patient_id = NULL, col_mo = NULL, col_testcode = NULL, col_specimen = NULL, col_icu = NULL, col_keyantimicrobials = NULL, episode_days = 365, testcodes_exclude = NULL, icu_exclude = FALSE, specimen_group = NULL, type = \"points\", method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"), ignore_I = TRUE, points_threshold = 2, info = interactive(), include_unknown = FALSE, include_untested_rsi = TRUE, ... ) filter_first_isolate( x = NULL, col_date = NULL, col_patient_id = NULL, col_mo = NULL, episode_days = 365, method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"), ... )"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology function strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab), defaults first column date class col_patient_id column name unique IDs patients, defaults first column starts 'patient' 'patid' (case insensitive) col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU). can also logical vector length rows x. col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). Defaults first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed, defaults TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_rsi logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_rsi = FALSE always return FALSE rows. checks data set columns class rsi consequently requires transforming columns antibiotic results using .rsi() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According Hindler et al. (2007, doi:10.1086/511864 ), different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. Although often inappropriate, method makes sure duplicate isolates selected patient. large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. example_isolates[first_isolate(), ] #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA #> # … with 1,369 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> Including isolates from ICU. #> ℹ Using column 'mo' as input for mo_is_gram_negative() #> # A tibble: 437 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 4 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 5 2002-02-05 067927 45 F ICU B_SERRT_MRCS R NA NA R #> 6 2002-02-27 066895 85 F Clinical B_KLBSL_PNMN R NA NA R #> 7 2002-03-08 4FC193 69 M Clinical B_ESCHR_COLI R NA NA R #> 8 2002-03-16 4FC193 69 M Clinical B_PSDMN_AERG R NA NA R #> 9 2002-04-01 496896 46 F ICU B_ESCHR_COLI R NA NA NA #> 10 2002-04-23 EE2510 69 F ICU B_ESCHR_COLI R NA NA NA #> # … with 427 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF if (require(\"dplyr\")) { # filter on first isolates using dplyr: example_isolates %>% filter(first_isolate()) } #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA #> # … with 1,369 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF if (require(\"dplyr\")) { # short-hand version: example_isolates %>% filter_first_isolate(info = FALSE) } #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 3 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 4 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> 5 2002-01-17 495616 67 M Clinical B_STPHY_EPDR R NA S NA #> 6 2002-01-19 738003 71 M Clinical B_ESCHR_COLI R NA NA NA #> 7 2002-01-21 462081 75 F Clinical B_CTRBC_FRND R NA NA R #> 8 2002-01-22 F35553 50 M ICU B_PROTS_MRBL R NA NA NA #> 9 2002-02-03 481442 76 M ICU B_STPHY_CONS R NA S NA #> 10 2002-02-05 023456 50 M Clinical B_STPHY_HMNS S NA S NA #> # … with 1,369 more rows, and 36 more variables: AMC , AMP , #> # TZP , CZO , FEP , CXM , FOX , CTX , #> # CAZ , CRO , GEN , TOB , AMK , KAN , #> # TMP , SXT , NIT , FOS , LNZ , CIP , #> # MFX , VAN , TEC , TCY , TGC , DOX , #> # ERY , CLI , AZM , IPM , MEM , MTR , #> # CHL , COL , MUP , RIF if (require(\"dplyr\")) { # flag the first isolates per group: example_isolates %>% group_by(ward) %>% mutate(first = first_isolate()) %>% select(ward, date, patient, mo, first) } #> Including isolates from ICU. #> Including isolates from ICU. #> Including isolates from ICU. #> # A tibble: 2,000 × 5 #> # Groups: ward [3] #> ward date patient mo first #> #> 1 Clinical 2002-01-02 A77334 B_ESCHR_COLI TRUE #> 2 Clinical 2002-01-03 A77334 B_ESCHR_COLI FALSE #> 3 ICU 2002-01-07 067927 B_STPHY_EPDR TRUE #> 4 ICU 2002-01-07 067927 B_STPHY_EPDR FALSE #> 5 ICU 2002-01-13 067927 B_STPHY_EPDR FALSE #> 6 ICU 2002-01-13 067927 B_STPHY_EPDR FALSE #> 7 Clinical 2002-01-14 462729 B_STPHY_AURS TRUE #> 8 Clinical 2002-01-14 462729 B_STPHY_AURS FALSE #> 9 ICU 2002-01-16 067927 B_STPHY_EPDR FALSE #> 10 ICU 2002-01-17 858515 B_STPHY_EPDR TRUE #> # … with 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also factors. y numeric vector; ignored x matrix. x factor, y factor length. p vector probabilities length x. error given entry p negative. rescale.p logical scalar; TRUE p rescaled (necessary) sum 1. rescale.p FALSE, p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following components: statistic value chi-squared test statistic. parameter degrees freedom approximate chi-squared distribution test statistic, NA p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test performed, whether Monte Carlo simulation continuity correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals, (observed - expected) / sqrt(expected). stdres standardized residuals, (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007, section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers. Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed . Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R : df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio. x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #> #> \tG-test of goodness-of-fit (likelihood ratio test) #> #> data: x #> X-squared = 4.1471, p-value = 0.1257 #> # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible. # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills. x <- c(1752, 1895) g.test(x) #> #> \tG-test of goodness-of-fit (likelihood ratio test) #> #> data: x #> X-squared = 5.6085, p-value = 0.01787 #> # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine (New) Episodes for Patients — get_episode","title":"Determine (New) Episodes for Patients — get_episode","text":"functions determine items vector can considered (start ) new episode, based argument episode_days. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns values TRUE/FALSE indicate whether item vector start new episode.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"get_episode(x, episode_days, ...) is_new_episode(x, episode_days, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine (New) Episodes for Patients — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days required episode length days, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine (New) Episodes for Patients — get_episode","text":"get_episode(): double vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine (New) Episodes for Patients — get_episode","text":"Dates first sorted old new. oldest date mark start first episode. date, next date marked least episode_days days later start first episode. second marked date , next date marked least episode_days days later start second episode start third episode, . vector returned, original order restored. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 200), ] get_episode(df$date, episode_days = 60) # indices #> [1] 61 15 27 7 41 50 27 14 30 57 61 25 26 60 34 11 53 45 4 9 6 11 25 37 59 #> [26] 14 63 44 23 23 36 48 5 14 46 2 33 33 60 39 62 57 52 52 56 34 66 22 57 5 #> [51] 62 64 47 12 7 65 1 39 12 34 12 64 13 59 22 6 17 63 48 14 23 45 36 32 4 #> [76] 34 24 50 42 8 3 14 53 45 21 55 21 53 52 6 35 19 43 48 6 55 3 5 29 21 #> [101] 22 47 38 65 30 51 8 54 62 2 40 28 11 45 8 31 4 63 53 14 57 57 62 45 57 #> [126] 9 3 52 31 53 40 51 49 8 52 36 18 54 20 11 10 7 59 18 43 63 63 51 16 49 #> [151] 12 10 48 33 43 50 1 58 33 43 8 42 66 56 44 13 60 7 40 41 34 8 45 13 12 #> [176] 55 43 45 18 33 1 32 62 9 34 57 42 30 57 36 4 42 24 28 35 42 28 4 3 43 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #> [1] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE #> [13] TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE #> [25] TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE #> [37] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE #> [49] FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE #> [61] FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE #> [73] FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE #> [85] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE #> [97] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE #> [109] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE #> [121] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE #> [133] FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE #> [145] FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE #> [157] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE #> [169] TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE #> [181] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE #> [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 4 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-07-23 F35553 51 M ICU B_STPHY_AURS R NA S R #> 2 2002-08-19 A49852 70 M Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-07-21 955940 82 F Clinical B_PSDMN_AERG R NA NA R #> 4 2002-08-14 785317 51 F ICU B_ESCHR_COLI R NA NA NA #> # … with 36 more variables: AMC , AMP , TZP , CZO , #> # FEP , CXM , FOX , CTX , CAZ , CRO , #> # GEN , TOB , AMK , KAN , TMP , SXT , #> # NIT , FOS , LNZ , CIP , MFX , VAN , #> # TEC , TCY , TGC , DOX , ERY , CLI , #> # AZM , IPM , MEM , MTR , CHL , COL , #> # MUP , RIF # the functions also work for less than a day, e.g. to include one per hour: get_episode(c( Sys.time(), Sys.time() + 60 * 60 ), episode_days = 1 / 24 ) #> [1] 1 2 # \\donttest{ if (require(\"dplyr\")) { # is_new_episode() can also be used in dplyr verbs to determine patient # episodes based on any (combination of) grouping variables: df %>% mutate(condition = sample( x = c(\"A\", \"B\", \"C\"), size = 200, replace = TRUE )) %>% group_by(condition) %>% mutate(new_episode = is_new_episode(date, 365)) %>% select(patient, date, condition, new_episode) } #> # A tibble: 200 × 4 #> # Groups: condition [3] #> patient date condition new_episode #> #> 1 D08278 2016-11-18 B TRUE #> 2 4047F6 2005-01-19 A FALSE #> 3 B66559 2007-11-28 A FALSE #> 4 E35356 2003-06-11 A TRUE #> 5 807228 2011-06-10 C FALSE #> 6 966513 2013-11-12 B FALSE #> 7 9C1B92 2008-01-25 A FALSE #> 8 C56827 2004-12-05 B FALSE #> 9 E95747 2008-06-30 C FALSE #> 10 A76045 2015-10-06 A TRUE #> # … with 190 more rows if (require(\"dplyr\")) { df %>% group_by(ward, patient) %>% transmute(date, patient, new_index = get_episode(date, 60), new_logical = is_new_episode(date, 60) ) } #> # A tibble: 200 × 5 #> # Groups: ward, patient [182] #> ward date patient new_index new_logical #> #> 1 Outpatient 2016-11-18 D08278 1 TRUE #> 2 ICU 2005-01-19 4047F6 1 TRUE #> 3 ICU 2007-11-28 B66559 1 TRUE #> 4 ICU 2003-06-11 E35356 1 TRUE #> 5 Clinical 2011-06-10 807228 1 TRUE #> 6 Clinical 2013-11-12 966513 1 TRUE #> 7 Clinical 2008-01-25 9C1B92 1 TRUE #> 8 Clinical 2004-12-05 C56827 1 TRUE #> 9 Clinical 2008-06-30 E95747 1 TRUE #> 10 ICU 2015-10-06 A76045 1 TRUE #> # … with 190 more rows if (require(\"dplyr\")) { df %>% group_by(ward) %>% summarise( n_patients = n_distinct(patient), n_episodes_365 = sum(is_new_episode(date, episode_days = 365)), n_episodes_60 = sum(is_new_episode(date, episode_days = 60)), n_episodes_30 = sum(is_new_episode(date, episode_days = 30)) ) } #> # A tibble: 3 × 5 #> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30 #> #> 1 Clinical 111 14 55 74 #> 2 ICU 57 13 37 42 #> 3 Outpatient 14 9 14 14 if (require(\"dplyr\")) { # grouping on patients and microorganisms leads to the same # results as first_isolate() when using 'episode-based': x <- df %>% filter_first_isolate( include_unknown = TRUE, method = \"episode-based\" ) y <- df %>% group_by(patient, mo) %>% filter(is_new_episode(date, 365)) %>% ungroup() identical(x, y) } #> Including isolates from ICU. #> [1] FALSE if (require(\"dplyr\")) { # but is_new_episode() has a lot more flexibility than first_isolate(), # since you can now group on anything that seems relevant: df %>% group_by(patient, mo, ward) %>% mutate(flag_episode = is_new_episode(date, 365)) %>% select(group_vars(.), flag_episode) } #> # A tibble: 200 × 4 #> # Groups: patient, mo, ward [187] #> patient mo ward flag_episode #> #> 1 D08278 B_ESCHR_COLI Outpatient TRUE #> 2 4047F6 B_ESCHR_COLI ICU TRUE #> 3 B66559 B_ESCHR_COLI ICU TRUE #> 4 E35356 B_STPHY_CONS ICU TRUE #> 5 807228 B_STRPT_PNMN Clinical TRUE #> 6 966513 B_STPHY_HMNS Clinical TRUE #> 7 9C1B92 B_STPHY_CONS Clinical TRUE #> 8 C56827 B_ESCHR_COLI Clinical TRUE #> 9 E95747 B_KLBSL_PNMN Clinical TRUE #> 10 A76045 B_ENTRC_FACM ICU TRUE #> # … with 190 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca( x, choices = 1:2, scale = 1, pc.biplot = TRUE, labels = NULL, labels_textsize = 3, labels_text_placement = 1.5, groups = NULL, ellipse = TRUE, ellipse_prob = 0.68, ellipse_size = 0.5, ellipse_alpha = 0.5, points_size = 2, points_alpha = 0.25, arrows = TRUE, arrows_colour = \"darkblue\", arrows_size = 0.5, arrows_textsize = 3, arrows_textangled = TRUE, arrows_alpha = 0.75, base_textsize = 10, ... )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default biplot strict sense. scale variables scaled lambda ^ scale observations scaled lambda ^ (1-scale) lambda singular values computed princomp. Normally 0 <= scale <= 1, warning issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component biplot\", lambda = 1 observations scaled sqrt(n) variables scaled sqrt(n). inner products variables approximate covariances distances observations approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. # \\donttest{ if (require(\"dplyr\")) { # calculate the resistance per group first resistance_data <- example_isolates %>% group_by( order = mo_order(mo), # group on anything, like order genus = mo_genus(mo) ) %>% # and genus as we do here; filter(n() >= 30) %>% # filter on only 30 results per group summarise_if(is.rsi, resistance) # then get resistance of all drugs # now conduct PCA for certain antimicrobial drugs pca_result <- resistance_data %>% pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT) summary(pca_result) # old base R plotting method: biplot(pca_result) # new ggplot2 plotting method using this package: if (require(\"ggplot2\")) { ggplot_pca(pca_result) # still extendible with any ggplot2 function ggplot_pca(pca_result) + scale_colour_viridis_d() + labs(title = \"Title here\") } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #> and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" #> # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_rsi","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"","code":"ggplot_rsi( data, position = NULL, x = \"antibiotic\", fill = \"interpretation\", facet = NULL, breaks = seq(0, 1, 0.1), limits = NULL, translate_ab = \"name\", combine_SI = TRUE, minimum = 30, language = get_AMR_locale(), nrow = NULL, colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R = \"#ED553B\"), datalabels = TRUE, datalabels.size = 2.5, datalabels.colour = \"grey15\", title = NULL, subtitle = NULL, caption = NULL, x.title = \"Antimicrobial\", y.title = \"Proportion\", ... ) geom_rsi( position = NULL, x = c(\"antibiotic\", \"interpretation\"), fill = \"interpretation\", translate_ab = \"name\", minimum = 30, language = get_AMR_locale(), combine_SI = TRUE, ... ) facet_rsi(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL) scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL) scale_rsi_colours(..., aesthetics = \"fill\") theme_rsi() labels_rsi_count( position = NULL, x = \"antibiotic\", translate_ab = \"name\", minimum = 30, language = get_AMR_locale(), combine_SI = TRUE, datalabels.size = 3, datalabels.colour = \"grey15\" )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"data data.frame column(s) class rsi (see .rsi()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_rsi_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_rsi() , case scale_rsi_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples. aesthetics aesthetics apply colours , defaults \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\"","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"default, names antibiotics shown plots using ab_name(). can set translate_ab argument. See count_df().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"the-functions","dir":"Reference","previous_headings":"","what":"The Functions","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"geom_rsi() take variable data rsi class (created .rsi()) using rsi_df() plot bars percentage R, S. default behaviour bars stacked different antibiotics x axis. facet_rsi() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_rsi_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_rsi() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_rsi_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_rsi() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"","code":"# \\donttest{ if (require(\"ggplot2\") && require(\"dplyr\")) { # get antimicrobial results for drugs against a UTI: ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) + geom_rsi() } if (require(\"ggplot2\") && require(\"dplyr\")) { # prettify the plot using some additional functions: df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP) ggplot(df) + geom_rsi() + scale_y_percent() + scale_rsi_colours() + labels_rsi_count() + theme_rsi() } if (require(\"ggplot2\") && require(\"dplyr\")) { # or better yet, simplify this using the wrapper function - a single command: example_isolates %>% select(AMX, NIT, FOS, TMP, CIP) %>% ggplot_rsi() } if (require(\"ggplot2\") && require(\"dplyr\")) { # get only proportions and no counts: example_isolates %>% select(AMX, NIT, FOS, TMP, CIP) %>% ggplot_rsi(datalabels = FALSE) } if (require(\"ggplot2\") && require(\"dplyr\")) { # add other ggplot2 arguments as you like: example_isolates %>% select(AMX, NIT, FOS, TMP, CIP) %>% ggplot_rsi( width = 0.5, colour = \"black\", size = 1, linetype = 2, alpha = 0.25 ) } if (require(\"ggplot2\") && require(\"dplyr\")) { # you can alter the colours with colour names: example_isolates %>% select(AMX) %>% ggplot_rsi(colours = c(SI = \"yellow\")) } if (require(\"ggplot2\") && require(\"dplyr\")) { # but you can also use the built-in colour-blind friendly colours for # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red: data.frame( x = c(\"Value1\", \"Value2\", \"Value3\"), y = c(1, 2, 3), z = c(\"Value4\", \"Value5\", \"Value6\") ) %>% ggplot() + geom_col(aes(x = x, y = y, fill = z)) + scale_rsi_colours(Value4 = \"S\", Value5 = \"I\", Value6 = \"R\") } if (require(\"ggplot2\") && require(\"dplyr\")) { # resistance of ciprofloxacine per age group example_isolates %>% mutate(first_isolate = first_isolate()) %>% filter( first_isolate == TRUE, mo == as.mo(\"Escherichia coli\") ) %>% # age_groups() is also a function in this AMR package: group_by(age_group = age_groups(age)) %>% select(age_group, CIP) %>% ggplot_rsi(x = \"age_group\") } #> Including isolates from ICU. #> Warning: Removed 6 rows containing missing values (`position_stack()`). #> Warning: Removed 6 rows containing missing values (`position_stack()`). if (require(\"ggplot2\") && require(\"dplyr\")) { # a shorter version which also adjusts data label colours: example_isolates %>% select(AMX, NIT, FOS, TMP, CIP) %>% ggplot_rsi(colours = FALSE) } if (require(\"ggplot2\") && require(\"dplyr\")) { # it also supports groups (don't forget to use the group var on `x` or `facet`): example_isolates %>% filter(mo_is_gram_negative(), ward != \"Outpatient\") %>% # select only UTI-specific drugs select(ward, AMX, NIT, FOS, TMP, CIP) %>% group_by(ward) %>% ggplot_rsi( x = \"ward\", facet = \"antibiotic\", nrow = 1, title = \"AMR of Anti-UTI Drugs Per Ward\", x.title = \"Ward\", datalabels = FALSE ) } #> ℹ Using column 'mo' as input for mo_is_gram_negative() # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antibiotics data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col( x = NULL, search_string = NULL, verbose = FALSE, only_rsi_columns = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_rsi_columns logical indicate whether antibiotic columns must detected transformed class rsi (see .rsi()) beforehand (defaults FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antibiotics data set column containing name code antibiotic.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame( amox = \"S\", tetr = \"R\" ) guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\" guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #> OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline). # WHONET codes df <- data.frame( AMP_ND10 = \"R\", AMC_ED20 = \"S\" ) guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 134,634 observations 2 variables: mo Microorganism ID ab Antibiotic ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 134,634 × 2 #> mo ab #> #> 1 B_GRAMP ATM #> 2 B_GRAMP COL #> 3 B_GRAMP NAL #> 4 B_GRAMP PLB #> 5 B_GRAMP TEM #> 6 B_ABTRP ATM #> 7 B_ABTRP COL #> 8 B_ABTRP NAL #> 9 B_ABTRP PLB #> 10 B_ABTRP TEM #> # … with 134,624 more rows"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\")) italicize_taxonomy(string, type = c(\"markdown\", \"ansi\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, using ANSI colours adding \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\" cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...) left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...) right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...) full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...) semi_join_microorganisms(x, by = NULL, ...) anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> # A tibble: 1 × 22 #> mo fullname status kingdom phylum class order family genus species #> #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies , rank , ref , #> # source , lpsn , lpsn_parent , lpsn_renamed_to , #> # gbif , gbif_parent , gbif_renamed_to , prevalence , #> # snomed left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 22 #> mo fullname status kingdom phylum class order family genus species #> #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies , rank , ref , #> # source , lpsn , lpsn_parent , lpsn_renamed_to , #> # gbif , gbif_parent , gbif_renamed_to , prevalence , #> # snomed df <- data.frame( date = seq( from = as.Date(\"2018-01-01\"), to = as.Date(\"2018-01-07\"), by = 1 ), bacteria = as.mo(c( \"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\", \"E. coli\", \"E. coli\", \"E. coli\" )), stringsAsFactors = FALSE ) colnames(df) #> [1] \"date\" \"bacteria\" df_joined <- left_join_microorganisms(df, \"bacteria\") colnames(df_joined) #> [1] \"date\" \"bacteria\" \"fullname\" \"status\" #> [5] \"kingdom\" \"phylum\" \"class\" \"order\" #> [9] \"family\" \"genus\" \"species\" \"subspecies\" #> [13] \"rank\" \"ref\" \"source\" \"lpsn\" #> [17] \"lpsn_parent\" \"lpsn_renamed_to\" \"gbif\" \"gbif_parent\" #> [21] \"gbif_renamed_to\" \"prevalence\" \"snomed\" # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% left_join_microorganisms() %>% colnames() } #> Joining, by = \"mo\" #> [1] \"date\" \"patient\" \"age\" \"gender\" #> [5] \"ward\" \"mo\" \"PEN\" \"OXA\" #> [9] \"FLC\" \"AMX\" \"AMC\" \"AMP\" #> [13] \"TZP\" \"CZO\" \"FEP\" \"CXM\" #> [17] \"FOX\" \"CTX\" \"CAZ\" \"CRO\" #> [21] \"GEN\" \"TOB\" \"AMK\" \"KAN\" #> [25] \"TMP\" \"SXT\" \"NIT\" \"FOS\" #> [29] \"LNZ\" \"CIP\" \"MFX\" \"VAN\" #> [33] \"TEC\" \"TCY\" \"TGC\" \"DOX\" #> [37] \"ERY\" \"CLI\" \"AZM\" \"IPM\" #> [41] \"MEM\" \"MTR\" \"CHL\" \"COL\" #> [45] \"MUP\" \"RIF\" \"fullname\" \"status\" #> [49] \"kingdom\" \"phylum\" \"class\" \"order\" #> [53] \"family\" \"genus\" \"species\" \"subspecies\" #> [57] \"rank\" \"ref\" \"source\" \"lpsn\" #> [61] \"lpsn_parent\" \"lpsn_renamed_to\" \"gbif\" \"gbif_parent\" #> [65] \"gbif_renamed_to\" \"prevalence\" \"snomed\" # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials( x = NULL, col_mo = NULL, universal = c(\"ampicillin\", \"amoxicillin/clavulanic acid\", \"cefuroxime\", \"piperacillin/tazobactam\", \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"), gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\", \"ceftazidime\", \"meropenem\"), gram_positive = c(\"vancomycin\", \"teicoplanin\", \"tetracycline\", \"erythromycin\", \"oxacillin\", \"rifampin\"), antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\", \"nystatin\", \"voriconazole\"), only_rsi_columns = FALSE, ... ) all_antimicrobials(x = NULL, only_rsi_columns = FALSE, ...) antimicrobials_equal( y, z, type = c(\"points\", \"keyantimicrobials\"), ignore_I = TRUE, points_threshold = 2, ... )"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antibiotics columns, like AMX amox. Can left blank determine automatically col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial drugs, case-insensitive. Set NULL ignore. See Details default antimicrobial drugs gram_negative names antibiotic drugs Gram-positives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs gram_positive names antibiotic drugs Gram-negatives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs antifungal names antifungal drugs fungi, case-insensitive. Set NULL ignore. See Details default antifungal drugs only_rsi_columns logical indicate whether columns must included transformed class rsi (see .rsi()) beforehand (defaults FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial drug results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial drugs used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial drugs used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial drugs used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial drugs used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\" # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values) antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs # \\donttest{ if (require(\"dplyr\")) { # set key antibiotics to a new variable my_patients <- example_isolates %>% mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x` mutate( # now calculate first isolates first_regular = first_isolate(col_keyantimicrobials = FALSE), # and first WEIGHTED isolates first_weighted = first_isolate(col_keyantimicrobials = \"keyab\") ) # Check the difference in this data set, 'weighted' results in more isolates: sum(my_patients$first_regular, na.rm = TRUE) sum(my_patients$first_weighted, na.rm = TRUE) } #> Including isolates from ICU. #> Including isolates from ICU. #> [1] 1395 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for default kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for matrix kurtosis(x, na.rm = FALSE, excess = FALSE) # S3 method for data.frame kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 3.007355 kurtosis(rnorm(10000), excess = TRUE) #> [1] -0.01576544"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE) x %like% pattern x %unlike% pattern x %like_case% pattern x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(\"case\", \"diff\", \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE a[1] %like% b #> [1] TRUE FALSE FALSE a %like% b[1] #> [1] TRUE FALSE FALSE # \\donttest{ # get isolates whose name start with 'Entero' (case-insensitive) example_isolates[which(mo_name() %like% \"^entero\"), ] #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinic… B_ENTRC_FACM NA NA NA NA #> 2 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 3 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 5 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 6 2003-04-21 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 7 2003-08-13 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC NA NA NA NA #> 10 2003-09-05 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> # … with 96 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF if (require(\"dplyr\")) { example_isolates %>% filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-02-21 4FC193 69 M Clinic… B_ENTRC_FACM NA NA NA NA #> 2 2002-04-08 130252 78 M ICU B_ENTRC_FCLS NA NA NA NA #> 3 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 4 2002-06-23 798871 82 M Clinic… B_ENTRC_FCLS NA NA NA NA #> 5 2003-04-20 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 6 2003-04-21 6BC362 62 M ICU B_ENTRC NA NA NA NA #> 7 2003-08-13 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> 8 2003-08-13 F35553 52 M ICU B_ENTRC_FCLS NA NA NA NA #> 9 2003-09-05 F35553 52 M ICU B_ENTRC NA NA NA NA #> 10 2003-09-05 F35553 52 M ICU B_ENTRBC_CLOC R NA NA R #> # … with 96 more rows, and 36 more variables: AMC , AMP , TZP , #> # CZO , FEP , CXM , FOX , CTX , CAZ , #> # CRO , GEN , TOB , AMK , KAN , TMP , #> # SXT , NIT , FOS , LNZ , CIP , MFX , #> # VAN , TEC , TCY , TGC , DOX , ERY , #> # CLI , AZM , IPM , MEM , MTR , CHL , #> # COL , MUP , RIF # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro( x = NULL, guideline = \"CMI2012\", col_mo = NULL, info = interactive(), pct_required_classes = 0.5, combine_SI = TRUE, verbose = FALSE, only_rsi_columns = FALSE, ... ) custom_mdro_guideline(..., as_factor = TRUE) brmo(x = NULL, only_rsi_columns = FALSE, ...) mrgn(x = NULL, only_rsi_columns = FALSE, ...) mdr_tb(x = NULL, only_rsi_columns = FALSE, ...) mdr_cmi2012(x = NULL, only_rsi_columns = FALSE, ...) eucast_exceptional_phenotypes(x = NULL, only_rsi_columns = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antibiotics columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_rsi_columns logical indicate whether antibiotic columns must detected transformed class rsi (see .rsi()) beforehand (defaults FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antibiotics . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu \"WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)\" (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde: row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: Rules can also combined custom rules using c(): rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\", ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #> 1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #> 2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #> 3. Otherwise -> Negative #> #> Unmatched rows will return NA. x <- mdro(example_isolates, guideline = custom) table(x) #> Negative Elderly Type A Elderly Type B #> 1070 198 732 x <- mdro(example_isolates, guideline = c(custom, custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #> Negative Elderly Type A Elderly Type B Elderly Type C #> 961 198 732 109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") #> (16 isolates had no test results) str(out) #> Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #> Negative Positive, unconfirmed Positive #> 1978 0 6 out <- mdro(example_isolates, guideline = custom_mdro_guideline( AMX == \"R\" ~ \"Custom MDRO 1\", VAN == \"R\" ~ \"Custom MDRO 2\" ) ) table(out) #> out #> Negative Custom MDRO 1 Custom MDRO 2 #> 870 804 326 # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% mdro() %>% table() # no need to define `x` when used inside dplyr verbs: example_isolates %>% mutate(MDRO = mdro()) %>% pull(MDRO) %>% table() } #> (16 isolates had no test results) #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> (16 isolates had no test results) #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #> Negative Multi-drug-resistant (MDR) #> 1601 128 #> Extensively drug-resistant (XDR) Pandrug-resistant (PDR) #> 0 0 # }"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean AMR Distance — mean_amr_distance","title":"Mean AMR Distance — mean_amr_distance","text":"function calculates normalised mean antimicrobial resistance multiple observations.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean AMR Distance — mean_amr_distance","text":"","code":"mean_amr_distance(x, ...) # S3 method for default mean_amr_distance(x, ...) # S3 method for mic mean_amr_distance(x, ...) # S3 method for disk mean_amr_distance(x, ...) # S3 method for rsi mean_amr_distance(x, ..., combine_SI = TRUE) # S3 method for data.frame mean_amr_distance(x, ..., combine_SI = TRUE) amr_distance_from_row(amr_distance, row)"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean AMR Distance — mean_amr_distance","text":"x vector class rsi, rsi rsi, data.frame containing columns classes ... variables select (supports tidy selection column1:column4 (.mic)), can thus also antibiotic selectors combine_SI logical indicate whether values S must merged one, input consists S+vs. R (susceptible vs. resistant), defaults TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mean AMR Distance — mean_amr_distance","text":"mean AMR distance normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. common numeric data distance equal Z scores (number standard deviations mean). MIC values (see .mic()) transformed log2() first; distance calculated (log2(x) - mean(log2(x))) / sd(log2(x)). R/SI values (see .rsi()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per variable, mean columns returned per row (using rowMeans()), see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean AMR Distance — mean_amr_distance","text":"","code":"x <- random_mic(10) x #> Class 'mic' #> [1] <=0.001 0.0625 0.01 0.005 32 0.01 4 0.025 2 #> [10] 32 mean_amr_distance(x) #> [1] -1.3194635 -0.2327999 -0.7143765 -0.8965258 1.4065439 -0.7143765 #> [7] 0.8600960 -0.4735882 0.6779467 1.4065439 y <- data.frame( id = LETTERS[1:10], amox = random_mic(10, ab = \"amox\", mo = \"Escherichia coli\"), cipr = random_mic(10, ab = \"cipr\", mo = \"Escherichia coli\"), gent = random_mic(10, ab = \"gent\", mo = \"Escherichia coli\"), tobr = random_mic(10, ab = \"tobr\", mo = \"Escherichia coli\") ) y #> id amox cipr gent tobr #> 1 A 16 1 4 2 #> 2 B 4 0.25 1 8 #> 3 C >=32 0.125 4 <=0.5 #> 4 D >=32 0.5 <=0.5 <=0.5 #> 5 E 4 0.125 1 8 #> 6 F 4 0.5 >=8 4 #> 7 G <=1 0.5 1 8 #> 8 H 16 1 2 1 #> 9 I 16 0.0625 >=8 <=0.5 #> 10 J 4 0.125 4 2 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #> \"id\" and \"tobr\" #> Warning: NAs introduced by coercion #> [1] 0.61790612 -0.10920284 -0.05949768 -0.23827191 -0.28797707 0.45880700 #> [7] -0.23661482 0.29204478 -0.21259079 -0.22460280 y$amr_distance <- mean_amr_distance(y, where(is.mic)) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\" and #> \"tobr\" y[order(y$amr_distance), ] #> id amox cipr gent tobr amr_distance #> 5 E 4 0.125 1 8 -0.28797707 #> 4 D >=32 0.5 <=0.5 <=0.5 -0.23827191 #> 7 G <=1 0.5 1 8 -0.23661482 #> 10 J 4 0.125 4 2 -0.22460280 #> 9 I 16 0.0625 >=8 <=0.5 -0.21259079 #> 2 B 4 0.25 1 8 -0.10920284 #> 3 C >=32 0.125 4 <=0.5 -0.05949768 #> 8 H 16 1 2 1 0.29204478 #> 6 F 4 0.5 >=8 4 0.45880700 #> 1 A 16 1 4 2 0.61790612 if (require(\"dplyr\")) { y %>% mutate( amr_distance = mean_amr_distance(., where(is.mic)), check_id_C = amr_distance_from_row(amr_distance, id == \"C\") ) %>% arrange(check_id_C) } #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\" and #> \"tobr\" #> id amox cipr gent tobr amr_distance check_id_C #> 1 C >=32 0.125 4 <=0.5 -0.05949768 0.00000000 #> 2 B 4 0.25 1 8 -0.10920284 0.04970516 #> 3 I 16 0.0625 >=8 <=0.5 -0.21259079 0.15309311 #> 4 J 4 0.125 4 2 -0.22460280 0.16510512 #> 5 G <=1 0.5 1 8 -0.23661482 0.17711714 #> 6 D >=32 0.5 <=0.5 <=0.5 -0.23827191 0.17877424 #> 7 E 4 0.125 1 8 -0.28797707 0.22847939 #> 8 H 16 1 2 1 0.29204478 0.35154246 #> 9 F 4 0.5 >=8 4 0.45880700 0.51830468 #> 10 A 16 1 4 2 0.61790612 0.67740380 if (require(\"dplyr\")) { # support for groups example_isolates %>% filter(mo_genus() == \"Enterococcus\" & mo_species() != \"\") %>% select(mo, TCY, carbapenems()) %>% group_by(mo) %>% mutate(d = mean_amr_distance(., where(is.rsi))) %>% arrange(mo, d) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ Using column 'mo' as input for mo_species() #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Calculating mean AMR distance based on columns \"IPM\", \"MEM\" and \"TCY\" #> # A tibble: 63 × 5 #> # Groups: mo [4] #> mo TCY IPM MEM d #> #> 1 B_ENTRC_AVIM S S NA NaN #> 2 B_ENTRC_AVIM S S NA NaN #> 3 B_ENTRC_CSSL NA S NA NA #> 4 B_ENTRC_FACM S S NA -2.66 #> 5 B_ENTRC_FACM S R R -0.423 #> 6 B_ENTRC_FACM S R R -0.423 #> 7 B_ENTRC_FACM NA R R 0.224 #> 8 B_ENTRC_FACM NA R R 0.224 #> 9 B_ENTRC_FACM NA R R 0.224 #> 10 B_ENTRC_FACM NA R R 0.224 #> # … with 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"tibble 5,910 observations 2 variables: code Commonly used code microorganism mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 5,910 × 2 #> code mo #> #> 1 _FAM_A- B_GRAMN #> 2 _FAM_A+ B_GRAMP #> 3 _FAM_AC B_GRAMP #> 4 _FAM_AE B_GRAMN #> 5 _FAM_AN B_GRAMN #> 6 _FAM_AO B_GRAMP #> 7 _FAM_AP UNKNOWN #> 8 _FAM_AS F_FUNGUS #> 9 _FAM_AT UNKNOWN #> 10 _FAM_AV UNKNOWN #> # … with 5,900 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 52,141 Microorganisms — microorganisms","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: 11 December, 2022) five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF). data set backbone AMR package. MO codes can looked using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"tibble 52,141 observations 22 variables: mo ID microorganism used package fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. status Status taxon, either \"accepted\" \"synonym\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism rank Text taxonomic rank microorganism, \"species\" \"genus\" ref Author(s) year related scientific publication. contains first surname year latest authors, e.g. \"Wallis et al. 2006 emend. Smith Jones 2018\" becomes \"Smith et al., 2018\". field directly retrieved source specified column source. Moreover, accents removed comply CRAN allows ASCII characters, e.g. \"Váňová\" becomes \"Vanova\". lpsn Identifier ('Record number') List Prokaryotic names Standing Nomenclature (LPSN). first/highest LPSN identifier keep one identifier per row. example, Acetobacter ascendens LPSN Record number 7864 11011. first available microorganisms data set. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF) gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon source Either \"GBIF\", \"LPSN\" \"manually added\" (see Source) prevalence Prevalence microorganism according Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version 1 July, 2021 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Grimont et al. (2007). Antigenic Formulae Salmonella Serovars, 9th Edition. Collaborating Centre Reference Research Salmonella (WHOCC-SALM). Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Please note entries based List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Included taxonomic data : ~36,000 (sub)species kingdoms Archaea Bacteria ~7,900 (sub)species kingdom Fungi. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package. relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histoplasma, Pneumocystis, Saccharomyces Trichophyton). ~5,100 (sub)species kingdom Protozoa ~1,400 (sub)species ~40 relevant genera kingdom Animalia (Strongyloides Taenia) ~9,800 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies identifier parent taxons year first author related scientific publication","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"convenience, entries added manually: ~1,500 entries city-like serovars Salmonellae 11 entries Streptococcus (beta-haemolytic: groups , B, C, D, F, G, H, K unspecified; : viridans, milleri) 2 entries Staphylococcus (coagulase-negative (CoNS) coagulase-positive (CoPS)) 1 entry Blastocystis (B. hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 6 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast, unknown fungus, unknown anaerobic bacteria) syntax used transform original data cleansed R format, can found : https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"about-the-records-from-lpsn-see-source-","dir":"Reference","previous_headings":"","what":"About the Records from LPSN (see Source)","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"LPSN main source bacteriological taxonomy AMR package. List Prokaryotic names Standing Nomenclature (LPSN) provides comprehensive information nomenclature prokaryotes. LPSN free use service founded Jean P. Euzeby 1997 later maintained Aidan C. Parte.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,141 × 22 #> mo fullname status kingdom phylum class order family genus #> #> 1 B_ANAER (unknown ana… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #> 2 F_FUNGUS (unknown fun… accep… Fungi (unkn… \"(un… \"(un… \"(unk… \"(un… #> 3 B_GRAMN (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #> 4 B_GRAMP (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #> 5 UNKNOWN (unknown nam… accep… (unkno… (unkn… \"(un… \"(un… \"(unk… \"(un… #> 6 F_YEAST (unknown yea… accep… Fungi (unkn… \"(un… \"(un… \"(unk… \"(un… #> 7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"Abdi… \"\" #> 8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"\" \"\" #> 9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… \"Abd… \"\" \"\" \"\" #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"\" \"\" \"\" \"\" #> # … with 52,131 more rows, and 13 more variables: species , #> # subspecies , rank , ref , source , lpsn , #> # lpsn_parent , lpsn_renamed_to , gbif , gbif_parent , #> # gbif_renamed_to , prevalence , snomed "},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm originally described : Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 . Later, work Bartlett et al. bacterial pathogens infecting humans (2022, doi:10.1099/mic.0.001269 ) incorporated.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"Dr. Matthijs Berends","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"as.mo(\"E. coli\") #> Class 'mo' #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans according to Bartlett et #> al. (2022). See ?mo_matching_score. #> #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Based on input \"K pneumoniae\" #> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella #> pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis #> (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500), #> Kingella potus (0.400), Kosakonia pseudosacchari (0.361), Kaistella #> palustris (0.333), Kocuria palustris (0.333), Kocuria pelophila #> (0.333), Kocuria polaris (0.333), Kroppenstedtia pulmonis (0.304), #> Kibdelosporangium phytohabitans (0.282), Kitasatospora putterlickiae #> (0.269), Kibdelosporangium philippinense (0.266), Kalamiella piersonii #> (0.262), Kitasatospora psammotica (0.260), Kallotenue papyrolyticum #> (0.250), Kangiella profundi (0.250), Kangsaoukella pontilimi (0.250), #> Katagnymene pelagic (0.250), Keratinibaculum paraultunense (0.250), #> Kibdelosporangium persicum (0.250), Kitasatoa purpurea (0.250) and #> Kitasatospora paracochleata (0.250) [showing first 25] mo_matching_score( x = \"E. coli\", n = c(\"Escherichia coli\", \"Entamoeba coli\") ) #> [1] 0.6875000 0.1587302"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_fullname( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_shortname( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_subspecies( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_species( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_genus( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_family( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_order( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_class( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_phylum( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_kingdom( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_domain( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_type( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_status( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_gramstain( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_is_gram_negative( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_is_gram_positive( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_is_yeast( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_is_intrinsic_resistant( x, ab, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_snomed( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_ref( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_authors( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_year( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_lpsn( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_gbif( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_rank( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_taxonomy( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_synonyms( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_current(x, language = get_AMR_locale(), ...) mo_info( x, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_url( x, open = FALSE, language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... ) mo_property( x, property = \"fullname\", language = get_AMR_locale(), keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ... )"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() list case mo_taxonomy(), mo_synonyms() mo_info() named character case mo_url() numeric case mo_snomed() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions , default, keep old taxonomic properties. Please refer example, knowing Escherichia blattae renamed Shimwellia blattae 2010: mo_name(\"Escherichia blattae\") return \"Shimwellia blattae\" (note renaming) mo_ref(\"Escherichia blattae\", keep_synonyms = TRUE) return \"Burgess et al., 1973\" (without note) mo_ref(\"Shimwellia blattae\", keep_synonyms = FALSE) return \"Priest et al., 2010\" (without note) short name - mo_shortname() - almost always returns first character genus full species, like \"E. coli\". Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. words, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Determination Gram stain - mo_gramstain() - based taxonomic kingdom phylum. Originally, Cavalier-Smith defined -called subkingdoms Negibacteria Posibacteria (2002, PMID 11837318), considered phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, Tenericutes. phyla renamed Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota (2021, PMID 34694987). Bacteria phyla considered Gram-positive AMR package, except members class Negativicutes (within phylum Bacillota) Gram-negative. bacteria considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (NA input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts - mo_is_yeast() - based taxonomic kingdom class. Budding yeasts fungi phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes). True yeasts aggregated underlying order Saccharomycetales. Thus, microorganisms member taxonomic class Saccharomycetes, function return TRUE. returns FALSE otherwise (NA input NA MO code UNKNOWN). Determination intrinsic resistance - mo_is_intrinsic_resistant() - based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antibiotics). output translated possible. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes - mo_snomed() - version 1 July, 2021. See Source microorganisms data set info. Old taxonomic names (-called 'synonyms') can retrieved mo_synonyms(), current taxonomic name can retrieved mo_current(). functions return full names.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"data sets AMR package (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree ----------------------------------------------------------- mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Pseudomonadota\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\" # colloquial properties ---------------------------------------------------- mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\" # other properties --------------------------------------------------------- mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [1] \"1098101000112102\" \"1098201000112108\" \"409801009\" \"446870005\" #> [5] \"56415008\" \"713926009\" \"714315002\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #> Klebsiella pneumoniae #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" mo_synonyms(\"Klebsiella pneumoniae\") #> NULL # scientific reference ----------------------------------------------------- mo_ref(\"Klebsiella pneumoniae\") #> [1] \"Trevisan, 1887\" mo_authors(\"Klebsiella pneumoniae\") #> [1] \"Trevisan\" mo_year(\"Klebsiella pneumoniae\") #> [1] 1887 mo_lpsn(\"Klebsiella pneumoniae\") #> [1] \"777151\" mo_gbif(\"Klebsiella pneumoniae\") #> [1] \"3221874\" # abbreviations known in the field ----------------------------------------- mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\" mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EHEC\") #> [1] \"coli\" # known subspecies --------------------------------------------------------- mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\" # \\donttest{ # Becker classification, see ?as.mo ---------------------------------------- mo_fullname(\"Staph. epidermidis\") #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"Staph. epidermidis\", Becker = TRUE) #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"Staph. epidermidis\") #> [1] \"S. epidermidis\" mo_shortname(\"Staph. epidermidis\", Becker = TRUE) #> [1] \"CoNS\" # Lancefield classification, see ?as.mo ------------------------------------ mo_fullname(\"S. pyo\") #> [1] \"Streptococcus pyogenes\" mo_fullname(\"S. pyo\", Lancefield = TRUE) #> [1] \"Streptococcus Group A\" mo_shortname(\"S. pyo\") #> [1] \"S. pyogenes\" mo_shortname(\"S. pyo\", Lancefield = TRUE) #> [1] \"GS. Group AS\" # language support -------------------------------------------------------- mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") # German #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") # Dutch #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") # Spanish #> [1] \"Gram negativo\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"el\") # Greek #> [1] \"Αρνητικό κατά Gram\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"uk\") # Ukrainian #> [1] \"Грамнегативні\" # mo_type is equal to mo_kingdom, but mo_kingdom will remain official mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_kingdom(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, no effect #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, translated #> [1] \"细菌\" mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"uk\") #> [1] \"Streptococcus Група A\" # other -------------------------------------------------------------------- mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1] TRUE FALSE FALSE # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) { example_isolates %>% filter(mo_is_gram_positive()) %>% count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 18 × 2 #> `mo_genus()` n #> #> 1 Staphylococcus 840 #> 2 Streptococcus 275 #> 3 Enterococcus 83 #> 4 Corynebacterium 17 #> 5 Micrococcus 6 #> 6 Gemella 3 #> 7 Aerococcus 2 #> 8 Cutibacterium 1 #> 9 Dermabacter 1 #> 10 Fusibacter 1 #> 11 Globicatella 1 #> 12 Granulicatella 1 #> 13 Lactobacillus 1 #> 14 Leuconostoc 1 #> 15 Listeria 1 #> 16 Paenibacillus 1 #> 17 Rothia 1 #> 18 Schaalia 1 if (require(\"dplyr\")) { example_isolates %>% filter(mo_is_intrinsic_resistant(ab = \"vanco\")) %>% count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 20 × 2 #> `mo_genus()` n #> #> 1 Escherichia 467 #> 2 Klebsiella 77 #> 3 Proteus 39 #> 4 Pseudomonas 30 #> 5 Serratia 25 #> 6 Enterobacter 23 #> 7 Citrobacter 11 #> 8 Haemophilus 8 #> 9 Acinetobacter 6 #> 10 Morganella 6 #> 11 Pantoea 4 #> 12 Salmonella 3 #> 13 Neisseria 2 #> 14 Stenotrophomonas 2 #> 15 Campylobacter 1 #> 16 Enterococcus 1 #> 17 Hafnia 1 #> 18 Lactobacillus 1 #> 19 Leuconostoc 1 #> 20 Pseudescherichia 1 # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #> #> $phylum #> [1] \"Pseudomonadota\" #> #> $class #> [1] \"Gammaproteobacteria\" #> #> $order #> [1] \"Enterobacterales\" #> #> $family #> [1] \"Enterobacteriaceae\" #> #> $genus #> [1] \"Klebsiella\" #> #> $species #> [1] \"pneumoniae\" #> #> $subspecies #> [1] \"\" #> # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #> #> $phylum #> [1] \"Pseudomonadota\" #> #> $class #> [1] \"Gammaproteobacteria\" #> #> $order #> [1] \"Enterobacterales\" #> #> $family #> [1] \"Enterobacteriaceae\" #> #> $genus #> [1] \"Klebsiella\" #> #> $species #> [1] \"pneumoniae\" #> #> $subspecies #> [1] \"\" #> #> $status #> [1] \"accepted\" #> #> $synonyms #> NULL #> #> $gramstain #> [1] \"Gram-negative\" #> #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #> #> $ref #> [1] \"Trevisan, 1887\" #> #> $snomed #> [1] \"1098101000112102\" \"1098201000112108\" \"409801009\" \"446870005\" #> [5] \"56415008\" \"713926009\" \"714315002\" #> # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source( path, destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") ) get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file, default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set R option, using options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names: save \"home//ourcodes.xlsx\". Now set source: now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions: edit Excel file , say, adding row 4 like : ...new usage MO function package update data file: delete reference data file, just use \"\", NULL FALSE input set_mo_source(): original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"| A | B | --|--------------------|-----------------------| 1 | Organisation XYZ | mo | 2 | lab_mo_ecoli | Escherichia coli | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | | | set_mo_source(\"home/me/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #> '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #> \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class 'mo' #> [1] B_ESCHR_COLI mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\" # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #> Use mo_uncertainties() to review it. #> Class 'mo' #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI | A | B | --|--------------------|-----------------------| 1 | Organisation XYZ | mo | 2 | lab_mo_ecoli | Escherichia coli | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus | Staphylococcus aureus | 5 | | | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #> '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #> \"Organisation XYZ\" and \"mo\" #> Class 'mo' #> [1] B_ESCHR_COLI mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca( x, ..., retx = TRUE, center = TRUE, scale. = TRUE, tol = NULL, rank. = NULL )"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables returned. center logical value indicating whether variables shifted zero centered. Alternately, vector length equal number columns x can supplied. value passed scale. scale. logical value indicating whether variables scaled unit variance analysis takes place. default FALSE consistency S, general scaling advisable. Alternatively, vector length equal number columns x can supplied. value passed scale. tol value indicating magnitude components omitted. (Components omitted standard deviations less equal tol times standard deviation first component.) default null setting, components omitted (unless rank. specified less min(dim(x)).). settings tol tol = 0 tol = sqrt(.Machine$double.eps), omit essentially constant components. rank. optionally, number specifying maximal rank, .e., maximal number principal components used. Can set alternative addition tol, useful notably desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. # \\donttest{ if (require(\"dplyr\")) { # calculate the resistance per group first resistance_data <- example_isolates %>% group_by( order = mo_order(mo), # group on anything, like order genus = mo_genus(mo) ) %>% # and genus as we do here; filter(n() >= 30) %>% # filter on only 30 results per group summarise_if(is.rsi, resistance) # then get resistance of all drugs # now conduct PCA for certain antimicrobial drugs pca_result <- resistance_data %>% pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT) pca_result summary(pca_result) # old base R plotting method: biplot(pca_result) # new ggplot2 plotting method using this package: if (require(\"ggplot2\")) { ggplot_pca(pca_result) ggplot_pca(pca_result) + scale_colour_viridis_d() + labs(title = \"Title here\") } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #> and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" #> # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting for Classes rsi, mic and disk — plot","title":"Plotting for Classes rsi, mic and disk — plot","text":"Functions plot classes rsi, mic disk, support base R ggplot2.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting for Classes rsi, mic and disk — plot","text":"","code":"# S3 method for mic plot( x, mo = NULL, ab = NULL, guideline = \"EUCAST\", main = deparse(substitute(x)), ylab = \"Frequency\", xlab = \"Minimum Inhibitory Concentration (mg/L)\", colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"), language = get_AMR_locale(), expand = TRUE, ... ) # S3 method for mic autoplot( object, mo = NULL, ab = NULL, guideline = \"EUCAST\", title = deparse(substitute(object)), ylab = \"Frequency\", xlab = \"Minimum Inhibitory Concentration (mg/L)\", colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"), language = get_AMR_locale(), expand = TRUE, ... ) # S3 method for mic fortify(object, ...) # S3 method for disk plot( x, main = deparse(substitute(x)), ylab = \"Frequency\", xlab = \"Disk diffusion diameter (mm)\", mo = NULL, ab = NULL, guideline = \"EUCAST\", colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"), language = get_AMR_locale(), expand = TRUE, ... ) # S3 method for disk autoplot( object, mo = NULL, ab = NULL, title = deparse(substitute(object)), ylab = \"Frequency\", xlab = \"Disk diffusion diameter (mm)\", guideline = \"EUCAST\", colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"), language = get_AMR_locale(), expand = TRUE, ... ) # S3 method for disk fortify(object, ...) # S3 method for rsi plot( x, ylab = \"Percentage\", xlab = \"Antimicrobial Interpretation\", main = deparse(substitute(x)), language = get_AMR_locale(), ... ) # S3 method for rsi autoplot( object, title = deparse(substitute(object)), xlab = \"Antimicrobial Interpretation\", ylab = \"Frequency\", colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"), language = get_AMR_locale(), ... ) # S3 method for rsi fortify(object, ...)"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting for Classes rsi, mic and disk — plot","text":"x, object values created .mic(), .disk() .rsi() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline interpretation guideline use, defaults latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title colours_RSI colours use filling bars, must vector three values (order R, S ). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant', defaults system language (see get_AMR_locale()) can overwritten setting option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL language = \"\" prevent translation. expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting for Classes rsi, mic and disk — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function. fortify() functions return data.frame extension usage ggplot2::ggplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plotting for Classes rsi, mic and disk — plot","text":"interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, supported guidelines used input guideline argument : \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\" \"CLSI 2013\". Simply using \"CLSI\" \"EUCAST\" input automatically select latest version guideline.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting for Classes rsi, mic and disk — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_rsi_values <- random_rsi(50, prob_RSI = c(0.30, 0.55, 0.05)) plot(some_mic_values) plot(some_disk_values) plot(some_rsi_values) # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\") plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\") plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"uk\") # \\donttest{ if (require(\"ggplot2\")) { autoplot(some_mic_values) } if (require(\"ggplot2\")) { autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\") } if (require(\"ggplot2\")) { autoplot(some_rsi_values) } # }"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Microbial Resistance — proportion","title":"Calculate Microbial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Microbial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) rsi_confidence_interval( ..., ab_result = \"R\", minimum = 30, as_percent = FALSE, only_all_tested = FALSE, confidence_level = 0.95, side = \"both\" ) proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) proportion_df( data, translate_ab = \"name\", language = get_AMR_locale(), minimum = 30, as_percent = FALSE, combine_SI = TRUE, confidence_level = 0.95 ) rsi_df( data, translate_ab = \"name\", language = get_AMR_locale(), minimum = 30, as_percent = FALSE, combine_SI = TRUE, confidence_level = 0.95 )"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate Microbial Resistance — proportion","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Microbial Resistance — proportion","text":"... one vectors (columns) antibiotic interpretations. transformed internally .rsi() needed. Use multiple columns calculate (lack ) co-resistance: probability one two drugs resistant susceptible result. See Examples. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. as_percent logical indicate whether output must returned hundred fold % sign (character). value 0.123456 returned \"12.3%\". only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy ab_result antibiotic results test , must one values \"R\", \"S\", \"\" confidence_level confidence level returned confidence interval. calculation, number S SI isolates, R isolates compared total number available isolates R, S, using binom.test(), .e., Clopper-Pearson method. side side confidence interval return. Defaults \"\" length 2 vector, can also (abbreviated ) \"min\"/\"left\"/\"lower\"/\"less\" \"max\"/\"right\"/\"higher\"/\"greater\". data data.frame containing columns class rsi (see .rsi()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Microbial Resistance — proportion","text":"double , as_percent = TRUE, character.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Microbial Resistance — proportion","text":"function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Use rsi_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set. functions meant count isolates, calculate proportion resistance/susceptibility. Use count() functions count isolates. function susceptibility() essentially equal count_susceptible() / count_all(). Low counts can influence outcome - proportion functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data rsi class (created .rsi()) calculates proportions R, S. also supports grouped variables. function rsi_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Microbial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI: Please note , combination therapies, only_all_tested = TRUE applies : , combination therapies, only_all_tested = FALSE applies : Using only_all_tested impact using one antibiotic input.","code":"-------------------------------------------------------------------- only_all_tested = FALSE only_all_tested = TRUE ----------------------- ----------------------- Drug A Drug B include as include as include as include as numerator denominator numerator denominator -------- -------- ---------- ----------- ---------- ----------- S or I S or I X X X X R S or I X X X X S or I X X - - S or I R X X X X R R - X - X R - - - - S or I X X - - R - - - - - - - - -------------------------------------------------------------------- count_S() + count_I() + count_R() = count_all() proportion_S() + proportion_I() + proportion_R() = 1 count_S() + count_I() + count_R() >= count_all() proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Calculate Microbial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Microbial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. # base R ------------------------------------------------------------ # determines %R resistance(example_isolates$AMX) #> [1] 0.5955556 rsi_confidence_interval(example_isolates$AMX) #> [1] 0.5688204 0.6218738 rsi_confidence_interval(example_isolates$AMX, confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670 # determines %S+I: susceptibility(example_isolates$AMX) #> [1] 0.4044444 rsi_confidence_interval(example_isolates$AMX, ab_result = c(\"S\", \"I\") ) #> [1] 0.3781262 0.4311796 # be more specific proportion_S(example_isolates$AMX) #> [1] 0.4022222 proportion_SI(example_isolates$AMX) #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> [1] 0.5977778 proportion_R(example_isolates$AMX) #> [1] 0.5955556 # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) { example_isolates %>% group_by(ward) %>% summarise( r = resistance(CIP), n = n_rsi(CIP) ) # n_rsi works like n_distinct in dplyr, see ?n_rsi } #> # A tibble: 3 × 3 #> ward r n #> #> 1 Clinical 0.147 869 #> 2 ICU 0.190 447 #> 3 Outpatient 0.161 93 if (require(\"dplyr\")) { example_isolates %>% group_by(ward) %>% summarise( cipro_R = resistance(CIP), ci_min = rsi_confidence_interval(CIP, side = \"min\"), ci_max = rsi_confidence_interval(CIP, side = \"max\"), ) } #> # A tibble: 3 × 4 #> ward cipro_R ci_min ci_max #> #> 1 Clinical 0.147 0.124 0.173 #> 2 ICU 0.190 0.155 0.230 #> 3 Outpatient 0.161 0.0932 0.252 if (require(\"dplyr\")) { # scoped dplyr verbs with antibiotic selectors # (you could also use across() of course) example_isolates %>% group_by(ward) %>% summarise_at( c(aminoglycosides(), carbapenems()), resistance ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> Warning: Introducing NA: only 23 results available for KAN in group: ward = #> \"Outpatient\" (minimum = 30). #> # A tibble: 3 × 7 #> ward GEN TOB AMK KAN IPM MEM #> #> 1 Clinical 0.229 0.315 0.626 1 0.0498 0.0458 #> 2 ICU 0.290 0.400 0.662 1 0.0862 0.0894 #> 3 Outpatient 0.2 0.368 0.605 NA 0.0541 0.0541 if (require(\"dplyr\")) { example_isolates %>% group_by(ward) %>% summarise( R = resistance(CIP, as_percent = TRUE), SI = susceptibility(CIP, as_percent = TRUE), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_rsi(CIP), # same - analogous to n_distinct total = n() ) # NOT the number of tested isolates! # Calculate co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy: example_isolates %>% susceptibility(AMC) # %SI = 76.3% example_isolates %>% count_all(AMC) # n = 1879 example_isolates %>% susceptibility(GEN) # %SI = 75.4% example_isolates %>% count_all(GEN) # n = 1855 example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1% example_isolates %>% count_all(AMC, GEN) # n = 1939 # See Details on how `only_all_tested` works. Example: example_isolates %>% summarise( numerator = count_susceptible(AMC, GEN), denominator = count_all(AMC, GEN), proportion = susceptibility(AMC, GEN) ) example_isolates %>% summarise( numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE), denominator = count_all(AMC, GEN, only_all_tested = TRUE), proportion = susceptibility(AMC, GEN, only_all_tested = TRUE) ) example_isolates %>% group_by(ward) %>% summarise( cipro_p = susceptibility(CIP, as_percent = TRUE), cipro_n = count_all(CIP), genta_p = susceptibility(GEN, as_percent = TRUE), genta_n = count_all(GEN), combination_p = susceptibility(CIP, GEN, as_percent = TRUE), combination_n = count_all(CIP, GEN) ) # Get proportions S/I/R immediately of all rsi columns example_isolates %>% select(AMX, CIP) %>% proportion_df(translate = FALSE) # It also supports grouping variables # (use rsi_df to also include the count) example_isolates %>% select(ward, AMX, CIP) %>% group_by(ward) %>% rsi_df(translate = FALSE) } #> # A tibble: 12 × 7 #> ward antibiotic interpretation value ci_min ci_max isolates #> * #> 1 Clinical AMX SI 0.423 0.389 0.457 357 #> 2 Clinical AMX R 0.577 0.543 0.611 487 #> 3 Clinical CIP SI 0.853 0.827 0.876 741 #> 4 Clinical CIP R 0.147 0.124 0.173 128 #> 5 ICU AMX SI 0.369 0.323 0.417 158 #> 6 ICU AMX R 0.631 0.583 0.677 270 #> 7 ICU CIP SI 0.810 0.770 0.845 362 #> 8 ICU CIP R 0.190 0.155 0.230 85 #> 9 Outpatient AMX SI 0.397 0.288 0.515 31 #> 10 Outpatient AMX R 0.603 0.485 0.712 47 #> 11 Outpatient CIP SI 0.839 0.748 0.907 78 #> 12 Outpatient CIP R 0.161 0.0932 0.252 15 # }"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":null,"dir":"Reference","previous_headings":"","what":"Random MIC Values/Disk Zones/RSI Generation — random","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"functions can used generating random MIC values disk diffusion diameters, AMR data analysis practice. providing microorganism antimicrobial drug, generated results reflect reality much possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"","code":"random_mic(size = NULL, mo = NULL, ab = NULL, ...) random_disk(size = NULL, mo = NULL, ab = NULL, ...) random_rsi(size = NULL, prob_RSI = c(0.33, 0.33, 0.33), ...)"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"size desired size returned vector. used data.frame call dplyr verb, get current (group) size left blank. mo character can coerced valid microorganism code .mo() ab character can coerced valid antimicrobial drug code .ab() ... ignored, place allow future extensions prob_RSI vector length 3: probabilities \"R\" (1st value), \"S\" (2nd value) \"\" (3rd value)","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"class mic random_mic() (see .mic()) class disk random_disk() (see .disk())","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"base R function sample() used generating values. Generated values based EUCAST 2022 guideline implemented rsi_translation data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"","code":"random_mic(25) #> Class 'mic' #> [1] 1 8 8 8 0.25 0.25 0.025 0.01 0.002 0.01 #> [11] >=64 0.5 2 1 0.01 0.0625 1 0.5 >=64 0.025 #> [21] 0.005 0.001 >=64 1 16 random_disk(25) #> Class 'disk' #> [1] 23 48 36 18 46 48 41 40 42 42 46 8 45 50 32 37 50 22 20 34 25 9 8 30 45 random_rsi(25) #> Class 'rsi' #> [1] I R S R R R I S R R S R S I S S I S R S I I R I R # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #> [1] 0.002 64 0.125 64 1 0.125 0.002 >=256 4 #> [10] 4 0.125 64 >=256 1 1 32 <=0.001 1 #> [19] 0.5 0.01 0.002 <=0.001 0.01 0.25 0.5 random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #> [1] 16 64 4 64 32 8 4 2 4 8 16 4 #> [13] 4 1 32 2 4 64 16 64 <=0.5 8 4 64 #> [25] 2 random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #> [1] 2 0.125 0.5 0.25 0.25 0.125 2 <=0.0625 #> [9] 0.25 >=4 0.5 0.25 0.5 2 1 1 #> [17] 0.25 <=0.0625 0.125 <=0.0625 0.25 0.5 2 1 #> [25] 0.125 random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #> [1] 31 18 17 13 25 50 33 11 43 28 47 34 32 30 10 48 13 11 44 40 46 8 32 19 19 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #> [1] 15 16 11 12 11 16 12 11 11 11 14 17 12 14 14 16 16 16 16 17 13 17 15 11 14 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #> [1] 25 17 21 26 26 15 18 19 24 23 20 24 18 18 25 24 17 17 25 15 16 20 15 27 21 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict( x, col_ab, col_date = NULL, year_min = NULL, year_max = NULL, year_every = 1, minimum = 30, model = NULL, I_as_S = TRUE, preserve_measurements = TRUE, info = interactive(), ... ) rsi_predict( x, col_ab, col_date = NULL, year_min = NULL, year_max = NULL, year_every = 1, minimum = 30, model = NULL, I_as_S = TRUE, preserve_measurements = TRUE, info = interactive(), ... ) # S3 method for resistance_predict plot(x, main = paste(\"Resistance Prediction of\", x_name), ...) ggplot_rsi_predict( x, main = paste(\"Resistance Prediction of\", x_name), ribbon = TRUE, ... ) # S3 method for resistance_predict autoplot( object, main = paste(\"Resistance Prediction of\", x_name), ribbon = TRUE, ... )"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already, defaults first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model, defaults 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates, col_ab = \"AMX\", year_min = 2010, model = \"binomial\" ) #> ℹ Using column 'date' as input for col_date. plot(x) # \\donttest{ if (require(\"ggplot2\")) { ggplot_rsi_predict(x) } # using dplyr: if (require(\"dplyr\")) { x <- example_isolates %>% filter_first_isolate() %>% filter(mo_genus(mo) == \"Staphylococcus\") %>% resistance_predict(\"PEN\", model = \"binomial\") print(plot(x)) # get the model from the object mymodel <- attributes(x)$model summary(mymodel) } #> Including isolates from ICU. #> NULL #> #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> -1.4150 -0.1855 0.0000 0.4096 0.9277 #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) 47.02915 72.98739 0.644 0.519 #> year -0.02280 0.03637 -0.627 0.531 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 5.9620 on 11 degrees of freedom #> Residual deviance: 5.5701 on 10 degrees of freedom #> AIC: 50.533 #> #> Number of Fisher Scoring iterations: 4 #> # create nice plots with ggplot2 yourself if (require(\"dplyr\") && require(\"ggplot2\")) { data <- example_isolates %>% filter(mo == as.mo(\"E. coli\")) %>% resistance_predict( col_ab = \"AMX\", col_date = \"date\", model = \"binomial\", info = FALSE, minimum = 15 ) head(data) autoplot(data) } # }"},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set for R/SI Interpretation — rsi_translation","title":"Data Set for R/SI Interpretation — rsi_translation","text":"Data set containing reference data interpret MIC disk diffusion R/SI values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .rsi() transform MICs disks measurements R/SI values.","code":""},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set for R/SI Interpretation — rsi_translation","text":"","code":"rsi_translation"},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set for R/SI Interpretation — rsi_translation","text":"tibble 18,308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set for R/SI Interpretation — rsi_translation","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set for R/SI Interpretation — rsi_translation","text":"","code":"rsi_translation #> # A tibble: 18,308 × 11 #> guideline method site mo rank_…¹ ab ref_tbl disk_…² break…³ #> #> 1 EUCAST 2… MIC NA F_ASPRG_MGTS 2 AMB Aspergi… NA 1 #> 2 EUCAST 2… MIC NA F_ASPRG_NIGR 2 AMB Aspergi… NA 1 #> 3 EUCAST 2… MIC NA F_CANDD_ALBC 2 AMB Candida NA 1 #> 4 EUCAST 2… MIC NA F_CANDD_DBLN 2 AMB Candida NA 1 #> 5 EUCAST 2… MIC NA F_CANDD_GLBR 2 AMB Candida NA 1 #> 6 EUCAST 2… MIC NA F_CANDD_KRUS 2 AMB Candida NA 1 #> 7 EUCAST 2… MIC NA F_CANDD_PRPS 2 AMB Candida NA 1 #> 8 EUCAST 2… MIC NA F_CANDD_TRPC 2 AMB Candida NA 1 #> 9 EUCAST 2… MIC NA F_CRYPT_NFRM 2 AMB Candida NA 1 #> 10 EUCAST 2… DISK NA B_[ORD]_ENTRBCTR 5 AMC Enterob… 20ug/1… 19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R , uti , and #> # abbreviated variable names ¹​rank_index, ²​disk_dose, ³​breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE) # S3 method for default skewness(x, na.rm = FALSE) # S3 method for matrix skewness(x, na.rm = FALSE) # S3 method for data.frame skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] -0.04641894"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, like mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale() set_AMR_locale(language) reset_AMR_locale() translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 16 supported languages English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set option AMR_locale .Rprofile file like : save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\") # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting R option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, R option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" # setting another language set_AMR_locale(\"Spanish\") #> ℹ Using Spanish (Español) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacina\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Staphylococcus coagulasa negativo (SCN)\" # setting yet another language set_AMR_locale(\"Greek\") #> ℹ Using Greek (Ελληνικά) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Σιπροφλοξασίνη\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Σταφυλόκοκκος με αρνητική πηκτικότητα (CoNS)\" # setting yet another language set_AMR_locale(\"Ukrainian\") #> ℹ Using Ukrainian (Українська) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ципрофлоксацин\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Коагулазонегативний стафілокок (КНС)\" # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> Warning: More than one result was found for item 1: amoxicillin and clavulansäure #> [1] \"Amoxicillin/Clavulansäure\" # reset to system default reset_AMR_locale() #> ℹ Using the English language (English) for the AMR package for this #> session. ab_name(\"amox/clav\") #> [1] \"Amoxicillin/clavulanic acid\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-1829066","dir":"Changelog","previous_headings":"","what":"AMR 1.8.2.9066","title":"AMR 1.8.2.9066","text":"(beta version eventually become v2.0! ’re happy reach new major milestone soon!) new major release AMR package, great new additions also breaking changes current users. listed . TL;DR Microbiological taxonomy (microorganisms data set) updated 2022 now based LPSN GBIF Much increased algorithms translate user input valid taxonomy, e.g. using recent scientific work per-species human pathogenicity Clinical breakpoints added EUCAST 2022 CLSI 2022 20 new antibiotics added updated DDDs ATC codes Extended support antiviral agents (antivirals data set), many new functions Now available 16 languages Many new interesting functions, rsi_confidence_interval() mean_amr_distance() Many small bug fixes","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"interpretation-of-mic-and-disk-diffusion-values-1-8-2-9066","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 1.8.2.9066","text":"EUCAST 2022 CLSI 2022 guidelines added .rsi(). EUCAST 2022 (v12.0) now new default guideline MIC disks diffusion interpretations, eucast_rules() apply EUCAST Expert Rules. default guideline (EUCAST) can now changed new AMR_guideline option, : options(AMR_guideline = \"CLSI 2020\"). Interpretation guidelines older 10 years removed, oldest now included guidelines EUCAST CLSI 2013.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"supported-languages-1-8-2-9066","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 1.8.2.9066","text":"added support following languages: Chinese, Greek, Japanese, Polish, Turkish Ukrainian. antibiotic names now available languages, AMR package automatically determine supported language based user system language. grateful valuable input colleagues countries. AMR package now available 16 languages according download stats used almost countries world!","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"microbiological-taxonomy-1-8-2-9066","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 1.8.2.9066","text":"microorganisms longer relies Catalogue Life, List Prokaryotic names Standing Nomenclature (LPSN) supplemented ‘backbone taxonomy’ Global Biodiversity Information Facility (GBIF). structure data set changed include separate LPSN GBIF identifiers. Almost previous MO codes retained. contains 1,400 taxonomic names 2022. previously relied experience categorise species pathogenic groups, happy encounter recent work Bartlett et al. (2022, DOI 10.1099/mic.0.001269) extensively studied medical-scientific literature categorise bacterial species groups. See mo_matching_score() work incorporated prevalence column microorganisms data set. Using results, .mo() mo_*() functions now much better capable converting user input valid taxonomic records. also made following changes regarding included taxonomy microorganisms functions: Updated full microbiological taxonomy according latest daily LPSN data set (December 2022) latest yearly GBIF taxonomy backbone (November 2022) Support 1,515 city-like serovars Salmonella, Salmonella Goldcoast. Formally, serovars belonging S. enterica species, reported name genus city. reason, serovars subspecies column microorganisms data set “enterica” species column, full name contain species name (enterica). new argument keep_synonyms allows correct updated taxonomy, favour now deleted argument allow_uncertain increased tremendously speed returns generally consequent results Sequential coercion now extremely fast results stored package environment, although coercion unknown values must run per session. Previous results can reset/removed new mo_reset_session() function. Support microorganism codes ASIan Antimicrobial Resistance Surveillance Network (ASIARS-Net) MO matching score algorithm (mo_matching_score()) now counts deletions substitutions 2 instead 1, impacts outcome .mo() mo_*() function CRAN allows packages around 5 MB maximum, packages exempted package one Chromista relevant comes antimicrobial resistance, thus lacking primary scope package Chromista almost never clinically relevant, thus lacking secondary scope package microorganisms.old data set removed, previously accepted names now included microorganisms data set. new column status contains \"accepted\" currently accepted names \"synonym\" taxonomic synonyms; currently invalid names. previously accepted names now microorganisms ID - available - LPSN, GBIF SNOMED CT identifier.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"antibiotic-agents-and-selectors-1-8-2-9066","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 1.8.2.9066","text":"new function add_custom_antimicrobials() allows users add custom antimicrobial codes names AMR package. antibiotics data set greatly updated: following 20 antibiotics added (also includes new J01RA ATC group): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), thioacetazone (TAT) Added missing ATC codes Updated DDDs PubChem Compound IDs Updated antibiotic name spelling, now used WHOCC (cephalexin -> cefalexin, phenethicillin -> pheneticillin) Antibiotic code “CEI” ceftolozane/tazobactam replaced “CZT” comply EARS-Net WHONET 2022. old code still work cases using .ab() ab_*() functions. Support antimicrobial interpretation anaerobic bacteria, adding ‘placeholder’ code B_ANAER microorganisms data set adding breakpoints anaerobics rsi_interpretation data set, used .rsi() interpretion MIC disk diffusion values Also, added support using antibiotic selectors scoped dplyr verbs (without using vars()), : ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() examples.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"antiviral-agents-1-8-2-9066","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 1.8.2.9066","text":"now added extensive support antiviral agents! first time, AMR package extensive support antiviral drugs work names, codes data way. antivirals data set extended 18 new drugs (also new J05AJ ATC group) now also contains antiviral identifiers LOINC codes new data type av (antivirals) added, functionally similar ab antibiotics Functions .av(), av_name(), av_atc(), av_synonyms(), av_from_text() added siblings ab_*() equivalents","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-new-functions-1-8-2-9066","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 1.8.2.9066","text":"Function rsi_confidence_interval() add confidence intervals AMR calculation. now also included rsi_df() proportion_df(). Function mean_amr_distance() calculate mean AMR distance. mean AMR distance normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. Function rsi_interpretation_history() view history previous runs .rsi(). returns ‘logbook’ selected guideline, reference table specific interpretation row data set .rsi() run. Function mo_current() get currently valid taxonomic name microorganism Function add_custom_antimicrobials() add custom antimicrobial codes names AMR package","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-1-8-2-9066","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.8.2.9066","text":"Argument combine_IR removed package (affecting functions count_df(), proportion_df(), rsi_df() plotting functions), since replaced combine_SI three years ago Using units ab_ddd(..., units = \"...\") deprecated time now supported anymore. Use ab_ddd_units() instead. Support data.frame-enhancing R packages, specifically: data.table::data.table, janitor::tabyl, tibble::tibble, tsibble::tsibble. AMR package functions data set output (rsi_df() bug_drug_combinations()), now return data type input. data sets package now tibble, instead base R data.frames. Older R versions still supported, even support tibbles. data sets now also continually exported Apache Feather Apache Parquet formats. can find info article website. Fixed certain EUCAST breakpoints MIC values Allow NA values (e.g. .rsi(.disk(NA), ...)) Fix bug-drug combinations multiple breakpoints different body sites Interpretation MIC disk zones now informative availability breakpoints robust Removed .integer() method MIC values, since MIC integer values running table() MIC values consequently failed able retrieve level position (’s normally .integer() factors work) Fixed determination Gram stains (mo_gramstain()), since taxonomic phyla Actinobacteria, Chloroflexi, Firmicutes, Tenericutes renamed respectively Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota 2021 droplevels() MIC now return common factor default lose mic class. Use droplevels(..., .mic = TRUE) keep mic class. Small fix using ab_from_text() Fixes reading text files using set_mo_source(), now also allows source file contain valid taxonomic names instead valid microorganism ID package Fixed bug mdro() using similar column names Magiorakos guideline Using random_*() function (random_mic()) now possible directly calling package without loading first: AMR::random_mic(10) Extended support vctrs package, used internally tidyverse. allows change values class mic, disk, rsi, mo ab tibbles, use antibiotic selectors selecting/filtering, e.g. df[carbapenems() == \"R\", ] Fix using info = FALSE mdro() interpretation guidelines using .rsi() amoxicillin, rules ampicillin used amoxicillin rules available Fix using ab_atc() non-existing ATC codes Black white message texts now reversed colour using RStudio dark theme mo_snomed() now returns class character, numeric anymore (make long SNOMED codes readable) Fix using .ab() NA values Updated support WHONET 2022 microorganism codes Antimicrobial interpretation ‘SDD’ (susceptible dose-dependent, coined CLSI) interpreted ‘’ comply EUCAST’s ‘’ .rsi() Fix mo_shortname() case higher taxonomic ranks (order, class, phylum) Cleaning columns .rsi(), .mic(), .disk() now show column name warning invalid results","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-2-9066","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.2.9066","text":"New website make use new Bootstrap 5 pkgdown 2.0. website now contains results examples automatically regenerated every change repository, using GitHub Actions Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Jonas Salm contributors, thank valuable input R Rmd files project now styled using styler package Set scalar conditional expressions (&& ||) possible comply upcoming R 4.3 enormous lot code cleaning, fixing small bugs way","code":""}]