Functions to simplify the analysis and prediction of Antimicrobial Resistance (AMR) to work with microbial and antimicrobial properties by using evidence-based methods.
This R package was created for academic research by PhD students of the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG).
▶️ Get it with
install.packages("AMR") or see below for other possibilities.
▶️ Read the changelog here.
1 Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands - rug.nl umcg.nl
2 Certe Medical Diagnostics & Advice, Groningen, the Netherlands - certe.nl
a R package author and thesis dissertant
b Thesis advisor
This R package was intended to make microbial epidemiology easier. Most functions contain extensive help pages to get started.
AMR package basically does four important things:
It cleanses existing data, by transforming it to reproducible and profound classes, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:
as.moto get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of Klebsiella pneumoniae is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of S. aureus is "B_STPHY_AUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even
as.mo("MRSA")will return the ID of S. aureus. Moreover, it can group all coagulase negative and positive Staphylococci, and can transform Streptococci into Lancefield groups. To find bacteria based on your input, it uses Artificial Intelligence to look up values in the included ITIS data, consisting of more than 18,000 microorganisms. It is very fast, see Benchmarks.
as.rsito transform values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
as.micto cleanse your MIC values. It produces a so-called factor (called ordinal in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
as.atcto get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantin", "nitro" all return the ATC code of Nitrofurantoine.
It enhances existing data and adds new data from data sets included in this package.
EUCAST_rulesto apply EUCAST expert rules to isolates.
first_isolateto identify the first isolates of every patient using guidelines from the CLSI (Clinical and Laboratory Standards Institute).
MDRO(abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
microorganismscontains the complete taxonomic tree of more than 18,000 microorganisms (bacteria, fungi/yeasts and protozoa). Furthermore, the colloquial name and Gram stain are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like
mo_phylum. As they use
as.mointernally, they also use artificial intelligence. For example,
mo_genus("S. aureus")will both return
"Staphylococcus". They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
antibioticscontains the ATC code, LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains a total of 298 trade names. Use functions like
ab_tradenamesto look up values. The
as.atcinternally so they support AI to guess your expected result. For example,
ab_name("J01CF05")will all return
"Flucloxacillin". These functions can again be used to add new variables to your data.
It analyses the data with convenient functions that use well-known methods.
portion_Sfunctions. Similarly, the number of isolates can be determined with the
count_Sfunctions. All these functions can be used with the
dplyrpackage (e.g. in conjunction with
geom_rsi, a function made for the
It teaches the user how to use all the above actions.
septic_patients. This data set contains:
This package contains the complete microbial taxonomic data (with all eight taxonomic ranks - from kingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, https://www.itis.gov).
All (sub)species from the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package, as well as all previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria.
ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists.
Get a note when a species was renamed
mo_shortname("Chlamydia psittaci")# Note: 'Chlamydia psittaci' (Page, 1968) was renamed 'Chlamydophila psittaci' (Everett et al., 1999)#  "C. psittaci"
Get any property from the entire taxonomic tree for all included species
mo_class("E. coli")#  "Gammaproteobacteria"mo_family("E. coli")#  "Enterobacteriaceae"mo_subkingdom("E. coli")#  "Negibacteria"mo_gramstain("E. coli") # based on subkingdom#  "Gram negative"mo_ref("E. coli")#  "Castellani and Chalmers, 1919"
Do not get mistaken - the package only includes microorganisms
mo_phylum("C. elegans")#  "Cyanobacteria" # Bacteria?!mo_fullname("C. elegans")#  "Chroococcus limneticus elegans" # Because a microorganism was found
All stable versions of this package are published on CRAN, the official R network with a peer-reviewed submission process.
Install using RStudio (recommended):
AMRand press Install
Install in R directly:
This package was also published on Zenodo (stable releases only): https://doi.org/10.5281/zenodo.1305355
This is the latest development version. Although it may contain bugfixes and even new functions compared to the latest released version on CRAN, it is also subject to change and may be unstable or behave unexpectedly. Always consider this a beta version. All below 'badges' should be green:
|All functions checked on Linux||GitLab CI [ref 1]|
|All functions checked on Windows||Appveyor Systems Inc. [ref 2]|
|Percentage of syntax lines checked||Codecov LLC [ref 3]|
If so, try it with:
# Call it with:library(AMR)# For a list of functions:help(package = "AMR")
This package contains two new S3 classes:
mic for MIC values (e.g. from Vitek or Phoenix) and
rsi for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of
2 being higher than
<=1 but lower than
>=32, and for class
rsi factors are ordered as
S < I < R).
Both classes have extensions for existing generic functions like
These functions also try to coerce valid values.
septic_patients data set comes with antimicrobial results of more than 40 different drugs. For example, columns
cipr contain results of amoxicillin and ciprofloxacin, respectively.
summary(septic_patients[, c("amox", "cipr")])# amox cipr# Mode :rsi Mode :rsi# <NA> :1002 <NA> :596# Sum S :336 Sum S :1108# Sum IR:662 Sum IR:296# -Sum R:659 -Sum R:227# -Sum I:3 -Sum I:69
You can use the
plot function from base R:
Or use the
dplyr packages to create more appealing plots:
library(dplyr)library(ggplot2)septic_patients %>%select(amox, nitr, fosf, trim, cipr) %>%ggplot_rsi()
Adjust it with any parameter you know from the
septic_patients %>%select(amox, nitr, fosf, trim, cipr) %>%ggplot_rsi(datalabels = FALSE,width = 0.5, colour = "purple", size = 1, linetype = 2, alpha = 0.5)
It also supports grouping variables. Let's say we want to compare resistance of drugs against Urine Tract Infections (UTI) between hospitals A to D (variable
septic_patients %>%select(hospital_id, amox, nitr, fosf, trim, cipr) %>%group_by(hospital_id) %>%ggplot_rsi(x = "hospital_id",facet = "Antibiotic",nrow = 1,datalabels = FALSE) +labs(title = "AMR of Anti-UTI Drugs Per Hospital",x = "Hospital")
You could use this to group on anything in your plots: Gram stain, age (group), genus, geographic location, et cetera.
Is there a significant difference between hospital A and D when it comes to Fosfomycin?
check_A_and_D <- septic_patients %>%filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and Dselect(hospital_id, fosf) %>% # select the hospitals and fosfomycingroup_by(hospital_id) %>%count_df(combine_IR = TRUE) %>% # count all isolates per group (hospital_id)tidyr::spread(hospital_id, Value) %>% # transform output so A and D are columnsselect(A, D) %>% # and select these onlyas.matrix() # transform to good old matrix for fisher.testcheck_A_and_D# A D# [1,] 24 33# [2,] 25 77
fisher.test(check_A_and_D)## Fisher's Exact Test for Count Data## data: check_A_and_D# p-value = 0.03104# alternative hypothesis: true odds ratio is not equal to 1# 95 percent confidence interval:# 1.054283 4.735995# sample estimates:# odds ratio# 2.228006
Well, there you go!
# Transform values to new classmic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))summary(mic_data)# Mode:mic# <NA>:0# Min.:<=0.128# Max.:>=32plot(mic_data)
This is also called interpretive reading.
a <- data.frame(mo = c("Staphylococcus aureus","Enterococcus faecalis","Escherichia coli","Klebsiella pneumoniae","Pseudomonas aeruginosa"),vanc = "-", # Vancomycinamox = "-", # Amoxicillincoli = "-", # Colistincfta = "-", # Ceftazidimecfur = "-", # Cefuroximepeni = "S", # Benzylpenicillincfox = "S", # CefoxitinstringsAsFactors = FALSE)a# mo vanc amox coli cfta cfur peni cfox# 1 Staphylococcus aureus - - - - - S S# 2 Enterococcus faecalis - - - - - S S# 3 Escherichia coli - - - - - S S# 4 Klebsiella pneumoniae - - - - - S S# 5 Pseudomonas aeruginosa - - - - - S Sb <- EUCAST_rules(a) # 18 results are forced as R or Sb# mo vanc amox coli cfta cfur peni cfox# 1 Staphylococcus aureus - S R R 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
Bacteria IDs can be retrieved with the
guess_mo function. It uses any type of info about a microorganism as input. For example, all these will return value
B_STPHY_AUR, the ID of S. aureus:
guess_mo("stau")guess_mo("STAU")guess_mo("staaur")guess_mo("S. aureus")guess_mo("S aureus")guess_mo("Staphylococcus aureus")guess_mo("MRSA") # Methicillin Resistant S. aureusguess_mo("MSSA") # Methicillin Susceptible S. aureusguess_mo("VISA") # Vancomycin Intermediate S. aureusguess_mo("VRSA") # Vancomycin Resistant S. aureus
# G-test to replace Chi squared testg.test(...)# Determine key antibiotic based on bacteria IDkey_antibiotics(...)# Selection of first isolates of any patientfirst_isolate(...)# Predict resistance levels of antibioticsresistance_predict(...)# Get name of antibiotic by ATC codeabname(...)abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types:
frequency_tbl). It can be used in two ways:
# Like base R:freq(mydata$myvariable)# And like tidyverse:mydata %>% freq(myvariable)
Frequency are of course sorted by count at default:
septic_patients %>% freq(hospital_id)# Class: factor (numeric)# Length: 2000 (of which NA: 0 = 0.00%)# Unique: 4## Item Count Percent Cum. Count Cum. Percent# --- ----- ------ -------- ----------- -------------# 1 D 762 38.1% 762 38.1%# 2 B 663 33.1% 1425 71.2%# 3 A 321 16.1% 1746 87.3%# 4 C 254 12.7% 2000 100.0%
This can be changed with the
septic_patients %>% freq(hospital_id, sort.count = FALSE)# Class: factor (numeric)# Length: 2000 (of which NA: 0 = 0.00%)# Unique: 4## Item Count Percent Cum. Count Cum. Percent# --- ----- ------ -------- ----------- -------------# 1 A 321 16.1% 321 16.1%# 2 B 663 33.1% 984 49.2%# 3 C 254 12.7% 1238 61.9%# 4 D 762 38.1% 2000 100.0%
For numeric values, some extra descriptive statistics will be calculated:
freq(runif(n = 10, min = 1, max = 5))# Frequency table# Class: numeric# Length: 10 (of which NA: 0 = 0.00%)# Unique: 10## Mean: 3.1# Std. dev.: 1.3 (CV: 0.43, MAD: 1.8)# Five-Num: 1.3 | 1.7 | 3.2 | 4.3 | 5.0 (IQR: 2.6, CQV: 0.43)# Outliers: 0## Item Count Percent Cum. Count Cum. Percent# --- --------- ------ -------- ----------- -------------# 1 1.271079 1 10.0% 1 10.0%# 2 1.333975 1 10.0% 2 20.0%# 3 1.714946 1 10.0% 3 30.0%# 4 2.751871 1 10.0% 4 40.0%# 5 3.090140 1 10.0% 5 50.0%# 6 3.260850 1 10.0% 6 60.0%# 7 3.824105 1 10.0% 7 70.0%# 8 4.278028 1 10.0% 8 80.0%# 9 4.436265 1 10.0% 9 90.0%# 10 4.996694 1 10.0% 10 100.0%## Warning message:# All observations are unique.
Learn more about this function with:
Data sets to work with antibiotics and bacteria properties.
# Data set with complete taxonomic trees from ITIS, containing of# the three kingdoms Bacteria, Fungi and Protozoamicroorganisms # data.frame: 18,833 x 15microorganisms.old # data.frame: 2,383 x 4# Data set with ATC antibiotics codes, official names, trade names# and DDDs (oral and parenteral)antibiotics # data.frame: 423 x 18# Data set with 2000 random blood culture isolates from anonymised# septic patients between 2001 and 2017 in 5 Dutch hospitalsseptic_patients # data.frame: 2,000 x 49
One of the most important features of this package is the complete microbial taxonomic database, supplied by ITIS (https://www.itis.gov). We created a function
as.mo that transforms any user input value to a valid microbial ID by using AI (Artificial Intelligence) and based on the taxonomic tree of ITIS.
microbenchmark package, we can review the calculation performance of this function.
In the next test, we try to 'coerce' different input values for Staphylococcus aureus. The actual result is the same every time: it returns its MO code
B_STPHY_AUR (B stands for Bacteria, the taxonomic kingdom).
But the calculation time differs a lot. Here, the AI effect can be reviewed best:
microbenchmark(A = as.mo("stau"),B = as.mo("staaur"),C = as.mo("S. aureus"),D = as.mo("S. aureus"),E = as.mo("STAAUR"),F = as.mo("Staphylococcus aureus"),G = as.mo("B_STPHY_AUR"),times = 10,unit = "ms")# Unit: milliseconds# expr min lq mean median uq max neval# A 34.745551 34.798630 35.2596102 34.8994810 35.258325 38.067062 10# B 7.095386 7.125348 7.2219948 7.1613865 7.240377 7.495857 10# C 11.677114 11.733826 11.8304789 11.7715050 11.843756 12.317559 10# D 11.694435 11.730054 11.9859313 11.8775585 12.206371 12.750016 10# E 7.044402 7.117387 7.2271630 7.1923610 7.246104 7.742396 10# F 6.642326 6.778446 6.8988042 6.8753165 6.923577 7.513945 10# G 0.106788 0.131023 0.1351229 0.1357725 0.144014 0.146458 10
In the table above, all measurements are shown in milliseconds (thousands of seconds), tested on a quite regular Linux server from 2007 (Core 2 Duo 2.7 GHz, 2 GB DDR2 RAM). A value of 6.9 milliseconds means it will roughly determine 144 input values per second. It case of 39.2 milliseconds, this is only 26 input values per second. The more an input value resembles a full name (like C, D and F), the faster the result will be found. In case of G, the input is already a valid MO code, so it only almost takes no time at all (0.0001 seconds on our server).
To achieve this speed, the
as.mo function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined far less faster. See this example for the ID of Burkholderia nodosa (
microbenchmark(A = as.mo("buno"),B = as.mo("burnod"),C = as.mo("B. nodosa"),D = as.mo("B. nodosa"),E = as.mo("BURNOD"),F = as.mo("Burkholderia nodosa"),G = as.mo("B_BRKHL_NOD"),times = 10,unit = "ms")# Unit: milliseconds# expr min lq mean median uq max neval# A 124.175427 124.474837 125.8610536 125.3750560 126.160945 131.485994 10# B 154.249713 155.364729 160.9077032 156.8738940 157.136183 197.315105 10# C 66.066571 66.162393 66.5538611 66.4488130 66.698077 67.623404 10# D 86.747693 86.918665 90.7831016 87.8149725 89.440982 116.767991 10# E 154.863827 155.208563 162.6535954 158.4062465 168.593785 187.378088 10# F 32.427028 32.638648 32.9929454 32.7860475 32.992813 34.674241 10# G 0.213155 0.216578 0.2369226 0.2338985 0.253734 0.285581 10
That takes up to 11 times as much time! A value of 158.4 milliseconds means it can only determine ~6 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance.
To relieve this pitfall and further improve performance, two important calculations take almost no time at all: repetive results and already precalculated results.
Repetive results mean that unique values are present more than once. Unique values will only be calculated once by
as.mo. We will use
mo_fullname for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) and uses
library(dplyr)# take 500,000 random MO codes from the septic_patients data setx = septic_patients %>%sample_n(500000, replace = TRUE) %>%pull(mo)# got the right length?length(x)#  500000# and how many unique values do we have?n_distinct(x)#  96# only 96, but distributed in 500,000 results. now let's see:microbenchmark(X = mo_fullname(x),times = 10,unit = "ms")# Unit: milliseconds# expr min lq mean median uq max neval# X 114.9342 117.1076 129.6448 120.2047 131.5005 168.6371 10
So transforming 500,000 values (!) of 96 unique values only takes 0.12 seconds (120 ms). You only lose time on your unique input values.
Results of a tenfold - 5,000,000 values:
# Unit: milliseconds# expr min lq mean median uq max neval# X 882.9045 901.3011 1001.677 940.3421 1168.088 1226.846 10
Even the full names of 5 Million values are calculated within a second.
What about precalculated results? If the input is an already precalculated result of a helper function like
mo_fullname, it almost doesn't take any time at all (see 'C' below):
microbenchmark(A = mo_fullname("B_STPHY_AUR"),B = mo_fullname("S. aureus"),C = mo_fullname("Staphylococcus aureus"),times = 10,unit = "ms")# Unit: milliseconds# expr min lq mean median uq max neval# A 11.364086 11.460537 11.5104799 11.4795330 11.524860 11.818263 10# B 11.976454 12.012352 12.1704592 12.0853020 12.210004 12.881737 10# C 0.095823 0.102528 0.1167754 0.1153785 0.132629 0.140661 10
So going from
mo_fullname("Staphylococcus aureus") to
"Staphylococcus aureus" takes 0.0001 seconds - it doesn't even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:
microbenchmark(A = mo_species("aureus"),B = mo_genus("Staphylococcus"),C = mo_fullname("Staphylococcus aureus"),D = mo_family("Staphylococcaceae"),E = mo_order("Bacillales"),F = mo_class("Bacilli"),G = mo_phylum("Firmicutes"),H = mo_subkingdom("Posibacteria"),I = mo_kingdom("Bacteria"),times = 10,unit = "ms")# Unit: milliseconds# expr min lq mean median uq max neval# A 0.105181 0.121314 0.1478538 0.1465265 0.166711 0.211409 10# B 0.132558 0.146388 0.1584278 0.1499835 0.164895 0.208477 10# C 0.135492 0.160355 0.2341847 0.1884665 0.348857 0.395931 10# D 0.109650 0.115727 0.1270481 0.1264130 0.128648 0.168317 10# E 0.081574 0.096940 0.0992582 0.0980915 0.101479 0.120477 10# F 0.081575 0.088489 0.0988463 0.0989650 0.103365 0.126482 10# G 0.091981 0.095333 0.1043568 0.1001530 0.111327 0.129625 10# H 0.092610 0.093169 0.1009135 0.0985455 0.101828 0.120406 10# I 0.087371 0.091213 0.1069758 0.0941815 0.109302 0.192831 10
Of course, when running
mo_phylum("Firmicutes") the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be
"Firmicutes" too, there is no point in calculating the result. And because this package 'knows' all phyla of all known microorganisms (according to ITIS), it can just return the initial value immediately.
When the system language is non-English and supported by this
AMR package, some functions take a little while longer:
mo_fullname("CoNS", language = "en") # or just mo_fullname("CoNS") on an English system# "Coagulase Negative Staphylococcus (CoNS)"mo_fullname("CoNS", language = "fr") # or just mo_fullname("CoNS") on a French system# "Staphylococcus à coagulase négative (CoNS)"microbenchmark(en = mo_fullname("CoNS", language = "en"),de = mo_fullname("CoNS", language = "de"),nl = mo_fullname("CoNS", language = "nl"),es = mo_fullname("CoNS", language = "es"),it = mo_fullname("CoNS", language = "it"),fr = mo_fullname("CoNS", language = "fr"),pt = mo_fullname("CoNS", language = "pt"),times = 10,unit = "ms")# Unit: milliseconds# expr min lq mean median uq max neval# en 6.093583 6.51724 6.555105 6.562986 6.630663 6.99698 100# de 13.934874 14.35137 16.891587 14.462210 14.764658 43.63956 100# nl 13.900092 14.34729 15.943268 14.424565 14.581535 43.76283 100# es 13.833813 14.34596 14.574783 14.439757 14.653994 17.49168 100# it 13.811883 14.36621 15.179060 14.453515 14.812359 43.64284 100# fr 13.798683 14.37019 16.344731 14.468775 14.697610 48.62923 100# pt 13.789674 14.36244 15.706321 14.443772 14.679905 44.76701 100
Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
This R package is licensed under the GNU General Public License (GPL) v2.0. In a nutshell, this means that this package:
May be used for commercial purposes
May be used for private purposes
May not be used for patent purposes
May be modified, although:
May be distributed, although:
Comes with a LIMITATION of liability
Comes with NO warranty
Published on CRAN: 2018-12-01
count_allto get all available isolates (that like all
count_*functions also supports
group_by), the old
n_rsiis now an alias of
get_localeto determine language for language-dependent output for some
mo_*functions. This is now the default value for their
languageparameter, by which the system language will be used at default.
microorganisms.oldDTto improve the speed of
as.mo. They are for reference only, since they are primarily for internal use of
read.4Dto read from the 4D database of the MMB department of the UMCG
mo_yearto get specific values about the scientific reference of a taxonomic entry
EUCAST_exceptional_phenotypeswere renamed to
EUCAST_ruleswas renamed to
eucast_rules, the old function still exists as a deprecated function
rulesto specify which rules should be applied (expert rules, breakpoints, others or all)
verbosewhich can be set to
TRUEto get very specific messages about which columns and rows were affected
septic_patientsnow reflects these changes
pipefor piperacillin (J01CA12), also to the
kingdomto the microorganisms data set, and function
mo_kingdomto look up values
as.mo(and subsequently all
mo_*functions), as empty values wil be ignored a priori
as.mowill return NA
mo_*wrappers) now supports genus abbreviations with "species" attached
as.mo("E. species") # B_ESCHRmo_fullname("E. spp.") # "Escherichia species"as.mo("S. spp") # B_STPHYmo_fullname("S. species") # "Staphylococcus species"
combine_IR(TRUE/FALSE) to functions
count_df, to indicate that all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
portion_*(..., as_percent = TRUE)when minimal number of isolates would not be met
count_*functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see
portion_*functions now throws a warning when total available isolate is below parameter
freqwill not set package name as attribute anymore
septic_patients %>%group_by(hospital_id) %>%freq(gender)
septic_patients %>%freq(hospital_id) %>%select(-count, -cum_count) # only get item, percent, cum_percent
na, to choose which character to print for empty values
headerto turn the header info off (default when
markdown = TRUE)
titleto manually setbthe title of the frequency table
first_isolatenow tries to find columns to use as input when parameters are left blank
septic_patientsis now a
data.frame, not a tibble anymore
microorganisms.old$ref) to comply with CRAN policy to only allow ASCII characters
mo_propertynot working properly
eucast_ruleswhere some Streptococci would become ceftazidime R in EUCAST rule 4.5
mo, useful for
"CRS"-> Stenotrophomonas maltophilia
"CRSM"-> Stenotrophomonas maltophilia
"MSSA"-> Staphylococcus aureus
"MSSE"-> Staphylococcus epidermidis
is.rsi.eligible, now 15-20 times faster
sum(x)is below 1000 or any of the expected values is below 5, Fisher's Exact Test will be suggested
ab_namewill try to fall back on
as.atcwhen no results are found
crayon, to support formatted text in the console
tidyris now mandatory (went to
count_dfrely on it
Published on CRAN: 2018-10-01
The data set
microorganisms now contains all microbial taxonomic data from ITIS (kingdoms Bacteria, Fungi and Protozoa), the Integrated Taxonomy Information System, available via https://itis.gov. The data set now contains more than 18,000 microorganisms with all known bacteria, fungi and protozoa according ITIS with genus, species, subspecies, family, order, class, phylum and subkingdom. The new data set
microorganisms.old contains all previously known taxonomic names from those kingdoms.
New functions based on the existing function
They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:
mo_gramstain("E. coli")#  "Gram negative"mo_gramstain("E. coli", language = "de") # German#  "Gramnegativ"mo_gramstain("E. coli", language = "es") # Spanish#  "Gram negativo"mo_fullname("S. group A", language = "pt") # Portuguese#  "Streptococcus grupo A"
Furthermore, former taxonomic names will give a note about the current taxonomic name:
mo_gramstain("Esc blattae")# Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)#  "Gram negative"
count_S to selectively count resistant or susceptible isolates
count_df(which works like
portion_df) to get all counts of S, I and R of a data set with antibiotic columns, with support for grouped variables
is.rsi.eligible to check for columns that have valid antimicrobial results, but do not have the
rsi class yet. Transform the columns of your raw data with:
data %>% mutate_if(is.rsi.eligible, as.rsi)
is.mo as replacements for
is.bactid (since the
microoganisms data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The
as.mo function determines microbial IDs using Artificial Intelligence (AI):
as.mo("E. coli")#  B_ESCHR_COLas.mo("MRSA")#  B_STPHY_AURas.mo("S group A")#  B_STRPTC_GRA
And with great speed too - on a quite regular Linux server from 2007 it takes us less than 0.02 seconds to transform 25,000 items:
thousands_of_E_colis <- rep("E. coli", 25000)microbenchmark::microbenchmark(as.mo(thousands_of_E_colis), unit = "s")# Unit: seconds# min median max neval# 0.01817717 0.01843957 0.03878077 100
as.mo, so users can supply their own microbial IDs, name or codes as a reference table
Renamed all previous references to
labels_rsi_count to print datalabels on a RSI
is.atc to transform/look up antibiotic ATC codes as defined by the WHO. The existing function
guess_atc is now an alias of
ab_property and its aliases:
Introduction to AMR as a vignette
Removed clipboard functions as it violated the CRAN policy
antibioticsdata set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)
antibioticsdata set, it now contains 298 different trade names in total, e.g.:
ab_official("Bactroban")#  "Mupirocin"ab_name(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))#  "Mupirocin" "Amoxicillin" "Azithromycin" "Flucloxacillin"ab_atc(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))#  "R01AX06" "J01CA04" "J01FA10" "J01CF05"
first_isolate, rows will be ignored when there's no species available
ratiois now deprecated and will be removed in a future release, as it is not really the scope of this package
as.micfor values ending in zeroes after a real number
prevalencecolumn to the
n_rsi. This allows to check for more than 2 vectors or columns.
septic_patients %>% select(amox, cipr) %>% count_IR()# which is the same as:septic_patients %>% count_IR(amox, cipr)septic_patients %>% portion_S(amcl)septic_patients %>% portion_S(amcl, gent)septic_patients %>% portion_S(amcl, gent, pita)
geom_rsiso they can cope with
count_df. The new
funparameter has value
portion_dfat default, but can be set to
ggplot2package was not loaded
ggplot_rsi) so you can set your own preferences
difffor frequency tables
freq) header of class
my_matrix = with(septic_patients, matrix(c(age, gender), ncol = 2))freq(my_matrix)
my_list = list(age = septic_patients$age, gender = septic_patients$gender)my_list %>% freq(age)my_list %>% freq(gender)
Published on CRAN: 2018-08-14
rsi_dfwas removed in favour of new functions
portion_Sto selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old
rsifunction. The old function still works, but is deprecated.
portion_dfto get all portions of S, I and R of a data set with antibiotic columns, with support for grouped variables
ggplot_rsito apply all above functions on a data set:
septic_patients %>% select(tobr, gent) %>% ggplot_rsiwill show portions of S, I and R immediately in a pretty plot
is.bactidto transform/ look up microbial ID's.
guess_bactidis now an alias of
skewnessthat are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
g.testto perform the Χ2 distributed G-test, which use is the same as
ratioto transform a vector of values to a preset ratio
ratio(c(10, 500, 10), ratio = "1:2:1")would return
130, 260, 130
%like%(and give them keyboard shortcuts), or to view the datasets that come with this package
p.symbolto transform p values to their related symbols:
0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
clipboard_exportas helper functions to quickly copy and paste from/to software like Excel and SPSS. These functions use the
cliprpackage, but are a little altered to also support headless Linux servers (so you can use it in RStudio Server)
rsi(antimicrobial resistance) to use as input
tableto use as input:
plotto use a frequency table as input:
freq(mydata, mycolumn)is the same as
mydata %>% freq(mycolumn)
top_freqfunction to return the top/below n items as vector
options(max.print.freq = n)where n is your preset value
resistance_predictand added more examples
septic_patientsdata set to better reflect the reality
rsiclasses now returns all values - use
freqto check distributions
key_antibioticsfunction are now generic: 6 for broadspectrum ABs, 6 for Gram-positive specific and 6 for Gram-negative specific ABs
%like%now supports multiple patterns
data.frames with altered console printing to make it look like a frequency table. Because of this, the parameter
toConsoleis not longer needed.
freqwhere the class of an item would be lost
septic_patientsdataset and the column
bactidnow has the new class
microorganismsdataset (especially for Salmonella) and the column
bactidnow has the new class
as.rsi("<=0.002; S")will return
as.mic("<=0.002; S")will return
as.mic("<= 0.002")now works
micdo not add the attribute
atc_property(..., property). It will return a vector of the ATC hierarchy as defined by the WHO. The new function
atc_groupsis a convenient wrapper around this.
atc_propertyas it requires the host set by
urlto be responsive
first_isolatealgorithm to exclude isolates where bacteria ID or genus is unavailable
924b62) from the
dplyrpackage v0.7.5 and above
yourdata %>% select(genus, species) %>% as.bactid()now also works
Published on CRAN: 2018-05-03
n_rsito count cases where antibiotic test results were available, to be used in conjunction with
dplyr::summarise, see ?rsi
guess_bactidto determine the ID of a microorganism based on genus/species or known abbreviations like MRSA
guess_atcto determine the ATC of an antibiotic based on name, trade name, or known abbreviations
freqto create frequency tables, with additional info in a header
MDROto determine Multi Drug Resistant Organisms (MDRO) with support for country-specific guidelines.
rsiclass for vectors that contain only invalid antimicrobial interpretations
%like%to make it case insensitive
EUCAST_rulescolumn names are now case-insensitive
as.micnow add the package name and version as attributes
README.mdwith more examples
Published on CRAN: 2018-03-14
EUCAST_rulesapplies for amoxicillin even if ampicillin is missing
Published on CRAN: 2018-02-22