benchmarks.RmdIn the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.
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 less fast. See this example for the ID of Thermus islandicus (B_THERMS_ISL), a bug probably never found before in humans:
T.islandicus <- microbenchmark(as.mo("theisl"),
@@ -229,12 +229,12 @@
print(T.islandicus, unit = "ms", signif = 2)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# as.mo("theisl") 260 270 280 280 290 310 10
-# as.mo("THEISL") 260 270 290 280 290 380 10
-# as.mo("T. islandicus") 130 140 150 150 150 160 10
-# as.mo("T. islandicus") 130 140 140 140 150 160 10
-# as.mo("Thermus islandicus") 47 50 58 62 65 68 10That takes 9.4 times as much time on average. A value of 100 milliseconds means it can only determine ~10 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. Full names (like Thermus islandicus) are almost fast - these are the most probable input from most data sets.
+# as.mo("theisl") 270 270 280 290 290 300 10 +# as.mo("THEISL") 280 290 290 290 300 300 10 +# as.mo("T. islandicus") 130 130 150 150 160 160 10 +# as.mo("T. islandicus") 130 130 150 150 150 160 10 +# as.mo("Thermus islandicus") 46 48 54 50 63 71 10 +That takes 8.8 times as much time on average. A value of 100 milliseconds means it can only determine ~10 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. Full names (like Thermus islandicus) are almost fast - these are the most probable input from most data sets.
In the figure below, we compare Escherichia coli (which is very common) with Prevotella brevis (which is moderately common) and with Thermus islandicus (which is very uncommon):
par(mar = c(5, 16, 4, 2)) # set more space for left margin text (16)
@@ -253,7 +253,7 @@
Repetitive results
-Repetitive results are unique values that 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) which uses as.mo() internally.
+Repetitive results are unique values that are present more than once. Unique values will only be calculated once by as.mo(). We will use mo_name() for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses as.mo() internally.
library(dplyr)
# take all MO codes from the septic_patients data set
x <- septic_patients$mo %>%
@@ -275,32 +275,32 @@
# [1] 50
# now let's see:
-run_it <- microbenchmark(mo_fullname(x),
+run_it <- microbenchmark(mo_name(x),
times = 10)
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
-# expr min lq mean median uq max neval
-# mo_fullname(x) 625 649 665 666 677 724 10
-So transforming 500,000 values (!!) of 50 unique values only takes 0.67 seconds (666 ms). You only lose time on your unique input values.
+# expr min lq mean median uq max neval
+# mo_name(x) 623 631 659 637 697 729 10
+So transforming 500,000 values (!!) of 50 unique values only takes 0.64 seconds (637 ms). You only lose time on your unique input values.
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):
run_it <- microbenchmark(A = mo_fullname("B_STPHY_AUR"),
- B = mo_fullname("S. aureus"),
- C = mo_fullname("Staphylococcus aureus"),
+What about precalculated results? If the input is an already precalculated result of a helper function like mo_name(), it almost doesn’t take any time at all (see ‘C’ below):
+run_it <- microbenchmark(A = mo_name("B_STPHY_AUR"),
+ B = mo_name("S. aureus"),
+ C = mo_name("Staphylococcus aureus"),
times = 10)
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
-# expr min lq mean median uq max neval
-# A 6.440 6.65 6.880 6.840 7.15 7.48 10
-# B 22.400 22.90 26.200 23.600 25.20 44.00 10
-# C 0.762 0.81 0.848 0.818 0.87 1.10 10
-So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0008 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:
+# expr min lq mean median uq max neval
+# A 6.290 6.730 7.170 7.010 7.760 8.09 10
+# B 22.600 22.700 26.200 23.000 25.400 44.30 10
+# C 0.798 0.806 0.874 0.844 0.891 1.05 10So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0008 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:
run_it <- microbenchmark(A = mo_species("aureus"),
B = mo_genus("Staphylococcus"),
- C = mo_fullname("Staphylococcus aureus"),
+ C = mo_name("Staphylococcus aureus"),
D = mo_family("Staphylococcaceae"),
E = mo_order("Bacillales"),
F = mo_class("Bacilli"),
@@ -310,47 +310,47 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 0.437 0.456 0.499 0.482 0.560 0.607 10
-# B 0.474 0.484 0.534 0.509 0.588 0.627 10
-# C 0.621 0.712 0.799 0.812 0.829 1.020 10
-# D 0.469 0.482 0.534 0.513 0.595 0.654 10
-# E 0.415 0.434 0.493 0.459 0.557 0.678 10
-# F 0.458 0.523 0.538 0.546 0.554 0.601 10
-# G 0.416 0.438 0.484 0.450 0.563 0.621 10
-# H 0.420 0.434 0.491 0.448 0.577 0.620 10Of 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 bacteria (according to the Catalogue of Life), it can just return the initial value immediately.
When the system language is non-English and supported by this AMR package, some functions will have a translated result. This almost does’t take extra time:
mo_fullname("CoNS", language = "en") # or just mo_fullname("CoNS") on an English system
+mo_name("CoNS", language = "en") # or just mo_name("CoNS") on an English system
# [1] "Coagulase-negative Staphylococcus (CoNS)"
-mo_fullname("CoNS", language = "es") # or just mo_fullname("CoNS") on a Spanish system
+mo_name("CoNS", language = "es") # or just mo_name("CoNS") on a Spanish system
# [1] "Staphylococcus coagulasa negativo (SCN)"
-mo_fullname("CoNS", language = "nl") # or just mo_fullname("CoNS") on a Dutch system
+mo_name("CoNS", language = "nl") # or just mo_name("CoNS") on a Dutch system
# [1] "Coagulase-negatieve Staphylococcus (CNS)"
-run_it <- 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"),
+run_it <- microbenchmark(en = mo_name("CoNS", language = "en"),
+ de = mo_name("CoNS", language = "de"),
+ nl = mo_name("CoNS", language = "nl"),
+ es = mo_name("CoNS", language = "es"),
+ it = mo_name("CoNS", language = "it"),
+ fr = mo_name("CoNS", language = "fr"),
+ pt = mo_name("CoNS", language = "pt"),
times = 10)
print(run_it, unit = "ms", signif = 4)
# Unit: milliseconds
-# expr min lq mean median uq max neval
-# en 17.02 17.26 17.89 17.85 18.50 18.84 10
-# de 18.28 18.65 22.91 18.84 19.67 41.64 10
-# nl 24.07 24.31 32.74 24.60 25.02 105.60 10
-# es 18.59 19.00 19.99 19.32 19.81 26.42 10
-# it 18.28 18.40 22.59 19.07 20.38 39.47 10
-# fr 18.34 18.70 21.48 19.37 20.83 34.67 10
-# pt 18.60 18.92 19.25 19.19 19.59 20.14 10
+# expr min lq mean median uq max neval
+# en 17.66 17.86 18.50 18.49 19.14 19.36 10
+# de 19.03 19.38 19.64 19.49 20.01 20.42 10
+# nl 24.40 25.23 30.77 25.78 41.94 44.93 10
+# es 19.18 19.22 23.30 19.53 21.34 39.20 10
+# it 19.02 19.24 23.53 19.57 20.35 50.89 10
+# fr 19.28 19.33 19.87 19.57 20.19 21.25 10
+# pt 18.89 19.14 19.77 19.67 20.21 20.99 10Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
WHO Collaborating Centre for Drug Statistics Methodology
This package contains all ~450 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission.
-NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.
+NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.
Read more about the data from WHOCC in our manual.
freq() has moved to a new package, clean (CRAN link). Creating frequency tables is actually not the scope of this package (never was) and this function has matured a lot over the last two years. Therefore, a new package was created for data cleaning and checking and it perfectly fits the freq() function. The clean package is available on CRAN and will be installed automatically when updating the AMR package, that now imports it. In a later stage, the skewness() and kurtosis() functions will be moved to the clean package too."UNKNOWN". They can be included with the new parameter include_unknown: first_isolates(..., include_unknown = TRUE). For WHONET users, this means that all records with organism code "con" (contamination) will be excluded at default, since as.mo("con") = "UNKNOWN".Determination of first isolates now excludes all ‘unknown’ microorganisms at default, i.e. microbial code "UNKNOWN". They can be included with the new parameter include_unknown:
For WHONET users, this means that all records with organism code "con" (contamination) will be excluded at default, since as.mo("con") = "UNKNOWN". The function always shows a note with the number of ‘unknown’ microorganisms that were included or excluded.
Additional way to calculate co-resistance, i.e. when using multiple antibiotics as input for portion_* functions or count_* functions. This can be used to determine the empiric susceptibily of a combination therapy. A new parameter only_all_tested (which defaults to FALSE) replaces the old also_single_tested and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the portion and count help pages), where the %SI is being determined:
# -------------------------------------------------------------------------
-# only_all_tested = FALSE only_all_tested = TRUE
-# Antibiotic Antibiotic ----------------------- -----------------------
-# A B include as include as include as include as
-# numerator denominator numerator denominator
-# ---------- ---------- ---------- ----------- ---------- -----------
-# S S X X X X
-# I S X X X X
-# R S X X X X
-# not tested S X X - -
-# S I X X X X
-# I I X X X X
-# R I X X X X
-# not tested I X X - -
-# S R X X X X
-# I R X X X X
-# R R - X - X
-# not tested R - - - -
-# S not tested X X - -
-# I not tested X X - -
-# R not tested - - - -
-# not tested not tested - - - -
-# -------------------------------------------------------------------------# -------------------------------------------------------------------------
+# only_all_tested = FALSE only_all_tested = TRUE
+# Antibiotic Antibiotic ----------------------- -----------------------
+# A B include as include as include as include as
+# numerator denominator numerator denominator
+# ---------- ---------- ---------- ----------- ---------- -----------
+# S S X X X X
+# I S X X X X
+# R S X X X X
+# not tested S X X - -
+# S I X X X X
+# I I X X X X
+# R I X X X X
+# not tested I X X - -
+# S R X X X X
+# I R X X X X
+# R R - X - X
+# not tested R - - - -
+# S not tested X X - -
+# I not tested X X - -
+# R not tested - - - -
+# not tested not tested - - - -
+# -------------------------------------------------------------------------Since this is a major change, usage of the old also_single_tested will throw an informative error that it has been replaced by only_all_tested.
Added tibble printing support for classes rsi, mic, ab and mo. When using tibbles containing antibiotic columns, values S will print in green, values I will print in yellow and values R will print in red:
(run this on your own console, as this page does not support colour printing)
-tibble(mo = sample(AMR::microorganisms$fullname, 10),
- drug1 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
- prob = c(0.6, 0.1, 0.3))),
- drug2 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
- prob = c(0.6, 0.1, 0.3))),
- drug3 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
- prob = c(0.6, 0.1, 0.3))))# (run this on your own console, as this page does not support colour printing)
+tibble(mo = sample(AMR::microorganisms$fullname, 10),
+ drug1 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
+ prob = c(0.6, 0.1, 0.3))),
+ drug2 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
+ prob = c(0.6, 0.1, 0.3))),
+ drug3 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
+ prob = c(0.6, 0.1, 0.3))))atc - using as.atc() is now deprecated in favour of ab_atc() and this will return a character, not the atc class anymoreabname(), ab_official(), atc_name(), atc_official(), atc_property(), atc_tradenames(), atc_trivial_nl()
@@ -312,7 +316,7 @@
mo_* functions where the coercion uncertainties and failures would not be available through mo_uncertainties() and mo_failures() anymorecountry parameter of mdro() in favour of the already existing guideline parameter to support multiple guidelines within one countryname of RIF is now Rifampicin instead of Rifampinantibiotics data set is now sorted by nameantibiotics data set is now sorted by name and all cephalosporines now have their generation between bracketsSpeed improvement for guess_ab_col() which is now 30 times faster for antibiotic abbreviations
Function rsi_df() to transform a data.frame to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combination of the existing functions count_df() and portion_df() to immediately show resistance percentages and number of available isolates:
Support for all scientifically published pathotypes of E. coli to date (that we could find). Supported are:
@@ -359,12 +363,12 @@All these lead to the microbial ID of E. coli:
-as.mo("UPEC")
-# B_ESCHR_COL
-mo_name("UPEC")
-# "Escherichia coli"
-mo_gramstain("EHEC")
-# "Gram-negative"as.mo("UPEC")
+# B_ESCHR_COL
+mo_name("UPEC")
+# "Escherichia coli"
+mo_gramstain("EHEC")
+# "Gram-negative"mo_info() as an analogy to ab_info(). The mo_info() prints a list with the full taxonomy, authors, and the URL to the online database of a microorganismFunction mo_synonyms() to get all previously accepted taxonomic names of a microorganism
septic_patients %>%
- freq(age) %>%
- boxplot()
-# grouped boxplots:
-septic_patients %>%
- group_by(hospital_id) %>%
- freq(age) %>%
- boxplot()septic_patients %>%
+ freq(age) %>%
+ boxplot()
+# grouped boxplots:
+septic_patients %>%
+ group_by(hospital_id) %>%
+ freq(age) %>%
+ boxplot()New filters for antimicrobial classes. Use these functions to filter isolates on results in one of more antibiotics from a specific class:
-filter_aminoglycosides()
-filter_carbapenems()
-filter_cephalosporins()
-filter_1st_cephalosporins()
-filter_2nd_cephalosporins()
-filter_3rd_cephalosporins()
-filter_4th_cephalosporins()
-filter_fluoroquinolones()
-filter_glycopeptides()
-filter_macrolides()
-filter_tetracyclines()filter_aminoglycosides()
+filter_carbapenems()
+filter_cephalosporins()
+filter_1st_cephalosporins()
+filter_2nd_cephalosporins()
+filter_3rd_cephalosporins()
+filter_4th_cephalosporins()
+filter_fluoroquinolones()
+filter_glycopeptides()
+filter_macrolides()
+filter_tetracyclines()The antibiotics data set will be searched, after which the input data will be checked for column names with a value in any abbreviations, codes or official names found in the antibiotics data set. For example:
All ab_* functions are deprecated and replaced by atc_* functions:
ab_property -> atc_property()
-ab_name -> atc_name()
-ab_official -> atc_official()
-ab_trivial_nl -> atc_trivial_nl()
-ab_certe -> atc_certe()
-ab_umcg -> atc_umcg()
-ab_tradenames -> atc_tradenames()ab_property -> atc_property()
+ab_name -> atc_name()
+ab_official -> atc_official()
+ab_trivial_nl -> atc_trivial_nl()
+ab_certe -> atc_certe()
+ab_umcg -> atc_umcg()
+ab_tradenames -> atc_tradenames()as.atc() internally. The old atc_property has been renamed atc_online_property(). This is done for two reasons: firstly, not all ATC codes are of antibiotics (ab) but can also be of antivirals or antifungals. Secondly, the input must have class atc or must be coerable to this class. Properties of these classes should start with the same class name, analogous to as.mo() and e.g. mo_genus.set_mo_source() and get_mo_source() to use your own predefined MO codes as input for as.mo() and consequently all mo_* functionsdplyr version 0.8.0as.atc()New function age_groups() to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
New function ggplot_rsi_predict() as well as the base R plot() function can now be used for resistance prediction calculated with resistance_predict():
-
+
Functions filter_first_isolate() and filter_first_weighted_isolate() to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
-
+
is equal to:
-
+
New function availability() to check the number of available (non-empty) results in a data.frame
@@ -634,33 +638,33 @@ These functions use as.atc()
Now handles incorrect spelling, like i instead of y and f instead of ph:
-
+
Uncertainty of the algorithm is now divided into four levels, 0 to 3, where the default allow_uncertain = TRUE is equal to uncertainty level 2. Run ?as.mo for more info about these levels.
-# equal:
-as.mo(..., allow_uncertain = TRUE)
-as.mo(..., allow_uncertain = 2)
-
-# also equal:
-as.mo(..., allow_uncertain = FALSE)
-as.mo(..., allow_uncertain = 0)
+# equal:
+as.mo(..., allow_uncertain = TRUE)
+as.mo(..., allow_uncertain = 2)
+
+# also equal:
+as.mo(..., allow_uncertain = FALSE)
+as.mo(..., allow_uncertain = 0)
Using as.mo(..., allow_uncertain = 3) could lead to very unreliable results.
Implemented the latest publication of Becker et al. (2019), for categorising coagulase-negative Staphylococci
All microbial IDs that found are now saved to a local file ~/.Rhistory_mo. Use the new function clean_mo_history() to delete this file, which resets the algorithms.
Incoercible results will now be considered ‘unknown’, MO code UNKNOWN. On foreign systems, properties of these will be translated to all languages already previously supported: German, Dutch, French, Italian, Spanish and Portuguese:
-
+
Fix for vector containing only empty values
Finds better results when input is in other languages
@@ -706,19 +710,19 @@ Using as.mo(..., allow_uncertain = 3)
Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:
-# Determine genus of microorganisms (mo) in `septic_patients` data set:
-# OLD WAY
-septic_patients %>%
- mutate(genus = mo_genus(mo)) %>%
- freq(genus)
-# NEW WAY
-septic_patients %>%
- freq(mo_genus(mo))
-
-# Even supports grouping variables:
-septic_patients %>%
- group_by(gender) %>%
- freq(mo_genus(mo))
+# Determine genus of microorganisms (mo) in `septic_patients` data set:
+# OLD WAY
+septic_patients %>%
+ mutate(genus = mo_genus(mo)) %>%
+ freq(genus)
+# NEW WAY
+septic_patients %>%
+ freq(mo_genus(mo))
+
+# Even supports grouping variables:
+septic_patients %>%
+ group_by(gender) %>%
+ freq(mo_genus(mo))
Header info is now available as a list, with the header function
The parameter header is now set to TRUE at default, even for markdown
@@ -793,10 +797,10 @@ Using as.mo(..., allow_uncertain = 3)Fewer than 3 characters as input for as.mo will return NA
Function as.mo (and all mo_* wrappers) now supports genus abbreviations with “species” attached
-
+
Added parameter combine_IR (TRUE/FALSE) to functions portion_df and 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)
Fix for portion_*(..., as_percent = TRUE) when minimal number of isolates would not be met
@@ -809,15 +813,15 @@ Using as.mo(..., allow_uncertain = 3)
Support for grouping variables, test with:
-
+
Support for (un)selecting columns:
-
+
Check for hms::is.hms
@@ -897,18 +901,18 @@ Using as.mo(..., allow_uncertain = 3)
They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:
-mo_gramstain("E. coli")
-# [1] "Gram negative"
-mo_gramstain("E. coli", language = "de") # German
-# [1] "Gramnegativ"
-mo_gramstain("E. coli", language = "es") # Spanish
-# [1] "Gram negativo"
-mo_fullname("S. group A", language = "pt") # Portuguese
-# [1] "Streptococcus grupo A"
+mo_gramstain("E. coli")
+# [1] "Gram negative"
+mo_gramstain("E. coli", language = "de") # German
+# [1] "Gramnegativ"
+mo_gramstain("E. coli", language = "es") # Spanish
+# [1] "Gram negativo"
+mo_fullname("S. group A", language = "pt") # Portuguese
+# [1] "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)
-# [1] "Gram negative"
+mo_gramstain("Esc blattae")
+# Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)
+# [1] "Gram negative"
Functions count_R, count_IR, count_I, count_SI and count_S to selectively count resistant or susceptible isolates
@@ -919,18 +923,18 @@ Using as.mo(..., allow_uncertain = 3)
-
Functions as.mo and is.mo as replacements for as.bactid and 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 intelligent rules:
-as.mo("E. coli")
-# [1] B_ESCHR_COL
-as.mo("MRSA")
-# [1] B_STPHY_AUR
-as.mo("S group A")
-# [1] B_STRPTC_GRA
+as.mo("E. coli")
+# [1] B_ESCHR_COL
+as.mo("MRSA")
+# [1] B_STPHY_AUR
+as.mo("S group A")
+# [1] 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:
-
+
- Added parameter
reference_df for as.mo, so users can supply their own microbial IDs, name or codes as a reference table
- Renamed all previous references to
bactid to mo, like:
@@ -958,12 +962,12 @@ Using as.mo(..., allow_uncertain = 3)Added three antimicrobial agents to the antibiotics data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)
-
Added 163 trade names to the antibiotics data set, it now contains 298 different trade names in total, e.g.:
-
+
- For
first_isolate, rows will be ignored when there’s no species available
- Function
ratio is now deprecated and will be removed in a future release, as it is not really the scope of this package
@@ -974,13 +978,13 @@ Using as.mo(..., allow_uncertain = 3)
-
Support for quasiquotation in the functions series count_* and portions_*, and n_rsi. This allows to check for more than 2 vectors or columns.
-
+
- Edited
ggplot_rsi and geom_rsi so they can cope with count_df. The new fun parameter has value portion_df at default, but can be set to count_df.
- Fix for
ggplot_rsi when the ggplot2 package was not loaded
@@ -994,12 +998,12 @@ Using as.mo(..., allow_uncertain = 3)
-
Support for types (classes) list and matrix for freq
-
+
For lists, subsetting is possible:
-
+
as.mo(..., allow_uncertain = 3)
Contents
an antibiotic code, generated with as.ab
an antimicrobial code, generated with as.ab
maximum fraction of x that is allowed to fail transformation, see Examples
maximum fraction of invalid antimicrobial interpretations of x, see Examples
Run unique(AMR::rsi_translation$guideline) for a list of all supported guidelines.
After using as.rsi, you can use eucast_rules to (1) apply inferred susceptibility and resistance based on results of other antibiotics and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
After using as.rsi, you can use eucast_rules to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
The function is.rsi.eligible returns TRUE when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and FALSE otherwise. The threshold of 5% can be set with the threshold parameter.
Idea from the like function from the data.table package, but made it case insensitive at default and let it support multiple patterns.
Idea from the like function from the data.table package, but made it case insensitive at default and let it support multiple patterns. Also, if the regex fails the first time, it tries again with perl = TRUE.
For language-dependent output of AMR functions, like mo_fullname and mo_type.
For language-dependent output of AMR functions, like mo_name, mo_type and ab_name.
Strings will be translated to foreign languages if they are defined in a local translation file. Additions to this file can be suggested at our repository. The file can be found here: https://gitlab.com/msberends/AMR/blob/master/data-raw/translations.tsv.
+Currently supported languages can be found if running: unique(AMR:::translations_file$lang).
Please suggest your own translations by creating a new issue on our repository.
This file will be read by all functions where a translated output can be desired, like all mo_property functions (mo_fullname, mo_type, etc.).
The system language will be used at default, if supported, using get_locale. The system language can be overwritten with getOption("AMR_locale").
The system language will be used at default, if that language is supported. The system language can be overwritten with getOption("AMR_locale").