Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 10 May 2019.
So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values M and F. From a researcher perspective: there are slightly more men. Nothing we didn’t already know.
The data is already quite clean, but we still need to transform some variables. The bacteria column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The mutate() function of the dplyr package makes this really easy:
We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The as.rsi() function ensures reliability and reproducibility in these kind of variables. The mutate_at() will run the as.rsi() function on defined variables:
Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the eucast_rules() function can also apply additional rules, like forcing ampicillin = R when amoxicillin/clavulanic acid = R.
-
Because the amoxicillin (column amox) and amoxicillin/clavulanic acid (column amcl) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The eucast_rules() fixes this:
+
Because the amoxicillin (column AMX) and amoxicillin/clavulanic acid (column AMC) in our data were generated randomly, some rows will undoubtedly contain AMX = S and AMC = R, which is technically impossible. The eucast_rules() fixes this:
For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:
@@ -520,39 +520,39 @@
date
patient_id
bacteria
-
amox
-
amcl
-
cipr
-
gent
+
AMX
+
AMC
+
CIP
+
GEN
first
1
-
2010-01-24
-
N5
+
2010-01-22
+
M1
B_ESCHR_COL
S
S
-
S
+
R
S
TRUE
2
-
2010-07-22
-
N5
+
2010-03-11
+
M1
B_ESCHR_COL
S
S
-
S
+
R
S
FALSE
3
-
2010-07-29
-
N5
+
2010-03-17
+
M1
B_ESCHR_COL
R
S
@@ -562,41 +562,41 @@
4
-
2010-09-17
-
N5
+
2010-04-28
+
M1
B_ESCHR_COL
R
-
I
S
+
R
S
FALSE
5
-
2011-02-27
-
N5
+
2010-05-26
+
M1
B_ESCHR_COL
+
R
S
S
S
-
S
-
TRUE
+
FALSE
6
-
2011-04-25
-
N5
+
2010-06-03
+
M1
B_ESCHR_COL
-
S
-
S
+
R
+
R
S
S
FALSE
7
-
2011-04-30
-
N5
+
2010-07-03
+
M1
B_ESCHR_COL
S
S
@@ -606,21 +606,21 @@
8
-
2011-05-27
-
N5
+
2010-07-07
+
M1
B_ESCHR_COL
-
S
-
S
+
R
+
R
S
S
FALSE
9
-
2011-06-24
-
N5
+
2010-07-30
+
M1
B_ESCHR_COL
-
R
+
S
S
S
S
@@ -628,18 +628,18 @@
10
-
2011-10-29
-
N5
+
2010-10-12
+
M1
B_ESCHR_COL
R
S
-
R
+
S
S
FALSE
-
Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
+
Only 1 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
If a column exists with a name like ‘key(…)ab’ the first_isolate() function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:
Instead of 2, now 5 isolates are flagged. In total, 75.3% of all isolates are marked ‘first weighted’ - 46.9% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
+
Instead of 1, now 9 isolates are flagged. In total, 75.6% of all isolates are marked ‘first weighted’ - 47.4% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
You might want to start by getting an idea of how the data is distributed. It’s an important start, because it also decides how you will continue your analysis.
+
You might want to start by getting an idea of how the data is distributed. It’s an important start, because it also decides how you will continue your analysis. Although this package contains a convenient function to make frequency tables, exploratory data analysis (EDA) is not the primary scope of this package. Use a package like DataExplorer for that, or read the free online book Exploratory Data Analysis with R by Roger D. Peng.
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the n_rsi() can be used, which works exactly like n_distinct() from the dplyr package. It counts all isolates available for every group (i.e. values S, I or R):
Omit the translate_ab = FALSE to have the antibiotic codes (amox, amcl, cipr, gent) translated to official WHO names (amoxicillin, amoxicillin and betalactamase inhibitor, ciprofloxacin, gentamicin).
+
Omit the translate_ab = FALSE to have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin and betalactamase inhibitor, ciprofloxacin, gentamicin).
If we group on e.g. the genus column and add some additional functions from our package, we can create this:
The next example uses the included septic_patients, which is an anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This data.frame can be used to practice AMR analysis.
-
We will compare the resistance to fosfomycin (column fosf) in hospital A and D. The input for the final fisher.test() will be this:
+
We will compare the resistance to fosfomycin (column FOS) in hospital A and D. The input for the final fisher.test() will be this:
@@ -1173,7 +1180,7 @@ Longest: 24
We can transform the data and apply the test in only a couple of lines:
septic_patients %>%filter(hospital_id %in%c("A", "D")) %>%# filter on only hospitals A and D
-select(hospital_id, fosf) %>%# select the hospitals and fosfomycin
+select(hospital_id, FOS) %>%# select the hospitals and fosfomycingroup_by(hospital_id) %>%# group on the hospitalscount_df(combine_IR =TRUE) %>%# count all isolates per group (hospital_id)tidyr::spread(hospital_id, Value) %>%# transform output so A and D are columns
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diff --git a/docs/articles/SPSS.html b/docs/articles/SPSS.html
index 4ff4c81f6..84c4f9b79 100644
--- a/docs/articles/SPSS.html
+++ b/docs/articles/SPSS.html
@@ -40,7 +40,7 @@
AMR (for R)
- 0.6.1.9002
+ 0.6.1.9003
If you sometimes write syntaxes in SPSS to run a complete analysis or to ‘automate’ some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS.
+
If you sometimes write syntaxes in SPSS to run a complete analysis or to ‘automate’ some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS. Still, as working with any statistical package, you will have to have knowledge about what you are doing (statistically) and what you are willing to accomplish.
In 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:
That takes 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.
That takes 8.2 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):
Uncommon microorganisms take a lot more time than common microorganisms. To relieve this pitfall and further improve performance, two important calculations take almost no time at all: repetitive results and already precalculated results.
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:
So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0018 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:
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 bacteria (according to the Catalogue of Life), it can just return the initial value immediately.
Our package contains a function resistance_predict(), which takes the same input as functions for other AMR analysis. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
#> NOTE: Using column `date` as input for `col_date`.
@@ -240,62 +240,62 @@
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
-#> -2.9224 -1.3120 0.0170 0.7586 3.1932
+#> -2.9203 -1.3066 0.0166 0.7641 3.1984
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
-#> (Intercept) -222.92857 45.93922 -4.853 1.22e-06 ***
-#> year 0.10994 0.02284 4.814 1.48e-06 ***
+#> (Intercept) -222.51053 45.94675 -4.843 1.28e-06 ***
+#> year 0.10973 0.02284 4.805 1.55e-06 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
-#> Null deviance: 59.794 on 14 degrees of freedom
-#> Residual deviance: 35.191 on 13 degrees of freedom
-#> AIC: 93.464
+#> Null deviance: 59.763 on 14 degrees of freedom
+#> Residual deviance: 35.261 on 13 degrees of freedom
+#> AIC: 93.537
#>
#> Number of Fisher Scoring iterations: 4
This text is only a printed summary - the actual result (output) of the function is a data.frame containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:
The function plot is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:
This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.
We also support the ggplot2 package with our custom function ggplot_rsi_predict() to create more appealing plots:
For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on the observed years:
AMR is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any data format, including WHONET/EARS-Net data.
We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen, the Netherlands, and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is actively maintained and is free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation. Read the full license here.
This package can be used for:
@@ -295,7 +295,7 @@
WHO Collaborating Centre for Drug Statistics Methodology
-
This package contains all ~500 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) 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.
+
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.
It cleanses existing data by providing new classes for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:
-
Use as.mo() to 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” or “esccol” and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our benchmarks. Moreover, it can group Staphylococci into coagulase negative and positive (CoNS and CoPS, see source) and can categorise Streptococci into Lancefield groups (like beta-haemolytic Streptococcus Group B, source).
+
Use as.mo() to get a microbial ID. 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” or “esccol” and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our benchmarks. Moreover, it can group Staphylococci into coagulase negative and positive (CoNS and CoPS, see source) and can categorise Streptococci into Lancefield groups (like beta-haemolytic Streptococcus Group B, source).
Use as.rsi() to 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”.
Use as.mic() to 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”.
-
Use as.atc() to 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.
+
Use as.ab() to get a antibiotic ID, which are abbreviations used by EARS-Net whenever available. Use as.atc() to 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 ID of Nitrofurantoine.
@@ -330,7 +330,7 @@
Use 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.
The data set microorganisms contains the complete taxonomic tree of ~65,000 microorganisms. Furthermore, some colloquial names and all Gram stains 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_genus(), mo_family(), mo_gramstain() or even mo_phylum(). As they use as.mo() internally, they also use the same intelligent rules for determination. For example, mo_genus("MRSA") and 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.
-
The data set antibiotics contains almost 500 antimicrobial drugs with their ATC code, EARS-Net code, common LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains hundreds of trade names. Use functions like atc_name() and atc_tradenames() to look up values. The atc_* functions use as.atc() internally so they support the same intelligent rules to guess the most probable result. For example, atc_name("Fluclox"), atc_name("Floxapen") and atc_name("J01CF05") will all return "Flucloxacillin". These functions can again be used to add new variables to your data.
+
The data set antibiotics contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. Use functions like ab_name(), ab_group() and ab_tradenames() to look up values. The ab_* functions use as.ab() internally so they support the same intelligent rules to guess the most probable result. For example, ab_name("Fluclox"), ab_name("Floxapen") and ab_name("J01CF05") will all return "Flucloxacillin". These functions can again be used to add new variables to your data.
Support for translation of disk diffusion and MIC values to RSI values (i.e. antimicrobial interpretations). Supported guidelines are EUCAST (2011 to 2019) and CLSI (2011 to 2019). Use as.rsi() on an MIC value (created with as.mic()), a disk diffusion value (created with the new as.disk()) or on a complete date set containing columns with MIC or disk diffusion values.
All entries now have 3 different identifiers: a human readable EARS-Net code (ab, used by ECDC and WHONET), an ATC code (atc, used by WHO), and a CID code (cid, Compound ID, used by PubChem)
+
Based on the Compound ID, more than a thousand official brand names have been added from many different countries
+
All references to antibiotics in our package now use EARS-Net codes, like AMX for amoxicillin
+
Functions atc_certe, ab_umcg and atc_trivial_nl have been removed
+
All atc_* functions are superceded by ab_* functions
+
+
All output will be translated by using an included, local translation file that can be found after install with:
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:
These functions use 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.
@@ -349,20 +373,20 @@ These functions use as.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 availability() to check the number of available (non-empty) results in a data.frame
@@ -384,40 +408,40 @@ These functions use as.atc()
Removed data sets microorganisms.oldDT, microorganisms.prevDT, microorganisms.unprevDT and microorganismsDT since they were no longer needed and only contained info already available in the microorganisms data set
Functions atc_ddd() and atc_groups() have been renamed atc_online_ddd() and atc_online_groups(). The old functions are deprecated and will be removed in a future version.
Function guess_mo() is now deprecated in favour of as.mo() and will be removed in future versions
Function guess_atc() is now deprecated in favour of as.atc() and will be removed in future versions
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.
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:
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
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.
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:
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:
@@ -715,12 +739,12 @@ 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.:
BREAKING: rsi_df was removed in favour of new functions portion_R, portion_IR, portion_I, portion_SI and portion_S to selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old rsi function. The old function still works, but is deprecated.
@@ -877,7 +901,7 @@ Using as.mo(..., allow_uncertain = 3)
Now possible to coerce MIC values with a space between operator and value, i.e. as.mic("<= 0.002") now works
Classes rsi and mic do not add the attribute package.version anymore
-
Added "groups" option for atc_property(..., property). It will return a vector of the ATC hierarchy as defined by the WHO. The new function atc_groups is a convenient wrapper around this.
+
Added "groups" option for atc_property(..., property). It will return a vector of the ATC hierarchy as defined by the WHO. The new function atc_groups is a convenient wrapper around this.
Build-in host check for atc_property as it requires the host set by url to be responsive
Improved first_isolate algorithm to exclude isolates where bacteria ID or genus is unavailable
Fix for warning hybrid evaluation forced for row_number (924b62) from the dplyr package v0.7.5 and above
Use these functions to return a specific property of an antibiotic from the antibiotics data set. All input values will be evaluated internally with as.ab.
a (vector of a) valid atc code or any text that can be coerced to a valid atc with as.atc
-
-
-
property
-
one of the column names of one of the antibiotics data set, like "atc" and "official"
+
any (vector of) text that can be coerced to a valid microorganism code with as.ab
language
-
language of the returned text, defaults to English ("en") and can be set with getOption("AMR_locale"). Either one of "en" (English) or "nl" (Dutch).
+
language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
+
+
+
tolower
+
logical to indicate whether the first character of every output should be transformed to a lower case character. This will lead to e.g. "polymyxin B" and not "polymyxin b".
values to split x at, defaults to age groups 0-11, 12-24, 26-54, 55-74 and 75+. See Details.
+
values to split x at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See Details.
@@ -264,7 +264,7 @@
To split ages, the input can be:
A numeric vector. A vector of e.g. c(10, 20) will split on 0-9, 10-19 and 20+. A value of only 50 will split on 0-49 and 50+.
- The default is to split on young children (0-11), youth (12-24), young adults (26-54), middle-aged adults (55-74) and elderly (75+).
+ The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).
A character:
"children", equivalent of: c(0, 1, 2, 4, 6, 13, 18). This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.
"elderly" or "seniors", equivalent of: c(65, 75, 85). This will split on 0-64, 65-74, 75-84, 85+.
A data set containing all antibiotics. Use as.ab or one of the ab_property functions to retrieve values from this data set. Three identifiers are included in this data set: an antibiotic ID (ab, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (atc) as defined by the WHO, and a Compound ID (cid) as found in PubChem. Other properties in this data set are derived from one or more of these codes.
@@ -245,33 +245,33 @@
Format
-
A data.frame with 488 observations and 17 variables:
-
atc
ATC code (Anatomical Therapeutic Chemical), like J01CR02
-
ears_net
EARS-Net code (European Antimicrobial Resistance Surveillance Network), like AMC
-
certe
Certe code, like amcl
-
umcg
UMCG code, like AMCL
-
abbr
Abbreviation as used by many countries, used internally by as.atc
-
official
Official name by the WHO, like "Amoxicillin and beta-lactamase inhibitor"
-
official_nl
Official name in the Netherlands, like "Amoxicilline met enzymremmer"
-
trivial_nl
Trivial name in Dutch, like "Amoxicilline/clavulaanzuur"
-
trade_name
Trade name as used by many countries (a total of 294), used internally by as.atc
+
A data.frame with 455 observations and 13 variables:
+
ab
Antibiotic ID as used in this package (like AMC), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
+
atc
ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like J01CR02
+
cid
Compound ID as found in PubChem
+
name
Official name as used by WHONET/EARS-Net or the WHO
+
group
A short and concise group name, based on WHONET and WHOCC definitions
+
atc_group1
Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like "Macrolides, lincosamides and streptogramins"
+
atc_group2
Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like "Macrolides"
+
abbr
List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)
+
synonyms
Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
oral_ddd
Defined Daily Dose (DDD), oral treatment
oral_units
Units of ddd_units
iv_ddd
Defined Daily Dose (DDD), parenteral treatment
iv_units
Units of iv_ddd
-
atc_group1
ATC group, like "Macrolides, lincosamides and streptogramins"
-
atc_group2
Subgroup of atc_group1, like "Macrolides"
-
useful_gramnegative
FALSE if not useful according to EUCAST, NA otherwise (see Source)
-
useful_grampositive
FALSE if not useful according to EUCAST, NA otherwise (see Source)
Properties that are based on an ATC code are only available when an ATC is available. These properties are: atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd and iv_units
+
Synonyms (i.e. trade names) are derived from the Compound ID (cid) and consequently only available where a CID is available.
+
WHOCC
@@ -299,6 +299,8 @@ This package contains all ~500 antimicrobial drugs and their An
Use this function to determine the antibiotic code of one or more antibiotics. The data set antibiotics will be searched for abbreviations, official names and synonyms (brand names).
+
+
+
+
as.ab(x)
+
+
Arguments
+
+
+
+
x
+
character vector to determine to antibiotic ID
+
+
+
+
Value
+
+
Character (vector) with class "act". Unknown values will return NA.
+
+
Details
+
+
Use the ab_property functions to get properties based on the returned ATC code, see Examples.
+
In the ATC classification system, the active substances are classified in a hierarchy with five different levels. The system has fourteen main anatomical/pharmacological groups or 1st levels. Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups. The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance. The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
+ Source: https://www.whocc.no/atc/structure_and_principles/
+This package contains all ~500 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) 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 (http://ec.europa.eu/health/documents/community-register/html/atc.htm).
+
These have become the gold standard for international drug utilisation monitoring and research.
+
The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
antibiotics for the dataframe that is being used to determine ATCs.
+
+
+
Examples
+
# NOT RUN {
+# These examples all return "ERY", the ID of Erythromycin:
+as.ab("J01FA01")
+as.ab("J 01 FA 01")
+as.ab("Erythromycin")
+as.ab("eryt")
+as.ab(" eryt 123")
+as.ab("ERYT")
+as.ab("ERY")
+as.ab("erytromicine") # spelled wrong
+as.ab("Erythrocin") # trade name
+as.ab("Romycin") # trade name
+
+# Use ab_* functions to get a specific properties (see ?ab_property);
+# they use as.ab() internally:
+ab_name("J01FA01") # "Erythromycin"
+ab_name("eryt") # "Erythromycin"
+# }
Use the ab_property functions to get properties based on the returned ATC code, see Examples.
+
Use the ab_property functions to get properties based on the returned ATC code, see Examples.
In the ATC classification system, the active substances are classified in a hierarchy with five different levels. The system has fourteen main anatomical/pharmacological groups or 1st levels. Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups. The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance. The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
Source: https://www.whocc.no/atc/structure_and_principles/
@@ -291,14 +293,6 @@ This package contains all ~500 antimicrobial drugs and their An
as.atc(" eryt 123")
as.atc("ERYT")
as.atc("ERY")
-as.atc("Erythrocin") # Trade name
-as.atc("Eryzole") # Trade name
-as.atc("Pediamycin") # Trade name
-
-# Use ab_* functions to get a specific property based on an ATC code
-Cipro<-as.atc("cipro") # returns `J01MA02`
-atc_official(Cipro) # returns "Ciprofloxacin"
-atc_umcg(Cipro) # returns "CIPR", the code used in the UMCG# }
[2] Becker K et al.Implications of identifying the recently defined members of the S. aureus complex, S. argenteus and S. schweitzeri: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).. 2019. Clin Microbiol Infect. 2019 Mar 11. https://doi.org/10.1016/j.cmi.2019.02.028
+
[2] Becker K et al.Implications of identifying the recently defined members of the S. aureus complex, S. argenteus and S. schweitzeri: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS). 2019. Clin Microbiol Infect. https://doi.org/10.1016/j.cmi.2019.02.028
[3] Lancefield RC A serological differentiation of human and other groups of hemolytic streptococci. 1933. J Exp Med. 57(4): 571–95. https://dx.doi.org/10.1084/jem.57.4.571
column name of the unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.
threshold
@@ -266,8 +296,9 @@
Details
-
NOTE: This function does not translate MIC values to RSI values. If more than 50% of the input resembles MIC values, it will warn about this. 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.
-
The function is.rsi.eligible returns TRUE when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and FALSE otherwise.
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.
+
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.
Read more on our website!
@@ -288,6 +319,16 @@
# this can also coerce combined MIC/RSI values:as.rsi("<= 0.002; S") # will return S
+# interpret MIC values
+as.rsi(x=as.mic(2),
+ mo=as.mo("S. pneumoniae"),
+ ab="AMX",
+ guideline="EUCAST")
+as.rsi(x=as.mic(4),
+ mo=as.mo("S. pneumoniae"),
+ ab="AMX",
+ guideline="EUCAST")
+
plot(rsi_data) # for percentagesbarplot(rsi_data) # for frequenciesfreq(rsi_data) # frequency table with informative header
@@ -295,7 +336,7 @@
# using dplyr's mutatelibrary(dplyr)
septic_patients%>%
- mutate_at(vars(peni:rifa), as.rsi)
+ mutate_at(vars(PEN:RIF), as.rsi)
# fastest way to transform all columns with already valid AB results to class `rsi`:
@@ -305,7 +346,7 @@
# default threshold of `is.rsi.eligible` is 5%.is.rsi.eligible(WHONET$`First name`) # fails, >80% is invalid
-is.rsi.eligible(WHONET$`First name`, threshold=0.9) # succeeds
+is.rsi.eligible(WHONET$`First name`, threshold=0.99) # succeeds# }
@@ -277,7 +277,11 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
translate_ab
-
a column name of the antibiotics data set to translate the antibiotic abbreviations to, using abname. This can be set with getOption("get_antibiotic_names").
+
a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property
+
+
+
language
+
language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
combine_IR
@@ -315,55 +319,55 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
?septic_patients# Count resistant isolates
-count_R(septic_patients$amox)
-count_IR(septic_patients$amox)
+count_R(septic_patients$AMX)
+count_IR(septic_patients$AMX)
# Or susceptible isolates
-count_S(septic_patients$amox)
-count_SI(septic_patients$amox)
+count_S(septic_patients$AMX)
+count_SI(septic_patients$AMX)
# Count all available isolates
-count_all(septic_patients$amox)
-n_rsi(septic_patients$amox)
+count_all(septic_patients$AMX)
+n_rsi(septic_patients$AMX)
# Since n_rsi counts available isolates, you can# calculate back to count e.g. non-susceptible isolates.# This results in the same:
-count_IR(septic_patients$amox)
-portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
+count_IR(septic_patients$AMX)
+portion_IR(septic_patients$AMX) * n_rsi(septic_patients$AMX)
library(dplyr)
septic_patients%>%group_by(hospital_id) %>%
- summarise(R=count_R(cipr),
- I=count_I(cipr),
- S=count_S(cipr),
- n1=count_all(cipr), # the actual total; sum of all three
- n2=n_rsi(cipr), # same - analogous to n_distinct
+ summarise(R=count_R(CIP),
+ I=count_I(CIP),
+ S=count_S(CIP),
+ n1=count_all(CIP), # the actual total; sum of all three
+ n2=n_rsi(CIP), # same - analogous to n_distincttotal=n()) # NOT the number of tested isolates!# Count co-resistance between amoxicillin/clav acid and gentamicin,# so we can see that combination therapy does a lot more than mono therapy.# Please mind that `portion_S` calculates percentages right away instead.
-count_S(septic_patients$amcl) # S = 1342 (71.4%)
-count_all(septic_patients$amcl) # n = 1879
+count_S(septic_patients$AMC) # S = 1342 (71.4%)
+count_all(septic_patients$AMC) # n = 1879
-count_S(septic_patients$gent) # S = 1372 (74.0%)
-count_all(septic_patients$gent) # n = 1855
+count_S(septic_patients$GEN) # S = 1372 (74.0%)
+count_all(septic_patients$GEN) # n = 1855with(septic_patients,
- count_S(amcl, gent)) # S = 1660 (92.3%)
+ count_S(AMC, GEN)) # S = 1660 (92.3%)with(septic_patients, # n = 1798
- n_rsi(amcl, gent))
+ n_rsi(AMC, GEN))
# Get portions S/I/R immediately of all rsi columnsseptic_patients%>%
- select(amox, cipr) %>%
+ select(AMX, CIP) %>%count_df(translate=FALSE)
# It also supports grouping variablesseptic_patients%>%
- select(hospital_id, amox, cipr) %>%
+ select(hospital_id, AMX, CIP) %>%group_by(hospital_id) %>%count_df(translate=FALSE)
diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html
index 512f3b8d5..007c9f6ff 100644
--- a/docs/reference/eucast_rules.html
+++ b/docs/reference/eucast_rules.html
@@ -80,7 +80,7 @@
AMR (for R)
- 0.6.1.9002
+ 0.6.1.9003
@@ -145,7 +145,7 @@
data with antibiotic columns, like e.g. amox and amcl
+
data with antibiotic columns, like e.g. AMX and AMC
col_mo
@@ -300,13 +269,9 @@
verbose
a logical to indicate whether extensive info should be returned as a data.frame with info about which rows and columns are effected. It runs all EUCAST rules, but will not be applied to an output - only an informative data.frame with changes will be returned as output.
For editing the reference file (which is available with eucast_rules_file), these values can all be used for target antibiotics: aminoglycosides, tetracyclines, polymyxins, macrolides, glycopeptides, streptogramins, cephalosporins, cephalosporins_without_cfta, carbapenems, aminopenicillins, ureidopenicillins, fluoroquinolones, all_betalactams, and all separate four letter codes like amcl. They can be separated by comma: "amcl, fluoroquinolones". The mo_property can be any column name from the microorganisms data set, or genus_species or gramstain. This file contains references to the 'Burkholderia cepacia complex'. The species in this group can be found in: LiPuma JJ, 2015 (PMID 16217180).
+
For editing the reference file (which is available with eucast_rules_file), these values can all be used for target antibiotics: aminoglycosides, tetracyclines, polymyxins, macrolides, glycopeptides, streptogramins, cephalosporins, cephalosporins_without_cfta, carbapenems, aminopenicillins, ureidopenicillins, fluoroquinolones, all_betalactams, and all separate four letter codes like AMC. They can be separated by comma: "AMC, fluoroquinolones". The mo_property can be any column name from the microorganisms data set, or genus_species or gramstain. This file contains references to the 'Burkholderia cepacia complex'. The species in this group can be found in: LiPuma JJ, 2015 (PMID 16217180).
Value
@@ -329,79 +294,81 @@
Details
-
NOTE: This function does not translate MIC values to RSI values. It only applies (1) inferred susceptibility and resistance based on results of other antibiotics and (2) intrinsic resistance based on taxonomic properties of a microorganism.
-
The file used for applying all EUCAST rules can be retrieved with eucast_rules_file(). It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by this function can be found when running this command:
+
Note: This function does not translate MIC values to RSI values. Use as.rsi for that.
+Note: When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.
+
The file used for applying all EUCAST rules can be retrieved with eucast_rules_file(). It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by eucast_rules() can be found by running this command: AMR::EUCAST_RULES_FILE_LOCATION (without brackets).
Note: When ampicillin (J01CA01) is not available but amoxicillin (J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.
+
In the source code the file containing all rules is located here.
Antibiotics
-
To define antibiotics column names, leave as it is to determine it automatically with guess_ab_col or input a text (case-insensitive) or use NULL to skip a column (e.g. tica = NULL). Non-existing columns will anyway be skipped with a warning.
-
Abbrevations of the column containing antibiotics in the form: abbreviation: generic name (ATC code)
To define antibiotics column names, leave as it is to determine it automatically with guess_ab_col or input a text (case-insensitive), or use NULL to skip a column (e.g. TIC = NULL to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
+
Available abbrevations of the column containing antibiotics in the form 'antimicrobial ID: name (ATC code)':
-
+
Get properties of an antibiotic
@@ -397,14 +397,14 @@ To conduct an analysis of antimicrobial resistance, you should only include the
# Now let's see if first isolates matter:A<-septic_patients%>%group_by(hospital_id) %>%
- summarise(count=n_rsi(gent), # gentamicin availability
- resistance=portion_IR(gent)) # gentamicin resistance
+ summarise(count=n_rsi(GEN), # gentamicin availability
+ resistance=portion_IR(GEN)) # gentamicin resistanceB<-septic_patients%>%
- filter_first_weighted_isolate() %>%# the 1st isolate filter
+ filter_first_weighted_isolate() %>%# the 1st isolate filtergroup_by(hospital_id) %>%
- summarise(count=n_rsi(gent), # gentamicin availability
- resistance=portion_IR(gent)) # gentamicin resistance
+ summarise(count=n_rsi(GEN), # gentamicin availability
+ resistance=portion_IR(GEN)) # gentamicin resistance# Have a look at A and B.# B is more reliable because every isolate is only counted once.
diff --git a/docs/reference/freq.html b/docs/reference/freq.html
index e456932a6..25a733559 100644
--- a/docs/reference/freq.html
+++ b/docs/reference/freq.html
@@ -81,7 +81,7 @@ top_freq can be used to get the top/bottom n items of a frequency table, with co
AMR (for R)
- 0.6.0
+ 0.6.1.9003
@@ -146,7 +146,7 @@ top_freq can be used to get the top/bottom n items of a frequency table, with co
a column name of the antibiotics data set to translate the antibiotic abbreviations into, using abname. Default behaviour is to translate to official names according to the WHO. Use translate_ab = FALSE to disable translation.
+
a column name of the antibiotics data set to translate the antibiotic abbreviations into, using ab_name. Default behaviour is to translate to official names according to the WHO. Use translate_ab = FALSE to disable translation.
+
+
+
language
+
the language used for translation of antibiotic names
fun
@@ -325,7 +329,7 @@
Details
-
At default, the names of antibiotics will be shown on the plots using abname. This can be set with the option get_antibiotic_names (a logical value), so change it e.g. to FALSE with options(get_antibiotic_names = FALSE).
+
At default, the names of antibiotics will be shown on the plots using ab_name. This can be set with the option get_antibiotic_names (a logical value), so change it e.g. to FALSE with options(get_antibiotic_names = FALSE).
The functions geom_rsi will take any variable from the data that has an rsi class (created with as.rsi) using fun (count_df at default, can also be portion_df) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
facet_rsi creates 2d plots (at default based on S/I/R) using facet_wrap.
@@ -347,11 +351,11 @@
library(ggplot2)
# get antimicrobial results for drugs against a UTI:
-ggplot(septic_patients%>%select(amox, nitr, fosf, trim, cipr)) +
+ggplot(septic_patients%>%select(AMX, NIT, FOS, TMP, CIP)) +
geom_rsi()
# prettify the plot using some additional functions:
-df<-septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
+df<-septic_patients[, c("AMX", "NIT", "FOS", "TMP", "CIP")]
ggplot(df) +
geom_rsi() +
scale_y_percent() +
@@ -361,17 +365,17 @@
# or better yet, simplify this using the wrapper function - a single command:septic_patients%>%
- select(amox, nitr, fosf, trim, cipr) %>%
+ select(AMX, NIT, FOS, TMP, CIP) %>%ggplot_rsi()
# get only portions and no counts:septic_patients%>%
- select(amox, nitr, fosf, trim, cipr) %>%
+ select(AMX, NIT, FOS, TMP, CIP) %>%ggplot_rsi(fun=portion_df)
# add other ggplot2 parameters as you like:septic_patients%>%
- select(amox, nitr, fosf, trim, cipr) %>%
+ select(AMX, NIT, FOS, TMP, CIP) %>%ggplot_rsi(width=0.5,
colour="black",
size=1,
@@ -386,18 +390,18 @@
# `age_group` is also a function of this package:group_by(age_group=age_groups(age)) %>%select(age_group,
- cipr) %>%
+ CIP) %>%ggplot_rsi(x="age_group")
# }# NOT RUN {# for colourblind mode, use divergent colours from the viridis package:septic_patients%>%
- select(amox, nitr, fosf, trim, cipr) %>%
+ select(AMX, NIT, FOS, TMP, CIP) %>%ggplot_rsi() + scale_fill_viridis_d()
# it also supports groups (don't forget to use the group var on `x` or `facet`):septic_patients%>%
- select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
+ select(hospital_id, AMX, NIT, FOS, TMP, CIP) %>%group_by(hospital_id) %>%ggplot_rsi(x=hospital_id,
facet=Antibiotic,
@@ -421,7 +425,7 @@
# get short MO names (like "E. coli")mutate(mo=mo_shortname(mo, Becker=TRUE)) %>%# select this short name and some antiseptic drugs
- select(mo, cfur, gent, cipr) %>%
+ select(mo, CXM, GEN, CIP) %>%# group by MOgroup_by(mo) %>%# plot the thing, putting MOs on the facet
diff --git a/docs/reference/guess_ab_col.html b/docs/reference/guess_ab_col.html
index 700425ec1..296680ca7 100644
--- a/docs/reference/guess_ab_col.html
+++ b/docs/reference/guess_ab_col.html
@@ -80,7 +80,7 @@
AMR (for R)
- 0.6.0
+ 0.6.1.9003
@@ -145,7 +145,7 @@
table with antibiotic columns, like e.g. amox and amcl
+
x
+
table with antibiotic columns, like e.g. AMX and AMC
country
@@ -298,253 +269,13 @@
info
print progress
-
-
amcl
-
column name of an antibiotic, see Antibiotics
-
-
-
amik
-
column name of an antibiotic, see Antibiotics
-
-
-
amox
-
column name of an antibiotic, see Antibiotics
-
-
-
ampi
-
column name of an antibiotic, see Antibiotics
-
-
-
azit
-
column name of an antibiotic, see Antibiotics
-
-
-
aztr
-
column name of an antibiotic, see Antibiotics
-
-
-
cefa
-
column name of an antibiotic, see Antibiotics
-
-
-
cfra
-
column name of an antibiotic, see Antibiotics
-
-
-
cfep
-
column name of an antibiotic, see Antibiotics
-
-
-
cfot
-
column name of an antibiotic, see Antibiotics
-
-
-
cfox
-
column name of an antibiotic, see Antibiotics
-
-
-
cfta
-
column name of an antibiotic, see Antibiotics
-
-
-
cftr
-
column name of an antibiotic, see Antibiotics
-
-
-
cfur
-
column name of an antibiotic, see Antibiotics
-
-
-
chlo
-
column name of an antibiotic, see Antibiotics
-
-
-
cipr
-
column name of an antibiotic, see Antibiotics
-
-
-
clar
-
column name of an antibiotic, see Antibiotics
-
-
-
clin
-
column name of an antibiotic, see Antibiotics
-
-
-
clox
-
column name of an antibiotic, see Antibiotics
-
-
-
coli
-
column name of an antibiotic, see Antibiotics
-
-
-
czol
-
column name of an antibiotic, see Antibiotics
-
-
-
dapt
-
column name of an antibiotic, see Antibiotics
-
-
-
doxy
-
column name of an antibiotic, see Antibiotics
-
-
-
erta
-
column name of an antibiotic, see Antibiotics
-
-
-
eryt
-
column name of an antibiotic, see Antibiotics
-
-
-
fosf
-
column name of an antibiotic, see Antibiotics
-
-
-
fusi
-
column name of an antibiotic, see Antibiotics
-
-
-
gent
-
column name of an antibiotic, see Antibiotics
-
-
-
imip
-
column name of an antibiotic, see Antibiotics
-
-
-
kana
-
column name of an antibiotic, see Antibiotics
-
-
-
levo
-
column name of an antibiotic, see Antibiotics
-
-
-
linc
-
column name of an antibiotic, see Antibiotics
-
-
-
line
-
column name of an antibiotic, see Antibiotics
-
-
-
mero
-
column name of an antibiotic, see Antibiotics
-
-
-
metr
-
column name of an antibiotic, see Antibiotics
-
-
-
mino
-
column name of an antibiotic, see Antibiotics
-
-
-
moxi
-
column name of an antibiotic, see Antibiotics
-
-
-
nali
-
column name of an antibiotic, see Antibiotics
-
-
-
neom
-
column name of an antibiotic, see Antibiotics
-
-
-
neti
-
column name of an antibiotic, see Antibiotics
-
-
-
nitr
-
column name of an antibiotic, see Antibiotics
-
-
-
novo
-
column name of an antibiotic, see Antibiotics
-
-
-
norf
-
column name of an antibiotic, see Antibiotics
-
-
-
oflo
-
column name of an antibiotic, see Antibiotics
-
-
-
peni
-
column name of an antibiotic, see Antibiotics
-
-
-
pipe
-
column name of an antibiotic, see Antibiotics
-
-
-
pita
-
column name of an antibiotic, see Antibiotics
-
-
-
poly
-
column name of an antibiotic, see Antibiotics
-
-
-
qida
-
column name of an antibiotic, see Antibiotics
-
-
-
rifa
-
column name of an antibiotic, see Antibiotics
-
-
-
roxi
-
column name of an antibiotic, see Antibiotics
-
-
-
siso
-
column name of an antibiotic, see Antibiotics
-
-
-
teic
-
column name of an antibiotic, see Antibiotics
-
-
-
tetr
-
column name of an antibiotic, see Antibiotics
-
-
-
tica
-
column name of an antibiotic, see Antibiotics
-
-
-
tige
-
column name of an antibiotic, see Antibiotics
-
-
-
tobr
-
column name of an antibiotic, see Antibiotics
-
-
-
trim
-
column name of an antibiotic, see Antibiotics
-
-
-
trsu
-
column name of an antibiotic, see Antibiotics
-
-
-
vanc
-
column name of an antibiotic, see Antibiotics
-
verbose
print additional info: missing antibiotic columns per parameter
...
-
parameters that are passed on to methods
+
column name of an antibiotic, see section Antibiotics
@@ -559,70 +290,72 @@
Antibiotics
-
To define antibiotics column names, leave as it is to determine it automatically with guess_ab_col or input a text (case-insensitive) or use NULL to skip a column (e.g. tica = NULL). Non-existing columns will anyway be skipped with a warning.
-
Abbrevations of the column containing antibiotics in the form: abbreviation: generic name (ATC code)
To define antibiotics column names, leave as it is to determine it automatically with guess_ab_col or input a text (case-insensitive), or use NULL to skip a column (e.g. TIC = NULL to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
+
Available abbrevations of the column containing antibiotics in the form 'antimicrobial ID: name (ATC code)':
language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
+
language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
...
@@ -323,13 +323,9 @@
mo_ref("Chlamydophila psittaci") will return "Everett et al., 1999" (without a warning)
The Gram stain - mo_gramstain() - will be determined on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002) who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram positive - all other bacteria are considered Gram negative. Species outside the kingdom of Bacteria will return a value NA.
[2] Becker K et al.Implications of identifying the recently defined members of the S. aureus complex, S. argenteus and S. schweitzeri: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).. 2019. Clin Microbiol Infect. 2019 Mar 11. https://doi.org/10.1016/j.cmi.2019.02.028
+
[2] Becker K et al.Implications of identifying the recently defined members of the S. aureus complex, S. argenteus and S. schweitzeri: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS). 2019. Clin Microbiol Infect. https://doi.org/10.1016/j.cmi.2019.02.028
[3] Lancefield RC A serological differentiation of human and other groups of hemolytic streptococci. 1933. J Exp Med. 57(4): 571–95. https://dx.doi.org/10.1084/jem.57.4.571
@@ -375,7 +371,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
mo_gramstain("E. coli") # "Gram negative"mo_type("E. coli") # "Bacteria" (equal to kingdom)mo_rank("E. coli") # "species"
-mo_url("E. coli") # get the direct url to the Catalogue of Life
+mo_url("E. coli") # get the direct url to the online database entry## scientific referencemo_ref("E. coli") # "Castellani et al., 1919"
@@ -433,7 +429,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
language="nl") # "Streptococcus groep A"
-# get a list with the complete taxonomy (kingdom to subspecies)
+# get a list with the complete taxonomy (from kingdom to subspecies)mo_taxonomy("E. coli")
# }
@@ -446,8 +442,6 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/portion.html b/docs/reference/portion.html
index 43621143a..a488db6a6 100644
--- a/docs/reference/portion.html
+++ b/docs/reference/portion.html
@@ -81,7 +81,7 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
AMR (for R)
- 0.6.1.9002
+ 0.6.1.9003
@@ -146,7 +146,7 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
@@ -286,7 +286,11 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
translate_ab
-
a column name of the antibiotics data set to translate the antibiotic abbreviations to, using abname. This can be set with getOption("get_antibiotic_names").
+
a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property
+
+
+
language
+
language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
combine_IR
@@ -307,8 +311,7 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
Remember that you should filter your table to let it contain only first isolates! Use first_isolate to determine them in your data set.
These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the count functions to count isolates. Low counts can infuence the outcome - these portion functions may camouflage this, since they only return the portion albeit being dependent on the minimum parameter.
-
portion_df takes any variable from data that has an "rsi" class (created with as.rsi) and calculates the portions R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
-
The old rsi function is still available for backwards compatibility but is deprecated.
+
portion_df takes any variable from data that has an "rsi" class (created with as.rsi) and calculates the portions R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
To calculate the probability (p) of susceptibility of one antibiotic, we use this formula:
@@ -338,63 +341,63 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
?septic_patients# Calculate resistance
-portion_R(septic_patients$amox)
-portion_IR(septic_patients$amox)
+portion_R(septic_patients$AMX)
+portion_IR(septic_patients$AMX)
# Or susceptibility
-portion_S(septic_patients$amox)
-portion_SI(septic_patients$amox)
+portion_S(septic_patients$AMX)
+portion_SI(septic_patients$AMX)
# Do the above with pipes:library(dplyr)
-septic_patients%>%portion_R(amox)
-septic_patients%>%portion_IR(amox)
-septic_patients%>%portion_S(amox)
-septic_patients%>%portion_SI(amox)
+septic_patients%>%portion_R(AMX)
+septic_patients%>%portion_IR(AMX)
+septic_patients%>%portion_S(AMX)
+septic_patients%>%portion_SI(AMX)
septic_patients%>%group_by(hospital_id) %>%
- summarise(p=portion_S(cipr),
- n=n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
+ summarise(p=portion_S(CIP),
+ n=n_rsi(CIP)) # n_rsi works like n_distinct in dplyrseptic_patients%>%group_by(hospital_id) %>%
- summarise(R=portion_R(cipr, as_percent=TRUE),
- I=portion_I(cipr, as_percent=TRUE),
- S=portion_S(cipr, as_percent=TRUE),
- n1=count_all(cipr), # the actual total; sum of all three
- n2=n_rsi(cipr), # same - analogous to n_distinct
+ summarise(R=portion_R(CIP, as_percent=TRUE),
+ I=portion_I(CIP, as_percent=TRUE),
+ S=portion_S(CIP, as_percent=TRUE),
+ n1=count_all(CIP), # the actual total; sum of all three
+ n2=n_rsi(CIP), # same - analogous to n_distincttotal=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:
-septic_patients%>%portion_S(amcl) # S = 71.4%
-septic_patients%>%count_all(amcl) # n = 1879
+septic_patients%>%portion_S(AMC) # S = 71.4%
+septic_patients%>%count_all(AMC) # n = 1879
-septic_patients%>%portion_S(gent) # S = 74.0%
-septic_patients%>%count_all(gent) # n = 1855
+septic_patients%>%portion_S(GEN) # S = 74.0%
+septic_patients%>%count_all(GEN) # n = 1855
-septic_patients%>%portion_S(amcl, gent) # S = 92.3%
-septic_patients%>%count_all(amcl, gent) # n = 1798
+septic_patients%>%portion_S(AMC, GEN) # S = 92.3%
+septic_patients%>%count_all(AMC, GEN) # n = 1798septic_patients%>%group_by(hospital_id) %>%
- summarise(cipro_p=portion_S(cipr, as_percent=TRUE),
- cipro_n=count_all(cipr),
- genta_p=portion_S(gent, as_percent=TRUE),
- genta_n=count_all(gent),
- combination_p=portion_S(cipr, gent, as_percent=TRUE),
- combination_n=count_all(cipr, gent))
+ summarise(cipro_p=portion_S(CIP, as_percent=TRUE),
+ cipro_n=count_all(CIP),
+ genta_p=portion_S(GEN, as_percent=TRUE),
+ genta_n=count_all(GEN),
+ combination_p=portion_S(CIP, GEN, as_percent=TRUE),
+ combination_n=count_all(CIP, GEN))
# Get portions S/I/R immediately of all rsi columnsseptic_patients%>%
- select(amox, cipr) %>%
+ select(AMX, CIP) %>%portion_df(translate=FALSE)
# It also supports grouping variablesseptic_patients%>%
- select(hospital_id, amox, cipr) %>%
+ select(hospital_id, AMX, CIP) %>%group_by(hospital_id) %>%portion_df(translate=FALSE)
@@ -404,8 +407,8 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
my_table%>%filter(first_isolate==TRUE,
genus=="Helicobacter") %>%
- summarise(p=portion_S(amox, metr), # amoxicillin with metronidazole
- n=count_all(amox, metr))
+ summarise(p=portion_S(AMX, MTR), # amoxicillin with metronidazole
+ n=count_all(AMX, MTR))
# }
Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use as.rsi to transform MICs or disks measurements to RSI values.
+
+
+
+
rsi_translation
+
+
Format
+
+
A data.frame with 11,559 observations and 9 variables:
For language-dependent output of AMR functions, like mo_fullname and mo_type.
+
+
+
+
get_locale()
+
+
Details
+
+
Strings will be translated to foreign languages if they are defined in a local translation file. This file comes with this package and can be found when running:
+
+
+
+
+
+
+
+
+
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index d641d6185..45016876d 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -16,7 +16,7 @@
https://msberends.gitlab.io/AMR/reference/WHONET.html
- https://msberends.gitlab.io/AMR/reference/abname.html
+ https://msberends.gitlab.io/AMR/reference/ab_property.htmlhttps://msberends.gitlab.io/AMR/reference/age.html
@@ -27,9 +27,15 @@
https://msberends.gitlab.io/AMR/reference/antibiotics.html
+
+ https://msberends.gitlab.io/AMR/reference/as.ab.html
+ https://msberends.gitlab.io/AMR/reference/as.atc.html
+
+ https://msberends.gitlab.io/AMR/reference/as.disk.html
+ https://msberends.gitlab.io/AMR/reference/as.mic.html
@@ -42,9 +48,6 @@
https://msberends.gitlab.io/AMR/reference/atc_online.html
-
- https://msberends.gitlab.io/AMR/reference/atc_property.html
- https://msberends.gitlab.io/AMR/reference/availability.html
@@ -72,9 +75,6 @@
https://msberends.gitlab.io/AMR/reference/g.test.html
-
- https://msberends.gitlab.io/AMR/reference/get_locale.html
- https://msberends.gitlab.io/AMR/reference/ggplot_rsi.html
@@ -124,7 +124,7 @@
https://msberends.gitlab.io/AMR/reference/resistance_predict.html
- https://msberends.gitlab.io/AMR/reference/rsi.html
+ https://msberends.gitlab.io/AMR/reference/rsi_translation.htmlhttps://msberends.gitlab.io/AMR/reference/septic_patients.html
@@ -132,6 +132,9 @@
https://msberends.gitlab.io/AMR/reference/skewness.html
+
+ https://msberends.gitlab.io/AMR/reference/translate.html
+ https://msberends.gitlab.io/AMR/articles/AMR.html
@@ -148,7 +151,7 @@
https://msberends.gitlab.io/AMR/articles/WHONET.html
- https://msberends.gitlab.io/AMR/articles/atc_property.html
+ https://msberends.gitlab.io/AMR/articles/ab_property.htmlhttps://msberends.gitlab.io/AMR/articles/benchmarks.html
diff --git a/index.md b/index.md
index 7677cd2a6..7ad967e69 100644
--- a/index.md
+++ b/index.md
@@ -6,7 +6,7 @@
`AMR` is a free and open-source [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any data format, including WHONET/EARS-Net data.
-After installing this package, R knows [**~65,000 microorganisms**](./reference/microorganisms.html) and [**~500 antibiotics**](./reference/antibiotics.html) by name and code, and knows all about valid RSI and MIC values.
+After installing this package, R knows [**~65,000 microorganisms**](./reference/microorganisms.html) and [**~450 antibiotics**](./reference/antibiotics.html) by name and code, and knows all about valid RSI and MIC values.
**Used to SPSS?** Read our [tutorial on how to import data from SPSS, SAS or Stata](./articles/SPSS.html) and learn in which ways R outclasses any of these statistical packages.
@@ -114,7 +114,7 @@ Read more about the data from the Catalogue of Life [in our manual](./reference/
WHO Collaborating Centre for Drug Statistics Methodology
-This package contains **all ~500 antimicrobial drugs** and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) 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](http://ec.europa.eu/health/documents/community-register/html/atc.htm).
+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](http://ec.europa.eu/health/documents/community-register/html/atc.htm).
Read more about the data from WHOCC [in our manual](./reference/WHOCC.html).
@@ -132,10 +132,10 @@ The `AMR` package basically does four important things:
1. It **cleanses existing data** by providing new *classes* for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:
- * Use `as.mo()` to 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" or "esccol" and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
+ * Use `as.mo()` to get a microbial ID. 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" or "esccol" and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
* Use `as.rsi()` to 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".
* Use `as.mic()` to 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".
- * Use `as.atc()` to 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.
+ * Use `as.ab()` to get a antibiotic ID, which are abbreviations used by EARS-Net whenever available. Use `as.atc()` to 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 ID of Nitrofurantoine.
2. It **enhances existing data** and **adds new data** from data sets included in this package.
@@ -144,7 +144,7 @@ The `AMR` package basically does four important things:
* You can also identify first *weighted* isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
* Use `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.
* The [data set `microorganisms`](./reference/microorganisms.html) contains the complete taxonomic tree of ~65,000 microorganisms. Furthermore, some colloquial names and all Gram stains 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_genus()`, `mo_family()`, `mo_gramstain()` or even `mo_phylum()`. As they use `as.mo()` internally, they also use the same intelligent rules for determination. For example, `mo_genus("MRSA")` and `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.
- * The [data set `antibiotics`](./reference/antibiotics.html) contains almost 500 antimicrobial drugs with their ATC code, EARS-Net code, common LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains hundreds of trade names. Use functions like `atc_name()` and `atc_tradenames()` to look up values. The `atc_*` functions use `as.atc()` internally so they support the same intelligent rules to guess the most probable result. For example, `atc_name("Fluclox")`, `atc_name("Floxapen")` and `atc_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
+ * The [data set `antibiotics`](./reference/antibiotics.html) contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. Use functions like `ab_name()`, `ab_group()` and `ab_tradenames()` to look up values. The `ab_*` functions use `as.ab()` internally so they support the same intelligent rules to guess the most probable result. For example, `ab_name("Fluclox")`, `ab_name("Floxapen")` and `ab_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
3. It **analyses the data** with convenient functions that use well-known methods.
diff --git a/inst/eucast/eucast_rules.tsv b/inst/eucast/eucast_rules.tsv
index f68ea5812..2537e5986 100644
--- a/inst/eucast/eucast_rules.tsv
+++ b/inst/eucast/eucast_rules.tsv
@@ -1,182 +1,182 @@
if_mo_property like_is_one_of this_value and_these_antibiotics have_these_values then_change_these_antibiotics to_value reference.rule reference.rule_group
-order is Enterobacteriales ampi S amox S Enterobacteriales (Order) Breakpoints
-order is Enterobacteriales ampi I amox I Enterobacteriales (Order) Breakpoints
-order is Enterobacteriales ampi R amox R Enterobacteriales (Order) Breakpoints
-genus is Staphylococcus peni, cfox S ampi, amox, pipe, tica S Staphylococcus Breakpoints
-genus is Staphylococcus peni, cfox R, S oxac, clox S Staphylococcus Breakpoints
-genus is Staphylococcus cfox R all_betalactams R Staphylococcus Breakpoints
-genus_species is Staphylococcus saprophyticus ampi S amox, amcl, pipe, pita S Staphylococcus Breakpoints
-genus is Staphylococcus cfox S carbapenems, cephalosporins_without_cfta S Staphylococcus Breakpoints
-genus is Staphylococcus cfox I carbapenems, cephalosporins_without_cfta I Staphylococcus Breakpoints
-genus is Staphylococcus cfox R carbapenems, cephalosporins_without_cfta R Staphylococcus Breakpoints
-genus is Staphylococcus norf S cipr, levo, moxi, oflo S Staphylococcus Breakpoints
-genus is Staphylococcus eryt S azit, clar, roxi S Staphylococcus Breakpoints
-genus is Staphylococcus eryt I azit, clar, roxi I Staphylococcus Breakpoints
-genus is Staphylococcus eryt R azit, clar, roxi R Staphylococcus Breakpoints
-genus is Staphylococcus tetr S doxy, mino S Staphylococcus Breakpoints
-genus_species is Enterococcus faecium ampi R all_betalactams R Enterococcus Breakpoints
-genus is Enterococcus ampi S amox, amcl, pipe, pita S Enterococcus Breakpoints
-genus is Enterococcus ampi I amox, amcl, pipe, pita I Enterococcus Breakpoints
-genus is Enterococcus ampi R amox, amcl, pipe, pita R Enterococcus Breakpoints
-genus is Enterococcus norf S cipr, levo S Enterococcus Breakpoints
-genus is Enterococcus norf I cipr, levo I Enterococcus Breakpoints
-genus is Enterococcus norf R cipr, levo R Enterococcus Breakpoints
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ peni S aminopenicillins, ureidopenicillins, cephalosporins, carbapenems, clox, amcl S Streptococcus groups A, B, C, G Breakpoints
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ peni I aminopenicillins, ureidopenicillins, cephalosporins, carbapenems, clox, amcl I Streptococcus groups A, B, C, G Breakpoints
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ peni R aminopenicillins, ureidopenicillins, cephalosporins, carbapenems, clox, amcl R Streptococcus groups A, B, C, G Breakpoints
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ norf S levo, moxi S Streptococcus groups A, B, C, G Breakpoints
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ eryt S azit, clar, roxi S Streptococcus groups A, B, C, G Breakpoints
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ eryt I azit, clar, roxi I Streptococcus groups A, B, C, G Breakpoints
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ eryt R azit, clar, roxi R Streptococcus groups A, B, C, G Breakpoints
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ tetr S doxy, mino S Streptococcus groups A, B, C, G Breakpoints
-genus_species is Streptococcus pneumoniae peni S ampi, amox, amcl, pipe, pita S Streptococcus pneumoniae Breakpoints
-genus_species is Streptococcus pneumoniae ampi S amox, amcl, pipe, pita S Streptococcus pneumoniae Breakpoints
-genus_species is Streptococcus pneumoniae ampi I amox, amcl, pipe, pita I Streptococcus pneumoniae Breakpoints
-genus_species is Streptococcus pneumoniae ampi R amox, amcl, pipe, pita R Streptococcus pneumoniae Breakpoints
-genus_species is Streptococcus pneumoniae norf S levo, moxi S Streptococcus pneumoniae Breakpoints
-genus_species is Streptococcus pneumoniae eryt S azit, clar, roxi S Streptococcus pneumoniae Breakpoints
-genus_species is Streptococcus pneumoniae eryt I azit, clar, roxi I Streptococcus pneumoniae Breakpoints
-genus_species is Streptococcus pneumoniae eryt R azit, clar, roxi R Streptococcus pneumoniae Breakpoints
-genus_species is Streptococcus pneumoniae tetr S doxy, mino S Streptococcus pneumoniae Breakpoints
-genus_species like ^Streptococcus (australis|bovis|constellatus|cristatus|gallolyticus|gordonii|infantarius|infantis|mitis|mutans|oligofermentans|oralis|peroris|pseudopneumoniae|salivarius|sinensis|sobrinus|thermophilus|vestibularis|anginosus|equinus|intermedius|parasanguinis|sanguinis)$ peni S ampi, amox, amcl, pipe, pita S Viridans group streptococci Breakpoints
-genus_species like ^Streptococcus (australis|bovis|constellatus|cristatus|gallolyticus|gordonii|infantarius|infantis|mitis|mutans|oligofermentans|oralis|peroris|pseudopneumoniae|salivarius|sinensis|sobrinus|thermophilus|vestibularis|anginosus|equinus|intermedius|parasanguinis|sanguinis)$ ampi S amox, amcl, pipe, pita S Viridans group streptococci Breakpoints
-genus_species like ^Streptococcus (australis|bovis|constellatus|cristatus|gallolyticus|gordonii|infantarius|infantis|mitis|mutans|oligofermentans|oralis|peroris|pseudopneumoniae|salivarius|sinensis|sobrinus|thermophilus|vestibularis|anginosus|equinus|intermedius|parasanguinis|sanguinis)$ ampi I amox, amcl, pipe, pita I Viridans group streptococci Breakpoints
-genus_species like ^Streptococcus (australis|bovis|constellatus|cristatus|gallolyticus|gordonii|infantarius|infantis|mitis|mutans|oligofermentans|oralis|peroris|pseudopneumoniae|salivarius|sinensis|sobrinus|thermophilus|vestibularis|anginosus|equinus|intermedius|parasanguinis|sanguinis)$ ampi R amox, amcl, pipe, pita R Viridans group streptococci Breakpoints
-genus_species is Haemophilus influenzae ampi S amox, pipe S Haemophilus influenzae Breakpoints
-genus_species is ^Haemophilus influenzae ampi I amox, pipe I Haemophilus influenzae Breakpoints
-genus_species is Haemophilus influenzae ampi R amox, pipe R Haemophilus influenzae Breakpoints
-genus_species is Haemophilus influenzae peni S ampi, amox, amcl, pipe, pita S Haemophilus influenzae Breakpoints
-genus_species is Haemophilus influenzae amcl S pita S Haemophilus influenzae Breakpoints
-genus_species is Haemophilus influenzae amcl I pita I Haemophilus influenzae Breakpoints
-genus_species is Haemophilus influenzae amcl R pita R Haemophilus influenzae Breakpoints
-genus_species is Haemophilus influenzae nali S cipr, levo, moxi, oflo S Haemophilus influenzae Breakpoints
-genus_species is Haemophilus influenzae tetr S doxy, mino S Haemophilus influenzae Breakpoints
-genus_species is Moraxella catarrhalis amcl S pita S Moraxella catarrhalis Breakpoints
-genus_species is Moraxella catarrhalis amcl I pita I Moraxella catarrhalis Breakpoints
-genus_species is Moraxella catarrhalis amcl R pita R Moraxella catarrhalis Breakpoints
-genus_species is Moraxella catarrhalis nali S cipr, levo, moxi, oflo S Moraxella catarrhalis Breakpoints
-genus_species is Moraxella catarrhalis eryt S azit, clar, roxi S Moraxella catarrhalis Breakpoints
-genus_species is Moraxella catarrhalis eryt I azit, clar, roxi I Moraxella catarrhalis Breakpoints
-genus_species is Moraxella catarrhalis eryt R azit, clar, roxi R Moraxella catarrhalis Breakpoints
-genus_species is Moraxella catarrhalis tetr S doxy, mino S Moraxella catarrhalis Breakpoints
-genus one_of Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus , Propionibacterium peni S ampi, amox, pipe, pita, tica S Anaerobic Gram positives Breakpoints
-genus one_of Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus , Propionibacterium peni I ampi, amox, pipe, pita, tica I Anaerobic Gram positives Breakpoints
-genus one_of Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus , Propionibacterium peni R ampi, amox, pipe, pita, tica R Anaerobic Gram positives Breakpoints
-genus one_of Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella peni S ampi, amox, pipe, pita, tica S Anaerobic Gram negatives Breakpoints
-genus one_of Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella peni I ampi, amox, pipe, pita, tica I Anaerobic Gram negatives Breakpoints
-genus one_of Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella peni R ampi, amox, pipe, pita, tica R Anaerobic Gram negatives Breakpoints
-genus_species is Pasteurella multocida peni S ampi, amox S Pasteurella multocida Breakpoints
-genus_species is Pasteurella multocida peni I ampi, amox I Pasteurella multocida Breakpoints
-genus_species is Pasteurella multocida peni R ampi, amox R Pasteurella multocida Breakpoints
-genus_species is Campylobacter coli eryt S azit, clar S Campylobacter coli Breakpoints
-genus_species is Campylobacter coli eryt I azit, clar I Campylobacter coli Breakpoints
-genus_species is Campylobacter coli eryt R azit, clar R Campylobacter coli Breakpoints
-genus_species is Campylobacter coli tetr S doxy S Campylobacter coli Breakpoints
-genus_species is Campylobacter coli tetr I doxy I Campylobacter coli Breakpoints
-genus_species is Campylobacter coli tetr R doxy R Campylobacter coli Breakpoints
-genus_species is Campylobacter jejuni eryt S azit, clar S Campylobacter jejuni Breakpoints
-genus_species is Campylobacter jejuni eryt I azit, clar I Campylobacter jejuni Breakpoints
-genus_species is Campylobacter jejuni eryt R azit, clar R Campylobacter jejuni Breakpoints
-genus_species is Campylobacter jejuni tetr S doxy S Campylobacter jejuni Breakpoints
-genus_species is Campylobacter jejuni tetr I doxy I Campylobacter jejuni Breakpoints
-genus_species is Campylobacter jejuni tetr R doxy R Campylobacter jejuni Breakpoints
-genus_species is Aerococcus sanguinicola norf S fluoroquinolones S Aerococcus sanguinicola Breakpoints
-genus_species is Aerococcus sanguinicola norf I fluoroquinolones I Aerococcus sanguinicola Breakpoints
-genus_species is Aerococcus sanguinicola norf R fluoroquinolones R Aerococcus sanguinicola Breakpoints
-genus_species is Aerococcus sanguinicola cipr S levo S Aerococcus sanguinicola Breakpoints
-genus_species is Aerococcus sanguinicola cipr I levo I Aerococcus sanguinicola Breakpoints
-genus_species is Aerococcus sanguinicola cipr R levo R Aerococcus urinae Breakpoints
-genus_species is Aerococcus urinae norf S fluoroquinolones S Aerococcus urinae Breakpoints
-genus_species is Aerococcus urinae norf I fluoroquinolones I Aerococcus urinae Breakpoints
-genus_species is Aerococcus urinae norf R fluoroquinolones R Aerococcus urinae Breakpoints
-genus_species is Aerococcus urinae cipr S levo S Aerococcus urinae Breakpoints
-genus_species is Aerococcus urinae cipr I levo I Aerococcus urinae Breakpoints
-genus_species is Aerococcus urinae cipr R levo R Aerococcus urinae Breakpoints
-genus_species is Kingella kingae peni S ampi, amox S Kingella kingae Breakpoints
-genus_species is Kingella kingae peni I ampi, amox I Kingella kingae Breakpoints
-genus_species is Kingella kingae peni R ampi, amox R Kingella kingae Breakpoints
-genus_species is Kingella kingae eryt S azit, clar S Kingella kingae Breakpoints
-genus_species is Kingella kingae eryt I azit, clar I Kingella kingae Breakpoints
-genus_species is Kingella kingae eryt R azit, clar R Kingella kingae Breakpoints
-genus_species is Kingella kingae tetr S doxy S Kingella kingae Breakpoints
-family is Enterobacteriaceae peni, glycopeptides, fusi, macrolides, linc, streptogramins, rifa, dapt, line R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-fullname like ^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium) aminopenicillins, tica R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-fullname like ^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae) aminopenicillins, amcl, czol, cfox R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Enterobacter cloacae aminopenicillins, amcl, czol, cfox R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Enterobacter aerogenes aminopenicillins, amcl, czol, cfox R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Escherichia hermanni aminopenicillins, tica R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Hafnia alvei aminopenicillins, amcl, czol, cfox R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus is Klebsiella aminopenicillins, tica R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Morganella morganii aminopenicillins, amcl, czol, tetracyclines, polymyxins, nitr R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Proteus mirabilis tetracyclines, tige, polymyxins, nitr R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Proteus penneri aminopenicillins, czol, cfur, tetracyclines, tige, polymyxins, nitr R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Proteus vulgaris aminopenicillins, czol, cfur, tetracyclines, tige, polymyxins, nitr R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Providencia rettgeri aminopenicillins, amcl, czol, cfur, tetracyclines, tige, polymyxins, nitr R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Providencia stuartii aminopenicillins, amcl, czol, cfur, tetracyclines, tige, polymyxins, nitr R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus is Raoultella aminopenicillins, tica R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Serratia marcescens aminopenicillins, amcl, czol, cfox, cfur, tetracyclines[tetracyclines != na.rm(mino)], polymyxins, nitr R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Yersinia enterocolitica aminopenicillins, amcl, tica, czol, cfox R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus_species is Yersinia pseudotuberculosis poly, coli R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
-genus one_of Achromobacter, Acinetobacter, Alcaligenes, Bordatella, Burkholderia, Elizabethkingia, Flavobacterium, Ochrobactrum, Pseudomonas, Stenotrophomonas peni, cfox, cfur, glycopeptides, fusi, macrolides, linc, streptogramins, rifa, dapt, line R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Acinetobacter baumannii aminopenicillins, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, doxy, tetr R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Acinetobacter pittii aminopenicillins, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, doxy, tetr R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Acinetobacter nosocomialis aminopenicillins, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, doxy, tetr R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Acinetobacter calcoaceticus aminopenicillins, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, doxy, tetr R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Achromobacter xylosoxidans aminopenicillins, czol, cfot, cftr, erta R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-fullname like ^Burkholderia (cepacia|multivorans|cenocepacia|stabilis|vietnamiensis|dolosa|ambifaria|anthina|pyrrocinia|ubonensis) aminopenicillins, amcl, tica, pipe, pita, czol, cfot, cftr, aztr, erta, cipr, chlo, aminoglycosides, trim, fosf, polymyxins R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Elizabethkingia meningoseptica aminopenicillins, amcl, tica, czol, cfot, cftr, cfta, cfep, aztr, erta, imip, mero, polymyxins R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Ochrobactrum anthropi aminopenicillins, amcl, tica, pipe, pita, czol, cfot, cftr, cfta, cfep, aztr, erta R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Pseudomonas aeruginosa aminopenicillins, amcl, czol, cfot, cftr, erta, chlo, kana, neom, trim, trsu, tetracyclines, tige R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus_species is Stenotrophomonas maltophilia aminopenicillins, amcl, tica, pipe, pita, czol, cfot, cftr, cfta, aztr, erta, imip, mero, aminoglycosides, trim, fosf, tetr R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
-genus one_of Haemophilus, Moraxella, Neisseria, Campylobacter glycopeptides, linc, dapt, line R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
-genus_species is Haemophilus influenzae fusi, streptogramins R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
-genus_species is Moraxella catarrhalis trim R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
-genus is Neisseria trim R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
-genus_species is Campylobacter fetus fusi, streptogramins, trim, nali R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
-genus_species is Campylobacter jejuni fusi, streptogramins, trim R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
-genus_species is Campylobacter coli fusi, streptogramins, trim R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
-gramstain is Gram positive aztr, polymyxins, nali R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus saprophyticus fusi, cfta, fosf, novo R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus cohnii cfta, novo R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus xylosus cfta, novo R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus capitis cfta, fosf R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus aureus cfta R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus epidermidis cfta R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus coagulase-negative cfta R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus hominis cfta R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus haemolyticus cfta R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus intermedius cfta R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Staphylococcus pseudintermedius cfta R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus is Streptococcus fusi, cfta, aminoglycosides R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Enterococcus faecalis fusi, cfta, cephalosporins_without_cfta, aminoglycosides, macrolides, clin, qida, trim, trsu R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Enterococcus gallinarum fusi, cfta, cephalosporins_without_cfta, aminoglycosides, macrolides, clin, qida, vanc, trim, trsu R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Enterococcus casseliflavus fusi, cfta, cephalosporins_without_cfta, aminoglycosides, macrolides, clin, qida, vanc, trim, trsu R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Enterococcus faecium fusi, cfta, cephalosporins_without_cfta, aminoglycosides, macrolides, trim, trsu R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus is Corynebacterium fosf R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+order is Enterobacteriales AMP S AMX S Enterobacteriales (Order) Breakpoints
+order is Enterobacteriales AMP I AMX I Enterobacteriales (Order) Breakpoints
+order is Enterobacteriales AMP R AMX R Enterobacteriales (Order) Breakpoints
+genus is Staphylococcus PEN, FOX S AMP, AMX, PIP, TIC S Staphylococcus Breakpoints
+genus is Staphylococcus PEN, FOX R, S OXA, FLC S Staphylococcus Breakpoints
+genus is Staphylococcus FOX R all_betalactams R Staphylococcus Breakpoints
+genus_species is Staphylococcus saprophyticus AMP S AMX, AMC, PIP, TZP S Staphylococcus Breakpoints
+genus is Staphylococcus FOX S carbapenems, cephalosporins_without_CAZ S Staphylococcus Breakpoints
+genus is Staphylococcus FOX I carbapenems, cephalosporins_without_CAZ I Staphylococcus Breakpoints
+genus is Staphylococcus FOX R carbapenems, cephalosporins_without_CAZ R Staphylococcus Breakpoints
+genus is Staphylococcus NOR S CIP, LVX, MFX, OFX S Staphylococcus Breakpoints
+genus is Staphylococcus ERY S AZM, CLR, RXT S Staphylococcus Breakpoints
+genus is Staphylococcus ERY I AZM, CLR, RXT I Staphylococcus Breakpoints
+genus is Staphylococcus ERY R AZM, CLR, RXT R Staphylococcus Breakpoints
+genus is Staphylococcus TCY S DOX, MNO S Staphylococcus Breakpoints
+genus_species is Enterococcus faecium AMP R all_betalactams R Enterococcus Breakpoints
+genus is Enterococcus AMP S AMX, AMC, PIP, TZP S Enterococcus Breakpoints
+genus is Enterococcus AMP I AMX, AMC, PIP, TZP I Enterococcus Breakpoints
+genus is Enterococcus AMP R AMX, AMC, PIP, TZP R Enterococcus Breakpoints
+genus is Enterococcus NOR S CIP, LVX S Enterococcus Breakpoints
+genus is Enterococcus NOR I CIP, LVX I Enterococcus Breakpoints
+genus is Enterococcus NOR R CIP, LVX R Enterococcus Breakpoints
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN S aminopenicillins, ureidopenicillins, cephalosporins_without_CAZ, carbapenems, FLC, AMC S Streptococcus groups A, B, C, G Breakpoints
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN I aminopenicillins, ureidopenicillins, cephalosporins_without_CAZ, carbapenems, FLC, AMC I Streptococcus groups A, B, C, G Breakpoints
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN R aminopenicillins, ureidopenicillins, cephalosporins_without_CAZ, carbapenems, FLC, AMC R Streptococcus groups A, B, C, G Breakpoints
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ NOR S LVX, MFX S Streptococcus groups A, B, C, G Breakpoints
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ ERY S AZM, CLR, RXT S Streptococcus groups A, B, C, G Breakpoints
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ ERY I AZM, CLR, RXT I Streptococcus groups A, B, C, G Breakpoints
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ ERY R AZM, CLR, RXT R Streptococcus groups A, B, C, G Breakpoints
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ TCY S DOX, MNO S Streptococcus groups A, B, C, G Breakpoints
+genus_species is Streptococcus pneumoniae PEN S AMP, AMX, AMC, PIP, TZP S Streptococcus pneumoniae Breakpoints
+genus_species is Streptococcus pneumoniae AMP S AMX, AMC, PIP, TZP S Streptococcus pneumoniae Breakpoints
+genus_species is Streptococcus pneumoniae AMP I AMX, AMC, PIP, TZP I Streptococcus pneumoniae Breakpoints
+genus_species is Streptococcus pneumoniae AMP R AMX, AMC, PIP, TZP R Streptococcus pneumoniae Breakpoints
+genus_species is Streptococcus pneumoniae NOR S LVX, MFX S Streptococcus pneumoniae Breakpoints
+genus_species is Streptococcus pneumoniae ERY S AZM, CLR, RXT S Streptococcus pneumoniae Breakpoints
+genus_species is Streptococcus pneumoniae ERY I AZM, CLR, RXT I Streptococcus pneumoniae Breakpoints
+genus_species is Streptococcus pneumoniae ERY R AZM, CLR, RXT R Streptococcus pneumoniae Breakpoints
+genus_species is Streptococcus pneumoniae TCY S DOX, MNO S Streptococcus pneumoniae Breakpoints
+genus_species like ^Streptococcus (australis|bovis|constellatus|cristatus|gallolyticus|gordonii|infantarius|infantis|mitis|mutans|oligofermentans|oralis|peroris|pseudopneumoniae|salivarius|sinensis|sobrinus|thermophilus|vestibularis|anginosus|equinus|intermedius|parasanguinis|sanguinis)$ PEN S AMP, AMX, AMC, PIP, TZP S Viridans group streptococci Breakpoints
+genus_species like ^Streptococcus (australis|bovis|constellatus|cristatus|gallolyticus|gordonii|infantarius|infantis|mitis|mutans|oligofermentans|oralis|peroris|pseudopneumoniae|salivarius|sinensis|sobrinus|thermophilus|vestibularis|anginosus|equinus|intermedius|parasanguinis|sanguinis)$ AMP S AMX, AMC, PIP, TZP S Viridans group streptococci Breakpoints
+genus_species like ^Streptococcus (australis|bovis|constellatus|cristatus|gallolyticus|gordonii|infantarius|infantis|mitis|mutans|oligofermentans|oralis|peroris|pseudopneumoniae|salivarius|sinensis|sobrinus|thermophilus|vestibularis|anginosus|equinus|intermedius|parasanguinis|sanguinis)$ AMP I AMX, AMC, PIP, TZP I Viridans group streptococci Breakpoints
+genus_species like ^Streptococcus (australis|bovis|constellatus|cristatus|gallolyticus|gordonii|infantarius|infantis|mitis|mutans|oligofermentans|oralis|peroris|pseudopneumoniae|salivarius|sinensis|sobrinus|thermophilus|vestibularis|anginosus|equinus|intermedius|parasanguinis|sanguinis)$ AMP R AMX, AMC, PIP, TZP R Viridans group streptococci Breakpoints
+genus_species is Haemophilus influenzae AMP S AMX, PIP S Haemophilus influenzae Breakpoints
+genus_species is ^Haemophilus influenzae AMP I AMX, PIP I Haemophilus influenzae Breakpoints
+genus_species is Haemophilus influenzae AMP R AMX, PIP R Haemophilus influenzae Breakpoints
+genus_species is Haemophilus influenzae PEN S AMP, AMX, AMC, PIP, TZP S Haemophilus influenzae Breakpoints
+genus_species is Haemophilus influenzae AMC S TZP S Haemophilus influenzae Breakpoints
+genus_species is Haemophilus influenzae AMC I TZP I Haemophilus influenzae Breakpoints
+genus_species is Haemophilus influenzae AMC R TZP R Haemophilus influenzae Breakpoints
+genus_species is Haemophilus influenzae NAL S CIP, LVX, MFX, OFX S Haemophilus influenzae Breakpoints
+genus_species is Haemophilus influenzae TCY S DOX, MNO S Haemophilus influenzae Breakpoints
+genus_species is Moraxella catarrhalis AMC S TZP S Moraxella catarrhalis Breakpoints
+genus_species is Moraxella catarrhalis AMC I TZP I Moraxella catarrhalis Breakpoints
+genus_species is Moraxella catarrhalis AMC R TZP R Moraxella catarrhalis Breakpoints
+genus_species is Moraxella catarrhalis NAL S CIP, LVX, MFX, OFX S Moraxella catarrhalis Breakpoints
+genus_species is Moraxella catarrhalis ERY S AZM, CLR, RXT S Moraxella catarrhalis Breakpoints
+genus_species is Moraxella catarrhalis ERY I AZM, CLR, RXT I Moraxella catarrhalis Breakpoints
+genus_species is Moraxella catarrhalis ERY R AZM, CLR, RXT R Moraxella catarrhalis Breakpoints
+genus_species is Moraxella catarrhalis TCY S DOX, MNO S Moraxella catarrhalis Breakpoints
+genus one_of Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus, Propionibacterium PEN S AMP, AMX, PIP, TZP, TIC S Anaerobic Gram positives Breakpoints
+genus one_of Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus, Propionibacterium PEN I AMP, AMX, PIP, TZP, TIC I Anaerobic Gram positives Breakpoints
+genus one_of Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus, Propionibacterium PEN R AMP, AMX, PIP, TZP, TIC R Anaerobic Gram positives Breakpoints
+genus one_of Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella PEN S AMP, AMX, PIP, TZP, TIC S Anaerobic Gram negatives Breakpoints
+genus one_of Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella PEN I AMP, AMX, PIP, TZP, TIC I Anaerobic Gram negatives Breakpoints
+genus one_of Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella PEN R AMP, AMX, PIP, TZP, TIC R Anaerobic Gram negatives Breakpoints
+genus_species is Pasteurella multocida PEN S AMP, AMX S Pasteurella multocida Breakpoints
+genus_species is Pasteurella multocida PEN I AMP, AMX I Pasteurella multocida Breakpoints
+genus_species is Pasteurella multocida PEN R AMP, AMX R Pasteurella multocida Breakpoints
+genus_species is Campylobacter coli ERY S AZM, CLR S Campylobacter coli Breakpoints
+genus_species is Campylobacter coli ERY I AZM, CLR I Campylobacter coli Breakpoints
+genus_species is Campylobacter coli ERY R AZM, CLR R Campylobacter coli Breakpoints
+genus_species is Campylobacter coli TCY S DOX S Campylobacter coli Breakpoints
+genus_species is Campylobacter coli TCY I DOX I Campylobacter coli Breakpoints
+genus_species is Campylobacter coli TCY R DOX R Campylobacter coli Breakpoints
+genus_species is Campylobacter jejuni ERY S AZM, CLR S Campylobacter jejuni Breakpoints
+genus_species is Campylobacter jejuni ERY I AZM, CLR I Campylobacter jejuni Breakpoints
+genus_species is Campylobacter jejuni ERY R AZM, CLR R Campylobacter jejuni Breakpoints
+genus_species is Campylobacter jejuni TCY S DOX S Campylobacter jejuni Breakpoints
+genus_species is Campylobacter jejuni TCY I DOX I Campylobacter jejuni Breakpoints
+genus_species is Campylobacter jejuni TCY R DOX R Campylobacter jejuni Breakpoints
+genus_species is Aerococcus sanguinicola NOR S fluoroquinolones S Aerococcus sanguinicola Breakpoints
+genus_species is Aerococcus sanguinicola NOR I fluoroquinolones I Aerococcus sanguinicola Breakpoints
+genus_species is Aerococcus sanguinicola NOR R fluoroquinolones R Aerococcus sanguinicola Breakpoints
+genus_species is Aerococcus sanguinicola CIP S LVX S Aerococcus sanguinicola Breakpoints
+genus_species is Aerococcus sanguinicola CIP I LVX I Aerococcus sanguinicola Breakpoints
+genus_species is Aerococcus sanguinicola CIP R LVX R Aerococcus urinae Breakpoints
+genus_species is Aerococcus urinae NOR S fluoroquinolones S Aerococcus urinae Breakpoints
+genus_species is Aerococcus urinae NOR I fluoroquinolones I Aerococcus urinae Breakpoints
+genus_species is Aerococcus urinae NOR R fluoroquinolones R Aerococcus urinae Breakpoints
+genus_species is Aerococcus urinae CIP S LVX S Aerococcus urinae Breakpoints
+genus_species is Aerococcus urinae CIP I LVX I Aerococcus urinae Breakpoints
+genus_species is Aerococcus urinae CIP R LVX R Aerococcus urinae Breakpoints
+genus_species is Kingella kingae PEN S AMP, AMX S Kingella kingae Breakpoints
+genus_species is Kingella kingae PEN I AMP, AMX I Kingella kingae Breakpoints
+genus_species is Kingella kingae PEN R AMP, AMX R Kingella kingae Breakpoints
+genus_species is Kingella kingae ERY S AZM, CLR S Kingella kingae Breakpoints
+genus_species is Kingella kingae ERY I AZM, CLR I Kingella kingae Breakpoints
+genus_species is Kingella kingae ERY R AZM, CLR R Kingella kingae Breakpoints
+genus_species is Kingella kingae TCY S DOX S Kingella kingae Breakpoints
+family is Enterobacteriaceae PEN, glycopeptides, FUS, macrolides, LIN, streptogramins, RIF, DAP, LNZ R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+fullname like ^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium) aminopenicillins, TIC R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+fullname like ^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae) aminopenicillins, AMC, CZO, FOX R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Enterobacter cloacae aminopenicillins, AMC, CZO, FOX R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Enterobacter aerogenes aminopenicillins, AMC, CZO, FOX R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Escherichia hermanni aminopenicillins, TIC R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Hafnia alvei aminopenicillins, AMC, CZO, FOX R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus is Klebsiella aminopenicillins, TIC R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Morganella morganii aminopenicillins, AMC, CZO, tetracyclines, polymyxins, NIT R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Proteus mirabilis tetracyclines, TGC, polymyxins, NIT R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Proteus penneri aminopenicillins, CZO, CXM, tetracyclines, TGC, polymyxins, NIT R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Proteus vulgaris aminopenicillins, CZO, CXM, tetracyclines, TGC, polymyxins, NIT R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Providencia rettgeri aminopenicillins, AMC, CZO, CXM, tetracyclines, TGC, polymyxins, NIT R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Providencia stuartii aminopenicillins, AMC, CZO, CXM, tetracyclines, TGC, polymyxins, NIT R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus is Raoultella aminopenicillins, TIC R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Serratia marcescens aminopenicillins, AMC, CZO, FOX, CXM, DOX, TCY, polymyxins, NIT R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Yersinia enterocolitica aminopenicillins, AMC, TIC, CZO, FOX R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus_species is Yersinia pseudotuberculosis poly, COL R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
+genus one_of Achromobacter, Acinetobacter, Alcaligenes, Bordatella, Burkholderia, Elizabethkingia, Flavobacterium, Ochrobactrum, Pseudomonas, Stenotrophomonas PEN, FOX, CXM, glycopeptides, FUS, macrolides, LIN, streptogramins, RIF, DAP, LNZ R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Acinetobacter baumannii aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Acinetobacter pittii aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Acinetobacter nosocomialis aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Acinetobacter calcoaceticus aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Achromobacter xylosoxidans aminopenicillins, CZO, CTX, CRO, ETP R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+fullname like ^Burkholderia (cepacia|multivorans|cenocepacia|stabilis|vietnamiensis|dolosa|ambifaria|anthina|pyrrocinia|ubonensis) aminopenicillins, AMC, TIC, PIP, TZP, CZO, CTX, CRO, ATM, ETP, CIP, CHL, aminoglycosides, TMP, FOS, polymyxins R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Elizabethkingia meningoseptica aminopenicillins, AMC, TIC, CZO, CTX, CRO, CAZ, FEP, ATM, ETP, IPM, MEM, polymyxins R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Ochrobactrum anthropi aminopenicillins, AMC, TIC, PIP, TZP, CZO, CTX, CRO, CAZ, FEP, ATM, ETP R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Pseudomonas aeruginosa aminopenicillins, AMC, CZO, CTX, CRO, ETP, CHL, KAN, NEO, TMP, SXT, tetracyclines, TGC R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus_species is Stenotrophomonas maltophilia aminopenicillins, AMC, TIC, PIP, TZP, CZO, CTX, CRO, CAZ, ATM, ETP, IPM, MEM, aminoglycosides, TMP, FOS, TCY R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
+genus one_of Haemophilus, Moraxella, Neisseria, Campylobacter glycopeptides, LIN, DAP, LNZ R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
+genus_species is Haemophilus influenzae FUS, streptogramins R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
+genus_species is Moraxella catarrhalis TMP R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
+genus is Neisseria TMP R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
+genus_species is Campylobacter fetus FUS, streptogramins, TMP, NAL R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
+genus_species is Campylobacter jejuni FUS, streptogramins, TMP R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
+genus_species is Campylobacter coli FUS, streptogramins, TMP R Table 03: Intrinsic resistance in other Gram-negative bacteria Expert Rules
+gramstain is Gram positive ATM, polymyxins, NAL R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus saprophyticus FUS, CAZ, FOS, NOV R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus cohnii CAZ, NOV R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus xylosus CAZ, NOV R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus capitis CAZ, FOS R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus aureus CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus epidermidis CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus coagulase-negative CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus hominis CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus haemolyticus CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus intermedius CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Staphylococcus pseudintermedius CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus is Streptococcus FUS, CAZ, aminoglycosides R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Enterococcus faecalis FUS, CAZ, cephalosporins_without_CAZ, aminoglycosides, macrolides, CLI, QDA, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Enterococcus gallinarum FUS, CAZ, cephalosporins_without_CAZ, aminoglycosides, macrolides, CLI, QDA, VAN, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Enterococcus casseliflavus FUS, CAZ, cephalosporins_without_CAZ, aminoglycosides, macrolides, CLI, QDA, VAN, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Enterococcus faecium FUS, CAZ, cephalosporins_without_CAZ, aminoglycosides, macrolides, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus is Corynebacterium FOS R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
genus_species is Listeria monocytogenes cephalosporins R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
genus is Leuconostoc, Pediococcus glycopeptides R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
genus is Lactobacillus glycopeptides R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Clostridium ramosum vanc R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species is Clostridium innocuum vanc R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
-genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G) peni S aminopenicillins, cephalosporins, carbapenems S Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules
-genus is Enterococcus ampi R ureidopenicillins, carbapenems R Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules
-genus is Enterococcus amox R ureidopenicillins, carbapenems R Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules
-family is Enterobacteriaceae tica, pipe R, S pipe R Table 09: Interpretive rules for B-lactam agents and Gram-negative rods Expert Rules
-genus is .* eryt S azit, clar S Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins Expert Rules
-genus is .* eryt I azit, clar I Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins Expert Rules
-genus is .* eryt R azit, clar R Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins Expert Rules
-genus is Staphylococcus tobr R kana, amik R Table 12: Interpretive rules for aminoglycosides Expert Rules
-genus is Staphylococcus gent R aminoglycosides R Table 12: Interpretive rules for aminoglycosides Expert Rules
-family is Enterobacteriaceae gent, tobr I, S gent R Table 12: Interpretive rules for aminoglycosides Expert Rules
-family is Enterobacteriaceae gent, tobr R, I tobr R Table 12: Interpretive rules for aminoglycosides Expert Rules
-genus is Staphylococcus moxi R fluoroquinolones R Table 13: Interpretive rules for quinolones Expert Rules
-genus_species is Streptococcus pneumoniae moxi R fluoroquinolones R Table 13: Interpretive rules for quinolones Expert Rules
-family is Enterobacteriaceae cipr R fluoroquinolones R Table 13: Interpretive rules for quinolones Expert Rules
-genus_species is Neisseria gonorrhoeae cipr R fluoroquinolones R Table 13: Interpretive rules for quinolones Expert Rules
-genus is .* amcl R ampi, amox R Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R Other rules
-genus is .* pita R pipe R Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R Other rules
-genus is .* trsu R trim R Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R Other rules
-genus is .* ampi S amcl S Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S Other rules
-genus is .* amox S amcl S Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S Other rules
-genus is .* pipe S pita S Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S Other rules
-genus is .* trim S trsu S Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S Other rules
+genus_species is Clostridium ramosum VAN R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species is Clostridium innocuum VAN R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules
+genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G) PEN S aminopenicillins, cephalosporins_without_CAZ, carbapenems S Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules
+genus is Enterococcus AMP R ureidopenicillins, carbapenems R Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules
+genus is Enterococcus AMX R ureidopenicillins, carbapenems R Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules
+family is Enterobacteriaceae TIC, PIP R, S PIP R Table 09: Interpretive rules for B-lactam agents and Gram-negative rods Expert Rules
+genus is .* ERY S AZM, CLR S Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins Expert Rules
+genus is .* ERY I AZM, CLR I Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins Expert Rules
+genus is .* ERY R AZM, CLR R Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins Expert Rules
+genus is Staphylococcus TOB R KAN, amik R Table 12: Interpretive rules for aminoglycosides Expert Rules
+genus is Staphylococcus GEN R aminoglycosides R Table 12: Interpretive rules for aminoglycosides Expert Rules
+family is Enterobacteriaceae GEN, TOB I, S GEN R Table 12: Interpretive rules for aminoglycosides Expert Rules
+family is Enterobacteriaceae GEN, TOB R, I TOB R Table 12: Interpretive rules for aminoglycosides Expert Rules
+genus is Staphylococcus MFX R fluoroquinolones R Table 13: Interpretive rules for quinolones Expert Rules
+genus_species is Streptococcus pneumoniae MFX R fluoroquinolones R Table 13: Interpretive rules for quinolones Expert Rules
+family is Enterobacteriaceae CIP R fluoroquinolones R Table 13: Interpretive rules for quinolones Expert Rules
+genus_species is Neisseria gonorrhoeae CIP R fluoroquinolones R Table 13: Interpretive rules for quinolones Expert Rules
+genus is .* AMC R AMP, AMX R Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R Other rules
+genus is .* TZP R PIP R Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R Other rules
+genus is .* SXT R TMP R Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R Other rules
+genus is .* AMP S AMC S Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S Other rules
+genus is .* AMX S AMC S Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S Other rules
+genus is .* PIP S TZP S Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S Other rules
+genus is .* TMP S SXT S Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S Other rules
diff --git a/inst/rstudio/addins.dcf b/inst/rstudio/addins.dcf
index a87253d28..d43c060a3 100644
--- a/inst/rstudio/addins.dcf
+++ b/inst/rstudio/addins.dcf
@@ -1,24 +1,3 @@
-# ==================================================================== #
-# TITLE #
-# Antimicrobial Resistance (AMR) Analysis #
-# #
-# SOURCE #
-# https://gitlab.com/msberends/AMR #
-# #
-# LICENCE #
-# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
-# #
-# This R package is free software; you can freely use and distribute #
-# it for both personal and commercial purposes under the terms of the #
-# GNU General Public License version 2.0 (GNU GPL-2), as published by #
-# the Free Software Foundation. #
-# #
-# This R package was created for academic research and was publicly #
-# released in the hope that it will be useful, but it comes WITHOUT #
-# ANY WARRANTY OR LIABILITY. #
-# Visit our website for more info: https://msberends.gitlab.io/AMR. #
-# ==================================================================== #
-
Name: Insert %in%
Binding: addin_insert_in
Interactive: false
diff --git a/inst/translations.tsv b/inst/translations.tsv
new file mode 100644
index 000000000..58870c3b0
--- /dev/null
+++ b/inst/translations.tsv
@@ -0,0 +1,456 @@
+lang pattern replacement fixed ignore.case
+de Coagulase-negative Staphylococcus Koagulase-negative Staphylococcus FALSE FALSE
+de Coagulase-positive Staphylococcus Koagulase-positive Staphylococcus FALSE FALSE
+de Beta-haemolytic Streptococcus Beta-hämolytischer Streptococcus FALSE FALSE
+de unknown Gram negatives unbekannte Gramnegativen FALSE FALSE
+de unknown Gram positives unbekannte Grampositiven FALSE FALSE
+de unknown name unbekannte Name FALSE FALSE
+de unknown kingdom unbekanntes Reich FALSE FALSE
+de unknown phylum unbekannter Stamm FALSE FALSE
+de unknown class unbekannte Klasse FALSE FALSE
+de unknown order unbekannte Ordnung FALSE FALSE
+de unknown family unbekannte Familie FALSE FALSE
+de unknown genus unbekannte Gattung FALSE FALSE
+de unknown species unbekannte Art FALSE FALSE
+de unknown subspecies unbekannte Unterart FALSE FALSE
+de unknown rank unbekannter Rang FALSE FALSE
+de (CoNS) (KNS) TRUE FALSE
+de (CoPS) (KPS) TRUE FALSE
+de Gram negative Gramnegativ FALSE FALSE
+de Gram positive Grampositiv FALSE FALSE
+de Bacteria Bakterien FALSE FALSE
+de Fungi Hefen/Pilze FALSE FALSE
+de Protozoa Protozoen FALSE FALSE
+de biogroup Biogruppe FALSE FALSE
+de biotype Biotyp FALSE FALSE
+de vegetative vegetativ FALSE FALSE
+de ([([ ]*?)group \\1Gruppe FALSE FALSE
+de ([([ ]*?)Group \\1Gruppe FALSE FALSE
+nl Coagulase-negative Staphylococcus Coagulase-negatieve Staphylococcus FALSE FALSE
+nl Coagulase-positive Staphylococcus Coagulase-positieve Staphylococcus FALSE FALSE
+nl Beta-haemolytic Streptococcus Beta-hemolytische Streptococcus FALSE FALSE
+nl unknown Gram negatives onbekende Gram-negatieven FALSE FALSE
+nl unknown Gram positives onbekende Gram-positieven FALSE FALSE
+nl unknown name onbekende naam FALSE FALSE
+nl unknown kingdom onbekend koninkrijk FALSE FALSE
+nl unknown phylum onbekend fylum FALSE FALSE
+nl unknown class onbekende klasse FALSE FALSE
+nl unknown order onbekende orde FALSE FALSE
+nl unknown family onbekende familie FALSE FALSE
+nl unknown genus onbekend geslacht FALSE FALSE
+nl unknown species onbekende soort FALSE FALSE
+nl unknown subspecies onbekende ondersoort FALSE FALSE
+nl unknown rank onbekende rang FALSE FALSE
+nl (CoNS) (CNS) TRUE FALSE
+nl (CoPS) (CPS) TRUE FALSE
+nl Gram negative Gram-negatief FALSE FALSE
+nl Gram positive Gram-positief FALSE FALSE
+nl Bacteria Bacteriën FALSE FALSE
+nl Fungi Schimmels/gisten FALSE FALSE
+nl Protozoa protozoën FALSE FALSE
+nl biogroup biogroep FALSE FALSE
+nl vegetative vegetatief FALSE FALSE
+nl ([([ ]*?)group \\1groep FALSE FALSE
+nl ([([ ]*?)Group \\1Groep FALSE FALSE
+es Coagulase-negative Staphylococcus Staphylococcus coagulasa negativo FALSE FALSE
+es Coagulase-positive Staphylococcus Staphylococcus coagulasa positivo FALSE FALSE
+es Beta-haemolytic Streptococcus Streptococcus Beta-hemolítico FALSE FALSE
+es unknown Gram negatives Gram negativos desconocidos FALSE FALSE
+es unknown Gram positives Gram positivos desconocidos FALSE FALSE
+es unknown name nombre desconocido FALSE FALSE
+es unknown kingdom reino desconocido FALSE FALSE
+es unknown phylum filo desconocido FALSE FALSE
+es unknown class clase desconocida FALSE FALSE
+es unknown order orden desconocido FALSE FALSE
+es unknown family familia desconocida FALSE FALSE
+es unknown genus género desconocido FALSE FALSE
+es unknown species especie desconocida FALSE FALSE
+es unknown subspecies subespecie desconocida FALSE FALSE
+es unknown rank rango desconocido FALSE FALSE
+es (CoNS) (SCN) TRUE FALSE
+es (CoPS) (SCP) TRUE FALSE
+es Gram negative Gram negativo FALSE FALSE
+es Gram positive Gram positivo FALSE FALSE
+es Bacteria Bacterias FALSE FALSE
+es Fungi Hongos FALSE FALSE
+es Protozoa Protozoarios FALSE FALSE
+es biogroup biogrupo FALSE FALSE
+es biotype biotipo FALSE FALSE
+es vegetative vegetativo FALSE FALSE
+es ([([ ]*?)group \\1grupo FALSE FALSE
+es ([([ ]*?)Group \\1Grupo FALSE FALSE
+it Coagulase-negative Staphylococcus Staphylococcus negativo coagulasi FALSE FALSE
+it Coagulase-positive Staphylococcus Staphylococcus positivo coagulasi FALSE FALSE
+it Beta-haemolytic Streptococcus Streptococcus Beta-emolitico FALSE FALSE
+it unknown Gram negatives Gram negativi sconosciuti FALSE FALSE
+it unknown Gram positives Gram positivi sconosciuti FALSE FALSE
+it unknown name nome sconosciuto FALSE FALSE
+it unknown kingdom regno sconosciuto FALSE FALSE
+it unknown phylum phylum sconosciuto FALSE FALSE
+it unknown class classe sconosciuta FALSE FALSE
+it unknown order ordine sconosciuto FALSE FALSE
+it unknown family famiglia sconosciuta FALSE FALSE
+it unknown genus genere sconosciuto FALSE FALSE
+it unknown species specie sconosciute FALSE FALSE
+it unknown subspecies sottospecie sconosciute FALSE FALSE
+it unknown rank grado sconosciuto FALSE FALSE
+it Gram negative Gram negativo FALSE FALSE
+it Gram positive Gram positivo FALSE FALSE
+it Bacteria Batteri FALSE FALSE
+it Fungi Fungo FALSE FALSE
+it Protozoa Protozoi FALSE FALSE
+it biogroup biogruppo FALSE FALSE
+it biotype biotipo FALSE FALSE
+it vegetative vegetativo FALSE FALSE
+it ([([ ]*?)group \\1gruppo FALSE FALSE
+it ([([ ]*?)Group \\1Gruppo FALSE FALSE
+fr Coagulase-negative Staphylococcus Staphylococcus à coagulase négative FALSE FALSE
+fr Coagulase-positive Staphylococcus Staphylococcus à coagulase positif FALSE FALSE
+fr Beta-haemolytic Streptococcus Streptococcus Bêta-hémolytique FALSE FALSE
+fr unknown Gram negatives Gram négatifs inconnus FALSE FALSE
+fr unknown Gram positives Gram positifs inconnus FALSE FALSE
+fr unknown name nom inconnu FALSE FALSE
+fr unknown kingdom règme inconnu FALSE FALSE
+fr unknown phylum embranchement inconnu FALSE FALSE
+fr unknown class classe inconnue FALSE FALSE
+fr unknown order ordre inconnu FALSE FALSE
+fr unknown family famille inconnue FALSE FALSE
+fr unknown genus genre inconnu FALSE FALSE
+fr unknown species espèce inconnue FALSE FALSE
+fr unknown subspecies sous-espèce inconnue FALSE FALSE
+fr unknown rank rang inconnu FALSE FALSE
+fr Gram negative Gram négatif FALSE FALSE
+fr Gram positive Gram positif FALSE FALSE
+fr Bacteria Bactéries FALSE FALSE
+fr Fungi Champignons FALSE FALSE
+fr Protozoa Protozoaires FALSE FALSE
+fr biogroup biogroupe FALSE FALSE
+fr vegetative végétatif FALSE FALSE
+fr ([([ ]*?)group \\1groupe FALSE FALSE
+fr ([([ ]*?)Group \\1Groupe FALSE FALSE
+pt Coagulase-negative Staphylococcus Staphylococcus coagulase negativo FALSE FALSE
+pt Coagulase-positive Staphylococcus Staphylococcus coagulase positivo FALSE FALSE
+pt Beta-haemolytic Streptococcus Streptococcus Beta-hemolítico FALSE FALSE
+pt unknown Gram negatives Gram negativos desconhecidos FALSE FALSE
+pt unknown Gram positives Gram positivos desconhecidos FALSE FALSE
+pt unknown name nome desconhecido FALSE FALSE
+pt unknown kingdom reino desconhecido FALSE FALSE
+pt unknown phylum filo desconhecido FALSE FALSE
+pt unknown class classe desconhecida FALSE FALSE
+pt unknown order ordem desconhecido FALSE FALSE
+pt unknown family família desconhecida FALSE FALSE
+pt unknown genus gênero desconhecido FALSE FALSE
+pt unknown species espécies desconhecida FALSE FALSE
+pt unknown subspecies subespécies desconhecida FALSE FALSE
+pt unknown rank classificação desconhecido FALSE FALSE
+pt Gram negative Gram negativo FALSE FALSE
+pt Gram positive Gram positivo FALSE FALSE
+pt Bacteria Bactérias FALSE FALSE
+pt Fungi Fungos FALSE FALSE
+pt Protozoa Protozoários FALSE FALSE
+pt biogroup biogrupo FALSE FALSE
+pt biotype biótipo FALSE FALSE
+pt vegetative vegetativo FALSE FALSE
+pt ([([ ]*?)group \\1grupo FALSE FALSE
+pt ([([ ]*?)Group \\1Grupo FALSE FALSE
+
+nl 4-aminosalicylic acid aminosalicylzuur
+nl adefovir dipivoxil adefovir
+nl aldesulfone sodium aldesulfon
+nl amikacin amikacine
+nl amoxicillin amoxicilline
+nl amoxicillin and beta-lactamase inhibitor amoxicilline met enzymremmer
+nl amphotericin B amfotericine B
+nl ampicillin ampicilline
+nl ampicillin and beta-lactamase inhibitor ampicilline met enzymremmer
+nl ampicillin, combinations ampicilline, combinatiepreparaten
+nl anidulafungin anidulafungine
+nl anthrax antigen anthrax-antigeen
+nl anti-d (rh) immunoglobulin rhesus(d)immunoglobuline
+nl azidocillin azidocilline
+nl azithromycin azitromycine
+nl azlocillin azlocilline
+nl bacampicillin bacampicilline
+nl bacitracin bacitracine
+nl benzathine benzylpenicillin benzylpenicillinebenzathine
+nl benzathine phenoxymethylpenicillin fenoxymethylpenicillinebenzathine
+nl benzylpenicillin benzylpenicilline
+nl botulinum antitoxin serum tegen botulisme
+nl brucella antigen brucella-antigeen
+nl calcium aminosalicylate aminosalicylzuur
+nl capreomycin capreomycine
+nl carbenicillin carbenicilline
+nl carindacillin carindacilline
+nl caspofungin caspofungine
+nl cefacetrile cefacetril
+nl cefalexin cefalexine
+nl cefalotin cefalotine
+nl cefamandole cefamandol
+nl cefapirin cefapirine
+nl cefazedone cefazedon
+nl cefazolin cefazoline
+nl cefepime cefepim
+nl cefixime cefixim
+nl cefmenoxime cefmenoxim
+nl cefmetazole cefmetazol
+nl cefodizime cefodizim
+nl cefonicid cefonicide
+nl cefoperazone cefoperazon
+nl cefoperazone and beta-lactamase inhibitor cefoperazon combinatiepreparaten
+nl cefotaxime cefotaxim
+nl cefoxitin cefoxitine
+nl cefpirome cefpirom
+nl cefpodoxime cefpodoxim
+nl cefsulodin cefsulodine
+nl ceftazidime ceftazidim
+nl ceftezole ceftezol
+nl ceftizoxime ceftizoxim
+nl ceftriaxone ceftriaxon
+nl ceftriaxone, combinations ceftriaxon, combinatiepreparaten
+nl cefuroxime cefuroxim
+nl cefuroxime and metronidazole cefuroxim met andere antibacteriele middelen
+nl chloramphenicol chlooramfenicol
+nl chlortetracycline chloortetracycline
+nl cholera, combinations with typhoid vaccine, inactivated, whole cell choleravaccin met tyfusvaccin, geinactiveerd, hele cel
+nl cholera, inactivated, whole cell cholera, geinactiveerd, hele cel
+nl cholera, live attenuated cholera, levend verzwakt
+nl cinoxacin cinoxacine
+nl ciprofloxacin ciprofloxacine
+nl clarithromycin claritromycine
+nl clavulanic acid clavulaanzuur
+nl clindamycin clindamycine
+nl clometocillin clometocilline
+nl clotrimazole clotrimazol
+nl cloxacillin cloxacilline
+nl colistin colistine
+nl combinations combinatiepreparaten
+nl combinations combinatiepreparaten
+nl combinations combinatiepreparaten
+nl combinations combinatiepreparaten
+nl combinations combinatiepreparaten
+nl combinations combinatiepreparaten
+nl combinations combinatiepreparaten
+nl combinations of penicillins combinatiepreparaten
+nl combinations of tetracyclines tetracycline combinatiepreparaten
+nl cytomegalovirus immunoglobulin cytomegalie-immunoglobuline
+nl dapsone dapson
+nl daptomycin daptomycine
+nl dibekacin dibekacine
+nl dicloxacillin dicloxacilline
+nl diphtheria antitoxin serum tegen difterie
+nl diphtheria immunoglobulin difterie-immunoglobuline
+nl diphtheria toxoid difterietoxoide
+nl diphtheria-hemophilus influenzae b-pertussis-poliomyelitis-tetanus difterie-haemophilus-kinkhoest-poliomyelitis-tetanus
+nl diphtheria-hemophilus influenzae b-pertussis-poliomyelitis-tetanus-hepatitis B difterie-haemophilus-kinkhoest-polio-tetanus-hepatitis B
+nl diphtheria-hemophilus influenzae b-pertussis-tetanus-hepatitis B difterie-haemophilus-kinkhoest-tetanus-hepatitis B
+nl diphtheria-hepatitis b-pertussis-tetanus difterie-hepatitis b-kinkhoest-tetanus
+nl diphtheria-hepatitis b-tetanus difterie-hepatitis b-tetanus
+nl diphtheria-pertussis-poliomyelitis-tetanus difterie-kinkhoest-poliomyelitis-tetanus
+nl diphtheria-pertussis-poliomyelitis-tetanus-hepatitis B difterie-kinkhoest-poliomyelitis-tetanus-hepatitis B
+nl diphtheria-poliomyelitis-tetanus difterie-poliomyelitis-tetanus
+nl diphtheria-rubella-tetanus difterie-rubella-tetanus
+nl dirithromycin diritromycine
+nl econazole econazol
+nl encephalitis, japanese, inactivated, whole virus japanse-encefalitis, geinactiveerd, intact virus
+nl encephalitis, tick borne immunoglobulin teken-encephalitis-immunoglobuline
+nl encephalitis, tick borne, inactivated, whole virus tekenencefalitis, geinactiveerd, intact virus
+nl enoxacin enoxacine
+nl epicillin epicilline
+nl erythromycin erytromycine
+nl ethambutol and isoniazid ethambutol met isoniazide
+nl fleroxacin fleroxacine
+nl flucloxacillin flucloxacilline
+nl fluconazole fluconazol
+nl flucytosine fluorocytosine
+nl flurithromycin fluritromycine
+nl fosfomycin fosfomycine
+nl fusidic acid fusidinezuur
+nl gas-gangrene sera serum tegen gasgangreen
+nl gatifloxacin gatifloxacine
+nl gemifloxacin gemifloxacine
+nl gentamicin gentamicine
+nl grepafloxacin grepafloxacine
+nl hachimycin hachimycine
+nl hemophilus influenzae B and hepatitis B haemophilus influenzae b-hepatitis B
+nl hemophilus influenzae B and poliomyelitis haemophilus influenzae b-poliomyelitis
+nl hemophilus influenzae B, combinations with pertussis and toxoids haemophilus influenzae B met kinkhoest en toxoiden
+nl hemophilus influenzae B, combinations with toxoids haemophilus influenzae B met toxoiden
+nl hemophilus influenzae B, purified antigen conjugated haemophilus influenzae B, gezuiverd geconjugeerd antigeen
+nl hepatitis A immunoglobulin hepatitis a-immunoglobuline
+nl hepatitis A, inactivated, whole virus hepatitis A, geinactiveerd, intact virus
+nl hepatitis B immunoglobulin hepatitis b-immunoglobuline
+nl hepatitis B, purified antigen hepatitis B, gezuiverd antigeen
+nl hetacillin hetacilline
+nl imipenem and cilastatin imipenem met enzymremmer
+nl immunoglobulins, normal human, for extravascular adm, immunoglobuline, normaal, extravasculair
+nl immunoglobulins, normal human, for intravascular adm, immunoglobuline, normaal, intravasculair
+nl influenza, inactivated, split virus or surface antigen influenza, gezuiverd antigeen
+nl influenza, inactivated, whole virus influenza, geinactiveerd, intact virus
+nl inosine pranobex inosiplex
+nl isepamicin isepamicine
+nl isoconazole isoconazol
+nl isoniazid isoniazide
+nl isoniazid, combinations isoniazide, combinatiepreparaten
+nl itraconazole itraconazol
+nl josamycin josamycine
+nl kanamycin kanamycine
+nl ketoconazole ketoconazol
+nl levofloxacin levofloxacine
+nl lincomycin lincomycine
+nl lomefloxacin lomefloxacine
+nl lysozyme lysozym
+nl mandelic acid amandelzuur
+nl measles immunoglobulin mazelenimmunoglobuline
+nl measles, combinations with mumps and rubella, live attenuated mazelen met bof en rubella, levend verzwakt
+nl measles, combinations with mumps, live attenuated mazelen met bof, levend verzwakt
+nl measles, combinations with mumps, rubella and varicella, live attenuated mazelen met bof, rubella en varicella, levend verzwakt
+nl measles, combinations with rubella, live attenuated mazelen met rubella, levend verzwakt
+nl measles, live attenuated mazelen, levend verzwakt
+nl meningococcus A, purified polysaccharides antigen meningokok A, gezuiverd polysaccharidenantigeen
+nl meningococcus A,c, bivalent purified polysaccharides antigen meningokok, 2 typen, gezuiverd polysaccharidenantigeen
+nl meningococcus A,c,y,w-135, tetravalent purified polysaccharides antigen meningokok, 4 typen, gezuiverd polysaccharidenantigeen
+nl meningococcus B, outer membrane vesicle vaccine meningokok B, buitenmembraanvaccin
+nl meningococcus C, purified polysaccharides antigen conjugated meningokok C, gezuiverd saccharidenantigeen geconjugeerd
+nl metampicillin metampicilline
+nl meticillin meticilline
+nl metisazone metisazon
+nl metronidazole metronidazol
+nl mezlocillin mezlocilline
+nl micafungin micafungine
+nl miconazole miconazol
+nl midecamycin midecamycine
+nl miocamycin miocamycine
+nl moxifloxacin moxifloxacine
+nl mumps immunoglobulin bofimmunoglobuline
+nl mumps, live attenuated bof, levend verzwakt
+nl mupirocin mupirocine
+nl nalidixic acid nalidixinezuur
+nl neomycin neomycine
+nl netilmicin netilmicine
+nl nitrofurantoin nitrofurantoine
+nl norfloxacin norfloxacine
+nl novobiocin novobiocine
+nl nystatin nystatine
+nl ofloxacin ofloxacine
+nl oleandomycin oleandomycine
+nl ornidazole ornidazol
+nl other meningococcal monovalent purified polysaccharides antigen meningokok, 1 type, gezuiverd polysaccaridenantigeen
+nl other meningococcal polyvalent purified polysaccharides antigen meningokok, meer typen, gezuiverd polysaccharidenantigeen
+nl oxacillin oxacilline
+nl oxolinic acid oxolinezuur
+nl oxytetracycline, combinations oxytetracycline, combinatiepreparaten
+nl pazufloxacin pazufloxacine
+nl pefloxacin pefloxacine
+nl penamecillin penamecilline
+nl penicillins, combinations with other antibacterials penicillines met andere antibacteriele middelen
+nl pertussis immunoglobulin kinkhoestimmunoglobuline
+nl pertussis, inactivated, whole cell pertussis, geinactiveerd, hele cel
+nl pertussis, inactivated, whole cell, combinations with toxoids pertussis, geinactiveerd hele cel, met toxoiden
+nl pertussis, purified antigen pertussis, gezuiverd antigeen
+nl pertussis, purified antigen, combinations with toxoids pertussis, gezuiverd antigeen, met toxoiden
+nl pheneticillin feneticilline
+nl phenoxymethylpenicillin fenoxymethylpenicilline
+nl pipemidic acid pipemidinezuur
+nl piperacillin piperacilline
+nl piperacillin and beta-lactamase inhibitor piperacilline met enzymremmer
+nl piromidic acid piromidinezuur
+nl pivampicillin pivampicilline
+nl plague, inactivated, whole cell yersinia pestis, geinactiveerd, hele cel
+nl pneumococcus, purified polysaccharides antigen pneumokok, gezuiverd polysaccharidenantigeen
+nl pneumococcus, purified polysaccharides antigen conjugated pneumokok, gezuiverd geconjugeerd polysaccharidenantigeen
+nl poliomyelitis oral, monovalent, live attenuated poliomyelitis, oraal, 1 type, levend verzwakt
+nl poliomyelitis oral, trivalent, live attenuated poliomyelitis, oraal, 3 typen, levend verzwakt
+nl poliomyelitis, trivalent, inactivated, whole virus poliomyelitis, 3 typen, geinactiveerd, intact virus
+nl polymyxin B polymyxine B
+nl posaconazole posaconazol
+nl pristinamycin pristinamycine
+nl procaine benzylpenicillin benzylpenicillineprocaine
+nl propicillin propicilline
+nl prulifloxacin prulifloxacine
+nl quinupristin/dalfopristin quinupristine/dalfopristine
+nl rabies immunoglobulin rabiesimmunoglobuline
+nl rabies serum serum tegen rabies
+nl rabies, inactivated, whole virus rabies, geinactiveerd, intact virus
+nl ribostamycin ribostamycine
+nl rifabutin rifabutine
+nl rifampicin rifampicine
+nl rifampicin and isoniazid rifampicine met isoniazide
+nl rifampicin, pyrazinamide and isoniazid rifampicine met pyrazinamide en isoniazide
+nl rifampicin, pyrazinamide, ethambutol and isoniazid rifampicine met pyrazinamide, ethambutol en isoniazide
+nl rifamycin rifamycine
+nl rifaximin rifaximine
+nl rokitamycin rokitamycine
+nl rosoxacin rosoxacine
+nl rota virus, live attenuated rotavirus, levend verzwakt
+nl roxithromycin roxitromycine
+nl rubella immunoglobulin rubella-immunoglobuline
+nl rubella, combinations with mumps, live attenuated rubella met bof, levend verzwakt
+nl rubella, live attenuated rubella, levend verzwakt
+nl rufloxacin rufloxacine
+nl sisomicin sisomicine
+nl snake venom antiserum serum tegen slangebeet
+nl sodium aminosalicylate aminosalicylzuur,4- (na-zout)
+nl sparfloxacin sparfloxacine
+nl spectinomycin spectinomycine
+nl spiramycin spiramycine
+nl spiramycin and metronidazole spiramycine met andere antibacteriele middelen
+nl staphylococcus immunoglobulin stafylokokkenimmunoglobuline
+nl streptoduocin streptoduocine
+nl streptomycin streptomycine
+nl streptomycin and isoniazid streptomycine met isoniazide
+nl sulbenicillin sulbenicilline
+nl sulfadiazine and tetroxoprim sulfadiazine met tetroxoprim
+nl sulfadiazine and trimethoprim sulfadiazine met trimethoprim
+nl sulfadimidine and trimethoprim sulfadimidine met trimethoprim
+nl sulfafurazole sulfafurazol
+nl sulfaisodimidine sulfisomidine
+nl sulfalene sulfaleen
+nl sulfamazone sulfamazon
+nl sulfamerazine and trimethoprim sulfamerazine met trimethoprim
+nl sulfamethizole sulfamethizol
+nl sulfamethoxazole sulfamethoxazol
+nl sulfamethoxazole and trimethoprim sulfamethoxazol met trimethoprim
+nl sulfametoxydiazine sulfamethoxydiazine
+nl sulfametrole and trimethoprim sulfametrol met trimethoprim
+nl sulfamoxole sulfamoxol
+nl sulfamoxole and trimethoprim sulfamoxol met trimethoprim
+nl sulfaperin sulfaperine
+nl sulfaphenazole sulfafenazol
+nl sulfathiazole sulfathiazol
+nl sulfathiourea sulfathioureum
+nl sulfonamides, combinations with other antibacterials (excl, trimethoprim) sulfonamiden met andere antibacteriele middelen (exc trim)
+nl sultamicillin sultamicilline
+nl talampicillin talampicilline
+nl teicoplanin teicoplanine
+nl telithromycin telitromycine
+nl temafloxacin temafloxacine
+nl temocillin temocilline
+nl tenofovir disoproxil tenofovir
+nl terizidone terizidon
+nl tetanus antitoxin serum tegen tetanus
+nl tetanus immunoglobulin tetanusimmunoglobuline
+nl tetanus toxoid tetanustoxoide
+nl tetanus toxoid, combinations with diphtheria toxoid tetanustoxoide met difterietoxoide
+nl tetanus toxoid, combinations with tetanus immunoglobulin tetanustoxoide met tetanusimmunoglobuline
+nl thiamphenicol thiamfenicol
+nl thiamphenicol, combinations thiamfenicol combinatiepreparaten
+nl thioacetazone and isoniazid thioacetazon met isoniazide
+nl ticarcillin ticarcilline
+nl ticarcillin and beta-lactamase inhibitor ticarcilline met enzymremmer
+nl tinidazole tinidazol
+nl tobramycin tobramycine
+nl troleandomycin troleandomycine
+nl trovafloxacin trovafloxacine
+nl tuberculosis, live attenuated bacillus calmette guerin, levend verzwakt
+nl typhoid, combinations with paratyphi types buiktyfus met paratyfus-typen
+nl typhoid, inactivated, whole cell buiktyfus, geinactiveerd, hele cel
+nl typhoid, oral, live attenuated buiktyfus, oraal, levend verzwakt
+nl typhoid, purified polysaccharide antigen buiktyfus, gezuiverd polysaccharidenantigeen
+nl typhoid-hepatitis A buiktyfus-hepatitis A
+nl typhus exanthematicus, inactivated, whole cell vlektyfus, geinactiveerd, hele cel
+nl vaccinia immunoglobulin vaccinia-immunoglobuline
+nl vancomycin vancomycine
+nl varicella, live attenuated varicella, levend verzwakt
+nl varicella/zoster immunoglobulin varicella-zoster-immunoglobuline
+nl voriconazole voriconazol
+nl yellow fever, live attenuated gele koorts, levend verzwakt
diff --git a/man/AMR-deprecated.Rd b/man/AMR-deprecated.Rd
index 743f9547e..9579966df 100644
--- a/man/AMR-deprecated.Rd
+++ b/man/AMR-deprecated.Rd
@@ -3,39 +3,30 @@
\name{AMR-deprecated}
\alias{AMR-deprecated}
\alias{ratio}
-\alias{ab_property}
-\alias{ab_atc}
+\alias{abname}
+\alias{atc_property}
+\alias{atc_official}
\alias{ab_official}
-\alias{ab_name}
-\alias{ab_trivial_nl}
-\alias{ab_certe}
-\alias{ab_umcg}
-\alias{ab_tradenames}
-\alias{atc_ddd}
-\alias{atc_groups}
+\alias{atc_name}
+\alias{atc_trivial_nl}
+\alias{atc_tradenames}
\title{Deprecated functions}
\usage{
ratio(x, ratio)
-ab_property(...)
+abname(...)
-ab_atc(...)
+atc_property(...)
+
+atc_official(...)
ab_official(...)
-ab_name(...)
+atc_name(...)
-ab_trivial_nl(...)
+atc_trivial_nl(...)
-ab_certe(...)
-
-ab_umcg(...)
-
-ab_tradenames(...)
-
-atc_ddd(...)
-
-atc_groups(...)
+atc_tradenames(...)
}
\description{
These functions are so-called '\link{Deprecated}'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
diff --git a/man/ab_property.Rd b/man/ab_property.Rd
new file mode 100644
index 000000000..8abb59dc5
--- /dev/null
+++ b/man/ab_property.Rd
@@ -0,0 +1,110 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ab_property.R
+\name{ab_property}
+\alias{ab_property}
+\alias{ab_name}
+\alias{ab_atc}
+\alias{ab_cid}
+\alias{ab_synonyms}
+\alias{ab_tradenames}
+\alias{ab_group}
+\alias{ab_atc_group1}
+\alias{ab_atc_group2}
+\alias{ab_ddd}
+\title{Property of an antibiotic}
+\usage{
+ab_name(x, language = get_locale(), tolower = FALSE, ...)
+
+ab_atc(x, ...)
+
+ab_cid(x, ...)
+
+ab_synonyms(x, ...)
+
+ab_tradenames(x, ...)
+
+ab_group(x, ...)
+
+ab_atc_group1(x, ...)
+
+ab_atc_group2(x, ...)
+
+ab_ddd(x, administration = "oral", units = FALSE, ...)
+
+ab_property(x, property = "name", language = get_locale(), ...)
+}
+\arguments{
+\item{x}{any (vector of) text that can be coerced to a valid microorganism code with \code{\link{as.ab}}}
+
+\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
+
+\item{tolower}{logical to indicate whether the first character of every output should be transformed to a lower case character. This will lead to e.g. "polymyxin B" and not "polymyxin b".}
+
+\item{...}{other parameters passed on to \code{\link{as.ab}}}
+
+\item{administration}{way of administration, either \code{"oral"} or \code{"iv"}}
+
+\item{units}{a logical to indicate whether the units instead of the DDDs itself must be returned, see Examples}
+
+\item{property}{one of the column names of one of the \code{\link{antibiotics}} data set}
+}
+\value{
+\itemize{
+ \item{An \code{integer} in case of \code{ab_cid}}
+ \item{A named \code{list} in case of multiple \code{ab_synonyms}}
+ \item{A \code{double} in case of \code{ab_ddd}}
+ \item{A \code{character} in all other cases}
+}
+}
+\description{
+Use these functions to return a specific property of an antibiotic from the \code{\link{antibiotics}} data set. All input values will be evaluated internally with \code{\link{as.ab}}.
+}
+\details{
+All output will be \link{translate}d where possible.
+}
+\section{Source}{
+
+World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
+
+WHONET 2019 software: \url{http://www.whonet.org/software.html}
+
+European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
+}
+
+\section{Read more on our website!}{
+
+On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
+}
+
+\examples{
+# all properties:
+ab_name("AMX") # "Amoxicillin"
+ab_atc("AMX") # J01CA04 (ATC code from the WHO)
+ab_cid("AMX") # 33613 (Compound ID from PubChem)
+
+ab_synonyms("AMX") # a list with brand names of amoxicillin
+ab_tradenames("AMX") # same
+
+ab_group("AMX") # "Beta-lactams/penicillins"
+ab_atc_group1("AMX") # "Beta-lactam antibacterials, penicillins"
+ab_atc_group2("AMX") # "Penicillins with extended spectrum"
+
+ab_name(x = c("AMC", "PLB")) # "Amoxicillin/clavulanic acid" "Polymyxin B"
+ab_name(x = c("AMC", "PLB"),
+ tolower = TRUE) # "amoxicillin/clavulanic acid" "polymyxin B"
+
+ab_ddd("AMX", "oral") # 1
+ab_ddd("AMX", "oral", units = TRUE) # "g"
+ab_ddd("AMX", "iv") # 1
+ab_ddd("AMX", "iv", units = TRUE) # "g"
+
+# all ab_* functions use as.ab() internally:
+ab_name("Fluclox") # "Flucloxacillin"
+ab_name("fluklox") # "Flucloxacillin"
+ab_name("floxapen") # "Flucloxacillin"
+ab_name(21319) # "Flucloxacillin" (using CID)
+ab_name("J01CF05") # "Flucloxacillin" (using ATC)
+}
+\seealso{
+\code{\link{antibiotics}}
+}
diff --git a/man/abname.Rd b/man/abname.Rd
deleted file mode 100644
index 5de0e51ba..000000000
--- a/man/abname.Rd
+++ /dev/null
@@ -1,74 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/abname.R
-\name{abname}
-\alias{abname}
-\title{Name of an antibiotic}
-\source{
-\code{\link{antibiotics}}
-}
-\usage{
-abname(abcode, from = c("guess", "atc", "certe", "umcg"),
- to = "official", textbetween = " + ", tolower = FALSE)
-}
-\arguments{
-\item{abcode}{a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}}
-
-\item{from, to}{type to transform from and to. See \code{\link{antibiotics}} for its column names. WIth \code{from = "guess"} the from will be guessed from \code{"atc"}, \code{"certe"} and \code{"umcg"}. When using \code{to = "atc"}, the ATC code will be searched using \code{\link{as.atc}}.}
-
-\item{textbetween}{text to put between multiple returned texts}
-
-\item{tolower}{return output as lower case with function \code{\link{tolower}}.}
-}
-\description{
-Convert antibiotic codes to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{antibiotics}}.
-}
-\details{
-\strong{The \code{\link{ab_property}} functions are faster and more concise}, but do not support concatenated strings, like \code{abname("AMCL+GENT"}.
-}
-\section{WHOCC}{
-
-\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
-This package contains \strong{all ~500 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
-
-These have become the gold standard for international drug utilisation monitoring and research.
-
-The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
-}
-
-\section{Read more on our website!}{
-
-On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
-}
-
-\examples{
-abname("AMCL")
-# "Amoxicillin and beta-lactamase inhibitor"
-
-# It is quite flexible at default (having `from = "guess"`)
-abname(c("amox", "J01CA04", "Trimox", "dispermox", "Amoxil"))
-# "Amoxicillin" "Amoxicillin" "Amoxicillin" "Amoxicillin" "Amoxicillin"
-
-# Multiple antibiotics can be combined with "+".
-# The second antibiotic will be set to lower case when `tolower` was not set:
-abname("AMCL+GENT", textbetween = "/")
-# "amoxicillin and enzyme inhibitor/gentamicin"
-
-abname(c("AMCL", "GENT"))
-# "Amoxicillin and beta-lactamase inhibitor" "Gentamicin"
-
-abname("AMCL", to = "trivial_nl")
-# "Amoxicilline/clavulaanzuur"
-
-abname("AMCL", to = "atc")
-# "J01CR02"
-
-# specific codes for University Medical Center Groningen (UMCG):
-abname("J01CR02", from = "atc", to = "umcg")
-# "AMCL"
-
-# specific codes for Certe:
-abname("J01CR02", from = "atc", to = "certe")
-# "amcl"
-}
-\keyword{ab}
-\keyword{antibiotics}
diff --git a/man/age_groups.Rd b/man/age_groups.Rd
index e6f0050c0..d4ef33220 100644
--- a/man/age_groups.Rd
+++ b/man/age_groups.Rd
@@ -9,7 +9,7 @@ age_groups(x, split_at = c(12, 25, 55, 75))
\arguments{
\item{x}{age, e.g. calculated with \code{\link{age}}}
-\item{split_at}{values to split \code{x} at, defaults to age groups 0-11, 12-24, 26-54, 55-74 and 75+. See Details.}
+\item{split_at}{values to split \code{x} at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See Details.}
}
\value{
Ordered \code{\link{factor}}
@@ -21,7 +21,7 @@ Split ages into age groups defined by the \code{split} parameter. This allows fo
To split ages, the input can be:
\itemize{
\item{A numeric vector. A vector of e.g. \code{c(10, 20)} will split on 0-9, 10-19 and 20+. A value of only \code{50} will split on 0-49 and 50+.
- The default is to split on young children (0-11), youth (12-24), young adults (26-54), middle-aged adults (55-74) and elderly (75+).}
+ The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).}
\item{A character:}
\itemize{
\item{\code{"children"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.}
@@ -66,7 +66,7 @@ septic_patients \%>\%
mo == as.mo("E. coli")) \%>\%
group_by(age_group = age_groups(age)) \%>\%
select(age_group,
- cipr) \%>\%
+ CIP) \%>\%
ggplot_rsi(x = "age_group")
}
\seealso{
diff --git a/man/antibiotics.Rd b/man/antibiotics.Rd
index ca42d689e..9f1b6cd25 100644
--- a/man/antibiotics.Rd
+++ b/man/antibiotics.Rd
@@ -3,33 +3,27 @@
\docType{data}
\name{antibiotics}
\alias{antibiotics}
-\title{Data set with ~500 antibiotics}
-\format{A \code{\link{data.frame}} with 488 observations and 17 variables:
+\title{Data set with ~450 antibiotics}
+\format{A \code{\link{data.frame}} with 455 observations and 13 variables:
\describe{
- \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical), like \code{J01CR02}}
- \item{\code{ears_net}}{EARS-Net code (European Antimicrobial Resistance Surveillance Network), like \code{AMC}}
- \item{\code{certe}}{Certe code, like \code{amcl}}
- \item{\code{umcg}}{UMCG code, like \code{AMCL}}
- \item{\code{abbr}}{Abbreviation as used by many countries, used internally by \code{\link{as.atc}}}
- \item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and beta-lactamase inhibitor"}}
- \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
- \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
- \item{\code{trade_name}}{Trade name as used by many countries (a total of 294), used internally by \code{\link{as.atc}}}
+ \item{\code{ab}}{Antibiotic ID as used in this package (like \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available}
+ \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like \code{J01CR02}}
+ \item{\code{cid}}{Compound ID as found in PubChem}
+ \item{\code{name}}{Official name as used by WHONET/EARS-Net or the WHO}
+ \item{\code{group}}{A short and concise group name, based on WHONET and WHOCC definitions}
+ \item{\code{atc_group1}}{Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like \code{"Macrolides, lincosamides and streptogramins"}}
+ \item{\code{atc_group2}}{Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like \code{"Macrolides"}}
+ \item{\code{abbr}}{List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)}
+ \item{\code{synonyms}}{Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID}
\item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
\item{\code{oral_units}}{Units of \code{ddd_units}}
\item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
\item{\code{iv_units}}{Units of \code{iv_ddd}}
- \item{\code{atc_group1}}{ATC group, like \code{"Macrolides, lincosamides and streptogramins"}}
- \item{\code{atc_group2}}{Subgroup of \code{atc_group1}, like \code{"Macrolides"}}
- \item{\code{useful_gramnegative}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
- \item{\code{useful_grampositive}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
}}
\source{
-World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
+World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): \url{https://www.whocc.no/atc_ddd_index/}
-Table antibiotic coding EARSS (from WHONET 5.3): \url{http://www.madsonline.dk/Tutorials/landskoder_antibiotika_WM.pdf}
-
-EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016: \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}
+WHONET 2019 software: \url{http://www.whonet.org/software.html}
European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
}
@@ -37,7 +31,12 @@ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{htt
antibiotics
}
\description{
-A data set containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDDs. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
+A data set containing all antibiotics. Use \code{\link{as.ab}} or one of the \code{\link{ab_property}} functions to retrieve values from this data set. Three identifiers are included in this data set: an antibiotic ID (\code{ab}, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (\code{atc}) as defined by the WHO, and a Compound ID (\code{cid}) as found in PubChem. Other properties in this data set are derived from one or more of these codes.
+}
+\details{
+Properties that are based on an ATC code are only available when an ATC is available. These properties are: \code{atc_group1}, \code{atc_group2}, \code{oral_ddd}, \code{oral_units}, \code{iv_ddd} and \code{iv_units}
+
+Synonyms (i.e. trade names) are derived from the Compound ID (\code{cid}) and consequently only available where a CID is available.
}
\section{WHOCC}{
diff --git a/man/as.ab.Rd b/man/as.ab.Rd
new file mode 100644
index 000000000..d8776bf12
--- /dev/null
+++ b/man/as.ab.Rd
@@ -0,0 +1,69 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ab.R
+\name{as.ab}
+\alias{as.ab}
+\title{Transform to antibiotic ID}
+\usage{
+as.ab(x)
+}
+\arguments{
+\item{x}{character vector to determine to antibiotic ID}
+}
+\value{
+Character (vector) with class \code{"act"}. Unknown values will return \code{NA}.
+}
+\description{
+Use this function to determine the antibiotic code of one or more antibiotics. The data set \code{\link{antibiotics}} will be searched for abbreviations, official names and synonyms (brand names).
+}
+\details{
+Use the \code{\link{ab_property}} functions to get properties based on the returned ATC code, see Examples.
+
+In the ATC classification system, the active substances are classified in a hierarchy with five different levels. The system has fourteen main anatomical/pharmacological groups or 1st levels. Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups. The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance. The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
+ Source: \url{https://www.whocc.no/atc/structure_and_principles/}
+}
+\section{Source}{
+
+World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
+
+WHONET 2019 software: \url{http://www.whonet.org/software.html}
+
+European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
+}
+
+\section{WHOCC}{
+
+\if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
+This package contains \strong{all ~500 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
+
+These have become the gold standard for international drug utilisation monitoring and research.
+
+The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
+}
+
+\section{Read more on our website!}{
+
+On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
+}
+
+\examples{
+# These examples all return "ERY", the ID of Erythromycin:
+as.ab("J01FA01")
+as.ab("J 01 FA 01")
+as.ab("Erythromycin")
+as.ab("eryt")
+as.ab(" eryt 123")
+as.ab("ERYT")
+as.ab("ERY")
+as.ab("erytromicine") # spelled wrong
+as.ab("Erythrocin") # trade name
+as.ab("Romycin") # trade name
+
+# Use ab_* functions to get a specific properties (see ?ab_property);
+# they use as.ab() internally:
+ab_name("J01FA01") # "Erythromycin"
+ab_name("eryt") # "Erythromycin"
+}
+\seealso{
+\code{\link{antibiotics}} for the dataframe that is being used to determine ATCs.
+}
+\keyword{atc}
diff --git a/man/as.atc.Rd b/man/as.atc.Rd
index 4c260377b..888b3bf12 100644
--- a/man/as.atc.Rd
+++ b/man/as.atc.Rd
@@ -1,11 +1,14 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/atc.R
-\name{as.atc}
+% Please edit documentation in R/ab.R, R/atc.R
+\name{is.ab}
+\alias{is.ab}
\alias{as.atc}
\alias{atc}
\alias{is.atc}
\title{Transform to ATC code}
\usage{
+is.ab(x)
+
as.atc(x)
is.atc(x)
@@ -48,14 +51,6 @@ as.atc("eryt")
as.atc(" eryt 123")
as.atc("ERYT")
as.atc("ERY")
-as.atc("Erythrocin") # Trade name
-as.atc("Eryzole") # Trade name
-as.atc("Pediamycin") # Trade name
-
-# Use ab_* functions to get a specific property based on an ATC code
-Cipro <- as.atc("cipro") # returns `J01MA02`
-atc_official(Cipro) # returns "Ciprofloxacin"
-atc_umcg(Cipro) # returns "CIPR", the code used in the UMCG
}
\seealso{
\code{\link{antibiotics}} for the dataframe that is being used to determine ATCs.
diff --git a/man/as.disk.Rd b/man/as.disk.Rd
new file mode 100644
index 000000000..ee597ad1d
--- /dev/null
+++ b/man/as.disk.Rd
@@ -0,0 +1,45 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/disk.R
+\name{as.disk}
+\alias{as.disk}
+\alias{is.disk}
+\title{Class 'disk'}
+\usage{
+as.disk(x, na.rm = FALSE)
+
+is.disk(x)
+}
+\arguments{
+\item{x}{vector}
+
+\item{na.rm}{a logical indicating whether missing values should be removed}
+}
+\value{
+Ordered integer factor with new class \code{disk}
+}
+\description{
+This transforms a vector to a new class \code{disk}, which is a growth zone size (around an antibiotic disk) in millimeters between 6 and 99.
+}
+\details{
+Interpret disk values as RSI values with \code{\link{as.rsi}}. It supports guidelines from EUCAST and CLSI.
+}
+\section{Read more on our website!}{
+
+On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
+}
+
+\examples{
+# interpret disk values
+as.rsi(x = 12,
+ mo = as.mo("S. pneumoniae"),
+ ab = "AMX",
+ guideline = "EUCAST")
+as.rsi(x = 12,
+ mo = as.mo("S. pneumoniae"),
+ ab = "AMX",
+ guideline = "CLSI")
+}
+\seealso{
+\code{\link{as.rsi}}
+}
+\keyword{disk}
diff --git a/man/as.mic.Rd b/man/as.mic.Rd
index d1f361e4d..90f4686c7 100755
--- a/man/as.mic.Rd
+++ b/man/as.mic.Rd
@@ -20,6 +20,9 @@ Ordered factor with new class \code{mic}
\description{
This transforms a vector to a new class \code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
}
+\details{
+Interpret MIC values as RSI values with \code{\link{as.rsi}}. It supports guidelines from EUCAST and CLSI.
+}
\section{Read more on our website!}{
On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
@@ -32,6 +35,16 @@ is.mic(mic_data)
# this can also coerce combined MIC/RSI values:
as.mic("<=0.002; S") # will return <=0.002
+# interpret MIC values
+as.rsi(x = as.mic(2),
+ mo = as.mo("S. pneumoniae"),
+ ab = "AMX",
+ guideline = "EUCAST")
+as.rsi(x = as.mic(4),
+ mo = as.mo("S. pneumoniae"),
+ ab = "AMX",
+ guideline = "EUCAST")
+
plot(mic_data)
barplot(mic_data)
freq(mic_data)
diff --git a/man/as.mo.Rd b/man/as.mo.Rd
index a3824e1ac..24c822c16 100644
--- a/man/as.mo.Rd
+++ b/man/as.mo.Rd
@@ -127,7 +127,7 @@ Group 2 probably contains all other microbial pathogens ever found in humans.
[1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13}
-[2] Becker K \emph{et al.} \strong{Implications of identifying the recently defined members of the S. aureus complex, S. argenteus and S. schweitzeri: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).}. 2019. Clin Microbiol Infect. 2019 Mar 11. \url{https://doi.org/10.1016/j.cmi.2019.02.028}
+[2] Becker K \emph{et al.} \strong{Implications of identifying the recently defined members of the \emph{S. aureus} complex, \emph{S. argenteus} and \emph{S. schweitzeri}: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).} 2019. Clin Microbiol Infect. \url{https://doi.org/10.1016/j.cmi.2019.02.028}
[3] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571}
diff --git a/man/as.rsi.Rd b/man/as.rsi.Rd
index a9d5e8ecc..b03eb6272 100755
--- a/man/as.rsi.Rd
+++ b/man/as.rsi.Rd
@@ -2,18 +2,38 @@
% Please edit documentation in R/rsi.R
\name{as.rsi}
\alias{as.rsi}
+\alias{as.rsi.mic}
+\alias{as.rsi.disk}
+\alias{as.rsi.data.frame}
\alias{is.rsi}
\alias{is.rsi.eligible}
\title{Class 'rsi'}
\usage{
-as.rsi(x)
+as.rsi(x, ...)
+
+\method{as.rsi}{mic}(x, mo, ab, guideline = "EUCAST", ...)
+
+\method{as.rsi}{disk}(x, mo, ab, guideline = "EUCAST", ...)
+
+\method{as.rsi}{data.frame}(x, col_mo = NULL, guideline = "EUCAST",
+ ...)
is.rsi(x)
is.rsi.eligible(x, threshold = 0.05)
}
\arguments{
-\item{x}{vector}
+\item{x}{vector of values (for class \code{mic}: an MIC value in mg/L, for class \code{disk}: a disk diffusion radius in millimeters)}
+
+\item{...}{parameters passed on to methods}
+
+\item{mo}{a microorganism code, generated with \code{\link{as.mo}}}
+
+\item{ab}{an antibiotic code, generated with \code{\link{as.ab}}}
+
+\item{guideline}{defaults to the latest included EUCAST guideline, run \code{unique(AMR::rsi_translation$guideline)} for all options}
+
+\item{col_mo}{column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
\item{threshold}{maximum fraction of \code{x} that is allowed to fail transformation, see Examples}
}
@@ -21,12 +41,14 @@ is.rsi.eligible(x, threshold = 0.05)
Ordered factor with new class \code{rsi}
}
\description{
-This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
+Interpret MIC values according to EUCAST or CLSI, or clean up existing RSI values. This transforms the input to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
}
\details{
-\strong{NOTE:} This function does not translate MIC values to RSI values. If more than 50\% of the input resembles MIC values, it will warn about this.\cr You can use \code{\link{eucast_rules}} to (1) apply inferred susceptibility and resistance based on results of other antibiotics and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
+Run \code{unique(AMR::rsi_translation$guideline)} for a list of all supported guidelines.
-The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
+After using \code{as.rsi}, you can use \code{\link{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.
+
+The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} parameter.
}
\section{Read more on our website!}{
@@ -41,6 +63,16 @@ is.rsi(rsi_data)
# this can also coerce combined MIC/RSI values:
as.rsi("<= 0.002; S") # will return S
+# interpret MIC values
+as.rsi(x = as.mic(2),
+ mo = as.mo("S. pneumoniae"),
+ ab = "AMX",
+ guideline = "EUCAST")
+as.rsi(x = as.mic(4),
+ mo = as.mo("S. pneumoniae"),
+ ab = "AMX",
+ guideline = "EUCAST")
+
plot(rsi_data) # for percentages
barplot(rsi_data) # for frequencies
freq(rsi_data) # frequency table with informative header
@@ -48,7 +80,7 @@ freq(rsi_data) # frequency table with informative header
# using dplyr's mutate
library(dplyr)
septic_patients \%>\%
- mutate_at(vars(peni:rifa), as.rsi)
+ mutate_at(vars(PEN:RIF), as.rsi)
# fastest way to transform all columns with already valid AB results to class `rsi`:
@@ -58,7 +90,7 @@ septic_patients \%>\%
# default threshold of `is.rsi.eligible` is 5\%.
is.rsi.eligible(WHONET$`First name`) # fails, >80\% is invalid
-is.rsi.eligible(WHONET$`First name`, threshold = 0.9) # succeeds
+is.rsi.eligible(WHONET$`First name`, threshold = 0.99) # succeeds
}
\seealso{
\code{\link{as.mic}}
diff --git a/man/atc_property.Rd b/man/atc_property.Rd
deleted file mode 100755
index 4f30e135e..000000000
--- a/man/atc_property.Rd
+++ /dev/null
@@ -1,55 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/atc_property.R
-\name{atc_property}
-\alias{atc_property}
-\alias{atc_official}
-\alias{atc_name}
-\alias{atc_trivial_nl}
-\alias{atc_certe}
-\alias{atc_umcg}
-\alias{atc_tradenames}
-\title{Property of an antibiotic}
-\usage{
-atc_property(x, property = "official")
-
-atc_official(x, language = NULL)
-
-atc_name(x, language = NULL)
-
-atc_trivial_nl(x)
-
-atc_certe(x)
-
-atc_umcg(x)
-
-atc_tradenames(x)
-}
-\arguments{
-\item{x}{a (vector of a) valid \code{\link{atc}} code or any text that can be coerced to a valid atc with \code{\link{as.atc}}}
-
-\item{property}{one of the column names of one of the \code{\link{antibiotics}} data set, like \code{"atc"} and \code{"official"}}
-
-\item{language}{language of the returned text, defaults to English (\code{"en"}) and can be set with \code{\link{getOption}("AMR_locale")}. Either one of \code{"en"} (English) or \code{"nl"} (Dutch).}
-}
-\value{
-A vector of values. In case of \code{atc_tradenames}, if \code{x} is of length one, a vector will be returned. Otherwise a \code{\link{list}}, with \code{x} as names.
-}
-\description{
-Use these functions to return a specific property of an antibiotic from the \code{\link{antibiotics}} data set, based on their ATC code. Get such a code with \code{\link{as.atc}}.
-}
-\section{Read more on our website!}{
-
-On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
-}
-
-\examples{
-as.atc("amcl") # J01CR02
-atc_name("amcl") # Amoxicillin and beta-lactamase inhibitor
-atc_name("amcl", "nl") # Amoxicilline met enzymremmer
-atc_trivial_nl("amcl") # Amoxicilline/clavulaanzuur
-atc_certe("amcl") # amcl
-atc_umcg("amcl") # AMCL
-}
-\seealso{
-\code{\link{antibiotics}}
-}
diff --git a/man/count.Rd b/man/count.Rd
index d0bd3b1d7..e32053ad8 100644
--- a/man/count.Rd
+++ b/man/count.Rd
@@ -29,8 +29,8 @@ count_all(...)
n_rsi(...)
-count_df(data, translate_ab = getOption("get_antibiotic_names",
- "official"), combine_IR = FALSE)
+count_df(data, translate_ab = "name", language = get_locale(),
+ combine_IR = FALSE)
}
\arguments{
\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.}
@@ -39,7 +39,9 @@ count_df(data, translate_ab = getOption("get_antibiotic_names",
\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
-\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.}
+\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}}}
+
+\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
\item{combine_IR}{a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)}
}
@@ -68,55 +70,55 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
?septic_patients
# Count resistant isolates
-count_R(septic_patients$amox)
-count_IR(septic_patients$amox)
+count_R(septic_patients$AMX)
+count_IR(septic_patients$AMX)
# Or susceptible isolates
-count_S(septic_patients$amox)
-count_SI(septic_patients$amox)
+count_S(septic_patients$AMX)
+count_SI(septic_patients$AMX)
# Count all available isolates
-count_all(septic_patients$amox)
-n_rsi(septic_patients$amox)
+count_all(septic_patients$AMX)
+n_rsi(septic_patients$AMX)
# Since n_rsi counts available isolates, you can
# calculate back to count e.g. non-susceptible isolates.
# This results in the same:
-count_IR(septic_patients$amox)
-portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
+count_IR(septic_patients$AMX)
+portion_IR(septic_patients$AMX) * n_rsi(septic_patients$AMX)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
- summarise(R = count_R(cipr),
- I = count_I(cipr),
- S = count_S(cipr),
- n1 = count_all(cipr), # the actual total; sum of all three
- n2 = n_rsi(cipr), # same - analogous to n_distinct
+ summarise(R = count_R(CIP),
+ I = count_I(CIP),
+ S = count_S(CIP),
+ n1 = count_all(CIP), # the actual total; sum of all three
+ n2 = n_rsi(CIP), # same - analogous to n_distinct
total = n()) # NOT the number of tested isolates!
# Count co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy.
# Please mind that `portion_S` calculates percentages right away instead.
-count_S(septic_patients$amcl) # S = 1342 (71.4\%)
-count_all(septic_patients$amcl) # n = 1879
+count_S(septic_patients$AMC) # S = 1342 (71.4\%)
+count_all(septic_patients$AMC) # n = 1879
-count_S(septic_patients$gent) # S = 1372 (74.0\%)
-count_all(septic_patients$gent) # n = 1855
+count_S(septic_patients$GEN) # S = 1372 (74.0\%)
+count_all(septic_patients$GEN) # n = 1855
with(septic_patients,
- count_S(amcl, gent)) # S = 1660 (92.3\%)
+ count_S(AMC, GEN)) # S = 1660 (92.3\%)
with(septic_patients, # n = 1798
- n_rsi(amcl, gent))
+ n_rsi(AMC, GEN))
# Get portions S/I/R immediately of all rsi columns
septic_patients \%>\%
- select(amox, cipr) \%>\%
+ select(AMX, CIP) \%>\%
count_df(translate = FALSE)
# It also supports grouping variables
septic_patients \%>\%
- select(hospital_id, amox, cipr) \%>\%
+ select(hospital_id, AMX, CIP) \%>\%
group_by(hospital_id) \%>\%
count_df(translate = FALSE)
diff --git a/man/eucast_rules.Rd b/man/eucast_rules.Rd
index 314a25533..b22a637b2 100644
--- a/man/eucast_rules.Rd
+++ b/man/eucast_rules.Rd
@@ -21,47 +21,16 @@
}
}
- For editing the reference file (which is available with \code{\link{eucast_rules_file}}), these values can all be used for target antibiotics: aminoglycosides, tetracyclines, polymyxins, macrolides, glycopeptides, streptogramins, cephalosporins, cephalosporins_without_cfta, carbapenems, aminopenicillins, ureidopenicillins, fluoroquinolones, all_betalactams, and all separate four letter codes like amcl. They can be separated by comma: \code{"amcl, fluoroquinolones"}. The mo_property can be any column name from the \code{\link{microorganisms}} data set, or \code{genus_species} or \code{gramstain}. This file contains references to the 'Burkholderia cepacia complex'. The species in this group can be found in: LiPuma JJ, 2015 (PMID 16217180).
+ For editing the reference file (which is available with \code{\link{eucast_rules_file}}), these values can all be used for target antibiotics: aminoglycosides, tetracyclines, polymyxins, macrolides, glycopeptides, streptogramins, cephalosporins, cephalosporins_without_cfta, carbapenems, aminopenicillins, ureidopenicillins, fluoroquinolones, all_betalactams, and all separate four letter codes like AMC. They can be separated by comma: \code{"AMC, fluoroquinolones"}. The mo_property can be any column name from the \code{\link{microorganisms}} data set, or \code{genus_species} or \code{gramstain}. This file contains references to the 'Burkholderia cepacia complex'. The species in this group can be found in: LiPuma JJ, 2015 (PMID 16217180).
}
\usage{
eucast_rules(x, col_mo = NULL, info = TRUE, rules = c("breakpoints",
- "expert", "other", "all"), verbose = FALSE, amcl = guess_ab_col(),
- amik = guess_ab_col(), amox = guess_ab_col(),
- ampi = guess_ab_col(), azit = guess_ab_col(),
- azlo = guess_ab_col(), aztr = guess_ab_col(),
- cefa = guess_ab_col(), cfep = guess_ab_col(),
- cfot = guess_ab_col(), cfox = guess_ab_col(),
- cfra = guess_ab_col(), cfta = guess_ab_col(),
- cftr = guess_ab_col(), cfur = guess_ab_col(),
- chlo = guess_ab_col(), cipr = guess_ab_col(),
- clar = guess_ab_col(), clin = guess_ab_col(),
- clox = guess_ab_col(), coli = guess_ab_col(),
- czol = guess_ab_col(), dapt = guess_ab_col(),
- doxy = guess_ab_col(), erta = guess_ab_col(),
- eryt = guess_ab_col(), fosf = guess_ab_col(),
- fusi = guess_ab_col(), gent = guess_ab_col(),
- imip = guess_ab_col(), kana = guess_ab_col(),
- levo = guess_ab_col(), linc = guess_ab_col(),
- line = guess_ab_col(), mero = guess_ab_col(),
- mezl = guess_ab_col(), mino = guess_ab_col(),
- moxi = guess_ab_col(), nali = guess_ab_col(),
- neom = guess_ab_col(), neti = guess_ab_col(),
- nitr = guess_ab_col(), norf = guess_ab_col(),
- novo = guess_ab_col(), oflo = guess_ab_col(),
- oxac = guess_ab_col(), peni = guess_ab_col(),
- pipe = guess_ab_col(), pita = guess_ab_col(),
- poly = guess_ab_col(), pris = guess_ab_col(),
- qida = guess_ab_col(), rifa = guess_ab_col(),
- roxi = guess_ab_col(), siso = guess_ab_col(),
- teic = guess_ab_col(), tetr = guess_ab_col(),
- tica = guess_ab_col(), tige = guess_ab_col(),
- tobr = guess_ab_col(), trim = guess_ab_col(),
- trsu = guess_ab_col(), vanc = guess_ab_col(), ...)
+ "expert", "other", "all"), verbose = FALSE, ...)
eucast_rules_file()
}
\arguments{
-\item{x}{data with antibiotic columns, like e.g. \code{amox} and \code{amcl}}
+\item{x}{data with antibiotic columns, like e.g. \code{AMX} and \code{AMC}}
\item{col_mo}{column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
@@ -71,9 +40,7 @@ eucast_rules_file()
\item{verbose}{a logical to indicate whether extensive info should be returned as a \code{data.frame} with info about which rows and columns are effected. It runs all EUCAST rules, but will not be applied to an output - only an informative \code{data.frame} with changes will be returned as output.}
-\item{amcl, amik, amox, ampi, azit, azlo, aztr, cefa, cfep, cfot, cfox, cfra, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli, czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana, levo, linc, line, mero, mezl, mino, moxi, nali, neom, neti, nitr, norf, novo, oflo, oxac, peni, pipe, pita, poly, pris, qida, rifa, roxi, siso, teic, tetr, tica, tige, tobr, trim, trsu, vanc}{column name of an antibiotic, see Antibiotics}
-
-\item{...}{parameters that are passed on to \code{eucast_rules}}
+\item{...}{column name of an antibiotic, see section Antibiotics}
}
\value{
The input of \code{tbl_}, possibly with edited values of antibiotics. Or, if \code{verbose = TRUE}, a \code{data.frame} with all original and new values of the affected bug-drug combinations.
@@ -82,83 +49,84 @@ The input of \code{tbl_}, possibly with edited values of antibiotics. Or, if \co
Apply susceptibility rules as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}. This includes (1) expert rules, (2) intrinsic resistance and (3) inferred resistance as defined in their breakpoint tables.
}
\details{
-\strong{NOTE:} This function does not translate MIC values to RSI values. It only applies (1) inferred susceptibility and resistance based on results of other antibiotics and (2) intrinsic resistance based on taxonomic properties of a microorganism.
+\strong{Note:} This function does not translate MIC values to RSI values. Use \code{\link{as.rsi}} for that. \cr
+\strong{Note:} When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.
-The file used for applying all EUCAST rules can be retrieved with \code{\link{eucast_rules_file}()}. It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by this function can be found when running this command: \cr
+The file used for applying all EUCAST rules can be retrieved with \code{\link{eucast_rules_file}()}. It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by \code{eucast_rules()} can be found by running this command: \cr
\code{AMR::EUCAST_RULES_FILE_LOCATION} (without brackets).
-In the source code it is located under \href{https://gitlab.com/msberends/AMR/blob/master/inst/eucast/eucast_rules.tsv}{\code{./inst/eucast/eucast_rules.tsv}}.
-
-\strong{Note:} When ampicillin (J01CA01) is not available but amoxicillin (J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.
+In the source code the file containing all rules is located \href{https://gitlab.com/msberends/AMR/blob/master/inst/eucast/eucast_rules.tsv}{here}.
}
\section{Antibiotics}{
-To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab_col}} or input a text (case-insensitive) or use \code{NULL} to skip a column (e.g. \code{tica = NULL}). Non-existing columns will anyway be skipped with a warning.
+To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab_col}} or input a text (case-insensitive), or use \code{NULL} to skip a column (e.g. \code{TIC = NULL} to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
-Abbrevations of the column containing antibiotics in the form: \strong{abbreviation}: generic name (\emph{ATC code})
+Available abbrevations of the column containing antibiotics in the form '\strong{antimicrobial ID}: name (\emph{ATC code})':
- \strong{amcl}: amoxicillin+clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}),
- \strong{amik}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}),
- \strong{amox}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}),
- \strong{ampi}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}),
- \strong{azit}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}),
- \strong{azlo}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}),
- \strong{aztr}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}),
- \strong{cefa}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}),
- \strong{cfep}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}),
- \strong{cfot}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}),
- \strong{cfox}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}),
- \strong{cfra}: cefradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}),
- \strong{cfta}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}),
- \strong{cftr}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}),
- \strong{cfur}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}),
- \strong{chlo}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}),
- \strong{cipr}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}),
- \strong{clar}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}),
- \strong{clin}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}),
- \strong{clox}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}),
- \strong{coli}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}),
- \strong{czol}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}),
- \strong{dapt}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}),
- \strong{doxy}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}),
- \strong{erta}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}),
- \strong{eryt}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}),
- \strong{fosf}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}),
- \strong{fusi}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}),
- \strong{gent}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}),
- \strong{imip}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}),
- \strong{kana}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}),
- \strong{levo}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}),
- \strong{linc}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}),
- \strong{line}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}),
- \strong{mero}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}),
- \strong{mezl}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}),
- \strong{mino}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}),
- \strong{moxi}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}),
- \strong{nali}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}),
- \strong{neom}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}),
- \strong{neti}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}),
- \strong{nitr}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}),
- \strong{norf}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}),
- \strong{novo}: novobiocin (an ATCvet code: \href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}),
- \strong{oflo}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}),
- \strong{peni}: (benzyl)penicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}),
- \strong{pipe}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}),
- \strong{pita}: piperacillin+tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}),
- \strong{poly}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}),
- \strong{pris}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}),
- \strong{qida}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}),
- \strong{rifa}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}),
- \strong{roxi}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}),
- \strong{siso}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}),
- \strong{teic}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}),
- \strong{tetr}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}),
- \strong{tica}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}),
- \strong{tige}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}),
- \strong{tobr}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}),
- \strong{trim}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
- \strong{trsu}: sulfamethoxazole and trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
- \strong{vanc}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
+ \strong{AMC}: amoxicillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}),
+ \strong{AMK}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}),
+ \strong{AMX}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}),
+ \strong{AMP}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}),
+ \strong{AZM}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}),
+ \strong{AZL}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}),
+ \strong{ATM}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}),
+ \strong{RID}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}),
+ \strong{FEP}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}),
+ \strong{CTX}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}),
+ \strong{FOX}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}),
+ \strong{CED}: cefradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}),
+ \strong{CAZ}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}),
+ \strong{CRO}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}),
+ \strong{CXM}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}),
+ \strong{CHL}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}),
+ \strong{CIP}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}),
+ \strong{CLR}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}),
+ \strong{CLI}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}),
+ \strong{FLC}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}),
+ \strong{COL}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}),
+ \strong{CZO}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}),
+ \strong{DAP}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}),
+ \strong{DOX}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}),
+ \strong{ETP}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}),
+ \strong{ERY}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}),
+ \strong{FOS}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}),
+ \strong{FUS}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}),
+ \strong{GEN}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}),
+ \strong{IPM}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}),
+ \strong{KAN}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}),
+ \strong{LVX}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}),
+ \strong{LIN}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}),
+ \strong{LNZ}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}),
+ \strong{MEM}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}),
+ \strong{MEZ}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}),
+ \strong{MNO}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}),
+ \strong{MFX}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}),
+ \strong{MTR}: metronidazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01XD01}),
+ \strong{NAL}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}),
+ \strong{NEO}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}),
+ \strong{NET}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}),
+ \strong{NIT}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}),
+ \strong{NOR}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}),
+ \strong{NOV}: novobiocin (an ATCvet code: \href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}),
+ \strong{OFX}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}),
+ \strong{OXA}: oxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01CF04}),
+ \strong{PEN}: penicillin G (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}),
+ \strong{PIP}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}),
+ \strong{TZP}: piperacillin/tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}),
+ \strong{PLB}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}),
+ \strong{PRI}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}),
+ \strong{QDA}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}),
+ \strong{RIF}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}),
+ \strong{RXT}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}),
+ \strong{SIS}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}),
+ \strong{TEC}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}),
+ \strong{TCY}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}),
+ \strong{TIC}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}),
+ \strong{TGC}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}),
+ \strong{TOB}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}),
+ \strong{TMP}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
+ \strong{SXT}: trimethoprim/sulfamethoxazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
+ \strong{VAN}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
}
\section{Read more on our website!}{
@@ -174,17 +142,17 @@ a <- data.frame(mo = c("Staphylococcus aureus",
"Escherichia coli",
"Klebsiella pneumoniae",
"Pseudomonas aeruginosa"),
- vanc = "-", # Vancomycin
- amox = "-", # Amoxicillin
- coli = "-", # Colistin
- cfta = "-", # Ceftazidime
- cfur = "-", # Cefuroxime
- peni = "S", # Benzylpenicillin
- cfox = "S", # Cefoxitin
+ VAN = "-", # Vancomycin
+ AMX = "-", # Amoxicillin
+ COL = "-", # Colistin
+ CAZ = "-", # Ceftazidime
+ CXM = "-", # Cefuroxime
+ PEN = "S", # Penicillin G
+ FOX = "S", # Cefoxitin
stringsAsFactors = FALSE)
a
-# mo vanc amox coli cfta cfur peni cfox
+# mo VAN AMX COL CAZ CXM PEN FOX
# 1 Staphylococcus aureus - - - - - S S
# 2 Enterococcus faecalis - - - - - S S
# 3 Escherichia coli - - - - - S S
@@ -196,7 +164,7 @@ a
b <- eucast_rules(a)
b
-# mo vanc amox coli cfta cfur peni cfox
+# mo VAN AMX COL CAZ CXM PEN FOX
# 1 Staphylococcus aureus - S R R S S S
# 2 Enterococcus faecalis - - R R R S R
# 3 Escherichia coli R - - - - R S
diff --git a/man/filter_ab_class.Rd b/man/filter_ab_class.Rd
index 19df430cb..31859f4b4 100644
--- a/man/filter_ab_class.Rd
+++ b/man/filter_ab_class.Rd
@@ -62,9 +62,9 @@ library(dplyr)
# filter on isolates that have any result for any aminoglycoside
septic_patients \%>\% filter_aminoglycosides()
-# this is essentially the same as:
+# this is essentially the same as (but without determination of column names):
septic_patients \%>\%
- filter_at(.vars = vars(c("gent", "tobr", "amik", "kana")),
+ filter_at(.vars = vars(c("GEN", "TOB", "AMK", "KAN")),
.vars_predicate = any_vars(. \%in\% c("S", "I", "R")))
diff --git a/man/first_isolate.Rd b/man/first_isolate.Rd
index b8cc97dff..4de198cf1 100755
--- a/man/first_isolate.Rd
+++ b/man/first_isolate.Rd
@@ -124,14 +124,14 @@ septic_patients \%>\%
# Now let's see if first isolates matter:
A <- septic_patients \%>\%
group_by(hospital_id) \%>\%
- summarise(count = n_rsi(gent), # gentamicin availability
- resistance = portion_IR(gent)) # gentamicin resistance
+ summarise(count = n_rsi(GEN), # gentamicin availability
+ resistance = portion_IR(GEN)) # gentamicin resistance
B <- septic_patients \%>\%
- filter_first_weighted_isolate() \%>\% # the 1st isolate filter
+ filter_first_weighted_isolate() \%>\% # the 1st isolate filter
group_by(hospital_id) \%>\%
- summarise(count = n_rsi(gent), # gentamicin availability
- resistance = portion_IR(gent)) # gentamicin resistance
+ summarise(count = n_rsi(GEN), # gentamicin availability
+ resistance = portion_IR(GEN)) # gentamicin resistance
# Have a look at A and B.
# B is more reliable because every isolate is only counted once.
diff --git a/man/freq.Rd b/man/freq.Rd
index fca5000c5..2e0c4be2c 100755
--- a/man/freq.Rd
+++ b/man/freq.Rd
@@ -215,8 +215,8 @@ septic_patients \%>\%
# check differences between frequency tables
-diff(freq(septic_patients$trim),
- freq(septic_patients$trsu))
+diff(freq(septic_patients$TMP),
+ freq(septic_patients$SXT))
}
\keyword{freq}
\keyword{frequency}
diff --git a/man/get_locale.Rd b/man/get_locale.Rd
deleted file mode 100644
index 3f943deee..000000000
--- a/man/get_locale.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/get_locale.R
-\name{get_locale}
-\alias{get_locale}
-\title{Get language for AMR}
-\usage{
-get_locale()
-}
-\description{
-Determines the system language to be used for language-dependent output of AMR functions, like \code{\link{mo_gramstain}} and \code{\link{mo_type}}.
-}
-\details{
-The system language can be overwritten with \code{\link{getOption}("AMR_locale")}.
-}
-\section{Supported languages}{
-
-Supported languages are \code{"en"} (English), \code{"de"} (German), \code{"nl"} (Dutch), \code{"es"} (Spanish), \code{"it"} (Italian), \code{"fr"} (French), and \code{"pt"} (Portuguese).
-}
-
-\section{Read more on our website!}{
-
-On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
-}
-
diff --git a/man/ggplot_rsi.Rd b/man/ggplot_rsi.Rd
index 709b2bc44..bc384174f 100644
--- a/man/ggplot_rsi.Rd
+++ b/man/ggplot_rsi.Rd
@@ -12,13 +12,13 @@
\usage{
ggplot_rsi(data, position = NULL, x = "Antibiotic",
fill = "Interpretation", facet = NULL, breaks = seq(0, 1, 0.1),
- limits = NULL, translate_ab = "official", fun = count_df,
- nrow = NULL, datalabels = TRUE, datalabels.size = 3,
- datalabels.colour = "grey15", ...)
+ limits = NULL, translate_ab = "name", language = get_locale(),
+ fun = count_df, nrow = NULL, datalabels = TRUE,
+ datalabels.size = 3, datalabels.colour = "grey15", ...)
geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
- fill = "Interpretation", translate_ab = "official", fun = count_df,
- ...)
+ fill = "Interpretation", translate_ab = "name",
+ language = get_locale(), fun = count_df, ...)
facet_rsi(facet = c("Interpretation", "Antibiotic"), nrow = NULL)
@@ -46,7 +46,9 @@ labels_rsi_count(position = NULL, x = "Antibiotic",
\item{limits}{numeric vector of length two providing limits of the scale, use \code{NA} to refer to the existing minimum or maximum}
-\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.}
+\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{ab_name}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.}
+
+\item{language}{the language used for translation of antibiotic names}
\item{fun}{function to transform \code{data}, either \code{\link{count_df}} (default) or \code{\link{portion_df}}}
@@ -64,7 +66,7 @@ labels_rsi_count(position = NULL, x = "Antibiotic",
Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
}
\details{
-At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
+At default, the names of antibiotics will be shown on the plots using \code{\link{ab_name}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
\strong{The functions}\cr
\code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{fun} (\code{\link{count_df}} at default, can also be \code{\link{portion_df}}) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
@@ -91,11 +93,11 @@ library(dplyr)
library(ggplot2)
# get antimicrobial results for drugs against a UTI:
-ggplot(septic_patients \%>\% select(amox, nitr, fosf, trim, cipr)) +
+ggplot(septic_patients \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
geom_rsi()
# prettify the plot using some additional functions:
-df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
+df <- septic_patients[, c("AMX", "NIT", "FOS", "TMP", "CIP")]
ggplot(df) +
geom_rsi() +
scale_y_percent() +
@@ -105,17 +107,17 @@ ggplot(df) +
# or better yet, simplify this using the wrapper function - a single command:
septic_patients \%>\%
- select(amox, nitr, fosf, trim, cipr) \%>\%
+ select(AMX, NIT, FOS, TMP, CIP) \%>\%
ggplot_rsi()
# get only portions and no counts:
septic_patients \%>\%
- select(amox, nitr, fosf, trim, cipr) \%>\%
+ select(AMX, NIT, FOS, TMP, CIP) \%>\%
ggplot_rsi(fun = portion_df)
# add other ggplot2 parameters as you like:
septic_patients \%>\%
- select(amox, nitr, fosf, trim, cipr) \%>\%
+ select(AMX, NIT, FOS, TMP, CIP) \%>\%
ggplot_rsi(width = 0.5,
colour = "black",
size = 1,
@@ -130,19 +132,19 @@ septic_patients \%>\%
# `age_group` is also a function of this package:
group_by(age_group = age_groups(age)) \%>\%
select(age_group,
- cipr) \%>\%
+ CIP) \%>\%
ggplot_rsi(x = "age_group")
\donttest{
# for colourblind mode, use divergent colours from the viridis package:
septic_patients \%>\%
- select(amox, nitr, fosf, trim, cipr) \%>\%
+ select(AMX, NIT, FOS, TMP, CIP) \%>\%
ggplot_rsi() + scale_fill_viridis_d()
# it also supports groups (don't forget to use the group var on `x` or `facet`):
septic_patients \%>\%
- select(hospital_id, amox, nitr, fosf, trim, cipr) \%>\%
+ select(hospital_id, AMX, NIT, FOS, TMP, CIP) \%>\%
group_by(hospital_id) \%>\%
ggplot_rsi(x = hospital_id,
facet = Antibiotic,
@@ -166,7 +168,7 @@ septic_patients \%>\%
# get short MO names (like "E. coli")
mutate(mo = mo_shortname(mo, Becker = TRUE)) \%>\%
# select this short name and some antiseptic drugs
- select(mo, cfur, gent, cipr) \%>\%
+ select(mo, CXM, GEN, CIP) \%>\%
# group by MO
group_by(mo) \%>\%
# plot the thing, putting MOs on the facet
diff --git a/man/guess_ab_col.Rd b/man/guess_ab_col.Rd
index 831c67d34..8770a4dc2 100644
--- a/man/guess_ab_col.Rd
+++ b/man/guess_ab_col.Rd
@@ -27,7 +27,7 @@ df <- data.frame(amox = "S",
guess_ab_col(df, "amoxicillin")
# [1] "amox"
-guess_ab_col(df, "J01AA07") # ATC code of Tetracycline
+guess_ab_col(df, "J01AA07") # ATC code of tetracycline
# [1] "tetr"
guess_ab_col(df, "J01AA07", verbose = TRUE)
@@ -41,6 +41,6 @@ guess_ab_col(df, "ampicillin")
# [1] "AMP_ND10"
guess_ab_col(df, "J01CR02")
# [1] "AMC_ED20"
-guess_ab_col(df, as.atc("augmentin"))
+guess_ab_col(df, as.ab("augmentin"))
# [1] "AMC_ED20"
}
diff --git a/man/key_antibiotics.Rd b/man/key_antibiotics.Rd
index b632b9677..e6f71f213 100755
--- a/man/key_antibiotics.Rd
+++ b/man/key_antibiotics.Rd
@@ -6,19 +6,19 @@
\title{Key antibiotics for first \emph{weighted} isolates}
\usage{
key_antibiotics(tbl, col_mo = NULL, universal_1 = guess_ab_col(tbl,
- "amox"), universal_2 = guess_ab_col(tbl, "amcl"),
- universal_3 = guess_ab_col(tbl, "cfur"),
- universal_4 = guess_ab_col(tbl, "pita"),
- universal_5 = guess_ab_col(tbl, "cipr"),
- universal_6 = guess_ab_col(tbl, "trsu"),
- GramPos_1 = guess_ab_col(tbl, "vanc"), GramPos_2 = guess_ab_col(tbl,
- "teic"), GramPos_3 = guess_ab_col(tbl, "tetr"),
- GramPos_4 = guess_ab_col(tbl, "eryt"), GramPos_5 = guess_ab_col(tbl,
- "oxac"), GramPos_6 = guess_ab_col(tbl, "rifa"),
- GramNeg_1 = guess_ab_col(tbl, "gent"), GramNeg_2 = guess_ab_col(tbl,
- "tobr"), GramNeg_3 = guess_ab_col(tbl, "coli"),
- GramNeg_4 = guess_ab_col(tbl, "cfot"), GramNeg_5 = guess_ab_col(tbl,
- "cfta"), GramNeg_6 = guess_ab_col(tbl, "mero"), warnings = TRUE, ...)
+ "AMX"), universal_2 = guess_ab_col(tbl, "AMC"),
+ universal_3 = guess_ab_col(tbl, "CXM"),
+ universal_4 = guess_ab_col(tbl, "TZP"),
+ universal_5 = guess_ab_col(tbl, "CIP"),
+ universal_6 = guess_ab_col(tbl, "SXT"), GramPos_1 = guess_ab_col(tbl,
+ "VAN"), GramPos_2 = guess_ab_col(tbl, "TEC"),
+ GramPos_3 = guess_ab_col(tbl, "TCY"), GramPos_4 = guess_ab_col(tbl,
+ "ERY"), GramPos_5 = guess_ab_col(tbl, "OXA"),
+ GramPos_6 = guess_ab_col(tbl, "RIF"), GramNeg_1 = guess_ab_col(tbl,
+ "GEN"), GramNeg_2 = guess_ab_col(tbl, "TOB"),
+ GramNeg_3 = guess_ab_col(tbl, "COL"), GramNeg_4 = guess_ab_col(tbl,
+ "CTX"), GramNeg_5 = guess_ab_col(tbl, "CAZ"),
+ GramNeg_6 = guess_ab_col(tbl, "MEM"), warnings = TRUE, ...)
key_antibiotics_equal(x, y, type = c("keyantibiotics", "points"),
ignore_I = TRUE, points_threshold = 2, info = FALSE)
diff --git a/man/mdro.Rd b/man/mdro.Rd
index e31e14218..b5028ad88 100644
--- a/man/mdro.Rd
+++ b/man/mdro.Rd
@@ -7,46 +7,17 @@
\alias{eucast_exceptional_phenotypes}
\title{Determine multidrug-resistant organisms (MDRO)}
\usage{
-mdro(tbl, country = NULL, col_mo = NULL, info = TRUE,
- amcl = guess_ab_col(), amik = guess_ab_col(),
- amox = guess_ab_col(), ampi = guess_ab_col(),
- azit = guess_ab_col(), aztr = guess_ab_col(),
- cefa = guess_ab_col(), cfra = guess_ab_col(),
- cfep = guess_ab_col(), cfot = guess_ab_col(),
- cfox = guess_ab_col(), cfta = guess_ab_col(),
- cftr = guess_ab_col(), cfur = guess_ab_col(),
- chlo = guess_ab_col(), cipr = guess_ab_col(),
- clar = guess_ab_col(), clin = guess_ab_col(),
- clox = guess_ab_col(), coli = guess_ab_col(),
- czol = guess_ab_col(), dapt = guess_ab_col(),
- doxy = guess_ab_col(), erta = guess_ab_col(),
- eryt = guess_ab_col(), fosf = guess_ab_col(),
- fusi = guess_ab_col(), gent = guess_ab_col(),
- imip = guess_ab_col(), kana = guess_ab_col(),
- levo = guess_ab_col(), linc = guess_ab_col(),
- line = guess_ab_col(), mero = guess_ab_col(),
- metr = guess_ab_col(), mino = guess_ab_col(),
- moxi = guess_ab_col(), nali = guess_ab_col(),
- neom = guess_ab_col(), neti = guess_ab_col(),
- nitr = guess_ab_col(), novo = guess_ab_col(),
- norf = guess_ab_col(), oflo = guess_ab_col(),
- peni = guess_ab_col(), pipe = guess_ab_col(),
- pita = guess_ab_col(), poly = guess_ab_col(),
- qida = guess_ab_col(), rifa = guess_ab_col(),
- roxi = guess_ab_col(), siso = guess_ab_col(),
- teic = guess_ab_col(), tetr = guess_ab_col(),
- tica = guess_ab_col(), tige = guess_ab_col(),
- tobr = guess_ab_col(), trim = guess_ab_col(),
- trsu = guess_ab_col(), vanc = guess_ab_col(), verbose = FALSE)
+mdro(x, country = NULL, col_mo = NULL, info = TRUE,
+ verbose = FALSE, ...)
brmo(..., country = "nl")
-mrgn(tbl, country = "de", ...)
+mrgn(x, country = "de", ...)
-eucast_exceptional_phenotypes(tbl, country = "EUCAST", ...)
+eucast_exceptional_phenotypes(x, country = "EUCAST", ...)
}
\arguments{
-\item{tbl}{table with antibiotic columns, like e.g. \code{amox} and \code{amcl}}
+\item{x}{table with antibiotic columns, like e.g. \code{AMX} and \code{AMC}}
\item{country}{country code to determine guidelines. EUCAST rules will be used when left empty, see Details. Should be or a code from the \href{https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2#Officially_assigned_code_elements}{list of ISO 3166-1 alpha-2 country codes}. Case-insensitive. Currently supported are \code{de} (Germany) and \code{nl} (the Netherlands).}
@@ -54,129 +25,9 @@ eucast_exceptional_phenotypes(tbl, country = "EUCAST", ...)
\item{info}{print progress}
-\item{amcl}{column name of an antibiotic, see Antibiotics}
-
-\item{amik}{column name of an antibiotic, see Antibiotics}
-
-\item{amox}{column name of an antibiotic, see Antibiotics}
-
-\item{ampi}{column name of an antibiotic, see Antibiotics}
-
-\item{azit}{column name of an antibiotic, see Antibiotics}
-
-\item{aztr}{column name of an antibiotic, see Antibiotics}
-
-\item{cefa}{column name of an antibiotic, see Antibiotics}
-
-\item{cfra}{column name of an antibiotic, see Antibiotics}
-
-\item{cfep}{column name of an antibiotic, see Antibiotics}
-
-\item{cfot}{column name of an antibiotic, see Antibiotics}
-
-\item{cfox}{column name of an antibiotic, see Antibiotics}
-
-\item{cfta}{column name of an antibiotic, see Antibiotics}
-
-\item{cftr}{column name of an antibiotic, see Antibiotics}
-
-\item{cfur}{column name of an antibiotic, see Antibiotics}
-
-\item{chlo}{column name of an antibiotic, see Antibiotics}
-
-\item{cipr}{column name of an antibiotic, see Antibiotics}
-
-\item{clar}{column name of an antibiotic, see Antibiotics}
-
-\item{clin}{column name of an antibiotic, see Antibiotics}
-
-\item{clox}{column name of an antibiotic, see Antibiotics}
-
-\item{coli}{column name of an antibiotic, see Antibiotics}
-
-\item{czol}{column name of an antibiotic, see Antibiotics}
-
-\item{dapt}{column name of an antibiotic, see Antibiotics}
-
-\item{doxy}{column name of an antibiotic, see Antibiotics}
-
-\item{erta}{column name of an antibiotic, see Antibiotics}
-
-\item{eryt}{column name of an antibiotic, see Antibiotics}
-
-\item{fosf}{column name of an antibiotic, see Antibiotics}
-
-\item{fusi}{column name of an antibiotic, see Antibiotics}
-
-\item{gent}{column name of an antibiotic, see Antibiotics}
-
-\item{imip}{column name of an antibiotic, see Antibiotics}
-
-\item{kana}{column name of an antibiotic, see Antibiotics}
-
-\item{levo}{column name of an antibiotic, see Antibiotics}
-
-\item{linc}{column name of an antibiotic, see Antibiotics}
-
-\item{line}{column name of an antibiotic, see Antibiotics}
-
-\item{mero}{column name of an antibiotic, see Antibiotics}
-
-\item{metr}{column name of an antibiotic, see Antibiotics}
-
-\item{mino}{column name of an antibiotic, see Antibiotics}
-
-\item{moxi}{column name of an antibiotic, see Antibiotics}
-
-\item{nali}{column name of an antibiotic, see Antibiotics}
-
-\item{neom}{column name of an antibiotic, see Antibiotics}
-
-\item{neti}{column name of an antibiotic, see Antibiotics}
-
-\item{nitr}{column name of an antibiotic, see Antibiotics}
-
-\item{novo}{column name of an antibiotic, see Antibiotics}
-
-\item{norf}{column name of an antibiotic, see Antibiotics}
-
-\item{oflo}{column name of an antibiotic, see Antibiotics}
-
-\item{peni}{column name of an antibiotic, see Antibiotics}
-
-\item{pipe}{column name of an antibiotic, see Antibiotics}
-
-\item{pita}{column name of an antibiotic, see Antibiotics}
-
-\item{poly}{column name of an antibiotic, see Antibiotics}
-
-\item{qida}{column name of an antibiotic, see Antibiotics}
-
-\item{rifa}{column name of an antibiotic, see Antibiotics}
-
-\item{roxi}{column name of an antibiotic, see Antibiotics}
-
-\item{siso}{column name of an antibiotic, see Antibiotics}
-
-\item{teic}{column name of an antibiotic, see Antibiotics}
-
-\item{tetr}{column name of an antibiotic, see Antibiotics}
-
-\item{tica}{column name of an antibiotic, see Antibiotics}
-
-\item{tige}{column name of an antibiotic, see Antibiotics}
-
-\item{tobr}{column name of an antibiotic, see Antibiotics}
-
-\item{trim}{column name of an antibiotic, see Antibiotics}
-
-\item{trsu}{column name of an antibiotic, see Antibiotics}
-
-\item{vanc}{column name of an antibiotic, see Antibiotics}
-
\item{verbose}{print additional info: missing antibiotic columns per parameter}
-\item{...}{parameters that are passed on to methods}
+\item{...}{column name of an antibiotic, see section Antibiotics}
}
\value{
Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}.
@@ -189,72 +40,74 @@ When \code{country} will be left blank, guidelines will be taken from EUCAST Exp
}
\section{Antibiotics}{
-To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab_col}} or input a text (case-insensitive) or use \code{NULL} to skip a column (e.g. \code{tica = NULL}). Non-existing columns will anyway be skipped with a warning.
+To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab_col}} or input a text (case-insensitive), or use \code{NULL} to skip a column (e.g. \code{TIC = NULL} to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
-Abbrevations of the column containing antibiotics in the form: \strong{abbreviation}: generic name (\emph{ATC code})
+Available abbrevations of the column containing antibiotics in the form '\strong{antimicrobial ID}: name (\emph{ATC code})':
- \strong{amcl}: amoxicillin+clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}),
- \strong{amik}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}),
- \strong{amox}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}),
- \strong{ampi}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}),
- \strong{azit}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}),
- \strong{azlo}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}),
- \strong{aztr}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}),
- \strong{cefa}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}),
- \strong{cfep}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}),
- \strong{cfot}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}),
- \strong{cfox}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}),
- \strong{cfra}: cefradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}),
- \strong{cfta}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}),
- \strong{cftr}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}),
- \strong{cfur}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}),
- \strong{chlo}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}),
- \strong{cipr}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}),
- \strong{clar}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}),
- \strong{clin}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}),
- \strong{clox}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}),
- \strong{coli}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}),
- \strong{czol}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}),
- \strong{dapt}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}),
- \strong{doxy}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}),
- \strong{erta}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}),
- \strong{eryt}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}),
- \strong{fosf}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}),
- \strong{fusi}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}),
- \strong{gent}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}),
- \strong{imip}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}),
- \strong{kana}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}),
- \strong{levo}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}),
- \strong{linc}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}),
- \strong{line}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}),
- \strong{mero}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}),
- \strong{mezl}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}),
- \strong{mino}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}),
- \strong{moxi}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}),
- \strong{nali}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}),
- \strong{neom}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}),
- \strong{neti}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}),
- \strong{nitr}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}),
- \strong{norf}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}),
- \strong{novo}: novobiocin (an ATCvet code: \href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}),
- \strong{oflo}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}),
- \strong{peni}: (benzyl)penicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}),
- \strong{pipe}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}),
- \strong{pita}: piperacillin+tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}),
- \strong{poly}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}),
- \strong{pris}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}),
- \strong{qida}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}),
- \strong{rifa}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}),
- \strong{roxi}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}),
- \strong{siso}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}),
- \strong{teic}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}),
- \strong{tetr}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}),
- \strong{tica}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}),
- \strong{tige}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}),
- \strong{tobr}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}),
- \strong{trim}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
- \strong{trsu}: sulfamethoxazole and trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
- \strong{vanc}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
+ \strong{AMC}: amoxicillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}),
+ \strong{AMK}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}),
+ \strong{AMX}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}),
+ \strong{AMP}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}),
+ \strong{AZM}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}),
+ \strong{AZL}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}),
+ \strong{ATM}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}),
+ \strong{RID}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}),
+ \strong{FEP}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}),
+ \strong{CTX}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}),
+ \strong{FOX}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}),
+ \strong{CED}: cefradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}),
+ \strong{CAZ}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}),
+ \strong{CRO}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}),
+ \strong{CXM}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}),
+ \strong{CHL}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}),
+ \strong{CIP}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}),
+ \strong{CLR}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}),
+ \strong{CLI}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}),
+ \strong{FLC}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}),
+ \strong{COL}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}),
+ \strong{CZO}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}),
+ \strong{DAP}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}),
+ \strong{DOX}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}),
+ \strong{ETP}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}),
+ \strong{ERY}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}),
+ \strong{FOS}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}),
+ \strong{FUS}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}),
+ \strong{GEN}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}),
+ \strong{IPM}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}),
+ \strong{KAN}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}),
+ \strong{LVX}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}),
+ \strong{LIN}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}),
+ \strong{LNZ}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}),
+ \strong{MEM}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}),
+ \strong{MEZ}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}),
+ \strong{MNO}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}),
+ \strong{MFX}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}),
+ \strong{MTR}: metronidazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01XD01}),
+ \strong{NAL}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}),
+ \strong{NEO}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}),
+ \strong{NET}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}),
+ \strong{NIT}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}),
+ \strong{NOR}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}),
+ \strong{NOV}: novobiocin (an ATCvet code: \href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}),
+ \strong{OFX}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}),
+ \strong{OXA}: oxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01CF04}),
+ \strong{PEN}: penicillin G (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}),
+ \strong{PIP}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}),
+ \strong{TZP}: piperacillin/tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}),
+ \strong{PLB}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}),
+ \strong{PRI}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}),
+ \strong{QDA}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}),
+ \strong{RIF}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}),
+ \strong{RXT}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}),
+ \strong{SIS}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}),
+ \strong{TEC}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}),
+ \strong{TCY}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}),
+ \strong{TIC}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}),
+ \strong{TGC}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}),
+ \strong{TOB}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}),
+ \strong{TMP}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
+ \strong{SXT}: trimethoprim/sulfamethoxazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
+ \strong{VAN}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
}
\section{Read more on our website!}{
diff --git a/man/mo_property.Rd b/man/mo_property.Rd
index e917ea5e7..6fccb9cd9 100644
--- a/man/mo_property.Rd
+++ b/man/mo_property.Rd
@@ -92,13 +92,10 @@ All functions will return the most recently known taxonomic property according t
The Gram stain - \code{mo_gramstain()} - will be determined on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002) who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram positive - all other bacteria are considered Gram negative. Species outside the kingdom of Bacteria will return a value \code{NA}.
+All output will be \link{translate}d where possible.
+
The function \code{mo_url()} will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
}
-\section{Supported languages}{
-
-Supported languages are \code{"en"} (English), \code{"de"} (German), \code{"nl"} (Dutch), \code{"es"} (Spanish), \code{"it"} (Italian), \code{"fr"} (French), and \code{"pt"} (Portuguese).
-}
-
\section{Catalogue of Life}{
\if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
@@ -111,7 +108,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
[1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13}
-[2] Becker K \emph{et al.} \strong{Implications of identifying the recently defined members of the S. aureus complex, S. argenteus and S. schweitzeri: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).}. 2019. Clin Microbiol Infect. 2019 Mar 11. \url{https://doi.org/10.1016/j.cmi.2019.02.028}
+[2] Becker K \emph{et al.} \strong{Implications of identifying the recently defined members of the \emph{S. aureus} complex, \emph{S. argenteus} and \emph{S. schweitzeri}: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).} 2019. Clin Microbiol Infect. \url{https://doi.org/10.1016/j.cmi.2019.02.028}
[3] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571}
@@ -142,7 +139,7 @@ mo_shortname("E. coli") # "E. coli"
mo_gramstain("E. coli") # "Gram negative"
mo_type("E. coli") # "Bacteria" (equal to kingdom)
mo_rank("E. coli") # "species"
-mo_url("E. coli") # get the direct url to the Catalogue of Life
+mo_url("E. coli") # get the direct url to the online database entry
## scientific reference
mo_ref("E. coli") # "Castellani et al., 1919"
@@ -200,7 +197,7 @@ mo_fullname("S. pyogenes",
language = "nl") # "Streptococcus groep A"
-# get a list with the complete taxonomy (kingdom to subspecies)
+# get a list with the complete taxonomy (from kingdom to subspecies)
mo_taxonomy("E. coli")
}
\seealso{
diff --git a/man/portion.Rd b/man/portion.Rd
index 705acf03d..fa81cdaca 100644
--- a/man/portion.Rd
+++ b/man/portion.Rd
@@ -30,8 +30,8 @@ portion_SI(..., minimum = 30, as_percent = FALSE,
portion_S(..., minimum = 30, as_percent = FALSE,
also_single_tested = FALSE)
-portion_df(data, translate_ab = getOption("get_antibiotic_names",
- "official"), minimum = 30, as_percent = FALSE, combine_IR = FALSE)
+portion_df(data, translate_ab = "name", language = get_locale(),
+ minimum = 30, as_percent = FALSE, combine_IR = FALSE)
}
\arguments{
\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
@@ -44,7 +44,9 @@ portion_df(data, translate_ab = getOption("get_antibiotic_names",
\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
-\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.}
+\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}}}
+
+\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
\item{combine_IR}{a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)}
}
@@ -62,8 +64,6 @@ These functions can be used to calculate the (co-)resistance of microbial isolat
These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. \emph{Low counts can infuence the outcome - these \code{portion} functions may camouflage this, since they only return the portion albeit being dependent on the \code{minimum} parameter.}
\code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
-
-The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
\if{html}{
\cr\cr
To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
@@ -89,63 +89,63 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
?septic_patients
# Calculate resistance
-portion_R(septic_patients$amox)
-portion_IR(septic_patients$amox)
+portion_R(septic_patients$AMX)
+portion_IR(septic_patients$AMX)
# Or susceptibility
-portion_S(septic_patients$amox)
-portion_SI(septic_patients$amox)
+portion_S(septic_patients$AMX)
+portion_SI(septic_patients$AMX)
# Do the above with pipes:
library(dplyr)
-septic_patients \%>\% portion_R(amox)
-septic_patients \%>\% portion_IR(amox)
-septic_patients \%>\% portion_S(amox)
-septic_patients \%>\% portion_SI(amox)
+septic_patients \%>\% portion_R(AMX)
+septic_patients \%>\% portion_IR(AMX)
+septic_patients \%>\% portion_S(AMX)
+septic_patients \%>\% portion_SI(AMX)
septic_patients \%>\%
group_by(hospital_id) \%>\%
- summarise(p = portion_S(cipr),
- n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
+ summarise(p = portion_S(CIP),
+ n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr
septic_patients \%>\%
group_by(hospital_id) \%>\%
- summarise(R = portion_R(cipr, as_percent = TRUE),
- I = portion_I(cipr, as_percent = TRUE),
- S = portion_S(cipr, as_percent = TRUE),
- n1 = count_all(cipr), # the actual total; sum of all three
- n2 = n_rsi(cipr), # same - analogous to n_distinct
+ summarise(R = portion_R(CIP, as_percent = TRUE),
+ I = portion_I(CIP, as_percent = TRUE),
+ S = portion_S(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:
-septic_patients \%>\% portion_S(amcl) # S = 71.4\%
-septic_patients \%>\% count_all(amcl) # n = 1879
+septic_patients \%>\% portion_S(AMC) # S = 71.4\%
+septic_patients \%>\% count_all(AMC) # n = 1879
-septic_patients \%>\% portion_S(gent) # S = 74.0\%
-septic_patients \%>\% count_all(gent) # n = 1855
+septic_patients \%>\% portion_S(GEN) # S = 74.0\%
+septic_patients \%>\% count_all(GEN) # n = 1855
-septic_patients \%>\% portion_S(amcl, gent) # S = 92.3\%
-septic_patients \%>\% count_all(amcl, gent) # n = 1798
+septic_patients \%>\% portion_S(AMC, GEN) # S = 92.3\%
+septic_patients \%>\% count_all(AMC, GEN) # n = 1798
septic_patients \%>\%
group_by(hospital_id) \%>\%
- summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
- cipro_n = count_all(cipr),
- genta_p = portion_S(gent, as_percent = TRUE),
- genta_n = count_all(gent),
- combination_p = portion_S(cipr, gent, as_percent = TRUE),
- combination_n = count_all(cipr, gent))
+ summarise(cipro_p = portion_S(CIP, as_percent = TRUE),
+ cipro_n = count_all(CIP),
+ genta_p = portion_S(GEN, as_percent = TRUE),
+ genta_n = count_all(GEN),
+ combination_p = portion_S(CIP, GEN, as_percent = TRUE),
+ combination_n = count_all(CIP, GEN))
# Get portions S/I/R immediately of all rsi columns
septic_patients \%>\%
- select(amox, cipr) \%>\%
+ select(AMX, CIP) \%>\%
portion_df(translate = FALSE)
# It also supports grouping variables
septic_patients \%>\%
- select(hospital_id, amox, cipr) \%>\%
+ select(hospital_id, AMX, CIP) \%>\%
group_by(hospital_id) \%>\%
portion_df(translate = FALSE)
@@ -156,8 +156,8 @@ septic_patients \%>\%
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
- summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
- n = count_all(amox, metr))
+ summarise(p = portion_S(AMX, MTR), # amoxicillin with metronidazole
+ n = count_all(AMX, MTR))
}
}
\seealso{
diff --git a/man/resistance_predict.Rd b/man/resistance_predict.Rd
index dae9b797c..cc98afd7b 100644
--- a/man/resistance_predict.Rd
+++ b/man/resistance_predict.Rd
@@ -86,7 +86,7 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
}
\examples{
-x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
+x <- resistance_predict(septic_patients, col_ab = "AMX", year_min = 2010)
plot(x)
ggplot_rsi_predict(x)
@@ -95,7 +95,7 @@ library(dplyr)
x <- septic_patients \%>\%
filter_first_isolate() \%>\%
filter(mo_genus(mo) == "Staphylococcus") \%>\%
- resistance_predict("peni")
+ resistance_predict("PEN")
plot(x)
@@ -109,7 +109,7 @@ if (!require(ggplot2)) {
data <- septic_patients \%>\%
filter(mo == as.mo("E. coli")) \%>\%
- resistance_predict(col_ab = "amox",
+ resistance_predict(col_ab = "AMX",
col_date = "date",
info = FALSE,
minimum = 15)
diff --git a/man/rsi.Rd b/man/rsi.Rd
deleted file mode 100644
index 9a96902b5..000000000
--- a/man/rsi.Rd
+++ /dev/null
@@ -1,23 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/portion.R
-\name{rsi}
-\alias{rsi}
-\title{Calculate resistance of isolates}
-\usage{
-rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
- as_percent = FALSE, ...)
-}
-\arguments{
-\item{ab1, ab2}{vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.}
-
-\item{interpretation}{antimicrobial interpretation to check for}
-
-\item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.}
-
-\item{as_percent}{a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.}
-
-\item{...}{deprecated parameters to support usage on older versions}
-}
-\description{
-This function is deprecated. Use the \code{\link{portion}} functions instead.
-}
diff --git a/man/rsi_translation.Rd b/man/rsi_translation.Rd
new file mode 100644
index 000000000..e89c881e9
--- /dev/null
+++ b/man/rsi_translation.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{rsi_translation}
+\alias{rsi_translation}
+\title{Data set for RSI interpretation}
+\format{A \code{\link{data.frame}} with 11,559 observations and 9 variables:
+\describe{
+ \item{\code{guideline}}{Name of the guideline}
+ \item{\code{mo}}{Microbial ID, see \code{\link{as.mo}}}
+ \item{\code{ab}}{Antibiotic ID, see \code{\link{as.ab}}}
+ \item{\code{ref_tbl}}{Info about where the guideline rule can be found}
+ \item{\code{S_mic}}{Lowest MIC value that leads to "S"}
+ \item{\code{R_mic}}{Highest MIC value that leads to "R"}
+ \item{\code{dose_disk}}{Dose of the used disk diffusion method}
+ \item{\code{S_disk}}{Lowest number of millimeters that leads to "S"}
+ \item{\code{R_disk}}{Highest number of millimeters that leads to "R"}
+}}
+\usage{
+rsi_translation
+}
+\description{
+Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use \code{\link{as.rsi}} to transform MICs or disks measurements to RSI values.
+}
+\section{Read more on our website!}{
+
+On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
+}
+
+\keyword{datasets}
diff --git a/man/translate.Rd b/man/translate.Rd
new file mode 100644
index 000000000..90a874823
--- /dev/null
+++ b/man/translate.Rd
@@ -0,0 +1,55 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/get_locale.R
+\name{translate}
+\alias{translate}
+\alias{get_locale}
+\title{Translate strings from AMR package}
+\usage{
+get_locale()
+}
+\description{
+For language-dependent output of AMR functions, like \code{\link{mo_fullname}} and \code{\link{mo_type}}.
+}
+\details{
+Strings will be translated to foreign languages if they are defined in a local translation file. This file comes with this package and can be found when running:
+
+\code{system.file("translations.tsv", package = "AMR")}
+
+This file will be read by all functions where a translated output can be desired, like all \code{\link{mo_property}} functions (\code{\link{mo_fullname}}, \code{\link{mo_type}}, etc.). Please suggest your own translations \href{https://gitlab.com/msberends/AMR/issues/new?issue[title]=Translation suggestion}{by creating a new issue on our repository}.
+
+The system language will be used at default, if supported, using \code{\link{get_locale}}. The system language can be overwritten with \code{\link{getOption}("AMR_locale")}.
+}
+\section{Read more on our website!}{
+
+On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
+}
+
+\examples{
+# The 'language' parameter of below functions
+# will be set automatically to your system language
+# with get_locale()
+
+# English
+mo_fullname("CoNS", language = "en")
+#> "Coagulase-negative Staphylococcus (CoNS)"
+
+# German
+mo_fullname("CoNS", language = "de")
+#> "Koagulase-negative Staphylococcus (KNS)"
+
+# Dutch
+mo_fullname("CoNS", language = "nl")
+#> "Coagulase-negatieve Staphylococcus (CNS)"
+
+# Spanish
+mo_fullname("CoNS", language = "es")
+#> "Staphylococcus coagulasa negativo (SCN)"
+
+# Italian
+mo_fullname("CoNS", language = "it")
+#> "Staphylococcus negativo coagulasi (CoNS)"
+
+# Portuguese
+mo_fullname("CoNS", language = "pt")
+#> "Staphylococcus coagulase negativo (CoNS)"
+}
diff --git a/pkgdown/extra.js b/pkgdown/extra.js
index 876bc3fb5..35858b3a1 100644
--- a/pkgdown/extra.js
+++ b/pkgdown/extra.js
@@ -24,6 +24,10 @@
// Add updated Font Awesome 5.6.3 library
$('head').append('');
+// Email template for new GitLab issues
+//https://stackoverflow.com/a/33190494/4575331
+//incoming+msberends-amr-9011429-5miwzuo1xo70wbz9r6fwv4dmg-issue@incoming.gitlab.com
+
// Edit footer
$( document ).ready(function() {
diff --git a/reproduction_of_antibiotics.R b/reproduction_of_antibiotics.R
index b1a657145..f7f0e2cb5 100644
--- a/reproduction_of_antibiotics.R
+++ b/reproduction_of_antibiotics.R
@@ -1,7 +1,145 @@
-# WORK IN PROGRESS --------------------------------------------------------
+library(dplyr)
-# vector with official names, return vector with CIDs
+# got EARS-Net codes (= ECDC/WHO codes) from here:
+
+# Installed WHONET 2019 software on Windows (http://www.whonet.org/software.html),
+# opened C:\WHONET\Codes\WHONETCodes.mdb in MS Access
+# and exported table 'DRGLST' to MS Excel
+library(readxl)
+DRGLST <- read_excel("DRGLST.xlsx")
+abx <- DRGLST %>%
+ select(ab = WHON5_CODE,
+ name = ANTIBIOTIC) %>%
+ # remove the ones without WHONET code
+ filter(!is.na(ab)) %>%
+ distinct(name, .keep_all = TRUE) %>%
+ # add the ones without WHONET code
+ bind_rows(
+ DRGLST %>%
+ select(ab = WHON5_CODE,
+ name = ANTIBIOTIC) %>%
+ filter(is.na(ab)) %>%
+ distinct(name, .keep_all = TRUE)
+ # add new ab code later
+ ) %>%
+ arrange(name)
+
+# add old ATC codes
+ab_old <- AMR::antibiotics %>%
+ mutate(official = gsub("( and |, )", "/", official),
+ abbr = tolower(paste(ifelse(is.na(abbr), "", abbr),
+ ifelse(is.na(certe), "", certe),
+ ifelse(is.na(umcg), "", umcg),
+ sep = "|")))
+for (i in 1:nrow(ab_old)) {
+ abbr <- ab_old[i, "abbr"]
+ abbr <- strsplit(abbr, "|", fixed = TRUE) %>% unlist() %>% unique()
+ abbr <- abbr[abbr != ""]
+ #print(abbr)
+ if (length(abbr) == 0) {
+ ab_old[i, "abbr"] <- NA_character_
+ } else {
+ ab_old[i, "abbr"] <- paste(abbr, collapse = "|")
+ }
+}
+
+# create reference data set: to be able to map ab to atc
+abx_atc1 <- abx %>%
+ mutate(name_lower = tolower(name)) %>%
+ left_join(ab_old %>%
+ select(ears_net, atc), by = c(ab = "ears_net")) %>%
+ rename(atc1 = atc) %>%
+ left_join(ab_old %>%
+ mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
+ transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
+ rename(atc2 = atc) %>%
+ left_join(ab_old %>%
+ mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
+ mutate(official = gsub("f", "ph", official)) %>%
+ transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
+ rename(atc3 = atc) %>%
+ left_join(ab_old %>%
+ mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
+ mutate(official = gsub("t", "th", official)) %>%
+ transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
+ rename(atc4 = atc) %>%
+ left_join(ab_old %>%
+ mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
+ mutate(official = gsub("f", "ph", official)) %>%
+ mutate(official = gsub("t", "th", official)) %>%
+ transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
+ rename(atc5 = atc) %>%
+ left_join(ab_old %>%
+ mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
+ mutate(official = gsub("f", "ph", official)) %>%
+ mutate(official = gsub("t", "th", official)) %>%
+ mutate(official = gsub("ine$", "in", official)) %>%
+ transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
+ rename(atc6 = atc) %>%
+ mutate(atc = case_when(!is.na(atc1) ~ atc1,
+ !is.na(atc2) ~ atc2,
+ !is.na(atc3) ~ atc3,
+ !is.na(atc4) ~ atc4,
+ !is.na(atc4) ~ atc5,
+ TRUE ~ atc6)) %>%
+ distinct(ab, name, .keep_all = TRUE) %>%
+ select(ab, atc, name)
+
+abx_atc2 <- ab_old %>%
+ filter(!atc %in% abx_atc1$atc,
+ is.na(ears_net),
+ !is.na(atc_group1),
+ !atc_group1 %like% ("virus|vaccin|viral|immun"),
+ !official %like% "(combinations| with )") %>%
+ mutate(ab = NA_character_) %>%
+ as.data.frame(stringsAsFactors = FALSE) %>%
+ select(ab, atc, name = official)
+
+abx2 <- bind_rows(abx_atc1, abx_atc2)
+
+rm(abx_atc1)
+rm(abx_atc2)
+
+abx2$ab[is.na(abx2$ab)] <- toupper(abbreviate(gsub("[/0-9-]",
+ " ",
+ abx2$name[is.na(abx2$ab)]),
+ minlength = 3,
+ method = "left.kept",
+ strict = TRUE))
+
+n_distinct(abx2$ab)
+
+abx2 <- abx2 %>% arrange(ab)
+seqnr <- 0
+# add follow up nrs
+for (i in 2:nrow(abx2)) {
+ if (abx2[i, "ab"] == abx2[i - 1, "ab"]) {
+ seqnr <- seqnr + 1
+ abx2[i, "seqnr"] <- seqnr
+ } else {
+ seqnr <- 0
+ }
+}
+for (i in 2:nrow(abx2)) {
+ if (!is.na(abx2[i, "seqnr"])) {
+ abx2[i, "ab"] <- paste0(abx2[i, "ab"], abx2[i, "seqnr"])
+ }
+}
+abx2 <- abx2 %>% select(-seqnr) %>% arrange(name)
+
+# everything unique??
+nrow(abx2) == n_distinct(abx2$ab)
+
+# get ATC properties
+abx2 <- abx2 %>%
+ left_join(ab_old %>%
+ select(atc, abbr, atc_group1, atc_group2,
+ oral_ddd, oral_units, iv_ddd, iv_units))
+
+abx2$abbr <- lapply(as.list(abx2$abbr), function(x) unlist(strsplit(x, "|", fixed = TRUE)))
+
+# vector with official names, returns vector with CIDs
get_CID <- function(ab) {
CID <- rep(NA_integer_, length(ab))
p <- progress_estimated(n = length(ab), min_time = 0)
@@ -10,49 +148,150 @@ get_CID <- function(ab) {
CID[i] <- tryCatch(
data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
- ab[i],
+ URLencode(ab[i], reserved = TRUE),
"/cids/TXT?name_type=complete"),
showProgress = FALSE)[[1]][1],
error = function(e) NA_integer_)
- Sys.sleep(0.2)
+ if (is.na(CID[i])) {
+ # try with removing the text in brackets
+ CID[i] <- tryCatch(
+ data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
+ URLencode(trimws(gsub("[(].*[)]", "", ab[i])), reserved = TRUE),
+ "/cids/TXT?name_type=complete"),
+ showProgress = FALSE)[[1]][1],
+ error = function(e) NA_integer_)
+ }
+ if (is.na(CID[i])) {
+ # try match on word and take the lowest CID value (sorted)
+ ab[i] <- gsub("[^a-z0-9]+", " ", ab[i], ignore.case = TRUE)
+ CID[i] <- tryCatch(
+ data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
+ URLencode(ab[i], reserved = TRUE),
+ "/cids/TXT?name_type=word"),
+ showProgress = FALSE)[[1]][1],
+ error = function(e) NA_integer_)
+ }
+ Sys.sleep(0.1)
}
CID
}
-# returns vector with synonyms (brand names) for a single CID
+# get CIDs (2-3 min)
+CIDs <- get_CID(abx2$name)
+# These could not be found:
+abx2[is.na(CIDs),] %>% View()
+
+# returns list with synonyms (brand names), with CIDs as names
get_synonyms <- function(CID, clean = TRUE) {
+ synonyms <- rep(NA_character_, length(CID))
p <- progress_estimated(n = length(CID), min_time = 0)
- p$tick()$print()
- synonyms_txt <- tryCatch(
- data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/fastidentity/cid/",
- CID,
- "/synonyms/TXT"),
- sep = "\n",
- showProgress = FALSE)[[1]],
- error = function(e) NA_character_)
+ for (i in 1:length(CID)) {
+ p$tick()$print()
- if (clean == TRUE) {
- # remove txt between brackets
- synonyms_txt <- trimws(gsub("[(].*[)]", "", gsub("[[].*[]]", "", synonyms_txt)))
- # only length 6 to 20 and no txt with reading marks or numbers
- synonyms_txt <- synonyms_txt[nchar(synonyms_txt) %in% c(6:20)
- & !synonyms_txt %like% "[-&{},_0-9]"]
- synonyms_txt <- unlist(strsplit(synonyms_txt, ";", fixed = TRUE))
+ synonyms_txt <- ""
+
+ if (is.na(CID[i])) {
+ next
+ }
+
+ synonyms_txt <- tryCatch(
+ data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/fastidentity/cid/",
+ CID[i],
+ "/synonyms/TXT"),
+ sep = "\n",
+ showProgress = FALSE)[[1]],
+ error = function(e) NA_character_)
+
+ Sys.sleep(0.1)
+
+ if (clean == TRUE) {
+ # remove text between brackets
+ synonyms_txt <- trimws(gsub("[(].*[)]", "",
+ gsub("[[].*[]]", "",
+ gsub("[(].*[]]", "",
+ gsub("[[].*[)]", "", synonyms_txt)))))
+ synonyms_txt <- gsub("Co-", "Co", synonyms_txt, fixed = TRUE)
+ # only length 6 to 20 and no txt with reading marks or numbers and must start with capital letter (= brand)
+ synonyms_txt <- synonyms_txt[nchar(synonyms_txt) %in% c(6:20)
+ & !grepl("[-&{},_0-9/]", synonyms_txt)
+ & grepl("^[A-Z]", synonyms_txt, ignore.case = FALSE)]
+ synonyms_txt <- unlist(strsplit(synonyms_txt, ";", fixed = TRUE))
+ }
+ synonyms_txt <- unique(trimws(synonyms_txt[tolower(synonyms_txt) %in% unique(tolower(synonyms_txt))]))
+ synonyms[i] <- list(sort(synonyms_txt))
}
- synonyms_txt <- synonyms_txt[tolower(synonyms_txt) %in% unique(tolower(synonyms_txt))]
- sort(synonyms_txt)
+ names(synonyms) <- CID
+ synonyms
}
-CIDs <- get_CID(antibiotics$official)
-synonyms <- character(length(CIDs))
-p <- progress_estimated(n = length(synonyms), min_time = 0)
-for (i in 365:length(synonyms)) {
- #p$tick()$print()
- if (!is.na(CIDs[i])) {
- synonyms[i] <- paste(get_synonyms(CIDs[i]), collapse = "|")
- }
-}
+# get brand names (2-3 min)
+synonyms <- get_synonyms(CIDs)
+synonyms <- lapply(synonyms,
+ function(x) {
+ if (length(x) == 0 | all(is.na(x))) {
+ ""
+ } else {
+ x
+ }})
-antibiotics$cid <- CIDs
-antibiotics$trade_name <- synonyms
+# add them to data set
+antibiotics <- abx2 %>%
+ left_join(DRGLST %>%
+ select(ab = WHON5_CODE, CLASS, SUBCLASS) %>%
+ distinct(ab, .keep_all = TRUE), by = "ab") %>%
+ transmute(ab,
+ atc,
+ cid = CIDs,
+ # no capital after a slash: Ampicillin/Sulbactam -> Ampicillin/sulbactam
+ name = gsub("edta", "EDTA", gsub("/([A-Z])", "/\\L\\1", name, perl = TRUE), ignore.case = TRUE),
+ group = case_when(
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "am(ph|f)enicol" ~ "Amphenicols",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "aminoglycoside" ~ "Aminoglycosides",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "carbapenem" | name %like% "(imipenem|meropenem)" ~ "Carbapenems",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "First-generation cephalosporin" ~ "Cephalosporins (1st gen.)",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "Second-generation cephalosporin" ~ "Cephalosporins (2nd gen.)",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "Third-generation cephalosporin" ~ "Cephalosporins (3rd gen.)",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "Fourth-generation cephalosporin" ~ "Cephalosporins (4th gen.)",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "(tuberculosis|mycobacter)" ~ "Antimycobacterials",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "cephalosporin" ~ "Cephalosporins",
+ name %like% "^Ce" & is.na(atc_group1) & paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "beta-?lactam" ~ "Cephalosporins",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "(beta-?lactam|penicillin)" ~ "Beta-lactams/penicillins",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "quinolone" ~ "Quinolones",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "glycopeptide" ~ "Glycopeptides",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "macrolide" ~ "Macrolides/lincosamides",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "tetracycline" ~ "Tetracyclines",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "trimethoprim" ~ "Trimethoprims",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "polymyxin" ~ "Polymyxins",
+ paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "(fungal|mycot)" ~ "Antifungals/antimycotics",
+ TRUE ~ "Other antibacterials"
+ ),
+ atc_group1, atc_group2,
+ abbreviations = unname(abbr),
+ synonyms = unname(synonyms),
+ oral_ddd, oral_units,
+ iv_ddd, iv_units) %>%
+ as.data.frame(stringsAsFactors = FALSE)
+
+# some exceptions
+antibiotics[which(antibiotics$ab == "DOX"), "abbreviations"][[1]] <- list(c("dox", "doxy"))
+antibiotics[which(antibiotics$ab == "FLC"), "abbreviations"][[1]] <- list(c("clox"))
+antibiotics[which(antibiotics$ab == "CEC"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "CEC"), "abbreviations"][[1]], "CFC")) # cefaclor old WHONET4 code
+# 'Polymixin B' (POL) and 'Polymyxin B' (PLB) both exist, so:
+antibiotics[which(antibiotics$ab == "PLB"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "PLB"), "abbreviations"][[1]], "POL", "Polymixin", "Polymixin B"))
+antibiotics <- filter(antibiotics, ab != "POL")
+# 'Latamoxef' (LTM) and 'Moxalactam (Latamoxef)' (MOX) both exist, so:
+antibiotics[which(antibiotics$ab == "LTM"), "abbreviations"][[1]] <- list(c("MOX", "moxa"))
+antibiotics <- filter(antibiotics, ab != "MOX")
+# ESBL E-test codes:
+antibiotics[which(antibiotics$ab == "CCV"), "abbreviations"][[1]] <- list(c("xtzl"))
+antibiotics[which(antibiotics$ab == "CAZ"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "CAZ"), "abbreviations"][[1]], "xtz"))
+antibiotics[which(antibiotics$ab == "CPC"), "abbreviations"][[1]] <- list(c("xpml"))
+antibiotics[which(antibiotics$ab == "FEP"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "FEP"), "abbreviations"][[1]], "xpm"))
+antibiotics[which(antibiotics$ab == "CTC"), "abbreviations"][[1]] <- list(c("xctl"))
+antibiotics[which(antibiotics$ab == "CTX"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "CTX"), "abbreviations"][[1]], "xct"))
+
+class(antibiotics$ab) <- "ab"
+class(antibiotics$atc) <- "atc"
+
+usethis::use_data(antibiotics, overwrite = TRUE)
diff --git a/reproduction_of_microorganisms.R b/reproduction_of_microorganisms.R
index 6b139e23e..ba2e899c4 100644
--- a/reproduction_of_microorganisms.R
+++ b/reproduction_of_microorganisms.R
@@ -96,8 +96,6 @@ MOs <- data_total %>%
(
# we only want all MICROorganisms and no viruses
!kingdom %in% c("Animalia", "Plantae", "Viruses")
- # and no entries above genus level - all species already have a taxonomic tree
- & !rank %in% c("kingdom", "phylum", "superfamily", "class", "order", "family")
# and not all fungi: Aspergillus, Candida, Trichphyton and Pneumocystis are the most important,
# so only keep these orders from the fungi:
& !(kingdom == "Fungi"
@@ -194,7 +192,12 @@ MOs.old <- MOs %>%
MOs <- MOs %>%
filter(is.na(col_id_new) | source == "DSMZ") %>%
transmute(col_id,
- fullname = trimws(paste(genus, species, subspecies)),
+ fullname = trimws(case_when(rank == "family" ~ family,
+ rank == "order" ~ order,
+ rank == "class" ~ class,
+ rank == "phylum" ~ phylum,
+ rank == "kingdom" ~ kingdom,
+ TRUE ~ paste(genus, species, subspecies))),
kingdom,
phylum,
class,
@@ -220,6 +223,34 @@ MOs <- MOs %>%
# These will become valid and unique microbial IDs for the AMR package.
MOs <- MOs %>%
group_by(kingdom) %>%
+ mutate(abbr_other = case_when(
+ rank == "family" ~ paste0("[FAM]_",
+ abbreviate(family,
+ minlength = 8,
+ use.classes = TRUE,
+ method = "both.sides",
+ strict = FALSE)),
+ rank == "order" ~ paste0("[ORD]_",
+ abbreviate(order,
+ minlength = 8,
+ use.classes = TRUE,
+ method = "both.sides",
+ strict = FALSE)),
+ rank == "class" ~ paste0("[CLS]_",
+ abbreviate(class,
+ minlength = 8,
+ use.classes = TRUE,
+ method = "both.sides",
+ strict = FALSE)),
+ rank == "phylum" ~ paste0("[PHL]_",
+ abbreviate(phylum,
+ minlength = 8,
+ use.classes = TRUE,
+ method = "both.sides",
+ strict = FALSE)),
+ rank == "kingdom" ~ paste0("[KNG]_", kingdom),
+ TRUE ~ NA_character_
+ )) %>%
# abbreviations may be same for genera between kingdoms,
# because each abbreviation starts with the the first character(s) of the kingdom
mutate(abbr_genus = abbreviate(genus,
@@ -247,9 +278,12 @@ MOs <- MOs %>%
toupper(paste(ifelse(kingdom %in% c("Animalia", "Plantae"),
substr(kingdom, 1, 2),
substr(kingdom, 1, 1)),
- abbr_genus,
- abbr_species,
- abbr_subspecies,
+ ifelse(is.na(abbr_other),
+ paste(abbr_genus,
+ abbr_species,
+ abbr_subspecies,
+ sep = "_"),
+ abbr_other),
sep = "_")))) %>%
mutate(mo = ifelse(duplicated(.$mo),
# these one or two must be unique too
@@ -259,7 +293,7 @@ MOs <- MOs %>%
trimws(paste(genus, species, subspecies)),
fullname)) %>%
# put `mo` in front, followed by the rest
- select(mo, everything(), -abbr_genus, -abbr_species, -abbr_subspecies)
+ select(mo, everything(), -abbr_other, -abbr_genus, -abbr_species, -abbr_subspecies)
# add non-taxonomic entries
@@ -419,7 +453,7 @@ sum(duplicated(MOs$mo))
colnames(MOs)
# save it
-MOs <- as.data.frame(MOs %>% arrange(mo), stringsAsFactors = FALSE)
+MOs <- as.data.frame(MOs %>% arrange(fullname), stringsAsFactors = FALSE)
MOs.old <- as.data.frame(MOs.old, stringsAsFactors = FALSE)
class(MOs$mo) <- "mo"
diff --git a/reproduction_of_rsi_translation.R b/reproduction_of_rsi_translation.R
new file mode 100644
index 000000000..3382d29ff
--- /dev/null
+++ b/reproduction_of_rsi_translation.R
@@ -0,0 +1,52 @@
+library(dplyr)
+library(readxl)
+
+# Installed WHONET 2019 software on Windows (http://www.whonet.org/software.html),
+# opened C:\WHONET\Codes\WHONETCodes.mdb in MS Access
+# and exported table 'DRGLST1' to MS Excel
+DRGLST1 <- read_excel("DRGLST1.xlsx")
+rsi_translation <- DRGLST1 %>%
+ # only keep CLSI and EUCAST guidelines:
+ filter(GUIDELINES %like% "^(CLSI|EUCST)") %>%
+ # set a nice layout:
+ transmute(guideline = gsub("([0-9]+)$", " 20\\1", gsub("EUCST", "EUCAST", GUIDELINES)),
+ method = TESTMETHOD,
+ mo = as.mo(ORG_CODE),
+ ab = as.ab(WHON5_CODE),
+ ref_tbl = REF_TABLE,
+ dose_disk = POTENCY,
+ S_disk = as.disk(DISK_S),
+ R_disk = as.disk(DISK_R),
+ S_mic = as.mic(MIC_S),
+ R_mic = as.mic(MIC_R)) %>%
+ filter(!is.na(mo) & !is.na(ab)) %>%
+ arrange(desc(guideline), mo, ab)
+
+# create 2 tables: MIC and disk
+tbl_mic <- rsi_translation %>%
+ filter(method == "MIC") %>%
+ select(-ends_with("_disk")) %>%
+ mutate(joinstring = paste(guideline, mo, ab))
+tbl_disk <- rsi_translation %>%
+ filter(method == "DISK") %>%
+ select(-S_mic, -R_mic) %>%
+ mutate(joinstring = paste(guideline, mo, ab)) %>%
+ select(joinstring, ends_with("_disk"))
+
+# merge them so every record is a unique combination of method, mo and ab
+rsi_translation <- tbl_mic %>%
+ left_join(tbl_disk,
+ by = "joinstring") %>%
+ select(-joinstring, -method) %>%
+ as.data.frame(stringsAsFactors = FALSE) %>%
+ # force classes again
+ mutate(mo = as.mo(mo),
+ ab = as.ab(ab),
+ S_mic = as.mic(S_mic),
+ R_mic = as.mic(R_mic),
+ S_disk = as.disk(S_disk),
+ R_disk = as.disk(R_disk))
+
+# save to package
+usethis::use_data(rsi_translation, overwrite = TRUE)
+rm(rsi_translation)
diff --git a/tests/testthat/test-atc.R b/tests/testthat/test-ab.R
similarity index 64%
rename from tests/testthat/test-atc.R
rename to tests/testthat/test-ab.R
index 5877ccf77..08c37dfff 100755
--- a/tests/testthat/test-atc.R
+++ b/tests/testthat/test-ab.R
@@ -19,27 +19,28 @@
# Visit our website for more info: https://msberends.gitlab.io/AMR. #
# ==================================================================== #
-context("atc.R")
+context("ab.R")
-test_that("as.atc works", {
- expect_equal(suppressWarnings(as.character(as.atc(c("J01FA01",
- "Erythromycin",
- "eryt",
- "ERYT",
- "ERY",
- "Erythrocin",
- "Eryzole",
- "Pediamycin")))),
- rep("J01FA01", 8))
+test_that("as.ab works", {
+ expect_equal(as.character(as.ab(c("J01FA01",
+ "J 01 FA 01",
+ "Erythromycin",
+ "eryt",
+ " eryt 123",
+ "ERYT",
+ "ERY",
+ "erytromicine",
+ "Erythrocin",
+ "Romycin"))),
+ rep("ERY", 10))
- expect_identical(class(as.atc("amox")), "atc")
- expect_identical(class(pull(antibiotics, atc)), "atc")
- expect_identical(atc_trivial_nl("Cefmenoxim"), "Cefmenoxim")
+ expect_identical(class(as.ab("amox")), "ab")
+ expect_identical(class(pull(antibiotics, ab)), "ab")
- expect_warning(as.atc("Z00ZZ00")) # not yet available in data set
- expect_warning(as.atc("UNKNOWN"))
+ expect_warning(as.ab("Z00ZZ00")) # not yet available in data set
+ expect_warning(as.ab("UNKNOWN"))
- expect_output(print(as.atc("amox")))
+ expect_output(print(as.ab("amox")))
# first 5 chars of official name
expect_equal(as.character(as.atc(c("nitro", "cipro"))),
diff --git a/tests/testthat/test-atc_property.R b/tests/testthat/test-ab_property.R
similarity index 50%
rename from tests/testthat/test-atc_property.R
rename to tests/testthat/test-ab_property.R
index bc3cae891..fb42cd105 100644
--- a/tests/testthat/test-atc_property.R
+++ b/tests/testthat/test-ab_property.R
@@ -19,19 +19,37 @@
# Visit our website for more info: https://msberends.gitlab.io/AMR. #
# ==================================================================== #
-context("atc_property.R")
+context("ab_property.R")
-test_that("atc_property works", {
- expect_equal(atc_certe("amox"), "amox")
- expect_equal(atc_name("amox", language = "en"), "Amoxicillin")
- expect_equal(atc_name("amox", language = "nl"), "Amoxicilline")
- expect_equal(atc_official("amox", language = "en"), "Amoxicillin")
- expect_equal(atc_trivial_nl("amox"), "Amoxicilline")
- expect_equal(atc_umcg("amox"), "AMOX")
- expect_equal(class(atc_tradenames("amox")), "character")
- expect_equal(class(atc_tradenames(c("amox", "amox"))), "list")
+test_that("ab_property works", {
- expect_error(atc_property("amox", "invalid property"))
- expect_error(atc_name("amox", language = "INVALID"))
- expect_output(print(atc_name("amox", language = NULL)))
+ expect_identical(ab_name("AMX"), "Amoxicillin")
+ expect_identical(as.character(ab_atc("AMX")), "J01CA04")
+ expect_identical(ab_cid("AMX"), as.integer(33613))
+
+ expect_equal(class(ab_tradenames("AMX")), "character")
+ expect_equal(class(ab_tradenames(c("AMX", "AMX"))), "list")
+
+ expect_identical(ab_group("AMX"), "Beta-lactams/penicillins")
+ expect_identical(ab_atc_group1("AMX"), "Beta-lactam antibacterials, penicillins")
+ expect_identical(ab_atc_group2("AMX"), "Penicillins with extended spectrum")
+
+ expect_identical(ab_name("Fluclox"), "Flucloxacillin")
+ expect_identical(ab_name("fluklox"), "Flucloxacillin")
+ expect_identical(ab_name("floxapen"), "Flucloxacillin")
+ expect_identical(ab_name(21319) , "Flucloxacillin")
+ expect_identical(ab_name("J01CF05"), "Flucloxacillin")
+
+ expect_identical(ab_ddd("AMX", "oral"), 1)
+ expect_identical(ab_ddd("AMX", "oral", units = TRUE) , "g")
+ expect_identical(ab_ddd("AMX", "iv"), 1)
+ expect_identical(ab_ddd("AMX", "iv", units = TRUE) , "g")
+
+ expect_identical(ab_name(x = c("AMC", "PLB")), c("Amoxicillin/clavulanic acid", "Polymyxin B"))
+ expect_identical(ab_name(x = c("AMC", "PLB"), tolower = TRUE),
+ c("amoxicillin/clavulanic acid", "polymyxin B"))
+
+ expect_error(ab_property("amox", "invalid property"))
+ expect_error(ab_name("amox", language = "INVALID"))
+ expect_output(print(ab_name("amox", language = NULL)))
})
diff --git a/tests/testthat/test-abname.R b/tests/testthat/test-abname.R
deleted file mode 100644
index a54d192f3..000000000
--- a/tests/testthat/test-abname.R
+++ /dev/null
@@ -1,43 +0,0 @@
-# ==================================================================== #
-# TITLE #
-# Antimicrobial Resistance (AMR) Analysis #
-# #
-# SOURCE #
-# https://gitlab.com/msberends/AMR #
-# #
-# LICENCE #
-# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
-# #
-# This R package is free software; you can freely use and distribute #
-# it for both personal and commercial purposes under the terms of the #
-# GNU General Public License version 2.0 (GNU GPL-2), as published by #
-# the Free Software Foundation. #
-# #
-# This R package was created for academic research and was publicly #
-# released in the hope that it will be useful, but it comes WITHOUT #
-# ANY WARRANTY OR LIABILITY. #
-# Visit our website for more info: https://msberends.gitlab.io/AMR. #
-# ==================================================================== #
-
-context("abname.R")
-
-test_that("abname works", {
- expect_equal(abname("AMOX"), "Amoxicillin")
- expect_equal(abname(c("AMOX", "GENT")), c("Amoxicillin", "Gentamicin"))
- expect_equal(abname(c("AMOX+GENT")), "Amoxicillin + gentamicin")
- expect_equal(abname("AMOX", from = 'umcg'), "Amoxicillin")
- expect_equal(abname("amox", from = 'certe', tolower = TRUE), "amoxicillin")
- expect_equal(abname("J01CA04", from = 'atc'), "Amoxicillin")
- expect_equal(abname(c("amox", "J01CA04", "Trimox", "dispermox", "Amoxil")),
- rep("Amoxicillin", 5))
- expect_equal(abname("AMOX", to = 'atc'), "J01CA04")
-
- expect_error(abname("AMOX", to = c(1:3)))
- expect_error(abname("AMOX", to = "test"))
- expect_warning(abname("NOTEXISTING
- "))
- expect_warning(abname("AMOX or GENT"))
-
- # this one is being found with as.atc internally
- expect_equal(abname("flu_clox123"), "Flucloxacillin")
-})
diff --git a/tests/testthat/test-count.R b/tests/testthat/test-count.R
index ad9507cc7..f8106302b 100644
--- a/tests/testthat/test-count.R
+++ b/tests/testthat/test-count.R
@@ -22,52 +22,52 @@
context("count.R")
test_that("counts work", {
- # amox resistance in `septic_patients`
- expect_equal(count_R(septic_patients$amox), 683)
- expect_equal(count_I(septic_patients$amox), 3)
- expect_equal(count_S(septic_patients$amox), 543)
- expect_equal(count_R(septic_patients$amox) + count_I(septic_patients$amox),
- count_IR(septic_patients$amox))
- expect_equal(count_S(septic_patients$amox) + count_I(septic_patients$amox),
- count_SI(septic_patients$amox))
+ # AMX resistance in `septic_patients`
+ expect_equal(count_R(septic_patients$AMX), 683)
+ expect_equal(count_I(septic_patients$AMX), 3)
+ expect_equal(count_S(septic_patients$AMX), 543)
+ expect_equal(count_R(septic_patients$AMX) + count_I(septic_patients$AMX),
+ count_IR(septic_patients$AMX))
+ expect_equal(count_S(septic_patients$AMX) + count_I(septic_patients$AMX),
+ count_SI(septic_patients$AMX))
library(dplyr)
- expect_equal(septic_patients %>% count_S(amcl), 1342)
- expect_equal(septic_patients %>% count_S(amcl, gent), 1660)
- expect_equal(septic_patients %>% count_all(amcl, gent), 1798)
- expect_identical(septic_patients %>% count_all(amcl, gent),
- septic_patients %>% count_S(amcl, gent) +
- septic_patients %>% count_IR(amcl, gent))
+ expect_equal(septic_patients %>% count_S(AMC), 1342)
+ expect_equal(septic_patients %>% count_S(AMC, GEN), 1660)
+ expect_equal(septic_patients %>% count_all(AMC, GEN), 1798)
+ expect_identical(septic_patients %>% count_all(AMC, GEN),
+ septic_patients %>% count_S(AMC, GEN) +
+ septic_patients %>% count_IR(AMC, GEN))
# count of cases
expect_equal(septic_patients %>%
group_by(hospital_id) %>%
- summarise(cipro = count_S(cipr),
- genta = count_S(gent),
- combination = count_S(cipr, gent)) %>%
+ summarise(cipro = count_S(CIP),
+ genta = count_S(GEN),
+ combination = count_S(CIP, GEN)) %>%
pull(combination),
c(192, 446, 184, 474))
# count_df
expect_equal(
- septic_patients %>% select(amox) %>% count_df() %>% pull(Value),
- c(septic_patients$amox %>% count_S(),
- septic_patients$amox %>% count_I(),
- septic_patients$amox %>% count_R())
+ septic_patients %>% select(AMX) %>% count_df() %>% pull(Value),
+ c(septic_patients$AMX %>% count_S(),
+ septic_patients$AMX %>% count_I(),
+ septic_patients$AMX %>% count_R())
)
expect_equal(
- septic_patients %>% select(amox) %>% count_df(combine_IR = TRUE) %>% pull(Value),
- c(septic_patients$amox %>% count_S(),
- septic_patients$amox %>% count_IR())
+ septic_patients %>% select(AMX) %>% count_df(combine_IR = TRUE) %>% pull(Value),
+ c(septic_patients$AMX %>% count_S(),
+ septic_patients$AMX %>% count_IR())
)
# warning for speed loss
- expect_warning(count_R(as.character(septic_patients$amcl)))
- expect_warning(count_I(as.character(septic_patients$amcl)))
- expect_warning(count_S(as.character(septic_patients$amcl,
- septic_patients$gent)))
- expect_warning(count_S(septic_patients$amcl,
- as.character(septic_patients$gent)))
+ expect_warning(count_R(as.character(septic_patients$AMC)))
+ expect_warning(count_I(as.character(septic_patients$AMC)))
+ expect_warning(count_S(as.character(septic_patients$AMC,
+ septic_patients$GEN)))
+ expect_warning(count_S(septic_patients$AMC,
+ as.character(septic_patients$GEN)))
# check for errors
expect_error(count_IR("test", minimum = "test"))
diff --git a/tests/testthat/test-data.R b/tests/testthat/test-data.R
index a74aa11d9..c9bd5ee1f 100644
--- a/tests/testthat/test-data.R
+++ b/tests/testthat/test-data.R
@@ -23,7 +23,7 @@ context("data.R")
test_that("data sets are valid", {
# IDs should always be unique
- expect_identical(nrow(antibiotics), length(unique(antibiotics$atc)))
+ expect_identical(nrow(antibiotics), length(unique(antibiotics$ab)))
expect_identical(nrow(microorganisms), length(unique(microorganisms$mo)))
# there should be no diacritics (i.e. non ASCII) characters in the datasets
diff --git a/tests/testthat/test-deprecated.R b/tests/testthat/test-deprecated.R
index 387205d70..c14a9fc4e 100644
--- a/tests/testthat/test-deprecated.R
+++ b/tests/testthat/test-deprecated.R
@@ -30,15 +30,11 @@ test_that("deprecated functions work", {
expect_identical(suppressWarnings(ratio(c(772, 1611, 737), ratio = "1:2:1")), c(780, 1560, 780))
expect_identical(suppressWarnings(ratio(c(1752, 1895), ratio = c(1, 1))), c(1823.5, 1823.5))
- expect_warning(ab_property("amox"))
- expect_warning(ab_atc("amox"))
+ expect_warning(atc_property("amox"))
+ expect_warning(atc_official("amox"))
expect_warning(ab_official("amox"))
- expect_warning(ab_name("amox"))
- expect_warning(ab_trivial_nl("amox"))
- expect_warning(ab_certe("amox"))
- expect_warning(ab_umcg("amox"))
- expect_warning(ab_tradenames("amox"))
- expect_warning(atc_ddd("amox"))
- expect_warning(atc_groups("amox"))
+ expect_warning(atc_name("amox"))
+ expect_warning(atc_trivial_nl("amox"))
+ expect_warning(atc_tradenames("amox"))
})
diff --git a/tests/testthat/test-eucast_rules.R b/tests/testthat/test-eucast_rules.R
index 1a85f5014..286bcd810 100755
--- a/tests/testthat/test-eucast_rules.R
+++ b/tests/testthat/test-eucast_rules.R
@@ -52,11 +52,11 @@ test_that("EUCAST rules work", {
a <- data.frame(mo = c("Staphylococcus aureus",
"Streptococcus group A"),
- coli = "-", # Colistin
+ COL = "-", # Colistin
stringsAsFactors = FALSE)
b <- data.frame(mo = c("Staphylococcus aureus",
"Streptococcus group A"),
- coli = "R", # Colistin
+ COL = "R", # Colistin
stringsAsFactors = FALSE)
expect_equal(suppressWarnings(eucast_rules(a, "mo", info = FALSE)), b)
@@ -64,30 +64,30 @@ test_that("EUCAST rules work", {
library(dplyr)
expect_equal(suppressWarnings(
septic_patients %>%
- mutate(tica = as.rsi("R"),
- pipe = as.rsi("S")) %>%
+ mutate(TIC = as.rsi("R"),
+ PIP = as.rsi("S")) %>%
eucast_rules(col_mo = "mo") %>%
left_join_microorganisms() %>%
filter(family == "Enterobacteriaceae") %>%
- pull(pipe) %>%
+ pull(PIP) %>%
unique() %>%
as.character()),
"R")
- # azit and clar must be equal to eryt
+ # Azithromicin and Clarythromycin must be equal to Erythromycin
a <- suppressWarnings(
septic_patients %>%
transmute(mo,
- eryt,
- azit = as.rsi("R"),
- clar = as.rsi("R")) %>%
+ ERY,
+ AZM = as.rsi("R"),
+ CLR = as.rsi("R")) %>%
eucast_rules(col_mo = "mo") %>%
- pull(clar))
+ pull(CLR))
b <- suppressWarnings(
septic_patients %>%
- select(mo, eryt) %>%
+ select(mo, ERY) %>%
eucast_rules(col_mo = "mo") %>%
- pull(eryt))
+ pull(ERY))
expect_identical(a[!is.na(b)],
b[!is.na(b)])
@@ -97,15 +97,15 @@ test_that("EUCAST rules work", {
suppressWarnings(
as.list(eucast_rules(
data.frame(mo = as.mo("Kingella kingae"),
- peni = "S",
- amox = "-",
+ PEN = "S",
+ AMX = "-",
stringsAsFactors = FALSE)
- , info = FALSE))$amox
+ , info = FALSE))$AMX
),
"S")
# also test norf
- expect_output(suppressWarnings(eucast_rules(septic_patients %>% mutate(norf = "S", nali = "S"))))
+ expect_output(suppressWarnings(eucast_rules(septic_patients %>% mutate(NOR = "S", NAL = "S"))))
# check verbose output
expect_output(suppressWarnings(eucast_rules(septic_patients, verbose = TRUE)))
diff --git a/tests/testthat/test-freq.R b/tests/testthat/test-freq.R
index 3bbdbe8e6..e757e7310 100755
--- a/tests/testthat/test-freq.R
+++ b/tests/testthat/test-freq.R
@@ -51,7 +51,7 @@ test_that("frequency table works", {
# mo
expect_output(print(freq(septic_patients$mo)))
# rsi
- expect_output(print(freq(septic_patients$amox)))
+ expect_output(print(freq(septic_patients$AMX)))
# integer
expect_output(print(freq(septic_patients$age)))
# date
@@ -61,7 +61,7 @@ test_that("frequency table works", {
# table
expect_output(print(freq(table(septic_patients$gender, septic_patients$age))))
# rsi
- expect_output(print(freq(septic_patients$amcl)))
+ expect_output(print(freq(septic_patients$AMC)))
# hms
expect_output(suppressWarnings(print(freq(hms::as.hms(sample(c(0:86399), 50))))))
# matrix
@@ -89,8 +89,8 @@ test_that("frequency table works", {
# grouping variable
expect_output(print(septic_patients %>% group_by(gender) %>% freq(hospital_id)))
- expect_output(print(septic_patients %>% group_by(gender) %>% freq(amox, quote = TRUE)))
- expect_output(print(septic_patients %>% group_by(gender) %>% freq(amox, markdown = TRUE)))
+ expect_output(print(septic_patients %>% group_by(gender) %>% freq(AMX, quote = TRUE)))
+ expect_output(print(septic_patients %>% group_by(gender) %>% freq(AMX, markdown = TRUE)))
# quasiquotation
expect_output(print(septic_patients %>% freq(mo_genus(mo))))
@@ -152,7 +152,7 @@ test_that("frequency table works", {
expect_error(septic_patients %>% freq(nonexisting))
expect_error(septic_patients %>% select(1:10) %>% freq())
- expect_error(septic_patients %>% freq(peni, oxac, clox, amox, amcl,
+ expect_error(septic_patients %>% freq(peni, oxac, clox, AMX, AMC,
ampi, pita, czol, cfep, cfur))
# (un)select columns
@@ -163,15 +163,15 @@ test_that("frequency table works", {
# run diff
expect_output(print(
- diff(freq(septic_patients$amcl),
- freq(septic_patients$amox))
+ diff(freq(septic_patients$AMC),
+ freq(septic_patients$AMX))
))
expect_output(print(
diff(freq(septic_patients$age),
freq(septic_patients$age)) # "No differences found."
))
expect_error(print(
- diff(freq(septic_patients$amcl),
+ diff(freq(septic_patients$AMX),
"Just a string") # not a freq tbl
))
diff --git a/tests/testthat/test-ggplot_rsi.R b/tests/testthat/test-ggplot_rsi.R
index b55b5741a..27465fc90 100644
--- a/tests/testthat/test-ggplot_rsi.R
+++ b/tests/testthat/test-ggplot_rsi.R
@@ -30,36 +30,36 @@ test_that("ggplot_rsi works", {
# data should be equal
expect_equal(
- (septic_patients %>% select(amcl, cipr) %>% ggplot_rsi())$data %>%
+ (septic_patients %>% select(AMC, CIP) %>% ggplot_rsi())$data %>%
summarise_all(portion_IR) %>% as.double(),
- septic_patients %>% select(amcl, cipr) %>%
+ septic_patients %>% select(AMC, CIP) %>%
summarise_all(portion_IR) %>% as.double()
)
expect_equal(
- (septic_patients %>% select(amcl, cipr) %>% ggplot_rsi(x = "Interpretation", facet = "Antibiotic"))$data %>%
+ (septic_patients %>% select(AMC, CIP) %>% ggplot_rsi(x = "Interpretation", facet = "Antibiotic"))$data %>%
summarise_all(portion_IR) %>% as.double(),
- septic_patients %>% select(amcl, cipr) %>%
+ septic_patients %>% select(AMC, CIP) %>%
summarise_all(portion_IR) %>% as.double()
)
expect_equal(
- (septic_patients %>% select(amcl, cipr) %>% ggplot_rsi(x = "Antibiotic", facet = "Interpretation"))$data %>%
+ (septic_patients %>% select(AMC, CIP) %>% ggplot_rsi(x = "Antibiotic", facet = "Interpretation"))$data %>%
summarise_all(portion_IR) %>% as.double(),
- septic_patients %>% select(amcl, cipr) %>%
+ septic_patients %>% select(AMC, CIP) %>%
summarise_all(portion_IR) %>% as.double()
)
expect_equal(
- (septic_patients %>% select(amcl, cipr) %>% ggplot_rsi(x = "Antibiotic",
+ (septic_patients %>% select(AMC, CIP) %>% ggplot_rsi(x = "Antibiotic",
facet = "Interpretation",
fun = count_df))$data %>%
summarise_all(count_IR) %>% as.double(),
- septic_patients %>% select(amcl, cipr) %>%
+ septic_patients %>% select(AMC, CIP) %>%
summarise_all(count_IR) %>% as.double()
)
- expect_equal(colnames(getlbls(septic_patients %>% select(amcl, cipr))),
+ expect_equal(colnames(getlbls(septic_patients %>% select(AMC, CIP))),
c("Interpretation", "Antibiotic", "Value", "lbl"))
expect_error(ggplot_rsi(septic_patients, fun = "invalid"))
diff --git a/tests/testthat/test-guess_ab_col.R b/tests/testthat/test-guess_ab_col.R
index 7ead1fa0e..c74ff0706 100644
--- a/tests/testthat/test-guess_ab_col.R
+++ b/tests/testthat/test-guess_ab_col.R
@@ -24,15 +24,15 @@ context("guess_ab_col.R")
test_that("guess_ab_col works", {
expect_equal(guess_ab_col(septic_patients, "amox"),
- "amox")
+ "AMX")
expect_equal(guess_ab_col(septic_patients, "amoxicillin"),
- "amox")
+ "AMX")
expect_equal(guess_ab_col(septic_patients, "J01AA07"),
- "tetr")
+ "TCY")
expect_equal(guess_ab_col(septic_patients, "tetracycline"),
- "tetr")
+ "TCY")
expect_equal(guess_ab_col(septic_patients, "TETR"),
- "tetr")
+ "TCY")
df <- data.frame(AMP_ND10 = "R",
AMC_ED20 = "S")
diff --git a/tests/testthat/test-portion.R b/tests/testthat/test-portion.R
index 95907770e..02b700a5d 100755
--- a/tests/testthat/test-portion.R
+++ b/tests/testthat/test-portion.R
@@ -22,40 +22,33 @@
context("portion.R")
test_that("portions works", {
- # amox resistance in `septic_patients`
- expect_equal(portion_R(septic_patients$amox), 0.5557364, tolerance = 0.0001)
- expect_equal(portion_I(septic_patients$amox), 0.002441009, tolerance = 0.0001)
- expect_equal(1 - portion_R(septic_patients$amox) - portion_I(septic_patients$amox),
- portion_S(septic_patients$amox))
- expect_equal(portion_R(septic_patients$amox) + portion_I(septic_patients$amox),
- portion_IR(septic_patients$amox))
- expect_equal(portion_S(septic_patients$amox) + portion_I(septic_patients$amox),
- portion_SI(septic_patients$amox))
+ # AMX resistance in `septic_patients`
+ expect_equal(portion_R(septic_patients$AMX), 0.5557364, tolerance = 0.0001)
+ expect_equal(portion_I(septic_patients$AMX), 0.002441009, tolerance = 0.0001)
+ expect_equal(1 - portion_R(septic_patients$AMX) - portion_I(septic_patients$AMX),
+ portion_S(septic_patients$AMX))
+ expect_equal(portion_R(septic_patients$AMX) + portion_I(septic_patients$AMX),
+ portion_IR(septic_patients$AMX))
+ expect_equal(portion_S(septic_patients$AMX) + portion_I(septic_patients$AMX),
+ portion_SI(septic_patients$AMX))
- expect_equal(septic_patients %>% portion_S(amcl),
+ expect_equal(septic_patients %>% portion_S(AMC),
0.7142097,
tolerance = 0.0001)
- expect_equal(septic_patients %>% portion_S(amcl, gent),
+ expect_equal(septic_patients %>% portion_S(AMC, GEN),
0.9232481,
tolerance = 0.0001)
- expect_equal(septic_patients %>% portion_S(amcl, gent, also_single_tested = TRUE),
+ expect_equal(septic_patients %>% portion_S(AMC, GEN, also_single_tested = TRUE),
0.926045,
tolerance = 0.0001)
- # amcl+genta susceptibility around 92.3%
- expect_equal(suppressWarnings(rsi(septic_patients$amcl,
- septic_patients$gent,
- interpretation = "S")),
- 0.9232481,
- tolerance = 0.000001)
-
# percentages
expect_equal(septic_patients %>%
group_by(hospital_id) %>%
- summarise(R = portion_R(cipr, as_percent = TRUE),
- I = portion_I(cipr, as_percent = TRUE),
- S = portion_S(cipr, as_percent = TRUE),
- n = n_rsi(cipr),
+ summarise(R = portion_R(CIP, as_percent = TRUE),
+ I = portion_I(CIP, as_percent = TRUE),
+ S = portion_S(CIP, as_percent = TRUE),
+ n = n_rsi(CIP),
total = n()) %>%
pull(n) %>%
sum(),
@@ -64,23 +57,23 @@ test_that("portions works", {
# count of cases
expect_equal(septic_patients %>%
group_by(hospital_id) %>%
- summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
- cipro_n = n_rsi(cipr),
- genta_p = portion_S(gent, as_percent = TRUE),
- genta_n = n_rsi(gent),
- combination_p = portion_S(cipr, gent, as_percent = TRUE),
- combination_n = n_rsi(cipr, gent)) %>%
+ summarise(CIPo_p = portion_S(CIP, as_percent = TRUE),
+ CIPo_n = n_rsi(CIP),
+ GENa_p = portion_S(GEN, as_percent = TRUE),
+ GENa_n = n_rsi(GEN),
+ combination_p = portion_S(CIP, GEN, as_percent = TRUE),
+ combination_n = n_rsi(CIP, GEN)) %>%
pull(combination_n),
c(202, 488, 201, 499))
- expect_warning(portion_R(as.character(septic_patients$amcl)))
- expect_warning(portion_S(as.character(septic_patients$amcl)))
- expect_warning(portion_S(as.character(septic_patients$amcl,
- septic_patients$gent)))
- expect_warning(n_rsi(as.character(septic_patients$amcl,
- septic_patients$gent)))
- expect_equal(suppressWarnings(n_rsi(as.character(septic_patients$amcl,
- septic_patients$gent))),
+ expect_warning(portion_R(as.character(septic_patients$AMC)))
+ expect_warning(portion_S(as.character(septic_patients$AMC)))
+ expect_warning(portion_S(as.character(septic_patients$AMC,
+ septic_patients$GEN)))
+ expect_warning(n_rsi(as.character(septic_patients$AMC,
+ septic_patients$GEN)))
+ expect_equal(suppressWarnings(n_rsi(as.character(septic_patients$AMC,
+ septic_patients$GEN))),
1879)
# check for errors
@@ -93,59 +86,29 @@ test_that("portions works", {
expect_error(portion_S("test", also_single_tested = "test"))
# check too low amount of isolates
- expect_identical(suppressWarnings(portion_R(septic_patients$amox, minimum = nrow(septic_patients) + 1)),
+ expect_identical(suppressWarnings(portion_R(septic_patients$AMX, minimum = nrow(septic_patients) + 1)),
NA)
- expect_identical(suppressWarnings(portion_I(septic_patients$amox, minimum = nrow(septic_patients) + 1)),
+ expect_identical(suppressWarnings(portion_I(septic_patients$AMX, minimum = nrow(septic_patients) + 1)),
NA)
- expect_identical(suppressWarnings(portion_S(septic_patients$amox, minimum = nrow(septic_patients) + 1)),
+ expect_identical(suppressWarnings(portion_S(septic_patients$AMX, minimum = nrow(septic_patients) + 1)),
NA)
# warning for speed loss
- expect_warning(portion_R(as.character(septic_patients$gent)))
- expect_warning(portion_I(as.character(septic_patients$gent)))
- expect_warning(portion_S(septic_patients$amcl, as.character(septic_patients$gent)))
-
-})
-
-test_that("old rsi works", {
- # amox resistance in `septic_patients` should be around 58.53%
- expect_equal(suppressWarnings(rsi(septic_patients$amox)), 0.5581774, tolerance = 0.0001)
- expect_equal(suppressWarnings(rsi(septic_patients$amox, interpretation = "S")), 1 - 0.5581774, tolerance = 0.0001)
-
- # pita+genta susceptibility around 95.3%
- expect_equal(suppressWarnings(rsi(septic_patients$pita,
- septic_patients$gent,
- interpretation = "S",
- info = TRUE)),
- 0.9526814,
- tolerance = 0.0001)
-
- # count of cases
- expect_equal(septic_patients %>%
- group_by(hospital_id) %>%
- summarise(cipro_S = suppressWarnings(rsi(cipr, interpretation = "S",
- as_percent = TRUE, warning = FALSE)),
- cipro_n = n_rsi(cipr),
- genta_S = suppressWarnings(rsi(gent, interpretation = "S",
- as_percent = TRUE, warning = FALSE)),
- genta_n = n_rsi(gent),
- combination_S = suppressWarnings(rsi(cipr, gent, interpretation = "S",
- as_percent = TRUE, warning = FALSE)),
- combination_n = n_rsi(cipr, gent)) %>%
- pull(combination_n),
- c(202, 488, 201, 499))
+ expect_warning(portion_R(as.character(septic_patients$GEN)))
+ expect_warning(portion_I(as.character(septic_patients$GEN)))
+ expect_warning(portion_S(septic_patients$AMC, as.character(septic_patients$GEN)))
# portion_df
expect_equal(
- septic_patients %>% select(amox) %>% portion_df() %>% pull(Value),
- c(septic_patients$amox %>% portion_S(),
- septic_patients$amox %>% portion_I(),
- septic_patients$amox %>% portion_R())
+ septic_patients %>% select(AMX) %>% portion_df() %>% pull(Value),
+ c(septic_patients$AMX %>% portion_S(),
+ septic_patients$AMX %>% portion_I(),
+ septic_patients$AMX %>% portion_R())
)
expect_equal(
- septic_patients %>% select(amox) %>% portion_df(combine_IR = TRUE) %>% pull(Value),
- c(septic_patients$amox %>% portion_S(),
- septic_patients$amox %>% portion_IR())
+ septic_patients %>% select(AMX) %>% portion_df(combine_IR = TRUE) %>% pull(Value),
+ c(septic_patients$AMX %>% portion_S(),
+ septic_patients$AMX %>% portion_IR())
)
diff --git a/tests/testthat/test-resistance_predict.R b/tests/testthat/test-resistance_predict.R
index cb511b1a4..b48ded4e3 100644
--- a/tests/testthat/test-resistance_predict.R
+++ b/tests/testthat/test-resistance_predict.R
@@ -22,17 +22,17 @@
context("portion.R")
test_that("prediction of rsi works", {
- amox_R <- septic_patients %>%
+ AMX_R <- septic_patients %>%
filter(mo == "B_ESCHR_COL") %>%
- rsi_predict(col_ab = "amox",
+ rsi_predict(col_ab = "AMX",
col_date = "date",
minimum = 10,
info = TRUE) %>%
pull("value")
- # amox resistance will increase according to data set `septic_patients`
- expect_true(amox_R[3] < amox_R[20])
+ # AMX resistance will increase according to data set `septic_patients`
+ expect_true(AMX_R[3] < AMX_R[20])
- x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
+ x <- resistance_predict(septic_patients, col_ab = "AMX", year_min = 2010)
plot(x)
ggplot_rsi_predict(x)
expect_error(ggplot_rsi_predict(septic_patients))
@@ -41,23 +41,23 @@ test_that("prediction of rsi works", {
expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
model = "binomial",
- col_ab = "amox",
+ col_ab = "AMX",
col_date = "date",
info = TRUE))
expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
model = "loglin",
- col_ab = "amox",
+ col_ab = "AMX",
col_date = "date",
info = TRUE))
expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
model = "lin",
- col_ab = "amox",
+ col_ab = "AMX",
col_date = "date",
info = TRUE))
expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
model = "INVALID MODEL",
- col_ab = "amox",
+ col_ab = "AMX",
col_date = "date",
info = TRUE))
expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
@@ -65,12 +65,12 @@ test_that("prediction of rsi works", {
col_date = "date",
info = TRUE))
expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
- col_ab = "amox",
+ col_ab = "AMX",
col_date = "NOT EXISTING COLUMN",
info = TRUE))
- # almost all E. coli are mero S in the Netherlands :)
+ # almost all E. coli are MEM S in the Netherlands :)
expect_error(resistance_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
- col_ab = "mero",
+ col_ab = "MEM",
col_date = "date",
info = TRUE))
diff --git a/tests/testthat/test-rsi.R b/tests/testthat/test-rsi.R
index 422ea462a..1b827836a 100644
--- a/tests/testthat/test-rsi.R
+++ b/tests/testthat/test-rsi.R
@@ -47,7 +47,7 @@ test_that("rsi works", {
library(dplyr)
# 40 rsi columns
expect_equal(septic_patients %>%
- mutate_at(vars(peni:rifa), as.character) %>%
+ mutate_at(vars(PEN:RIF), as.character) %>%
lapply(is.rsi.eligible) %>%
as.logical() %>%
sum(),
diff --git a/vignettes/AMR.Rmd b/vignettes/AMR.Rmd
index dab834d42..12b80a264 100755
--- a/vignettes/AMR.Rmd
+++ b/vignettes/AMR.Rmd
@@ -35,8 +35,8 @@ You can skip to [Cleaning the data](#cleaning-the-data) if you already have your
knitr::kable(dplyr::tibble(date = Sys.Date(),
patient_id = c("abcd", "abcd", "efgh"),
mo = "Escherichia coli",
- amox = c("S", "S", "R"),
- cipr = c("S", "R", "S")),
+ AMX = c("S", "S", "R"),
+ CIP = c("S", "R", "S")),
align = "c")
```
@@ -113,13 +113,13 @@ data <- data.frame(date = sample(dates, 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)),
- amox = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ AMX = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.60, 0.05, 0.35)),
- amcl = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ AMC = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.75, 0.10, 0.15)),
- cipr = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.80, 0.00, 0.20)),
- gent = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.92, 0.00, 0.08))
)
```
@@ -166,12 +166,12 @@ We also want to transform the antibiotics, because in real life data we don't kn
```{r transform abx}
data <- data %>%
- mutate_at(vars(amox:gent), as.rsi)
+ mutate_at(vars(AMX:GEN), as.rsi)
```
Finally, we will apply [EUCAST rules](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the `eucast_rules()` function can also apply additional rules, like forcing ampicillin = R when amoxicillin/clavulanic acid = R.
-Because the amoxicillin (column `amox`) and amoxicillin/clavulanic acid (column `amcl`) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The `eucast_rules()` fixes this:
+Because the amoxicillin (column `AMX`) and amoxicillin/clavulanic acid (column `AMC`) in our data were generated randomly, some rows will undoubtedly contain AMX = S and AMC = R, which is technically impossible. The `eucast_rules()` fixes this:
```{r eucast, warning = FALSE, message = FALSE}
data <- eucast_rules(data, col_mo = "bacteria")
@@ -226,7 +226,7 @@ weighted_df <- data %>%
# only most prevalent patient
filter(patient_id == top_freq(freq(., patient_id), 1)[1]) %>%
arrange(date) %>%
- select(date, patient_id, bacteria, amox:gent, first) %>%
+ select(date, patient_id, bacteria, AMX:GEN, first) %>%
# maximum of 10 rows
.[1:min(10, nrow(.)),] %>%
mutate(isolate = row_number()) %>%
@@ -240,7 +240,7 @@ Only `r sum(weighted_df$first)` isolates are marked as 'first' according to CLSI
If a column exists with a name like 'key(...)ab' the `first_isolate()` function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:
-```{r 1st weighted}
+```{r 1st weighted, warning = FALSE}
data <- data %>%
mutate(keyab = key_antibiotics(.)) %>%
mutate(first_weighted = first_isolate(.))
@@ -252,7 +252,7 @@ weighted_df2 <- data %>%
# only most prevalent patient
filter(patient_id == top_freq(freq(., patient_id), 1)[1]) %>%
arrange(date) %>%
- select(date, patient_id, bacteria, amox:gent, first, first_weighted) %>%
+ select(date, patient_id, bacteria, AMX:GEN, first, first_weighted) %>%
# maximum of 10 rows
.[1:min(10, nrow(.)),] %>%
mutate(isolate = row_number()) %>%
@@ -292,7 +292,7 @@ knitr::kable(head(data_1st), align = "c")
Time for the analysis!
# Analysing the data
-You might want to start by getting an idea of how the data is distributed. It's an important start, because it also decides how you will continue your analysis.
+You might want to start by getting an idea of how the data is distributed. It's an important start, because it also decides how you will continue your analysis. Although this package contains a convenient function to make frequency tables, exploratory data analysis (EDA) is not the primary scope of this package. Use a package like [`DataExplorer`](https://cran.r-project.org/package=DataExplorer) for that, or read the free online book [Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/) by Roger D. Peng.
## Dispersion of species
To just get an idea how the species are distributed, create a frequency table with our `freq()` function. We created the `genus` and `species` column earlier based on the microbial ID. With `paste()`, we can concatenate them together.
@@ -318,7 +318,7 @@ data_1st %>%
The functions `portion_S()`, `portion_SI()`, `portion_I()`, `portion_IR()` and `portion_R()` can be used to determine the portion of a specific antimicrobial outcome. They can be used on their own:
```{r}
-data_1st %>% portion_IR(amox)
+data_1st %>% portion_IR(AMX)
```
Or can be used in conjuction with `group_by()` and `summarise()`, both from the `dplyr` package:
@@ -326,12 +326,12 @@ Or can be used in conjuction with `group_by()` and `summarise()`, both from the
```{r, eval = FALSE}
data_1st %>%
group_by(hospital) %>%
- summarise(amoxicillin = portion_IR(amox))
+ summarise(amoxicillin = portion_IR(AMX))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
- summarise(amoxicillin = portion_IR(amox)) %>%
+ summarise(amoxicillin = portion_IR(AMX)) %>%
knitr::kable(align = "c", big.mark = ",")
```
@@ -340,14 +340,14 @@ Of course it would be very convenient to know the number of isolates responsible
```{r, eval = FALSE}
data_1st %>%
group_by(hospital) %>%
- summarise(amoxicillin = portion_IR(amox),
- available = n_rsi(amox))
+ summarise(amoxicillin = portion_IR(AMX),
+ available = n_rsi(AMX))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
- summarise(amoxicillin = portion_IR(amox),
- available = n_rsi(amox)) %>%
+ summarise(amoxicillin = portion_IR(AMX),
+ available = n_rsi(AMX)) %>%
knitr::kable(align = "c", big.mark = ",")
```
@@ -356,16 +356,16 @@ These functions can also be used to get the portion of multiple antibiotics, to
```{r, eval = FALSE}
data_1st %>%
group_by(genus) %>%
- summarise(amoxiclav = portion_S(amcl),
- gentamicin = portion_S(gent),
- amoxiclav_genta = portion_S(amcl, gent))
+ summarise(amoxiclav = portion_S(AMC),
+ gentamicin = portion_S(GEN),
+ amoxiclav_genta = portion_S(AMC, GEN))
```
```{r, echo = FALSE}
data_1st %>%
group_by(genus) %>%
- summarise(amoxiclav = portion_S(amcl),
- gentamicin = portion_S(gent),
- amoxiclav_genta = portion_S(amcl, gent)) %>%
+ summarise(amoxiclav = portion_S(AMC),
+ gentamicin = portion_S(GEN),
+ amoxiclav_genta = portion_S(AMC, GEN)) %>%
knitr::kable(align = "c", big.mark = ",")
```
@@ -374,9 +374,9 @@ To make a transition to the next part, let's see how this difference could be pl
```{r plot 1}
data_1st %>%
group_by(genus) %>%
- summarise("1. Amoxi/clav" = portion_S(amcl),
- "2. Gentamicin" = portion_S(gent),
- "3. Amoxi/clav + gent" = portion_S(amcl, gent)) %>%
+ summarise("1. Amoxi/clav" = portion_S(AMC),
+ "2. Gentamicin" = portion_S(GEN),
+ "3. Amoxi/clav + GEN" = portion_S(AMC, GEN)) %>%
tidyr::gather("Antibiotic", "S", -genus) %>%
ggplot(aes(x = genus,
y = S,
@@ -409,7 +409,7 @@ ggplot(data_1st) +
geom_rsi(translate_ab = FALSE)
```
-Omit the `translate_ab = FALSE` to have the antibiotic codes (amox, amcl, cipr, gent) translated to official WHO names (amoxicillin, amoxicillin and betalactamase inhibitor, ciprofloxacin, gentamicin).
+Omit the `translate_ab = FALSE` to have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin and betalactamase inhibitor, ciprofloxacin, gentamicin).
If we group on e.g. the `genus` column and add some additional functions from our package, we can create this:
@@ -452,12 +452,12 @@ data_1st %>%
The next example uses the included `septic_patients`, which is an anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This `data.frame` can be used to practice AMR analysis.
-We will compare the resistance to fosfomycin (column `fosf`) in hospital A and D. The input for the final `fisher.test()` will be this:
+We will compare the resistance to fosfomycin (column `FOS`) in hospital A and D. The input for the final `fisher.test()` will be this:
```{r, echo = FALSE, results = 'asis'}
septic_patients %>%
filter(hospital_id %in% c("A", "D")) %>%
- select(hospital_id, fosf) %>%
+ select(hospital_id, FOS) %>%
group_by(hospital_id) %>%
count_df(combine_IR = TRUE) %>%
tidyr::spread(hospital_id, Value) %>%
@@ -472,7 +472,7 @@ We can transform the data and apply the test in only a couple of lines:
```{r}
septic_patients %>%
filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
- select(hospital_id, fosf) %>% # select the hospitals and fosfomycin
+ select(hospital_id, FOS) %>% # select the hospitals and fosfomycin
group_by(hospital_id) %>% # group on the hospitals
count_df(combine_IR = TRUE) %>% # count all isolates per group (hospital_id)
tidyr::spread(hospital_id, Value) %>% # transform output so A and D are columns
diff --git a/vignettes/SPSS.Rmd b/vignettes/SPSS.Rmd
index e15765c87..ee8dc864e 100755
--- a/vignettes/SPSS.Rmd
+++ b/vignettes/SPSS.Rmd
@@ -64,7 +64,7 @@ As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major
If you are working at a midsized or small company, you can save it tens of thousands of dollars by using R instead of e.g. SPSS - gaining even more functions and flexibility. And all R enthousiasts can do as much PR as they want (like I do here), because nobody is officially associated with or affiliated by R. It is really free.
-If you sometimes write syntaxes in SPSS to run a complete analysis or to 'automate' some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS.
+If you sometimes write syntaxes in SPSS to run a complete analysis or to 'automate' some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS. Still, as working with any statistical package, you will have to have knowledge about what you are doing (statistically) and what you are willing to accomplish.
To demonstrate the first point:
@@ -83,10 +83,10 @@ klebsiella_test <- data.frame(mo = "klebsiella",
klebsiella_test
eucast_rules(klebsiella_test, info = FALSE)
-# hundreds of trade names can be translated to an ATC or name:
-atc_name("floxapen")
-as.atc("floxapen")
-atc_tradenames("floxapen")
+# hundreds of trade names can be translated to a name, trade name or an ATC code:
+ab_name("floxapen")
+ab_tradenames("floxapen")
+ab_atc("floxapen")
```
## Import data from SPSS/SAS/Stata
diff --git a/vignettes/atc_property.Rmd b/vignettes/ab_property.Rmd
similarity index 100%
rename from vignettes/atc_property.Rmd
rename to vignettes/ab_property.Rmd
diff --git a/vignettes/benchmarks.Rmd b/vignettes/benchmarks.Rmd
index 46ebf8b0e..a62dc7ea6 100755
--- a/vignettes/benchmarks.Rmd
+++ b/vignettes/benchmarks.Rmd
@@ -81,7 +81,7 @@ boxplot(microbenchmark(as.mo("Thermus islandicus"),
as.mo("E. coli"),
times = 10),
horizontal = TRUE, las = 1, unit = "s", log = FALSE,
- xlab = "", ylab = "Time in seconds", ylim = c(0, 0.5),
+ xlab = "", ylab = "Time in seconds",
main = "Benchmarks per prevalence")
```
diff --git a/vignettes/resistance_predict.Rmd b/vignettes/resistance_predict.Rmd
index 463d1ae61..72ff87738 100755
--- a/vignettes/resistance_predict.Rmd
+++ b/vignettes/resistance_predict.Rmd
@@ -41,16 +41,16 @@ Our package contains a function `resistance_predict()`, which takes the same inp
It is basically as easy as:
```{r, eval = FALSE}
-# resistance prediction of piperacillin/tazobactam (pita):
-resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "pita")
+# resistance prediction of piperacillin/tazobactam (TZP):
+resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "TZP")
# or:
septic_patients %>%
- resistance_predict(col_ab = "pita")
+ resistance_predict(col_ab = "TZP")
-# to bind it to object 'predict_pita' for example:
-predict_pita <- septic_patients %>%
- resistance_predict(col_ab = "pita")
+# to bind it to object 'predict_TZP' for example:
+predict_TZP <- septic_patients %>%
+ resistance_predict(col_ab = "TZP")
```
The function will look for a date column itself if `col_date` is not set.
@@ -58,20 +58,20 @@ The function will look for a date column itself if `col_date` is not set.
When running any of these commands, a summary of the regression model will be printed unless using `resistance_predict(..., info = FALSE)`.
```{r, echo = FALSE}
-predict_pita <- septic_patients %>%
- resistance_predict(col_ab = "pita")
+predict_TZP <- septic_patients %>%
+ resistance_predict(col_ab = "TZP")
```
This text is only a printed summary - the actual result (output) of the function is a `data.frame` containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:
```{r}
-predict_pita
+predict_TZP
```
The function `plot` is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:
```{r, fig.height = 5.5}
-plot(predict_pita)
+plot(predict_TZP)
```
This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.
@@ -79,10 +79,10 @@ This is the fastest way to plot the result. It automatically adds the right axes
We also support the `ggplot2` package with our custom function `ggplot_rsi_predict()` to create more appealing plots:
```{r}
-ggplot_rsi_predict(predict_pita)
+ggplot_rsi_predict(predict_TZP)
# choose for error bars instead of a ribbon
-ggplot_rsi_predict(predict_pita, ribbon = FALSE)
+ggplot_rsi_predict(predict_TZP, ribbon = FALSE)
```
### Choosing the right model
@@ -92,7 +92,7 @@ Resistance is not easily predicted; if we look at vancomycin resistance in Gram
```{r}
septic_patients %>%
filter(mo_gramstain(mo) == "Gram positive") %>%
- resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE) %>%
+ resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE) %>%
ggplot_rsi_predict()
```
@@ -113,7 +113,7 @@ For the vancomycin resistance in Gram positive bacteria, a linear model might be
```{r}
septic_patients %>%
filter(mo_gramstain(mo) == "Gram positive") %>%
- resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE, model = "linear") %>%
+ resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "linear") %>%
ggplot_rsi_predict()
```
@@ -121,7 +121,7 @@ This seems more likely, doesn't it?
The model itself is also available from the object, as an `attribute`:
```{r}
-model <- attributes(predict_pita)$model
+model <- attributes(predict_TZP)$model
summary(model)$family
