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 14 March 2022.
+
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 27 March 2022.
Introduction
-
Conducting AMR data analysis unfortunately requires in-depth
-knowledge from different scientific fields, which makes it hard to do
-right. At least, it requires:
+
Conducting AMR data analysis unfortunately requires in-depth knowledge from different scientific fields, which makes it hard to do right. At least, it requires:
Good questions (always start with those!)
-
A thorough understanding of (clinical) epidemiology, to understand
-the clinical and epidemiological relevance and possible bias of
-results
-
A thorough understanding of (clinical) microbiology/infectious
-diseases, to understand which microorganisms are causal to which
-infections and the implications of pharmaceutical treatment, as well as
-understanding intrinsic and acquired microbial resistance
-
Experience with data analysis with microbiological tests and their
-results, to understand the determination and limitations of MIC values
-and their interpretations to RSI values
-
Availability of the biological taxonomy of microorganisms and
-probably normalisation factors for pharmaceuticals, such as defined
-daily doses (DDD)
-
Available (inter-)national guidelines, and profound methods to apply
-them
+
A thorough understanding of (clinical) epidemiology, to understand the clinical and epidemiological relevance and possible bias of results
+
A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment, as well as understanding intrinsic and acquired microbial resistance
+
Experience with data analysis with microbiological tests and their results, to understand the determination and limitations of MIC values and their interpretations to RSI values
+
Availability of the biological taxonomy of microorganisms and probably normalisation factors for pharmaceuticals, such as defined daily doses (DDD)
+
Available (inter-)national guidelines, and profound methods to apply them
-
Of course, we cannot instantly provide you with knowledge and
-experience. But with this AMR package, we aimed at
-providing (1) tools to simplify antimicrobial resistance data cleaning,
-transformation and analysis, (2) methods to easily incorporate
-international guidelines and (3) scientifically reliable reference data,
-including the requirements mentioned above.
-
The AMR package enables standardised and reproducible
-AMR data analysis, with the application of evidence-based rules,
-determination of first isolates, translation of various codes for
-microorganisms and antimicrobial agents, determination of (multi-drug)
-resistant microorganisms, and calculation of antimicrobial resistance,
-prevalence and future trends.
+
Of course, we cannot instantly provide you with knowledge and experience. But with this AMR package, we aimed at providing (1) tools to simplify antimicrobial resistance data cleaning, transformation and analysis, (2) methods to easily incorporate international guidelines and (3) scientifically reliable reference data, including the requirements mentioned above.
+
The AMR package enables standardised and reproducible AMR data analysis, with the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends.
Preparation
-
For this tutorial, we will create fake demonstration data to work
-with.
-
You can skip to Cleaning the data if
-you already have your own data ready. If you start your analysis, try to
-make the structure of your data generally look like this:
+
For this tutorial, we will create fake demonstration data to work with.
+
You can skip to Cleaning the data if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:
date
@@ -261,21 +231,21 @@ make the structure of your data generally look like this:
-
2022-03-14
+
2022-03-27
abcd
Escherichia coli
S
S
-
2022-03-14
+
2022-03-27
abcd
Escherichia coli
S
R
-
2022-03-14
+
2022-03-27
efgh
Escherichia coli
R
@@ -286,13 +256,8 @@ make the structure of your data generally look like this:
Needed R packages
-
As with many uses in R, we need some additional packages for AMR data
-analysis. Our package works closely together with the tidyverse packagesdplyr and ggplot2 by
-RStudio. The tidyverse tremendously improves the way we conduct data
-science - it allows for a very natural way of writing syntaxes and
-creating beautiful plots in R.
-
We will also use the cleaner package, that can be used
-for cleaning data and creating frequency tables.
+
As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the tidyverse packagesdplyr and ggplot2 by RStudio. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
+
We will also use the cleaner package, that can be used for cleaning data and creating frequency tables.
We will create some fake example data to use for analysis. For AMR
-data analysis, we need at least: a patient ID, name or code of a
-microorganism, a date and antimicrobial results (an antibiogram). It
-could also include a specimen type (e.g. to filter on blood or urine),
-the ward type (e.g. to filter on ICUs).
-
With additional columns (like a hospital name, the patients gender of
-even [well-defined] clinical properties) you can do a comparative
-analysis, as this tutorial will demonstrate too.
+
We will create some fake example data to use for analysis. For AMR data analysis, we need at least: a patient ID, name or code of a microorganism, a date and antimicrobial results (an antibiogram). It could also include a specimen type (e.g. to filter on blood or urine), the ward type (e.g. to filter on ICUs).
+
With additional columns (like a hospital name, the patients gender of even [well-defined] clinical properties) you can do a comparative analysis, as this tutorial will demonstrate too.
Patients
To start with patients, we need a unique list of patients.
The LETTERS object is available in R - it’s a vector
-with 26 characters: A to Z. The
-patients object we just created is now a vector of length
-260, with values (patient IDs) varying from A1 to
-Z10. Now we we also set the gender of our patients, by
-putting the ID and the gender in a table:
+
The LETTERS object is available in R - it’s a vector with 26 characters: A to Z. The patients object we just created is now a vector of length 260, with values (patient IDs) varying from A1 to Z10. Now we we also set the gender of our patients, by putting the ID and the gender in a table:
patients_table<-data.frame(patient_id =patients,
gender =c(rep("M", 135),
@@ -335,19 +289,14 @@ putting the ID and the gender in a table:
Dates
-
Let’s pretend that our data consists of blood cultures isolates from
-between 1 January 2010 and 1 January 2018.
+
Let’s pretend that our data consists of blood cultures isolates from between 1 January 2010 and 1 January 2018.
This dates object now contains all days in our date
-range.
+
This dates object now contains all days in our date range.
Microorganisms
-
For this tutorial, we will uses four different microorganisms:
-Escherichia coli, Staphylococcus aureus,
-Streptococcus pneumoniae, and Klebsiella
-pneumoniae:
+
For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:
Using the sample() function, we can randomly select
-items from all objects we defined earlier. To let our fake data reflect
-reality a bit, we will also approximately define the probabilities of
-bacteria and the antibiotic results, using the random_rsi()
-function.
+
Using the sample() function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results, using the random_rsi() function.
We also created a package dedicated to data cleaning and checking,
-called the cleaner package. It freq() function
-can be used to create frequency tables.
+
We also created a package dedicated to data cleaning and checking, called the cleaner package. It freq() function can be used to create frequency tables.
So, we can draw at least two conclusions immediately. From a data
-scientists perspective, the data looks clean: only values M
-and F. From a researchers 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:
+
So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values M and F. From a researchers 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 is.rsi.eligible() can check which columns
-are probably columns with R/SI test results. Using mutate()
-and across(), we can apply the transformation to the formal
-<rsi> class:
+
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 is.rsi.eligible() can check which columns are probably columns with R/SI test results. Using mutate() and across(), we can apply the transformation to the formal <rsi> class:
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 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:
+
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 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:
We also need to know which isolates we can actually use for
-analysis.
-
To conduct an analysis of antimicrobial resistance, you must only include the first
-isolate of every patient per episode (Hindler et al., Clin
-Infect Dis. 2007). If you would not do this, you could easily get an
-overestimate or underestimate of the resistance of an antibiotic.
-Imagine that a patient was admitted with an MRSA and that it was found
-in 5 different blood cultures the following weeks (yes, some countries
-like the Netherlands have these blood drawing policies). The resistance
-percentage of oxacillin of all isolates would be overestimated, because
-you included this MRSA more than once. It would clearly be selection
-bias.
-
The Clinical and Laboratory Standards Institute (CLSI) appoints this
-as follows:
+
We also need to know which isolates we can actually use for analysis.
+
To conduct an analysis of antimicrobial resistance, you must only include the first isolate of every patient per episode (Hindler et al., Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be selection bias.
+
The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:
-
(…) When preparing a cumulative antibiogram to guide clinical
-decisions about empirical antimicrobial therapy of initial infections,
-only the first isolate of a given species per patient, per
-analysis period (eg, one year) should be included, irrespective of body
-site, antimicrobial susceptibility profile, or other phenotypical
-characteristics (eg, biotype). The first isolate is easily
-identified, and cumulative antimicrobial susceptibility test data
-prepared using the first isolate are generally comparable to cumulative
-antimicrobial susceptibility test data calculated by other methods,
-providing duplicate isolates are excluded. M39-A4
-Analysis and Presentation of Cumulative Antimicrobial Susceptibility
-Test Data, 4th Edition. CLSI, 2014. Chapter 6.4
+
(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype). The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded. M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4
-
This AMR package includes this methodology with the
-first_isolate() function and is able to apply the four
-different methods as defined by Hindler
-et al. in 2007: phenotype-based, episode-based,
-patient-based, isolate-based. The right method depends on your goals and
-analysis, but the default phenotype-based method is in any case the
-method to properly correct for most duplicate isolates. This method also
-takes into account the antimicrobial susceptibility test results using
-all_microbials(). Read more about the methods on the
-first_isolate() page.
+
This AMR package includes this methodology with the first_isolate() function and is able to apply the four different methods as defined by Hindler et al. in 2007: phenotype-based, episode-based, patient-based, isolate-based. The right method depends on your goals and analysis, but the default phenotype-based method is in any case the method to properly correct for most duplicate isolates. This method also takes into account the antimicrobial susceptibility test results using all_microbials(). Read more about the methods on the first_isolate() page.
The outcome of the function can easily be added to our data:
data<-data%>%
@@ -630,11 +502,9 @@ takes into account the antimicrobial susceptibility test results using
# ℹ Using column 'patient_id' as input for `col_patient_id`.# Basing inclusion on all antimicrobial results, using a points threshold of# 2
-# => Found 10,676 'phenotype-based' first isolates (53.4% of total where a
+# => Found 10,589 'phenotype-based' first isolates (52.9% of total where a# microbial ID was available)
-
So only 53.4% is suitable for resistance analysis! We can now filter
-on it with the filter() function, also from the
-dplyr package:
+
So only 52.9% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
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.
+
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.
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.
-
The freq() function can be used like the base R language
-was intended:
+
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.
+
The freq() function can be used like the base R language was intended:
@@ -910,58 +757,28 @@ antibiotic class they are in:
-
2015-06-11
-
R8
-
Hospital A
-
B_STRPT_PNMN
-
S
+
2015-07-03
+
F1
+
Hospital B
+
B_KLBSL_PNMN
+
R
S
S
R
-
F
-
Gram-positive
-
Streptococcus
+
M
+
Gram-negative
+
Klebsiella
pneumoniae
TRUE
-
2011-11-10
-
L4
-
Hospital A
-
B_STRPT_PNMN
-
R
-
R
-
R
-
R
-
M
-
Gram-positive
-
Streptococcus
-
pneumoniae
-
TRUE
-
-
-
2013-03-20
-
G10
-
Hospital A
-
B_STRPT_PNMN
-
S
-
S
-
R
-
R
-
M
-
Gram-positive
-
Streptococcus
-
pneumoniae
-
TRUE
-
-
-
2010-09-24
-
T9
-
Hospital A
+
2014-05-04
+
O4
+
Hospital B
B_ESCHR_COLI
S
S
-
R
+
S
R
F
Gram-negative
@@ -970,28 +787,28 @@ antibiotic class they are in:
TRUE
-
2014-12-18
-
D8
-
Hospital B
-
B_STPHY_AURS
+
2016-11-18
+
G10
+
Hospital D
+
B_ESCHR_COLI
R
S
S
R
M
-
Gram-positive
-
Staphylococcus
-
aureus
+
Gram-negative
+
Escherichia
+
coli
TRUE
-
2014-10-24
-
V7
-
Hospital A
+
2017-08-15
+
N7
+
Hospital C
B_STRPT_PNMN
-
R
-
R
-
R
+
S
+
S
+
S
R
F
Gram-positive
@@ -999,11 +816,39 @@ antibiotic class they are in:
pneumoniae
TRUE
+
+
2015-03-31
+
H3
+
Hospital D
+
B_STRPT_PNMN
+
S
+
S
+
S
+
R
+
M
+
Gram-positive
+
Streptococcus
+
pneumoniae
+
TRUE
+
+
+
2015-08-12
+
Q8
+
Hospital A
+
B_STPHY_AURS
+
R
+
R
+
R
+
R
+
F
+
Gram-positive
+
Staphylococcus
+
aureus
+
TRUE
+
-
If you want to get a quick glance of the number of isolates in
-different bug/drug combinations, you can use the
-bug_drug_combinations() function:
+
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the bug_drug_combinations() function:
data_1st%>%bug_drug_combinations()%>%
@@ -1022,50 +867,50 @@ different bug/drug combinations, you can use the
E. coli
AMX
-
2208
-
135
-
2334
-
4677
+
2163
+
127
+
2211
+
4501
E. coli
AMC
-
3416
-
135
-
1126
-
4677
+
3304
+
152
+
1045
+
4501
E. coli
CIP
-
3415
+
3294
0
-
1262
-
4677
+
1207
+
4501
E. coli
GEN
-
4110
+
3917
0
-
567
-
4677
+
584
+
4501
K. pneumoniae
AMX
0
0
-
1203
-
1203
+
1198
+
1198
K. pneumoniae
AMC
-
926
-
38
-
239
-
1203
+
946
+
45
+
207
+
1198
@@ -1088,26 +933,26 @@ different bug/drug combinations, you can use the
E. coli
GEN
-
4110
+
3917
0
-
567
-
4677
+
584
+
4501
K. pneumoniae
GEN
-
1083
+
1077
0
-
120
-
1203
+
121
+
1198
S. aureus
GEN
-
2348
+
2426
0
-
304
-
2652
+
320
+
2746
S. pneumoniae
@@ -1119,37 +964,18 @@ different bug/drug combinations, you can use the
-
This will only give you the crude numbers in the data. To calculate
-antimicrobial resistance in a more sensible way, also by correcting for
-too few results, we use the resistance() and
-susceptibility() functions.
+
This will only give you the crude numbers in the data. To calculate antimicrobial resistance in a more sensible way, also by correcting for too few results, we use the resistance() and susceptibility() functions.
All these functions contain a minimum argument, denoting
-the minimum required number of test results for returning a value. These
-functions will otherwise return NA. The default is
-minimum = 30, following the CLSI
-M39-A4 guideline for applying microbial epidemiology.
-
As per the EUCAST guideline of 2019, we calculate resistance as the
-proportion of R (proportion_R(), equal to
-resistance()) and susceptibility as the proportion of S and
-I (proportion_SI(), equal to
-susceptibility()). These functions can be used on their
-own:
All these functions contain a minimum argument, denoting the minimum required number of test results for returning a value. These functions will otherwise return NA. The default is minimum = 30, following the CLSI M39-A4 guideline for applying microbial epidemiology.
+
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
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):
+
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):
data_1st%>%group_by(hospital)%>%
@@ -1197,29 +1019,27 @@ all isolates available for every group (i.e. values S, I or R):
Hospital A
-
0.5442031
-
3269
+
0.5514896
+
3088
Hospital B
-
0.5462647
-
3815
+
0.5451879
+
3751
Hospital C
-
0.5565553
-
1556
+
0.5297061
+
1599
Hospital D
-
0.5569745
-
2036
+
0.5443980
+
2151
-
These functions can also be used to get the proportion of multiple
-antibiotics, to calculate empiric susceptibility of combination
-therapies very easily:
+
These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
data_1st%>%group_by(genus)%>%
@@ -1236,34 +1056,31 @@ therapies very easily:
Escherichia
-
0.7592474
-
0.8787684
-
0.9775497
+
0.7678294
+
0.8702511
+
0.9748945
Klebsiella
-
0.8013300
-
0.9002494
-
0.9758936
+
0.8272120
+
0.8989983
+
0.9841402
Staphylococcus
-
0.7929864
-
0.8853695
-
0.9807692
+
0.7825929
+
0.8834669
+
0.9766934
Streptococcus
-
0.5247201
+
0.5391791
0.0000000
-
0.5247201
+
0.5391791
-
Or if you are curious for the resistance within certain antibiotic
-classes, use a antibiotic class selector such as
-penicillins(), which automatically will include the columns
-AMX and AMC of our data:
+
Or if you are curious for the resistance within certain antibiotic classes, use a antibiotic class selector such as penicillins(), which automatically will include the columns AMX and AMC of our data:
data_1st%>%# group by hospital
@@ -1284,28 +1101,27 @@ classes, use a antibiotic class selector such as
Hospital A
-
54.4%
-
26.2%
+
55.1%
+
27.0%
Hospital B
-
54.6%
-
28.2%
+
54.5%
+
26.0%
Hospital C
-
55.7%
-
26.6%
+
53.0%
+
27.0%
Hospital D
-
55.7%
-
28.9%
+
54.4%
+
27.8%
-
To make a transition to the next part, let’s see how differences in
-the previously calculated combination therapies could be plotted:
+
To make a transition to the next part, let’s see how differences in the previously calculated combination therapies could be plotted:
data_1st%>%group_by(genus)%>%
@@ -1323,10 +1139,7 @@ the previously calculated combination therapies could be plotted:
Plots
-
To show results in plots, most R users would nowadays use the
-ggplot2 package. This package lets you create plots in
-layers. You can read more about it on their website. A quick
-example would look like these syntaxes:
+
To show results in plots, most R users would nowadays use the ggplot2 package. This package lets you create plots in layers. You can read more about it on their website. A quick example would look like these syntaxes:
ggplot(data =a_data_set,
mapping =aes(x =year,
@@ -1340,21 +1153,13 @@ example would look like these syntaxes:
# or as short as:ggplot(a_data_set)+geom_bar(aes(year))
-
The AMR package contains functions to extend this
-ggplot2 package, for example geom_rsi(). It
-automatically transforms data with count_df() or
-proportion_df() and show results in stacked bars. Its
-simplest and shortest example:
+
The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or proportion_df() and show results in stacked bars. Its simplest and shortest example:
Omit the translate_ab = FALSE to have the antibiotic
-codes (AMX, AMC, CIP, GEN) translated to official WHO names
-(amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin,
-gentamicin).
-
If we group on e.g. the genus column and add some
-additional functions from our package, we can create this:
+
Omit the translate_ab = FALSE to have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin).
+
If we group on e.g. the genus column and add some additional functions from our package, we can create this:
# group the data on `genus`ggplot(data_1st%>%group_by(genus))+
@@ -1377,8 +1182,7 @@ additional functions from our package, we can create this:
# (is now y axis because we turned the plot)theme(axis.text.y =element_text(face ="italic"))
-
To simplify this, we also created the ggplot_rsi()
-function, which combines almost all above functions:
+
To simplify this, we also created the ggplot_rsi() function, which combines almost all above functions:
data_1st%>%group_by(genus)%>%
@@ -1391,29 +1195,22 @@ function, which combines almost all above functions:
Plotting MIC and disk diffusion values
-
The AMR package also extends the plot() and
-ggplot2::autoplot() functions for plotting minimum
-inhibitory concentrations (MIC, created with as.mic()) and
-disk diffusion diameters (created with as.disk()).
-
With the random_mic() and random_disk()
-functions, we can generate sampled values for the new data types (S3
-classes) <mic> and <disk>:
+
The AMR package also extends the plot() and ggplot2::autoplot() functions for plotting minimum inhibitory concentrations (MIC, created with as.mic()) and disk diffusion diameters (created with as.disk()).
+
With the random_mic() and random_disk() functions, we can generate sampled values for the new data types (S3 classes) <mic> and <disk>:
@@ -1422,17 +1219,11 @@ classes) <mic> and <disk>:
# ggplot2:autoplot(mic_values)
-
But we could also be more specific, by generating MICs that are
-likely to be found in E. coli for ciprofloxacin:
+
But we could also be more specific, by generating MICs that are likely to be found in E. coli for ciprofloxacin:
mic_values<-random_mic(size =100, mo ="E. coli", ab ="cipro")
-
For the plot() and autoplot() function, we
-can define the microorganism and an antimicrobial agent the same way.
-This will add the interpretation of those values according to a chosen
-guidelines (defaults to the latest EUCAST guideline).
-
Default colours are colour-blind friendly, while maintaining the
-convention that e.g. ‘susceptible’ should be green and ‘resistant’
-should be red:
+
For the plot() and autoplot() function, we can define the microorganism and an antimicrobial agent the same way. This will add the interpretation of those values according to a chosen guidelines (defaults to the latest EUCAST guideline).
+
Default colours are colour-blind friendly, while maintaining the convention that e.g. ‘susceptible’ should be green and ‘resistant’ should be red:
# base R:plot(mic_values, mo ="E. coli", ab ="cipro")
@@ -1441,25 +1232,22 @@ should be red:
# ggplot2:autoplot(mic_values, mo ="E. coli", ab ="cipro")
-
For disk diffusion values, there is not much of a difference in
-plotting:
+
For disk diffusion values, there is not much of a difference in plotting:
# base R:plot(disk_values, mo ="E. coli", ab ="cipro")
-
And when using the ggplot2 package, but now choosing the
-latest implemented CLSI guideline (notice that the EUCAST-specific term
-“Susceptible, incr. exp.” has changed to “Intermediate”):
+
And when using the ggplot2 package, but now choosing the latest implemented CLSI guideline (notice that the EUCAST-specific term “Susceptible, incr. exp.” has changed to “Intermediate”):
autoplot(disk_values,
mo ="E. coli",
@@ -1471,14 +1259,8 @@ latest implemented CLSI guideline (notice that the EUCAST-specific term
Independence test
-
The next example uses the example_isolates data set.
-This is a data set included with this package and contains 2,000
-microbial isolates with their full antibiograms. It reflects reality and
-can be used to practice AMR data analysis.
-
We will compare the resistance to fosfomycin (column
-FOS) in hospital A and D. The input for the
-fisher.test() can be retrieved with a transformation like
-this:
+
The next example uses the example_isolates data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis.
+
We will compare the resistance to fosfomycin (column FOS) in hospital A and D. The input for the fisher.test() can be retrieved with a transformation like this:
# use package 'tidyr' to pivot data:library(tidyr)
@@ -1512,9 +1294,7 @@ this:
# sample estimates:# odds ratio # 0.4488318
-
As can be seen, the p value is 0.031, which means that the fosfomycin
-resistance found in isolates from patients in hospital A and D are
-really different.
+
As can be seen, the p value is 0.031, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.
What are EUCAST rules? The European Committee on Antimicrobial
-Susceptibility Testing (EUCAST) states on
-their website:
+
What are EUCAST rules? The European Committee on Antimicrobial Susceptibility Testing (EUCAST) states on their website:
-
EUCAST expert rules are a tabulated collection of expert
-knowledge on intrinsic resistances, exceptional resistance phenotypes
-and interpretive rules that may be applied to antimicrobial
-susceptibility testing in order to reduce errors and make appropriate
-recommendations for reporting particular resistances.
+
EUCAST expert rules are a tabulated collection of expert knowledge on intrinsic resistances, exceptional resistance phenotypes and interpretive rules that may be applied to antimicrobial susceptibility testing in order to reduce errors and make appropriate recommendations for reporting particular resistances.
-
In Europe, a lot of medical microbiological laboratories already
-apply these rules (Brown
-et al., 2015). Our package features their latest insights
-on intrinsic resistance and unusual phenotypes (v3.3, 2021).
-
Moreover, the eucast_rules() function we use for this
-purpose can also apply additional rules, like forcing
-ampicillin = R in isolates when
-amoxicillin/clavulanic acid = R.
+
In Europe, a lot of medical microbiological laboratories already apply these rules (Brown et al., 2015). Our package features their latest insights on intrinsic resistance and unusual phenotypes (v3.3, 2021).
+
Moreover, the eucast_rules() function we use for this purpose can also apply additional rules, like forcing ampicillin = R in isolates when amoxicillin/clavulanic acid = R.
Examples
-
These rules can be used to discard impossible bug-drug combinations
-in your data. For example, Klebsiella produces beta-lactamase
-that prevents ampicillin (or amoxicillin) from working against it. In
-other words, practically every strain of Klebsiella is
-resistant to ampicillin.
-
Sometimes, laboratory data can still contain such strains with
-ampicillin being susceptible to ampicillin. This could be because an
-antibiogram is available before an identification is available, and the
-antibiogram is then not re-interpreted based on the identification
-(namely, Klebsiella). EUCAST expert rules solve this, that can
-be applied using eucast_rules():
+
These rules can be used to discard impossible bug-drug combinations in your data. For example, Klebsiella produces beta-lactamase that prevents ampicillin (or amoxicillin) from working against it. In other words, practically every strain of Klebsiella is resistant to ampicillin.
+
Sometimes, laboratory data can still contain such strains with ampicillin being susceptible to ampicillin. This could be because an antibiogram is available before an identification is available, and the antibiogram is then not re-interpreted based on the identification (namely, Klebsiella). EUCAST expert rules solve this, that can be applied using eucast_rules():
A more convenient function is
-mo_is_intrinsic_resistant() that uses the same guideline,
-but allows to check for one or more specific microorganisms or
-antibiotics:
+
A more convenient function is mo_is_intrinsic_resistant() that uses the same guideline, but allows to check for one or more specific microorganisms or antibiotics:
EUCAST rules can not only be used for correction, they can also be
-used for filling in known resistance and susceptibility based on results
-of other antimicrobials drugs. This process is called interpretive
-reading, is basically a form of imputation, and is part of the
-eucast_rules() function as well:
+
EUCAST rules can not only be used for correction, they can also be used for filling in known resistance and susceptibility based on results of other antimicrobials drugs. This process is called interpretive reading, is basically a form of imputation, and is part of the eucast_rules() function as well:
With the function mdro(), you can determine which
-micro-organisms are multi-drug resistant organisms (MDRO).
+
With the function mdro(), you can determine which micro-organisms are multi-drug resistant organisms (MDRO).
Type of input
-
The mdro() function takes a data set as input, such as a
-regular data.frame. It tries to automatically determine the
-right columns for info about your isolates, such as the name of the
-species and all columns with results of antimicrobial agents. See the
-help page for more info about how to set the right settings for your
-data with the command ?mdro.
-
For WHONET data (and most other data), all settings are automatically
-set correctly.
+
The mdro() function takes a data set as input, such as a regular data.frame. It tries to automatically determine the right columns for info about your isolates, such as the name of the species and all columns with results of antimicrobial agents. See the help page for more info about how to set the right settings for your data with the command ?mdro.
+
For WHONET data (and most other data), all settings are automatically set correctly.
Guidelines
-
The mdro() function support multiple guidelines. You can
-select a guideline with the guideline parameter. Currently
-supported guidelines are (case-insensitive):
+
The mdro() function support multiple guidelines. You can select a guideline with the guideline parameter. Currently supported guidelines are (case-insensitive):
guideline = "CMI2012" (default)
-
Magiorakos AP, Srinivasan A et al. “Multidrug-resistant,
-extensively drug-resistant and pandrug-resistant bacteria: an
-international expert proposal for interim standard definitions for
-acquired resistance.” Clinical Microbiology and Infection (2012) (link)
+
Magiorakos AP, Srinivasan A et al. “Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.” Clinical Microbiology and Infection (2012) (link)
The European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance and Unusual Phenotypes” (link)
guideline = "EUCAST3.1"
-
The European international guideline - EUCAST Expert Rules Version
-3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (link)
+
The European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (link)
guideline = "TB"
-
The international guideline for multi-drug resistant tuberculosis -
-World Health Organization “Companion handbook to the WHO guidelines for
-the programmatic management of drug-resistant tuberculosis” (link)
+
The international guideline for multi-drug resistant tuberculosis - World Health Organization “Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis” (link)
guideline = "MRGN"
-
The German national guideline - Mueller et al. (2015)
-Antimicrobial Resistance and Infection Control 4:7. DOI:
-10.1186/s13756-015-0047-6
+
The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
guideline = "BRMO"
-
The Dutch national guideline - Rijksinstituut voor Volksgezondheid en
-Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen)
-(ZKH)” (link)
+
The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link)
You can also use your own custom guideline. Custom guidelines can be
-set with the custom_mdro_guideline() function. This is of
-great importance if you have custom rules to determine MDROs in your
-hospital, e.g., rules that are dependent on ward, state of contact
-isolation or other variables in your data.
-
If you are familiar with case_when() of the
-dplyr package, you will recognise the input method to set
-your own rules. Rules must be set using what R considers to be the
-‘formula notation’:
+
You can also use your own custom guideline. Custom guidelines can be set with the custom_mdro_guideline() function. This is of great importance if you have custom rules to determine MDROs in your hospital, e.g., rules that are dependent on ward, state of contact isolation or other variables in your data.
+
If you are familiar with case_when() of the dplyr package, you will recognise the input method to set your own rules. Rules must be set using what R considers to be the ‘formula notation’:
custom<-custom_mdro_guideline(CIP=="R"&age>60~"Elderly Type A",
ERY=="R"&age>60~"Elderly Type B")
-
If a row/an isolate matches the first rule, the value after the first
-~ (in this case ‘Elderly Type A’) will be set as
-MDRO value. Otherwise, the second rule will be tried and so on. The
-maximum number of rules is unlimited.
-
You can print the rules set in the console for an overview. Colours
-will help reading it if your console supports colours.
+
If a row/an isolate matches the first rule, the value after the first ~ (in this case ‘Elderly Type A’) will be set as MDRO value. Otherwise, the second rule will be tried and so on. The maximum number of rules is unlimited.
+
You can print the rules set in the console for an overview. Colours will help reading it if your console supports colours.
custom# A set of custom MDRO rules:
@@ -289,8 +255,7 @@ will help reading it if your console supports colours.
# # Unmatched rows will return NA.# Results will be of class <factor>, with ordered levels: Negative < Elderly Type A < Elderly Type B
-
The outcome of the function can be used for the
-guideline argument in the mdro() function:
+
The outcome of the function can be used for the guideline argument in the mdro() function:
x<-mdro(example_isolates, guideline =custom)# Determining MDROs based on custom rules, resulting in factor levels:
@@ -301,25 +266,14 @@ will help reading it if your console supports colours.
# x# Negative Elderly Type A Elderly Type B # 1070 198 732
-
The rules set (the custom object in this case) could be
-exported to a shared file location using saveRDS() if you
-collaborate with multiple users. The custom rules set could then be
-imported using readRDS().
+
The rules set (the custom object in this case) could be exported to a shared file location using saveRDS() if you collaborate with multiple users. The custom rules set could then be imported using readRDS().
Examples
-
The mdro() function always returns an ordered
-factor for predefined guidelines. For example, the output
-of the default guideline by Magiorakos et al. returns a
-factor with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in
-that order.
-
The next example uses the example_isolates data set.
-This is a data set included with this package and contains full
-antibiograms of 2,000 microbial isolates. It reflects reality and can be
-used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline
-on this data set, we get:
+
The mdro() function always returns an ordered factor for predefined guidelines. For example, the output of the default guideline by Magiorakos et al. returns a factor with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.
+
The next example uses the example_isolates data set. This is a data set included with this package and contains full antibiograms of 2,000 microbial isolates. It reflects reality and can be used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:
@@ -343,32 +297,16 @@ on this data set, we get:
# Table 5 - Acinetobacter spp.... OK.# Warning: in `mdro()`: NA introduced for isolates where the available percentage of# antimicrobial classes was below 50% (set with `pct_required_classes`)
-
Only results with ‘R’ are considered as resistance. Use
-combine_SI = FALSE to also consider ‘I’ as resistance.
-
Determining multidrug-resistant organisms (MDRO), according to:
-Guideline: Multidrug-resistant, extensively drug-resistant and
-pandrug-resistant bacteria: an international expert proposal for interim
-standard definitions for acquired resistance. Author(s): Magiorakos AP,
-Srinivasan A, Carey RB, …, Vatopoulos A, Weber JT, Monnet DL Source:
-Clinical Microbiology and Infection 18:3, 2012; doi:
-10.1111/j.1469-0691.2011.03570.x
+
Only results with ‘R’ are considered as resistance. Use combine_SI = FALSE to also consider ‘I’ as resistance.
+
Determining multidrug-resistant organisms (MDRO), according to: Guideline: Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Author(s): Magiorakos AP, Srinivasan A, Carey RB, …, Vatopoulos A, Weber JT, Monnet DL Source: Clinical Microbiology and Infection 18:3, 2012; doi: 10.1111/j.1469-0691.2011.03570.x
For another example, I will create a data set to determine multi-drug
-resistant TB:
+
For another example, I will create a data set to determine multi-drug resistant TB:
# random_rsi() is a helper function to generate# a random vector with values S, I and R
@@ -408,8 +345,7 @@ resistant TB:
pyrazinamide =random_rsi(5000),
moxifloxacin =random_rsi(5000),
kanamycin =random_rsi(5000))
-
Because all column names are automatically verified for valid drug
-names or codes, this would have worked exactly the same way:
+
Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same way:
my_TB_data<-data.frame(RIF =random_rsi(5000),
INH =random_rsi(5000),
@@ -422,21 +358,20 @@ names or codes, this would have worked exactly the same way:
head(my_TB_data)# rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-# 1 I S I I S S
-# 2 R S I S R R
-# 3 I R I S R S
-# 4 I I I R S R
-# 5 R S I R S I
-# 6 S S I S R R
+# 1 S R S I R S
+# 2 I I S S I I
+# 3 I S R R R S
+# 4 S S I S R S
+# 5 R R S I R I
+# 6 R S R S S I# kanamycin
-# 1 R
-# 2 I
+# 1 I
+# 2 S# 3 S
-# 4 S
-# 5 S
-# 6 R
-
We can now add the interpretation of MDR-TB to our data set. You can
-use:
+# 4 I
+# 5 I
+# 6 I
+
We can now add the interpretation of MDR-TB to our data set. You can use:
Now to transform this to a data set with only resistance percentages
-per taxonomic order and genus:
+
Now to transform this to a data set with only resistance percentages per taxonomic order and genus:
resistance_data<-example_isolates%>%group_by(order =mo_order(mo), # group on anything, like order
@@ -290,15 +286,12 @@ per taxonomic order and genus:
Perform principal component analysis
-
The new pca() function will automatically filter on rows
-that contain numeric values in all selected variables, so we now only
-need to do:
+
The new pca() function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:
pca_result<-pca(resistance_data)# ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP"# and "TOB". Total observations available: 7.
-
The result can be reviewed with the good old summary()
-function:
+
The result can be reviewed with the good old summary() function:
summary(pca_result)# Groups (n=4, named as 'order'):
@@ -310,11 +303,7 @@ function:
# Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
# Groups (n=4, named as 'order'):# [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
-
Good news. The first two components explain a total of 93.3% of the
-variance (see the PC1 and PC2 values of the Proportion of
-Variance. We can create a so-called biplot with the base R
-biplot() function, to see which antimicrobial resistance
-per drug explain the difference per microorganism.
+
Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the Proportion of Variance. We can create a so-called biplot with the base R biplot() function, to see which antimicrobial resistance per drug explain the difference per microorganism.
Plotting the results
@@ -322,9 +311,7 @@ per drug explain the difference per microorganism.
But we can’t see the explanation of the points. Perhaps this works
-better with our new ggplot_pca() function, that
-automatically adds the right labels and even groups:
+
But we can’t see the explanation of the points. Perhaps this works better with our new ggplot_pca() function, that automatically adds the right labels and even groups:
SPSS (Statistical Package for the Social Sciences) is probably the
-most well-known software package for statistical analysis. SPSS is
-easier to learn than R, because in SPSS you only have to click a menu to
-run parts of your analysis. Because of its user-friendliness, it is
-taught at universities and particularly useful for students who are new
-to statistics. From my experience, I would guess that pretty much all
-(bio)medical students know it at the time they graduate. SAS and Stata
-are comparable statistical packages popular in big industries.
+
SPSS (Statistical Package for the Social Sciences) is probably the most well-known software package for statistical analysis. SPSS is easier to learn than R, because in SPSS you only have to click a menu to run parts of your analysis. Because of its user-friendliness, it is taught at universities and particularly useful for students who are new to statistics. From my experience, I would guess that pretty much all (bio)medical students know it at the time they graduate. SAS and Stata are comparable statistical packages popular in big industries.
Compared to R
-
As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come
-with major downsides when comparing it with R:
+
As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major downsides when comparing it with R:
R is highly modular.
-
The official R network
-(CRAN) features more than 16,000 packages at the time of writing,
-our AMR package being one of them. All these packages were
-peer-reviewed before publication. Aside from this official channel,
-there are also developers who choose not to submit to CRAN, but rather
-keep it on their own public repository, like GitHub. So there may even
-be a lot more than 14,000 packages out there.
-
Bottom line is, you can really extend it yourself or ask somebody to
-do this for you. Take for example our AMR package. Among
-other things, it adds reliable reference data to R to help you with the
-data cleaning and analysis. SPSS, SAS and Stata will never know what a
-valid MIC value is or what the Gram stain of E. coli is. Or
-that all species of Klebiella are resistant to amoxicillin and
-that Floxapen® is a trade name of flucloxacillin. These facts
-and properties are often needed to clean existing data, which would be
-very inconvenient in a software package without reliable reference data.
-See below for a demonstration.
+
The official R network (CRAN) features more than 16,000 packages at the time of writing, our AMR package being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitHub. So there may even be a lot more than 14,000 packages out there.
+
Bottom line is, you can really extend it yourself or ask somebody to do this for you. Take for example our AMR package. Among other things, it adds reliable reference data to R to help you with the data cleaning and analysis. SPSS, SAS and Stata will never know what a valid MIC value is or what the Gram stain of E. coli is. Or that all species of Klebiella are resistant to amoxicillin and that Floxapen® is a trade name of flucloxacillin. These facts and properties are often needed to clean existing data, which would be very inconvenient in a software package without reliable reference data. See below for a demonstration.
R is extremely flexible.
-
Because you write the syntax yourself, you can do anything you want.
-The flexibility in transforming, arranging, grouping and summarising
-data, or drawing plots, is endless - with SPSS, SAS or Stata you are
-bound to their algorithms and format styles. They may be a bit flexible,
-but you can probably never create that very specific publication-ready
-plot without using other (paid) software. If you sometimes write
-syntaxes in SPSS to run a complete analysis or to ‘automate’ some of
-your work, you could do this a lot less time 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.
+
Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, arranging, grouping and summarising data, or drawing plots, is endless - with SPSS, SAS or Stata you are bound to their algorithms and format styles. They may be a bit flexible, but you can probably never create that very specific publication-ready plot without using other (paid) software. If you sometimes write syntaxes in SPSS to run a complete analysis or to ‘automate’ some of your work, you could do this a lot less time 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.
R can be easily automated.
-
Over the last years, R
-Markdown has really made an interesting development. With R
-Markdown, you can very easily produce reports, whether the format has to
-be Word, PowerPoint, a website, a PDF document or just the raw data to
-Excel. It even allows the use of a reference file containing the layout
-style (e.g. fonts and colours) of your organisation. I use this a lot to
-generate weekly and monthly reports automatically. Just write the code
-once and enjoy the automatically updated reports at any interval you
-like.
-
For an even more professional environment, you could create Shiny apps: live manipulation of
-data using a custom made website. The webdesign knowledge needed
-(JavaScript, CSS, HTML) is almost zero.
+
Over the last years, R Markdown has really made an interesting development. With R Markdown, you can very easily produce reports, whether the format has to be Word, PowerPoint, a website, a PDF document or just the raw data to Excel. It even allows the use of a reference file containing the layout style (e.g. fonts and colours) of your organisation. I use this a lot to generate weekly and monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.
+
For an even more professional environment, you could create Shiny apps: live manipulation of data using a custom made website. The webdesign knowledge needed (JavaScript, CSS, HTML) is almost zero.
R has a huge community.
-
Many R users just ask questions on websites like StackOverflow.com, the largest
-online community for programmers. At the time of writing, 439,954
-R-related questions have already been asked on this platform (that
-covers questions and answers for any programming language). In my own
-experience, most questions are answered within a couple of
-minutes.
+
Many R users just ask questions on websites like StackOverflow.com, the largest online community for programmers. At the time of writing, 440,893 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
-
R understands any data type, including
-SPSS/SAS/Stata.
-
And that’s not vice versa I’m afraid. You can import data from any
-source into R. For example from SPSS, SAS and Stata (link), from Minitab, Epi Info
-and EpiData (link), from Excel
-(link), from flat files like
-CSV, TXT or TSV (link), or
-directly from databases and datawarehouses from anywhere on the world
-(link). You can even scrape
-websites to download tables that are live on the internet (link) or get the results of
-an API call and transform it into data in only one command (link).
-
And the best part - you can export from R to most data formats as
-well. So you can import an SPSS file, do your analysis neatly in R and
-export the resulting tables to Excel files for sharing.
+
R understands any data type, including SPSS/SAS/Stata.
+
And that’s not vice versa I’m afraid. You can import data from any source into R. For example from SPSS, SAS and Stata (link), from Minitab, Epi Info and EpiData (link), from Excel (link), from flat files like CSV, TXT or TSV (link), or directly from databases and datawarehouses from anywhere on the world (link). You can even scrape websites to download tables that are live on the internet (link) or get the results of an API call and transform it into data in only one command (link).
+
And the best part - you can export from R to most data formats as well. So you can import an SPSS file, do your analysis neatly in R and export the resulting tables to Excel files for sharing.
R is completely free and open-source.
-
No strings attached. It was created and is being maintained by
-volunteers who believe that (data) science should be open and publicly
-available to everybody. SPSS, SAS and Stata are quite expensive. IBM
-SPSS Staticstics only comes with subscriptions nowadays, varying between USD
-1,300 and USD 8,500 per user per year. SAS Analytics Pro
-costs around
-USD 10,000 per computer. Stata also has a business model with
-subscription fees, varying between
-USD 600 and USD 2,800 per computer per year, but lower prices come
-with a limitation of the number of variables you can work with. And
-still they do not offer the above benefits of R.
-
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.
+
No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying between USD 1,300 and USD 8,500 per user per year. SAS Analytics Pro costs around USD 10,000 per computer. Stata also has a business model with subscription fees, varying between USD 600 and USD 2,800 per computer per year, but lower prices come with a limitation of the number of variables you can work with. And still they do not offer the above benefits of R.
+
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.
-
R is (nowadays) the preferred analysis software in
-academic papers.
-
At present, R is among the world most powerful statistical languages,
-and it is generally very popular in science (Bollmann et al.,
-2017). For all the above reasons, the number of references to R as an
-analysis method in academic papers is
-rising continuously and has even surpassed SPSS for academic use
-(Muenchen, 2014).
-
I believe that the thing with SPSS is, that it has always had a great
-user interface which is very easy to learn and use. Back when they
-developed it, they had very little competition, let alone from R. R
-didn’t even had a professional user interface until the last decade
-(called RStudio, see below). How people used R between the nineties and
-2010 is almost completely incomparable to how R is being used now. The
-language itself has been
-restyled completely by volunteers who are dedicated professionals in
-the field of data science. SPSS was great when there was nothing else
-that could compete. But now in 2022, I don’t see any reason why SPSS
-would be of any better use than R.
+
R is (nowadays) the preferred analysis software in academic papers.
+
At present, R is among the world most powerful statistical languages, and it is generally very popular in science (Bollmann et al., 2017). For all the above reasons, the number of references to R as an analysis method in academic papers is rising continuously and has even surpassed SPSS for academic use (Muenchen, 2014).
+
I believe that the thing with SPSS is, that it has always had a great user interface which is very easy to learn and use. Back when they developed it, they had very little competition, let alone from R. R didn’t even had a professional user interface until the last decade (called RStudio, see below). How people used R between the nineties and 2010 is almost completely incomparable to how R is being used now. The language itself has been restyled completely by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else that could compete. But now in 2022, I don’t see any reason why SPSS would be of any better use than R.
To demonstrate the first point:
@@ -376,23 +285,13 @@ would be of any better use than R.
RStudio
-
To work with R, probably the best option is to use RStudio. It is an
-open-source and free desktop environment which not only allows you to
-run R code, but also supports project management, version management,
-package management and convenient import menus to work with other data
-sources. You can also install RStudio Server on a
-private or corporate server, which brings nothing less than the complete
-RStudio software to you as a website (at home or at work).
-
To import a data file, just click Import Dataset in the
-Environment tab:
+
To work with R, probably the best option is to use RStudio. It is an open-source and free desktop environment which not only allows you to run R code, but also supports project management, version management, package management and convenient import menus to work with other data sources. You can also install RStudio Server on a private or corporate server, which brings nothing less than the complete RStudio software to you as a website (at home or at work).
+
To import a data file, just click Import Dataset in the Environment tab:
-
If additional packages are needed, RStudio will ask you if they
-should be installed on beforehand.
-
In the the window that opens, you can define all options (parameters)
-that should be used for import and you’re ready to go:
+
If additional packages are needed, RStudio will ask you if they should be installed on beforehand.
+
In the the window that opens, you can define all options (parameters) that should be used for import and you’re ready to go:
-
If you want named variables to be imported as factors so it resembles
-SPSS more, use as_factor().
+
If you want named variables to be imported as factors so it resembles SPSS more, use as_factor().
This tutorial assumes you already imported the WHONET data with
-e.g. the readxl
-package. In RStudio, this can be done using the menu button ‘Import
-Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
-select your exported file. Make sure date fields are imported
-correctly.
+
This tutorial assumes you already imported the WHONET data with e.g. the readxl package. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.
First, load the relevant packages if you did not yet did this. I use
-the tidyverse for all of my analyses. All of them. If you don’t know it
-yet, I suggest you read about it on their website: https://www.tidyverse.org/.
+
First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: https://www.tidyverse.org/.
We will have to transform some variables to simplify and automate the
-analysis:
+
We will have to transform some variables to simplify and automate the analysis:
-
Microorganisms should be transformed to our own microorganism codes
-(called an mo) using our
-Catalogue of Life reference data set, which contains all ~70,000
-microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
-We do the tranformation with as.mo(). This function also
-recognises almost all WHONET abbreviations of microorganisms.
-
Antimicrobial results or interpretations have to be clean and valid.
-In other words, they should only contain values "S",
-"I" or "R". That is exactly where the
-as.rsi() function is for.
+
Microorganisms should be transformed to our own microorganism codes (called an mo) using our Catalogue of Life reference data set, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with as.mo(). This function also recognises almost all WHONET abbreviations of microorganisms.
+
Antimicrobial results or interpretations have to be clean and valid. In other words, they should only contain values "S", "I" or "R". That is exactly where the as.rsi() function is for.
# transform variables
@@ -247,9 +230,7 @@ In other words, they should only contain values "S",
# transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` classmutate_at(vars(AMP_ND10:CIP_EE), as.rsi)
No errors or warnings, so all values are transformed succesfully.
-
We also created a package dedicated to data cleaning and checking,
-called the cleaner package. Its freq()
-function can be used to create frequency tables.
+
We also created a package dedicated to data cleaning and checking, called the cleaner package. Its freq() function can be used to create frequency tables.
So let’s check our data, with a couple of frequency tables:
# our newly created `mo` variable, put in the mo_name() function
@@ -262,14 +243,6 @@ Unique: 37
Shortest: 11
Longest: 40
-
-
-
-
-
-
-
-
Item
@@ -415,8 +388,7 @@ Drug group: Beta-lactams/penicillins
A first glimpse at results
-
An easy ggplot will already give a lot of information,
-using the included ggplot_rsi() function:
+
An easy ggplot will already give a lot of information, using the included ggplot_rsi() function:
Using the microbenchmark package, we can review the
-calculation performance of this function. Its function
-microbenchmark() runs different input expressions
-independently of each other and measures their time-to-result.
+
One of the most important features of this package is the complete microbial taxonomic database, supplied by the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). We created a function as.mo() that transforms any user input value to a valid microbial ID by using intelligent rules combined with the microbial taxonomy.
+
Using the microbenchmark package, we can review the calculation performance of this function. Its function microbenchmark() runs different input expressions independently of each other and measures their time-to-result.
In the next test, we try to ‘coerce’ different input values into the
-microbial code of Staphylococcus aureus. Coercion is a
-computational process of forcing output based on an input. For
-microorganism names, coercing user input to taxonomically valid
-microorganism names is crucial to ensure correct interpretation and to
-enable grouping based on taxonomic properties.
-
The actual result is the same every time: it returns its
-microorganism code B_STPHY_AURS (B stands for
-Bacteria, its taxonomic kingdom).
+
In the next test, we try to ‘coerce’ different input values into the microbial code of Staphylococcus aureus. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.
+
The actual result is the same every time: it returns its microorganism code B_STPHY_AURS (B stands for Bacteria, its taxonomic kingdom).
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 200 milliseconds, this is only 5
-input values per second. It is clear that accepted taxonomic names are
-extremely fast, but some variations are up to 61 times slower to
-determine.
-
To improve performance, we implemented two important algorithms to
-save unnecessary calculations: repetitive results and
-already precalculated results.
+
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 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 69 times slower to determine.
+
To improve performance, we implemented two important algorithms to save unnecessary calculations: repetitive results and already precalculated results.
Repetitive results
-
Repetitive results are values that are present more than once in a
-vector. Unique values will only be calculated once by
-as.mo(). So running
-as.mo(c("E. coli", "E. coli")) will check the value
-"E. coli" only once.
-
To prove this, we will use mo_name() for testing - a
-helper function that returns the full microbial name (genus, species and
-possibly subspecies) which uses as.mo() internally.
+
Repetitive results are values that are present more than once in a vector. Unique values will only be calculated once by as.mo(). So running as.mo(c("E. coli", "E. coli")) will check the value "E. coli" only once.
+
To prove this, we will use mo_name() for testing - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses as.mo() internally.
# start with the example_isolates data setx<-example_isolates%>%
@@ -287,8 +258,8 @@ possibly subspecies) which uses as.mo()<
# what do these values look like? They are of class <mo>:head(x)# Class <mo>
-# [1] B_ACNTB B_ESCHR_COLI B_STRPT_GRPC B_STPHY_HMNS B_STPHY_CONS
-# [6] B_ESCHR_COLI
+# [1] B_ENTRBC_CLOC B_ESCHR_COLI B_STRPT_PYGN B_STPHY_AURS B_ESCHR_COLI
+# [6] B_STRPT_PNMN# as the example_isolates data set has 2,000 rows, we should have 2 million itemslength(x)
@@ -301,38 +272,28 @@ possibly subspecies) which uses as.mo()<
# now let's see:run_it<-microbenchmark(mo_name(x),
times =10)
-print(run_it, unit ="ms", signif =3)
+print(run_it, unit ="ms", signif =3)# Unit: milliseconds# expr min lq mean median uq max neval
-# mo_name(x) 200 204 265 225 320 392 10
-
So getting official taxonomic names of 2,000,000 (!!) items
-consisting of 90 unique values only takes 0.225 seconds. That is 112
-nanoseconds on average. You only lose time on your unique input
-values.
+# mo_name(x) 259 264 357 299 451 509 10
+
So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.299 seconds. That is 149 nanoseconds on average. You only lose time on your unique input values.
Precalculated results
-
What about precalculated results? If the input is an already
-precalculated result of a helper function such as
-mo_name(), it almost doesn’t take any time at all. In other
-words, if you run mo_name() on a valid taxonomic name, it
-will return the results immediately (see ‘C’ below):
+
What about precalculated results? If the input is an already precalculated result of a helper function such as mo_name(), it almost doesn’t take any time at all. In other words, if you run mo_name() on a valid taxonomic name, it will return the results immediately (see ‘C’ below):
run_it<-microbenchmark(A =mo_name("STAAUR"),
B =mo_name("S. aureus"),
C =mo_name("Staphylococcus aureus"),
times =10)
-print(run_it, unit ="ms", signif =3)
+print(run_it, unit ="ms", signif =3)# Unit: milliseconds
-# expr min lq mean median uq max neval
-# A 8.35 8.84 9.10 9.01 9.32 10.20 10
-# B 23.00 24.70 30.30 25.00 26.90 72.70 10
-# C 2.05 2.07 2.44 2.41 2.83 3.05 10
-
So going from mo_name("Staphylococcus aureus") to
-"Staphylococcus aureus" takes 0.0024 seconds - it doesn’t
-even start calculating if the result would be the same as the
-expected resulting value. That goes for all helper functions:
+# expr min lq mean median uq max neval
+# A 11.90 12.10 13.0 13.50 13.70 13.80 10
+# B 60.90 61.20 67.7 66.20 66.90 99.70 10
+# C 2.91 2.94 3.2 3.32 3.38 3.46 10
+
So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0033 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:
run_it<-microbenchmark(A =mo_species("aureus"),
B =mo_genus("Staphylococcus"),
@@ -343,31 +304,23 @@ expected resulting value. That goes for all helper functions:
G =mo_phylum("Firmicutes"),
H =mo_kingdom("Bacteria"),
times =10)
-print(run_it, unit ="ms", signif =3)
+print(run_it, unit ="ms", signif =3)# Unit: milliseconds# expr min lq mean median uq max neval
-# A 1.89 1.93 2.09 2.05 2.17 2.40 10
-# B 1.89 1.93 2.08 2.00 2.19 2.63 10
-# C 1.91 1.92 2.10 1.97 2.30 2.43 10
-# D 1.90 1.94 2.21 2.02 2.53 2.88 10
-# E 1.87 1.95 2.09 2.04 2.22 2.33 10
-# F 1.84 1.91 1.97 1.92 2.04 2.14 10
-# G 1.87 1.92 2.10 1.96 2.12 2.96 10
-# H 1.90 1.96 2.12 2.06 2.21 2.47 10
-
Of course, when running mo_phylum("Firmicutes") the
-function has zero knowledge about the actual microorganism, namely
-S. aureus. But since the result would be
-"Firmicutes" anyway, there is no point in calculating the
-result. And because this package contains all phyla of all known
-bacteria, it can just return the initial value immediately.
+# A 2.92 2.93 3.02 2.94 3.02 3.40 10
+# B 2.87 2.90 3.14 3.09 3.32 3.71 10
+# C 2.91 2.94 3.15 3.12 3.33 3.46 10
+# D 2.86 2.90 3.05 2.96 3.27 3.30 10
+# E 2.87 2.88 3.03 2.96 3.16 3.29 10
+# F 2.92 2.95 3.08 2.98 3.29 3.35 10
+# G 2.89 2.96 3.04 2.99 3.11 3.29 10
+# H 2.85 2.95 3.11 3.08 3.31 3.38 10
+
Of course, when running mo_phylum("Firmicutes") the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be "Firmicutes" anyway, there is no point in calculating the result. And because this package contains all phyla of all known bacteria, it can just return the initial value immediately.
Results in other languages
-
When the system language is non-English and supported by this
-AMR package, some functions will have a translated result.
-This almost does’t take extra time (compare “en” from the table below
-with the other languages):
+
When the system language is non-English and supported by this AMR package, some functions will have a translated result. This almost does’t take extra time (compare “en” from the table below with the other languages):
CoNS<-as.mo("CoNS")CoNS
@@ -394,21 +347,20 @@ with the other languages):
ru =mo_name(CoNS, language ="ru"),
sv =mo_name(CoNS, language ="sv"),
times =100)
-print(run_it, unit ="ms", signif =4)
+print(run_it, unit ="ms", signif =4)# Unit: milliseconds# expr min lq mean median uq max neval
-# da 2.133 2.304 3.442 2.494 2.816 46.020 100
-# de 2.128 2.312 3.068 2.520 2.699 53.220 100
-# en 1.014 1.115 1.262 1.227 1.362 2.424 100
-# es 2.133 2.338 2.981 2.570 2.737 43.770 100
-# fr 1.986 2.149 3.139 2.377 2.567 41.610 100
-# it 2.072 2.268 2.911 2.468 2.656 44.560 100
-# nl 2.115 2.286 2.962 2.521 2.723 43.240 100
-# pt 2.055 2.205 2.912 2.520 2.687 39.520 100
-# ru 1.998 2.210 2.866 2.474 2.631 39.820 100
-# sv 2.022 2.187 2.759 2.357 2.536 38.560 100
-
Currently supported languages are Danish, Dutch, English, French,
-German, Italian, Portuguese, Russian, Spanish and Swedish.
All reference data (about microorganisms, antibiotics, R/SI
-interpretation, EUCAST rules, etc.) in this AMR package are
-reliable, up-to-date and freely available. We continually export our
-data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also
-supply tab separated files that are machine-readable and suitable for
-input in any software program, such as laboratory information
-systems.
-
On this page, we explain how to download them and how the structure
-of the data sets look like.
+
All reference data (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply tab separated files that are machine-readable and suitable for input in any software program, such as laboratory information systems.
+
On this page, we explain how to download them and how the structure of the data sets look like.
-If you are reading this page from within R, please
-visit
-our website, which is automatically updated with every code change.
+If you are reading this page from within R, please visit our website, which is automatically updated with every code change.
Microorganisms (currently accepted names)
-
A data set with 70,760 rows and 16 columns, containing the following
-column names: mo, fullname, kingdom, phylum,
-class, order, family, genus,
-species, subspecies, rank, ref,
-species_id, source, prevalence and
-snomed.
-
This data set is in R available as microorganisms, after
-you load the AMR package.
-
It was last updated on 29 November 2021 11:38:23 UTC. Find more info
-about the structure of this data set here.
+
A data set with 70,760 rows and 16 columns, containing the following column names: mo, fullname, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, species_id, source, prevalence and snomed.
+
This data set is in R available as microorganisms, after you load the AMR package.
+
It was last updated on 1 February 2022 22:08:20 UTC. Find more info about the structure of this data set here.
NOTE: The exported files for SAS, SPSS and Stata do not
-contain SNOMED codes, as their file size would exceed 100 MB; the file
-size limit of GitHub. Advice? Use R instead.
+
NOTE: The exported files for SAS, SPSS and Stata do not contain SNOMED codes, as their file size would exceed 100 MB; the file size limit of GitHub. Advice? Use R instead.
Source
-
Our full taxonomy of microorganisms is based on the authoritative and
-comprehensive:
+
Our full taxonomy of microorganisms is based on the authoritative and comprehensive:
@@ -456,64 +427,40 @@ Health Information Network Vocabulary Access and Distribution System
Microorganisms (previously accepted names)
-
A data set with 14,338 rows and 4 columns, containing the following
-column names: fullname, fullname_new, ref and
-prevalence.
-
Note: remember that the ‘ref’ columns contains the
-scientific reference to the old taxonomic entries, i.e. of column
-‘fullname’. For the scientific reference of the new names,
-i.e. of column ‘fullname_new’, see the
-microorganisms data set.
-
This data set is in R available as microorganisms.old,
-after you load the AMR package.
-
It was last updated on 6 October 2021 14:38:29 UTC. Find more info
-about the structure of this data set here.
+
A data set with 14,338 rows and 4 columns, containing the following column names: fullname, fullname_new, ref and prevalence.
+
Note: remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e. of column ‘fullname’. For the scientific reference of the new names, i.e. of column ‘fullname_new’, see the microorganisms data set.
+
This data set is in R available as microorganisms.old, after you load the AMR package.
+
It was last updated on 1 February 2022 22:08:19 UTC. Find more info about the structure of this data set here.
@@ -546,50 +493,31 @@ Standing in Nomenclature (LPSN, last updated: 5 October 2021)
Antibiotic agents
-
A data set with 464 rows and 14 columns, containing the following
-column names: ab, cid, name, group, atc,
-atc_group1, atc_group2, abbreviations,
-synonyms, oral_ddd, oral_units,
-iv_ddd, iv_units and loinc.
-
This data set is in R available as antibiotics, after
-you load the AMR package.
-
It was last updated on 14 December 2021 21:59:33 UTC. Find more info
-about the structure of this data set here.
+
A data set with 464 rows and 14 columns, containing the following column names: ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units and loinc.
+
This data set is in R available as antibiotics, after you load the AMR package.
+
It was last updated on 1 February 2022 22:08:19 UTC. Find more info about the structure of this data set here.
This data set contains all EARS-Net and ATC codes gathered from WHO
-and WHONET, and all compound IDs from PubChem. It also contains all
-brand names (synonyms) as found on PubChem and Defined Daily Doses
-(DDDs) for oral and parenteral administration.
+
This data set contains all EARS-Net and ATC codes gathered from WHO and WHONET, and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.
Combinations of penicillins, incl. beta-lactamase
-inhibitors
+
Combinations of penicillins, incl. beta-lactamase inhibitors
a/c, amcl, aml, …
amocla, amoclan, amoclav, …
1.5
@@ -734,49 +661,31 @@ inhibitors
Antiviral agents
-
A data set with 102 rows and 9 columns, containing the following
-column names: atc, cid, name, atc_group,
-synonyms, oral_ddd, oral_units,
-iv_ddd and iv_units.
-
This data set is in R available as antivirals, after you
-load the AMR package.
-
It was last updated on 29 August 2020 19:53:07 UTC. Find more info
-about the structure of this data set here.
+
A data set with 102 rows and 9 columns, containing the following column names: atc, cid, name, atc_group, synonyms, oral_ddd, oral_units, iv_ddd and iv_units.
+
This data set is in R available as antivirals, after you load the AMR package.
+
It was last updated on 23 July 2021 20:35:47 UTC. Find more info about the structure of this data set here.
This data set contains all ATC codes gathered from WHO and all
-compound IDs from PubChem. It also contains all brand names (synonyms)
-as found on PubChem and Defined Daily Doses (DDDs) for oral and
-parenteral administration.
+
This data set contains all ATC codes gathered from WHO and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.
Example content
@@ -1162,40 +1055,27 @@ v3.3 (2021).
Interpretation from MIC values / disk diameters to R/SI
-
A data set with 20,318 rows and 11 columns, containing the following
-column names: guideline, method, site, mo,
-rank_index, ab, ref_tbl, disk_dose,
-breakpoint_S, breakpoint_R and uti.
-
This data set is in R available as rsi_translation,
-after you load the AMR package.
-
It was last updated on 14 December 2021 21:59:33 UTC. Find more info
-about the structure of this data set here.
+
A data set with 20,318 rows and 11 columns, containing the following column names: guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R and uti.
+
This data set is in R available as rsi_translation, after you load the AMR package.
+
It was last updated on 1 February 2022 22:08:20 UTC. Find more info about the structure of this data set here.
This data set contains interpretation rules for MIC values and disk
-diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST
-(2011-2021).
+
This data set contains interpretation rules for MIC values and disk diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST (2011-2021).
Example content
@@ -1313,57 +1193,33 @@ diffusion diameters. Included guidelines are CLSI (2010-2021) and EUCAST
Dosage guidelines from EUCAST
-
A data set with 169 rows and 9 columns, containing the following
-column names: ab, name, type, dose,
-dose_times, administration, notes,
-original_txt and eucast_version.
-
This data set is in R available as dosage, after you
-load the AMR package.
-
It was last updated on 25 January 2021 20:58:20 UTC. Find more info
-about the structure of this data set here.
+
A data set with 169 rows and 9 columns, containing the following column names: ab, name, type, dose, dose_times, administration, notes, original_txt and eucast_version.
+
This data set is in R available as dosage, after you load the AMR package.
+
It was last updated on 23 July 2021 20:35:47 UTC. Find more info about the structure of this data set here.
As with many uses in R, we need some additional packages for AMR data
-analysis. Our package works closely together with the tidyverse packagesdplyr and ggplot2 by Dr
-Hadley Wickham. The tidyverse tremendously improves the way we conduct
-data science - it allows for a very natural way of writing syntaxes and
-creating beautiful plots in R.
-
Our AMR package depends on these packages and even
-extends their use and functions.
+
As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the tidyverse packagesdplyr and ggplot2 by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
+
Our AMR package depends on these packages and even extends their use and functions.
Our package contains a function resistance_predict(),
-which takes the same input as functions for other
-AMR data analysis. Based on a date column, it calculates cases per
-year and uses a regression model to predict antimicrobial
-resistance.
+
Our package contains a function resistance_predict(), which takes the same input as functions for other AMR data analysis. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
It is basically as easy as:
-
# resistance prediction of piperacillin/tazobactam (TZP):
-resistance_predict(tbl = example_isolates, col_date ="date", col_ab ="TZP", model ="binomial")
-
-# or:
-example_isolates %>%
-resistance_predict(col_ab ="TZP",
- model "binomial")
-
-# to bind it to object 'predict_TZP' for example:
-predict_TZP <- example_isolates %>%
-resistance_predict(col_ab ="TZP",
-model ="binomial")
-
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).
+
# resistance prediction of piperacillin/tazobactam (TZP):
+resistance_predict(tbl = example_isolates, col_date ="date", col_ab ="TZP", model ="binomial")
+
+# or:
+example_isolates %>%
+resistance_predict(col_ab ="TZP",
+ model "binomial")
+
+# to bind it to object 'predict_TZP' for example:
+predict_TZP <-example_isolates %>%
+resistance_predict(col_ab ="TZP",
+model ="binomial")
+
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).
# ℹ Using column 'date' as input for `col_date`.
-
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:
+
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:
predict_TZP# year value se_min se_max observations observed estimated
@@ -281,18 +266,12 @@ resistance and the standard error below and above the estimation:
# 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359# 30 2031 0.51109592 0.3973697 0.6248221 NA NA 0.51109592# 31 2032 0.53574417 0.4169574 0.6545309 NA NA 0.53574417
-
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:
+
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:
+
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:
Resistance is not easily predicted; if we look at vancomycin
-resistance in Gram-positive bacteria, the spread (i.e. standard error)
-is enormous:
+
Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:
example_isolates%>%filter(mo_gramstain(mo, language =NULL)=="Gram-positive")%>%
@@ -314,13 +291,8 @@ is enormous:
ggplot_rsi_predict()# ℹ Using column 'date' as input for `col_date`.
-
Vancomycin resistance could be 100% in ten years, but might also stay
-around 0%.
-
You can define the model with the model parameter. The
-model chosen above is a generalised linear regression model using a
-binomial distribution, assuming that a period of zero resistance was
-followed by a period of increasing resistance leading slowly to more and
-more resistance.
+
Vancomycin resistance could be 100% in ten years, but might also stay around 0%.
+
You can define the model with the model parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.
Valid values are:
@@ -336,8 +308,7 @@ more resistance.
-"binomial" or "binom" or
-"logit"
+"binomial" or "binom" or "logit"
glm(..., family = binomial)
Generalised linear model with binomial distribution
@@ -358,9 +329,7 @@ more resistance.
-
For the vancomycin resistance in Gram-positive bacteria, a linear
-model might be more appropriate since no binomial distribution is to be
-expected based on the observed years:
+
For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:
example_isolates%>%filter(mo_gramstain(mo, language =NULL)=="Gram-positive")%>%
@@ -369,8 +338,7 @@ expected based on the observed years:
# ℹ Using column 'date' as input for `col_date`.
This seems more likely, doesn’t it?
-
The model itself is also available from the object, as an
-attribute:
+
The model itself is also available from the object, as an attribute:
Note: to keep the package size as small as possible, we only included this vignette on CRAN. You can read more vignettes on our website about how to conduct AMR data analysis, determine MDRO’s, find explanation of EUCAST rules, and much more: https://msberends.github.io/AMR/articles/.
-
AMR is a free, open-source and independent R package
-(see Copyright)
-to simplify the analysis and prediction of Antimicrobial Resistance
-(AMR) and to work with microbial and antimicrobial data and properties,
-by using evidence-based methods. Our aim is to provide a
-standard for clean and reproducible antimicrobial resistance
-data analysis, that can therefore empower epidemiological analyses to
-continuously enable surveillance and treatment evaluation in any
-setting.
-
After installing this package, R knows ~71,000 distinct microbial
-species and all ~570 antibiotic, antimycotic and antiviral drugs by name
-and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and
-knows all about valid R/SI and MIC values. It supports any data format,
-including WHONET/EARS-Net data.
-
The AMR package is available in Danish, Dutch, English,
-French, German, Italian, Portuguese, Russian, Spanish and Swedish.
-Antimicrobial drug (group) names and colloquial microorganism names are
-provided in these languages.
-
This package is fully independent of any other R package and works on
-Windows, macOS and Linux with all versions of R since R-3.0 (April
-2013). It was designed to work in any setting, including those
-with very limited resources. Since its first public release in
-early 2018, this package has been downloaded from more than 175
-countries.
+
AMR is a free, open-source and independent R package (see Copyright) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
+
After installing this package, R knows ~71,000 distinct microbial species and all ~570 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
+
The AMR package is available in Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish and Swedish. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
+
This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). It was designed to work in any setting, including those with very limited resources. Since its first public release in early 2018, this package has been downloaded from more than 175 countries.
This package can be used for:
-
Reference for the taxonomy of microorganisms, since the package
-contains all microbial (sub)species from the Catalogue of Life and List
-of Prokaryotic names with Standing in Nomenclature
-
Interpreting raw MIC and disk diffusion values, based on the latest
-CLSI or EUCAST guidelines
-
Retrieving antimicrobial drug names, doses and forms of
-administration from clinical health care records
+
Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature
+
Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
+
Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records
Determining first isolates to be used for AMR data analysis
Calculating (empirical) susceptibility of both mono therapy and combination therapies
+
Predicting future antimicrobial resistance using regression models
+
Getting properties for any microorganism (like Gram stain, species, genus or family)
+
Getting properties for any antibiotic (like name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)
Plotting antimicrobial resistance
Applying EUCAST expert rules
-
Getting SNOMED codes of a microorganism, or getting properties of a
-microorganism based on a SNOMED code
-
Getting LOINC codes of an antibiotic, or getting properties of an
-antibiotic based on a LOINC code
-
Machine reading the EUCAST and CLSI guidelines from 2011-2020 to
-translate MIC values and disk diffusion diameters to R/SI
+
Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code
+
Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code
+
Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to R/SI
Principal component analysis for AMR
-
All reference data sets (about microorganisms, antibiotics, R/SI
-interpretation, EUCAST rules, etc.) in this AMR package are
-publicly and freely available. We continually export our data sets to
-formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat
-files that are machine-readable and suitable for input in any software
-program, such as laboratory information systems. Please find all
-download links on our website, which is automatically updated with
-every code change.
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.
Update: The latest EUCAST
-guideline for intrinsic resistance (v3.3, October 2021) is now
-supported, the CLSI 2021 interpretation guideline is now supported, and
-our taxonomy tables have been updated as well (LPSN, 5 October
-2021).
+
Update: The latest EUCAST guideline for intrinsic resistance (v3.3, October 2021) is now supported, the CLSI 2021 interpretation guideline is now supported, and our taxonomy tables have been updated as well (LPSN, 5 October 2021).
What is AMR (for R)?
-
AMR is a free, open-source and independent R package (see Copyright) to simplify the analysis and prediction
-of Antimicrobial Resistance (AMR) and to work with microbial and
-antimicrobial data and properties, by using evidence-based methods.
-Our aim is to provide a standard for clean and
-reproducible AMR data analysis, that can therefore empower
-epidemiological analyses to continuously enable surveillance and
-treatment evaluation in any setting.
The AMR package is available in
-
-Danish,
-
-Dutch,
-
-English,
-
-French,
-
-German,
-
-Italian,
-
-Portuguese,
-
-Russian,
-
-Spanish and
-
-Swedish. Antimicrobial drug (group) names and colloquial microorganism
-names are provided in these languages.
AMR is a free, open-source and independent R package (see Copyright) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
The AMR package is available in Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish and Swedish. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
-
-Used in 175 countries Since its first public
-release in early 2018, this R package has been used in almost all
-countries in the world. Click the map to enlarge and to see the country
-names.
+Used in 175 countries Since its first public release in early 2018, this R package has been used in almost all countries in the world. Click the map to enlarge and to see the country names.
-
With AMR (for R), there’s always a knowledgeable
-microbiologist by your side!
+
With AMR (for R), there’s always a knowledgeable microbiologist by your side!
# AMR works great with dplyr, but it's not required or neccesary
@@ -277,12 +224,7 @@ microbiologist by your side!select(bacteria,
aminoglycosides(),
carbapenems())
It will be downloaded and installed automatically. For RStudio, click
-on the menu Tools > Install Packages… and then type
-in “AMR” and press Install.
-
Note: Not all functions on this website may be
-available in this latest release. To use all functions and data sets
-mentioned on this website, install the latest development version.
+
It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install.
+
Note: Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version.
Latest development version
-
The latest and unpublished development version can be installed from
-GitHub in two ways:
+
The latest and unpublished development version can be installed from GitHub in two ways:
Manually, using:
@@ -475,16 +393,11 @@ GitHub in two ways:
remotes::install_github("msberends/AMR")
After this, you can install and update this AMR package
-like any official release (e.g., using
-install.packages("AMR") or in RStudio via Tools
-> Check for Package Updates…).
+
After this, you can install and update this AMR package like any official release (e.g., using install.packages("AMR") or in RStudio via Tools > Check for Package Updates…).
This package contains the complete taxonomic tree of almost all
-~71,000 microorganisms from the authoritative and comprehensive
-Catalogue of Life (CoL, www.catalogueoflife.org),
-supplemented by data from the List of Prokaryotic names with Standing in
-Nomenclature (LPSN, lpsn.dsmz.de).
-This supplementation is needed until the CoL+ project is finished,
-which we await. With catalogue_of_life_version() can be
-checked which version of the CoL is included in this package.
+
This package contains the complete taxonomic tree of almost all ~71,000 microorganisms from the authoritative and comprehensive Catalogue of Life (CoL, www.catalogueoflife.org), supplemented by data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, lpsn.dsmz.de). This supplementation is needed until the CoL+ project is finished, which we await. With catalogue_of_life_version() can be checked which version of the CoL is included in this package.
Read more about which data from the Catalogue of Life in our manual.
Antimicrobial reference data
-
This package contains all ~570 antibiotic, antimycotic and
-antiviral 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.
This package contains all ~570 antibiotic, antimycotic and antiviral 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.
We support WHONET and EARS-Net data. Exported files from WHONET can
-be imported into R and can be analysed easily using this package. For
-education purposes, we created an example data set WHONET
-with the exact same structure as a WHONET export file. Furthermore, this
-package also contains a data set
-antibiotics with all EARS-Net antibiotic abbreviations, and knows
-almost all WHONET abbreviations for microorganisms. When using WHONET
-data as input for analysis, all input parameters will be set
-automatically.
We support WHONET and EARS-Net data. Exported files from WHONET can be imported into R and can be analysed easily using this package. For education purposes, we created an example data set WHONET with the exact same structure as a WHONET export file. Furthermore, this package also contains a data set antibiotics with all EARS-Net antibiotic abbreviations, and knows almost all WHONET abbreviations for microorganisms. When using WHONET data as input for analysis, all input parameters will be set automatically.
The AMR package basically does four important
-things:
+
The AMR package basically does four important things:
-
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:
+
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 a microbial ID. The IDs are human
-readable for the trained eye - the ID of Klebsiella pneumoniae
-is “B_KLBSL_PNMN” (B stands for Bacteria) and the ID of S.
-aureus is “B_STPHY_AURS”. 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.ab() to get an antibiotic ID. Like microbial
-IDs, these IDs are also human readable based on those used by EARS-Net.
-For example, the ID of amoxicillin is AMX and the ID of
-gentamicin is GEN. The as.ab() function also
-uses intelligent rules to find results like accepting misspelling, trade
-names and abbrevations used in many laboratory systems. For instance,
-the values “Furabid”, “Furadantin”, “nitro” all return the ID of
-Nitrofurantoine. To accomplish this, the package contains a database
-with most LIS codes, official names, trade names, ATC codes, defined
-daily doses (DDD) and drug categories of antibiotics.
-
Use as.rsi() to get antibiotic interpretations based on
-raw MIC values (in mg/L) or disk diffusion values (in mm), or transform
-existing 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.mo() to get a microbial ID. The IDs are human readable for the trained eye - the ID of Klebsiella pneumoniae is “B_KLBSL_PNMN” (B stands for Bacteria) and the ID of S. aureus is “B_STPHY_AURS”. 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.ab() to get an antibiotic ID. Like microbial IDs, these IDs are also human readable based on those used by EARS-Net. For example, the ID of amoxicillin is AMX and the ID of gentamicin is GEN. The as.ab() function also uses intelligent rules to find results like accepting misspelling, trade names and abbrevations used in many laboratory systems. For instance, the values “Furabid”, “Furadantin”, “nitro” all return the ID of Nitrofurantoine. To accomplish this, the package contains a database with most LIS codes, official names, trade names, ATC codes, defined daily doses (DDD) and drug categories of antibiotics.
+
Use as.rsi() to get antibiotic interpretations based on raw MIC values (in mg/L) or disk diffusion values (in mm), or transform existing 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”.
-
It enhances existing data and adds new
-data from data sets included in this package.
+
It enhances existing data and adds new data from data sets included in this package.
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.
+
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() to determine which micro-organisms are
-multi-drug resistant organisms (MDRO). It supports a variety of
-international guidelines, such as the MDR-paper by Magiorakos et
-al. (2012, PMID
-21793988), the exceptional phenotype definitions of EUCAST and the
-WHO guideline on multi-drug resistant TB. It also supports the national
-guidelines of the Netherlands and Germany.
-
The data set
-microorganisms contains the complete taxonomic tree of ~70,000
-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(). Use mo_snomed() to look up any
-SNOMED CT code associated with a microorganism. As all these function
-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,
-Danish, Dutch, Spanish, Italian, French and Portuguese. These functions
-can 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, LOINC code, 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(), ab_atc(),
-ab_loinc() 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.
+
Use mdro() to determine which micro-organisms are multi-drug resistant organisms (MDRO). It supports a variety of international guidelines, such as the MDR-paper by Magiorakos et al. (2012, PMID 21793988), the exceptional phenotype definitions of EUCAST and the WHO guideline on multi-drug resistant TB. It also supports the national guidelines of the Netherlands and Germany.
+
The data set microorganisms contains the complete taxonomic tree of ~70,000 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(). Use mo_snomed() to look up any SNOMED CT code associated with a microorganism. As all these function 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, Danish, Dutch, Spanish, Italian, French and Portuguese. These functions can 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, LOINC code, 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(), ab_atc(), ab_loinc() 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.
-
It analyses the data with convenient functions
-that use well-known methods.
+
It analyses the data with convenient functions that use well-known methods.
Plot AMR results with geom_rsi(), a function made for the ggplot2 package
+
Predict antimicrobial resistance for the nextcoming years using logistic regression models with the resistance_predict() function
-
It teaches the user how to use all the above
-actions.
+
It teaches the user how to use all the above actions.
-
Aside from this website with many tutorials, the package itself
-contains extensive help pages with many examples for all functions.
+
Aside from this website with many tutorials, the package itself contains extensive help pages with many examples for all functions.
The package also contains example data sets:
-
The example_isolates
-data set. This data set contains 2,000 microbial isolates with their
-full antibiograms. It reflects reality and can be used to practice AMR
-data analysis.
-
The WHONET data
-set. This data set only contains fake data, but with the exact same
-structure as files exported by WHONET. Read more about WHONET on its tutorial page.
+
The example_isolates data set. This data set contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis.
+
The WHONET data set. This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET on its tutorial page.
@@ -687,9 +484,7 @@ structure as files exported by WHONET. Read more about WHONET
All functions in this package are considered to be stable. Updates to
-the AMR interpretation rules (such as by EUCAST and CLSI), the microbial
-taxonomy, and the antibiotic dosages will all be updated every 6 to 12
-months.
+
+AMR 1.8.12022-03-24
+
All functions in this package are considered to be stable. Updates to the AMR interpretation rules (such as by EUCAST and CLSI), the microbial taxonomy, and the antibiotic dosages will all be updated every 6 to 12 months.
-
Changed
-
Fix for using as.rsi() on values containing capped
-values (such as >=), sometimes leading to
-NA
-
Support for antibiotic interpretations of the MIPS laboratory
-system: "U" for S (‘susceptible urine’), "D"
-for I (‘susceptible dose-dependent’)
+
Changed
+
Fix for using as.rsi() on values containing capped values (such as >=), sometimes leading to NA
+
Support for antibiotic interpretations of the MIPS laboratory system: "U" for S (‘susceptible urine’), "D" for I (‘susceptible dose-dependent’)
-
Improved algorithm of as.mo(), especially for
-ignoring non-taxonomic text, such as:
+
Improved algorithm of as.mo(), especially for ignoring non-taxonomic text, such as:
mo_name("methicillin-resistant S. aureus (MRSA)")
@@ -187,221 +177,112 @@ ignoring non-taxonomic text, such as:
Increased speed for loading the package
-
Other
+
Other
Fix for unit testing on R 3.3
Fix for size of some image elements, as requested by CRAN
-
AMR 1.8.02022-01-07
Breaking changes
-
Removed p_symbol() and all filter_*()
-functions (except for filter_first_isolate()), which were
-all deprecated in a previous package version
Removed all previously implemented ggplot2::ggplot()
-generics for classes <mic>,
-<disk>, <rsi> and
-<resistance_predict> as they did not follow the
-ggplot2 logic. They were replaced with
-ggplot2::autoplot() generics.
-
Renamed function get_locale() to
-get_AMR_locale() to prevent conflicts with other
-packages
+
Removed all previously implemented ggplot2::ggplot() generics for classes <mic>, <disk>, <rsi> and <resistance_predict> as they did not follow the ggplot2 logic. They were replaced with ggplot2::autoplot() generics.
+
Renamed function get_locale() to get_AMR_locale() to prevent conflicts with other packages
New
-
Support for the CLSI 2021 guideline for interpreting MIC/disk
-diffusion values, which are incorporated in the
-rsi_translation data set. This data set now more strictly
-follows the WHONET software as well.
-
Support for EUCAST Intrinsic Resistance and Unusual Phenotypes
-v3.3 (October 2021). This is now the default EUCAST guideline in the
-package (all older guidelines are still available) for
-eucast_rules(), mo_is_intrinsic_resistant()
-and mdro(). The intrinsic_resistant data set
-was also updated accordingly.
-
Support for all antimicrobial drug (group) names and colloquial
-microorganism names in Danish, Dutch, English, French, German, Italian,
-Portuguese, Russian, Spanish and Swedish
+
Support for the CLSI 2021 guideline for interpreting MIC/disk diffusion values, which are incorporated in the rsi_translation data set. This data set now more strictly follows the WHONET software as well.
+
Support for EUCAST Intrinsic Resistance and Unusual Phenotypes v3.3 (October 2021). This is now the default EUCAST guideline in the package (all older guidelines are still available) for eucast_rules(), mo_is_intrinsic_resistant() and mdro(). The intrinsic_resistant data set was also updated accordingly.
+
Support for all antimicrobial drug (group) names and colloquial microorganism names in Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish and Swedish
-
Function set_ab_names() to rename data set columns
-that resemble antimicrobial drugs. This allows for quickly renaming
-columns to official names, ATC codes, etc. Its second argument can be a
-tidyverse way of selecting:
+
Function set_ab_names() to rename data set columns that resemble antimicrobial drugs. This allows for quickly renaming columns to official names, ATC codes, etc. Its second argument can be a tidyverse way of selecting:
Function ab_ddd_units() to get units of DDDs (daily
-defined doses), deprecating the use of
-ab_ddd(..., units = TRUE) to be more consistent in data
-types of function output
+
Function ab_ddd_units() to get units of DDDs (daily defined doses), deprecating the use of ab_ddd(..., units = TRUE) to be more consistent in data types of function output
Changed
-
Updated the bacterial taxonomy to 5 October 2021 (according to LPSN), including all 11 new
-staphylococcal species named since 1 January last year
-
The antibiotics data set now contains all ATC
-codes that are available through the WHOCC website, regardless of drugs being
-present in more than one ATC group. This means that:
-
Some drugs now contain multiple ATC codes (e.g., metronidazole
-contains 5)
+
Updated the bacterial taxonomy to 5 October 2021 (according to LPSN), including all 11 new staphylococcal species named since 1 January last year
+
The antibiotics data set now contains all ATC codes that are available through the WHOCC website, regardless of drugs being present in more than one ATC group. This means that:
+
Some drugs now contain multiple ATC codes (e.g., metronidazole contains 5)
-antibiotics$atc is now a list containing
-character vectors, and this atc column was
-moved to the 5th position of the antibiotics data set
+antibiotics$atc is now a list containing character vectors, and this atc column was moved to the 5th position of the antibiotics data set
-ab_atc() does not always return a character vector of
-length 1, and returns a list if the input is larger than
-length 1
+ab_atc() does not always return a character vector of length 1, and returns a list if the input is larger than length 1
Added specific selectors for certain types for treatment:
-administrable_per_os() and administrable_iv(),
-which are based on available Defined Daily Doses (DDDs), as defined by
-the WHOCC. These are ideal for e.g. analysing pathogens in primary care
-where IV treatment is not an option. They can be combined with other AB
-selectors, e.g. to select penicillins that are only administrable per os
-(i.e., orally):
+
Added specific selectors for certain types for treatment: administrable_per_os() and administrable_iv(), which are based on available Defined Daily Doses (DDDs), as defined by the WHOCC. These are ideal for e.g. analysing pathogens in primary care where IV treatment is not an option. They can be combined with other AB selectors, e.g. to select penicillins that are only administrable per os (i.e., orally):
Added the selector ab_selector(), which accepts a
-filter to be used internally on the antibiotics data set,
-yielding great flexibility on drug properties, such as selecting
-antibiotic columns with an oral DDD of at least 1 gram:
+
Added the selector ab_selector(), which accepts a filter to be used internally on the antibiotics data set, yielding great flexibility on drug properties, such as selecting antibiotic columns with an oral DDD of at least 1 gram:
example_isolates[, ab_selector(oral_ddd>1&oral_units=="g")]# base Rexample_isolates%>%select(ab_selector(oral_ddd>1&oral_units=="g"))# dplyr
-
Added the selector not_intrinsic_resistant(), which
-only keeps antibiotic columns that are not intrinsic resistant for all
-microorganisms in a data set, based on the latest EUCAST guideline on
-intrinsic resistance. For example, if a data set contains only
-microorganism codes or names of E. coli and K.
-pneumoniae and contains a column “vancomycin”, this column will be
-removed (or rather, unselected) using this function.
-
Added argument only_treatable, which defaults to
-TRUE and will exclude drugs that are only for laboratory
-tests and not for treating patients (such as imipenem/EDTA and
-gentamicin-high)
-
Fix for using selectors multiple times in one call (e.g., using
-them in dplyr::filter() and immediately after in
-dplyr::select())
-
Fix for using having multiple columns that are coerced to the
-same antibiotic agent
-
Fixed for using all() or any() on
-antibiotic selectors in an R Markdown file
+
Added the selector not_intrinsic_resistant(), which only keeps antibiotic columns that are not intrinsic resistant for all microorganisms in a data set, based on the latest EUCAST guideline on intrinsic resistance. For example, if a data set contains only microorganism codes or names of E. coli and K. pneumoniae and contains a column “vancomycin”, this column will be removed (or rather, unselected) using this function.
+
Added argument only_treatable, which defaults to TRUE and will exclude drugs that are only for laboratory tests and not for treating patients (such as imipenem/EDTA and gentamicin-high)
+
Fix for using selectors multiple times in one call (e.g., using them in dplyr::filter() and immediately after in dplyr::select())
+
Fix for using having multiple columns that are coerced to the same antibiotic agent
+
Fixed for using all() or any() on antibiotic selectors in an R Markdown file
-
Added the following antimicrobial agents that are now covered by the
-WHO: aztreonam/nacubactam (ANC), cefepime/nacubactam (FNC), exebacase
-(EXE), ozenoxacin (OZN), zoliflodacin (ZFD), manogepix (MGX),
-ibrexafungerp (IBX), and rezafungin (RZF). None of these agents have an
-ATC code yet.
-
Fixed the Gram stain (mo_gramstain()) determination of
-the taxonomic class Negativicutes within the phylum of Firmicutes - they
-were considered Gram-positives because of their phylum but are actually
-Gram-negative. This impacts 137 taxonomic species, genera and families,
-such as Negativicoccus and Veillonella.
+
Added the following antimicrobial agents that are now covered by the WHO: aztreonam/nacubactam (ANC), cefepime/nacubactam (FNC), exebacase (EXE), ozenoxacin (OZN), zoliflodacin (ZFD), manogepix (MGX), ibrexafungerp (IBX), and rezafungin (RZF). None of these agents have an ATC code yet.
+
Fixed the Gram stain (mo_gramstain()) determination of the taxonomic class Negativicutes within the phylum of Firmicutes - they were considered Gram-positives because of their phylum but are actually Gram-negative. This impacts 137 taxonomic species, genera and families, such as Negativicoccus and Veillonella.
When warnings are thrown because of too few isolates in any
-count_*(), proportion_*() function (or
-resistant() or susceptible()), the
-dplyr group will be shown, if available
-
Fix for legends created with scale_rsi_colours() when
-using ggplot2 v3.3.4 or higher (this is ggplot2 bug 4511,
-soon to be fixed)
+
When warnings are thrown because of too few isolates in any count_*(), proportion_*() function (or resistant() or susceptible()), the dplyr group will be shown, if available
+
Fix for legends created with scale_rsi_colours() when using ggplot2 v3.3.4 or higher (this is ggplot2 bug 4511, soon to be fixed)
Fix for minor translation errors
-
Fix for the MIC interpretation of Morganellaceae (such as
-Morganella and Proteus) when using the EUCAST 2021
-guideline
+
Fix for the MIC interpretation of Morganellaceae (such as Morganella and Proteus) when using the EUCAST 2021 guideline
-NA values of the classes <mic>,
-<disk> and <rsi> are now exported
-objects of this package, e.g. NA_mic_ is an NA
-of class mic (just like the base R
-NA_character_ is an NA of class
-character)
-
The proportion_df(), count_df() and
-rsi_df() functions now return with the additional S3 class
-‘rsi_df’ so they can be extended by other packages
-
The mdro() function now returns NA for all
-rows that have no test results
-
The species_id column in the
-microorganisms data set now only contains LPSN record
-numbers. For this reason, this column is now numeric instead of a
-character, and mo_url() has been updated to reflect this
-change.
-
Fixed a small bug in the functions get_episode() and
-is_new_episode()
+NA values of the classes <mic>, <disk> and <rsi> are now exported objects of this package, e.g. NA_mic_ is an NA of class mic (just like the base R NA_character_ is an NA of class character)
+
The proportion_df(), count_df() and rsi_df() functions now return with the additional S3 class ‘rsi_df’ so they can be extended by other packages
+
The mdro() function now returns NA for all rows that have no test results
+
The species_id column in the microorganisms data set now only contains LPSN record numbers. For this reason, this column is now numeric instead of a character, and mo_url() has been updated to reflect this change.
This package is now being maintained by two epidemiologists and a
-data scientist from two different non-profit healthcare
-organisations.
+
This package is now being maintained by two epidemiologists and a data scientist from two different non-profit healthcare organisations.
@@ -409,14 +290,7 @@ organisations.
Breaking change
-
All antibiotic class selectors (such as
-carbapenems(), aminoglycosides()) can now be
-used for filtering as well, making all their accompanying
-filter_*() functions redundant (such as
-filter_carbapenems(),
-filter_aminoglycosides()). These functions are now
-deprecated and will be removed in a next release. Examples of how the
-selectors can be used for filtering:
+
All antibiotic class selectors (such as carbapenems(), aminoglycosides()) can now be used for filtering as well, making all their accompanying filter_*() functions redundant (such as filter_carbapenems(), filter_aminoglycosides()). These functions are now deprecated and will be removed in a next release. Examples of how the selectors can be used for filtering:
# select columns with results for carbapenems
@@ -437,144 +311,71 @@ selectors can be used for filtering:
New
-
Support for CLSI 2020 guideline for interpreting MICs and disk
-diffusion values (using as.rsi())
Function italicise_taxonomy() to make taxonomic names
-within a string italic, with support for markdown and ANSI
-
Support for all four methods to determine first isolates as
-summarised by Hindler et al. (doi: 10.1086/511864):
-isolate-based, patient-based, episode-based and phenotype-based. The
-last method is now the default.
-
The first_isolate() function gained the argument
-method that has to be “phenotype-based”, “episode-based”,
-“patient-based”, or “isolate-based”. The old behaviour is equal to
-“episode-based”. The new default is “phenotype-based” if antimicrobial
-test results are available, and “episode-based” otherwise. This new
-default will yield slightly more isolates for selection (which is a good
-thing).
-
Since fungal isolates can also be selected, the functions
-key_antibiotics() and key_antibiotics_equal()
-are now deprecated in favour of the key_antimicrobials()
-and antimicrobials_equal() functions. Also, the new
-all_antimicrobials() function works like the old
-key_antibiotics() function, but includes any column with
-antimicrobial test results. Using key_antimicrobials()
-still only selects six preferred antibiotics for Gram-negatives, six for
-Gram-positives, and six universal antibiotics. It has a new
-antifungal argument to set antifungal agents
-(antimycotics).
-
Using type == "points" in the
-first_isolate() function for phenotype-based selection will
-now consider all antimicrobial drugs in the data set, using the new
-all_antimicrobials()
+
Function italicise_taxonomy() to make taxonomic names within a string italic, with support for markdown and ANSI
+
Support for all four methods to determine first isolates as summarised by Hindler et al. (doi: 10.1086/511864): isolate-based, patient-based, episode-based and phenotype-based. The last method is now the default.
+
The first_isolate() function gained the argument method that has to be “phenotype-based”, “episode-based”, “patient-based”, or “isolate-based”. The old behaviour is equal to “episode-based”. The new default is “phenotype-based” if antimicrobial test results are available, and “episode-based” otherwise. This new default will yield slightly more isolates for selection (which is a good thing).
+
Since fungal isolates can also be selected, the functions key_antibiotics() and key_antibiotics_equal() are now deprecated in favour of the key_antimicrobials() and antimicrobials_equal() functions. Also, the new all_antimicrobials() function works like the old key_antibiotics() function, but includes any column with antimicrobial test results. Using key_antimicrobials() still only selects six preferred antibiotics for Gram-negatives, six for Gram-positives, and six universal antibiotics. It has a new antifungal argument to set antifungal agents (antimycotics).
+
Using type == "points" in the first_isolate() function for phenotype-based selection will now consider all antimicrobial drugs in the data set, using the new all_antimicrobials()
-
The first_isolate() function can now take a vector of
-values for col_keyantibiotics and can have an episode
-length of Inf
+
The first_isolate() function can now take a vector of values for col_keyantibiotics and can have an episode length of Inf
-
Since the phenotype-based method is the new default,
-filter_first_isolate() renders the
-filter_first_weighted_isolate() function redundant. For
-this reason, filter_first_weighted_isolate() is now
-deprecated.
Since the phenotype-based method is the new default, filter_first_isolate() renders the filter_first_weighted_isolate() function redundant. For this reason, filter_first_weighted_isolate() is now deprecated.
Function betalactams() as additional antbiotic column
-selector and function filter_betalactams() as additional
-antbiotic column filter. The group of betalactams consists of all
-carbapenems, cephalosporins and penicillins.
Function betalactams() as additional antbiotic column selector and function filter_betalactams() as additional antbiotic column filter. The group of betalactams consists of all carbapenems, cephalosporins and penicillins.
Custom MDRO guidelines can now be combined with other custom MDRO
-guidelines using c()
+bug_drug_combinations() now supports grouping using the dplyr package
Custom MDRO guidelines can now be combined with other custom MDRO guidelines using c()
-
Fix for applying the rules; in previous versions, rows were
-interpreted according to the last matched rule. Now, rows are
-interpreted according to the first matched rule
+
Fix for applying the rules; in previous versions, rows were interpreted according to the last matched rule. Now, rows are interpreted according to the first matched rule
The example_isolates data set now contains some
-(fictitious) zero-year old patients
+
The example_isolates data set now contains some (fictitious) zero-year old patients
Fix for minor translation errors
-
Printing of microbial codes in a data.frame or
-tibble now gives a warning if the data contains old
-microbial codes (from a previous AMR package version)
+
Printing of microbial codes in a data.frame or tibble now gives a warning if the data contains old microbial codes (from a previous AMR package version)
Altered the RStudio addin, so it now iterates over
-%like% -> %unlike% ->
-%like_case% -> %unlike_case% if you keep
-pressing your keyboard shortcut
+
Altered the RStudio addin, so it now iterates over %like% -> %unlike% -> %like_case% -> %unlike_case% if you keep pressing your keyboard shortcut
Fixed an installation error on R-3.0
-
Added info argument to as.mo() to turn
-on/off the progress bar
-
Fixed a bug where col_mo in some functions
-(esp. eucast_rules() and mdro()) could not be
-a column name of the microorganisms data set as it would
-throw an error
-
Fix for transforming numeric values to RSI (as.rsi())
-when the vctrs package is loaded (i.e., when using
-tidyverse)
+
Added info argument to as.mo() to turn on/off the progress bar
+
Fixed a bug where col_mo in some functions (esp. eucast_rules() and mdro()) could not be a column name of the microorganisms data set as it would throw an error
+
Fix for transforming numeric values to RSI (as.rsi()) when the vctrs package is loaded (i.e., when using tidyverse)
-age() now vectorises over both x and
-reference
+age() now vectorises over both x and reference
Other
-
As requested by CRAN administrators: decreased package size by 3 MB
-in costs of a slower loading time of the package
-
All unit tests are now processed by the tinytest
-package, instead of the testthat package. The
-testthat package unfortunately requires tons of
-dependencies that are also heavy and only usable for recent R versions,
-disallowing developers to test a package under any R 3.* version. On the
-contrary, the tinytest package is very lightweight and
-dependency-free.
+
As requested by CRAN administrators: decreased package size by 3 MB in costs of a slower loading time of the package
+
All unit tests are now processed by the tinytest package, instead of the testthat package. The testthat package unfortunately requires tons of dependencies that are also heavy and only usable for recent R versions, disallowing developers to test a package under any R 3.* version. On the contrary, the tinytest package is very lightweight and dependency-free.
@@ -582,44 +383,22 @@ dependency-free.
New
-
Support for EUCAST Clinical Breakpoints v11.0 (2021), effective
-in the eucast_rules() function and in as.rsi()
-to interpret MIC and disk diffusion values. This is now the default
-guideline in this package.
-
Added function eucast_dosage() to get a
-data.frame with advised dosages of a certain bug-drug
-combination, which is based on the new dosage data set
-
Added data set dosage to fuel the new
-eucast_dosage() function and to make this data available in
-a structured way
-
Existing data set example_isolates now reflects the
-latest EUCAST rules
+
Support for EUCAST Clinical Breakpoints v11.0 (2021), effective in the eucast_rules() function and in as.rsi() to interpret MIC and disk diffusion values. This is now the default guideline in this package.
+
Added function eucast_dosage() to get a data.frame with advised dosages of a certain bug-drug combination, which is based on the new dosage data set
+
Added data set dosage to fuel the new eucast_dosage() function and to make this data available in a structured way
+
Existing data set example_isolates now reflects the latest EUCAST rules
-
Added argument only_rsi_columns for some functions,
-which defaults to FALSE, to indicate if the functions must
-only be applied to columns that are of class <rsi>
-(i.e., transformed with as.rsi()). This increases speed
-since automatic determination of antibiotic columns is not needed
-anymore. Affected functions are:
All antibiotic filter functions (filter_ab_class() and
-its wrappers, such as filter_aminoglycosides(),
-filter_carbapenems(),
-filter_penicillins())
+
Added argument only_rsi_columns for some functions, which defaults to FALSE, to indicate if the functions must only be applied to columns that are of class <rsi> (i.e., transformed with as.rsi()). This increases speed since automatic determination of antibiotic columns is not needed anymore. Affected functions are:
Functions oxazolidinones() (an antibiotic selector
-function) and filter_oxazolidinones() (an antibiotic filter
-function) to select/filter on e.g. linezolid and tedizolid
+
Functions oxazolidinones() (an antibiotic selector function) and filter_oxazolidinones() (an antibiotic filter function) to select/filter on e.g. linezolid and tedizolid
library(dplyr)
@@ -629,15 +408,10 @@ function) to select/filter on e.g. linezolid and tedizolid
x<-example_isolates%>%filter_oxazolidinones()#> Filtering on oxazolidinones: value in column `LNZ` (linezolid) is either "R", "S" or "I"
-
Support for custom MDRO guidelines, using the new
-custom_mdro_guideline() function, please see
-mdro() for additional info
-
ggplot() generics for classes
-<mic> and <disk>
+
Support for custom MDRO guidelines, using the new custom_mdro_guideline() function, please see mdro() for additional info
Function mo_is_yeast(), which determines whether a
-microorganism is a member of the taxonomic class Saccharomycetes or the
-taxonomic order Saccharomycetales:
+
Function mo_is_yeast(), which determines whether a microorganism is a member of the taxonomic class Saccharomycetes or the taxonomic order Saccharomycetales:
mo_kingdom(c("Aspergillus", "Candida"))
@@ -649,8 +423,7 @@ taxonomic order Saccharomycetales:
# usage for filtering data:example_isolates[which(mo_is_yeast()), ]# base Rexample_isolates%>%filter(mo_is_yeast())# dplyr
-
The mo_type() function has also been updated to reflect
-this change:
+
The mo_type() function has also been updated to reflect this change:
mo_type(c("Aspergillus", "Candida"))
@@ -658,14 +431,9 @@ this change:
mo_type(c("Aspergillus", "Candida"), language ="es")# also supported: de, nl, fr, it, pt#> [1] "Hongos" "Levaduras"
-
Added Pretomanid (PMD, J04AK08) to the antibiotics
-data set
+
Added Pretomanid (PMD, J04AK08) to the antibiotics data set
-
MIC values (see as.mic()) can now be used in any
-mathematical processing, such as usage inside functions
-min(), max(), range(), and with
-binary operators (+, -, etc.). This allows for
-easy distribution analysis and fast filtering on MIC values:
+
MIC values (see as.mic()) can now be used in any mathematical processing, such as usage inside functions min(), max(), range(), and with binary operators (+, -, etc.). This allows for easy distribution analysis and fast filtering on MIC values:
x<-random_mic(10)
@@ -684,107 +452,49 @@ easy distribution analysis and fast filtering on MIC values:
Changed
Updated the bacterial taxonomy to 3 March 2021 (using LPSN)
-
Added 3,372 new species and 1,523 existing species became
-synomyms
-first_isolate() can be used with
-group_by() (also when using a dot . as input
-for the data) and now returns the names of the groups
-
Updated the data set microorganisms.codes (which
-contains popular LIS and WHONET codes for microorganisms) for some
-species of Mycobacterium that previously incorrectly returned
-M. africanum
+first_isolate() can be used with group_by() (also when using a dot . as input for the data) and now returns the names of the groups
+
Updated the data set microorganisms.codes (which contains popular LIS and WHONET codes for microorganisms) for some species of Mycobacterium that previously incorrectly returned M. africanum
-
WHONET code "PNV" will now correctly be interpreted as
-PHN, the antibiotic code for phenoxymethylpenicillin (‘peni
-V’)
-
Fix for verbose output of mdro(..., verbose = TRUE) for
-German guideline (3MGRN and 4MGRN) and Dutch guideline (BRMO, only
-P. aeruginosa)
+
WHONET code "PNV" will now correctly be interpreted as PHN, the antibiotic code for phenoxymethylpenicillin (‘peni V’)
+
Fix for verbose output of mdro(..., verbose = TRUE) for German guideline (3MGRN and 4MGRN) and Dutch guideline (BRMO, only P. aeruginosa)
-is.rsi.eligible() now detects if the column name
-resembles an antibiotic name or code and now returns TRUE
-immediately if the input contains any of the values “R”, “S” or “I”.
-This drastically improves speed, also for a lot of other functions that
-rely on automatic determination of antibiotic columns.
-
Functions get_episode() and
-is_new_episode() now support less than a day as value for
-argument episode_days (e.g., to include one patient/test
-per hour)
-
Argument ampc_cephalosporin_resistance in
-eucast_rules() now also applies to value “I” (not only
-“S”)
-
Functions print() and summary() on a
-Principal Components Analysis object (pca()) now print
-additional group info if the original data was grouped using
-dplyr::group_by()
+is.rsi.eligible() now detects if the column name resembles an antibiotic name or code and now returns TRUE immediately if the input contains any of the values “R”, “S” or “I”. This drastically improves speed, also for a lot of other functions that rely on automatic determination of antibiotic columns.
+
Functions get_episode() and is_new_episode() now support less than a day as value for argument episode_days (e.g., to include one patient/test per hour)
+
Argument ampc_cephalosporin_resistance in eucast_rules() now also applies to value “I” (not only “S”)
+
Functions print() and summary() on a Principal Components Analysis object (pca()) now print additional group info if the original data was grouped using dplyr::group_by()
-
Improved speed and reliability of guess_ab_col(). As
-this also internally improves the reliability of
-first_isolate() and mdro(), this might have a
-slight impact on the results of those functions.
-
Fix for mo_name() when used in other languages than
-English
-
The like() function (and its fast alias
-%like%) now always use Perl compatibility, improving speed
-for many functions in this package (e.g., as.mo() is now up
-to 4 times faster)
+
Improved speed and reliability of guess_ab_col(). As this also internally improves the reliability of first_isolate() and mdro(), this might have a slight impact on the results of those functions.
+
Fix for mo_name() when used in other languages than English
+
The like() function (and its fast alias %like%) now always use Perl compatibility, improving speed for many functions in this package (e.g., as.mo() is now up to 4 times faster)
-Staphylococcus cornubiensis is now correctly categorised as
-coagulase-positive
+Staphylococcus cornubiensis is now correctly categorised as coagulase-positive
Support for GISA (glycopeptide-intermediate S. aureus), so
-e.g. mo_genus("GISA") will return
-"Staphylococcus"
+random_disk() and random_mic() now have an expanded range in their randomisation
+
Support for GISA (glycopeptide-intermediate S. aureus), so e.g. mo_genus("GISA") will return "Staphylococcus"
-
Added translations of German and Spanish for more than 200
-antimicrobial drugs
-
Speed improvement for as.ab() when the input is an
-official name or ATC code
-
Added argument include_untested_rsi to the
-first_isolate() functions (defaults to TRUE to
-keep existing behaviour), to be able to exclude rows where all R/SI
-values (class <rsi>, see as.rsi()) are
-empty
+
Added translations of German and Spanish for more than 200 antimicrobial drugs
+
Speed improvement for as.ab() when the input is an official name or ATC code
+
Added argument include_untested_rsi to the first_isolate() functions (defaults to TRUE to keep existing behaviour), to be able to exclude rows where all R/SI values (class <rsi>, see as.rsi()) are empty
Other
Big documentation updates
-
Loading the package (i.e., library(AMR)) now is ~50
-times faster than before, in costs of package size (which increased by
-~3 MB)
+
Loading the package (i.e., library(AMR)) now is ~50 times faster than before, in costs of package size (which increased by ~3 MB)
@@ -792,14 +502,7 @@ times faster than before, in costs of package size (which increased by
New
-
Functions get_episode() and
-is_new_episode() to determine (patient) episodes which are
-not necessarily based on microorganisms. The get_episode()
-function returns the index number of the episode per group, while the
-is_new_episode() function returns values
-TRUE/FALSE to indicate whether an item in a
-vector is the start of a new episode. They also support
-dplyrs grouping (i.e. using group_by()):
+
Functions get_episode() and is_new_episode() to determine (patient) episodes which are not necessarily based on microorganisms. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE to indicate whether an item in a vector is the start of a new episode. They also support dplyrs grouping (i.e. using group_by()):
library(dplyr)
@@ -807,52 +510,25 @@ vector is the start of a new episode. They also support
group_by(patient_id, hospital_id)%>%filter(is_new_episode(date, episode_days =60))
-
Functions mo_is_gram_negative() and
-mo_is_gram_positive() as wrappers around
-mo_gramstain(). They always return TRUE or
-FALSE (except when the input is NA or the MO
-code is UNKNOWN), thus always return FALSE for
-species outside the taxonomic kingdom of Bacteria.
Functions mo_is_gram_negative() and mo_is_gram_positive() as wrappers around mo_gramstain(). They always return TRUE or FALSE (except when the input is NA or the MO code is UNKNOWN), thus always return FALSE for species outside the taxonomic kingdom of Bacteria.
+
Function mo_is_intrinsic_resistant() to test for intrinsic resistance, based on EUCAST Intrinsic Resistance and Unusual Phenotypes v3.2 from 2020.
New argument ampc_cephalosporin_resistance in
-eucast_rules() to correct for AmpC de-repressed
-cephalosporin-resistant mutants
+
New argument ampc_cephalosporin_resistance in eucast_rules() to correct for AmpC de-repressed cephalosporin-resistant mutants
-
Interpretation of antimicrobial resistance -
-as.rsi():
-
Reference data used for as.rsi() can now be set by the
-user, using the reference_data argument. This allows for
-using own interpretation guidelines. The user-set data must have the
-same structure as rsi_translation.
-
Better determination of disk zones and MIC values when running
-as.rsi() on a data.frame
-
Fix for using as.rsi() on a data.frame in older R
-versions
+
Interpretation of antimicrobial resistance - as.rsi():
+
Reference data used for as.rsi() can now be set by the user, using the reference_data argument. This allows for using own interpretation guidelines. The user-set data must have the same structure as rsi_translation.
+
Better determination of disk zones and MIC values when running as.rsi() on a data.frame
+
Fix for using as.rsi() on a data.frame in older R versions
-as.rsi() on a data.frame will not print a message
-anymore if the values are already clean R/SI values
-
If using as.rsi() on MICs or disk diffusion while there
-is intrinsic antimicrobial resistance, a warning will be thrown to
-remind about this
-
Fix for using as.rsi() on a data.frame
-that only contains one column for antibiotic interpretations
+as.rsi() on a data.frame will not print a message anymore if the values are already clean R/SI values
+
If using as.rsi() on MICs or disk diffusion while there is intrinsic antimicrobial resistance, a warning will be thrown to remind about this
+
Fix for using as.rsi() on a data.frame that only contains one column for antibiotic interpretations
-
Some functions are now context-aware when used inside
-dplyr verbs, such as filter(),
-mutate() and summarise(). This means that then
-the data argument does not need to be set anymore. This is the case for
-the new functions:
+
Some functions are now context-aware when used inside dplyr verbs, such as filter(), mutate() and summarise(). This means that then the data argument does not need to be set anymore. This is the case for the new functions:
@@ -883,72 +559,36 @@ the new functions:
as_tibble()
-
For antibiotic selection functions (such as
-cephalosporins(), aminoglycosides()) to select
-columns based on a certain antibiotic group, the dependency on the
-tidyselect package was removed, meaning that they can now
-also be used without the need to have this package installed and now
-also work in base R function calls (they rely on R 3.2 or later):
+
For antibiotic selection functions (such as cephalosporins(), aminoglycosides()) to select columns based on a certain antibiotic group, the dependency on the tidyselect package was removed, meaning that they can now also be used without the need to have this package installed and now also work in base R function calls (they rely on R 3.2 or later):
For all function arguments in the code, it is now defined what
-the exact type of user input should be (inspired by the typed
-package). If the user input for a certain function does not meet the
-requirements for a specific argument (such as the class or length), an
-informative error will be thrown. This makes the package more robust and
-the use of it more reproducible and reliable. In total, more than 420
-arguments were defined.
-
Fix for set_mo_source(), that previously would not
-remember the file location of the original file
-
Deprecated function p_symbol() that not really fits
-the scope of this package. It will be removed in a future version. See
-here
-for the source code to preserve it.
-
Updated coagulase-negative staphylococci determination with
-Becker et al. 2020 (PMID 32056452), meaning that the species
-S. argensis, S. caeli, S. debuckii, S.
-edaphicus and S. pseudoxylosus are now all considered
-CoNS
-
Fix for using argument reference_df in
-as.mo() and mo_*() functions that contain old
-microbial codes (from previous package versions)
-
Fixed a bug where mo_uncertainties() would not
-return the results based on the MO matching score
-
Fixed a bug where as.mo() would not return results
-for known laboratory codes for microorganisms
For all function arguments in the code, it is now defined what the exact type of user input should be (inspired by the typed package). If the user input for a certain function does not meet the requirements for a specific argument (such as the class or length), an informative error will be thrown. This makes the package more robust and the use of it more reproducible and reliable. In total, more than 420 arguments were defined.
+
Fix for set_mo_source(), that previously would not remember the file location of the original file
+
Deprecated function p_symbol() that not really fits the scope of this package. It will be removed in a future version. See here for the source code to preserve it.
+
Updated coagulase-negative staphylococci determination with Becker et al. 2020 (PMID 32056452), meaning that the species S. argensis, S. caeli, S. debuckii, S. edaphicus and S. pseudoxylosus are now all considered CoNS
+
Fix for using argument reference_df in as.mo() and mo_*() functions that contain old microbial codes (from previous package versions)
+
Fixed a bug where mo_uncertainties() would not return the results based on the MO matching score
+
Fixed a bug where as.mo() would not return results for known laboratory codes for microorganisms
LA-MRSA and CA-MRSA are now recognised as an abbreviation for
-Staphylococcus aureus, meaning that
-e.g. mo_genus("LA-MRSA") will return
-"Staphylococcus" and
-mo_is_gram_positive("LA-MRSA") will return
-TRUE.
-
Fix for printing class in tibbles when all values are
-NA
LA-MRSA and CA-MRSA are now recognised as an abbreviation for Staphylococcus aureus, meaning that e.g. mo_genus("LA-MRSA") will return "Staphylococcus" and mo_is_gram_positive("LA-MRSA") will return TRUE.
+
Fix for printing class in tibbles when all values are NA
@@ -958,31 +598,11 @@ of a new internal environment pkg_env
AMR 1.4.02020-10-08
New
-
Support for ‘EUCAST Expert Rules’ / ‘EUCAST Intrinsic Resistance
-and Unusual Phenotypes’ version 3.2 of May 2020. With this addition to
-the previously implemented version 3.1 of 2016, the
-eucast_rules() function can now correct for more than 180
-different antibiotics and the mdro() function can determine
-multidrug resistance based on more than 150 different antibiotics. All
-previously implemented versions of the EUCAST rules are now maintained
-and kept available in this package. The eucast_rules()
-function consequently gained the arguments
-version_breakpoints (at the moment defaults to v10.0, 2020)
-and version_expertrules (at the moment defaults to v3.2,
-2020). The example_isolates data set now also reflects the
-change from v3.1 to v3.2. The mdro() function now accepts
-guideline == "EUCAST3.1" and
-guideline == "EUCAST3.2".
-
A new vignette and website page with info about all our public
-and freely available data sets, that can be downloaded as flat files or
-in formats for use in R, SPSS, SAS, Stata and Excel: https://msberends.github.io/AMR/articles/datasets.html
+
Support for ‘EUCAST Expert Rules’ / ‘EUCAST Intrinsic Resistance and Unusual Phenotypes’ version 3.2 of May 2020. With this addition to the previously implemented version 3.1 of 2016, the eucast_rules() function can now correct for more than 180 different antibiotics and the mdro() function can determine multidrug resistance based on more than 150 different antibiotics. All previously implemented versions of the EUCAST rules are now maintained and kept available in this package. The eucast_rules() function consequently gained the arguments version_breakpoints (at the moment defaults to v10.0, 2020) and version_expertrules (at the moment defaults to v3.2, 2020). The example_isolates data set now also reflects the change from v3.1 to v3.2. The mdro() function now accepts guideline == "EUCAST3.1" and guideline == "EUCAST3.2".
+
A new vignette and website page with info about all our public and freely available data sets, that can be downloaded as flat files or in formats for use in R, SPSS, SAS, Stata and Excel: https://msberends.github.io/AMR/articles/datasets.html
-
Data set intrinsic_resistant. This data set contains
-all bug-drug combinations where the ‘bug’ is intrinsic resistant to the
-‘drug’ according to the latest EUCAST insights. It contains just two
-columns: microorganism and antibiotic.
-
Curious about which enterococci are actually intrinsic resistant to
-vancomycin?
+
Data set intrinsic_resistant. This data set contains all bug-drug combinations where the ‘bug’ is intrinsic resistant to the ‘drug’ according to the latest EUCAST insights. It contains just two columns: microorganism and antibiotic.
+
Curious about which enterococci are actually intrinsic resistant to vancomycin?
Support for skimming classes <rsi>,
-<mic>, <disk> and
-<mo> with the skimr package
+
Support for skimming classes <rsi>, <mic>, <disk> and <mo> with the skimr package
Changed
-
Although advertised that this package should work under R 3.0.0,
-we still had a dependency on R 3.6.0. This is fixed, meaning that our
-package should now work under R 3.0.0.
+
Although advertised that this package should work under R 3.0.0, we still had a dependency on R 3.6.0. This is fixed, meaning that our package should now work under R 3.0.0.
Support for using dplyr’s across() to
-interpret MIC values or disk zone diameters, which also automatically
-determines the column with microorganism names or codes.
+
Support for using dplyr’s across() to interpret MIC values or disk zone diameters, which also automatically determines the column with microorganism names or codes.
# until dplyr 1.0.0
@@ -1018,23 +632,13 @@ determines the column with microorganism names or codes.
your_data%>%mutate(across(where(is.mic), as.rsi))your_data%>%mutate(across(where(is.disk), as.rsi))
-
Cleaning columns in a data.frame now allows you to specify those
-columns with tidy selection,
-e.g. as.rsi(df, col1:col9)
-
Big speed improvement for interpreting MIC values and disk zone
-diameters. When interpreting 5,000 MIC values of two antibiotics (10,000
-values in total), our benchmarks showed a total run time going from
-80.7-85.1 seconds to 1.8-2.0 seconds.
-
Added argument ‘add_intrinsic_resistance’ (defaults to
-FALSE), that considers intrinsic resistance according to
-EUCAST
-
Fixed a bug where in EUCAST rules the breakpoint for R would be
-interpreted as “>=” while this should have been “<”
+
Cleaning columns in a data.frame now allows you to specify those columns with tidy selection, e.g. as.rsi(df, col1:col9)
+
Big speed improvement for interpreting MIC values and disk zone diameters. When interpreting 5,000 MIC values of two antibiotics (10,000 values in total), our benchmarks showed a total run time going from 80.7-85.1 seconds to 1.8-2.0 seconds.
+
Added argument ‘add_intrinsic_resistance’ (defaults to FALSE), that considers intrinsic resistance according to EUCAST
+
Fixed a bug where in EUCAST rules the breakpoint for R would be interpreted as “>=” while this should have been “<”
-
Added intelligent data cleaning to as.disk(), so
-numbers can also be extracted from text and decimal numbers will always
-be rounded up:
+
Added intelligent data cleaning to as.disk(), so numbers can also be extracted from text and decimal numbers will always be rounded up:
A completely new matching score for ambiguous user input, using
-mo_matching_score(). Any user input value that could mean
-more than one taxonomic entry is now considered ‘uncertain’. Instead of
-a warning, a message will be thrown and the accompanying
-mo_uncertainties() has been changed completely; it now
-prints all possible candidates with their matching score.
-
Big speed improvement for already valid microorganism ID. This also
-means an significant speed improvement for using mo_*
-functions like mo_name() on microoganism IDs.
-
Added argument ignore_pattern to as.mo()
-which can also be given to mo_* functions like
-mo_name(), to exclude known non-relevant input from
-analysing. This can also be set with the option
-AMR_ignore_pattern.
+
A completely new matching score for ambiguous user input, using mo_matching_score(). Any user input value that could mean more than one taxonomic entry is now considered ‘uncertain’. Instead of a warning, a message will be thrown and the accompanying mo_uncertainties() has been changed completely; it now prints all possible candidates with their matching score.
+
Big speed improvement for already valid microorganism ID. This also means an significant speed improvement for using mo_* functions like mo_name() on microoganism IDs.
+
Added argument ignore_pattern to as.mo() which can also be given to mo_* functions like mo_name(), to exclude known non-relevant input from analysing. This can also be set with the option AMR_ignore_pattern.
-
get_locale() now uses at default
-Sys.getenv("LANG") or, if LANG is not set,
-Sys.getlocale(). This can be overwritten by setting the
-option AMR_locale.
+
get_locale() now uses at default Sys.getenv("LANG") or, if LANG is not set, Sys.getlocale(). This can be overwritten by setting the option AMR_locale.
Overall speed improvement by tweaking joining functions
-
Function mo_shortname() now returns the genus for
-input where the species is unknown
-
BORSA is now recognised as an abbreviation for Staphylococcus
-aureus, meaning that e.g. mo_genus("BORSA") will
-return “Staphylococcus”
-
Added a feature from AMR 1.1.0 and earlier again, but now without
-other package dependencies: tibble printing support for
-classes <rsi>, <mic>,
-<disk>, <ab> and
-<mo>. When using tibbles containing
-antimicrobial columns (class <rsi>), “S” will print
-in green, “I” will print in yellow and “R” will print in red. Microbial
-IDs (class <mo>) will emphasise on the genus and
-species, not on the kingdom.
-
Names of antiviral agents in data set antivirals now
-have a starting capital letter, like it is the case in the
-antibiotics data set
-
Updated the documentation of the WHONET data set to
-clarify that all patient names are fictitious
+
Function mo_shortname() now returns the genus for input where the species is unknown
+
BORSA is now recognised as an abbreviation for Staphylococcus aureus, meaning that e.g. mo_genus("BORSA") will return “Staphylococcus”
+
Added a feature from AMR 1.1.0 and earlier again, but now without other package dependencies: tibble printing support for classes <rsi>, <mic>, <disk>, <ab> and <mo>. When using tibbles containing antimicrobial columns (class <rsi>), “S” will print in green, “I” will print in yellow and “R” will print in red. Microbial IDs (class <mo>) will emphasise on the genus and species, not on the kingdom.
+
Names of antiviral agents in data set antivirals now have a starting capital letter, like it is the case in the antibiotics data set
+
Updated the documentation of the WHONET data set to clarify that all patient names are fictitious
Added abbreviation “piptazo” to ‘Piperacillin/tazobactam’ (TZP)
-
‘Penicillin G’ (for intravenous use) is now named ‘Benzylpenicillin’
-(code PEN)
-
‘Penicillin V’ (for oral use, code PNV) was removed,
-since its actual entry ‘Phenoxymethylpenicillin’ (code PHN)
-already existed
-
The group name (antibiotics$group) of ‘Linezolid’
-(LNZ), ‘Cycloserine’ (CYC), ‘Tedizolid’
-(TZD) and ‘Thiacetazone’ (THA) is now
-“Oxazolidinones” instead of “Other antibacterials”
+
‘Penicillin G’ (for intravenous use) is now named ‘Benzylpenicillin’ (code PEN)
+
‘Penicillin V’ (for oral use, code PNV) was removed, since its actual entry ‘Phenoxymethylpenicillin’ (code PHN) already existed
+
The group name (antibiotics$group) of ‘Linezolid’ (LNZ), ‘Cycloserine’ (CYC), ‘Tedizolid’ (TZD) and ‘Thiacetazone’ (THA) is now “Oxazolidinones” instead of “Other antibacterials”
-
Added support for using unique() on classes
-<rsi>, <mic>,
-<disk>, <ab> and
-<mo>
-
Added argument excess to the kurtosis()
-function (defaults to FALSE), to return the excess
-kurtosis, defined as the kurtosis minus three.
+
Added support for using unique() on classes <rsi>, <mic>, <disk>, <ab> and <mo>
+
Added argument excess to the kurtosis() function (defaults to FALSE), to return the excess kurtosis, defined as the kurtosis minus three.
Other
-
Removed functions portion_R(), portion_S()
-and portion_I() that were deprecated since version 0.9.0
-(November 2019) and were replaced with proportion_R(),
-proportion_S() and proportion_I()
+
Removed functions portion_R(), portion_S() and portion_I() that were deprecated since version 0.9.0 (November 2019) and were replaced with proportion_R(), proportion_S() and proportion_I()
Removed unnecessary references to the base package
-
Added packages that could be useful for some functions to the
-Suggests field of the DESCRIPTION file
+
Added packages that could be useful for some functions to the Suggests field of the DESCRIPTION file
AMR 1.3.02020-07-31
New
-
Function ab_from_text() to retrieve antimicrobial
-drug names, doses and forms of administration from clinical texts in
-e.g. health care records, which also corrects for misspelling since it
-uses as.ab() internally
+
Function ab_from_text() to retrieve antimicrobial drug names, doses and forms of administration from clinical texts in e.g. health care records, which also corrects for misspelling since it uses as.ab() internally
-
Tidyverse
-selection helpers for antibiotic classes, that help to select the
-columns of antibiotics that are of a specific antibiotic class, without
-the need to define the columns or antibiotic abbreviations. They can be
-used in any function that allows selection helpers, like
-dplyr::select() and tidyr::pivot_longer():
+
Tidyverse selection helpers for antibiotic classes, that help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations. They can be used in any function that allows selection helpers, like dplyr::select() and tidyr::pivot_longer():
library(dplyr)
@@ -1148,105 +697,54 @@ used in any function that allows selection helpers, like
select(carbapenems())#> Selecting carbapenems: `IPM` (imipenem), `MEM` (meropenem)
Added function filter_penicillins() to filter
-isolates on a specific result in any column with a name in the
-antimicrobial ‘penicillins’ class (more specific: ATC subgroup
-Beta-lactam antibacterials, penicillins)
-
Added official antimicrobial names to all
-filter_ab_class() functions, such as
-filter_aminoglycosides()
-
Added antibiotics code “FOX1” for cefoxitin screening
-(abbreviation “cfsc”) to the antibiotics data set
Added function filter_penicillins() to filter isolates on a specific result in any column with a name in the antimicrobial ‘penicillins’ class (more specific: ATC subgroup Beta-lactam antibacterials, penicillins)
+
Added official antimicrobial names to all filter_ab_class() functions, such as filter_aminoglycosides()
+
Added antibiotics code “FOX1” for cefoxitin screening (abbreviation “cfsc”) to the antibiotics data set
Added Monuril as trade name for fosfomycin
-
Added argument conserve_capped_values to
-as.rsi() for interpreting MIC values - it makes sure that
-values starting with “<” (but not “<=”) will always return “S” and
-values starting with “>” (but not “>=”) will always return “R”.
-The default behaviour of as.rsi() has not changed, so you
-need to specifically do
-as.rsi(..., conserve_capped_values = TRUE).
+
Added argument conserve_capped_values to as.rsi() for interpreting MIC values - it makes sure that values starting with “<” (but not “<=”) will always return “S” and values starting with “>” (but not “>=”) will always return “R”. The default behaviour of as.rsi() has not changed, so you need to specifically do as.rsi(..., conserve_capped_values = TRUE).
As a consequence, very old microbial codes (from AMR
-v0.5.0 and lower) are not supported anymore. Use
-as.mo() on your microorganism names or codes to transform
-them to current abbreviations used in this package.
As a consequence, very old microbial codes (from AMR v0.5.0 and lower) are not supported anymore. Use as.mo() on your microorganism names or codes to transform them to current abbreviations used in this package.
Dramatic improvement of the algorithm behind as.ab(),
-making many more input errors translatable, such as digitalised health
-care records, using too few or too many vowels or consonants and many
-more
+
Dramatic improvement of the algorithm behind as.ab(), making many more input errors translatable, such as digitalised health care records, using too few or too many vowels or consonants and many more
Added progress bar
-
Fixed a bug where as.ab() would return an error on
-invalid input values
-
The as.ab() function will now throw a note if more than
-1 antimicrobial drug could be retrieved from a single input value.
+
Fixed a bug where as.ab() would return an error on invalid input values
+
The as.ab() function will now throw a note if more than 1 antimicrobial drug could be retrieved from a single input value.
-
Fixed a bug where eucast_rules() would not work on a
-tibble when the tibble or dplyr package was
-loaded
-
Fixed a bug for CLSI 2019 guidelines (using
-as.rsi()), that also included results for animals. It now
-only contains interpretation guidelines for humans.
-
All *_join_microorganisms() functions and
-bug_drug_combinations() now return the original data class
-(e.g. tibbles and data.tables)
+
Fixed a bug where eucast_rules() would not work on a tibble when the tibble or dplyr package was loaded
+
Fixed a bug for CLSI 2019 guidelines (using as.rsi()), that also included results for animals. It now only contains interpretation guidelines for humans.
+
All *_join_microorganisms() functions and bug_drug_combinations() now return the original data class (e.g. tibbles and data.tables)
Changed the summary for class <rsi>, to
-highlight the %SI vs. %R
-
Improved error handling, giving more useful info when functions
-return an error
-
Any progress bar will now only show in interactive mode (i.e. not
-in R Markdown)
-
Speed improvement for mdro() and
-filter_ab_class()
-
New option arrows_textangled for
-ggplot_pca() to indicate whether the text at the end of the
-arrows should be angled (defaults to TRUE, as it was in
-previous versions)
+
Improved auto-determination for columns of types <mo> and <Date>
Changed the summary for class <rsi>, to highlight the %SI vs. %R
+
Improved error handling, giving more useful info when functions return an error
+
Any progress bar will now only show in interactive mode (i.e. not in R Markdown)
+
Speed improvement for mdro() and filter_ab_class()
+
New option arrows_textangled for ggplot_pca() to indicate whether the text at the end of the arrows should be angled (defaults to TRUE, as it was in previous versions)
Added parenteral DDD to benzylpenicillin
-
Fixed a bug where as.mic() could not handle dots
-without a leading zero (like "<=.25)
+
Fixed a bug where as.mic() could not handle dots without a leading zero (like "<=.25)
Other
-
Moved primary location of this project from GitLab to GitHub, giving us native
-support for automated syntax checking without being dependent on
-external services such as AppVeyor and Travis CI.
+
Moved primary location of this project from GitLab to GitHub, giving us native support for automated syntax checking without being dependent on external services such as AppVeyor and Travis CI.
@@ -1254,144 +752,73 @@ external services such as AppVeyor and Travis CI.
Breaking
-
Removed code dependency on all other R packages, making this
-package fully independent of the development process of others. This is
-a major code change, but will probably not be noticeable by most
-users.
-
Making this package independent of especially the tidyverse
-(e.g. packages dplyr and tidyr) tremendously
-increases sustainability on the long term, since tidyverse functions
-change quite often. Good for users, but hard for package maintainers.
-Most of our functions are replaced with versions that only rely on base
-R, which keeps this package fully functional for many years to come,
-without requiring a lot of maintenance to keep up with other packages
-anymore. Another upside it that this package can now be used with all
-versions of R since R-3.0.0 (April 2013). Our package is being used in
-settings where the resources are very limited. Fewer dependencies on
-newer software is helpful for such settings.
+
Removed code dependency on all other R packages, making this package fully independent of the development process of others. This is a major code change, but will probably not be noticeable by most users.
+
Making this package independent of especially the tidyverse (e.g. packages dplyr and tidyr) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Good for users, but hard for package maintainers. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. Another upside it that this package can now be used with all versions of R since R-3.0.0 (April 2013). Our package is being used in settings where the resources are very limited. Fewer dependencies on newer software is helpful for such settings.
Negative effects of this change are:
-
Function freq() that was borrowed from the
-cleaner package was removed. Use
-cleaner::freq(), or run library("cleaner")
-before you use freq().
-
Printing values of class mo or rsi in
-a tibble will no longer be in colour and printing rsi in a
-tibble will show the class <ord>, not
-<rsi> anymore. This is purely a visual
-effect.
-
All functions from the mo_* family (like
-mo_name() and mo_gramstain()) are noticeably
-slower when running on hundreds of thousands of rows.
-
For developers: classes mo and ab now both
-also inherit class character, to support any data
-transformation. This change invalidates code that checks for class
-length == 1.
Printing values of class mo or rsi in a tibble will no longer be in colour and printing rsi in a tibble will show the class <ord>, not <rsi> anymore. This is purely a visual effect.
+
All functions from the mo_* family (like mo_name() and mo_gramstain()) are noticeably slower when running on hundreds of thousands of rows.
+
For developers: classes mo and ab now both also inherit class character, to support any data transformation. This change invalidates code that checks for class length == 1.
Changed
Taxonomy:
-
Updated the taxonomy of microorganisms to May 2020, using the
-Catalogue of Life (CoL), the Global Biodiversity Information Facility
-(GBIF) and the List of Prokaryotic names with Standing in Nomenclature
-(LPSN, hosted by DSMZ since February 2020). Note: a
-taxonomic update may always impact determination of first isolates
-(using first_isolate()), since some bacterial names might
-be renamed to other genera or other (sub)species. This is expected
-behaviour.
-
Removed the Catalogue of Life IDs (like 776351), since they now work
-with a species ID (hexadecimal string)
+
Updated the taxonomy of microorganisms to May 2020, using the Catalogue of Life (CoL), the Global Biodiversity Information Facility (GBIF) and the List of Prokaryotic names with Standing in Nomenclature (LPSN, hosted by DSMZ since February 2020). Note: a taxonomic update may always impact determination of first isolates (using first_isolate()), since some bacterial names might be renamed to other genera or other (sub)species. This is expected behaviour.
+
Removed the Catalogue of Life IDs (like 776351), since they now work with a species ID (hexadecimal string)
EUCAST rules:
-
The eucast_rules() function no longer applies “other”
-rules at default that are made available by this package (like setting
-ampicillin = R when ampicillin + enzyme inhibitor = R). The default
-input value for rules is now
-c("breakpoints", "expert") instead of "all",
-but this can be changed by the user. To return to the old behaviour, set
-options(AMR.eucast_rules = "all").
-
Fixed a bug where checking antimicrobial results in the original
-data were not regarded as valid R/SI values
-
All “other” rules now apply for all drug combinations in the
-antibiotics data set these two rules:
-
A drug with enzyme inhibitor will be set to S if
-the drug without enzyme inhibitor is S
-
A drug without enzyme inhibitor will be set to R if
-the drug with enzyme inhibitor is R
+
The eucast_rules() function no longer applies “other” rules at default that are made available by this package (like setting ampicillin = R when ampicillin + enzyme inhibitor = R). The default input value for rules is now c("breakpoints", "expert") instead of "all", but this can be changed by the user. To return to the old behaviour, set options(AMR.eucast_rules = "all").
+
Fixed a bug where checking antimicrobial results in the original data were not regarded as valid R/SI values
+
All “other” rules now apply for all drug combinations in the antibiotics data set these two rules:
+
A drug with enzyme inhibitor will be set to S if the drug without enzyme inhibitor is S
+
A drug without enzyme inhibitor will be set to R if the drug with enzyme inhibitor is R
-This works for all drug combinations, such as ampicillin/sulbactam,
-ceftazidime/avibactam, trimethoprim/sulfamethoxazole, etc.
-
Added official drug names to verbose output of
-eucast_rules()
+This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/avibactam, trimethoprim/sulfamethoxazole, etc.
+
Added official drug names to verbose output of eucast_rules()
-
Added function ab_url() to return the direct URL of an
-antimicrobial agent from the official WHO website
-
Improvements for algorithm in as.ab(), so that
-e.g. as.ab("ampi sul") and ab_name("ampi sul")
-work
-
Functions ab_atc() and ab_group() now
-return NA if no antimicrobial agent could be found
-
Small fix for some text input that could not be coerced as valid MIC
-values
-
Fix for interpretation of generic CLSI interpretation rules (thanks
-to Anthony Underwood)
-
Fix for set_mo_source() to make sure that column
-mo will always be the second column
-
Added abbreviation “cfsc” for Cefoxitin and “cfav” for
-Ceftazidime/avibactam
+
Added function ab_url() to return the direct URL of an antimicrobial agent from the official WHO website
+
Improvements for algorithm in as.ab(), so that e.g. as.ab("ampi sul") and ab_name("ampi sul") work
+
Functions ab_atc() and ab_group() now return NA if no antimicrobial agent could be found
+
Small fix for some text input that could not be coerced as valid MIC values
+
Fix for interpretation of generic CLSI interpretation rules (thanks to Anthony Underwood)
+
Fix for set_mo_source() to make sure that column mo will always be the second column
+
Added abbreviation “cfsc” for Cefoxitin and “cfav” for Ceftazidime/avibactam
Other
-
Removed previously deprecated function p.symbol() - it
-was replaced with p_symbol()
+
Removed previously deprecated function p.symbol() - it was replaced with p_symbol()
-
Removed function read.4d(), that was only useful for
-reading data from an old test database.
+
Removed function read.4d(), that was only useful for reading data from an old test database.
AMR 1.1.02020-04-15
New
-
Support for easy principal component analysis for AMR, using the new
-pca() function
-
Plotting biplots for principal component analysis using the new
-ggplot_pca() function
+
Support for easy principal component analysis for AMR, using the new pca() function
+
Plotting biplots for principal component analysis using the new ggplot_pca() function
Changed
-
Improvements for the algorithm used by as.mo() (and
-consequently all mo_* functions, that use
-as.mo() internally):
-
Support for codes ending with SPE for species, like
-"ESCSPE" for Escherichia coli
+
Improvements for the algorithm used by as.mo() (and consequently all mo_* functions, that use as.mo() internally):
+
Support for codes ending with SPE for species, like "ESCSPE" for Escherichia coli
-
Support for any encoding, which means that any language-specific
-character with accents can be used for input
-
Support for more arbitrary IDs used in laboratory information
-systems
+
Support for any encoding, which means that any language-specific character with accents can be used for input
+
Support for more arbitrary IDs used in laboratory information systems
Small fix for preventing viruses being treated as bacteria
-
Small fix for preventing contamination and lack of growth being
-treated as valid microorganisms
+
Small fix for preventing contamination and lack of growth being treated as valid microorganisms
-
Support for all abbreviations of antibiotics and antimycotics used
-by the Netherlands National Institute for Public Health and the
-Environment (Rijksinstituut voor Volksgezondheid en Milieu; RIVM)
-
Added more abbreviations to the antibiotics data
-set
-
Reloaded original EUCAST master tables from 2019 (2020 was already
-available). This seems more reliable than the data we used from
-WHONET.
-
Added generic CLSI rules for R/SI interpretation using
-as.rsi() for years 2010-2019 (thanks to Anthony
-Underwood)
+
Support for all abbreviations of antibiotics and antimycotics used by the Netherlands National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu; RIVM)
+
Added more abbreviations to the antibiotics data set
+
Reloaded original EUCAST master tables from 2019 (2020 was already available). This seems more reliable than the data we used from WHONET.
+
Added generic CLSI rules for R/SI interpretation using as.rsi() for years 2010-2019 (thanks to Anthony Underwood)
Other
Support for the upcoming dplyr version 1.0.0
-
More robust assigning for classes rsi and
-mic
+
More robust assigning for classes rsi and mic
@@ -1399,12 +826,9 @@ Underwood)
AMR 1.0.12020-02-23
Changed
-
Fixed important floating point error for some MIC comparisons in
-EUCAST 2020 guideline
+
Fixed important floating point error for some MIC comparisons in EUCAST 2020 guideline
-
Interpretation from MIC values (and disk zones) to R/SI can now
-be used with mutate_at() of the dplyr
-package:
+
Interpretation from MIC values (and disk zones) to R/SI can now be used with mutate_at() of the dplyr package:
yourdata%>%
@@ -1413,43 +837,21 @@ package:
yourdata%>%mutate_at(vars(antibiotic1:antibiotic25), as.rsi, mo =.$mybacteria)
-
Added antibiotic abbreviations for a laboratory manufacturer
-(GLIMS) for cefuroxime, cefotaxime, ceftazidime, cefepime, cefoxitin and
-trimethoprim/sulfamethoxazole
-
Added uti (as abbreviation of urinary tract
-infections) as argument to as.rsi(), so interpretation of
-MIC values and disk zones can be made dependent on isolates specifically
-from UTIs
Added antibiotic abbreviations for a laboratory manufacturer (GLIMS) for cefuroxime, cefotaxime, ceftazidime, cefepime, cefoxitin and trimethoprim/sulfamethoxazole
+
Added uti (as abbreviation of urinary tract infections) as argument to as.rsi(), so interpretation of MIC values and disk zones can be made dependent on isolates specifically from UTIs
The repository of this package now contains a clean version of the
-EUCAST and CLSI guidelines from 2011-2020 to translate MIC and disk
-diffusion values to R/SI: https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt.
-This allows for machine reading these guidelines, which
-is almost impossible with the Excel and PDF files distributed by EUCAST
-and CLSI. This file used to process the EUCAST Clinical Breakpoints
-Excel file can
-be found here.
The repository of this package now contains a clean version of the EUCAST and CLSI guidelines from 2011-2020 to translate MIC and disk diffusion values to R/SI: https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt. This allows for machine reading these guidelines, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. This file used to process the EUCAST Clinical Breakpoints Excel file can be found here.
Support for LOINC and SNOMED codes
-
Support for LOINC codes in the antibiotics data set.
-Use ab_loinc() to retrieve LOINC codes, or use a LOINC code
-for input in any ab_* function:
+
Support for LOINC codes in the antibiotics data set. Use ab_loinc() to retrieve LOINC codes, or use a LOINC code for input in any ab_* function:
ab_loinc("ampicillin")
@@ -1460,9 +862,7 @@ for input in any ab_* function:
#> [1] "J01CA01"
-
Support for SNOMED CT codes in the microorganisms
-data set. Use mo_snomed() to retrieve SNOMED codes, or use
-a SNOMED code for input in any mo_* function:
+
Support for SNOMED CT codes in the microorganisms data set. Use mo_snomed() to retrieve SNOMED codes, or use a SNOMED code for input in any mo_* function:
mo_snomed("S. aureus")
@@ -1476,47 +876,25 @@ a SNOMED code for input in any mo_* function:
Changes
-
The as.mo() function previously wrote to the package
-folder to improve calculation speed for previously calculated results.
-This is no longer the case, to comply with CRAN policies. Consequently,
-the function clear_mo_history() was removed.
-
Bugfix for some WHONET microorganism codes that were not interpreted
-correctly when using as.rsi()
+
The as.mo() function previously wrote to the package folder to improve calculation speed for previously calculated results. This is no longer the case, to comply with CRAN policies. Consequently, the function clear_mo_history() was removed.
+
Bugfix for some WHONET microorganism codes that were not interpreted correctly when using as.rsi()
-
Improvements for the algorithm used by as.mo() (and
-consequently all mo_* functions, that use
-as.mo() internally):
-
Support for missing spaces, e.g. in
-as.mo("Methicillin-resistant S.aureus")
+
Improvements for the algorithm used by as.mo() (and consequently all mo_* functions, that use as.mo() internally):
+
Support for missing spaces, e.g. in as.mo("Methicillin-resistant S.aureus")
Better support for determination of Salmonella biovars
-
Speed improvements, especially for the G. species format (G
-for genus), like E. coli and K pneumoniae
+
Speed improvements, especially for the G. species format (G for genus), like E. coli and K pneumoniae
-
Support for more common codes used in laboratory information
-systems
+
Support for more common codes used in laboratory information systems
-
Input values for as.disk() limited to a maximum of 50
-millimeters
-
Added a lifecycle state to every function, following the lifecycle
-circle of the tidyverse
+
Input values for as.disk() limited to a maximum of 50 millimeters
+
Added a lifecycle state to every function, following the lifecycle circle of the tidyverse
-
For in as.ab(): support for drugs starting with “co-”
-like co-amoxiclav, co-trimoxazole, co-trimazine and co-trimazole (thanks
-to Peter Dutey)
-
Changes to the antibiotics data set (thanks to Peter
-Dutey):
-
Added more synonyms to colistin, imipenem and
-piperacillin/tazobactam
Moved synonyms Bactrimel and Cotrimazole from sulfamethoxazole (SMX) to trimethoprim/sulfamethoxazole (SXT)
@@ -1530,19 +908,9 @@ e.g. ab_ddd("Rimactazid") will now return
AMR 0.9.02019-11-29
Breaking
-
Adopted Adeolu et al. (2016), PMID 27620848 for
-the microorganisms data set, which means that the new order
-Enterobacterales now consists of a part of the existing family
-Enterobacteriaceae, but that this family has been split into other
-families as well (like Morganellaceae and
-Yersiniaceae). Although published in 2016, this information is
-not yet in the Catalogue of Life version of 2019. All MDRO
-determinations with mdro() will now use the
-Enterobacterales order for all guidelines before 2016 that were
-dependent on the Enterobacteriaceae family.
+
Adopted Adeolu et al. (2016), PMID 27620848 for the microorganisms data set, which means that the new order Enterobacterales now consists of a part of the existing family Enterobacteriaceae, but that this family has been split into other families as well (like Morganellaceae and Yersiniaceae). Although published in 2016, this information is not yet in the Catalogue of Life version of 2019. All MDRO determinations with mdro() will now use the Enterobacterales order for all guidelines before 2016 that were dependent on the Enterobacteriaceae family.
-
If you were dependent on the old Enterobacteriaceae family
-e.g. by using in your code:
+
If you were dependent on the old Enterobacteriaceae family e.g. by using in your code:
Support for a new MDRO guideline: Magiorakos AP, Srinivasan A
-et al. “Multidrug-resistant, extensively drug-resistant and
-pandrug-resistant bacteria: an international expert proposal for interim
-standard definitions for acquired resistance.” Clinical Microbiology and
-Infection (2012).
-
This is now the new default guideline for the mdro()
-function
-
The new Verbose mode (mdro(...., verbose = TRUE))
-returns an informative data set where the reason for MDRO determination
-is given for every isolate, and an list of the resistant antimicrobial
-agents
+
Support for a new MDRO guideline: Magiorakos AP, Srinivasan A et al. “Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.” Clinical Microbiology and Infection (2012).
+
This is now the new default guideline for the mdro() function
+
The new Verbose mode (mdro(...., verbose = TRUE)) returns an informative data set where the reason for MDRO determination is given for every isolate, and an list of the resistant antimicrobial agents
-
Data set antivirals, containing all entries from the
-ATC J05 group with their DDDs for oral and parenteral treatment
+
Data set antivirals, containing all entries from the ATC J05 group with their DDDs for oral and parenteral treatment
Removed previously deprecated function as.atc() - this
-function was replaced by ab_atc()
+
Removed previously deprecated function as.atc() - this function was replaced by ab_atc()
-
Renamed all portion_* functions to
-proportion_*. All portion_* functions are
-still available as deprecated functions, and will return a warning when
-used.
-
When running as.rsi() over a data set, it will now
-print the guideline that will be used if it is not specified by the
-user
+
Renamed all portion_* functions to proportion_*. All portion_* functions are still available as deprecated functions, and will return a warning when used.
+
When running as.rsi() over a data set, it will now print the guideline that will be used if it is not specified by the user
Fix where Stenotrophomonas maltophilia would always become
-ceftazidime R (following EUCAST v3.1)
-
Fix where Leuconostoc and Pediococcus would not
-always become glycopeptides R
-
non-EUCAST rules in eucast_rules() are now applied
-first and not as last anymore. This is to improve the dependency on
-certain antibiotics for the official EUCAST rules. Please see
-?eucast_rules.
+
Fix where Stenotrophomonas maltophilia would always become ceftazidime R (following EUCAST v3.1)
+
Fix where Leuconostoc and Pediococcus would not always become glycopeptides R
+
non-EUCAST rules in eucast_rules() are now applied first and not as last anymore. This is to improve the dependency on certain antibiotics for the official EUCAST rules. Please see ?eucast_rules.
-
Fix for interpreting MIC values with as.rsi() where the
-input is NA
+
Fix for interpreting MIC values with as.rsi() where the input is NA
-
Added “imi” and “imp” as allowed abbreviation for Imipenem
-(IPM)
-
Fix for automatically determining columns with antibiotic results in
-mdro() and eucast_rules()
+
Added “imi” and “imp” as allowed abbreviation for Imipenem (IPM)
+
Fix for automatically determining columns with antibiotic results in mdro() and eucast_rules()
-
Added ATC codes for ceftaroline, ceftobiprole and faropenem and
-fixed two typos in the antibiotics data set
+
Added ATC codes for ceftaroline, ceftobiprole and faropenem and fixed two typos in the antibiotics data set
More robust way of determining valid MIC values
-
Small changed to the example_isolates data set to
-better reflect reality
-
Added more microorganisms codes from laboratory systems
-(esp. species of Pseudescherichia and
-Rodentibacter)
+
Small changed to the example_isolates data set to better reflect reality
+
Added more microorganisms codes from laboratory systems (esp. species of Pseudescherichia and Rodentibacter)
Rewrote the complete documentation to markdown format, to be able to
-use the very latest version of the great Roxygen2, released in
-November 2019. This tremously improved the documentation quality, since
-the rewrite forced us to go over all texts again and make changes where
-needed.
-
Change dependency on clean to cleaner, as
-this package was renamed accordingly upon CRAN request
+
Rewrote the complete documentation to markdown format, to be able to use the very latest version of the great Roxygen2, released in November 2019. This tremously improved the documentation quality, since the rewrite forced us to go over all texts again and make changes where needed.
+
Change dependency on clean to cleaner, as this package was renamed accordingly upon CRAN request
Added Dr. Sofia Ny as contributor
@@ -1667,24 +995,14 @@ this package was renamed accordingly upon CRAN request
Breaking
-
Determination of first isolates now excludes all
-‘unknown’ microorganisms at default, i.e. microbial code
-"UNKNOWN". They can be included with the new argument
-include_unknown:
+
Determination of first isolates now excludes all ‘unknown’ microorganisms at default, i.e. microbial code "UNKNOWN". They can be included with the new argument include_unknown:
For WHONET users, this means that all records/isolates with organism
-code "con" (contamination) will be excluded at
-default, since as.mo("con") = "UNKNOWN". The function
-always shows a note with the number of ‘unknown’ microorganisms that
-were included or excluded.
+
For WHONET users, this means that all records/isolates with organism code "con" (contamination) will be excluded at default, since as.mo("con") = "UNKNOWN". The function always shows a note with the number of ‘unknown’ microorganisms that were included or excluded.
-
For code consistency, classes ab and mo
-will now be preserved in any subsetting or assignment. For the sake of
-data integrity, this means that invalid assignments will now result in
-NA:
+
For code consistency, classes ab and mo will now be preserved in any subsetting or assignment. For the sake of data integrity, this means that invalid assignments will now result in NA:
# how it works in base R:
@@ -1698,26 +1016,15 @@ data integrity, this means that invalid assignments will now result in
x[1]<-"testvalue"#> Warning message:#> invalid microorganism code, NA generated
-
This is important, because a value like "testvalue"
-could never be understood by e.g. mo_name(), although the
-class would suggest a valid microbial code.
+
This is important, because a value like "testvalue" could never be understood by e.g. mo_name(), although the class would suggest a valid microbial code.
-
Function freq() has moved to a new package, clean (CRAN link), since
-creating frequency tables actually does not fit the scope of this
-package. The freq() function still works, since it is
-re-exported from the clean package (which will be installed
-automatically upon updating this AMR package).
-
Renamed data set septic_patients to
-example_isolates
+
Function freq() has moved to a new package, clean (CRAN link), since creating frequency tables actually does not fit the scope of this package. The freq() function still works, since it is re-exported from the clean package (which will be installed automatically upon updating this AMR package).
+
Renamed data set septic_patients to example_isolates
New
-
Function bug_drug_combinations() to quickly get a
-data.frame with the results of all bug-drug combinations in
-a data set. The column containing microorganism codes is guessed
-automatically and its input is transformed with
-mo_shortname() at default:
+
Function bug_drug_combinations() to quickly get a data.frame with the results of all bug-drug combinations in a data set. The column containing microorganism codes is guessed automatically and its input is transformed with mo_shortname() at default:
x<-bug_drug_combinations(example_isolates)
@@ -1740,25 +1047,13 @@ automatically and its input is transformed with
#> 3 Gram-negative AMP 227 0 405 632#> 4 Gram-negative AMX 227 0 405 632#> NOTE: Use 'format()' on this result to get a publicable/printable format.
-
You can format this to a printable format, ready for reporting or
-exporting to e.g. Excel with the base R format()
-function:
+
You can format this to a printable format, ready for reporting or exporting to e.g. Excel with the base R format() function:
Additional way to calculate co-resistance, i.e. when using
-multiple antimicrobials as input for portion_* functions or
-count_* functions. This can be used to determine the
-empiric susceptibility of a combination therapy. A new argument
-only_all_tested (which defaults to
-FALSE) replaces the old
-also_single_tested and can be used to select one of the two
-methods to count isolates and calculate portions. The difference can be
-seen in this example table (which is also on the portion
-and count help pages), where the %SI is being
-determined:
+
Additional way to calculate co-resistance, i.e. when using multiple antimicrobials as input for portion_* functions or count_* functions. This can be used to determine the empiric susceptibility of a combination therapy. A new argument only_all_tested (which defaults to FALSE) replaces the old also_single_tested and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the portion and count help pages), where the %SI is being determined:
Since this is a major change, usage of the old
-also_single_tested will throw an informative error that it
-has been replaced by only_all_tested.
+
Since this is a major change, usage of the old also_single_tested will throw an informative error that it has been replaced by only_all_tested.
-
tibble printing support for classes
-rsi, mic, disk, ab
-mo. When using tibbles containing
-antimicrobial columns, values S will print in green, values
-I will print in yellow and values R will print
-in red. Microbial IDs (class mo) will emphasise on the
-genus and species, not on the kingdom.
+
tibble printing support for classes rsi, mic, disk, abmo. When using tibbles containing antimicrobial columns, values S will print in green, values I will print in yellow and values R will print in red. Microbial IDs (class mo) will emphasise on the genus and species, not on the kingdom.
# (run this on your own console, as this page does not support colour printing)
@@ -1800,93 +1087,44 @@ genus and species, not on the kingdom.
Changed
-
Many algorithm improvements for as.mo() (of which some
-led to additions to the microorganisms data set). Many
-thanks to all contributors that helped improving the algorithms.
-
Self-learning algorithm - the function now gains experience from
-previously determined microorganism IDs and learns from it (yielding
-80-95% speed improvement for any guess after the first try)
+
Many algorithm improvements for as.mo() (of which some led to additions to the microorganisms data set). Many thanks to all contributors that helped improving the algorithms.
+
Self-learning algorithm - the function now gains experience from previously determined microorganism IDs and learns from it (yielding 80-95% speed improvement for any guess after the first try)
Big improvement for misspelled input
-
These new trivial names known to the field are now understood:
-meningococcus, gonococcus, pneumococcus
-
Updated to the latest taxonomic data (updated to August 2019, from
-the International Journal of Systematic and Evolutionary
-Microbiology
-
Added support for Viridans Group Streptococci (VGS) and Milleri
-Group Streptococci (MGS)
+
These new trivial names known to the field are now understood: meningococcus, gonococcus, pneumococcus
+
Updated to the latest taxonomic data (updated to August 2019, from the International Journal of Systematic and Evolutionary Microbiology
+
Added support for Viridans Group Streptococci (VGS) and Milleri Group Streptococci (MGS)
Added support for Blastocystis
Added support for 5,000 new fungi
Added support for unknown yeasts and fungi
-
Changed most microorganism IDs to improve readability. For example,
-the old code B_ENTRC_FAE could have been both E.
-faecalis and E. faecium. Its new code is
-B_ENTRC_FCLS and E. faecium has become
-B_ENTRC_FACM. Also, the Latin character ae is now preserved
-at the start of each genus and species abbreviation. For example, the
-old code for Aerococcus urinae was B_ARCCC_NAE.
-This is now B_AERCC_URIN. IMPORTANT: Old
-microorganism IDs are still supported, but support will be dropped in a
-future version. Use as.mo() on your old codes to transform
-them to the new format. Using functions from the mo_*
-family (like mo_name() and mo_gramstain()) on
-old codes, will throw a warning.
+
Changed most microorganism IDs to improve readability. For example, the old code B_ENTRC_FAE could have been both E. faecalis and E. faecium. Its new code is B_ENTRC_FCLS and E. faecium has become B_ENTRC_FACM. Also, the Latin character ae is now preserved at the start of each genus and species abbreviation. For example, the old code for Aerococcus urinae was B_ARCCC_NAE. This is now B_AERCC_URIN. IMPORTANT: Old microorganism IDs are still supported, but support will be dropped in a future version. Use as.mo() on your old codes to transform them to the new format. Using functions from the mo_* family (like mo_name() and mo_gramstain()) on old codes, will throw a warning.
-
More intelligent guessing for as.ab(), including
-bidirectional language support
-
Added support for the German national guideline (3MRGN/4MRGN) in the
-mdro() function, to determine multi-drug resistant
-organisms
+
More intelligent guessing for as.ab(), including bidirectional language support
+
Added support for the German national guideline (3MRGN/4MRGN) in the mdro() function, to determine multi-drug resistant organisms
Fix for using mo_* functions where the coercion
-uncertainties and failures would not be available through
-mo_uncertainties() and mo_failures()
-anymore
-
Deprecated the country argument of mdro()
-in favour of the already existing guideline argument to
-support multiple guidelines within one country
-
The name of RIF is now Rifampicin instead
-of Rifampin
-
The antibiotics data set is now sorted by name and all
-cephalosporins now have their generation between brackets
-
Speed improvement for guess_ab_col() which is now 30
-times faster for antibiotic abbreviations
-
Improved filter_ab_class() to be more reliable and to
-support 5th generation cephalosporins
-
Function availability() now uses
-portion_R() instead of portion_IR(), to comply
-with EUCAST insights
-
Functions age() and age_groups() now have
-a na.rm argument to remove empty values
-
Renamed function p.symbol() to p_symbol()
-(the former is now deprecated and will be removed in a future
-version)
-
Using negative values for x in
-age_groups() will now introduce NAs and not
-return an error anymore
+
Fix for using mo_* functions where the coercion uncertainties and failures would not be available through mo_uncertainties() and mo_failures() anymore
+
Deprecated the country argument of mdro() in favour of the already existing guideline argument to support multiple guidelines within one country
+
The name of RIF is now Rifampicin instead of Rifampin
+
The antibiotics data set is now sorted by name and all cephalosporins now have their generation between brackets
+
Speed improvement for guess_ab_col() which is now 30 times faster for antibiotic abbreviations
+
Improved filter_ab_class() to be more reliable and to support 5th generation cephalosporins
+
Function availability() now uses portion_R() instead of portion_IR(), to comply with EUCAST insights
+
Functions age() and age_groups() now have a na.rm argument to remove empty values
+
Renamed function p.symbol() to p_symbol() (the former is now deprecated and will be removed in a future version)
+
Using negative values for x in age_groups() will now introduce NAs and not return an error anymore
Added Prof. Dr. Casper Albers as doctoral advisor and added
-Dr. Judith Fonville, Eric Hazenberg, Dr. Bart Meijer, Dr. Dennis
-Souverein and Annick Lenglet as contributors
-
Cleaned the coding style of every single syntax line in this package
-with the help of the lintr package
+
Added Prof. Dr. Casper Albers as doctoral advisor and added Dr. Judith Fonville, Eric Hazenberg, Dr. Bart Meijer, Dr. Dennis Souverein and Annick Lenglet as contributors
+
Cleaned the coding style of every single syntax line in this package with the help of the lintr package
@@ -1908,13 +1143,7 @@ with the help of the lintr package
New
-
Function rsi_df() to transform a
-data.frame to a data set containing only the microbial
-interpretation (S, I, R), the antibiotic, the percentage of S/I/R and
-the number of available isolates. This is a convenient combination of
-the existing functions count_df() and
-portion_df() to immediately show resistance percentages and
-number of available isolates:
+
Function rsi_df() to transform a data.frame to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combination of the existing functions count_df() and portion_df() to immediately show resistance percentages and number of available isolates:
septic_patients%>%
@@ -1927,8 +1156,7 @@ number of available isolates:
# 4 Ciprofloxacin R 0.1618169 228
-
Support for all scientifically published pathotypes of E.
-coli to date (that we could find). Supported are:
+
Support for all scientifically published pathotypes of E. coli to date (that we could find). Supported are:
AIEC (Adherent-Invasive E. coli)
ATEC (Atypical Entero-pathogenic E. coli)
DAEC (Diffusely Adhering E. coli)
@@ -1950,52 +1178,31 @@ coli to date (that we could find). Supported are:
mo_gramstain("EHEC")# "Gram-negative"
-
Function mo_info() as an analogy to
-ab_info(). The mo_info() prints a list with
-the full taxonomy, authors, and the URL to the online database of a
-microorganism
-
Function mo_synonyms() to get all previously
-accepted taxonomic names of a microorganism
+
Function mo_info() as an analogy to ab_info(). The mo_info() prints a list with the full taxonomy, authors, and the URL to the online database of a microorganism
+
Function mo_synonyms() to get all previously accepted taxonomic names of a microorganism
Changed
-
Column names of output count_df() and
-portion_df() are now lowercase
+
Column names of output count_df() and portion_df() are now lowercase
Fixed bug in translation of microorganism names
Fixed bug in determining taxonomic kingdoms
-
Algorithm improvements for as.ab() and
-as.mo() to understand even more severely misspelled
-input
-
Function as.ab() now allows spaces for coercing
-antibiotics names
-
Added ggplot2 methods for automatically determining the
-scale type of classes mo and ab
+
Algorithm improvements for as.ab() and as.mo() to understand even more severely misspelled input
+
Function as.ab() now allows spaces for coercing antibiotics names
+
Added ggplot2 methods for automatically determining the scale type of classes mo and ab
-
Added names of object in the header in frequency tables, even when
-using pipes
-
Prevented "bacteria" from getting coerced by
-as.ab() because Bacterial is a brand name of trimethoprim
-(TMP)
-
Fixed a bug where setting an antibiotic would not work for
-eucast_rules() and mdro()
+
Added names of object in the header in frequency tables, even when using pipes
+
Prevented "bacteria" from getting coerced by as.ab() because Bacterial is a brand name of trimethoprim (TMP)
+
Fixed a bug where setting an antibiotic would not work for eucast_rules() and mdro()
-
Fixed a EUCAST rule for Staphylococci, where amikacin resistance
-would not be inferred from tobramycin
Removed antibiotic code PVM1 from the antibiotics data set as this was a duplicate of PME
-
Fixed bug where not all old taxonomic names would be printed, when
-using a vector as input for as.mo()
+
Fixed bug where not all old taxonomic names would be printed, when using a vector as input for as.mo()
-
Manually added Trichomonas vaginalis from the kingdom of
-Protozoa, which is missing from the Catalogue of Life
-
Small improvements to plot() and barplot()
-for MIC and RSI classes
-
Allow Catalogue of Life IDs to be coerced by
-as.mo()
+
Manually added Trichomonas vaginalis from the kingdom of Protozoa, which is missing from the Catalogue of Life
+
Small improvements to plot() and barplot() for MIC and RSI classes
+
Allow Catalogue of Life IDs to be coerced by as.mo()
@@ -2007,124 +1214,77 @@ for MIC and RSI classes
AMR 0.7.02019-06-03
New
-
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.
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.
Added guidelines of the WHO to determine multi-drug resistance (MDR)
-for TB (mdr_tb()) and added a new vignette about MDR. Read
-this tutorial here on our
-website.
+
Added guidelines of the WHO to determine multi-drug resistance (MDR) for TB (mdr_tb()) and added a new vignette about MDR. Read this tutorial here on our website.
Changed
-
Fixed a critical bug in first_isolate() where missing
-species would lead to incorrect FALSEs. This bug was not present in AMR
-v0.5.0, but was in v0.6.0 and v0.6.1.
-
Fixed a bug in eucast_rules() where antibiotics from
-WHONET software would not be recognised
+
Fixed a critical bug in first_isolate() where missing species would lead to incorrect FALSEs. This bug was not present in AMR v0.5.0, but was in v0.6.0 and v0.6.1.
+
Fixed a bug in eucast_rules() where antibiotics from WHONET software would not be recognised
Completely reworked the antibiotics data set:
All entries now have 3 different identifiers:
-
Column ab contains a human readable EARS-Net code, used
-by ECDC and WHO/WHONET - this is the primary identifier used in this
-package
-
Column atc contains the ATC code, used by
-WHO/WHOCC
-
Column cid contains the CID code (Compound ID), used by
-PubChem
+
Column ab contains a human readable EARS-Net code, used by ECDC and WHO/WHONET - this is the primary identifier used in this package
+
Column atc contains the ATC code, used by WHO/WHOCC
+
Column cid contains the CID code (Compound ID), used by PubChem
-
Based on the Compound ID, almost 5,000 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 superseded by ab_*
-functions
-
All output will be translated by using an included translation file
-which can
-be viewed here
+
Based on the Compound ID, almost 5,000 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 superseded by ab_* functions
+
All output will be translated by using an included translation file which can be viewed here
Improvements to plotting AMR results with ggplot_rsi():
New argument colours to set the bar colours
-
New arguments title, subtitle,
-caption, x.title and y.title to
-set titles and axis descriptions
+
New arguments title, subtitle, caption, x.title and y.title to set titles and axis descriptions
-
Improved intelligence of looking up antibiotic columns in a data set
-using guess_ab_col()
+
Improved intelligence of looking up antibiotic columns in a data set using guess_ab_col()
-
Added ~5,000 more old taxonomic names to the
-microorganisms.old data set, which leads to better results
-finding when using the as.mo() function
-
This package now honours the new EUCAST insight (2019) that S and I
-are but classified as susceptible, where I is defined as ‘increased
-exposure’ and not ‘intermediate’ anymore. For functions like
-portion_df() and count_df() this means that
-their new argument combine_SI is TRUE at default. Our
-plotting function ggplot_rsi() also reflects this change
-since it uses count_df() internally.
-
The age() function gained a new argument
-exact to determine ages with decimals
Added ~5,000 more old taxonomic names to the microorganisms.old data set, which leads to better results finding when using the as.mo() function
+
This package now honours the new EUCAST insight (2019) that S and I are but classified as susceptible, where I is defined as ‘increased exposure’ and not ‘intermediate’ anymore. For functions like portion_df() and count_df() this means that their new argument combine_SI is TRUE at default. Our plotting function ggplot_rsi() also reflects this change since it uses count_df() internally.
+
The age() function gained a new argument exact to determine ages with decimals
Contains the complete manual of this package and all of its functions with an explanation of their arguments
+
Contains a comprehensive tutorial about how to conduct AMR data analysis, import data from WHONET or SPSS and many more.
New
-
BREAKING: removed deprecated functions,
-arguments and references to ‘bactid’. Use as.mo() to
-identify an MO code.
+
BREAKING: removed deprecated functions, arguments and references to ‘bactid’. Use as.mo() to identify an MO code.
-
Catalogue of Life as a new taxonomic source for data about
-microorganisms, which also contains all ITIS data we used previously.
-The microorganisms data set now contains:
-
All ~55,000 (sub)species from the kingdoms of Archaea, Bacteria
-and Protozoa
-
All ~3,000 (sub)species from these orders of the kingdom of
-Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales and
-Schizosaccharomycetales (covering at least like all species of
-Aspergillus, Candida, Pneumocystis,
-Saccharomyces and Trichophyton)
-
All ~2,000 (sub)species from ~100 other relevant genera, from the
-kingdoms of Animalia and Plantae (like Strongyloides and
-Taenia)
-
All ~15,000 previously accepted names of included (sub)species
-that have been taxonomically renamed
+
Catalogue of Life as a new taxonomic source for data about microorganisms, which also contains all ITIS data we used previously. The microorganisms data set now contains:
+
All ~55,000 (sub)species from the kingdoms of Archaea, Bacteria and Protozoa
+
All ~3,000 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales and Schizosaccharomycetales (covering at least like all species of Aspergillus, Candida, Pneumocystis, Saccharomyces and Trichophyton)
+
All ~2,000 (sub)species from ~100 other relevant genera, from the kingdoms of Animalia and Plantae (like Strongyloides and Taenia)
+
All ~15,000 previously accepted names of included (sub)species that have been taxonomically renamed
The responsible author(s) and year of scientific publication
-
This data is updated annually - check the included version with the
-new function catalogue_of_life_version().
+
This data is updated annually - check the included version with the new function catalogue_of_life_version().
-
Due to this change, some mo codes changed
-(e.g. Streptococcus changed from B_STRPTC to
-B_STRPT). A translation table is used internally to support
-older microorganism IDs, so users will not notice this
-difference.
-
New function mo_rank() for the taxonomic rank
-(genus, species, infraspecies, etc.)
-
New function mo_url() to get the direct URL of a
-species from the Catalogue of Life
+
Due to this change, some mo codes changed (e.g. Streptococcus changed from B_STRPTC to B_STRPT). A translation table is used internally to support older microorganism IDs, so users will not notice this difference.
+
New function mo_rank() for the taxonomic rank (genus, species, infraspecies, etc.)
+
New function mo_url() to get the direct URL of a species from the Catalogue of Life
-
Support for data from WHONET
-and EARS-Net
-(European Antimicrobial Resistance Surveillance Network):
-
Exported files from WHONET can be read and used in this package. For
-functions like first_isolate() and
-eucast_rules(), all arguments will be filled in
-automatically.
-
This package now knows all antibiotic abbrevations by EARS-Net
-(which are also being used by WHONET) - the antibiotics
-data set now contains a column ears_net.
-
The function as.mo() now knows all WHONET species
-abbreviations too, because almost 2,000 microbial abbreviations were
-added to the microorganisms.codes data set.
+
Support for data from WHONET and EARS-Net (European Antimicrobial Resistance Surveillance Network):
+
Exported files from WHONET can be read and used in this package. For functions like first_isolate() and eucast_rules(), all arguments will be filled in automatically.
+
This package now knows all antibiotic abbrevations by EARS-Net (which are also being used by WHONET) - the antibiotics data set now contains a column ears_net.
+
The function as.mo() now knows all WHONET species abbreviations too, because almost 2,000 microbial abbreviations were added to the microorganisms.codes data set.
-
New filters for antimicrobial classes. Use these functions to
-filter isolates on results in one of more antibiotics from a specific
-class:
+
New filters for antimicrobial classes. Use these functions to filter isolates on results in one of more antibiotics from a specific class:
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:
+
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:
septic_patients%>%filter_glycopeptides(result ="R")
@@ -2228,8 +1350,7 @@ abbreviations, codes or official names found in the
# Filtering on glycopeptide antibacterials: all of `vanc` and `teic` is R
-
All ab_* functions are deprecated and replaced by
-atc_* functions:
+
All ab_* functions are deprecated and replaced by atc_* functions:
ab_property->atc_property()
@@ -2239,42 +1360,18 @@ abbreviations, codes or official names found in the
ab_certe->atc_certe()ab_umcg->atc_umcg()ab_tradenames->atc_tradenames()
-
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.
+
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.
New function guess_ab_col() to find an antibiotic
-column in a table
-
New function mo_failures() to review values that
-could not be coerced to a valid MO code, using as.mo().
-This latter function will now only show a maximum of 10 uncoerced values
-and will refer to mo_failures().
-
New function mo_uncertainties() to review values
-that could be coerced to a valid MO code using as.mo(), but
-with uncertainty.
-
New function mo_renamed() to get a list of all
-returned values from as.mo() that have had taxonomic
-renaming
-
New function age() to calculate the (patients) age
-in years
-
New function age_groups() to split ages into custom
-or predefined groups (like children or elderly). This allows for easier
-demographic AMR data analysis per age group.
New function guess_ab_col() to find an antibiotic column in a table
+
New function mo_failures() to review values that could not be coerced to a valid MO code, using as.mo(). This latter function will now only show a maximum of 10 uncoerced values and will refer to mo_failures().
+
New function mo_uncertainties() to review values that could be coerced to a valid MO code using as.mo(), but with uncertainty.
+
New function mo_renamed() to get a list of all returned values from as.mo() that have had taxonomic renaming
+
New function age() to calculate the (patients) age in years
+
New function age_groups() to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic AMR data analysis per age group.
Functions filter_first_isolate() and
-filter_first_weighted_isolate() to shorten and fasten
-filtering on data sets with antimicrobial results, e.g.:
+
Functions filter_first_isolate() and filter_first_weighted_isolate() to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
septic_patients%>%filter_first_isolate(...)
@@ -2298,50 +1393,26 @@ filtering on data sets with antimicrobial results, e.g.:
filter(only_firsts==TRUE)%>%select(-only_firsts)
-
New function availability() to check the number of
-available (non-empty) results in a data.frame
-
New vignettes about how to conduct AMR analysis, predict
-antimicrobial resistance, use the G-test and more. These are
-also available (and even easier readable) on our website: https://msberends.gitlab.io/AMR.
+
New function availability() to check the number of available (non-empty) results in a data.frame
+
New vignettes about how to conduct AMR analysis, predict antimicrobial resistance, use the G-test and more. These are also available (and even easier readable) on our website: https://msberends.gitlab.io/AMR.
Updated EUCAST Clinical breakpoints to version 9.0 of 1
-January 2019, the data set septic_patients now reflects
-these changes
-
Fixed a critical bug where some rules that depend on previous
-applied rules would not be applied adequately
-
Emphasised in manual that penicillin is meant as benzylpenicillin
-(ATC J01CE01)
-
New info is returned when running this function, stating exactly
-what has been changed or added. Use
-eucast_rules(..., verbose = TRUE) to get a data set with
-all changed per bug and drug combination.
+
Updated EUCAST Clinical breakpoints to version 9.0 of 1 January 2019, the data set septic_patients now reflects these changes
+
Fixed a critical bug where some rules that depend on previous applied rules would not be applied adequately
+
Emphasised in manual that penicillin is meant as benzylpenicillin (ATC J01CE01)
+
New info is returned when running this function, stating exactly what has been changed or added. Use eucast_rules(..., verbose = TRUE) to get a data set with all changed per bug and drug combination.
-
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
Removed columns atc_group1_nl and
-atc_group2_nl from the antibiotics 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
+
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
Removed columns atc_group1_nl and atc_group2_nl from the antibiotics 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
Now handles incorrect spelling, like i instead of
-y and f instead of ph:
+
Now handles incorrect spelling, like i instead of y and f instead of ph:
# mo_fullname() uses as.mo() internally
@@ -2353,10 +1424,7 @@ will be removed in a future version.
#> [1] "Staphylococcus kloosii"
-
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.
+
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.
Using as.mo(..., allow_uncertain = 3) could lead to very
-unreliable results.
+
Using as.mo(..., allow_uncertain = 3) could lead to very unreliable results.
-
Implemented the latest publication of Becker et al.
-(2019), for categorising coagulase-negative
-Staphylococci
-
All microbial IDs that found are now saved to a local file
-~/.Rhistory_mo. Use the new function
-clean_mo_history() to delete this file, which resets the
-algorithms.
-
Incoercible results will now be considered ‘unknown’, MO code
-UNKNOWN. On foreign systems, properties of these will be
-translated to all languages already previously supported: German, Dutch,
-French, Italian, Spanish and Portuguese.
+
Implemented the latest publication of Becker et al. (2019), for categorising coagulase-negative Staphylococci
+
All microbial IDs that found are now saved to a local file ~/.Rhistory_mo. Use the new function clean_mo_history() to delete this file, which resets the algorithms.
+
Incoercible results will now be considered ‘unknown’, MO code UNKNOWN. On foreign systems, properties of these will be translated to all languages already previously supported: German, Dutch, French, Italian, Spanish and Portuguese.
Fix for vector containing only empty values
Finds better results when input is in other languages
Better handling for subspecies
-
Better handling for Salmonellae, especially the ‘city
-like’ serovars like Salmonella London
-
Understanding of highly virulent E. coli strains like
-EIEC, EPEC and STEC
-
There will be looked for uncertain results at default - these
-results will be returned with an informative warning
-
Manual (help page) now contains more info about the
-algorithms
-
Progress bar will be shown when it takes more than 3 seconds to
-get results
+
Better handling for Salmonellae, especially the ‘city like’ serovars like Salmonella London
+
Understanding of highly virulent E. coli strains like EIEC, EPEC and STEC
+
There will be looked for uncertain results at default - these results will be returned with an informative warning
+
Manual (help page) now contains more info about the algorithms
+
Progress bar will be shown when it takes more than 3 seconds to get results
Support for formatted console text
Console will return the percentage of uncoercable input
Fixed a bug where distances between dates would not be calculated
-right - in the septic_patients data set this yielded a
-difference of 0.15% more isolates
-
Will now use a column named like “patid” for the patient ID
-(argument col_patientid), when this argument was left
-blank
-
Will now use a column named like “key(…)ab” or “key(…)antibiotics”
-for the key antibiotics (argument col_keyantibiotics()),
-when this argument was left blank
-
Removed argument output_logical, the function will now
-always return a logical value
-
Renamed argument filter_specimen to
-specimen_group, although using filter_specimen
-will still work
+
Fixed a bug where distances between dates would not be calculated right - in the septic_patients data set this yielded a difference of 0.15% more isolates
+
Will now use a column named like “patid” for the patient ID (argument col_patientid), when this argument was left blank
+
Will now use a column named like “key(…)ab” or “key(…)antibiotics” for the key antibiotics (argument col_keyantibiotics()), when this argument was left blank
+
Removed argument output_logical, the function will now always return a logical value
+
Renamed argument filter_specimen to specimen_group, although using filter_specimen will still work
-
A note to the manual pages of the portion functions,
-that low counts can influence the outcome and that the
-portion functions may camouflage this, since they only
-return the portion (albeit being dependent on the minimum
-argument)
-
Merged data sets microorganisms.certe and
-microorganisms.umcg into
-microorganisms.codes
+
A note to the manual pages of the portion functions, that low counts can influence the outcome and that the portion functions may camouflage this, since they only return the portion (albeit being dependent on the minimum argument)
+
Merged data sets microorganisms.certe and microorganisms.umcg into microorganisms.codes
-
Function mo_taxonomy() now contains the kingdom
-too
-
Reduce false positives for is.rsi.eligible() using the
-new threshold argument
Support for tidyverse quasiquotation! Now you can create
-frequency tables of function outcomes:
+
Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:
# Determine genus of microorganisms (mo) in `septic_patients` data set:# OLD WAYseptic_patients%>%mutate(genus =mo_genus(mo))%>%
- freq(genus)
+ freq(genus)# NEW WAYseptic_patients%>%
- freq(mo_genus(mo))
+ freq(mo_genus(mo))# Even supports grouping variables:septic_patients%>%group_by(gender)%>%
- freq(mo_genus(mo))
Fix for as.mic() to support more values ending in (several) zeroes
+
if using different lengths of pattern and x in %like%, it will now return the call
Other
-
Updated licence text to emphasise GPL 2.0 and that this is an R
-package.
+
Updated licence text to emphasise GPL 2.0 and that this is an R package.
@@ -2502,64 +1519,33 @@ package.
New
Repository moved to GitLab
-
Function count_all to get all available isolates (that
-like all portion_* and count_* functions also
-supports summarise and group_by), the old
-n_rsi is now an alias of count_all
+
Function count_all to get all available isolates (that like all portion_* and count_* functions also supports summarise and group_by), the old n_rsi is now an alias of count_all
-
Function get_locale to determine language for
-language-dependent output for some mo_* functions. This is
-now the default value for their language argument, by which
-the system language will be used at default.
-
Data sets microorganismsDT,
-microorganisms.prevDT, microorganisms.unprevDT
-and microorganisms.oldDT to improve the speed of
-as.mo. They are for reference only, since they are
-primarily for internal use of as.mo.
-
Function read.4D to read from the 4D database of the
-MMB department of the UMCG
-
Functions mo_authors and mo_year to get
-specific values about the scientific reference of a taxonomic entry
+
Function get_locale to determine language for language-dependent output for some mo_* functions. This is now the default value for their language argument, by which the system language will be used at default.
+
Data sets microorganismsDT, microorganisms.prevDT, microorganisms.unprevDT and microorganisms.oldDT to improve the speed of as.mo. They are for reference only, since they are primarily for internal use of as.mo.
+
Function read.4D to read from the 4D database of the MMB department of the UMCG
+
Functions mo_authors and mo_year to get specific values about the scientific reference of a taxonomic entry
Changed
-
Functions MDRO, BRMO, MRGN
-and EUCAST_exceptional_phenotypes were renamed to
-mdro, brmo, mrgn and
-eucast_exceptional_phenotypes
-
EUCAST_rules was renamed to
-eucast_rules, the old function still exists as a deprecated
-function
+
Functions MDRO, BRMO, MRGN and EUCAST_exceptional_phenotypes were renamed to mdro, brmo, mrgn and eucast_exceptional_phenotypes
+
EUCAST_rules was renamed to eucast_rules, the old function still exists as a deprecated function
New argument rules to specify which rules should be applied (expert rules, breakpoints, others or all)
+
New argument verbose which can be set to TRUE to get very specific messages about which columns and rows were affected
+
Better error handling when rules cannot be applied (i.e. new values could not be inserted)
+
The number of affected values will now only be measured once per row/column combination
+
Data set septic_patients now reflects these changes
+
Added argument pipe for piperacillin (J01CA12), also to the mdro function
Small fixes to EUCAST clinical breakpoint rules
-
Added column kingdom to the microorganisms data set,
-and function mo_kingdom to look up values
-
Tremendous speed improvement for as.mo (and
-subsequently all mo_* functions), as empty values wil be
-ignored a priori
-
Fewer than 3 characters as input for as.mo will
-return NA
+
Added column kingdom to the microorganisms data set, and function mo_kingdom to look up values
+
Tremendous speed improvement for as.mo (and subsequently all mo_* functions), as empty values wil be ignored a priori
+
Fewer than 3 characters as input for as.mo will return NA
-
Function as.mo (and all mo_* wrappers)
-now supports genus abbreviations with “species” attached
+
Function as.mo (and all mo_* wrappers) now supports genus abbreviations with “species” attached
as.mo("E. species")# B_ESCHR
@@ -2567,69 +1553,45 @@ now supports genus abbreviations with “species” attached
as.mo("S. spp")# B_STPHYmo_fullname("S. species")# "Staphylococcus species"
-
Added argument 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
-
Added argument also_single_tested for
-portion_* and count_* functions to also
-include cases where not all antibiotics were tested but at least one of
-the tested antibiotics includes the target antimicribial interpretation,
-see ?portion
-
Using portion_* functions now throws a warning when
-total available isolate is below argument minimum
-
Functions as.mo, as.rsi,
-as.mic, as.atc and freq will not
-set package name as attribute anymore
+
Added argument 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
+
Added argument also_single_tested for portion_* and count_* functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see ?portion
+
Using portion_* functions now throws a warning when total available isolate is below argument minimum
+
Functions as.mo, as.rsi, as.mic, as.atc and freq will not set package name as attribute anymore
Now prints in markdown at default in non-interactive
-sessions
-
No longer adds the factor level column and sorts factors on count
-again
+
Now prints in markdown at default in non-interactive sessions
+
No longer adds the factor level column and sorts factors on count again
Support for class difftime
-
New argument na, to choose which character to print
-for empty values
-
New argument header to turn the header info off
-(default when markdown = TRUE)
-
New argument title to manually setbthe title of the
-frequency table
+
New argument na, to choose which character to print for empty values
+
New argument header to turn the header info off (default when markdown = TRUE)
+
New argument title to manually setbthe title of the frequency table
-
first_isolate now tries to find columns to use as
-input when arguments are left blank
-
Improvements for MDRO algorithm (function
-mdro)
-
Data set septic_patients is now a
-data.frame, not a tibble anymore
-
Removed diacritics from all authors (columns
-microorganisms$ref and microorganisms.old$ref)
-to comply with CRAN policy to only allow ASCII characters
+
first_isolate now tries to find columns to use as input when arguments are left blank
+
Improvements for MDRO algorithm (function mdro)
+
Data set septic_patients is now a data.frame, not a tibble anymore
+
Removed diacritics from all authors (columns microorganisms$ref and microorganisms.old$ref) to comply with CRAN policy to only allow ASCII characters
Fix for mo_property not working properly
-
Fix for eucast_rules where some Streptococci would
-become ceftazidime R in EUCAST rule 4.5
-
Support for named vectors of class mo, useful for
-top_freq()
-
ggplot_rsi and scale_y_percent have
-breaks argument
+
Fix for eucast_rules where some Streptococci would become ceftazidime R in EUCAST rule 4.5
+
Support for named vectors of class mo, useful for top_freq()
+
ggplot_rsi and scale_y_percent have breaks argument
AI improvements for as.mo:
@@ -2646,24 +1608,16 @@ become ceftazidime R in EUCAST rule 4.5
Fix for join functions
-
Speed improvement for is.rsi.eligible, now 15-20
-times faster
-
In g.test, when sum(x) is below 1000 or
-any of the expected values is below 5, Fisher’s Exact Test will be
-suggested
-
ab_name will try to fall back on as.atc
-when no results are found
+
Speed improvement for is.rsi.eligible, now 15-20 times faster
+
In g.test, when sum(x) is below 1000 or any of the expected values is below 5, Fisher’s Exact Test will be suggested
+
ab_name will try to fall back on as.atc when no results are found
Removed the addin to view data sets
-
Percentages will now will rounded more logically (e.g. in
-freq function)
+
Percentages will now will rounded more logically (e.g. in freq function)
Other
-
New dependency on package crayon, to support formatted
-text in the console
-
Dependency tidyr is now mandatory (went to
-Import field) since portion_df and
-count_df rely on it
+
New dependency on package crayon, to support formatted text in the console
+
Dependency tidyr is now mandatory (went to Import field) since portion_df and count_df rely on it
Updated vignettes to comply with README
@@ -2671,32 +1625,18 @@ text in the console
AMR 0.4.02018-10-01
New
-
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.
+
The data set microorganisms now contains all microbial taxonomic data from ITIS (kingdoms Bacteria, Fungi and Protozoa), the Integrated Taxonomy Information System, available via https://itis.gov. The data set now contains more than 18,000 microorganisms with all known bacteria, fungi and protozoa according ITIS with genus, species, subspecies, family, order, class, phylum and subkingdom. The new data set microorganisms.old contains all previously known taxonomic names from those kingdoms.
-
New functions based on the existing function
-mo_property:
Functions count_R, count_IR,
-count_I, count_SI and count_S to
-selectively count resistant or susceptible isolates
-
Extra function count_df (which works like
-portion_df) to get all counts of S, I and R of a data set
-with antibiotic columns, with support for grouped variables
+
Functions count_R, count_IR, count_I, count_SI and count_S to selectively count resistant or susceptible isolates
+
Extra function count_df (which works like portion_df) to get all counts of S, I and R of a data set with antibiotic columns, with support for grouped variables
-
Function is.rsi.eligible to check for columns that
-have valid antimicrobial results, but do not have the rsi
-class yet. Transform the columns of your raw data with:
-data %>% mutate_if(is.rsi.eligible, as.rsi)
+
Function is.rsi.eligible to check for columns that have valid antimicrobial results, but do not have the rsi class yet. Transform the columns of your raw data with: data %>% mutate_if(is.rsi.eligible, as.rsi)
-
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:
+
Functions as.mo and is.mo as replacements for as.bactid and is.bactid (since the microoganisms data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The as.mo function determines microbial IDs using intelligent rules:
as.mo("E. coli")
@@ -2742,8 +1669,7 @@ microbial IDs using intelligent rules:
# [1] B_STPHY_AURas.mo("S group A")# [1] B_STRPTC_GRA
-
And with great speed too - on a quite regular Linux server from 2007
-it takes us less than 0.02 seconds to transform 25,000 items:
+
And with great speed too - on a quite regular Linux server from 2007 it takes us less than 0.02 seconds to transform 25,000 items:
thousands_of_E_colis<-rep("E. coli", 25000)
@@ -2752,44 +1678,27 @@ it takes us less than 0.02 seconds to transform 25,000 items:
# min median max neval# 0.01817717 0.01843957 0.03878077 100
-
Added argument reference_df for as.mo,
-so users can supply their own microbial IDs, name or codes as a
-reference table
+
Added argument 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:
-
Column names inputs of EUCAST_rules,
-first_isolate and key_antibiotics
+
Renamed all previous references to bactid to mo, like:
+
Column names inputs of EUCAST_rules, first_isolate and key_antibiotics
-
Column names of datasets microorganisms and
-septic_patients
+
Column names of datasets microorganisms and septic_patients
-
All old syntaxes will still work with this version, but will throw
-warnings
+
All old syntaxes will still work with this version, but will throw warnings
-
Function labels_rsi_count to print datalabels on a
-RSI ggplot2 model
-
Functions as.atc and is.atc to
-transform/look up antibiotic ATC codes as defined by the WHO. The
-existing function guess_atc is now an alias of
-as.atc.
-
Function ab_property and its aliases:
-ab_name, ab_tradenames, ab_certe,
-ab_umcg and ab_trivial_nl
+
Function labels_rsi_count to print datalabels on a RSI ggplot2 model
+
Functions as.atc and is.atc to transform/look up antibiotic ATC codes as defined by the WHO. The existing function guess_atc is now an alias of as.atc.
+
Function ab_property and its aliases: ab_name, ab_tradenames, ab_certe, ab_umcg and ab_trivial_nl
Introduction to AMR as a vignette
-
Removed clipboard functions as it violated the CRAN
-policy
-
Renamed septic_patients$sex to
-septic_patients$gender
+
Removed clipboard functions as it violated the CRAN policy
+
Renamed septic_patients$sex to septic_patients$gender
Changed
-
Added three antimicrobial agents to the antibiotics
-data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole
-(D01AC05)
+
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.:
+
Added 163 trade names to the antibiotics data set, it now contains 298 different trade names in total, e.g.:
ab_official("Bactroban")
@@ -2799,24 +1708,14 @@ it now contains 298 different trade names in total, e.g.:
ab_atc(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))# [1] "R01AX06" "J01CA04" "J01FA10" "J01CF05"
-
For first_isolate, rows will be ignored when there’s
-no species available
-
Function ratio is now deprecated and will be removed
-in a future release, as it is not really the scope of this
-package
-
Fix for as.mic for values ending in zeroes after a
-real number
-
Small fix where B. fragilis would not be found in the
-microorganisms.umcg data set
-
Added prevalence column to the
-microorganisms data set
-
Added arguments minimum and as_percent
-to portion_df
+
For first_isolate, rows will be ignored when there’s no species available
+
Function ratio is now deprecated and will be removed in a future release, as it is not really the scope of this package
+
Fix for as.mic for values ending in zeroes after a real number
+
Small fix where B. fragilis would not be found in the microorganisms.umcg data set
+
Added prevalence column to the microorganisms data set
+
Added arguments minimum and as_percent to portion_df
-
Support for quasiquotation in the functions series
-count_* and portions_*, and
-n_rsi. This allows to check for more than 2 vectors or
-columns.
+
Support for quasiquotation in the functions series count_* and portions_*, and n_rsi. This allows to check for more than 2 vectors or columns.
-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.
-
New function portion_df to get all portions of S, I and
-R of a data set with antibiotic columns, with support for grouped
-variables
+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.
+
New function portion_df to get all portions of S, I and R of a data set with antibiotic columns, with support for grouped variables
-BREAKING: the methodology for determining first
-weighted isolates was changed. The antibiotics that are compared between
-isolates (call key antibiotics) to include more first isolates
-(afterwards called first weighted isolates) are now as follows:
-
+BREAKING: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call key antibiotics) to include more first isolates (afterwards called first weighted isolates) are now as follows:
+
New functions as.bactid and is.bactid to
-transform/ look up microbial ID’s.
-
The existing function guess_bactid is now an alias of
-as.bactid
+
New functions as.bactid and is.bactid to transform/ look up microbial ID’s.
+
The existing function guess_bactid is now an alias of as.bactid
-
New Becker classification for Staphylococcus to categorise
-them into Coagulase Negative Staphylococci (CoNS) and Coagulase
-Positve Staphylococci (CoPS)
-
New Lancefield classification for Streptococcus to
-categorise them into Lancefield groups
+
New Becker classification for Staphylococcus to categorise them into Coagulase Negative Staphylococci (CoNS) and Coagulase Positve Staphylococci (CoPS)
+
New Lancefield classification for Streptococcus to categorise them into Lancefield groups
-
For convience, new descriptive statistical functions
-kurtosis and skewness that are lacking in base
-R - they are generic functions and have support for vectors, data.frames
-and matrices
-
Function g.test to perform the X2
-distributed G-test, which
-use is the same as chisq.test
+
For convience, new descriptive statistical functions kurtosis and skewness that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
+
Function g.test to perform the X2 distributed G-test, which use is the same as chisq.test
-Function ratio to transform a vector of values to
-a preset ratio
-
For example:
-ratio(c(10, 500, 10), ratio = "1:2:1") would return
-130, 260, 130
+Function ratio to transform a vector of values to a preset ratio
+
For example: ratio(c(10, 500, 10), ratio = "1:2:1") would return 130, 260, 130
-
Support for Addins menu in RStudio to quickly insert
-%in% or %like% (and give them keyboard
-shortcuts), or to view the datasets that come with this package
-
Function p.symbol to transform p values to their
-related symbols:
-0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
Support for Addins menu in RStudio to quickly insert %in% or %like% (and give them keyboard shortcuts), or to view the datasets that come with this package
+
Function p.symbol to transform p values to their related symbols: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
Functions clipboard_import and
-clipboard_export as helper functions to quickly copy and
-paste from/to software like Excel and SPSS. These functions use the
-clipr package, but are a little altered to also support
-headless Linux servers (so you can use it in RStudio Server)
+
Functions clipboard_import and clipboard_export as helper functions to quickly copy and paste from/to software like Excel and SPSS. These functions use the clipr package, but are a little altered to also support headless Linux servers (so you can use it in RStudio Server)
New for frequency tables (function freq):
A vignette to explain its usage
-
Support for rsi (antimicrobial resistance) to use as
-input
-
Support for table to use as input:
-freq(table(x, y))
+
Support for rsi (antimicrobial resistance) to use as input
+
Support for table to use as input: freq(table(x, y))
-
Support for existing functions hist and
-plot to use a frequency table as input:
-hist(freq(df$age))
+
Support for existing functions hist and plot to use a frequency table as input: hist(freq(df$age))
-
Support for as.vector, as.data.frame,
-as_tibble and format
+
Support for as.vector, as.data.frame, as_tibble and format
-
Support for quasiquotation: freq(mydata, mycolumn) is
-the same as mydata %>% freq(mycolumn)
+
Support for quasiquotation: freq(mydata, mycolumn) is the same as mydata %>% freq(mycolumn)
-
Function top_freq function to return the top/below
-n items as vector
-
Header of frequency tables now also show Mean Absolute Deviaton
-(MAD) and Interquartile Range (IQR)
-
Possibility to globally set the default for the amount of items to
-print, with options(max.print.freq = n) where n is
-your preset value
+
Function top_freq function to return the top/below n items as vector
+
Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR)
+
Possibility to globally set the default for the amount of items to print, with options(max.print.freq = n) where n is your preset value
Changed
-
Improvements for forecasting with resistance_predict
-and added more examples
+
Improvements for forecasting with resistance_predict and added more examples
More antibiotics added as arguments for EUCAST rules
-
Updated version of the septic_patients data set to
-better reflect the reality
-
Pretty printing for tibbles removed as it is not really the scope of
-this package
-
Printing of mic and rsi classes now
-returns all values - use freq to check distributions
-
Improved speed of key antibiotics comparison for determining first
-isolates
-
Column names for the key_antibiotics function are now
-generic: 6 for broadspectrum ABs, 6 for Gram-positive specific and 6 for
-Gram-negative specific ABs
+
Updated version of the septic_patients data set to better reflect the reality
+
Pretty printing for tibbles removed as it is not really the scope of this package
+
Printing of mic and rsi classes now returns all values - use freq to check distributions
+
Improved speed of key antibiotics comparison for determining first isolates
+
Column names for the key_antibiotics function are now generic: 6 for broadspectrum ABs, 6 for Gram-positive specific and 6 for Gram-negative specific ABs
Speed improvement for the abname function
%like% now supports multiple patterns
-
Frequency tables are now actual data.frames with
-altered console printing to make it look like a frequency table. Because
-of this, the argument toConsole is not longer needed.
-
Fix for freq where the class of an item would be
-lost
-
Small translational improvements to the septic_patients
-dataset and the column bactid now has the new class
-"bactid"
+
Frequency tables are now actual data.frames with altered console printing to make it look like a frequency table. Because of this, the argument toConsole is not longer needed.
+
Fix for freq where the class of an item would be lost
+
Small translational improvements to the septic_patients dataset and the column bactid now has the new class "bactid"
-
Small improvements to the microorganisms dataset
-(especially for Salmonella) and the column bactid
-now has the new class "bactid"
+
Small improvements to the microorganisms dataset (especially for Salmonella) and the column bactid now has the new class "bactid"
-
Combined MIC/RSI values will now be coerced by the rsi
-and mic functions:
+
Combined MIC/RSI values will now be coerced by the rsi and mic functions:
as.rsi("<=0.002; S") will return S
-as.mic("<=0.002; S") will return
-<=0.002
+as.mic("<=0.002; S") will return <=0.002
-
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.
-
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
-
Support for empty values and for 1 or 2 columns as input for
-guess_bactid (now called as.bactid)
-
So
-yourdata %>% select(genus, species) %>% as.bactid()
-now also works
+
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.
+
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
+
Support for empty values and for 1 or 2 columns as input for guess_bactid (now called as.bactid)
+
So yourdata %>% select(genus, species) %>% as.bactid() now also works
Other small fixes
Other
-
Added integration tests (check if everything works as expected) for
-all releases of R 3.1 and higher
+
Added integration tests (check if everything works as expected) for all releases of R 3.1 and higher
diff --git a/docs/reference/ab_from_text.html b/docs/reference/ab_from_text.html
index a7dc06a5..19d04b41 100644
--- a/docs/reference/ab_from_text.html
+++ b/docs/reference/ab_from_text.html
@@ -275,13 +275,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/ab_property.html b/docs/reference/ab_property.html
index c74aa078..e43a281c 100644
--- a/docs/reference/ab_property.html
+++ b/docs/reference/ab_property.html
@@ -344,13 +344,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/age.html b/docs/reference/age.html
index f1a27b59..3bc80b34 100644
--- a/docs/reference/age.html
+++ b/docs/reference/age.html
@@ -226,13 +226,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/age_groups.html b/docs/reference/age_groups.html
index d4578dbb..c3129f13 100644
--- a/docs/reference/age_groups.html
+++ b/docs/reference/age_groups.html
@@ -250,13 +250,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
The ab_class() function can be used to filter/select on a manually defined antibiotic class. It searches for results in the antibiotics data set within the columns group, atc_group1 and atc_group2.
The ab_selector() function can be used to internally filter the antibiotics data set on any results, see Examples. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
The administrable_per_os() and administrable_iv() functions also rely on the antibiotics data set - antibiotic columns will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in the antibiotics data set.
-
The not_intrinsic_resistant() function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of E. coli and K. pneumoniae and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021) to determine intrinsic resistance, using the eucast_rules() function internally. Because of this determination, this function is quite slow in terms of performance.
+
The not_intrinsic_resistant() function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of E. coli and K. pneumoniae and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021) to determine intrinsic resistance, using the eucast_rules() function internally. Because of this determination, this function is quite slow in terms of performance.
Full list of supported (antibiotic) classes
@@ -430,13 +430,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/as.mic.html b/docs/reference/as.mic.html
index 58914b73..0daf4317 100644
--- a/docs/reference/as.mic.html
+++ b/docs/reference/as.mic.html
@@ -273,13 +273,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/as.mo.html b/docs/reference/as.mo.html
index 0b3375d3..80bc9a5f 100644
--- a/docs/reference/as.mo.html
+++ b/docs/reference/as.mo.html
@@ -377,13 +377,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/as.rsi.html b/docs/reference/as.rsi.html
index 7fa7fad6..7bf8fa72 100644
--- a/docs/reference/as.rsi.html
+++ b/docs/reference/as.rsi.html
@@ -231,7 +231,7 @@
conserve_capped_values
a logical to indicate that MIC values starting with ">" (but not ">=") must always return "R" , and that MIC values starting with "<" (but not "<=") must always return "S"
a data.frame to be used for interpretation, which defaults to the rsi_translation data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the rsi_translation data set (same column names and column types). Please note that the guideline argument will be ignored when reference_data is manually set.
col_mo
@@ -334,7 +334,7 @@ The lifecycle of this function is stable# \donttest{
if(require("skimr")){# class <rsi> supported in skim() too:
- skim(example_isolates)
+ skim(example_isolates)}# }# For INTERPRETING disk diffusion and MIC values -----------------------
@@ -423,13 +423,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/atc_online.html b/docs/reference/atc_online.html
index 599ecd4e..05b4b79e 100644
--- a/docs/reference/atc_online.html
+++ b/docs/reference/atc_online.html
@@ -257,13 +257,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/availability.html b/docs/reference/availability.html
index 0aae951d..c726909d 100644
--- a/docs/reference/availability.html
+++ b/docs/reference/availability.html
@@ -216,13 +216,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/bug_drug_combinations.html b/docs/reference/bug_drug_combinations.html
index f363023c..a0eb2142 100644
--- a/docs/reference/bug_drug_combinations.html
+++ b/docs/reference/bug_drug_combinations.html
@@ -265,13 +265,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/catalogue_of_life.html b/docs/reference/catalogue_of_life.html
index b628f62b..b04f5ae6 100644
--- a/docs/reference/catalogue_of_life.html
+++ b/docs/reference/catalogue_of_life.html
@@ -235,13 +235,11 @@ Function as.mo() to use the data for intel
diff --git a/docs/reference/catalogue_of_life_version.html b/docs/reference/catalogue_of_life_version.html
index a06ce87b..de8d8437 100644
--- a/docs/reference/catalogue_of_life_version.html
+++ b/docs/reference/catalogue_of_life_version.html
@@ -200,13 +200,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/count.html b/docs/reference/count.html
index 9c6a5887..2b39f4a3 100644
--- a/docs/reference/count.html
+++ b/docs/reference/count.html
@@ -358,13 +358,11 @@ A microorganism is categorised as Susceptible, Increased exposure when
diff --git a/docs/reference/custom_eucast_rules.html b/docs/reference/custom_eucast_rules.html
index eb4bc8cd..7da86cd1 100644
--- a/docs/reference/custom_eucast_rules.html
+++ b/docs/reference/custom_eucast_rules.html
@@ -301,13 +301,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/g.test.html b/docs/reference/g.test.html
index 72160550..2723bd3f 100644
--- a/docs/reference/g.test.html
+++ b/docs/reference/g.test.html
@@ -323,13 +323,11 @@ The lifecycle of this function is questioni
diff --git a/docs/reference/get_episode.html b/docs/reference/get_episode.html
index adf012be..f2f3c229 100644
--- a/docs/reference/get_episode.html
+++ b/docs/reference/get_episode.html
@@ -278,13 +278,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/ggplot_pca.html b/docs/reference/ggplot_pca.html
index 30606c55..aac9ad2d 100644
--- a/docs/reference/ggplot_pca.html
+++ b/docs/reference/ggplot_pca.html
@@ -304,13 +304,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/ggplot_rsi.html b/docs/reference/ggplot_rsi.html
index 0dc16eef..941f6578 100644
--- a/docs/reference/ggplot_rsi.html
+++ b/docs/reference/ggplot_rsi.html
@@ -395,13 +395,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/guess_ab_col.html b/docs/reference/guess_ab_col.html
index 0eb75062..1e61bfba 100644
--- a/docs/reference/guess_ab_col.html
+++ b/docs/reference/guess_ab_col.html
@@ -243,13 +243,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
These functions are meant to get taxonomically valid properties of
-microorganisms from any input. Use mo_source() to teach
-this package how to translate your own codes to valid microorganism
-codes.
+
These functions are meant to get taxonomically valid properties of microorganisms from any input. Use mo_source() to teach this package how to translate your own codes to valid microorganism codes.
User-Defined Reference Data Set for Microorganisms
Preparing data: antibiotics
-
Use these functions to get valid properties of antibiotics from any
-input or to clean your input. You can even retrieve drug names and doses
-from clinical text records, using ab_from_text().
+
Use these functions to get valid properties of antibiotics from any input or to clean your input. You can even retrieve drug names and doses from clinical text records, using ab_from_text().
With as.mic() and as.disk() you can
-transform your raw input to valid MIC or disk diffusion values. Use
-as.rsi() for cleaning raw data to let it only contain “R”,
-“I” and “S”, or to interpret MIC or disk diffusion values as R/SI based
-on the lastest EUCAST and CLSI guidelines. Afterwards, you can extend
-antibiotic interpretations by applying EUCAST
-rules with eucast_rules().
+
With as.mic() and as.disk() you can transform your raw input to valid MIC or disk diffusion values. Use as.rsi() for cleaning raw data to let it only contain “R”, “I” and “S”, or to interpret MIC or disk diffusion values as R/SI based on the lastest EUCAST and CLSI guidelines. Afterwards, you can extend antibiotic interpretations by applying EUCAST rules with eucast_rules().
Use these function for the analysis part. You can use
-susceptibility() or resistance() on any
-antibiotic column. Be sure to first select the isolates that are
-appropiate for analysis, by using first_isolate() or
-is_new_episode(). You can also filter your data on certain
-resistance in certain antibiotic classes (carbapenems(),
-aminoglycosides()), or determine multi-drug resistant
-microorganisms (MDRO, mdro()).
+
Use these function for the analysis part. You can use susceptibility() or resistance() on any antibiotic column. Be sure to first select the isolates that are appropiate for analysis, by using first_isolate() or is_new_episode(). You can also filter your data on certain resistance in certain antibiotic classes (carbapenems(), aminoglycosides()), or determine multi-drug resistant microorganisms (MDRO, mdro()).
Some pages about our package and its external sources. Be sure to
-read our How To’s for more
-information about how to work with functions in this package.
+
Some pages about our package and its external sources. Be sure to read our How To’s for more information about how to work with functions in this package.
@@ -353,9 +333,7 @@ information about how to work with functions in this package.
Data Set with 500 Isolates - WHONET Example
Other: miscellaneous functions
-
These functions are mostly for internal use, but some of them may
-also be suitable for your analysis. Especially the ‘like’ function can
-be useful: if (x %like% y) {...}.
+
These functions are mostly for internal use, but some of them may also be suitable for your analysis. Especially the ‘like’ function can be useful: if (x %like% y) {...}.
The repository of this AMR package contains a file comprising this data set with full taxonomic and antibiotic names: https://github.com/msberends/AMR/blob/main/data-raw/intrinsic_resistant.txt. This file allows for machine reading EUCAST guidelines about intrinsic resistance, which is almost impossible with the Excel and PDF files distributed by EUCAST. The file is updated automatically.
diff --git a/docs/reference/italicise_taxonomy.html b/docs/reference/italicise_taxonomy.html
index 2d3f8f86..2c5dcf52 100644
--- a/docs/reference/italicise_taxonomy.html
+++ b/docs/reference/italicise_taxonomy.html
@@ -221,13 +221,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/join.html b/docs/reference/join.html
index a08d9d1d..b5430cb3 100644
--- a/docs/reference/join.html
+++ b/docs/reference/join.html
@@ -242,13 +242,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/key_antimicrobials.html b/docs/reference/key_antimicrobials.html
index 640b31e9..e4626ba1 100644
--- a/docs/reference/key_antimicrobials.html
+++ b/docs/reference/key_antimicrobials.html
@@ -308,13 +308,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/kurtosis.html b/docs/reference/kurtosis.html
index 02427b3f..3c02562c 100644
--- a/docs/reference/kurtosis.html
+++ b/docs/reference/kurtosis.html
@@ -210,13 +210,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/lifecycle.html b/docs/reference/lifecycle.html
index f7766476..5eb1aaf3 100644
--- a/docs/reference/lifecycle.html
+++ b/docs/reference/lifecycle.html
@@ -211,13 +211,11 @@ The lifecycle of this function is questioning. This function mi
diff --git a/docs/reference/like.html b/docs/reference/like.html
index f75dc7e2..fc9be9bc 100644
--- a/docs/reference/like.html
+++ b/docs/reference/like.html
@@ -260,13 +260,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (link)
+
The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (link)
guideline = "MRGN"
The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7; doi: 10.1186/s13756-015-0047-6
guideline = "BRMO"
@@ -352,13 +352,11 @@ A microorganism is categorised as Susceptible, Increased exposure when
diff --git a/docs/reference/microorganisms.codes.html b/docs/reference/microorganisms.codes.html
index 96a33477..ca5c110d 100644
--- a/docs/reference/microorganisms.codes.html
+++ b/docs/reference/microorganisms.codes.html
@@ -203,13 +203,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/microorganisms.html b/docs/reference/microorganisms.html
index a9259dc7..2d294306 100644
--- a/docs/reference/microorganisms.html
+++ b/docs/reference/microorganisms.html
@@ -254,13 +254,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/microorganisms.old.html b/docs/reference/microorganisms.old.html
index e0b91c8a..27a6fd2d 100644
--- a/docs/reference/microorganisms.old.html
+++ b/docs/reference/microorganisms.old.html
@@ -210,13 +210,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/mo_matching_score.html b/docs/reference/mo_matching_score.html
index 040c928c..870d327c 100644
--- a/docs/reference/mo_matching_score.html
+++ b/docs/reference/mo_matching_score.html
@@ -231,13 +231,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
Since the top-level of the taxonomy is sometimes referred to as 'kingdom' and sometimes as 'domain', the functions mo_kingdom() and mo_domain() return the exact same results.
The Gram stain - mo_gramstain() - will be determined based on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002, PMID 11837318), who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram-positive, except for members of the class Negativicutes which are Gram-negative. Members of other bacterial phyla are all considered Gram-negative. Species outside the kingdom of Bacteria will return a value NA. Functions mo_is_gram_negative() and mo_is_gram_positive() always return TRUE or FALSE (except when the input is NA or the MO code is UNKNOWN), thus always return FALSE for species outside the taxonomic kingdom of Bacteria.
Determination of yeasts - mo_is_yeast() - will be based on the taxonomic kingdom and class. Budding yeasts are fungi of the phylum Ascomycetes, class Saccharomycetes (also called Hemiascomycetes). True yeasts are aggregated into the underlying order Saccharomycetales. Thus, for all microorganisms that are fungi and member of the taxonomic class Saccharomycetes, the function will return TRUE. It returns FALSE otherwise (except when the input is NA or the MO code is UNKNOWN).
The function mo_url() will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
SNOMED codes - mo_snomed() - are from the US Edition of SNOMED CT from 1 September 2020. See Source and the microorganisms data set for more info.
@@ -433,13 +433,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/mo_source.html b/docs/reference/mo_source.html
index 12ce5775..f3e10490 100644
--- a/docs/reference/mo_source.html
+++ b/docs/reference/mo_source.html
@@ -267,13 +267,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/pca.html b/docs/reference/pca.html
index dfc3f036..3d6f420a 100644
--- a/docs/reference/pca.html
+++ b/docs/reference/pca.html
@@ -266,13 +266,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/plot.html b/docs/reference/plot.html
index fc39d863..7d67ef6b 100644
--- a/docs/reference/plot.html
+++ b/docs/reference/plot.html
@@ -332,13 +332,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/proportion.html b/docs/reference/proportion.html
index 15c1124d..b0347e8c 100644
--- a/docs/reference/proportion.html
+++ b/docs/reference/proportion.html
@@ -381,13 +381,11 @@ A microorganism is categorised as Susceptible, Increased exposure when
diff --git a/docs/reference/random.html b/docs/reference/random.html
index d8c7470c..be5e5dc9 100644
--- a/docs/reference/random.html
+++ b/docs/reference/random.html
@@ -232,13 +232,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.
diff --git a/docs/reference/translate.html b/docs/reference/translate.html
index bfae1c7b..83b711ea 100644
--- a/docs/reference/translate.html
+++ b/docs/reference/translate.html
@@ -237,13 +237,11 @@ The lifecycle of this function is stable
-
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
-Erwin E. A. Hassing.
+
Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.