How to conduct AMR analysis
Matthijs S. Berends
-01 July 2019
+06 August 2019
AMR.RmdNote: 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 01 July 2019.
+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 06 August 2019.
Introduction
@@ -217,21 +210,21 @@As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the tidyverse packages dplyr 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.
library(dplyr)
-library(ggplot2)
-library(AMR)
-
-# (if not yet installed, install with:)
-# install.packages(c("tidyverse", "AMR"))library(dplyr)
+library(ggplot2)
+library(AMR)
+
+# (if not yet installed, install with:)
+# install.packages(c("tidyverse", "AMR"))
@@ -261,58 +254,58 @@
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 first 135 patient IDs are now male, the other 125 are female.
Dates
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.
Microorganisms
For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:
- +Other variables
For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:
- +Put everything together
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 with the prob parameter.
sample_size <- 20000
-data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
- patient_id = sample(patients, size = sample_size, replace = TRUE),
- hospital = sample(hospitals, size = sample_size, replace = TRUE,
- prob = c(0.30, 0.35, 0.15, 0.20)),
- bacteria = sample(bacteria, size = sample_size, replace = TRUE,
- prob = c(0.50, 0.25, 0.15, 0.10)),
- AMX = sample(ab_interpretations, size = sample_size, replace = TRUE,
- prob = c(0.60, 0.05, 0.35)),
- AMC = sample(ab_interpretations, size = sample_size, replace = TRUE,
- prob = c(0.75, 0.10, 0.15)),
- CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
- prob = c(0.80, 0.00, 0.20)),
- GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
- prob = c(0.92, 0.00, 0.08))
- )sample_size <- 20000
+data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
+ patient_id = sample(patients, size = sample_size, replace = TRUE),
+ hospital = sample(hospitals, size = sample_size, replace = TRUE,
+ prob = c(0.30, 0.35, 0.15, 0.20)),
+ bacteria = sample(bacteria, size = sample_size, replace = TRUE,
+ prob = c(0.50, 0.25, 0.15, 0.10)),
+ AMX = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ prob = c(0.60, 0.05, 0.35)),
+ AMC = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ prob = c(0.75, 0.10, 0.15)),
+ CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ prob = c(0.80, 0.00, 0.20)),
+ GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ prob = c(0.92, 0.00, 0.08))
+ )Using the left_join() function from the dplyr package, we can ‘map’ the gender to the patient ID using the patients_table object we created earlier:
The resulting data set contains 20,000 blood culture isolates. With the head() function we can preview the first 6 values of this data set:
For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on the observed years:
-septic_patients %>%
- filter(mo_gramstain(mo, language = NULL) == "Gram-positive") %>%
- resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "linear") %>%
- ggplot_rsi_predict()
-# NOTE: Using column `date` as input for `col_date`.septic_patients %>%
+ filter(mo_gramstain(mo, language = NULL) == "Gram-positive") %>%
+ resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "linear") %>%
+ ggplot_rsi_predict()
+# NOTE: 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:
-
+
@@ -204,16 +197,15 @@
@@ -256,8 +248,8 @@
+(
@@ -204,16 +197,15 @@
- Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life (manual)
- Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines (manual) +
- Determining first isolates to be used for AMR analysis (manual)
- Calculating antimicrobial resistance (tutorial)
- Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) -
- Calculating empirical susceptibility of both mono therapy and combination therapy (tutorial) +
- Calculating (empirical) susceptibility of both mono therapy and combination therapies (tutorial)
- Predicting future antimicrobial resistance using regression models (tutorial)
- Getting properties for any microorganism (like Gram stain, species, genus or family) (manual)
- Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name) (manual)
- Plotting antimicrobial resistance (tutorial) -
- Determining first isolates to be used for AMR analysis (manual)
- Applying EUCAST expert rules (manual) -
- Descriptive statistics: frequency tables, kurtosis and skewness (tutorial)
This package is ready-to-use for a professional environment by specialists in the following fields:
Medical Microbiology
@@ -248,7 +240,7 @@Latest released version
This package is available on the official R network (CRAN), which has a peer-reviewed submission process. Install this package in R with:
- +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 with (precaution: may be unstable):
- +
@@ -296,11 +288,12 @@
WHO Collaborating Centre for Drug Statistics Methodology
This package contains all ~450 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission.
+NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.
Read more about the data from WHOCC in our manual.
+
Use
The data set
-The data set
+The data set
@@ -339,8 +332,6 @@
Calculate the resistance (and even co-resistance) of microbial isolates with the
Plot AMR results with
Predict antimicrobial resistance for the nextcoming years using logistic regression models with the
-Conduct descriptive statistics to enhance base R: calculate
@@ -386,9 +377,7 @@
-
-
-
-
- Create frequency tables
-
-
@@ -232,39 +225,46 @@
-
-
-
-
- Create frequency tables
-
-
diff --git a/docs/reference/as.mic.html b/docs/reference/as.mic.html
index 0e02ee0b9..4764d16bb 100644
--- a/docs/reference/as.mic.html
+++ b/docs/reference/as.mic.html
@@ -80,7 +80,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
-WHONET / EARS-Net
+WHONET / EARS-Net
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.
Read our tutorial about how to work with WHONET data here.
@@ -314,7 +307,7 @@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_PNE” (B stands for Bacteria) and the ID of S. aureus is “B_STPHY_AUR”. The function takes almost any text as input that looks like the name or code of a microorganism like “E. coli”, “esco” or “esccol” and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our benchmarks. Moreover, it can group Staphylococci into coagulase negative and positive (CoNS and CoPS, see source) and can categorise Streptococci into Lancefield groups (like beta-haemolytic Streptococcus Group B, source).
- - Use
as.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 isAMXand the ID of gentamicin isGEN. Theas.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, DDDs and categories of antibiotics. The functionas.atc()will return the ATC code of an antibiotic as defined by the WHO.
+ - 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 isAMXand the ID of gentamicin isGEN. Theas.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”.
mdro() (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.microorganisms contains the complete taxonomic tree of ~65,000 microorganisms. Furthermore, some colloquial names and all Gram stains are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like mo_genus(), mo_family(), mo_gramstain() or even mo_phylum(). As they use as.mo() internally, they also use the same intelligent rules for determination. For example, mo_genus("MRSA") and mo_genus("S. aureus") will both return "Staphylococcus". They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.antibiotics contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. Use functions like ab_name(), ab_group() and ab_tradenames() to look up values. The ab_* functions use as.ab() internally so they support the same intelligent rules to guess the most probable result. For example, ab_name("Fluclox"), ab_name("Floxapen") and ab_name("J01CF05") will all return "Flucloxacillin". These functions can again be used to add new variables to your data.antibiotics contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. The function ab_atc() will return the ATC code of an antibiotic as defined by the WHO. Use functions like ab_name(), ab_group() and ab_tradenames() to look up values. The ab_* functions use as.ab() internally so they support the same intelligent rules to guess the most probable result. For example, ab_name("Fluclox"), ab_name("Floxapen") and ab_name("J01CF05") will all return "Flucloxacillin". These functions can again be used to add new variables to your data.portion_R(), portion_IR(), portion_I(), portion_SI() and portion_S() functions. Similarly, the number of isolates can be determined with the count_R(), count_IR(), count_I(), count_SI() and count_S() functions. All these functions can be used with the dplyr package (e.g. in conjunction with summarise())geom_rsi(), a function made for the ggplot2 packageresistance_predict() functionkurtosis(), skewness() and create frequency tables with freq()
-
diff --git a/docs/news/index.html b/docs/news/index.html
index b2a19f2e7..49872dce0 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -78,7 +78,7 @@
@@ -156,13 +156,6 @@
Get properties of an antibiotic
+
-
-
-
-
- Create frequency tables
-
-
@@ -274,7 +267,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
- -AMR 0.7.1.9008 Unreleased +AMR 0.7.1.9026 Unreleased
+
+
+Breaking
+-
+
- Function
freq()has moved to a new package,clean(CRAN link). Creating frequency tables is actually not the scope of this package (never was) and this function has matured a lot over the last two years. Therefore, a new package was created for data cleaning and checking and it perfectly fits thefreq()function. Thecleanpackage is available on CRAN and will be installed automatically when updating theAMRpackage, that now imports it. In a later stage, theskewness()andkurtosis()functions will be moved to thecleanpackage too.
+
New
-
Additional way to calculate co-resistance, i.e. when using multiple antibiotics as input for
-portion_*functions orcount_*functions. This can be used to determine the empiric susceptibily of a combination therapy. A new parameteronly_all_tested(which defaults toFALSE) replaces the oldalso_single_testedand 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 theportionandcounthelp pages), where the %SI is being determined:+# ------------------------------------------------------------------------- -# only_all_tested = FALSE only_all_tested = TRUE -# Antibiotic Antibiotic ----------------------- ----------------------- -# A B include as include as include as include as -# numerator denominator numerator denominator -# ---------- ---------- ---------- ----------- ---------- ----------- -# S S X X X X -# I S X X X X -# R S X X X X -# not tested S X X - - -# S I X X X X -# I I X X X X -# R I X X X X -# not tested I X X - - -# S R X X X X -# I R X X X X -# R R - X - X -# not tested R - - - - -# S not tested X X - - -# I not tested X X - - -# R not tested - - - - -# not tested not tested - - - - -# -------------------------------------------------------------------------# ------------------------------------------------------------------------- +# only_all_tested = FALSE only_all_tested = TRUE +# Antibiotic Antibiotic ----------------------- ----------------------- +# A B include as include as include as include as +# numerator denominator numerator denominator +# ---------- ---------- ---------- ----------- ---------- ----------- +# S S X X X X +# I S X X X X +# R S X X X X +# not tested S X X - - +# S I X X X X +# I I X X X X +# R I X X X X +# not tested I X X - - +# S R X X X X +# I R X X X X +# R R - X - X +# not tested R - - - - +# S not tested X X - - +# I not tested X X - - +# R not tested - - - - +# not tested not tested - - - - +# -------------------------------------------------------------------------Since this is a major change, usage of the old
also_single_testedwill throw an informative error that it has been replaced byonly_all_tested.
Changed
-
+
- Fixed a bug in
eucast_rules()that caused an error when the input was a specific kind oftibble+ - Removed class
atc- usingas.atc()is now deprecated in favour ofab_atc()and this will return a character, not theatcclass anymore - Removed deprecated functions
abname(),ab_official(),atc_name(),atc_official(),atc_property(),atc_tradenames(),atc_trivial_nl() - Fix and speed improvement for
mo_shortname()
- - Fix for
as.mo()where misspelled input would not be understood
+ - Algorithm improvements for
as.mo(): +-
+
- Some misspelled input were not understood +
- These new trivial names known to the field are now understood: meningococcus, gonococcus, pneumococcus +
- Added support for unknown yeasts and fungi +
+ - Added the newest taxonomic data from the IJSEM journal (now up to date until August 2019)
- Fix for using
mo_*functions where the coercion uncertainties and failures would not be available throughmo_uncertainties()andmo_failures()anymore - Deprecated the
countryparameter ofmdro()in favour of the already existingguidelineparameter to support multiple guidelines within one country
- - Fix for frequency tables when creating one directly on a group (using
group_by())
- - The name of
RIFis now Rifampicin instead of Rifampin
+ - The
nameofRIFis now Rifampicin instead of Rifampin - The
antibioticsdata set is now sorted by name
+ - Using verbose mode with
eucast_rules(..., verbose = TRUE)returns more informative and readable output
+ - Speed improvement for
guess_ab_col()which is now 30 times faster for antibiotic abbreviations
+
@@ -297,14 +307,14 @@
+
Function
-
-
-
- Create frequency tables
-
-
@@ -276,7 +269,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
- -AMR 0.7.1 2019-06-23 +AMR 0.7.1 2019-06-23
@@ -297,14 +307,14 @@
-
Function rsi_df() to transform a data.frame to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combination of the existing functions count_df() and portion_df() to immediately show resistance percentages and number of available isolates:
-
+
-
Support for all scientifically published pathotypes of E. coli to date (that we could find). Supported are:
@@ -322,12 +332,12 @@
- UPEC (Uropathogenic E. coli)
Function rsi_df() to transform a data.frame to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combination of the existing functions count_df() and portion_df() to immediately show resistance percentages and number of available isolates:
Support for all scientifically published pathotypes of E. coli to date (that we could find). Supported are:
@@ -322,12 +332,12 @@All these lead to the microbial ID of E. coli:
-as.mo("UPEC")
-# B_ESCHR_COL
-mo_name("UPEC")
-# "Escherichia coli"
-mo_gramstain("EHEC")
-# "Gram-negative"as.mo("UPEC")
+# B_ESCHR_COL
+mo_name("UPEC")
+# "Escherichia coli"
+mo_gramstain("EHEC")
+# "Gram-negative"mo_info() as an analogy to ab_info(). The mo_info() prints a list with the full taxonomy, authors, and the URL to the online database of a microorganismFunction mo_synonyms() to get all previously accepted taxonomic names of a microorganism
+
@@ -419,21 +429,21 @@ Please age() function gained a new parameter
Removed deprecated functions
-Frequency tables ( Frequency tables (
@@ -443,7 +453,7 @@ Please age_groups(), to let groups of fives and tens end with 100+ instead of 120+
-Fix for Fix for
Fix for
Improved speed of
@@ -479,9 +489,9 @@ Please
+
-
-
-
-
- Create frequency tables
-
-
diff --git a/docs/reference/ab_property.html b/docs/reference/ab_property.html
index 956260632..5972d9884 100644
--- a/docs/reference/ab_property.html
+++ b/docs/reference/ab_property.html
@@ -80,7 +80,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
-
-
-
-
- Create frequency tables
-
-
diff --git a/docs/reference/age.html b/docs/reference/age.html
index af8125076..9c6d191bc 100644
--- a/docs/reference/age.html
+++ b/docs/reference/age.html
@@ -80,7 +80,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
-
-
-
-
- Create frequency tables
-
-
diff --git a/docs/reference/age_groups.html b/docs/reference/age_groups.html
index f1b5113bb..ef94ec085 100644
--- a/docs/reference/age_groups.html
+++ b/docs/reference/age_groups.html
@@ -80,7 +80,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
-
-
-
-
- Create frequency tables
-
-
diff --git a/docs/reference/antibiotics.html b/docs/reference/antibiotics.html
index d396612f1..fbbf2e7f4 100644
--- a/docs/reference/antibiotics.html
+++ b/docs/reference/antibiotics.html
@@ -80,7 +80,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
- -AMR 0.7.0 2019-06-03 +AMR 0.7.0 2019-06-03
@@ -419,21 +429,21 @@ Please age() function gained a new parameter exact to determine ages with decimals
guess_mo(), guess_atc(), EUCAST_rules(), interpretive_reading(), rsi()
freq()):
+freq()):
- speed improvement for microbial IDs
- fixed factor level names for R Markdown
- when all values are unique it now shows a message instead of a warning
-
support for boxplots:
- +
freq() for when all values are NA
+freq() for when all values are NA
first_isolate() for when dates are missingguess_ab_col()
@@ -465,9 +475,9 @@ Please
+ -AMR 0.6.1 2019-03-29 +AMR 0.6.1 2019-03-29
@@ -479,9 +489,9 @@ Please
+
-AMR 0.6.0 2019-03-27
+AMR 0.6.0 2019-03-27
New website!
We’ve got a new website: https://msberends.gitlab.io/AMR (built with the great pkgdown)
@@ -504,7 +514,7 @@ Please 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.
+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
@@ -518,33 +528,33 @@ This data is updated annually - check the included version with the new function
New filters for antimicrobial classes. Use these functions to filter isolates on results in one of more antibiotics from a specific class:
-filter_aminoglycosides()
-filter_carbapenems()
-filter_cephalosporins()
-filter_1st_cephalosporins()
-filter_2nd_cephalosporins()
-filter_3rd_cephalosporins()
-filter_4th_cephalosporins()
-filter_fluoroquinolones()
-filter_glycopeptides()
-filter_macrolides()
-filter_tetracyclines()
+filter_aminoglycosides()
+filter_carbapenems()
+filter_cephalosporins()
+filter_1st_cephalosporins()
+filter_2nd_cephalosporins()
+filter_3rd_cephalosporins()
+filter_4th_cephalosporins()
+filter_fluoroquinolones()
+filter_glycopeptides()
+filter_macrolides()
+filter_tetracyclines()
The antibiotics data set will be searched, after which the input data will be checked for column names with a value in any abbreviations, codes or official names found in the antibiotics data set. For example:
-
+
All ab_* functions are deprecated and replaced by atc_* functions:
-ab_property -> atc_property()
-ab_name -> atc_name()
-ab_official -> atc_official()
-ab_trivial_nl -> atc_trivial_nl()
-ab_certe -> atc_certe()
-ab_umcg -> atc_umcg()
-ab_tradenames -> atc_tradenames()
-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.
+ab_property -> atc_property()
+ab_name -> atc_name()
+ab_official -> atc_official()
+ab_trivial_nl -> atc_trivial_nl()
+ab_certe -> atc_certe()
+ab_umcg -> atc_umcg()
+ab_tradenames -> atc_tradenames()
+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 functions set_mo_source() and get_mo_source() to use your own predefined MO codes as input for as.mo() and consequently all mo_* functions
Support for the upcoming dplyr version 0.8.0
New function guess_ab_col() to find an antibiotic column in a table
@@ -555,20 +565,20 @@ These functions use as.atc()New function age_groups() to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
New function ggplot_rsi_predict() as well as the base R plot() function can now be used for resistance prediction calculated with resistance_predict():
-
+
Functions filter_first_isolate() and filter_first_weighted_isolate() to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
-
+
is equal to:
-
+
New function availability() to check the number of available (non-empty) results in a data.frame
@@ -597,33 +607,33 @@ These functions use as.atc()
Now handles incorrect spelling, like i instead of y and f instead of ph:
-
+
Uncertainty of the algorithm is now divided into four levels, 0 to 3, where the default allow_uncertain = TRUE is equal to uncertainty level 2. Run ?as.mo for more info about these levels.
-# equal:
-as.mo(..., allow_uncertain = TRUE)
-as.mo(..., allow_uncertain = 2)
-
-# also equal:
-as.mo(..., allow_uncertain = FALSE)
-as.mo(..., allow_uncertain = 0)
+# equal:
+as.mo(..., allow_uncertain = TRUE)
+as.mo(..., allow_uncertain = 2)
+
+# also equal:
+as.mo(..., allow_uncertain = FALSE)
+as.mo(..., allow_uncertain = 0)
Using as.mo(..., allow_uncertain = 3) could lead to very unreliable results.
Implemented the latest publication of Becker et al. (2019), for categorising coagulase-negative Staphylococci
All microbial IDs that found are now saved to a local file ~/.Rhistory_mo. Use the new function clean_mo_history() to delete this file, which resets the algorithms.
Incoercible results will now be considered ‘unknown’, MO code UNKNOWN. On foreign systems, properties of these will be translated to all languages already previously supported: German, Dutch, French, Italian, Spanish and Portuguese:
-
+
Fix for vector containing only empty values
Finds better results when input is in other languages
@@ -665,23 +675,23 @@ Using as.mo(..., allow_uncertain = 3)
-Frequency tables (freq() function):
+ Frequency tables (freq() function):
-
Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:
-# Determine genus of microorganisms (mo) in `septic_patients` data set:
-# OLD WAY
-septic_patients %>%
- mutate(genus = mo_genus(mo)) %>%
- freq(genus)
-# NEW WAY
-septic_patients %>%
- freq(mo_genus(mo))
-
-# Even supports grouping variables:
-septic_patients %>%
- group_by(gender) %>%
- freq(mo_genus(mo))
+# Determine genus of microorganisms (mo) in `septic_patients` data set:
+# OLD WAY
+septic_patients %>%
+ mutate(genus = mo_genus(mo)) %>%
+ freq(genus)
+# NEW WAY
+septic_patients %>%
+ freq(mo_genus(mo))
+
+# Even supports grouping variables:
+septic_patients %>%
+ group_by(gender) %>%
+ freq(mo_genus(mo))
- Header info is now available as a list, with the
header function
- The parameter
header is now set to TRUE at default, even for markdown
@@ -712,9 +722,9 @@ Using as.mo(..., allow_uncertain = 3)
-AMR 0.6.0 2019-03-27 +AMR 0.6.0 2019-03-27
New website!
We’ve got a new website: https://msberends.gitlab.io/AMR (built with the great pkgdown)
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.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.mo_rank() for the taxonomic rank (genus, species, infraspecies, etc.)mo_url() to get the direct URL of a species from the Catalogue of LifeNew filters for antimicrobial classes. Use these functions to filter isolates on results in one of more antibiotics from a specific class:
-filter_aminoglycosides()
-filter_carbapenems()
-filter_cephalosporins()
-filter_1st_cephalosporins()
-filter_2nd_cephalosporins()
-filter_3rd_cephalosporins()
-filter_4th_cephalosporins()
-filter_fluoroquinolones()
-filter_glycopeptides()
-filter_macrolides()
-filter_tetracyclines()filter_aminoglycosides()
+filter_carbapenems()
+filter_cephalosporins()
+filter_1st_cephalosporins()
+filter_2nd_cephalosporins()
+filter_3rd_cephalosporins()
+filter_4th_cephalosporins()
+filter_fluoroquinolones()
+filter_glycopeptides()
+filter_macrolides()
+filter_tetracyclines()The antibiotics data set will be searched, after which the input data will be checked for column names with a value in any abbreviations, codes or official names found in the antibiotics data set. For example:
All ab_* functions are deprecated and replaced by atc_* functions:
ab_property -> atc_property()
-ab_name -> atc_name()
-ab_official -> atc_official()
-ab_trivial_nl -> atc_trivial_nl()
-ab_certe -> atc_certe()
-ab_umcg -> atc_umcg()
-ab_tradenames -> atc_tradenames()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.ab_property -> atc_property()
+ab_name -> atc_name()
+ab_official -> atc_official()
+ab_trivial_nl -> atc_trivial_nl()
+ab_certe -> atc_certe()
+ab_umcg -> atc_umcg()
+ab_tradenames -> atc_tradenames()as.atc() internally. The old atc_property has been renamed atc_online_property(). This is done for two reasons: firstly, not all ATC codes are of antibiotics (ab) but can also be of antivirals or antifungals. Secondly, the input must have class atc or must be coerable to this class. Properties of these classes should start with the same class name, analogous to as.mo() and e.g. mo_genus.
set_mo_source() and get_mo_source() to use your own predefined MO codes as input for as.mo() and consequently all mo_* functionsdplyr version 0.8.0guess_ab_col() to find an antibiotic column in a tableas.atc()New function age_groups() to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
New function ggplot_rsi_predict() as well as the base R plot() function can now be used for resistance prediction calculated with resistance_predict():
-
+
Functions filter_first_isolate() and filter_first_weighted_isolate() to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
-
+
is equal to:
-
+
New function availability() to check the number of available (non-empty) results in a data.frame
@@ -597,33 +607,33 @@ These functions use as.atc()
Now handles incorrect spelling, like i instead of y and f instead of ph:
-
+
Uncertainty of the algorithm is now divided into four levels, 0 to 3, where the default allow_uncertain = TRUE is equal to uncertainty level 2. Run ?as.mo for more info about these levels.
-# equal:
-as.mo(..., allow_uncertain = TRUE)
-as.mo(..., allow_uncertain = 2)
-
-# also equal:
-as.mo(..., allow_uncertain = FALSE)
-as.mo(..., allow_uncertain = 0)
+# equal:
+as.mo(..., allow_uncertain = TRUE)
+as.mo(..., allow_uncertain = 2)
+
+# also equal:
+as.mo(..., allow_uncertain = FALSE)
+as.mo(..., allow_uncertain = 0)
Using as.mo(..., allow_uncertain = 3) could lead to very unreliable results.
Implemented the latest publication of Becker et al. (2019), for categorising coagulase-negative Staphylococci
All microbial IDs that found are now saved to a local file ~/.Rhistory_mo. Use the new function clean_mo_history() to delete this file, which resets the algorithms.
Incoercible results will now be considered ‘unknown’, MO code UNKNOWN. On foreign systems, properties of these will be translated to all languages already previously supported: German, Dutch, French, Italian, Spanish and Portuguese:
-
+
Fix for vector containing only empty values
Finds better results when input is in other languages
@@ -665,23 +675,23 @@ Using as.mo(..., allow_uncertain = 3)
-Frequency tables (freq() function):
+ Frequency tables (freq() function):
-
Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:
-# Determine genus of microorganisms (mo) in `septic_patients` data set:
-# OLD WAY
-septic_patients %>%
- mutate(genus = mo_genus(mo)) %>%
- freq(genus)
-# NEW WAY
-septic_patients %>%
- freq(mo_genus(mo))
-
-# Even supports grouping variables:
-septic_patients %>%
- group_by(gender) %>%
- freq(mo_genus(mo))
+# Determine genus of microorganisms (mo) in `septic_patients` data set:
+# OLD WAY
+septic_patients %>%
+ mutate(genus = mo_genus(mo)) %>%
+ freq(genus)
+# NEW WAY
+septic_patients %>%
+ freq(mo_genus(mo))
+
+# Even supports grouping variables:
+septic_patients %>%
+ group_by(gender) %>%
+ freq(mo_genus(mo))
- Header info is now available as a list, with the
header function
- The parameter
header is now set to TRUE at default, even for markdown
@@ -712,9 +722,9 @@ Using as.mo(..., allow_uncertain = 3)
+
@@ -756,10 +766,10 @@ Using
-
-
-
-
- Create frequency tables
-
-
@@ -246,7 +239,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
- -AMR 0.5.0 2018-11-30 +AMR 0.5.0 2018-11-30
@@ -756,10 +766,10 @@ Using as.mo(..., allow_uncertain = 3)Fewer than 3 characters as input for as.mo will return NA
Function as.mo (and all mo_* wrappers) now supports genus abbreviations with “species” attached
-
+
Added parameter combine_IR (TRUE/FALSE) to functions portion_df and count_df, to indicate that all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
Fix for portion_*(..., as_percent = TRUE) when minimal number of isolates would not be met
@@ -768,21 +778,21 @@ Using as.mo(..., allow_uncertain = 3)Using portion_* functions now throws a warning when total available isolate is below parameter minimum
Functions as.mo, as.rsi, as.mic, as.atc and freq will not set package name as attribute anymore
-Frequency tables - freq():
+ Frequency tables - freq():
-
Support for grouping variables, test with:
-
+
-
Support for (un)selecting columns:
-
+
-- Check for
hms::is.hms
+ - Check for
hms::is.hms
- Now prints in markdown at default in non-interactive sessions
- No longer adds the factor level column and sorts factors on count again
@@ -800,7 +810,7 @@ Using as.mo(..., allow_uncertain = 3)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()
+ Support for named vectors of class mo, useful for top_freq()
ggplot_rsi and scale_y_percent have breaks parameter
@@ -839,9 +849,9 @@ Using as.mo(..., allow_uncertain = 3)
as.mo will return NA
Function as.mo (and all mo_* wrappers) now supports genus abbreviations with “species” attached
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)portion_*(..., as_percent = TRUE) when minimal number of isolates would not be metas.mo(..., allow_uncertain = 3)Using portion_* functions now throws a warning when total available isolate is below parameter minimum
Functions as.mo, as.rsi, as.mic, as.atc and freq will not set package name as attribute anymore
-Frequency tables - freq():
+ Frequency tables - freq():
-
Support for grouping variables, test with:
-
+
-
Support for (un)selecting columns:
-
+
-- Check for
hms::is.hms
+ - Check for
hms::is.hms
- Now prints in markdown at default in non-interactive sessions
- No longer adds the factor level column and sorts factors on count again
@@ -800,7 +810,7 @@ Using as.mo(..., allow_uncertain = 3)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()
+ Support for named vectors of class mo, useful for top_freq()
ggplot_rsi and scale_y_percent have breaks parameter
@@ -839,9 +849,9 @@ Using as.mo(..., allow_uncertain = 3)
+
@@ -860,18 +870,18 @@ Using
@@ -974,9 +984,9 @@ Using
- -AMR 0.4.0 2018-10-01 +AMR 0.4.0 2018-10-01
@@ -860,18 +870,18 @@ Using as.mo(..., allow_uncertain = 3)
They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:
-mo_gramstain("E. coli")
-# [1] "Gram negative"
-mo_gramstain("E. coli", language = "de") # German
-# [1] "Gramnegativ"
-mo_gramstain("E. coli", language = "es") # Spanish
-# [1] "Gram negativo"
-mo_fullname("S. group A", language = "pt") # Portuguese
-# [1] "Streptococcus grupo A"
+mo_gramstain("E. coli")
+# [1] "Gram negative"
+mo_gramstain("E. coli", language = "de") # German
+# [1] "Gramnegativ"
+mo_gramstain("E. coli", language = "es") # Spanish
+# [1] "Gram negativo"
+mo_fullname("S. group A", language = "pt") # Portuguese
+# [1] "Streptococcus grupo A"
Furthermore, former taxonomic names will give a note about the current taxonomic name:
-mo_gramstain("Esc blattae")
-# Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)
-# [1] "Gram negative"
+mo_gramstain("Esc blattae")
+# Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)
+# [1] "Gram negative"
Functions count_R, count_IR, count_I, count_SI and count_S to selectively count resistant or susceptible isolates
@@ -882,18 +892,18 @@ Using as.mo(..., allow_uncertain = 3)
-
Functions as.mo and is.mo as replacements for as.bactid and is.bactid (since the microoganisms data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The as.mo function determines microbial IDs using intelligent rules:
-as.mo("E. coli")
-# [1] B_ESCHR_COL
-as.mo("MRSA")
-# [1] B_STPHY_AUR
-as.mo("S group A")
-# [1] B_STRPTC_GRA
+as.mo("E. coli")
+# [1] B_ESCHR_COL
+as.mo("MRSA")
+# [1] B_STPHY_AUR
+as.mo("S group A")
+# [1] B_STRPTC_GRA
And with great speed too - on a quite regular Linux server from 2007 it takes us less than 0.02 seconds to transform 25,000 items:
-
+
- Added parameter
reference_df for as.mo, so users can supply their own microbial IDs, name or codes as a reference table
- Renamed all previous references to
bactid to mo, like:
@@ -921,12 +931,12 @@ Using as.mo(..., allow_uncertain = 3)Added three antimicrobial agents to the antibiotics data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)
-
Added 163 trade names to the antibiotics data set, it now contains 298 different trade names in total, e.g.:
-
+
- For
first_isolate, rows will be ignored when there’s no species available
- Function
ratio is now deprecated and will be removed in a future release, as it is not really the scope of this package
@@ -937,13 +947,13 @@ Using as.mo(..., allow_uncertain = 3)
-
Support for quasiquotation in the functions series count_* and portions_*, and n_rsi. This allows to check for more than 2 vectors or columns.
-
+
- Edited
ggplot_rsi and geom_rsi so they can cope with count_df. The new fun parameter has value portion_df at default, but can be set to count_df.
- Fix for
ggplot_rsi when the ggplot2 package was not loaded
@@ -957,12 +967,12 @@ Using as.mo(..., allow_uncertain = 3)
-
Support for types (classes) list and matrix for freq
-
+
For lists, subsetting is possible:
-
+
They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:
-mo_gramstain("E. coli")
-# [1] "Gram negative"
-mo_gramstain("E. coli", language = "de") # German
-# [1] "Gramnegativ"
-mo_gramstain("E. coli", language = "es") # Spanish
-# [1] "Gram negativo"
-mo_fullname("S. group A", language = "pt") # Portuguese
-# [1] "Streptococcus grupo A"mo_gramstain("E. coli")
+# [1] "Gram negative"
+mo_gramstain("E. coli", language = "de") # German
+# [1] "Gramnegativ"
+mo_gramstain("E. coli", language = "es") # Spanish
+# [1] "Gram negativo"
+mo_fullname("S. group A", language = "pt") # Portuguese
+# [1] "Streptococcus grupo A"Furthermore, former taxonomic names will give a note about the current taxonomic name:
-mo_gramstain("Esc blattae")
-# Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)
-# [1] "Gram negative"mo_gramstain("Esc blattae")
+# Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)
+# [1] "Gram negative"count_R, count_IR, count_I, count_SI and count_S to selectively count resistant or susceptible isolates
-
@@ -882,18 +892,18 @@ Using
-
Functions
-as.moandis.moas replacements foras.bactidandis.bactid(since themicrooganismsdata set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. Theas.mofunction determines microbial IDs using intelligent rules:+as.mo("E. coli") -# [1] B_ESCHR_COL -as.mo("MRSA") -# [1] B_STPHY_AUR -as.mo("S group A") -# [1] B_STRPTC_GRAas.mo("E. coli") +# [1] B_ESCHR_COL +as.mo("MRSA") +# [1] B_STPHY_AUR +as.mo("S group A") +# [1] B_STRPTC_GRAAnd with great speed too - on a quite regular Linux server from 2007 it takes us less than 0.02 seconds to transform 25,000 items:
- + - Added parameter
reference_dfforas.mo, so users can supply their own microbial IDs, name or codes as a reference table - Renamed all previous references to
bactidtomo, like: @@ -921,12 +931,12 @@ Usingas.mo(..., allow_uncertain = 3)Added three antimicrobial agents to theantibioticsdata set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05) -
Added 163 trade names to the
- +antibioticsdata set, it now contains 298 different trade names in total, e.g.: - For
first_isolate, rows will be ignored when there’s no species available - Function
ratiois now deprecated and will be removed in a future release, as it is not really the scope of this package
@@ -937,13 +947,13 @@ Using -
Support for quasiquotation in the functions series
- +count_*andportions_*, andn_rsi. This allows to check for more than 2 vectors or columns. - Edited
ggplot_rsiandgeom_rsiso they can cope withcount_df. The newfunparameter has valueportion_dfat default, but can be set tocount_df. - Fix for
ggplot_rsiwhen theggplot2package was not loaded
@@ -957,12 +967,12 @@ Using -
Support for types (classes) list and matrix for
- +freqFor lists, subsetting is possible:
- +
as.mo(..., allow_uncertain = 3)
as.mo(..., allow_uncertain = 3)
as.mo(..., allow_uncertain = 3)
as.mo(..., allow_uncertain = 3)
+
@@ -1036,13 +1046,13 @@ Using
-
-
-
-
- Create frequency tables
-
-
diff --git a/docs/reference/ITIS.html b/docs/reference/ITIS.html
deleted file mode 100644
index 12a08129e..000000000
--- a/docs/reference/ITIS.html
+++ /dev/null
@@ -1,337 +0,0 @@
-
-
-
-
-
-
-
-
-ITIS: Integrated Taxonomic Information System — ITIS • AMR (for R)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
@@ -158,13 +158,6 @@
Get properties of an antibiotic
- -AMR 0.3.0 2018-08-14 +AMR 0.3.0 2018-08-14
@@ -1036,13 +1046,13 @@ Using as.mo(..., allow_uncertain = 3)
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 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$age))
Support for as.vector, as.data.frame, as_tibble and format
-Support for quasiquotation: freq(mydata, mycolumn) is the same as mydata %>% freq(mycolumn)
+ Support for quasiquotation: freq(mydata, mycolumn) is the same as mydata %>% freq(mycolumn)
Function top_freq function to return the top/below n items as vector
Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR)
@@ -1081,7 +1091,7 @@ Using as.mo(..., allow_uncertain = 3)
-Now possible to coerce MIC values with a space between operator and value, i.e. as.mic("<= 0.002") now works
+Now possible to coerce MIC values with a space between operator and value, i.e. as.mic("<= 0.002") now works
Classes rsi and mic do not add the attribute package.version anymore
Added "groups" option for atc_property(..., property). It will return a vector of the ATC hierarchy as defined by the WHO. The new function atc_groups is a convenient wrapper around this.
Build-in host check for atc_property as it requires the host set by url to be responsive
@@ -1111,9 +1121,9 @@ Using as.mo(..., allow_uncertain = 3)
rsi (antimicrobial resistance) to use as inputtable to use as input: freq(table(x, y))
+table to use as input: freq(table(x, y))
hist and plot to use a frequency table as input: hist(freq(df$age))
as.vector, as.data.frame, as_tibble and format
freq(mydata, mycolumn) is the same as mydata %>% freq(mycolumn)
+freq(mydata, mycolumn) is the same as mydata %>% freq(mycolumn)
top_freq function to return the top/below n items as vectoras.mo(..., allow_uncertain = 3)
-Now possible to coerce MIC values with a space between operator and value, i.e. as.mic("<= 0.002") now works
+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
@@ -1111,9 +1121,9 @@ Using as.mo(..., allow_uncertain = 3)
+
@@ -1169,9 +1179,9 @@ Using
-
-
-
-
- Create frequency tables
-
-
diff --git a/docs/reference/AMR.html b/docs/reference/AMR.html
index 6559b61c6..dea7cd8c4 100644
--- a/docs/reference/AMR.html
+++ b/docs/reference/AMR.html
@@ -80,7 +80,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
- -AMR 0.2.0 2018-05-03 +AMR 0.2.0 2018-05-03
@@ -1169,9 +1179,9 @@ Using as.mo(..., allow_uncertain = 3)
+
-
@@ -158,13 +158,6 @@
Get properties of an antibiotic
- -AMR 0.1.1 2018-03-14 +AMR 0.1.1 2018-03-14
-
@@ -1182,9 +1192,9 @@ Using
as.mo(..., allow_uncertain = 3)Added barplots forrsiandmicclasses
+
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 9f781c72c..a569ce0ce 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,4 +1,4 @@
-pandoc: '2.6'
+pandoc: 2.3.1
pkgdown: 1.3.0
pkgdown_sha: ~
articles:
@@ -8,7 +8,6 @@ articles:
SPSS: SPSS.html
WHONET: WHONET.html
benchmarks: benchmarks.html
- freq: freq.html
resistance_predict: resistance_predict.html
urls:
reference: https://msberends.gitlab.io/AMR/reference
diff --git a/docs/reference/AMR-deprecated.html b/docs/reference/AMR-deprecated.html
index 368607bfd..6e3451992 100644
--- a/docs/reference/AMR-deprecated.html
+++ b/docs/reference/AMR-deprecated.html
@@ -80,7 +80,7 @@
-AMR 0.1.0 2018-02-22 +AMR 0.1.0 2018-02-22
- First submission to CRAN. @@ -1196,17 +1206,17 @@ Using
as.mo(..., allow_uncertain = 3)
Contents
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/docs/reference/WHOCC.html b/docs/reference/WHOCC.html
index bcb3bbcee..cfedd039a 100644
--- a/docs/reference/WHOCC.html
+++ b/docs/reference/WHOCC.html
@@ -80,7 +80,7 @@
-
-
-
-
-
-
-
-
-
-
- ITIS: Integrated Taxonomic Information System
- -ITIS.Rd
-
-
-
-
- All taxonomic names of all microorganisms are included in this package, using the authoritative Integrated Taxonomic Information System (ITIS).
- -ITIS
- - -
-This package contains the complete microbial taxonomic data (with all nine taxonomic ranks - from kingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, https://www.itis.gov).
All ~20,000 (sub)species from the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package, as well as all their ~2,500 previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since ITIS honours the taxonomic branching order of bacterial phyla according to Cavalier-Smith (2002), which defines that all bacteria are classified into either subkingdom Negibacteria or subkingdom Posibacteria.
-ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists [3].
- -Read more on our website!
- - -
-On our website https://msberends.gitlab.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
Examples
-# NOT RUN { -# Get a note when a species was renamed -mo_shortname("Chlamydia psittaci") -# Note: 'Chlamydia psittaci' (Page, 1968) was renamed -# 'Chlamydophila psittaci' (Everett et al., 1999) -# [1] "C. psittaci" - -# Get any property from the entire taxonomic tree for all included species -mo_class("E. coli") -# [1] "Gammaproteobacteria" - -mo_family("E. coli") -# [1] "Enterobacteriaceae" - -mo_subkingdom("E. coli") -# [1] "Negibacteria" - -mo_gramstain("E. coli") # based on subkingdom -# [1] "Gram negative" - -mo_ref("E. coli") -# [1] "Castellani and Chalmers, 1919" - -# Do not get mistaken - the package only includes microorganisms -mo_phylum("C. elegans") -# [1] "Cyanobacteria" # Bacteria?! -mo_fullname("C. elegans") -# [1] "Chroococcus limneticus elegans" # Because a microorganism was found -# }-
-This package contains all ~450 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission (http://ec.europa.eu/health/documents/community-register/html/atc.htm).
These have become the gold standard for international drug utilisation monitoring and research.
The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
diff --git a/docs/reference/WHONET.html b/docs/reference/WHONET.html index 205a3cd2f..8366c8c8a 100644 --- a/docs/reference/WHONET.html +++ b/docs/reference/WHONET.html @@ -80,7 +80,7 @@
-This package contains all ~450 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission (http://ec.europa.eu/health/documents/community-register/html/atc.htm).
These have become the gold standard for international drug utilisation monitoring and research.
The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
diff --git a/docs/reference/as.ab.html b/docs/reference/as.ab.html index b2fb70b55..80e2ce26e 100644 --- a/docs/reference/as.ab.html +++ b/docs/reference/as.ab.html @@ -80,7 +80,7 @@
-This package contains all ~450 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission (http://ec.europa.eu/health/documents/community-register/html/atc.htm).
These have become the gold standard for international drug utilisation monitoring and research.
The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
diff --git a/docs/reference/as.disk.html b/docs/reference/as.disk.html index b2aa1c906..2b43b8e6a 100644 --- a/docs/reference/as.disk.html +++ b/docs/reference/as.disk.html @@ -80,7 +80,7 @@Examples
# NOT RUN { library(dplyr) -microorganisms %>% freq(kingdom) -microorganisms %>% group_by(kingdom) %>% freq(phylum, nmax = NULL) +library(clean) +microorganisms %>% freq(kingdom) +microorganisms %>% group_by(kingdom) %>% freq(phylum, nmax = NULL) # }@@ -159,13 +159,6 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_ Get properties of an antibiotic
a logical to indicate whether extensive info should be returned as a data.frame with info about which rows and columns are effected. It runs all EUCAST rules, but will not be applied to an output - only an informative data.frame with changes will be returned as output.
a logical to turn Verbose mode on and off (default is off). In Verbose mode, the function does not apply rules to the data, but instead returns a data.frame with extensive info about which rows and columns would be effected and in which way.
Examples
# NOT RUN { -a <- eucast_rules(septic_patients) - a <- data.frame(mo = c("Staphylococcus aureus", "Enterococcus faecalis", "Escherichia coli", @@ -418,7 +409,7 @@ # 5 Pseudomonas aeruginosa R R - - R R R -# do not apply EUCAST rules, but rather get a a data.frame +# do not apply EUCAST rules, but rather get a data.frame # with 18 rows, containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) # }diff --git a/docs/reference/extended-functions.html b/docs/reference/extended-functions.html index ce2e53b53..60d1a7c1e 100644 --- a/docs/reference/extended-functions.html +++ b/docs/reference/extended-functions.html @@ -80,7 +80,7 @@ @@ -158,13 +158,6 @@ Get properties of an antibiotic -
Analysing your data
-Functions for conducting AMR analysis, like counting isolates, calculating resistance or susceptibility, creating frequency tables or make plots.
+Functions for conducting AMR analysis, like counting isolates, calculating resistance or susceptibility, or make plots.
Filter isolates on result in antibiotic class
Frequency table
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/docs/reference/join.html b/docs/reference/join.html
index 81e39cafc..8f972cd66 100644
--- a/docs/reference/join.html
+++ b/docs/reference/join.html
@@ -80,7 +80,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
-
-
-
-
-
-
-
-
-
-
-
- ITIS: Integrated Taxonomic Information System
- -ITIS.Rd
-
-
-
-
- All taxonomic names of all microorganisms are included in this package, using the authoritative Integrated Taxonomic Information System (ITIS).
- -ITIS
- - -
-This package contains the complete microbial taxonomic data (with all nine taxonomic ranks - from kingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, https://www.itis.gov).
All ~20,000 (sub)species from the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package, as well as all their ~2,500 previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since ITIS honours the taxonomic branching order of bacterial phyla according to Cavalier-Smith (2002), which defines that all bacteria are classified into either subkingdom Negibacteria or subkingdom Posibacteria.
-ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists [3].
- -Read more on our website!
- - -
-On our website https://msberends.gitlab.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
Examples
-# NOT RUN { -# Get a note when a species was renamed -mo_shortname("Chlamydia psittaci") -# Note: 'Chlamydia psittaci' (Page, 1968) was renamed -# 'Chlamydophila psittaci' (Everett et al., 1999) -# [1] "C. psittaci" - -# Get any property from the entire taxonomic tree for all included species -mo_class("E. coli") -# [1] "Gammaproteobacteria" - -mo_family("E. coli") -# [1] "Enterobacteriaceae" - -mo_subkingdom("E. coli") -# [1] "Negibacteria" - -mo_gramstain("E. coli") # based on subkingdom -# [1] "Gram negative" - -mo_ref("E. coli") -# [1] "Castellani and Chalmers, 1919" - -# Do not get mistaken - the package only includes microorganisms -mo_phylum("C. elegans") -# [1] "Cyanobacteria" # Bacteria?! -mo_fullname("C. elegans") -# [1] "Chroococcus limneticus elegans" # Because a microorganism was found -# }-
mdro(x, guideline = NULL, col_mo = NULL, info = TRUE, verbose = FALSE, ...) -brmo(..., guideline = "BRMO") +brmo(x, guideline = "BRMO", ...) mrgn(x, guideline = "MRGN", ...) diff --git a/docs/reference/microorganisms.codes.html b/docs/reference/microorganisms.codes.html index 0bc2192b7..541e64c09 100644 --- a/docs/reference/microorganisms.codes.html +++ b/docs/reference/microorganisms.codes.html @@ -80,7 +80,7 @@ @@ -158,13 +158,6 @@ Get properties of an antibiotic
Format
-A data.frame with 67,906 observations and 16 variables:
-
+
moID of microorganism as used by this package
col_idCatalogue of Life ID
fullnameFull name, like
"Escherichia coli"
@@ -268,8 +261,8 @@
A data.frame with 68,260 observations and 16 variables:
9 entries of Streptococcus (beta haemolytic groups A, B, C, D, F, G, H, K and unspecified)
2 entries of Staphylococcus (coagulase-negative [CoNS] and coagulase-positive [CoPS])
3 entries of Trichomonas (Trichomonas vaginalis, and its family and genus)
3 other 'undefined' entries (unknown, unknown Gram negatives and unknown Gram positives)
8,830 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) that are not in the Catalogue of Life
5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)
8,970 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) that are not in the Catalogue of Life
About the records from DSMZ (see source)
diff --git a/docs/reference/microorganisms.old.html b/docs/reference/microorganisms.old.html index 5024ae93a..15bf81e6f 100644 --- a/docs/reference/microorganisms.old.html +++ b/docs/reference/microorganisms.old.html @@ -80,7 +80,7 @@ @@ -158,13 +158,6 @@ Get properties of an antibioticFormat
-A data.frame with 21,342 observations and 4 variables:
-
+
col_idCatalogue of Life ID that was originally given
col_id_newNew Catalogue of Life ID that responds to an entry in the
microorganismsdata setfullnameOld full taxonomic name of the microorganism
diff --git a/docs/reference/mo_property.html b/docs/reference/mo_property.html
index 201789259..ff83d0d8e 100644
--- a/docs/reference/mo_property.html
+++ b/docs/reference/mo_property.html
@@ -80,7 +80,7 @@
@@ -158,13 +158,6 @@
Get properties of an antibiotic
A data.frame with 21,743 observations and 4 variables:
the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.
the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution (i.e. using glm(..., family = binomial)), assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.
a logical to indicate whether values I should be treated as S
a logical to indicate whether values I should be treated as S (will otherwise be treated as R)