NEWS.md
- Note: some changes in this version were suggested by anonymous reviewers from the journal we submitted our manuscipt to. We are those reviewers very grateful for going through our code so thoroughly!
Support for veterinary ATC codes
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 parameter ‘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 “<”
Improvements for as.mo():
mo_uncertainties() has been changed completely; it now prints all possible candidates with their matching score.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.mo_* functions like mo_name() on microoganism IDs.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.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
Small as.ab() algorithm improvements
Fix for combining MIC values with raw numbers, i.e. c(as.mic(2), 2) previously failed but now returns a valid MIC class
ggplot_rsi() and geom_rsi() gained parameters minimum and language, to influence the internal use of rsi_df()
Added abbreviation “piptazo” to piperacillin/tazobactam (TZP)
A data.frame with 500 observations and 53 variables:
A data.frame with 500 observations and 53 variables:
Identification number
ID of the sample
Specimen number
ID of the specimen
Organism
Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using as.mo().
A list, or a character if collapse is not NULL
A list, or a character if collapse is not NULL
This function is also internally used by as.ab(), although it then only searches for the first drug name and will throw a note if more drug names could have been returned.
typeAn integer in case of ab_cid()
A named list in case of ab_info() and multiple ab_synonyms()/ab_tradenames()
A double in case of ab_ddd()
A character in all other cases
An integer in case of ab_cid()
A named list in case of ab_info() and multiple ab_synonyms()/ab_tradenames()
A double in case of ab_ddd()
A character in all other cases
data.frame with 456 observations and 14 variables:ab
Antibiotic ID as used in this package (like AMC), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
data.frame with 102 observations and 9 variables:atc
ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC
Character (vector) with class ab. Unknown values will return NA.
A character vector with additional class ab
All entries in the antibiotics data set have three different identifiers: a human readable EARS-Net code (column ab, used by ECDC and WHONET), an ATC code (column atc, used by WHO), and a CID code (column cid, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem.
antibiotics for the dataframe that is being used to determine ATCs
antibiotics for the data.frame that is being used to determine ATCs
ab_from_text() for a function to retrieve antimicrobial drugs from clinical text (from health care records)
An integer with additional new class disk
An integer with additional class disk
Interpret disk values as RSI values with as.rsi(). It supports guidelines from EUCAST and CLSI.
R/mic.R
as.mic.RdThis transforms a vector to a new class mic, which is an ordered factor with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as NA with a warning.
This transforms a vector to a new class mic, which is an ordered factor with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as NA with a warning.
as.mic(x, na.rm = FALSE) @@ -261,7 +261,7 @@Value
-Ordered
+factorwith new classmicOrdered factor with additional class
micDetails
To interpret MIC values as RSI values, use
diff --git a/docs/reference/as.mo.html b/docs/reference/as.mo.html index 174c4d889..e8c2f31bf 100644 --- a/docs/reference/as.mo.html +++ b/docs/reference/as.mo.html @@ -50,7 +50,7 @@ - + @@ -82,7 +82,7 @@as.rsi()on MIC values. It supports guidelines from EUCAST and CLSI.
a character vector or a data.frame with one or two columns
a character vector or a data.frame with one or two columns
a data.frame to be used for extra reference when translating x to a valid mo. See set_mo_source() and get_mo_source() to automate the usage of your own codes (e.g. used in your analysis or organisation).
a data.frame to be used for extra reference when translating x to a valid mo. See set_mo_source() and get_mo_source() to automate the usage of your own codes (e.g. used in your analysis or organisation).
A character vector with additional class mo
A character vector with additional class mo
There are three helper functions that can be run after using the as.mo() function:
Use mo_uncertainties() to get a data.frame that prints in a pretty format with all taxonomic names that were guessed. The output contains a score that is based on the human pathogenic prevalence and the Levenshtein distance between the user input and the full taxonomic name.
Use mo_failures() to get a character vector with all values that could not be coerced to a valid value.
Use mo_renamed() to get a data.frame with all values that could be coerced based on old, previously accepted taxonomic names.
Use mo_uncertainties() to get a data.frame that prints in a pretty format with all taxonomic names that were guessed. The output contains the matching score for all matches (see Background on matching score).
Use mo_failures() to get a character vector with all values that could not be coerced to a valid value.
Use mo_renamed() to get a data.frame with all values that could be coerced based on old, previously accepted taxonomic names.
Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is Aspergillus, Bacteroides, Candida, Capnocytophaga, Chryseobacterium, Cryptococcus, Elisabethkingia, Flavobacterium, Fusobacterium, Giardia, Leptotrichia, Mycoplasma, Prevotella, Rhodotorula, Treponema, Trichophyton or Ureaplasma. This group consequently contains all less common and rare human pathogens.
Group 3 (least prevalent microorganisms) consists of all other microorganisms. This group contains microorganisms most probably not found in humans.
+With ambiguous user input, the returned results are chosen based on their matching score using mo_matching_score(). This matching score is based on four parameters:
The prevalence \(P\) is categorised into group 1, 2 and 3 as stated above;
A kingdom index \(K\) is set as follows: Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, and all others = 5;
The level of uncertainty \(U\) needed to get to the result, as stated above (1 to 3);
The Levenshtein distance \(L\) is the distance between the user input and all taxonomic full names, with the text length of the user input being the maximum distance. A modified version of the Levenshtein distance \(L'\) based on the text length of the full name \(F\) is calculated as:
$$L' = F - \frac{0.5 \times L}{F}$$
+The final matching score \(M\) is calculated as: +$$M = L' \times \frac{1}{P \times K} * \frac{1}{U}$$
+All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
+On our website https://msberends.github.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. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
microorganisms for the data.frame that is being used to determine ID's.
microorganisms for the data.frame that is being used to determine ID's.
The mo_property() functions (like mo_genus(), mo_gramstain()) to get properties based on the returned code.
Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit.
+Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic), such as the name, defined daily dose (DDD) or standard unit.
atc_online_property( atc_code, property, administration = "O", - url = "https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no" + url = "https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no", + url_vet = "https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no" ) atc_online_groups(atc_code, ...) @@ -270,7 +271,11 @@
url of website of the WHO. The sign %s can be used as a placeholder for ATC codes.
url of website of the WHOCC. The sign %s can be used as a placeholder for ATC codes.
url of website of the WHOCC for veterinary medicine. The sign %s can be used as a placeholder for ATC_vet codes (that all start with "Q").
a data.frame or list
a data.frame or list
data.frame with column names of tbl as row names
data.frame with column names of tbl as row names
The function returns a data.frame with columns "resistant" and "visual_resistance". The values in that columns are calculated with resistance().
The function returns a data.frame with columns "resistant" and "visual_resistance". The values in that columns are calculated with resistance().
M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
The function bug_drug_combinations() returns a data.frame with columns "mo", "ab", "S", "I", "R" and "total".
The function bug_drug_combinations() returns a data.frame with columns "mo", "ab", "S", "I", "R" and "total".
The function format() calculates the resistance per bug-drug combination. Use combine_IR = FALSE (default) to test R vs. S+I and combine_IR = TRUE to test R+I vs. S.
a list, which prints in pretty format
a list, which prints in pretty format
For DSMZ, see microorganisms.
diff --git a/docs/reference/count.html b/docs/reference/count.html index a10357cd6..b8852153c 100644 --- a/docs/reference/count.html +++ b/docs/reference/count.html @@ -51,7 +51,7 @@ - + @@ -83,7 +83,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible( @@ -283,7 +283,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(a data.frame containing columns with class rsi (see as.rsi())
a data.frame containing columns with class rsi (see as.rsi())
An integer
An integer
These functions are meant to count isolates. Use the resistance()/susceptibility() functions to calculate microbial resistance/susceptibility.
Please note that, in combination therapies, for only_all_tested = TRUE applies that:
count_S() + count_I() + count_R() = count_all() - proportion_S() + proportion_I() + proportion_R() = 1+ proportion_S() + proportion_I() + proportion_R() = 1 +
and that, in combination therapies, for only_all_tested = FALSE applies that:
count_S() + count_I() + count_R() >= count_all() - proportion_S() + proportion_I() + proportion_R() >= 1+ proportion_S() + proportion_I() + proportion_R() >= 1 +
Using only_all_tested has no impact when only using one antibiotic as input.
The input of x, possibly with edited values of antibiotics. Or, if verbose = TRUE, a data.frame with all original and new values of the affected bug-drug combinations.
The input of x, possibly with edited values of antibiotics. Or, if verbose = TRUE, a data.frame with all original and new values of the affected bug-drug combinations.
Note: This function does not translate MIC values to RSI values. Use A A data.frame with 2,000 observations and 49 variables: A A data.frame with 3,000 observations and 8 variables: a a data.frame containing isolates. WHY THIS IS SO IMPORTANT All isolates with a microbial ID of The functions The functions The function a a data.frame with column(s) of class a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter the minimum allowed number of available (tested) isolates. Any isolate count lower than language of the returned text, defaults to system language (see Transform input to minimum inhibitory concentrations Transform input to minimum inhibitory concentrations (MIC) Translate strings from AMR package Pattern Matching Age in years of individuals Join microorganisms to a data set Principal Component Analysis (for AMR) Translate strings from AMR package PCA biplot with Join microorganisms to a data set Pattern Matching Principal Component Analysis (for AMR) A A data.frame with 49,462 observations and 2 variables: Note: As opposed to the Note: As opposed to the These functions rely on a vector of values, a a vector of values, a a character string containing a regular expression (or a character string containing a regular expression (or character string for CMI 2012 paper - function TB guideline - function German guideline - function Everything else: A A data.frame with 5,583 observations and 2 variables: A A data.frame with 67,151 observations and 16 variables: A A data.frame with 12,708 observations and 4 variables: This helper function is used by Any user input value(s) A full taxonomic name, that exists in The level of uncertainty set in The matching score is based on four parameters: A human pathogenic prevalence \(P\), that is categorised into group 1, 2 and 3 (see A kingdom index \(K\) is set as follows: Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, and all others = 5; The level of uncertainty \(U\) that is needed to get to a result (1 to 3, see The Levenshtein distance \(L\) is the distance between the user input and all taxonomic full names, with the text length of the user input being the maximum distance. A modified version of the Levenshtein distance \(L'\) based on the text length of the full name \(F\) is calculated as: $$L' = F - \frac{0.5 \times L}{F}$$ The final matching score \(M\) is calculated as:
+$$M = L' \times \frac{1}{P \times K} * \frac{1}{U}$$ An A A named A A An integer in case of A list in case of A named character in case of A double in case of A character in all other cases The reference file can be a text file seperated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you need to have the The reference file can be a text file separated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you will need to have the The created compressed data file The function Reading an Excel file ( We save it as It has now created a file And now we can use it in our functions: If we edit the Excel file by, let's say, adding row 4 like this: To delete the reference data file, just use If the original Excel file is moved or deleted, the mo_source file will be removed upon the next use of a a data.frame containing columns with class M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. A A double or, when The function Please note that, in combination therapies, for and that, in combination therapies, for Using a a data.frame containing isolates. A A data.frame with extra class A A data.frame with 18,650 observations and 10 variables: a vector of values, a a vector of values, a as.rsi() for that.
@@ -451,7 +451,8 @@ The lifecycle of this function is maturing<
# 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)
-# }
+# }
+
@@ -247,7 +247,7 @@
Format
- data.frame with 2,000 observations and 49 variables:
+
date
date of receipt at the laboratoryhospital_id
ID of the hospital, from A to Dward_icu
logical to determine if ward is an intensive care unitFormat
- data.frame with 3,000 observations and 8 variables:
+
patient_id
ID of the patientdate
date of receipt at the laboratoryhospital
ID of the hospital, from A to C
x
- data.frame containing isolates.
col_date
@@ -369,14 +369,16 @@
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode (ref). 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 week. The resistance percentage of oxacillin of all S. aureus isolates would be overestimated, because you included this MRSA more than once. It would be selection bias.NA will be excluded as first isolate.filter_first_isolate() and filter_first_weighted_isolate() are helper functions to quickly filter on first isolates. The function filter_first_isolate() is essentially equal to one of: x %>% filter(first_isolate(., ...))
+filter_first_isolate() and filter_first_weighted_isolate() are helper functions to quickly filter on first isolates. The function filter_first_isolate() is essentially equal to one of: x %>% filter(first_isolate(., ...))
+
filter_first_weighted_isolate() is essentially equal to: x %>%
mutate(keyab = key_antibiotics(.)) %>%
mutate(only_weighted_firsts = first_isolate(x,
col_keyantibiotics = "keyab", ...)) %>%
filter(only_weighted_firsts == TRUE) %>%
- select(-only_weighted_firsts, -keyab)
+ select(-only_weighted_firsts, -keyab)
+
Key antibiotics
@@ -452,7 +454,8 @@ The lifecycle of this function is stablex$first_isolate_weighed <- first_isolate(x, col_keyantibiotics = 'keyab')
x$first_blood_isolate <- first_isolate(x, specimen_group = "Blood")
-}
+}
+
@@ -253,6 +253,7 @@
translate_ab = "name",
combine_SI = TRUE,
combine_IR = FALSE,
+ minimum = 30,
language = get_locale(),
nrow = NULL,
colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR = "#ff6961", R =
@@ -273,6 +274,7 @@
x = c("antibiotic", "interpretation"),
fill = "interpretation",
translate_ab = "name",
+ minimum = 30,
language = get_locale(),
combine_SI = TRUE,
combine_IR = FALSE,
@@ -294,6 +296,8 @@
position = NULL,
x = "antibiotic",
translate_ab = "name",
+ minimum = 30,
+ language = get_locale(),
combine_SI = TRUE,
combine_IR = FALSE,
datalabels.size = 3,
@@ -305,7 +309,7 @@
data
- data.frame with column(s) of class rsi (see as.rsi())rsi (see as.rsi())
+ position
@@ -343,6 +347,10 @@
combine_IR
combine_SI.
+
minimum
+ minimum will return NA with a warning. The default number of 30 isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
language
get_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
x
-
+
search_string
@@ -306,7 +306,8 @@ The lifecycle of this function is maturing<
df <- data.frame(AMP_ED2 = "S",
AMP_ED20 = "S")
guess_ab_col(df, "ampicillin")
-# [1] "AMP_ED20"
+# [1] "AMP_ED20"
+
@@ -431,7 +431,7 @@
-
@@ -544,18 +544,6 @@
-
-
-
-
-
-
-
-
-
-
@@ -568,12 +556,6 @@
-
-
- inner_join_microorganisms() left_join_microorganisms() right_join_microorganisms() full_join_microorganisms() semi_join_microorganisms() anti_join_microorganisms()
-
@@ -581,15 +563,33 @@
-
+
-
ggplot2
+
+
+
+ inner_join_microorganisms() left_join_microorganisms() right_join_microorganisms() full_join_microorganisms() semi_join_microorganisms() anti_join_microorganisms()
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/intrinsic_resistant.html b/docs/reference/intrinsic_resistant.html
index 1af42371e..52abb7c89 100644
--- a/docs/reference/intrinsic_resistant.html
+++ b/docs/reference/intrinsic_resistant.html
@@ -50,7 +50,7 @@
-
+
@@ -82,7 +82,7 @@
@@ -247,7 +247,7 @@
Format
- data.frame with 49,462 observations and 2 variables:
+
@@ -273,7 +273,8 @@
filter(antibiotic == "Vancomycin", microorganism %like% "Enterococcus") %>%
pull(microorganism)
# [1] "Enterococcus casseliflavus" "Enterococcus gallinarum"
-}
+}
+
@@ -277,7 +277,7 @@
microorganism
Name of the microorganismantibiotic
Name of the antibiotic drugDetails
- join() functions of dplyr, character vectors are supported and at default existing columns will get a suffix "2" and the newly joined columns will not get a suffix.join() functions of dplyr, character vectors are supported and at default existing columns will get a suffix "2" and the newly joined columns will not get a suffix.merge(), a base R function to do joins.Stable lifecycle
@@ -309,7 +309,8 @@ The lifecycle of this function is stablecolnames(df)
df_joined <- left_join_microorganisms(df, "bacteria")
colnames(df_joined)
-}
+}
+
@@ -258,7 +258,7 @@
x
- matrix or a data.framematrix or a data.frame
na.rm
diff --git a/docs/reference/like.html b/docs/reference/like.html
index e7122de85..e5b474c47 100644
--- a/docs/reference/like.html
+++ b/docs/reference/like.html
@@ -50,7 +50,7 @@
-
+
@@ -82,7 +82,7 @@
@@ -257,7 +257,7 @@
pattern
- character string for fixed = TRUE) to be matched in the given character vector. Coerced by as.character() to a character string if possible. If a character vector of length 2 or more is supplied, the first element is used with a warning.fixed = TRUE) to be matched in the given character vector. Coerced by as.character() to a character string if possible. If a character vector of length 2 or more is supplied, the first element is used with a warning.
ignore.case
diff --git a/docs/reference/mdro.html b/docs/reference/mdro.html
index 6fff058ef..65aa0845a 100644
--- a/docs/reference/mdro.html
+++ b/docs/reference/mdro.html
@@ -50,7 +50,7 @@
-
+
@@ -82,7 +82,7 @@
@@ -308,13 +308,13 @@
mdr_cmi2012() or mdro():
-Ordered factor with levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR)Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR)
mdr_tb() or mdro(..., guideline = "TB"):
-Ordered factor with levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistantNegative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant
mrgn() or mdro(..., guideline = "MRGN"):
-Ordered factor with levels Negative < 3MRGN < 4MRGNNegative < 3MRGN < 4MRGN
-Ordered factor with levels Negative < Positive, unconfirmed < Positive. The value "Positive, unconfirmed" means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) testsNegative < Positive, unconfirmed < Positive. The value "Positive, unconfirmed" means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) tests
Details
@@ -459,7 +459,8 @@ A microorganism is categorised as Susceptible, Increased exposure when
mutate(EUCAST = eucast_exceptional_phenotypes(.),
BRMO = brmo(.),
MRGN = mrgn(.))
-}
+}
+
@@ -247,7 +247,7 @@
Format
- data.frame with 5,583 observations and 2 variables:
+
diff --git a/docs/reference/microorganisms.html b/docs/reference/microorganisms.html
index 1f2b3b880..4039b3015 100644
--- a/docs/reference/microorganisms.html
+++ b/docs/reference/microorganisms.html
@@ -50,7 +50,7 @@
-
+
@@ -82,7 +82,7 @@
@@ -247,7 +247,7 @@
code
Commonly used code of a microorganismmo
ID of the microorganism in the microorganisms data setFormat
- data.frame with 67,151 observations and 16 variables:
+
mo
ID of microorganism as used by this packagefullname
Full name, like "Escherichia coli"kingdom, phylum, class, order, family, genus, species, subspecies
Taxonomic rank of the microorganismFormat
- data.frame with 12,708 observations and 4 variables:
+
fullname
Old full taxonomic name of the microorganismfullname_new
New full taxonomic name of the microorganismref
Author(s) and year of concerning scientific publicationCalculate the matching score for microorganisms
+ Source: R/mo_matching_score.R
+ mo_matching_score.Rdas.mo() to determine the most probable match of taxonomic records, based on user input.mo_matching_score(x, fullname, uncertainty = 1)
+
+ Arguments
+
+
+
+
+
+ x
+
+
+ fullname
+ microorganisms$fullname
+
+ uncertainty
+ as.mo(), see allow_uncertain in that function (here, it defaults to 1, but is automatically determined in as.mo() based on the number of transformations needed to get to a result)Details
+
+
+
+
+as.mo());as.mo());Examples
+ as.mo("E. coli")
+mo_uncertainties()
+
+
-
integer in case of mo_year()list in case of mo_taxonomy() and mo_info()character in case of mo_url()double in case of mo_snomed()character in all other casesmo_year()mo_taxonomy() and mo_info()mo_url()mo_snomed()Details
diff --git a/docs/reference/mo_source.html b/docs/reference/mo_source.html
index 1efc0d96a..2d801d620 100644
--- a/docs/reference/mo_source.html
+++ b/docs/reference/mo_source.html
@@ -51,7 +51,7 @@
-
+
@@ -83,7 +83,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
@@ -259,9 +259,10 @@ This is the fastest way to have your organisation (or analysis) specific codes p
Details
- readxl package installed.set_mo_source() will check the file for validity: it must be a data.frame, must have a column named "mo" which contains values from microorganisms$mo and must have a reference column with your own defined values. If all tests pass, set_mo_source() will read the file into R and export it to "~/.mo_source.rds". This compressed data file will then be used at default for MO determination (function as.mo() and consequently all mo_* functions like mo_genus() and mo_gramstain()). The location of the original file will be saved as option with options(mo_source = path). Its timestamp will be saved with options(mo_source_datetime = ...).get_mo_source() will return the data set by reading "~/.mo_source.rds" with readRDS(). If the original file has changed (the file defined with path), it will call set_mo_source() to update the data file automatically.readxl package installed.set_mo_source() will check the file for validity: it must be a data.frame, must have a column named "mo" which contains values from microorganisms$mo and must have a reference column with your own defined values. If all tests pass, set_mo_source() will read the file into R and export it to "~/.mo_source.rds" after the user specifically confirms and allows that this file will be created. For this reason, this function only works in interactive sessions."~/.mo_source.rds" will be used at default for MO determination (function as.mo() and consequently all mo_* functions like mo_genus() and mo_gramstain()). The location of the original file will be saved as an R option with options(mo_source = path). Its timestamp will be saved with options(mo_source_datetime = ...).get_mo_source() will return the data set by reading "~/.mo_source.rds" with readRDS(). If the original file has changed (by checking the aforementioned options mo_source and mo_source_datetime), it will call set_mo_source() to update the data file automatically..xlsx) with only one row has a size of 8-9 kB. The compressed file created with set_mo_source() will then have a size of 0.1 kB and can be read by get_mo_source() in only a couple of microseconds (millionths of a second).How to setup
@@ -278,7 +279,8 @@ This is the fastest way to have your organisation (or analysis) specific codes p
"home/me/ourcodes.xlsx". Now we have to set it as a source:set_mo_source("home/me/ourcodes.xlsx")
#> NOTE: Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'
-#> (columns "Organisation XYZ" and "mo")
+#> (columns "Organisation XYZ" and "mo")
+
"~/.mo_source.rds" with the contents of our Excel file. Only the first column with foreign values and the 'mo' column will be kept when creating the RDS file.as.mo("lab_mo_ecoli")
@@ -289,7 +291,8 @@ This is the fastest way to have your organisation (or analysis) specific codes p
# other input values still work too
as.mo(c("Escherichia coli", "E. coli", "lab_mo_ecoli"))
-#> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI
+#> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI
+
| A | B |
--|--------------------|--------------|
@@ -306,10 +309,12 @@ This is the fastest way to have your organisation (or analysis) specific codes p
#> [1] B_ESCHR_COLI
mo_genus("lab_Staph_aureus")
-#> [1] "Staphylococcus"
+#> [1] "Staphylococcus"
+
"", NULL or FALSE as input for set_mo_source():set_mo_source(NULL)
-# Removed mo_source file '~/.mo_source.rds'.
+# Removed mo_source file '~/.mo_source.rds'.
+
as.mo(). If the mo_source file is manually deleted (i.e. without using set_mo_source()), the references to the mo_source file will be removed upon the next use of as.mo().Stable lifecycle
diff --git a/docs/reference/plot.html b/docs/reference/plot.html
index 1b626cd7a..de16fc1dd 100644
--- a/docs/reference/plot.html
+++ b/docs/reference/plot.html
@@ -50,7 +50,7 @@
-
+
@@ -82,7 +82,7 @@
diff --git a/docs/reference/proportion.html b/docs/reference/proportion.html
index 357269270..f7efc04d9 100644
--- a/docs/reference/proportion.html
+++ b/docs/reference/proportion.html
@@ -51,7 +51,7 @@
-
+
@@ -83,7 +83,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
@@ -299,7 +299,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
data
- data.frame containing columns with class rsi (see as.rsi())rsi (see as.rsi())
translate_ab
@@ -324,7 +324,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
Value
- double or, when as_percent = TRUE, a character.as_percent = TRUE, a character.Details
resistance() is equal to the function proportion_R(). The function susceptibility() is equal to the function proportion_SI().only_all_tested = TRUE applies that: count_S() + count_I() + count_R() = count_all()
- proportion_S() + proportion_I() + proportion_R() = 1
+ proportion_S() + proportion_I() + proportion_R() = 1
+
only_all_tested = FALSE applies that: count_S() + count_I() + count_R() >= count_all()
- proportion_S() + proportion_I() + proportion_R() >= 1
+ proportion_S() + proportion_I() + proportion_R() >= 1
+
only_all_tested has no impact when only using one antibiotic as input.Stable lifecycle
@@ -470,7 +472,8 @@ A microorganism is categorised as Susceptible, Increased exposure when
genus == "Helicobacter") %>%
summarise(p = susceptibility(AMX, MTR), # amoxicillin with metronidazole
n = count_all(AMX, MTR))
-}
+}
+
@@ -273,7 +273,7 @@
)
# S3 method for resistance_predict
-plot(x, main = paste("Resistance Prediction of", x_name), ...)
+plot(x, main = paste("Resistance Prediction of", x_name), ...)
ggplot_rsi_predict(
x,
@@ -287,7 +287,7 @@
x
- data.frame containing isolates.
col_ab
@@ -345,7 +345,7 @@
Value
- data.frame with extra class resistance_predict with columns:
+
resistance_predict with columns:
yearvalue, the same as estimated when preserve_measurements = FALSE, and a combination of observed and estimated otherwisese_min, the lower bound of the standard error with a minimum of 0 (so the standard error will never go below 0%)Format
- data.frame with 18,650 observations and 10 variables:
+
guideline
Name of the guidelinemethod
Either "MIC" or "DISK"site
Body site, e.g. "Oral" or "Respiratory"
x
- matrix or a data.framematrix or a data.frame
na.rm
diff --git a/docs/reference/translate.html b/docs/reference/translate.html
index 8c9167e75..76af823f7 100644
--- a/docs/reference/translate.html
+++ b/docs/reference/translate.html
@@ -82,7 +82,7 @@
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index a6844cfc8..e21019553 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -123,6 +123,9 @@