vignettes/AMR.Rmd
AMR.Rmd
Now, let’s start the cleaning and the analysis!
So only 53% is suitable for resistance analysis! We can now filter on -it with the filter() function, also from the +
filter()
So only 52.8% is suitable for resistance analysis! We can now filter +on it with the filter() function, also from the dplyr package:
dplyr
data_1st <- data %>% @@ -634,7 +634,7 @@ it with the data_1st <- data %>% filter_first_isolate() # Including isolates from ICU.
data_1st <- data %>% filter_first_isolate() # Including isolates from ICU.
So we end up with 10,607 isolates for analysis. Now our data looks +
So we end up with 10,556 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,607 -Available: 10,607 (100%, NA: 0 = 0%) +Length: 10,556 +Available: 10,556 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5453946
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1152,19 +1152,19 @@ own: Hospital A -0.5539478 +0.5429724 Hospital B -0.5484475 +0.5454295 Hospital C -0.5440806 +0.5458488 Hospital D -0.5280418 +0.5428845 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5539478 -3179 +0.5429724 +3223 Hospital B -0.5484475 -3736 +0.5454295 +3632 Hospital C -0.5440806 -1588 +0.5458488 +1614 Hospital D -0.5280418 -2104 +0.5428845 +2087 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7746142 -0.8772006 -0.9780483 +0.7735184 +0.8766248 +0.9806125 Klebsiella -0.8227425 -0.8963211 -0.9790970 +0.8250853 +0.9052901 +0.9880546 Staphylococcus -0.7789356 -0.9036100 -0.9810197 +0.7991924 +0.8883994 +0.9801762 Streptococcus -0.5336788 +0.5299387 0.0000000 -0.5336788 +0.5299387 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -55.4% -27.0% +54.3% +25.1% Hospital B -54.8% -26.5% +54.5% +26.6% Hospital C -54.4% -26.2% +54.6% +26.7% Hospital D -52.8% -27.1% +54.3% +27.4% @@ -1410,16 +1410,18 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 8 16 16 0.01 8 0.01 0.001 0.01 0.125 32 -# [11] 2 4 0.002 0.002 0.002 16 0.0625 0.005 0.005 8 -# [21] 0.001 64 2 0.125 16 >=256 0.005 0.001 4 0.005 -# [31] 2 0.005 2 32 2 4 16 1 0.01 0.025 -# [41] 0.0625 0.01 8 128 0.25 0.01 0.01 128 0.0625 0.25 -# [51] 2 8 0.0625 32 0.01 32 32 0.01 8 0.002 -# [61] 0.25 4 2 0.25 16 64 4 >=256 64 64 -# [71] 64 32 32 0.25 >=256 0.125 0.01 0.0625 128 4 -# [81] 0.01 0.25 0.001 2 16 0.002 0.01 0.002 >=256 0.025 -# [91] 8 >=256 16 0.002 2 0.125 0.0625 0.002 >=256 0.025
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 8 16 16 0.01 8 0.01 0.001 0.01 0.125 32 -# [11] 2 4 0.002 0.002 0.002 16 0.0625 0.005 0.005 8 -# [21] 0.001 64 2 0.125 16 >=256 0.005 0.001 4 0.005 -# [31] 2 0.005 2 32 2 4 16 1 0.01 0.025 -# [41] 0.0625 0.01 8 128 0.25 0.01 0.01 128 0.0625 0.25 -# [51] 2 8 0.0625 32 0.01 32 32 0.01 8 0.002 -# [61] 0.25 4 2 0.25 16 64 4 >=256 64 64 -# [71] 64 32 32 0.25 >=256 0.125 0.01 0.0625 128 4 -# [81] 0.01 0.25 0.001 2 16 0.002 0.01 0.002 >=256 0.025 -# [91] 8 >=256 16 0.002 2 0.125 0.0625 0.002 >=256 0.025
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 17 22 17 18 28 30 30 31 20 20 17 23 18 30 27 28 18 27 25 25 25 25 17 19 20 -# [26] 24 19 25 20 23 30 25 23 18 20 23 24 29 19 27 29 21 17 21 25 25 17 29 24 20 -# [51] 24 20 17 30 23 22 25 17 17 21 19 20 27 29 26 25 22 21 29 31 19 21 29 24 29 -# [76] 31 24 22 20 22 28 18 28 28 17 20 23 31 19 23 26 31 19 30 29 17 26 28 29 30
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 S R R S I S -# 2 I R S S S S -# 3 I R S R S S -# 4 R R S I I R -# 5 S I R I S R -# 6 S I I I I R +# 1 R I I S S S +# 2 R R I R S R +# 3 R R I R R I +# 4 R R R R S S +# 5 S I R I I I +# 6 S R R S I S # kanamycin -# 1 R -# 2 S +# 1 S +# 2 I # 3 I -# 4 S -# 5 R -# 6 S
We can now add the interpretation of MDR-TB to our data set. You can use:
@@ -428,40 +428,40 @@ Unique: 5 1 Mono-resistant -3211 -64.22% -3211 -64.22% +3215 +64.30% +3215 +64.30% 2 Negative -1047 -20.94% -4258 -85.16% +997 +19.94% +4212 +84.24% 3 Multi-drug-resistant -410 -8.20% -4668 -93.36% +445 +8.90% +4657 +93.14% 4 Poly-resistant -248 -4.96% -4916 -98.32% +242 +4.84% +4899 +97.98% 5 Extensively drug-resistant -84 -1.68% +101 +2.02% 5000 100.00% diff --git a/articles/PCA.html b/articles/PCA.html index 49775cf0..c4e56e46 100644 --- a/articles/PCA.html +++ b/articles/PCA.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/articles/SPSS.html b/articles/SPSS.html index ffcd7e0e..c504c6a1 100644 --- a/articles/SPSS.html +++ b/articles/SPSS.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 @@ -168,7 +168,7 @@ Dr. Matthijs Berends - 20 December 2022 + 21 December 2022 Source: vignettes/SPSS.Rmd SPSS.Rmd diff --git a/articles/WHONET.html b/articles/WHONET.html index d3178e97..0c28e72b 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/articles/datasets.html b/articles/datasets.html index 09bb458f..2452ef54 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 @@ -166,7 +166,7 @@ Data sets for download / own use - 20 December 2022 + 21 December 2022 Source: vignettes/datasets.Rmd datasets.Rmd diff --git a/articles/index.html b/articles/index.html index c5197a7c..4ddd25a6 100644 --- a/articles/index.html +++ b/articles/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html index 8db0632d..8a9ef4b5 100644 --- a/articles/resistance_predict.html +++ b/articles/resistance_predict.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html index 681b1660..154ad85a 100644 --- a/articles/welcome_to_AMR.html +++ b/articles/welcome_to_AMR.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/authors.html b/authors.html index a3fdaad6..1e553637 100644 --- a/authors.html +++ b/authors.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/index.html b/index.html index 3968757a..d63c15e5 100644 --- a/index.html +++ b/index.html @@ -42,7 +42,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 @@ -176,7 +176,7 @@ Corrects for duplicate isolates, calculates and predicts AMR per antibiotic class Integrates with WHONET, ATC, EARS-Net, PubChem, LOINC and SNOMED CT -Works on Windows, macOS and Linux with all versions of R since R-3.0 and is ompletely dependency-free, highly suitable for places with limited resources +Works on Windows, macOS and Linux with all versions of R since R-3.0 and is completely dependency-free, highly suitable for places with limited resources diff --git a/news/index.html b/news/index.html index 7d5f22b0..c4ef4c69 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 @@ -132,7 +132,7 @@ -AMR 1.8.2.9068 +AMR 1.8.2.9069 (this beta version will eventually become v2.0! We’re happy to reach a new major milestone soon!) This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below. TL;DR @@ -146,19 +146,19 @@ Many small bug fixes -New - -Interpretation of MIC and disk diffusion values +New + +Interpretation of MIC and disk diffusion values EUCAST 2022 and CLSI 2022 guidelines have been added for as.rsi(). EUCAST 2022 (v12.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for eucast_rules() to apply EUCAST Expert Rules. The default guideline (EUCAST) can now be changed with the new AMR_guideline option, such as: options(AMR_guideline = "CLSI 2020"). Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013. - -Supported languages + +Supported languages We added support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. All antibiotic names are now available in these languages, and the AMR package will automatically determine a supported language based on the user system language. We are very grateful for the valuable input by our colleagues from other countries. The AMR package is now available in 16 languages and according to download stats used in almost all countries in the world! - -Microbiological taxonomy + +Microbiological taxonomy The microorganisms no longer relies on the Catalogue of Life, but on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the ‘backbone taxonomy’ from the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,400 taxonomic names from 2022. We previously relied on our own experience to categorise species into pathogenic groups, but we were very happy to encounter the very recent work of Bartlett et al. (2022, DOI 10.1099/mic.0.001269) who extensively studied medical-scientific literature to categorise all bacterial species into groups. See mo_matching_score() on how their work was incorporated into the prevalence column of the microorganisms data set. Using their results, the as.mo() and all mo_*() functions are now much better capable of converting user input to valid taxonomic records. We also made the following changes regarding the included taxonomy or microorganisms functions: @@ -180,8 +180,8 @@ The microorganisms.old data set was removed, and all previously accepted names are now included in the microorganisms data set. A new column status contains "accepted" for currently accepted names and "synonym" for taxonomic synonyms; currently invalid names. All previously accepted names now have a microorganisms ID and - if available - an LPSN, GBIF and SNOMED CT identifier. - -Antibiotic agents and selectors + +Antibiotic agents and selectors The new function add_custom_antimicrobials() allows users to add custom antimicrobial codes and names to the AMR package. The antibiotics data set was greatly updated: The following 20 antibiotics have been added (also includes the new J01RA ATC group): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), and thioacetazone (TAT) @@ -192,15 +192,15 @@ Support for antimicrobial interpretation of anaerobic bacteria, by adding a ‘placeholder’ code B_ANAER to the microorganisms data set and adding the breakpoints of anaerobics to the rsi_interpretation data set, which is used by as.rsi() for interpretion of MIC and disk diffusion values Also, we added support for using antibiotic selectors in scoped dplyr verbs (with or without using vars()), such as in: ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() for examples. - -Antiviral agents + +Antiviral agents We now added extensive support for antiviral agents! For the first time, the AMR package has extensive support for antiviral drugs and to work with their names, codes and other data in any way. The antivirals data set has been extended with 18 new drugs (also from the new J05AJ ATC group) and now also contains antiviral identifiers and LOINC codes A new data type av (antivirals) has been added, which is functionally similar to ab for antibiotics Functions as.av(), av_name(), av_atc(), av_synonyms(), av_from_text() have all been added as siblings to their ab_*() equivalents - -Other new functions + +Other new functions Function rsi_confidence_interval() to add confidence intervals in AMR calculation. This is now also included in rsi_df() and proportion_df(). Function mean_amr_distance() to calculate the mean AMR distance. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. Function rsi_interpretation_history() to view the history of previous runs of as.rsi(). This returns a ‘logbook’ with the selected guideline, reference table and specific interpretation of each row in a data set on which as.rsi() was run. @@ -209,7 +209,7 @@ -Changes +Changes Argument combine_IR has been removed from this package (affecting functions count_df(), proportion_df(), and rsi_df() and some plotting functions), since it was replaced with combine_SI three years ago Using units in ab_ddd(..., units = "...") had been deprecated for some time and is now not supported anymore. Use ab_ddd_units() instead. Support for data.frame-enhancing R packages, more specifically: data.table::data.table, janitor::tabyl, tibble::tibble, and tsibble::tsibble. AMR package functions that have a data set as output (such as rsi_df() and bug_drug_combinations()), will now return the same data type as the input. @@ -248,13 +248,14 @@ Cleaning columns with as.rsi(), as.mic(), or as.disk() will now show the column name in the warning for invalid results -Other +Other New website to make use of the new Bootstrap 5 and pkgdown 2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, and Jonas Salm as contributors, to thank them for their valuable input All R and Rmd files in this project are now styled using the styler package Set scalar conditional expressions (&& and ||) where possible to comply with the upcoming R 4.3 An enormous lot of code cleaning, fixing some small bugs on the way - +This changelog only contains changes from AMR v2.0 and later. For prior versions, please see our archive. + diff --git a/pkgdown.yml b/pkgdown.yml index d04a313b..46290724 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -11,7 +11,7 @@ articles: datasets: datasets.html resistance_predict: resistance_predict.html welcome_to_AMR: welcome_to_AMR.html -last_built: 2022-12-20T21:45Z +last_built: 2022-12-21T08:18Z urls: reference: https://msberends.github.io/AMR/reference article: https://msberends.github.io/AMR/articles diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html index 0d4dd4b5..f7a269bc 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/AMR.html b/reference/AMR.html index f55988ec..3abfee68 100644 --- a/reference/AMR.html +++ b/reference/AMR.html @@ -62,7 +62,7 @@ Principal component analysis for AMR AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/Rplot005.png b/reference/Rplot005.png index b36fcad3..2fa10ddf 100644 Binary files a/reference/Rplot005.png and b/reference/Rplot005.png differ diff --git a/reference/Rplot006.png b/reference/Rplot006.png index 37ca23ac..536abd05 100644 Binary files a/reference/Rplot006.png and b/reference/Rplot006.png differ diff --git a/reference/Rplot007.png b/reference/Rplot007.png index 82b93218..fc1dde1b 100644 Binary files a/reference/Rplot007.png and b/reference/Rplot007.png differ diff --git a/reference/Rplot008.png b/reference/Rplot008.png index 583f888e..b1611b7a 100644 Binary files a/reference/Rplot008.png and b/reference/Rplot008.png differ diff --git a/reference/Rplot009.png b/reference/Rplot009.png index 98ddd32c..132211bc 100644 Binary files a/reference/Rplot009.png and b/reference/Rplot009.png differ diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 3dfdf46e..74329875 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/WHONET.html b/reference/WHONET.html index cba152c6..3b6df662 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 1f48a060..54237679 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/ab_property.html b/reference/ab_property.html index 17fee227..0584a41b 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index ea7a2d0a..ee82bc22 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/age.html b/reference/age.html index 0b9cd1f8..edb98180 100644 --- a/reference/age.html +++ b/reference/age.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 @@ -195,16 +195,16 @@ df #> birth_date age age_exact age_at_y2k -#> 1 1999-08-14 23 23.35068 0 -#> 2 1939-01-30 83 83.88767 60 -#> 3 1974-06-28 48 48.47945 25 -#> 4 1976-03-17 46 46.76164 23 -#> 5 1985-12-22 36 36.99452 14 -#> 6 1960-12-10 62 62.02740 39 -#> 7 1996-02-09 26 26.86027 3 -#> 8 1969-05-20 53 53.58630 30 -#> 9 1950-09-14 72 72.26575 49 -#> 10 1962-08-23 60 60.32603 37 +#> 1 1970-07-07 52 52.45753 29 +#> 2 1997-08-06 25 25.37534 2 +#> 3 1933-06-01 89 89.55616 66 +#> 4 1956-01-20 66 66.91781 43 +#> 5 1937-05-20 85 85.58904 62 +#> 6 1967-08-29 55 55.31233 32 +#> 7 1989-05-15 33 33.60274 10 +#> 8 1988-11-01 34 34.13699 11 +#> 9 1931-08-12 91 91.35890 68 +#> 10 1936-08-10 86 86.36438 63 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 36d7adf4..7b42c1f2 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/ggplot_rsi.html b/reference/ggplot_rsi.html index 7cfae5a8..462a64b1 100644 --- a/reference/ggplot_rsi.html +++ b/reference/ggplot_rsi.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index 35176429..e429201b 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/index.html b/reference/index.html index 7d8babda..c494c981 100644 --- a/reference/index.html +++ b/reference/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index 09508944..6f8f7890 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index 2b94c6a5..5e375bdc 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/join.html b/reference/join.html index ada73b77..3417639a 100644 --- a/reference/join.html +++ b/reference/join.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index f5f842f1..1befad9b 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index abd037c9..e0e5d253 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9068 + 1.8.2.9069 @@ -172,9 +172,9 @@ Examples kurtosis(rnorm(10000)) -#> [1] 2.979785 +#> [1] 2.940008 kurtosis(rnorm(10000), excess = TRUE) -#> [1] -0.01457832 +#> [1] -0.03402398
vignettes/SPSS.Rmd
SPSS.Rmd
vignettes/datasets.Rmd
datasets.Rmd
(this beta version will eventually become v2.0! We’re happy to reach a new major milestone soon!)
This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below.
TL;DR
EUCAST 2022 and CLSI 2022 guidelines have been added for as.rsi(). EUCAST 2022 (v12.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for eucast_rules() to apply EUCAST Expert Rules. The default guideline (EUCAST) can now be changed with the new AMR_guideline option, such as: options(AMR_guideline = "CLSI 2020").
as.rsi()
eucast_rules()
AMR_guideline
options(AMR_guideline = "CLSI 2020")
Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013.
We added support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. All antibiotic names are now available in these languages, and the AMR package will automatically determine a supported language based on the user system language.
We are very grateful for the valuable input by our colleagues from other countries. The AMR package is now available in 16 languages and according to download stats used in almost all countries in the world!
AMR
The microorganisms no longer relies on the Catalogue of Life, but on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the ‘backbone taxonomy’ from the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,400 taxonomic names from 2022.
microorganisms
We previously relied on our own experience to categorise species into pathogenic groups, but we were very happy to encounter the very recent work of Bartlett et al. (2022, DOI 10.1099/mic.0.001269) who extensively studied medical-scientific literature to categorise all bacterial species into groups. See mo_matching_score() on how their work was incorporated into the prevalence column of the microorganisms data set. Using their results, the as.mo() and all mo_*() functions are now much better capable of converting user input to valid taxonomic records.
mo_matching_score()
prevalence
as.mo()
mo_*()
We also made the following changes regarding the included taxonomy or microorganisms functions:
microorganisms.old
status
"accepted"
"synonym"
The new function add_custom_antimicrobials() allows users to add custom antimicrobial codes and names to the AMR package.
add_custom_antimicrobials()
The antibiotics data set was greatly updated:
antibiotics
B_ANAER
rsi_interpretation
Also, we added support for using antibiotic selectors in scoped dplyr verbs (with or without using vars()), such as in: ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() for examples.
vars()
... %>% summarise_at(aminoglycosides(), resistance)
resistance()
We now added extensive support for antiviral agents! For the first time, the AMR package has extensive support for antiviral drugs and to work with their names, codes and other data in any way.
antivirals
av
ab
as.av()
av_name()
av_atc()
av_synonyms()
av_from_text()
ab_*()
rsi_confidence_interval()
rsi_df()
proportion_df()
mean_amr_distance()
rsi_interpretation_history()
combine_IR
count_df()
combine_SI
units
ab_ddd(..., units = "...")
ab_ddd_units()
data.frame
data.table::data.table
janitor::tabyl
tibble::tibble
tsibble::tsibble
bug_drug_combinations()
as.mic()
as.disk()
styler
&&
||
This changelog only contains changes from AMR v2.0 and later. For prior versions, please see our archive.
kurtosis(rnorm(10000)) -#> [1] 2.979785 +#> [1] 2.940008 kurtosis(rnorm(10000), excess = TRUE) -#> [1] -0.01457832 +#> [1] -0.03402398