vignettes/AMR.Rmd
AMR.Rmd
So only 52.7% is suitable for resistance analysis! We can now filter +
So only 53.8% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -634,11 +634,11 @@ on 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 10,540 isolates for analysis. Now our data looks +
So we end up with 10,762 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,540 -Available: 10,540 (100%, NA: 0 = 0%) +Length: 10,762 +Available: 10,762 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.542315
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 -0.5399227 +0.5354760 Hospital B -0.5454792 +0.5500402 Hospital C -0.5585914 +0.5372946 Hospital D -0.5274725 +0.5447796 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5399227 -3106 +0.5354760 +3298 Hospital B -0.5454792 -3694 +0.5500402 +3727 Hospital C -0.5585914 -1647 +0.5372946 +1582 Hospital D -0.5274725 -2093 +0.5447796 +2155 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7834758 -0.8691650 -0.9783037 +0.7640257 +0.8713062 +0.9781585 Klebsiella -0.8121264 -0.8983775 -0.9803587 +0.8166804 +0.8976053 +0.9785301 Staphylococcus -0.7984352 -0.8930700 -0.9813711 +0.8016679 +0.8836113 +0.9815083 Streptococcus -0.5278040 +0.5463966 0.0000000 -0.5278040 +0.5463966 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -54.0% -26.2% +53.5% +25.9% Hospital B -54.5% -25.9% +55.0% +26.5% Hospital C -55.9% -27.6% +53.7% +27.1% Hospital D -52.7% -25.0% +54.5% +26.2% @@ -1410,16 +1410,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 4 0.125 1 2 1 0.25 4 >=256 2 0.001 -# [11] 0.01 0.25 0.002 32 0.125 0.005 0.01 0.001 2 0.025 -# [21] 8 0.0625 0.002 2 16 128 0.005 0.125 0.01 2 -# [31] 0.01 0.125 4 0.002 0.005 4 1 0.125 2 0.002 -# [41] 0.025 0.125 16 2 0.125 0.01 0.0625 0.125 0.005 4 -# [51] 16 0.001 0.005 1 4 1 >=256 0.002 0.001 0.5 -# [61] 32 0.5 0.005 0.125 0.002 2 0.005 0.025 0.002 0.002 -# [71] 0.25 128 64 64 2 0.001 0.25 32 32 0.025 -# [81] 64 0.125 16 8 0.5 8 4 0.01 0.001 0.001 -# [91] 32 0.002 0.0625 128 0.0625 128 0.001 32 2 0.002
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 4 0.125 1 2 1 0.25 4 >=256 2 0.001 -# [11] 0.01 0.25 0.002 32 0.125 0.005 0.01 0.001 2 0.025 -# [21] 8 0.0625 0.002 2 16 128 0.005 0.125 0.01 2 -# [31] 0.01 0.125 4 0.002 0.005 4 1 0.125 2 0.002 -# [41] 0.025 0.125 16 2 0.125 0.01 0.0625 0.125 0.005 4 -# [51] 16 0.001 0.005 1 4 1 >=256 0.002 0.001 0.5 -# [61] 32 0.5 0.005 0.125 0.002 2 0.005 0.025 0.002 0.002 -# [71] 0.25 128 64 64 2 0.001 0.25 32 32 0.025 -# [81] 64 0.125 16 8 0.5 8 4 0.01 0.001 0.001 -# [91] 32 0.002 0.0625 128 0.0625 128 0.001 32 2 0.002
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 27 28 28 21 22 25 29 24 25 25 28 30 27 23 25 23 30 29 31 23 17 18 18 28 28 -# [26] 17 20 23 22 23 31 22 23 25 25 20 28 31 28 20 18 24 20 26 26 21 21 20 28 27 -# [51] 30 19 20 31 27 20 21 27 27 30 18 24 18 19 22 20 31 26 25 26 26 29 22 24 22 -# [76] 23 25 27 21 29 17 24 22 21 17 20 28 20 27 20 19 21 29 29 31 27 25 27 21 30
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 R I I I I R -# 2 I S R I R S -# 3 R S S R R S -# 4 R S S S S S -# 5 S I S S I R -# 6 R S I S R S +# 1 S S I I R R +# 2 I R S I R R +# 3 R I I I R R +# 4 R R R R I R +# 5 S R I I R R +# 6 I R R S S I # kanamycin # 1 R -# 2 R -# 3 S -# 4 R -# 5 I -# 6 R
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 -3280 -65.60% -3280 -65.60% +3215 +64.30% +3215 +64.30% 2 Negative -952 -19.04% -4232 -84.64% +978 +19.56% +4193 +83.86% 3 Multi-drug-resistant -402 -8.04% -4634 -92.68% +447 +8.94% +4640 +92.80% 4 Poly-resistant -241 -4.82% -4875 -97.50% +249 +4.98% +4889 +97.78% 5 Extensively drug-resistant -125 -2.50% +111 +2.22% 5000 100.00% diff --git a/articles/PCA.html b/articles/PCA.html index 252148ff..056a20a7 100644 --- a/articles/PCA.html +++ b/articles/PCA.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 @@ -280,7 +280,7 @@ function: # [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales" # Importance of components: # PC1 PC2 PC3 PC4 PC5 PC6 PC7 -# Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 +# Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 # Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 # Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
# Groups (n=4, named as 'order'): diff --git a/articles/SPSS.html b/articles/SPSS.html index 4ce59813..98b89032 100644 --- a/articles/SPSS.html +++ b/articles/SPSS.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 @@ -168,7 +168,7 @@ Dr. Matthijs Berends - 27 December 2022 + 28 December 2022 Source: vignettes/SPSS.Rmd SPSS.Rmd diff --git a/articles/WHONET.html b/articles/WHONET.html index d328fc7a..3513015e 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/articles/datasets.html b/articles/datasets.html index 20c9a33d..ec0e34a9 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 @@ -166,7 +166,7 @@ Data sets for download / own use - 27 December 2022 + 28 December 2022 Source: vignettes/datasets.Rmd datasets.Rmd diff --git a/articles/index.html b/articles/index.html index f212b4a1..740d90a7 100644 --- a/articles/index.html +++ b/articles/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html index b093192a..0ff7fbe6 100644 --- a/articles/resistance_predict.html +++ b/articles/resistance_predict.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html index 3bd50df8..d7197c7c 100644 --- a/articles/welcome_to_AMR.html +++ b/articles/welcome_to_AMR.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/authors.html b/authors.html index 68e4e7c2..d140302b 100644 --- a/authors.html +++ b/authors.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/index.html b/index.html index 4df48a31..ecd2ccd7 100644 --- a/index.html +++ b/index.html @@ -42,7 +42,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/news/index.html b/news/index.html index 06591fc6..cb756b15 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 @@ -132,7 +132,7 @@ -AMR 1.8.2.9071 +AMR 1.8.2.9072 (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 @@ -145,19 +145,19 @@ Many new interesting functions, such as rsi_confidence_interval() and mean_amr_distance(), and add_custom_microorganisms() to add custom microorganisms to this package Many small bug fixes -New +New -Interpretation of MIC and disk diffusion values +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. The new function add_custom_microorganisms() allows users to add custom microorganisms to the AMR package. @@ -181,7 +181,7 @@ 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) @@ -193,14 +193,14 @@ 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. @@ -248,7 +248,7 @@ 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 diff --git a/pkgdown.yml b/pkgdown.yml index 48373026..0f983d85 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: 2022-12-27T15:09Z +last_built: 2022-12-28T13:24Z 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 ace50a99..2e4561f5 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/AMR.html b/reference/AMR.html index e67fcbe3..1acfc87a 100644 --- a/reference/AMR.html +++ b/reference/AMR.html @@ -62,7 +62,7 @@ Principal component analysis for AMR AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/Rplot005.png b/reference/Rplot005.png index f52008bb..b606fb89 100644 Binary files a/reference/Rplot005.png and b/reference/Rplot005.png differ diff --git a/reference/Rplot006.png b/reference/Rplot006.png index 7cc7ebde..dba14580 100644 Binary files a/reference/Rplot006.png and b/reference/Rplot006.png differ diff --git a/reference/Rplot007.png b/reference/Rplot007.png index f02a10f6..baad59da 100644 Binary files a/reference/Rplot007.png and b/reference/Rplot007.png differ diff --git a/reference/Rplot008.png b/reference/Rplot008.png index bffb63d8..6789b51b 100644 Binary files a/reference/Rplot008.png and b/reference/Rplot008.png differ diff --git a/reference/Rplot009.png b/reference/Rplot009.png index 9fe8dc22..2265be79 100644 Binary files a/reference/Rplot009.png and b/reference/Rplot009.png differ diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 823e4c33..ba24bf92 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/WHONET.html b/reference/WHONET.html index 78765642..677ddbfd 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 9b676d4e..387f3277 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/ab_property.html b/reference/ab_property.html index 2e07d1c9..2fe5e152 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index bbabe83c..142afe7d 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index 6b0a2550..89c7d5f8 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 @@ -146,7 +146,7 @@ Arguments x -a data.frame resembling the microorganisms data set, at least containing columns "mo", "genus" and "species" +a data.frame resembling the microorganisms data set, at least containing columns "genus" and "species" @@ -154,7 +154,7 @@ This function will fill in missing taxonomy for you, if specific taxonomic columns are missing, see Examples. Important: Due to how R works, the add_custom_microorganisms() function has to be run in every R session - added microorganisms are not stored between sessions and are thus lost when R is exited. There are two ways to automate this process: -Method 1: Save the microorganisms to a local or remote file (can even be the internet). To use this method:Create a data set in the structure of the microorganisms data set (containing at the very least columns "ab" and "name") and save it with saveRDS() to a location of choice, e.g. "~/my_custom_mo.rds", or any remote location. +Method 1: Save the microorganisms to a local or remote file (can even be the internet). To use this method:Create a data set in the structure of the microorganisms data set (containing at the very least columns "genus" and "species") and save it with saveRDS() to a location of choice, e.g. "~/my_custom_mo.rds", or any remote location. Set the file location to the AMR_custom_mo R option: options(AMR_custom_mo = "~/my_custom_mo.rds"). This can even be a remote file location, such as an https URL. Since options are not saved between R sessions, it is best to save this option to the .Rprofile file so that it will loaded on start-up of R. To do this, open the .Rprofile file using e.g. utils::file.edit("~/.Rprofile"), add this text and save the file: # Add custom microorganism codes: options(AMR_custom_mo = "~/my_custom_mo.rds") @@ -164,8 +164,7 @@ # Add custom antibiotic drug codes: library(AMR) add_custom_microorganisms( - data.frame(mo = "ENT_ASB_CLO", - genus = "Enterobacter", + data.frame(genus = "Enterobacter", species = "asburiae/cloacae") ) Use clear_custom_microorganisms() to clear the previously added antimicrobials. @@ -187,32 +186,30 @@ # now add a custom entry - it will be considered by as.mo() and # all mo_*() functions add_custom_microorganisms( - data.frame(mo = "ENT_ASB_CLO", - genus = "Enterobacter", + data.frame(genus = "Enterobacter", species = "asburiae/cloacae" ) ) #> ℹ Added one record to the internal microorganisms data set. -# "ENT_ASB_CLO" is now a new microorganism: +# E. asburiae/cloacae is now a new microorganism: mo_name("Enterobacter asburiae/cloacae") #> [1] "Enterobacter asburiae/cloacae" -as.mo("ent_asb_clo") +# its code: +as.mo("Enterobacter asburiae/cloacae") #> Class 'mo' -#> [1] ENT_ASB_CLO -mo_name("ent_asb_clo") -#> [1] "Enterobacter asburiae/cloacae" +#> [1] CUSTOM_ENTRBC_A_C # all internal algorithms will work as well: mo_name("Ent asburia cloacae") #> [1] "Enterobacter asburiae/cloacae" # and even the taxonomy was added based on the genus! -mo_family("ent_asb_clo") +mo_family("E. asburiae/cloacae") #> [1] "Enterobacteriaceae" mo_gramstain("Enterobacter asburiae/cloacae") #> [1] "Gram-negative" -mo_info("ent_asb_clo") +mo_info("Enterobacter asburiae/cloacae") #> $kingdom #> [1] "Bacteria" #> @@ -235,7 +232,7 @@ #> [1] "asburiae/cloacae" #> #> $subspecies -#> [1] NA +#> [1] "" #> #> $status #> [1] "accepted" diff --git a/reference/age.html b/reference/age.html index 86c024ff..5794b255 100644 --- a/reference/age.html +++ b/reference/age.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 @@ -195,16 +195,16 @@ df #> birth_date age age_exact age_at_y2k -#> 1 1965-03-23 57 57.76438 34 -#> 2 1979-07-13 43 43.45753 20 -#> 3 1939-08-31 83 83.32329 60 -#> 4 1948-04-18 74 74.69315 51 -#> 5 1947-06-06 75 75.55890 52 -#> 6 1997-09-22 25 25.26301 2 -#> 7 1977-04-26 45 45.67123 22 -#> 8 1999-10-17 23 23.19452 0 -#> 9 1984-10-22 38 38.18082 15 -#> 10 1944-01-31 78 78.90411 55 +#> 1 1972-04-12 50 50.71233 27 +#> 2 1937-10-17 85 85.19726 62 +#> 3 1956-07-07 66 66.47671 43 +#> 4 1967-07-04 55 55.48493 32 +#> 5 1962-01-28 60 60.91507 37 +#> 6 1962-12-05 60 60.06301 37 +#> 7 1961-03-12 61 61.79726 38 +#> 8 1967-09-09 55 55.30137 32 +#> 9 1967-01-22 55 55.93151 32 +#> 10 1936-09-15 86 86.28493 63 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index cbee1fdc..3dbc1497 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/ggplot_rsi.html b/reference/ggplot_rsi.html index 9c10ac96..2312a05d 100644 --- a/reference/ggplot_rsi.html +++ b/reference/ggplot_rsi.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index 2e30005c..77994cff 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/index.html b/reference/index.html index d95bb30c..8f89541e 100644 --- a/reference/index.html +++ b/reference/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index d0e0adbe..09bc2c0b 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index 2c65744c..eee55736 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/join.html b/reference/join.html index 27a81153..154ba95b 100644 --- a/reference/join.html +++ b/reference/join.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index e05d3e98..599bac23 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index dd3c7902..f97377da 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9071 + 1.8.2.9072 @@ -172,9 +172,9 @@ Examples kurtosis(rnorm(10000)) -#> [1] 3.135239 +#> [1] 3.008528 kurtosis(rnorm(10000), excess = TRUE) -#> [1] -0.01331429 +#> [1] -0.0002890877
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
rsi_confidence_interval()
mean_amr_distance()
add_custom_microorganisms()
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_*()
The new function add_custom_microorganisms() allows users to add custom microorganisms to the AMR package.
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
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_df()
proportion_df()
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
a data.frame resembling the microorganisms data set, at least containing columns "mo", "genus" and "species"
a data.frame resembling the microorganisms data set, at least containing columns "genus" and "species"
This function will fill in missing taxonomy for you, if specific taxonomic columns are missing, see Examples.
Important: Due to how R works, the add_custom_microorganisms() function has to be run in every R session - added microorganisms are not stored between sessions and are thus lost when R is exited.
There are two ways to automate this process:
Method 1: Save the microorganisms to a local or remote file (can even be the internet). To use this method:
Create a data set in the structure of the microorganisms data set (containing at the very least columns "ab" and "name") and save it with saveRDS() to a location of choice, e.g. "~/my_custom_mo.rds", or any remote location.
saveRDS()
"~/my_custom_mo.rds"
Create a data set in the structure of the microorganisms data set (containing at the very least columns "genus" and "species") and save it with saveRDS() to a location of choice, e.g. "~/my_custom_mo.rds", or any remote location.
Set the file location to the AMR_custom_mo R option: options(AMR_custom_mo = "~/my_custom_mo.rds"). This can even be a remote file location, such as an https URL. Since options are not saved between R sessions, it is best to save this option to the .Rprofile file so that it will loaded on start-up of R. To do this, open the .Rprofile file using e.g. utils::file.edit("~/.Rprofile"), add this text and save the file:
AMR_custom_mo
options(AMR_custom_mo = "~/my_custom_mo.rds")
.Rprofile
utils::file.edit("~/.Rprofile")
# Add custom microorganism codes: options(AMR_custom_mo = "~/my_custom_mo.rds")
# Add custom antibiotic drug codes: library(AMR) add_custom_microorganisms( - data.frame(mo = "ENT_ASB_CLO", - genus = "Enterobacter", + data.frame(genus = "Enterobacter", species = "asburiae/cloacae") )
Use clear_custom_microorganisms() to clear the previously added antimicrobials.
clear_custom_microorganisms()
kurtosis(rnorm(10000)) -#> [1] 3.135239 +#> [1] 3.008528 kurtosis(rnorm(10000), excess = TRUE) -#> [1] -0.01331429 +#> [1] -0.0002890877