vignettes/AMR.Rmd
AMR.Rmd
Now, let’s start the cleaning and the analysis!
So only 52.9% is suitable for resistance analysis! We can now filter +
So only 53.9% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -634,7 +634,7 @@ 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 10,583 isolates for analysis. Now our data looks +
So we end up with 10,772 isolates for analysis. Now our data looks like:
head(data_1st)
Time for the analysis!
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,583 -Available: 10,583 (100%, NA: 0 = 0%) +Length: 10,772 +Available: 10,772 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5408674
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 -0.5445669 +0.5404491 Hospital B -0.5366764 +0.5344360 Hospital C -0.5427167 +0.5435294 Hospital D -0.5413386 +0.5391467 @@ -1189,23 +1189,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5445669 -3175 +0.5404491 +3251 Hospital B -0.5366764 -3749 +0.5344360 +3688 Hospital C -0.5427167 -1627 +0.5435294 +1700 Hospital D -0.5413386 -2032 +0.5391467 +2133 @@ -1230,27 +1230,27 @@ therapies very easily: Escherichia -0.7651007 -0.8809266 -0.9742368 +0.7727176 +0.8703980 +0.9765908 Klebsiella -0.8231707 -0.9128920 -0.9799652 +0.8240271 +0.9060914 +0.9780034 Staphylococcus -0.7911677 -0.8910928 -0.9805389 +0.7986823 +0.8868960 +0.9795022 Streptococcus -0.5270522 +0.5368226 0.0000000 -0.5270522 +0.5368226 @@ -1278,23 +1278,23 @@ classes, use a antibiotic class selector such as Hospital A -54.5% -27.0% +54.0% +25.9% Hospital B -53.7% -26.7% +53.4% +25.6% Hospital C -54.3% -27.8% +54.4% +28.5% Hospital D -54.1% -27.0% +53.9% +26.0% @@ -1410,18 +1410,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 32 8 128 256 0.5 32 0.025 0.005 0.005 -# [10] 64 0.002 16 0.5 0.002 1 <=0.001 256 0.125 -# [19] 0.25 256 0.025 0.0625 8 0.0625 256 0.125 128 -# [28] 0.005 256 64 0.025 4 8 2 0.0625 8 -# [37] 32 1 16 0.5 0.01 16 8 0.025 16 -# [46] 0.005 <=0.001 1 1 64 0.25 16 <=0.001 0.025 -# [55] 256 0.002 0.25 0.01 2 1 0.5 0.5 4 -# [64] 0.01 0.5 2 0.5 0.0625 16 16 0.025 64 -# [73] 256 0.5 0.5 0.125 16 16 0.125 0.002 128 -# [82] <=0.001 64 64 64 0.005 0.25 64 0.5 256 -# [91] 0.025 16 4 0.025 0.0625 <=0.001 64 128 128 -# [100] 8
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 32 8 128 256 0.5 32 0.025 0.005 0.005 -# [10] 64 0.002 16 0.5 0.002 1 <=0.001 256 0.125 -# [19] 0.25 256 0.025 0.0625 8 0.0625 256 0.125 128 -# [28] 0.005 256 64 0.025 4 8 2 0.0625 8 -# [37] 32 1 16 0.5 0.01 16 8 0.025 16 -# [46] 0.005 <=0.001 1 1 64 0.25 16 <=0.001 0.025 -# [55] 256 0.002 0.25 0.01 2 1 0.5 0.5 4 -# [64] 0.01 0.5 2 0.5 0.0625 16 16 0.025 64 -# [73] 256 0.5 0.5 0.125 16 16 0.125 0.002 128 -# [82] <=0.001 64 64 64 0.005 0.25 64 0.5 256 -# [91] 0.025 16 4 0.025 0.0625 <=0.001 64 128 128 -# [100] 8
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 24 19 26 26 21 31 28 19 25 29 20 26 22 18 20 29 18 18 20 18 26 20 17 19 25 -# [26] 25 24 23 28 22 21 23 21 29 19 22 31 30 25 26 31 20 21 21 27 26 20 30 22 20 -# [51] 25 17 23 24 17 19 26 25 30 20 20 20 26 26 29 17 29 24 22 18 26 25 28 23 20 -# [76] 23 31 17 17 18 24 19 27 30 22 17 23 22 17 21 18 29 19 26 31 27 26 22 22 25
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 S S R S S S -# 2 R I S S R I -# 3 S S R S S R -# 4 R R I S S I -# 5 I S I I I S -# 6 R R S R I I +# 1 I S S R I R +# 2 R S S R R R +# 3 R S I S S S +# 4 I S I I R R +# 5 I S S I S S +# 6 S I R I R S # kanamycin -# 1 I -# 2 S -# 3 I -# 4 R -# 5 S -# 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 -3144 -62.88% -3144 -62.88% +3261 +65.22% +3261 +65.22% 2 Negative -1012 -20.24% -4156 -83.12% +959 +19.18% +4220 +84.40% 3 Multi-drug-resistant -482 -9.64% -4638 -92.76% +434 +8.68% +4654 +93.08% 4 Poly-resistant -261 -5.22% -4899 -97.98% +243 +4.86% +4897 +97.94% 5 Extensively drug-resistant -101 -2.02% +103 +2.06% 5000 100.00% diff --git a/articles/PCA.html b/articles/PCA.html index f24680ad..f601c23d 100644 --- a/articles/PCA.html +++ b/articles/PCA.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 diff --git a/articles/SPSS.html b/articles/SPSS.html index 8a73c7f4..03ce83e8 100644 --- a/articles/SPSS.html +++ b/articles/SPSS.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 @@ -168,7 +168,7 @@ Dr. Matthijs Berends - 30 December 2022 + 05 January 2023 Source: vignettes/SPSS.Rmd SPSS.Rmd @@ -247,7 +247,7 @@ data using a custom made website. The webdesign knowledge needed R has a huge community. Many R users just ask questions on websites like StackOverflow.com, the largest -online community for programmers. At the time of writing, 475,704 +online community for programmers. At the time of writing, 476,194 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of @@ -306,7 +306,7 @@ didn’t even had a professional user interface until the last decade language itself has been restyled completely by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else -that could compete. But now in 2022, I don’t see any reason why SPSS +that could compete. But now in 2023, I don’t see any reason why SPSS would be of any better use than R. diff --git a/articles/WHONET.html b/articles/WHONET.html index 2781fb84..3d4d3fa0 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 diff --git a/articles/datasets.html b/articles/datasets.html index e3fde4ff..d033941e 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 @@ -166,7 +166,7 @@ Data sets for download / own use - 30 December 2022 + 05 January 2023 Source: vignettes/datasets.Rmd datasets.Rmd diff --git a/articles/index.html b/articles/index.html index fe5084d2..7760b546 100644 --- a/articles/index.html +++ b/articles/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html index c0f2139d..cd843aca 100644 --- a/articles/resistance_predict.html +++ b/articles/resistance_predict.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 @@ -228,7 +228,7 @@ number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation: predict_TZP -# # A tibble: 31 × 7 +# # A tibble: 32 × 7 # year value se_min se_max observations observed estimated # * <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> # 1 2002 0.2 NA NA 15 0.2 0.0562 @@ -241,7 +241,7 @@ resistance and the standard error below and above the estimation: # 8 2009 0.0164 NA NA 61 0.0164 0.106 # 9 2010 0.0566 NA NA 53 0.0566 0.116 # 10 2011 0.183 NA NA 93 0.183 0.127 -# # … with 21 more rows +# # … with 22 more rows The function plot is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions: diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-4-1.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-4-1.png index b65784ad..d7f40bdc 100644 Binary files a/articles/resistance_predict_files/figure-html/unnamed-chunk-4-1.png and b/articles/resistance_predict_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-5-1.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-5-1.png index a419c10d..11a4302a 100644 Binary files a/articles/resistance_predict_files/figure-html/unnamed-chunk-5-1.png and b/articles/resistance_predict_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-5-2.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-5-2.png index 17da220b..261f9679 100644 Binary files a/articles/resistance_predict_files/figure-html/unnamed-chunk-5-2.png and b/articles/resistance_predict_files/figure-html/unnamed-chunk-5-2.png differ diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-6-1.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-6-1.png index c0fea349..bcd9c7c0 100644 Binary files a/articles/resistance_predict_files/figure-html/unnamed-chunk-6-1.png and b/articles/resistance_predict_files/figure-html/unnamed-chunk-6-1.png differ diff --git a/articles/resistance_predict_files/figure-html/unnamed-chunk-7-1.png b/articles/resistance_predict_files/figure-html/unnamed-chunk-7-1.png index 01b47606..6ce08ff0 100644 Binary files a/articles/resistance_predict_files/figure-html/unnamed-chunk-7-1.png and b/articles/resistance_predict_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html index 839c192e..c2386ce6 100644 --- a/articles/welcome_to_AMR.html +++ b/articles/welcome_to_AMR.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 diff --git a/authors.html b/authors.html index 8f423535..1cdb95d7 100644 --- a/authors.html +++ b/authors.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 diff --git a/index.html b/index.html index 5cad6e54..96b0e096 100644 --- a/index.html +++ b/index.html @@ -42,7 +42,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 diff --git a/news/index.html b/news/index.html index ba9d15c9..b194b2d6 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9076 + 1.8.2.9078 @@ -131,7 +131,134 @@ Source: NEWS.md
vignettes/SPSS.Rmd
SPSS.Rmd
R has a huge community.
Many R users just ask questions on websites like StackOverflow.com, the largest -online community for programmers. At the time of writing, 475,704 +online community for programmers. At the time of writing, 476,194 R-related questions have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of @@ -306,7 +306,7 @@ didn’t even had a professional user interface until the last decade language itself has been restyled completely by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else -that could compete. But now in 2022, I don’t see any reason why SPSS +that could compete. But now in 2023, I don’t see any reason why SPSS would be of any better use than R.
vignettes/datasets.Rmd
datasets.Rmd
predict_TZP -# # A tibble: 31 × 7 +# # A tibble: 32 × 7 # year value se_min se_max observations observed estimated # * <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> # 1 2002 0.2 NA NA 15 0.2 0.0562 @@ -241,7 +241,7 @@ resistance and the standard error below and above the estimation: # 8 2009 0.0164 NA NA 61 0.0164 0.106 # 9 2010 0.0566 NA NA 53 0.0566 0.116 # 10 2011 0.183 NA NA 93 0.183 0.127 -# # … with 21 more rows
The function plot is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:
plot
NEWS.md
(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
microorganisms
antivirals
rsi_confidence_interval()
mean_amr_distance()
add_custom_microorganisms()
The clinical breakpoints and intrinsic resistance of EUCAST 2022 and CLSI 2022 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 data set 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.
mo_matching_score()
prevalence
as.mo()
mo_*()
The new function add_custom_microorganisms() allows users to add custom microorganisms to the AMR package.
We also made the following changes regarding the included taxonomy or microorganisms functions:
subspecies
species
keep_synonyms
allow_uncertain
mo_reset_session()
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
as.ab()
ab_*()
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.
av
ab
as.av()
av_name()
av_atc()
av_synonyms()
av_from_text()
rsi_df()
proportion_df()
rsi_interpretation_history()
mo_current()
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()
tibble
NA
as.rsi(as.disk(NA), ...)
as.integer()
table()
factor
mo_gramstain()
droplevels()
mic
droplevels(..., as.mic = TRUE)
ab_from_text()
set_mo_source()
mdro()
random_*()
random_mic()
AMR::random_mic(10)
vctrs
disk
rsi
mo
df[carbapenems() == "R", ]
info = FALSE
ab_atc()
mo_snomed()
character
numeric
mo_shortname()
as.mic()
as.disk()
styler
&&
||
This changelog only contains changes from AMR v2.0 and later. For prior versions, please see our archive.