vignettes/AMR.Rmd
AMR.Rmd
So only 53.2% is suitable for resistance analysis! We can now filter +
So only 53.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
@@ -629,7 +629,7 @@ 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 10,638 isolates for analysis. Now our data looks +
So we end up with 10,672 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,638 -Available: 10,638 (100%, NA: 0 = 0%) +Length: 10,672 +Available: 10,672 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5418312
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
@@ -1149,19 +1149,19 @@ own: Hospital A -0.5577628 +0.5467467 Hospital B -0.5364725 +0.5433633 Hospital C -0.5250313 +0.5498433 Hospital D -0.5391595 +0.5359324 @@ -1186,23 +1186,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5577628 -3272 +0.5467467 +3166 Hospital B -0.5364725 -3674 +0.5433633 +3782 Hospital C -0.5250313 -1598 +0.5498433 +1595 Hospital D -0.5391595 -2094 +0.5359324 +2129 @@ -1227,27 +1227,27 @@ therapies very easily: Escherichia -0.7643678 -0.8769689 -0.9753086 +0.7757188 +0.8739084 +0.9782748 Klebsiella -0.8315080 -0.9106992 -0.9831508 +0.8259740 +0.9142857 +0.9844156 Staphylococcus -0.7935147 -0.9034663 -0.9836004 +0.7937432 +0.8821390 +0.9792652 Streptococcus -0.5526570 +0.5344911 0.0000000 -0.5526570 +0.5344911 @@ -1275,23 +1275,23 @@ classes, use a antibiotic class selector such as Hospital A -55.8% -26.8% +54.7% +26.2% Hospital B -53.6% -26.5% +54.3% +25.5% Hospital C -52.5% -25.1% +55.0% +27.5% Hospital D -53.9% -25.5% +53.6% +26.0% @@ -1407,18 +1407,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 8 <=0.001 64 128 8 0.01 16 0.0625 0.5 -# [10] 0.01 128 8 0.125 1 0.25 8 0.01 32 -# [19] 4 0.5 1 0.002 0.25 1 4 0.025 0.005 -# [28] 0.01 128 0.005 0.125 0.125 2 32 16 16 -# [37] <=0.001 0.01 0.125 0.5 <=0.001 4 0.25 8 8 -# [46] 0.025 4 1 128 0.0625 0.025 1 0.125 1 -# [55] 0.025 <=0.001 8 0.002 0.25 2 >=256 32 0.005 -# [64] 2 0.0625 4 0.5 128 0.002 0.01 1 0.005 -# [73] 0.125 0.125 0.25 <=0.001 8 128 0.125 128 4 -# [82] >=256 16 8 16 64 8 <=0.001 4 0.002 -# [91] 64 0.005 128 1 0.5 32 1 0.0625 0.01 -# [100] 0.0625
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 8 <=0.001 64 128 8 0.01 16 0.0625 0.5 -# [10] 0.01 128 8 0.125 1 0.25 8 0.01 32 -# [19] 4 0.5 1 0.002 0.25 1 4 0.025 0.005 -# [28] 0.01 128 0.005 0.125 0.125 2 32 16 16 -# [37] <=0.001 0.01 0.125 0.5 <=0.001 4 0.25 8 8 -# [46] 0.025 4 1 128 0.0625 0.025 1 0.125 1 -# [55] 0.025 <=0.001 8 0.002 0.25 2 >=256 32 0.005 -# [64] 2 0.0625 4 0.5 128 0.002 0.01 1 0.005 -# [73] 0.125 0.125 0.25 <=0.001 8 128 0.125 128 4 -# [82] >=256 16 8 16 64 8 <=0.001 4 0.002 -# [91] 64 0.005 128 1 0.5 32 1 0.0625 0.01 -# [100] 0.0625
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 22 30 22 24 23 21 30 20 25 25 31 26 21 31 20 24 18 31 28 28 27 28 20 18 29 -# [26] 27 31 26 19 28 28 17 29 30 23 22 31 24 31 17 18 24 26 21 26 17 21 26 19 27 -# [51] 22 19 19 21 23 24 27 20 31 18 23 22 30 24 31 26 25 22 22 24 23 30 19 28 18 -# [76] 28 29 25 22 25 23 27 20 23 20 19 23 18 22 28 21 23 26 17 17 18 27 26 28 30
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 S R S R R I -# 2 R S I I S I -# 3 I S I I S I -# 4 I I S R S R -# 5 S I S S R I -# 6 R S I R S R +# 1 S I R S S R +# 2 I I I I S I +# 3 I I R I I S +# 4 I S I R I R +# 5 I R I R R I +# 6 I R I R S S # kanamycin -# 1 I -# 2 I +# 1 S +# 2 S # 3 R -# 4 I -# 5 I -# 6 S
We can now add the interpretation of MDR-TB to our data set. You can use:
@@ -423,40 +423,40 @@ Unique: 5 1 Mono-resistant -3190 -63.80% -3190 -63.80% +3192 +63.84% +3192 +63.84% 2 Negative -978 -19.56% -4168 -83.36% +989 +19.78% +4181 +83.62% 3 Multi-drug-resistant -498 -9.96% -4666 -93.32% +453 +9.06% +4634 +92.68% 4 Poly-resistant -242 -4.84% -4908 -98.16% +268 +5.36% +4902 +98.04% 5 Extensively drug-resistant -92 -1.84% +98 +1.96% 5000 100.00% diff --git a/articles/PCA.html b/articles/PCA.html index afa66e67..c266dedd 100644 --- a/articles/PCA.html +++ b/articles/PCA.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9037 + 1.8.2.9038 diff --git a/articles/SPSS.html b/articles/SPSS.html index b6e229d7..2f5c06c3 100644 --- a/articles/SPSS.html +++ b/articles/SPSS.html @@ -38,7 +38,7 @@ AMR (for R) - 1.8.2.9037 + 1.8.2.9038 @@ -163,7 +163,7 @@ Dr. Matthijs Berends - 29 October 2022 + 30 October 2022 Source: vignettes/SPSS.Rmd SPSS.Rmd @@ -242,7 +242,7 @@ 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, 467,961 +online community for programmers. At the time of writing, 468,730 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 @@ -336,9 +336,10 @@ would be of any better use than R. ab_name("floxapen") # [1] "Flucloxacillin" ab_tradenames("floxapen") -# [1] "floxacillin" "floxapen" "floxapen sodium salt" -# [4] "fluclox" "flucloxacilina" "flucloxacillin" -# [7] "flucloxacilline" "flucloxacillinum" "fluorochloroxacillin" +# [1] "culpen" "floxacillin" "floxacillin sodium" +# [4] "floxapen" "floxapen sodium salt" "fluclox" +# [7] "flucloxacilina" "flucloxacillin" "flucloxacilline" +# [10] "flucloxacillinum" "fluorochloroxacillin" "staphylex" ab_atc("floxapen") # [1] "J01CF05"
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, 467,961 +online community for programmers. At the time of writing, 468,730 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 @@ -336,9 +336,10 @@ would be of any better use than R.
vignettes/datasets.Rmd
datasets.Rmd
This data set is in R available as microorganisms, after you load the AMR package.
microorganisms
AMR
It was last updated on 29 October 2022 17:02:33 UTC. Find more info +
It was last updated on 30 October 2022 13:33:29 UTC. Find more info about the structure of this data set here.
Direct download links:
antibiotics
A data set with 464 rows and 14 columns, containing the following +
A data set with 484 rows and 14 columns, containing the following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units and loinc.
This data set is in R available as antibiotics, after you load the AMR package.
The tab-separated text file and Microsoft Excel workbook, and SAS, SPSS and Stata files all contain the ATC codes, common abbreviations, @@ -650,7 +650,7 @@ inhibitors
This data set is in R available as antivirals, after you load the AMR package.
antivirals
This data set is in R available as rsi_translation, after you load the AMR package.
rsi_translation
This data set is in R available as dosage, after you load the AMR package.
dosage
This data set is in R available as example_isolates, after you load the AMR package.
example_isolates
This data set is in R available as example_isolates_unclean, after you load the AMR package.
example_isolates_unclean
After installing this package, R knows ~49,000 distinct microbial -species and all ~570 antibiotic, antimycotic and antiviral drugs by name +species and all ~590 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 diff --git a/authors.html b/authors.html index bdf9b7d9..90a22b42 100644 --- a/authors.html +++ b/authors.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9037 + 1.8.2.9038 diff --git a/index.html b/index.html index b0d1643d..d79a99b5 100644 --- a/index.html +++ b/index.html @@ -42,7 +42,7 @@ AMR (for R) - 1.8.2.9037 + 1.8.2.9038 diff --git a/news/index.html b/news/index.html index 7e2137f2..58d1d2db 100644 --- a/news/index.html +++ b/news/index.html @@ -10,7 +10,7 @@ AMR (for R) - 1.8.2.9037 + 1.8.2.9038 @@ -127,10 +127,10 @@
This version will eventually become v2.0! We’re happy to reach a new major milestone soon!
as.rsi()
as.mo()
mo_*()
keep_synonyms
allow_uncertain
vars()
... %>% summarise_at(aminoglycosides(), resistance)
resistance()
B_ANAER
rsi_interpretation
NA
as.rsi(as.disk(NA), ...)
mo_shortname()
styler
After installing this package, R knows ~49,000 distinct microbial species and all ~570 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
After installing this package, R knows ~49,000 distinct microbial species and all ~590 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
This package can be used for:
Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF)
Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline from the last 10 years