Used in 175 countries
Since its first public release in early 2018, this R package has been used in almost all countries in the world. Click the map to enlarge and to see the country names.
diff --git a/docs/news/index.html b/docs/news/index.html
index 802ff96e..2fcfb759 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -17,7 +17,7 @@
How to conduct AMR data analysis
Dr. Matthijs Berends
-06 December 2021
+11 December 2021
Source:vignettes/AMR.Rmd
AMR.RmdNote: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 06 December 2021.
+Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 11 December 2021.
Introduction
@@ -231,21 +231,21 @@So only 52.8% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 53.7% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
data_1st <- data %>%
filter_first_isolate()So we end up with 10,565 isolates for analysis. Now our data looks like:
+So we end up with 10,742 isolates for analysis. Now our data looks like:
head(data_1st)| 3 | -2012-09-21 | -Q2 | -Hospital C | -B_STRPT_PNMN | -R | -R | -R | -R | +1 | +2011-06-27 | +V5 | +Hospital B | +B_ESCHR_COLI | +S | +S | +S | +S | F | -Gram-positive | -Streptococcus | -pneumoniae | +Gram-negative | +Escherichia | +coli | TRUE |
| 4 | -2011-11-26 | -E2 | -Hospital B | -B_STRPT_PNMN | -S | -S | -R | -R | -M | -Gram-positive | -Streptococcus | -pneumoniae | -TRUE | -||||||||||||
| 6 | -2016-12-04 | -K5 | +3 | +2013-08-29 | +Q6 | Hospital A | B_ESCHR_COLI | +R | +R | +S | +S | +F | +Gram-negative | +Escherichia | +coli | +TRUE | +|||||||||
| 4 | +2012-05-28 | +S1 | +Hospital A | +B_KLBSL_PNMN | +R | I | +R | +S | +F | +Gram-negative | +Klebsiella | +pneumoniae | +TRUE | +||||||||||||
| 6 | +2011-12-02 | +E2 | +Hospital D | +B_ESCHR_COLI | +S | S | S | S | @@ -597,52 +613,36 @@ Longest: 1coli | TRUE | |||||||||||||||
| 18 | -2010-04-02 | -J4 | -Hospital B | -B_STPHY_AURS | -I | -S | -S | -S | -M | -Gram-positive | -Staphylococcus | -aureus | -TRUE | -||||||||||||
| 19 | -2012-12-04 | -Z1 | -Hospital C | -B_STPHY_AURS | -S | -S | +8 | +2010-05-20 | +X1 | +Hospital A | +B_STRPT_PNMN | +R | +R | S | R | F | Gram-positive | -Staphylococcus | -aureus | +Streptococcus | +pneumoniae | TRUE | |||
| 20 | -2013-10-17 | -I4 | -Hospital B | -B_ESCHR_COLI | -R | -R | +11 | +2013-03-04 | +T9 | +Hospital C | +B_STRPT_PNMN | S | S | -M | -Gram-negative | -Escherichia | -coli | +R | +R | +F | +Gram-positive | +Streptococcus | +pneumoniae | TRUE | |
| 1 | Escherichia coli | -4,622 | -43.75% | -4,622 | -43.75% | +4,726 | +44.00% | +4,726 | +44.00% | ||||||||||||||||
| 2 | Staphylococcus aureus | -2,676 | -25.33% | -7,298 | -69.08% | +2,781 | +25.89% | +7,507 | +69.88% | ||||||||||||||||
| 3 | Streptococcus pneumoniae | -2,072 | -19.61% | -9,370 | -88.69% | +2,090 | +19.46% | +9,597 | +89.34% | ||||||||||||||||
| 4 | Klebsiella pneumoniae | -1,195 | -11.31% | -10,565 | +1,145 | +10.66% | +10,742 | 100.00% | |||||||||||||||||
| 2012-09-21 | -Q2 | -Hospital C | -B_STRPT_PNMN | -R | -R | -R | -R | -F | -Gram-positive | -Streptococcus | -pneumoniae | -TRUE | -|||||||||||||
| 2011-11-26 | -E2 | -Hospital B | -B_STRPT_PNMN | -S | -S | -R | -R | -M | -Gram-positive | -Streptococcus | -pneumoniae | -TRUE | -|||||||||||||
| 2012-12-04 | -Z1 | -Hospital C | -B_STPHY_AURS | -S | -S | -S | -R | -F | -Gram-positive | -Staphylococcus | -aureus | -TRUE | -|||||||||||||
| 2017-08-02 | -Y9 | +2010-05-20 | +X1 | Hospital A | -B_STPHY_AURS | -S | -S | +B_STRPT_PNMN | +R | +R | S | R | F | Gram-positive | -Staphylococcus | -aureus | +Streptococcus | +pneumoniae | TRUE | ||||||
| 2014-01-21 | -B3 | -Hospital B | +|||||||||||||||||||||||
| 2013-03-04 | +T9 | +Hospital C | B_STRPT_PNMN | S | S | +R | +R | +F | +Gram-positive | +Streptococcus | +pneumoniae | +TRUE | +|||||||||||||
| 2011-04-02 | +K4 | +Hospital B | +B_STRPT_PNMN | +R | +R | S | R | M | @@ -832,9 +802,24 @@ Longest: 24TRUE | ||||||||||||||||
| 2012-06-21 | -S7 | -Hospital B | +2016-11-29 | +X8 | +Hospital D | +B_STRPT_PNMN | +R | +R | +R | +R | +F | +Gram-positive | +Streptococcus | +pneumoniae | +TRUE | +||||||||||
| 2014-06-19 | +V9 | +Hospital A | B_STRPT_PNMN | S | S | @@ -846,6 +831,21 @@ Longest: 24pneumoniae | TRUE | ||||||||||||||||||
| 2015-11-16 | +M7 | +Hospital B | +B_ESCHR_COLI | +S | +S | +R | +R | +M | +Gram-negative | +Escherichia | +coli | +TRUE | +
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the bug_drug_combinations() function:
data_1st %>%
bug_drug_combinations() %>%
head() # show first 6 rows
-# ℹ Using column 'bacteria' as input for `col_mo`.| mo | @@ -867,59 +866,58 @@ Longest: 24|||||||||
|---|---|---|---|---|---|---|---|---|---|
| E. coli | AMX | -2194 | -140 | -2288 | -4622 | +2242 | +112 | +2372 | +4726 |
| E. coli | AMC | -3372 | -174 | -1076 | -4622 | +3500 | +154 | +1072 | +4726 |
| E. coli | CIP | -3363 | +3435 | 0 | -1259 | -4622 | +1291 | +4726 | |
| E. coli | GEN | -4050 | +4126 | 0 | -572 | -4622 | +600 | +4726 | |
| K. pneumoniae | AMX | 0 | 0 | -1195 | -1195 | +1145 | +1145 | ||
| K. pneumoniae | AMC | -949 | -40 | -206 | -1195 | +902 | +39 | +204 | +1145 |
+-data_1st %>% select(bacteria, aminoglycosides()) %>% bug_drug_combinations()+# ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) -# ℹ Using column 'bacteria' as input for `col_mo`.# ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
| mo | @@ -933,34 +931,34 @@ Longest: 24||||||||
|---|---|---|---|---|---|---|---|---|
| E. coli | GEN | -4050 | +4126 | 0 | -572 | -4622 | +600 | +4726 |
| K. pneumoniae | GEN | -1084 | +1049 | 0 | -111 | -1195 | +96 | +1145 |
| S. aureus | GEN | -2373 | +2469 | 0 | -303 | -2676 | +312 | +2781 |
| S. pneumoniae | GEN | 0 | 0 | -2072 | -2072 | +2090 | +2090 |
The functions resistance() and susceptibility() can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() and proportion_R() can be used to determine the proportion of a specific antimicrobial outcome.
All these functions contain a minimum argument, denoting the minimum required number of test results for returning a value. These functions will otherwise return NA. The default is minimum = 30, following the CLSI M39-A4 guideline for applying microbial epidemiology.
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
++# [1] 0.5448706data_1st %>% resistance(AMX) -# [1] 0.5433034
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
+@@ -988,24 +986,24 @@ Longest: 24data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX))Hospital A -0.5414451 +0.5393604 Hospital B -0.5422872 +0.5352956 Hospital C -0.5453988 +0.5694087 Hospital D -0.5461221 +0.5527446
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the n_rsi() can be used, which works exactly like n_distinct() from the dplyr package. It counts all isolates available for every group (i.e. values S, I or R):
+data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX), @@ -1019,28 +1017,28 @@ Longest: 24Hospital A -0.5414451 -3197 +0.5393604 +3252 Hospital B -0.5422872 -3559 +0.5352956 +3839 Hospital C -0.5453988 -1630 +0.5694087 +1556 Hospital D -0.5461221 -2179 +0.5527446 +2095 These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
-+data_1st %>% group_by(genus) %>% summarise(amoxiclav = susceptibility(AMC), @@ -1056,32 +1054,32 @@ Longest: 24Escherichia -0.7672003 -0.8762441 -0.9794461 +0.7731697 +0.8730427 +0.9769361 Klebsiella -0.8276151 -0.9071130 -0.9774059 +0.8218341 +0.9161572 +0.9834061 Staphylococcus -0.8045590 -0.8867713 -0.9828102 +0.7802949 +0.8878101 +0.9748292 Streptococcus -0.5361969 +0.5382775 0.0000000 -0.5361969 +0.5382775 Or if you are curious for the resistance within certain antibiotic classes, use a antibiotic class selector such as
-penicillins(), which automatically will include the columnsAMXandAMCof our data:+data_1st %>% # group by hospital group_by(hospital) %>% @@ -1101,28 +1099,28 @@ Longest: 24Hospital A -54.1% +53.9% 26.1% Hospital B -54.2% -27.3% +53.5% +25.7% Hospital C -54.5% -25.0% +56.9% +28.8% Hospital D -54.6% -25.3% +55.3% +27.1% To make a transition to the next part, let’s see how differences in the previously calculated combination therapies could be plotted:
-+data_1st %>% group_by(genus) %>% summarise("1. Amoxi/clav" = susceptibility(AMC), @@ -1140,7 +1138,7 @@ Longest: 24Plots
To show results in plots, most R users would nowadays use the
-ggplot2package. This package lets you create plots in layers. You can read more about it on their website. A quick example would look like these syntaxes:+ggplot(data = a_data_set, mapping = aes(x = year, y = value)) + @@ -1154,13 +1152,13 @@ Longest: 24 ggplot(a_data_set) + geom_bar(aes(year))The
-AMRpackage contains functions to extend thisggplot2package, for examplegeom_rsi(). It automatically transforms data withcount_df()orproportion_df()and show results in stacked bars. Its simplest and shortest example:+
Omit the
translate_ab = FALSEto have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin).If we group on e.g. the
-genuscolumn and add some additional functions from our package, we can create this:+# group the data on `genus` ggplot(data_1st %>% group_by(genus)) + # create bars with genus on x axis @@ -1183,7 +1181,7 @@ Longest: 24 theme(axis.text.y = element_text(face = "italic"))
To simplify this, we also created the
-ggplot_rsi()function, which combines almost all above functions:+data_1st %>% group_by(genus) %>% ggplot_rsi(x = "genus", @@ -1197,58 +1195,58 @@ Longest: 24The AMR package also extends the
plot()andggplot2::autoplot()functions for plotting minimum inhibitory concentrations (MIC, created withas.mic()) and disk diffusion diameters (created withas.disk()).With the
-random_mic()andrandom_disk()functions, we can generate sampled values for the new data types (S3 classes)<mic>and<disk>:+-mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.01 16 16 2 0.005 0.125 128 4 1 0.005 -# [11] 0.001 0.025 8 0.025 0.01 0.002 0.0625 8 64 1 -# [21] 0.0625 >=256 0.5 0.002 32 64 0.001 0.002 >=256 0.002 -# [31] 0.001 0.5 0.5 0.25 2 128 8 0.025 0.025 0.025 -# [41] 0.5 64 0.5 16 0.25 0.002 32 16 4 32 -# [51] 0.0625 0.5 8 0.25 1 16 8 0.002 0.25 4 -# [61] 0.5 0.25 4 0.125 128 0.125 1 128 32 1 -# [71] >=256 0.25 1 0.0625 0.5 2 0.01 32 0.0625 0.125 -# [81] 0.002 0.001 0.002 0.001 0.0625 8 32 1 4 64 -# [91] 1 0.025 2 2 1 0.005 0.25 0.25 >=256 128++# [1] 8 <=0.001 16 64 0.0625 16 0.01 0.025 0.002 +# [10] 0.01 <=0.001 >=256 128 4 0.01 2 0.002 0.025 +# [19] 1 0.0625 2 >=256 0.0625 8 32 0.25 2 +# [28] 2 0.25 0.002 8 32 64 8 0.5 0.125 +# [37] 8 0.5 0.002 4 32 32 2 0.0625 64 +# [46] 0.125 64 0.0625 16 8 >=256 0.125 0.0625 32 +# [55] 1 0.25 0.5 0.025 16 0.25 16 0.01 >=256 +# [64] 0.25 64 8 2 0.025 32 0.25 4 <=0.001 +# [73] 128 1 0.125 1 1 8 >=256 4 0.0625 +# [82] 0.0625 <=0.001 8 32 16 0.0625 0.005 64 0.25 +# [91] 32 0.025 0.005 1 >=256 0.25 128 0.025 0.0625 +# [100] 0.025# base R: plot(mic_values)-
+# ggplot2: autoplot(mic_values)
But we could also be more specific, by generating MICs that are likely to be found in E. coli for ciprofloxacin:
-+mic_values <- random_mic(size = 100, mo = "E. coli", ab = "cipro")For the
plot()andautoplot()function, we can define the microorganism and an antimicrobial agent the same way. This will add the interpretation of those values according to a chosen guidelines (defaults to the latest EUCAST guideline).Default colours are colour-blind friendly, while maintaining the convention that e.g. ‘susceptible’ should be green and ‘resistant’ should be red:
-+# base R: plot(mic_values, mo = "E. coli", ab = "cipro")-
+# ggplot2: autoplot(mic_values, mo = "E. coli", ab = "cipro")
For disk diffusion values, there is not much of a difference in plotting:
-+-disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") -# ℹ Translation is uncertain of one microorganism. Use `mo_uncertainties()` -# to review it. disk_values # Class <disk> -# [1] 28 28 30 17 27 26 31 26 17 29 19 23 28 31 17 23 29 27 25 19 20 29 17 21 30 -# [26] 17 30 31 27 29 31 22 28 27 17 19 23 21 21 22 27 22 24 30 26 23 31 25 30 20 -# [51] 24 31 25 22 23 17 31 18 26 31 25 27 24 19 30 21 26 30 24 24 28 23 18 23 26 -# [76] 27 18 29 20 19 24 30 31 26 17 18 20 21 17 29 24 26 21 27 19 31 23 19 25 21++# [1] 27 22 20 22 29 28 22 19 18 23 28 26 22 22 30 22 23 30 23 27 25 17 31 18 18 +# [26] 17 28 29 17 22 29 27 30 19 22 17 29 17 18 31 28 27 28 24 27 22 24 17 31 20 +# [51] 19 28 31 24 27 21 29 27 28 22 27 26 31 28 30 21 25 27 25 23 29 17 29 20 20 +# [76] 27 26 17 30 23 25 24 30 27 23 23 26 21 20 23 17 29 28 19 21 20 24 28 17 28# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
And when using the
-ggplot2package, but now choosing the latest implemented CLSI guideline (notice that the EUCAST-specific term “Susceptible, incr. exp.” has changed to “Intermediate”):+autoplot(disk_values, mo = "E. coli", ab = "cipro", @@ -1261,7 +1259,7 @@ Longest: 24The next example uses the
example_isolatesdata set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis.We will compare the resistance to fosfomycin (column
-FOS) in hospital A and D. The input for thefisher.test()can be retrieved with a transformation like this:+# use package 'tidyr' to pivot data: library(tidyr) @@ -1280,7 +1278,7 @@ Longest: 24 # [1,] 25 77 # [2,] 24 33We can apply the test now with:
-@@ -190,7 +190,7 @@+@@ -191,7 +191,7 @@# do Fisher's Exact Test fisher.test(check_FOS) # diff --git a/docs/articles/AMR_files/figure-html/disk_plots-1.png b/docs/articles/AMR_files/figure-html/disk_plots-1.png index acdbacb1..5f5638e7 100644 Binary files a/docs/articles/AMR_files/figure-html/disk_plots-1.png and b/docs/articles/AMR_files/figure-html/disk_plots-1.png differ diff --git a/docs/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png b/docs/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png index c04e6542..d101c4d6 100644 Binary files a/docs/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png and b/docs/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png differ diff --git a/docs/articles/AMR_files/figure-html/mic_plots-1.png b/docs/articles/AMR_files/figure-html/mic_plots-1.png index 39016b9e..deebc450 100644 Binary files a/docs/articles/AMR_files/figure-html/mic_plots-1.png and b/docs/articles/AMR_files/figure-html/mic_plots-1.png differ diff --git a/docs/articles/AMR_files/figure-html/mic_plots-2.png b/docs/articles/AMR_files/figure-html/mic_plots-2.png index 33f30a31..7490bcad 100644 Binary files a/docs/articles/AMR_files/figure-html/mic_plots-2.png and b/docs/articles/AMR_files/figure-html/mic_plots-2.png differ diff --git a/docs/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png b/docs/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png index 091465e5..54887cc3 100644 Binary files a/docs/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png and b/docs/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png differ diff --git a/docs/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png b/docs/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png index dd44ada2..e98ae1cc 100644 Binary files a/docs/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png and b/docs/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png index 1b0cb250..28376a8f 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 1-1.png and b/docs/articles/AMR_files/figure-html/plot 1-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 3-1.png b/docs/articles/AMR_files/figure-html/plot 3-1.png index 8de1e75e..9930737b 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 3-1.png and b/docs/articles/AMR_files/figure-html/plot 3-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 4-1.png b/docs/articles/AMR_files/figure-html/plot 4-1.png index 8837a710..8152d9a1 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 4-1.png and b/docs/articles/AMR_files/figure-html/plot 4-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 5-1.png b/docs/articles/AMR_files/figure-html/plot 5-1.png index 9bd47475..c52ef33d 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 5-1.png and b/docs/articles/AMR_files/figure-html/plot 5-1.png differ diff --git a/docs/articles/SPSS.html b/docs/articles/SPSS.html index b04162df..b0c68415 100644 --- a/docs/articles/SPSS.html +++ b/docs/articles/SPSS.html @@ -44,7 +44,7 @@How to import data from SPSS / SAS / Stata
Dr. Matthijs Berends
-06 December 2021
+11 December 2021
Source:vignettes/SPSS.Rmddiff --git a/docs/articles/datasets.html b/docs/articles/datasets.html index 63cf0e3f..31caecb3 100644 --- a/docs/articles/datasets.html +++ b/docs/articles/datasets.html @@ -44,7 +44,7 @@SPSS.RmdData sets for download / own use
-09 December 2021
+11 December 2021
Source:vignettes/datasets.Rmd@@ -788,22 +788,22 @@ If you are reading this page from within R, pleasedatasets.RmdIntrinsic bacterial resistance
-A data set with 93,892 rows and 2 columns, containing the following column names:
+
microorganism and antibiotic.A data set with 134,956 rows and 2 columns, containing the following column names:
microorganism and antibiotic.This data set is in R available as
-intrinsic_resistant, after you load theAMRpackage.It was last updated on 6 October 2021 14:38:29 UTC. Find more info about the structure of this data set here.
+It was last updated on 11 December 2021 11:49:52 UTC. Find more info about the structure of this data set here.
Direct download links:
-
- Download as R file (69 kB)
+- Download as R file (78 kB)
- Download as Excel file (0.9 MB)
-
- Download as plain text file (3.5 MB)
+- Download as plain text file (5.1 MB)
-
- Download as SAS file (7.1 MB)
+- Download as SAS file (10.4 MB)
-
- Download as SPSS file (5.1 MB)
+- Download as SPSS file (7.4 MB)
-
- Download as Stata file (7 MB)
+- Download as Stata file (10.2 MB)
Source @@ -822,6 +822,14 @@ If you are reading this page from within R, please
Enterobacter cloacae +Acetylmidecamycin + ++ +Enterobacter cloacae +Acetylspiramycin ++ Enterobacter cloacae Amoxicillin @@ -846,6 +854,14 @@ If you are reading this page from within R, please +Enterobacter cloacae +Benzylpenicillin ++ +Enterobacter cloacae +Cadazolid ++ Enterobacter cloacae Cefadroxil @@ -898,8 +914,16 @@ If you are reading this page from within R, please +Enterobacter cloacae +Gamithromycin ++ +Enterobacter cloacae Josamycin + Enterobacter cloacae +Kitasamycin (Leucomycin) +Enterobacter cloacae Lincomycin @@ -910,12 +934,20 @@ If you are reading this page from within R, pleaseEnterobacter cloacae -Midecamycin +Meleumycin + Enterobacter cloacae +Midecamycin ++ +Enterobacter cloacae Miocamycin + Enterobacter cloacae +Nafithromycin +Enterobacter cloacae Norvancomycin @@ -930,11 +962,11 @@ If you are reading this page from within R, pleaseEnterobacter cloacae -Benzylpenicillin +Pirlimycin Enterobacter cloacae -Pirlimycin +Primycin Enterobacter cloacae @@ -962,19 +994,19 @@ If you are reading this page from within R, pleaseEnterobacter cloacae -Spiramycin +Solithromycin Enterobacter cloacae -Tedizolid +Spiramycin Enterobacter cloacae -Teicoplanin +Tedizolid Enterobacter cloacae -Teicoplanin-macromethod +Teicoplanin + Enterobacter cloacae @@ -990,10 +1022,30 @@ If you are reading this page from within R, pleaseEnterobacter cloacae +Tildipirosin ++ +Enterobacter cloacae +Tilmicosin ++ Enterobacter cloacae Troleandomycin + Enterobacter cloacae +Tulathromycin ++ +Enterobacter cloacae +Tylosin ++ +Enterobacter cloacae +Tylvalosin ++ Enterobacter cloacae Vancomycin
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.
This R package 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 Foundation and University Medical Center Groningen. This R package formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.177417131) but is actively and durably maintained (see changelog)) by two public healthcare organisations in the Netherlands.
+This R package 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 Foundation and University Medical Center Groningen. This R package formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.192486375) but is actively and durably maintained (see changelog)) by two public healthcare organisations in the Netherlands.
diff --git a/docs/index.html b/docs/index.html index 4af4fd44..f03ee645 100644 --- a/docs/index.html +++ b/docs/index.html @@ -47,7 +47,7 @@ @@ -195,14 +195,14 @@AMR (for R) 
-Update: The latest EUCAST guideline for intrinsic resistance (v3.3, October 2021) is now supported, and our taxonomy tables has been updated as well (LPSN, 5 October 2021). A new version will be released after the EUCAST guideline for clinical breakpoints (v12.0, likely January 2022) are implemented, to be expected shortly after the official guideline release.
+Update: The latest EUCAST guideline for intrinsic resistance (v3.3, October 2021) is now supported, and our taxonomy tables have been updated as well (LPSN, 5 October 2021). A new version will be released after the EUCAST guideline for clinical breakpoints (v12.0, likely January 2022) are implemented, to be expected shortly after the official guideline release.
What is AMR (for R)?
AMR is a free, open-source and independent R package (see Copyright) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
After installing this package, R knows ~71,000 distinct microbial species and all ~560 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. Antimicrobial names and group names are available in Danish, Dutch, English, French, German, Italian, Portuguese and Spanish.
-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 (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 Foundation and University Medical Center Groningen. This R package formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.177417131) but is actively and durably maintained by two public healthcare organisations in the Netherlands.
+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 (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 Foundation and University Medical Center Groningen. This R package formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.192486375) but is actively and durably maintained by two public healthcare organisations in the Netherlands.
-
-
+
+
Fixed the Gram stain (
Dramatic speed improvement for
-AMR 1.7.1.9063
+
+AMR 1.7.1.9064
-
Last updated: 9 December 2021
+Last updated: 11 December 2021
-
Breaking changes
+Breaking changes
- Removed
p_symbol()and allfilter_*()functions (except forfilter_first_isolate()), which were all deprecated in a previous package version - Removed the
key_antibiotics()andkey_antibiotics_equal()functions, which were deprecated and superseded bykey_antimicrobials()andantimicrobials_equal() - Removed all previously implemented
ggplot2::ggplot()generics for classes<mic>,<disk>,<rsi>and<resistance_predict>as they did not follow theggplot2logic. They were replaced withggplot2::autoplot()generics.
-
New
-Support for EUCAST Intrinsic Resistance and Unusual Phenotypes v3.3 (October 2021), effective in the
eucast_rules()function. This is now the default guideline (all other guidelines are still available).
+Support for EUCAST Intrinsic Resistance and Unusual Phenotypes v3.3 (October 2021). This is now the default EUCAST guideline in the package (all older guidelines are still available) for
eucast_rules(),mo_intrinsic_resistant()andmdro(). Theintrinsic_resistantdata set was also updated accordingly.Support for Danish, and also added missing translations of all antimicrobial drugs in Italian, French and Portuguese
-
Function
@@ -180,9 +180,10 @@ example_isolates %>% set_ab_names(AMC:GEN, property = "atc")set_ab_names()to rename data set columns that resemble antimicrobial drugs. This allows for quickly renaming columns to official names, ATC codes, etc. Its second argument can be a tidyverse way of selecting:
New
+Function ab_ddd_units() to get units of DDDs (daily defined doses), deprecating the use of ab_ddd(..., units = TRUE) to be more consistent in data types of function output
-
Changed
+Changed
- Updated the bacterial taxonomy to 5 October 2021 (according to LPSN), including all 11 new staphylococcal species named since 1 January last year
- The
antibioticsdata set now contains all ATC codes that are available through the WHOCC website, regardless of drugs being present in more than one ATC group. This means that:- Some drugs now contain multiple ATC codes (e.g., metronidazole contains 5) @@ -212,9 +213,10 @@ example_isolates %>% select(ab_selector(oral_ddd > 1 & oral_units == "g")) # dplyr
Added the selector not_intrinsic_resistant(), which only keeps antibiotic columns that are not intrinsic resistant for all microorganisms in a data set, based on the latest EUCAST guideline on intrinsic resistance. For example, if a data set contains only microorganism codes or names of E. coli and K. pneumoniae and contains a column “vancomycin”, this column will be removed (or rather, unselected) using this function.
Added argument only_treatable, which defaults to TRUE and will exclude drugs that are only for laboratory tests and not for treating patients (such as imipenem/EDTA and gentamicin-high)
Fix for using selectors multiple times in one call (e.g., using them in dplyr::filter() and immediately after in dplyr::select())
Fix for using having multiple columns that are coerced to the same antibiotic agent
Added argument only_treatable, which defaults to TRUE and will exclude drugs that are only for laboratory tests and not for treating patients (such as imipenem/EDTA and gentamicin-high)
Fixed for using all() or any() on antibiotic selectors in an R Markdown file
mo_gramstain()) determination of the taxonomic class Negativicutes within the phylum of Firmicutes - they were considered Gram-positives because of their phylum but are actually Gram-negative. This impacts 137 taxonomic species, genera and families, such as Negativicoccus and Veillonella.first_isolate()
@@ -250,7 +252,7 @@
get_episode() and is_new_episode() can now cope with NAs
-
diff --git a/docs/reference/ab_property.html b/docs/reference/ab_property.html
index b5d5dcc8..fced647f 100644
--- a/docs/reference/ab_property.html
+++ b/docs/reference/ab_property.html
@@ -17,7 +17,7 @@
@@ -206,7 +206,7 @@
Other
+Other
- This package is now being maintained by two epidemiologists and a data scientist from two different non-profit healthcare organisations. All functions in this package are now all considered to be stable. Updates to the AMR interpretation rules (such as by EUCAST and CLSI), the microbial taxonomy, and the antibiotic dosages will all be updated every 6 to 12 months from now on.
a logical to indicate whether the first character of every output should be transformed to a lower case character. This will lead to e.g. "polymyxin B" and not "polymyxin b".
in case of set_ab_names() and data is a data.frame: variables to select (supports tidy selection like AMX:VAN), otherwise other arguments passed on to as.ab()
in case of set_ab_names() and data is a data.frame: variables to select (supports tidy selection such as column1:column4), otherwise other arguments passed on to as.ab()
a logical to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group)
- x
vector of values (for class
mic: MIC values in mg/L, for classdisk: a disk diffusion radius in millimetres)- ... -
for using on a data.frame: names of columns to apply
as.rsi()on (supports tidy selection likeAMX:VAN). Otherwise: arguments passed on to methods.
+for using on a data.frame: names of columns to apply
as.rsi()on (supports tidy selection such ascolumn1:column4). Otherwise: arguments passed on to methods.- threshold
maximum fraction of invalid antimicrobial interpretations of
x, see Examples- mo @@ -229,7 +229,7 @@
- conserve_capped_values
a logical to indicate that MIC values starting with
">"(but not">=") must always return "R" , and that MIC values starting with"<"(but not"<=") must always return "S"- add_intrinsic_resistance -
(only useful when using a EUCAST guideline) a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in Klebsiella species. Determination is based on the intrinsic_resistant data set, that itself is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.2 (2020).
+(only useful when using a EUCAST guideline) a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in Klebsiella species. Determination is based on the intrinsic_resistant data set, that itself is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021).
- reference_data
a data.frame to be used for interpretation, which defaults to the rsi_translation data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the rsi_translation data set (same column names and column types). Please note that the
guidelineargument will be ignored whenreference_datais manually set.- col_mo diff --git a/docs/reference/index.html b/docs/reference/index.html index bf93c518..7a2fc584 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ diff --git a/docs/reference/intrinsic_resistant.html b/docs/reference/intrinsic_resistant.html index 59385a07..f2de9e83 100644 --- a/docs/reference/intrinsic_resistant.html +++ b/docs/reference/intrinsic_resistant.html @@ -17,7 +17,7 @@ @@ -165,13 +165,13 @@
microorganism
Name of the microorganism
-antibiotic
Name of the antibiotic drug
+ microorganism
Official taxonomic name of the microorganism, according to the LPSN
+antibiotic
Official name of the antibiotic drug, according to the WHOCCguideline = "CMI2012"(default)Magiorakos AP, Srinivasan A et al. "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) (link)
-
+guideline = "EUCAST3.2"(or simplyguideline = "EUCAST")
+guideline = "EUCAST3.3"(or simplyguideline = "EUCAST")The European international guideline - EUCAST Expert Rules Version 3.3 "Intrinsic Resistance and Unusual Phenotypes" (link)
+guideline = "EUCAST3.2"The European international guideline - EUCAST Expert Rules Version 3.2 "Intrinsic Resistance and Unusual Phenotypes" (link)
guideline = "EUCAST3.1"The European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (link)
diff --git a/docs/reference/mo_property.html b/docs/reference/mo_property.html
index 3f8562e8..fc20b674 100644
--- a/docs/reference/mo_property.html
+++ b/docs/reference/mo_property.html
@@ -17,7 +17,7 @@
Format
-A data.frame with 93,892 observations and 2 variables:
A data.frame with 134,956 observations and 2 variables:
Details
The repository of this AMR package contains a file comprising this exact data set: https://github.com/msberends/AMR/blob/main/data-raw/intrinsic_resistant.txt. This file allows for machine reading EUCAST guidelines about intrinsic resistance, which is almost impossible with the Excel and PDF files distributed by EUCAST. The file is updated automatically.
This data set is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.2 (2020).
+This data set is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021).
Reference Data Publicly Available
@@ -188,12 +188,16 @@Examples
-# \donttest{
+ subset(intrinsic_resistant,
+ antibiotic == "Vancomycin" & microorganism %like% "Enterococcus")$microorganism
+#> [1] "Enterococcus casseliflavus" "Enterococcus gallinarum"
+
+# \donttest{
if (require("dplyr")) {
intrinsic_resistant %>%
- filter(antibiotic == "Vancomycin", microorganism %like% "Enterococcus") %>%
+ filter(antibiotic == "Vancomycin" & microorganism %like% "Enterococcus") %>%
pull(microorganism)
- # [1] "Enterococcus casseliflavus" "Enterococcus gallinarum"
+ #> [1] "Enterococcus casseliflavus" "Enterococcus gallinarum"
}
# }
diff --git a/docs/reference/like.html b/docs/reference/like.html
index 32b92f15..bf040b7c 100644
--- a/docs/reference/like.html
+++ b/docs/reference/like.html
@@ -17,7 +17,7 @@
Currently supported guidelines are (case-insensitive):
Since the top-level of the taxonomy is sometimes referred to as 'kingdom' and sometimes as 'domain', the functions mo_kingdom() and mo_domain() return the exact same results.
The Gram stain - mo_gramstain() - will be determined based on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002, PMID 11837318), who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram-positive, except for members of the class Negativicutes which are Gram-negative. Members of other bacterial phyla are all considered Gram-negative. Species outside the kingdom of Bacteria will return a value NA. Functions mo_is_gram_negative() and mo_is_gram_positive() always return TRUE or FALSE (except when the input is NA or the MO code is UNKNOWN), thus always return FALSE for species outside the taxonomic kingdom of Bacteria.
Determination of yeasts - mo_is_yeast() - will be based on the taxonomic kingdom and class. Budding yeasts are fungi of the phylum Ascomycetes, class Saccharomycetes (also called Hemiascomycetes). True yeasts are aggregated into the underlying order Saccharomycetales. Thus, for all microorganisms that are fungi and member of the taxonomic class Saccharomycetes, the function will return TRUE. It returns FALSE otherwise (except when the input is NA or the MO code is UNKNOWN).
Intrinsic resistance - mo_is_intrinsic_resistant() - will be determined based on the intrinsic_resistant data set, which is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.2 (2020). The mo_is_intrinsic_resistant() functions can be vectorised over arguments x (input for microorganisms) and over ab (input for antibiotics).
Intrinsic resistance - mo_is_intrinsic_resistant() - will be determined based on the intrinsic_resistant data set, which is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). The mo_is_intrinsic_resistant() functions can be vectorised over arguments x (input for microorganisms) and over ab (input for antibiotics).
All output will be translated where possible.
The function mo_url() will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
SNOMED codes - mo_snomed() - are from the US Edition of SNOMED CT from 1 September 2020. See Source and the microorganisms data set for more info.
-> Update: The latest [EUCAST guideline for intrinsic resistance](https://www.eucast.org/expert_rules_and_intrinsic_resistance/) (v3.3, October 2021) is now supported, and our taxonomy tables has been updated as well (LPSN, 5 October 2021).
+> Update: The latest [EUCAST guideline for intrinsic resistance](https://www.eucast.org/expert_rules_and_intrinsic_resistance/) (v3.3, October 2021) is now supported, and our taxonomy tables have been updated as well (LPSN, 5 October 2021).
> **A new version will be released after the [EUCAST guideline for clinical breakpoints](https://www.eucast.org/clinical_breakpoints/) (v12.0, likely January 2022) are implemented, to be expected shortly after the official guideline release.**
### What is `AMR` (for R)?
@@ -9,7 +9,7 @@
After installing this package, R knows [**~71,000 distinct microbial species**](./reference/microorganisms.html) and all [**~560 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. Antimicrobial names and group names are available in Danish, Dutch, English, French, German, Italian, Portuguese and Spanish.
-This package is [fully independent of any other R package](https://en.wikipedia.org/wiki/Dependency_hell) and works on Windows, macOS and Linux with all versions of R since R-3.0 (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](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl). This R package formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.192486375)) but is [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
+This package is [fully independent of any other R package](https://en.wikipedia.org/wiki/Dependency_hell) and works on Windows, macOS and Linux with all versions of R since R-3.0 (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](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl). This R package formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)) but is [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
diff --git a/man/ab_property.Rd b/man/ab_property.Rd index 4fba759b..24874ccd 100644 --- a/man/ab_property.Rd +++ b/man/ab_property.Rd @@ -62,7 +62,7 @@ set_ab_names( \item{tolower}{a \link{logical} to indicate whether the first \link{character} of every output should be transformed to a lower case \link{character}. This will lead to e.g. "polymyxin B" and not "polymyxin b".} -\item{...}{in case of \code{\link[=set_ab_names]{set_ab_names()}} and \code{data} is a \link{data.frame}: variables to select (supports tidy selection like \code{AMX:VAN}), otherwise other arguments passed on to \code{\link[=as.ab]{as.ab()}}} +\item{...}{in case of \code{\link[=set_ab_names]{set_ab_names()}} and \code{data} is a \link{data.frame}: variables to select (supports tidy selection such as \code{column1:column4}), otherwise other arguments passed on to \code{\link[=as.ab]{as.ab()}}} \item{only_first}{a \link{logical} to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group)} diff --git a/man/as.rsi.Rd b/man/as.rsi.Rd index e0a4cd27..5e722a51 100755 --- a/man/as.rsi.Rd +++ b/man/as.rsi.Rd @@ -60,7 +60,7 @@ is.rsi.eligible(x, threshold = 0.05) \arguments{ \item{x}{vector of values (for class \code{\link{mic}}: MIC values in mg/L, for class \code{\link{disk}}: a disk diffusion radius in millimetres)} -\item{...}{for using on a \link{data.frame}: names of columns to apply \code{\link[=as.rsi]{as.rsi()}} on (supports tidy selection like \code{AMX:VAN}). Otherwise: arguments passed on to methods.} +\item{...}{for using on a \link{data.frame}: names of columns to apply \code{\link[=as.rsi]{as.rsi()}} on (supports tidy selection such as \code{column1:column4}). Otherwise: arguments passed on to methods.} \item{threshold}{maximum fraction of invalid antimicrobial interpretations of \code{x}, see \emph{Examples}} @@ -74,7 +74,7 @@ is.rsi.eligible(x, threshold = 0.05) \item{conserve_capped_values}{a \link{logical} to indicate that MIC values starting with \code{">"} (but not \code{">="}) must always return "R" , and that MIC values starting with \code{"<"} (but not \code{"<="}) must always return "S"} -\item{add_intrinsic_resistance}{\emph{(only useful when using a EUCAST guideline)} a \link{logical} to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in \emph{Klebsiella} species. Determination is based on the \link{intrinsic_resistant} data set, that itself is based on \href{https://www.eucast.org/expert_rules_and_intrinsic_resistance/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.2} (2020).} +\item{add_intrinsic_resistance}{\emph{(only useful when using a EUCAST guideline)} a \link{logical} to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in \emph{Klebsiella} species. Determination is based on the \link{intrinsic_resistant} data set, that itself is based on \href{https://www.eucast.org/expert_rules_and_intrinsic_resistance/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021).} \item{reference_data}{a \link{data.frame} to be used for interpretation, which defaults to the \link{rsi_translation} data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the \link{rsi_translation} data set (same column names and column types). Please note that the \code{guideline} argument will be ignored when \code{reference_data} is manually set.} @@ -105,7 +105,7 @@ your_data \%>\% mutate(across(where(is.mic), as.rsi)) # since dplyr 1.0.0 your_data \%>\% mutate(across(where(is.disk), as.rsi)) # since dplyr 1.0.0 } } -\item For \strong{interpreting a complete data set}, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running \code{as.rsi(data)}. +\item For \strong{interpreting a complete data set}, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running \code{as.rsi(your_data)}. } } diff --git a/man/intrinsic_resistant.Rd b/man/intrinsic_resistant.Rd index 0ac78493..bb6c636d 100644 --- a/man/intrinsic_resistant.Rd +++ b/man/intrinsic_resistant.Rd @@ -5,10 +5,10 @@ \alias{intrinsic_resistant} \title{Data Set with Bacterial Intrinsic Resistance} \format{ -A \link{data.frame} with 93,892 observations and 2 variables: +A \link{data.frame} with 134,956 observations and 2 variables: \itemize{ -\item \code{microorganism}\cr Name of the microorganism -\item \code{antibiotic}\cr Name of the antibiotic drug +\item \code{microorganism}\cr Official taxonomic name of the microorganism, according to the LPSN +\item \code{antibiotic}\cr Official name of the antibiotic drug, according to the WHOCC } } \usage{ @@ -20,7 +20,7 @@ Data set containing defined intrinsic resistance by EUCAST of all bug-drug combi \details{ The repository of this \code{AMR} package contains a file comprising this exact data set: \url{https://github.com/msberends/AMR/blob/main/data-raw/intrinsic_resistant.txt}. This file \strong{allows for machine reading EUCAST guidelines about intrinsic resistance}, which is almost impossible with the Excel and PDF files distributed by EUCAST. The file is updated automatically. -This data set is based on \href{https://www.eucast.org/expert_rules_and_intrinsic_resistance/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.2} (2020). +This data set is based on \href{https://www.eucast.org/expert_rules_and_intrinsic_resistance/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021). } \section{Reference Data Publicly Available}{ @@ -33,12 +33,16 @@ On our website \url{https://msberends.github.io/AMR/} you can find \href{https:/ } \examples{ +subset(intrinsic_resistant, + antibiotic == "Vancomycin" & microorganism \%like\% "Enterococcus")$microorganism +#> [1] "Enterococcus casseliflavus" "Enterococcus gallinarum" + \donttest{ if (require("dplyr")) { intrinsic_resistant \%>\% - filter(antibiotic == "Vancomycin", microorganism \%like\% "Enterococcus") \%>\% + filter(antibiotic == "Vancomycin" & microorganism \%like\% "Enterococcus") \%>\% pull(microorganism) - # [1] "Enterococcus casseliflavus" "Enterococcus gallinarum" + #> [1] "Enterococcus casseliflavus" "Enterococcus gallinarum" } } } diff --git a/man/like.Rd b/man/like.Rd index 7e136108..407a647e 100755 --- a/man/like.Rd +++ b/man/like.Rd @@ -82,7 +82,7 @@ a \%like\% b[1] # get isolates whose name start with 'Ent' or 'ent' example_isolates[which(mo_name(example_isolates$mo) \%like\% "^ent"), ] \donttest{ -# faster way, only works in R 3.2 and later: +# faster way, since mo_name() is context-aware: example_isolates[which(mo_name() \%like\% "^ent"), ] if (require("dplyr")) { diff --git a/man/mdro.Rd b/man/mdro.Rd index 43fb3976..b42a36cf 100644 --- a/man/mdro.Rd +++ b/man/mdro.Rd @@ -94,7 +94,10 @@ Currently supported guidelines are (case-insensitive): \item \code{guideline = "CMI2012"} (default) Magiorakos AP, Srinivasan A \emph{et al.} "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) (\href{https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext}{link}) -\item \code{guideline = "EUCAST3.2"} (or simply \code{guideline = "EUCAST"}) +\item \code{guideline = "EUCAST3.3"} (or simply \code{guideline = "EUCAST"}) + +The European international guideline - EUCAST Expert Rules Version 3.3 "Intrinsic Resistance and Unusual Phenotypes" (\href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2021/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.3_20211018.pdf}{link}) +\item \code{guideline = "EUCAST3.2"} The European international guideline - EUCAST Expert Rules Version 3.2 "Intrinsic Resistance and Unusual Phenotypes" (\href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf}{link}) \item \code{guideline = "EUCAST3.1"} diff --git a/man/mo_property.Rd b/man/mo_property.Rd index b6310216..d3064000 100644 --- a/man/mo_property.Rd +++ b/man/mo_property.Rd @@ -131,7 +131,7 @@ The Gram stain - \code{\link[=mo_gramstain]{mo_gramstain()}} - will be determine Determination of yeasts - \code{\link[=mo_is_yeast]{mo_is_yeast()}} - will be based on the taxonomic kingdom and class. \emph{Budding yeasts} are fungi of the phylum Ascomycetes, class Saccharomycetes (also called Hemiascomycetes). \emph{True yeasts} are aggregated into the underlying order Saccharomycetales. Thus, for all microorganisms that are fungi and member of the taxonomic class Saccharomycetes, the function will return \code{TRUE}. It returns \code{FALSE} otherwise (except when the input is \code{NA} or the MO code is \code{UNKNOWN}). -Intrinsic resistance - \code{\link[=mo_is_intrinsic_resistant]{mo_is_intrinsic_resistant()}} - will be determined based on the \link{intrinsic_resistant} data set, which is based on \href{https://www.eucast.org/expert_rules_and_intrinsic_resistance/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.2} (2020). The \code{\link[=mo_is_intrinsic_resistant]{mo_is_intrinsic_resistant()}} functions can be vectorised over arguments \code{x} (input for microorganisms) and over \code{ab} (input for antibiotics). +Intrinsic resistance - \code{\link[=mo_is_intrinsic_resistant]{mo_is_intrinsic_resistant()}} - will be determined based on the \link{intrinsic_resistant} data set, which is based on \href{https://www.eucast.org/expert_rules_and_intrinsic_resistance/}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021). The \code{\link[=mo_is_intrinsic_resistant]{mo_is_intrinsic_resistant()}} functions can be vectorised over arguments \code{x} (input for microorganisms) and over \code{ab} (input for antibiotics). All output \link[=translate]{will be translated} where possible. diff --git a/vignettes/welcome_to_AMR.Rmd b/vignettes/welcome_to_AMR.Rmd index cc66878d..d55edded 100644 --- a/vignettes/welcome_to_AMR.Rmd +++ b/vignettes/welcome_to_AMR.Rmd @@ -53,4 +53,4 @@ This package can be used for: All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this `AMR` package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find [all download links on our website](https://msberends.github.io/AMR/articles/datasets.html), which is automatically updated with every code change. -This R package was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl). This R package formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.192486375)) but is actively and durably maintained (see [changelog)](https://msberends.github.io/AMR/news/index.html)) by two public healthcare organisations in the Netherlands. +This R package was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl). This R package formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)) but is actively and durably maintained (see [changelog)](https://msberends.github.io/AMR/news/index.html)) by two public healthcare organisations in the Netherlands.