diff --git a/DESCRIPTION b/DESCRIPTION
index 029c4b853..58fe84c04 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: AMR
-Version: 1.3.0.9005
-Date: 2020-08-17
+Version: 1.3.0.9006
+Date: 2020-08-21
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(role = c("aut", "cre"),
diff --git a/NEWS.md b/NEWS.md
index 52d745004..c415c0ef8 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,5 @@
-# AMR 1.3.0.9005
-## Last updated: 17 August 2020
+# AMR 1.3.0.9006
+## Last updated: 21 August 2020
### New
* Data set `intrinsic_resistant`. This data set contains all bug-drug combinations where the 'bug' is intrinsic resistant to the 'drug' according to the latest EUCAST insights. It contains just two columns: `microorganism` and `antibiotic`.
diff --git a/R/ab.R b/R/ab.R
index 3f651de82..4a926ed76 100755
--- a/R/ab.R
+++ b/R/ab.R
@@ -51,6 +51,7 @@
#' @seealso
#' * [antibiotics] for the dataframe that is being used to determine ATCs
#' * [ab_from_text()] for a function to retrieve antimicrobial drugs from clinical text (from health care records)
+#' @inheritSection AMR Reference data publicly available
#' @inheritSection AMR Read more on our website!
#' @export
#' @examples
diff --git a/R/ab_class_selectors.R b/R/ab_class_selectors.R
index bd3592657..0d9b723df 100644
--- a/R/ab_class_selectors.R
+++ b/R/ab_class_selectors.R
@@ -30,6 +30,7 @@
#' @seealso [filter_ab_class()] for the `filter()` equivalent.
#' @name antibiotic_class_selectors
#' @export
+#' @inheritSection AMR Read more on our website!
#' @examples
#' \dontrun{
#' library(dplyr)
diff --git a/R/ab_property.R b/R/ab_property.R
index 0b262f2cd..fd09b1959 100644
--- a/R/ab_property.R
+++ b/R/ab_property.R
@@ -44,6 +44,7 @@
#' - A [`character`] in all other cases
#' @export
#' @seealso [antibiotics]
+#' @inheritSection AMR Reference data publicly available
#' @inheritSection AMR Read more on our website!
#' @examples
#' # all properties:
diff --git a/R/amr.R b/R/amr.R
index 9a212237c..aa0ff9dfb 100644
--- a/R/amr.R
+++ b/R/amr.R
@@ -47,6 +47,8 @@
#' - Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to R/SI
#' - Principal component analysis for AMR
#'
+#' @section Reference data publicly available:
+#' 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.
#' @section Read more on our website!:
#' On our website 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 14 August 2020. 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 21 August 2020. Now, let’s start the cleaning and the analysis! So only 28.4% is suitable for resistance analysis! We can now filter on it with the So only 28.3% is suitable for resistance analysis! We can now filter on it with the We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient N8, sorted on date: We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient C8, sorted on date: Only 1 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The If a column exists with a name like ‘key(…)ab’ the Instead of 1, now 9 isolates are flagged. In total, 78.0% of all isolates are marked ‘first weighted’ - 49.7% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline. Instead of 2, now 8 isolates are flagged. In total, 78.4% of all isolates are marked ‘first weighted’ - 50.1% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline. As with So we end up with 15,607 isolates for analysis. So we end up with 15,686 isolates for analysis. We can remove unneeded columns: Time for the analysis! Frequency table Class: character Shortest: 16 As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R ( Or can be used in conjuction with In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. So transforming 500,000 values (!!) of 50 unique values only takes 1.94 seconds. You only lose time on your unique input values. So transforming 500,000 values (!!) of 50 unique values only takes 1.81 seconds. You only lose time on your unique input values. So going from So going from Of course, when running Currently supported are German, Dutch, Spanish, Italian, French and Portuguese. This package contains a lot of reference data sets that are all reliable, up-to-date and free to download. You can even use them outside of R, for example to teach your laboratory information system (LIS) about intrinsic resistance! We included them in our Note: Years and dates of updates mentioned on this page, are from on All reference data (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this On this page, we explain how to download them and how the structure of the data sets look like. If you are reading this page from within R, please visit our website, which is automatically updated with every code change. This data set is in R available as It was last updated on 28 July 2020 20:52:40 CEST. Direct download links: Direct download links: This data set is in R available as It was last updated on 28 May 2020 11:17:56 CEST. Direct download links: Direct download links: This data set is in R available as It was last updated on 31 July 2020 12:12:13 CEST. Direct download links: Direct download links: This data set is in R available as It was last updated on 23 November 2019 19:03:43 CET. Direct download links: Direct download links: This data set is in R available as It was last updated on 14 August 2020 14:18:20 CEST. Direct download links: Direct download links: This data set is in R available as It was last updated on 29 July 2020 13:12:34 CEST. Direct download links: Direct download links: Principal component analysis for AMR All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this How to conduct AMR analysis
Matthijs S. Berends
- 14 August 2020
+ 21 August 2020
Source: vignettes/AMR.Rmd
AMR.Rmd
Introduction
@@ -226,21 +233,21 @@
-
2020-08-14
+2020-08-21
abcd
Escherichia coli
S
S
-
2020-08-14
+2020-08-21
abcd
Escherichia coli
S
R
-
2020-08-14
+2020-08-21
efgh
Escherichia coli
R
@@ -354,71 +361,71 @@
-
+2010-03-16
+2011-03-20
+W2
+Hospital A
+Streptococcus pneumoniae
+S
+S
+S
+R
+F
+
+
-2011-12-03
X9
Hospital B
-Staphylococcus aureus
+Klebsiella pneumoniae
R
-S
+I
S
S
F
- 2013-10-27
-G3
-Hospital A
+
+
-2012-11-11
+A5
+Hospital D
Staphylococcus aureus
S
-S
R
S
+S
M
- 2014-12-09
-F7
+
+
-2012-09-03
+Y3
Hospital C
Escherichia coli
R
S
-S
-S
-M
-
-
2014-06-08
-S9
-Hospital B
-Klebsiella pneumoniae
-S
-S
-S
-S
+R
+R
F
-
+2015-01-01
-N10
-Hospital A
-Streptococcus pneumoniae
+2012-11-19
+I3
+Hospital C
+Escherichia coli
+S
+S
+S
R
+M
+
+
-2015-06-05
+Q7
+Hospital B
+Escherichia coli
+S
S
S
S
F
-
2015-11-12
-H6
-Hospital B
-Staphylococcus aureus
-S
-S
-S
-S
-M
-
1
M
-10,276
-51.38%
-10,276
-51.38%
+10,358
+51.79%
+10,358
+51.79%
@@ -511,7 +518,7 @@ Longest: 1
# NOTE: Using column `date` as input for `col_date`.
# NOTE: Using column `patient_id` as input for `col_patient_id`.
2
F
-9,724
-48.62%
+9,642
+48.21%
20,000
100.00%
filter()
function, also from the dplyr
package:filter()
function, also from the dplyr
package:
data_1st <- data %>%
filter(first == TRUE)
@@ -525,7 +532,7 @@ Longest: 1
First weighted isolates
-
-
isolate
@@ -541,10 +548,10 @@ Longest: 1
1
-2010-05-17
-N8
+2010-01-13
+C8
B_ESCHR_COLI
-R
+S
S
S
S
@@ -552,8 +559,8 @@ Longest: 1
2
-2010-07-03
-N8
+2010-02-19
+C8
B_ESCHR_COLI
S
S
@@ -563,52 +570,52 @@ Longest: 1
3
-2010-07-31
-N8
+2010-03-12
+C8
B_ESCHR_COLI
R
-R
-R
-R
+S
+S
+S
FALSE
4
-2010-09-13
-N8
+2010-03-25
+C8
B_ESCHR_COLI
S
S
R
-S
+R
FALSE
5
-2010-09-15
-N8
+2010-04-28
+C8
B_ESCHR_COLI
-I
-S
+R
S
+R
S
FALSE
6
-2010-10-16
-N8
+2010-07-22
+C8
B_ESCHR_COLI
-R
-R
+S
+S
S
S
FALSE
7
-2010-10-17
-N8
+2010-12-05
+C8
B_ESCHR_COLI
S
S
@@ -618,30 +625,30 @@ Longest: 1
8
-2010-10-24
-N8
+2011-01-18
+C8
B_ESCHR_COLI
R
+R
S
S
-S
-FALSE
+TRUE
9
-2010-12-27
-N8
+2011-05-24
+C8
B_ESCHR_COLI
-S
-S
-S
+R
+I
+R
S
FALSE
10
-2011-02-25
-N8
+2011-06-24
+C8
B_ESCHR_COLI
S
S
@@ -651,7 +658,7 @@ Longest: 1
key_antibiotics()
function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.key_antibiotics()
function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.first_isolate()
function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:
data <- data %>%
@@ -679,10 +686,10 @@ Longest: 1
1
-2010-05-17
-N8
+2010-01-13
+C8
B_ESCHR_COLI
-R
+S
S
S
S
@@ -691,59 +698,59 @@ Longest: 1
2
-2010-07-03
-N8
+2010-02-19
+C8
B_ESCHR_COLI
S
S
S
S
FALSE
-TRUE
+FALSE
3
-2010-07-31
-N8
+2010-03-12
+C8
B_ESCHR_COLI
R
-R
-R
-R
+S
+S
+S
FALSE
TRUE
4
-2010-09-13
-N8
+2010-03-25
+C8
B_ESCHR_COLI
S
S
R
-S
+R
FALSE
TRUE
5
-2010-09-15
-N8
+2010-04-28
+C8
B_ESCHR_COLI
-I
-S
+R
S
+R
S
FALSE
TRUE
6
-2010-10-16
-N8
+2010-07-22
+C8
B_ESCHR_COLI
-R
-R
+S
+S
S
S
FALSE
@@ -751,61 +758,61 @@ Longest: 1
7
-2010-10-17
-N8
+2010-12-05
+C8
B_ESCHR_COLI
S
S
S
S
FALSE
-TRUE
+FALSE
8
-2010-10-24
-N8
+2011-01-18
+C8
B_ESCHR_COLI
R
+R
S
S
-S
-FALSE
+TRUE
TRUE
9
-2010-12-27
-N8
+2011-05-24
+C8
B_ESCHR_COLI
-S
-S
-S
+R
+I
+R
S
FALSE
TRUE
-10
-2011-02-25
-N8
+2011-06-24
+C8
B_ESCHR_COLI
S
S
S
S
FALSE
-FALSE
+TRUE
filter_first_isolate()
, there’s a shortcut for this new algorithm too:
data_1st <- data %>%
filter_first_weighted_isolate()
data_1st <- data_1st %>%
@@ -851,76 +858,12 @@ Longest: 1
-1
-2010-03-16
-X9
-Hospital B
-B_STPHY_AURS
-R
-S
-S
-S
-F
-Gram-positive
-Staphylococcus
-aureus
-TRUE
-
-
-2
-2013-10-27
-G3
-Hospital A
-B_STPHY_AURS
-S
-S
-R
-S
-M
-Gram-positive
-Staphylococcus
-aureus
-TRUE
-
-
-3
-2014-12-09
-F7
-Hospital C
-B_ESCHR_COLI
-R
-S
-S
-S
-M
-Gram-negative
-Escherichia
-coli
-TRUE
-
-
-4
-2014-06-08
-S9
-Hospital B
-B_KLBSL_PNMN
-R
-S
-S
-S
-F
-Gram-negative
-Klebsiella
-pneumoniae
-TRUE
-
-
5
-2015-01-01
-N10
+2011-03-20
+W2
Hospital A
B_STRPT_PNMN
-R
-R
+S
+S
S
R
F
@@ -930,21 +873,85 @@ Longest: 1
TRUE
-
+7
-2014-08-04
-O9
+3
+2012-11-11
+A5
Hospital D
B_STPHY_AURS
R
-I
R
S
-F
+S
+M
Gram-positive
Staphylococcus
aureus
TRUE
+
+4
+2012-09-03
+Y3
+Hospital C
+B_ESCHR_COLI
+R
+S
+R
+R
+F
+Gram-negative
+Escherichia
+coli
+TRUE
+
+
+5
+2012-11-19
+I3
+Hospital C
+B_ESCHR_COLI
+S
+S
+S
+R
+M
+Gram-negative
+Escherichia
+coli
+TRUE
+
+
+8
+2013-11-06
+B1
+Hospital B
+B_KLBSL_PNMN
+R
+S
+S
+S
+M
+Gram-negative
+Klebsiella
+pneumoniae
+TRUE
+
+
9
+2017-01-21
+U3
+Hospital D
+B_KLBSL_PNMN
+R
+S
+S
+S
+F
+Gram-negative
+Klebsiella
+pneumoniae
+TRUE
+
-Length: 15,607
-Available: 15,607 (100%, NA: 0 = 0%)
+Length: 15,686
+Available: 15,686 (100%, NA: 0 = 0%)
Unique: 4
Longest: 24
1
Escherichia coli
-7,836
-50.21%
-7,836
-50.21%
+7,774
+49.56%
+7,774
+49.56%
2
Staphylococcus aureus
-3,899
-24.98%
-11,735
-75.19%
+3,952
+25.19%
+11,726
+74.75%
3
Streptococcus pneumoniae
-2,337
-14.97%
-14,072
-90.16%
+2,367
+15.09%
+14,093
+89.84%
@@ -1041,50 +1048,50 @@ Longest: 24
4
Klebsiella pneumoniae
-1,535
-9.84%
-15,607
+1,593
+10.16%
+15,686
100.00%
E. coli
AMX
-3854
-265
-3717
-7836
+3685
+257
+3832
+7774
E. coli
AMC
-6244
-287
-1305
-7836
+6104
+275
+1395
+7774
E. coli
CIP
-5880
+5979
0
-1956
-7836
+1795
+7774
E. coli
GEN
-7050
+6986
0
-786
-7836
+788
+7774
K. pneumoniae
AMX
0
0
-1535
-1535
+1593
+1593
@@ -1109,34 +1116,34 @@ Longest: 24
K. pneumoniae
AMC
-1199
-50
-286
-1535
+1269
+57
+267
+1593
E. coli
CIP
-5880
+5979
0
-1956
-7836
+1795
+7774
K. pneumoniae
CIP
-1166
+1213
0
-369
-1535
+380
+1593
S. aureus
CIP
-2942
+3003
0
-957
-3899
+949
+3952
@@ -1149,7 +1156,7 @@ Longest: 24
S. pneumoniae
CIP
-1799
+1820
0
-538
-2337
+547
+2367
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:
data_1st %>% resistance(AMX)
-# [1] 0.5233549
+# [1] 0.5350631
group_by()
and summarise()
, both from the dplyr
package:
@@ -1166,19 +1173,19 @@ Longest: 24
Hospital A
-0.5238399
+0.5338783
Hospital B
-0.5201802
+0.5368875
Hospital C
-0.5259455
+0.5367068
@@ -1199,23 +1206,23 @@ Longest: 24
Hospital D
-0.5264182
+0.5322422
Hospital A
-0.5238399
-4698
+0.5338783
+4649
Hospital B
-0.5201802
-5550
+0.5368875
+5571
Hospital C
-0.5259455
-2274
+0.5367068
+2411
@@ -1238,27 +1245,27 @@ Longest: 24
Hospital D
-0.5264182
-3085
+0.5322422
+3055
Escherichia
-0.8334609
-0.8996937
-0.9857070
+0.8205557
+0.8986365
+0.9855930
Klebsiella
-0.8136808
-0.8983713
-0.9798046
+0.8323917
+0.8901444
+0.9855618
Staphylococcus
-0.8304694
-0.9253655
-0.9894845
+0.8276822
+0.9210526
+0.9868421
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 cc528e063..7fda4f19a 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 34c7ae86f..ed7101612 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 d1e70c2b0..b5de9a4d2 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 4b95609c6..d2d6fe207 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/EUCAST.html b/docs/articles/EUCAST.html
index 39ad84a92..3ecd97a7a 100644
--- a/docs/articles/EUCAST.html
+++ b/docs/articles/EUCAST.html
@@ -39,7 +39,7 @@
Streptococcus
-0.5537013
+0.5504858
0.0000000
-0.5537013
+0.5504858
head(my_TB_data)
# rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-# 1 S S S S S R
-# 2 S R S R S S
-# 3 R S S S I R
-# 4 R S R S R R
-# 5 R S S R S R
-# 6 S R I S R S
+# 1 I S S S S R
+# 2 S S S R R I
+# 3 R S S R S I
+# 4 R S S R S S
+# 5 S R I R I R
+# 6 R R S R R R
# kanamycin
# 1 S
# 2 R
# 3 R
-# 4 S
+# 4 I
# 5 R
# 6 R
1
Mono-resistant
-3203
-64.06%
-3203
-64.06%
+3233
+64.66%
+3233
+64.66%
2
Negative
-682
-13.64%
-3885
-77.70%
+698
+13.96%
+3931
+78.62%
3
Multi-drug-resistant
-627
-12.54%
-4512
-90.24%
+556
+11.12%
+4487
+89.74%
4
Poly-resistant
-268
-5.36%
-4780
-95.60%
+300
+6.00%
+4787
+95.74%
diff --git a/docs/articles/PCA.html b/docs/articles/PCA.html
index 9b7942afd..dc573f075 100644
--- a/docs/articles/PCA.html
+++ b/docs/articles/PCA.html
@@ -39,7 +39,7 @@
5
Extensively drug-resistant
-220
-4.40%
+213
+4.26%
5000
100.00%
How to import data from SPSS / SAS / Stata
Matthijs S. Berends
- 17 August 2020
+ 21 August 2020
Source: vignettes/SPSS.Rmd
SPSS.Rmd
@@ -293,12 +293,12 @@
times = 10)
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
-# expr min lq mean median uq max neval
-# A 5.080 5.220 5.81 5.66 6.46 7.16 10
-# B 10.000 10.200 14.40 10.60 11.30 49.00 10
-# C 0.862 0.875 1.04 1.05 1.14 1.40 10
+# expr min lq mean median uq max neval
+# A 6.08 6.23 10.40 6.56 7.03 44.90 10
+# B 11.70 12.00 12.70 12.70 13.60 13.90 10
+# C 1.05 1.11 1.19 1.13 1.25 1.55 10
mo_name("Staphylococcus aureus")
to "Staphylococcus aureus"
takes 0.001 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:mo_name("Staphylococcus aureus")
to "Staphylococcus aureus"
takes 0.0011 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:
run_it <- microbenchmark(A = mo_species("aureus"),
B = mo_genus("Staphylococcus"),
@@ -311,15 +311,15 @@
times = 10)
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
-# expr min lq mean median uq max neval
-# A 0.869 0.889 0.951 0.904 1.010 1.19 10
-# B 0.837 0.873 0.977 0.937 1.010 1.36 10
-# C 0.869 0.874 1.020 0.921 1.130 1.40 10
-# D 0.829 0.858 0.898 0.862 0.873 1.21 10
-# E 0.862 0.870 0.983 0.918 1.050 1.36 10
-# F 0.841 0.850 0.915 0.867 0.907 1.24 10
-# G 0.842 0.851 0.940 0.898 1.000 1.16 10
-# H 0.854 0.864 1.030 0.920 1.170 1.60 10
+# expr min lq mean median uq max neval
+# A 0.886 1.010 1.040 1.020 1.06 1.25 10
+# B 1.010 1.030 1.150 1.040 1.27 1.64 10
+# C 0.885 1.030 1.110 1.060 1.26 1.29 10
+# D 0.812 0.822 1.000 1.000 1.05 1.43 10
+# E 0.827 0.989 1.070 1.030 1.23 1.35 10
+# F 0.887 0.994 1.070 1.040 1.08 1.35 10
+# G 0.812 0.839 0.969 0.916 1.04 1.32 10
+# H 0.815 1.020 1.090 1.050 1.30 1.37 10
mo_phylum("Firmicutes")
the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be "Firmicutes"
anyway, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.AMR
package, but also automatically ‘mirror’ them to our public repository in different software formats. On this page, we explain how to download them and how the structure of the data sets look like. The tab separated files allow for machine reading taxonomic data and EUCAST and CLSI interpretation guidelines, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. We also offer all data sets in formats for R, SPSS, SAS, Stata and Excel.AMR
package version 1.3.0.9005, online released on 17 August 2020. If you are reading this page from within R, please visit our website for the latest update.AMR
package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply tab separated files that are machine-readable and suitable for input in any software program, such as laboratory information systems.
Microorganisms (currently accepted names)
microorganisms
, after you load the AMR
package.
R file (.rds), 2.7 MB – Excel workbook (.xlsx), 6.1 MB – SPSS file (.sav), 28.2 MB – Stata file (.dta), 28.2 MB – SAS file (.sas), 25.2 MB – Tab separated file (.txt), 13.3 MB.
R file (.rds), 2.7 MB – Excel workbook (.xlsx), 6.1 MB – SPSS file (.sav), 28.2 MB – Stata file (.dta), 25.2 MB – SAS file (.sas), 26.2 MB – tab separated file (.txt), 13.3 MB.
Source
@@ -413,7 +412,7 @@
Microorganisms (previously accepted names)
microorganisms.old
, after you load the AMR
package.
R file (.rds), 0.3 MB – Excel workbook (.xlsx), 0.4 MB – SPSS file (.sav), 1.9 MB – Stata file (.dta), 1.9 MB – SAS file (.sas), 1.8 MB – Tab separated file (.txt), 0.8 MB.
R file (.rds), 0.3 MB – Excel workbook (.xlsx), 0.4 MB – SPSS file (.sav), 1.9 MB – Stata file (.dta), 1.8 MB – SAS file (.sas), 1.9 MB – tab separated file (.txt), 0.8 MB.
Source
@@ -466,7 +465,7 @@
Antibiotic agents
antibiotics
, after you load the AMR
package.
R file (.rds), 37 kB – Excel workbook (.xlsx), 65 kB – SPSS file (.sav), 1.3 MB – Stata file (.dta), 1.3 MB – SAS file (.sas), 0.3 MB – Tab separated file (.txt), 0.1 MB.
R file (.rds), 37 kB – Excel workbook (.xlsx), 65 kB – SPSS file (.sav), 1.3 MB – Stata file (.dta), 0.3 MB – SAS file (.sas), 1.8 MB – tab separated file (.txt), 0.1 MB.
Source
@@ -622,7 +621,7 @@
Antiviral agents
antivirals
, after you load the AMR
package.
R file (.rds), 5 kB – Excel workbook (.xlsx), 14 kB – SPSS file (.sav), 68 kB – Stata file (.dta), 68 kB – SAS file (.sas), 67 kB – Tab separated file (.txt), 16 kB.
R file (.rds), 5 kB – Excel workbook (.xlsx), 14 kB – SPSS file (.sav), 68 kB – Stata file (.dta), 67 kB – SAS file (.sas), 80 kB – tab separated file (.txt), 16 kB.
Source
@@ -737,7 +736,7 @@
Intrinsic bacterial resistance
intrinsic_resistant
, after you load the AMR
package.
R file (.rds), 97 kB – Excel workbook (.xlsx), 0.5 MB – SPSS file (.sav), 4.2 MB – Stata file (.dta), 4.2 MB – SAS file (.sas), 3.7 MB – Tab separated file (.txt), 1.8 MB.
R file (.rds), 97 kB – Excel workbook (.xlsx), 0.5 MB – SPSS file (.sav), 4.2 MB – Stata file (.dta), 3.7 MB – SAS file (.sas), 3.8 MB – tab separated file (.txt), 1.8 MB.
Source
@@ -788,7 +787,7 @@
Interpretation from MIC values / disk diameters to R/SI
rsi_translation
, after you load the AMR
package.
R file (.rds), 55 kB – Excel workbook (.xlsx), 0.6 MB – SPSS file (.sav), 3.4 MB – Stata file (.dta), 3.4 MB – SAS file (.sas), 3 MB – Tab separated file (.txt), 1.5 MB.
R file (.rds), 55 kB – Excel workbook (.xlsx), 0.6 MB – SPSS file (.sav), 3.4 MB – Stata file (.dta), 3 MB – SAS file (.sas), 3.2 MB – tab separated file (.txt), 1.5 MB.NEWS.md
-AMR 1.3.0.9005 Unreleased
+
+AMR 1.3.0.9006 Unreleased
-
-Last updated: 17 August 2020
+Last updated: 21 August 2020
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index cd8971df9..bb42e8f4d 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -2,7 +2,7 @@ pandoc: 2.7.3
pkgdown: 1.5.1.9000
pkgdown_sha: eae56f08694abebf93cdfc0dd8e9ede06d8c815f
articles: []
-last_built: 2020-08-17T19:18Z
+last_built: 2020-08-21T09:34Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
diff --git a/docs/reference/AMR-deprecated.html b/docs/reference/AMR-deprecated.html
index 7aa64a120..e72f1e4b0 100644
--- a/docs/reference/AMR-deprecated.html
+++ b/docs/reference/AMR-deprecated.html
@@ -82,7 +82,7 @@
Reference data publicly available
+
+
+
+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.Read more on our website!
diff --git a/docs/reference/WHOCC.html b/docs/reference/WHOCC.html
index a0d04162e..4b1c7269a 100644
--- a/docs/reference/WHOCC.html
+++ b/docs/reference/WHOCC.html
@@ -82,7 +82,7 @@
AMP_ND10:CIP_EE
28 different antibiotics. You can lookup the abbreviations in the antibiotics data set, or use e.g. ab_name("AMP")
to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using as.rsi()
.Reference data publicly available
+
+
+
+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.Read more on our website!
diff --git a/docs/reference/ab_from_text.html b/docs/reference/ab_from_text.html
index 73d5b1a2f..9cfec18d2 100644
--- a/docs/reference/ab_from_text.html
+++ b/docs/reference/ab_from_text.html
@@ -82,7 +82,7 @@
WHONET 2019 software: http://www.whonet.org/software.html
European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: http://ec.europa.eu/health/documents/community-register/html/atc.htm
+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.
All columns will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.). This means that a selector like e.g. aminoglycosides()
will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
These functions only work if the tidyselect
package is installed, that comes with the dplyr
package. An error will be thrown if tidyselect
package is not installed, or if the functions are used outside a function that allows Tidyverse selections like select()
or pivot_longer()
.
On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
filter_ab_class()
for the filter()
equivalent.
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.
These have become the gold standard for international drug utilisation monitoring and research.
The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.
+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.
Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version()
.
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.
The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
+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.
These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in summarise()
from the dplyr
package and also support grouped variables, please see Examples.
These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in summarise()
from the dplyr
package and also support grouped variables, please see Examples.
count_resistant()
should be used to count resistant isolates, count_susceptible()
should be used to count susceptible isolates.
The lifecycle of this function is maturing. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome to suggest changes at our repository or write us an email (see section 'Contact Us').
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.
PEN:RIF
40 different antibiotics with class rsi
(see as.rsi()
); these column names occur in the antibiotics data set and can be translated with ab_name()
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.
AMX:GEN
4 different antibiotics that have to be transformed with as.rsi()
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.
The repository of this AMR
package contains a file comprising this exact data set: https://github.com/msberends/AMR/blob/master/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, Intrinsic Resistance and Exceptional Phenotypes', version 3.1, 2016.
+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.
mo
ID of the microorganism in the microorganisms data set
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 package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version()
.
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 package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version()
.
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.
Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()
).
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.
The repository of this AMR
package contains a file comprising this exact data set: https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt. This file allows for machine reading EUCAST and CLSI guidelines, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. The file is updated automatically.
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.