How to conduct AMR analysis
Matthijs S. Berends
-14 August 2020
+21 August 2020
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 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.
Introduction
@@ -226,21 +233,21 @@Now, let’s start the cleaning and the analysis!
@@ -452,16 +459,16 @@ Longest: 1So only 28.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 28.3% 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 == TRUE) @@ -525,7 +532,7 @@ Longest: 1diff --git a/docs/articles/SPSS.html b/docs/articles/SPSS.html index 5630c12f4..a7fa1ef4f 100644 --- a/docs/articles/SPSS.html +++ b/docs/articles/SPSS.html @@ -39,7 +39,7 @@First weighted isolates
-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:
-
isolate @@ -541,10 +548,10 @@ Longest: 11 -2010-05-17 -N8 +2010-01-13 +C8 B_ESCHR_COLI -R +S S S S @@ -552,8 +559,8 @@ Longest: 12 -2010-07-03 -N8 +2010-02-19 +C8 B_ESCHR_COLI S S @@ -563,52 +570,52 @@ Longest: 13 -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: 18 -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: 1Only 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
+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.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
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.If a column exists with a name like ‘key(…)ab’ the
first_isolate()function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:@@ -320,17 +320,17 @@ Unique: 2data <- data %>% @@ -679,10 +686,10 @@ Longest: 11 -2010-05-17 -N8 +2010-01-13 +C8 B_ESCHR_COLI -R +S S S S @@ -691,59 +698,59 @@ Longest: 12 -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: 17 -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 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
filter_first_isolate(), there’s a shortcut for this new algorithm too:-data_1st <- data %>% filter_first_weighted_isolate()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:
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: 1TRUE - +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 +Time for the analysis!
@@ -968,8 +975,8 @@ Longest: 1Frequency table
Class: character
-Length: 15,607
-Available: 15,607 (100%, NA: 0 = 0%)
+Length: 15,686
+Available: 15,686 (100%, NA: 0 = 0%)
Unique: 4Shortest: 16
@@ -986,33 +993,33 @@ Longest: 24
Longest: 241 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 As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (
proportion_R(), equal toresistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal tosusceptibility()). These functions can be used on their own:data_1st %>% resistance(AMX) -# [1] 0.5233549 +# [1] 0.5350631Or can be used in conjuction with
group_by()andsummarise(), both from thedplyrpackage:diff --git a/docs/articles/MDR.html b/docs/articles/MDR.html index 3db87980a..2f74d5d6f 100644 --- a/docs/articles/MDR.html +++ b/docs/articles/MDR.html @@ -39,7 +39,7 @@@@ -1166,19 +1173,19 @@ Longest: 24Hospital 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 @@ -368,40 +368,40 @@ Unique: 5head(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 R1 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
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.
@@ -278,9 +278,9 @@ print(run_it, unit = "ms", signif = 3) # Unit: milliseconds # expr min lq mean median uq max neval -# mo_name(x) 1810 1850 1980 1940 2130 2230 10 +# mo_name(x) 1750 1790 1830 1810 1850 1950 10 -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.
@@ -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
So going from 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:
So going from 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
Of course, when running 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.
Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png index 42aa53ed8..b281d9986 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/docs/articles/datasets.html b/docs/articles/datasets.html index 93142c345..6535e2e92 100644 --- a/docs/articles/datasets.html +++ b/docs/articles/datasets.html @@ -39,7 +39,7 @@ @@ -200,15 +200,14 @@ -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 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.
Note: Years and dates of updates mentioned on this page, are from on 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.
All reference data (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this 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.
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.
Microorganisms (currently accepted names)
This data set is in R available as microorganisms, after you load the AMR package.
It was last updated on 28 July 2020 20:52:40 CEST.
-Direct download links:
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.
Direct download links:
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)This data set is in R available as microorganisms.old, after you load the AMR package.
It was last updated on 28 May 2020 11:17:56 CEST.
-Direct download links:
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.
Direct download links:
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 agentsThis data set is in R available as antibiotics, after you load the AMR package.
It was last updated on 31 July 2020 12:12:13 CEST.
-Direct download links:
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.
Direct download links:
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 agentsThis data set is in R available as antivirals, after you load the AMR package.
It was last updated on 23 November 2019 19:03:43 CET.
-Direct download links:
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.
Direct download links:
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 resistanceThis data set is in R available as intrinsic_resistant, after you load the AMR package.
It was last updated on 14 August 2020 14:18:20 CEST.
-Direct download links:
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.
Direct download links:
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/SIThis data set is in R available as rsi_translation, after you load the AMR package.
It was last updated on 29 July 2020 13:12:34 CEST.
-Direct download links:
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.
Direct download links:
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 @@
@@ -118,6 +118,13 @@
Predict antimicrobial resistance
+
+
+
+
+ Download our reference data sets for own use
+
+
diff --git a/docs/reference/AMR.html b/docs/reference/AMR.html
index 2b6eb132d..e415b85dc 100644
--- a/docs/reference/AMR.html
+++ b/docs/reference/AMR.html
@@ -82,7 +82,7 @@
@@ -118,6 +118,13 @@
Predict antimicrobial resistance
+
+
+
+
+ Download our reference data sets for own use
+
+
@@ -261,6 +268,11 @@
Principal component analysis for AMR
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, 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 @@
@@ -118,6 +118,13 @@
Predict antimicrobial resistance
+
+
+
+
+ Download our reference data sets for own use
+
+
diff --git a/docs/reference/WHONET.html b/docs/reference/WHONET.html
index d51061b7f..a3159984c 100644
--- a/docs/reference/WHONET.html
+++ b/docs/reference/WHONET.html
@@ -82,7 +82,7 @@
@@ -118,6 +118,13 @@
Predict antimicrobial resistance
+
+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 @@
Principal component analysis for AMR
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, 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
+ + + +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.
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
+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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/age.html b/docs/reference/age.html index dd2962af4..087963122 100644 --- a/docs/reference/age.html +++ b/docs/reference/age.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistanceAll 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().
Read more on our website!
+ + + +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!
See also
filter_ab_class() for the filter() equivalent.
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, which is automatically updated with every code change.
WHOCC
diff --git a/docs/reference/as.ab.html b/docs/reference/as.ab.html index 02cf04f05..f6c78ea9a 100644 --- a/docs/reference/as.ab.html +++ b/docs/reference/as.ab.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistanceThese 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/.
+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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/as.disk.html b/docs/reference/as.disk.html index acdb7c4cd..38174ce7d 100644 --- a/docs/reference/as.disk.html +++ b/docs/reference/as.disk.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistance
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().
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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/as.rsi.html b/docs/reference/as.rsi.html index 67485157e..c1b7bde9c 100644 --- a/docs/reference/as.rsi.html +++ b/docs/reference/as.rsi.html @@ -82,7 +82,7 @@ @@ -393,6 +393,11 @@ A microorganism is categorised as Susceptible, Increased exposure when
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.
+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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/atc_online.html b/docs/reference/atc_online.html index ad3ac8cf4..8a7f02cd5 100644 --- a/docs/reference/atc_online.html +++ b/docs/reference/atc_online.html @@ -83,7 +83,7 @@ This function requires an internet connection." /> @@ -119,6 +119,13 @@ This function requires an internet connection." /> Predict antimicrobial resistanceThese 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').
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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/example_isolates.html b/docs/reference/example_isolates.html index 2b0ac055a..f61030df3 100644 --- a/docs/reference/example_isolates.html +++ b/docs/reference/example_isolates.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistancePEN: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()
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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/example_isolates_unclean.html b/docs/reference/example_isolates_unclean.html index 4222b1629..6bf854451 100644 --- a/docs/reference/example_isolates_unclean.html +++ b/docs/reference/example_isolates_unclean.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistanceAMX:GEN
4 different antibiotics that have to be transformed with as.rsi()
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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/filter_ab_class.html b/docs/reference/filter_ab_class.html index 0c80155b4..3e057353c 100644 --- a/docs/reference/filter_ab_class.html +++ b/docs/reference/filter_ab_class.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistanceThe 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.
+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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/join.html b/docs/reference/join.html index 62f15d42f..ebcb2aada 100644 --- a/docs/reference/join.html +++ b/docs/reference/join.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistancemo
ID of the microorganism in the microorganisms data set
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, which is automatically updated with every code change.
Catalogue of Life
diff --git a/docs/reference/microorganisms.html b/docs/reference/microorganisms.html index bfc893e64..7068a1712 100644 --- a/docs/reference/microorganisms.html +++ b/docs/reference/microorganisms.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistance
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().
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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/microorganisms.old.html b/docs/reference/microorganisms.old.html index cc31b91b0..07cb42bb3 100644 --- a/docs/reference/microorganisms.old.html +++ b/docs/reference/microorganisms.old.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistance
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().
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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/mo_property.html b/docs/reference/mo_property.html index 9bd09f2b0..40b490136 100644 --- a/docs/reference/mo_property.html +++ b/docs/reference/mo_property.html @@ -82,7 +82,7 @@ @@ -118,6 +118,13 @@ Predict antimicrobial resistanceCatalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
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, which is automatically updated with every code change.
Read more on our website!
diff --git a/docs/reference/mo_source.html b/docs/reference/mo_source.html index 64cc1d3b7..b9d9627e9 100644 --- a/docs/reference/mo_source.html +++ b/docs/reference/mo_source.html @@ -83,7 +83,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p @@ -119,6 +119,13 @@ This is the fastest way to have your organisation (or analysis) specific codes p Predict antimicrobial resistanceDetails
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.
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, which is automatically updated with every code change.