How to apply EUCAST rules
-Matthijs S. Berends
-14 August 2020
Source:vignettes/EUCAST.Rmd
EUCAST.RmdHow to determine multi-drug resistance (MDR)
-Matthijs S. Berends
-14 August 2020
Source:vignettes/MDR.Rmd
MDR.Rmdhead(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 R R R R R S -# 2 S R S I I S -# 3 R R R S S S -# 4 S I S R I S -# 5 S R R R S R -# 6 S S R S S R +# 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 # kanamycin # 1 S -# 2 I +# 2 R # 3 R -# 4 R -# 5 S -# 6 I +# 4 S +# 5 R +# 6 R
We can now add the interpretation of MDR-TB to our data set. You can use:
@@ -363,40 +368,40 @@ Unique: 51 Mono-resistant -3241 -64.82% -3241 -64.82% +3203 +64.06% +3203 +64.06% 2 Negative -642 -12.84% -3883 -77.66% +682 +13.64% +3885 +77.70% 3 Multi-drug-resistant -595 -11.90% -4478 -89.56% +627 +12.54% +4512 +90.24% 4 Poly-resistant -311 -6.22% -4789 -95.78% +268 +5.36% +4780 +95.60% diff --git a/docs/articles/PCA.html b/docs/articles/PCA.html index 7a22eada1..9b7942afd 100644 --- a/docs/articles/PCA.html +++ b/docs/articles/PCA.html @@ -39,7 +39,7 @@ 5 Extensively drug-resistant -211 -4.22% +220 +4.40% 5000 100.00%
How to conduct principal component analysis (PCA) for AMR
-Matthijs S. Berends
-14 August 2020
Source:vignettes/PCA.Rmd
PCA.RmdHow to import data from SPSS / SAS / Stata
Matthijs S. Berends
-14 August 2020
+17 August 2020
Source:vignettes/SPSS.Rmd
SPSS.Rmd-
R is highly modular.
-The official R network (CRAN) features almost 14,000 packages at the time of writing, our
+AMRpackage being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitHub. So there may even be a lot more than 14,000 packages out there.The official R network (CRAN) features more than 16,000 packages at the time of writing, our
AMRpackage being one of them. All these packages were peer-reviewed before publication. Aside from this official channel, there are also developers who choose not to submit to CRAN, but rather keep it on their own public repository, like GitHub. So there may even be a lot more than 14,000 packages out there.Bottom line is, you can really extend it yourself or ask somebody to do this for you. Take for example our
AMRpackage. Among other things, it adds reliable reference data to R to help you with the data cleaning and analysis. SPSS, SAS and Stata will never know what a valid MIC value is or what the Gram stain of E. coli is. Or that all species of Klebiella are resistant to amoxicillin and that Floxapen® is a trade name of flucloxacillin. These facts and properties are often needed to clean existing data, which would be very inconvenient in a software package without reliable reference data. See below for a demonstration. - @@ -221,7 +228,7 @@
-
R has a huge community.
-Many R users just ask questions on websites like StackOverflow.com, the largest online community for programmers. At the time of writing, more than 300,000 R-related questions have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
+Many R users just ask questions on websites like StackOverflow.com, the largest online community for programmers. At the time of writing, more than 360,000 R-related questions have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
-
R understands any data type, including SPSS/SAS/Stata.
diff --git a/docs/articles/WHONET.html b/docs/articles/WHONET.html index ce1fd0de1..574daebba 100644 --- a/docs/articles/WHONET.html +++ b/docs/articles/WHONET.html @@ -39,7 +39,7 @@
How to work with WHONET data
-Matthijs S. Berends
-14 August 2020
Source:vignettes/WHONET.Rmd
WHONET.RmdBenchmarks
-Matthijs S. Berends
-14 August 2020
Source:vignettes/benchmarks.Rmd
benchmarks.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.
-To achieve this speed, the as.mo function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of Methanosarcina semesiae (B_MTHNSR_SEMS), a bug probably never found before in humans:
-M.semesiae <- microbenchmark(as.mo("metsem"), - as.mo("METSEM"), - as.mo("M. semesiae"), - as.mo("M. semesiae"), - as.mo("Methanosarcina semesiae"), - times = 10) -print(M.semesiae, unit = "ms", signif = 4) -# Unit: milliseconds -# expr min lq mean median uq max -# as.mo("metsem") 193.600 200.200 209.30 202.800 218.00 238.20 -# as.mo("METSEM") 186.500 193.500 208.40 202.600 228.90 244.80 -# as.mo("M. semesiae") 12.280 13.090 18.86 14.430 15.48 59.40 -# as.mo("M. semesiae") 14.060 14.520 20.98 16.420 17.56 46.18 -# as.mo("Methanosarcina semesiae") 8.065 9.203 12.65 9.715 10.65 39.96 -# neval -# 10 -# 10 -# 10 -# 10 -# 10 -
Looking up arbitrary codes of less prevalent microorganisms costs the most time. Full names (like Methanosarcina semesiae) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.
-In the figure below, we compare Escherichia coli (which is very common) with Prevotella brevis (which is moderately common) and with Methanosarcina semesiae (which is uncommon):
-
Uncommon microorganisms take some more time than common microorganisms. To further improve performance, two important calculations take almost no time at all: repetitive results and already precalculated results.
+To improve performance, two important calculations take almost no time at all: repetitive results and already precalculated results.
Repetitive results
Repetitive results are unique values that are present more than once. Unique values will only be calculated once by as.mo(). We will use mo_name() for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses as.mo() internally.
+-# take all MO codes from the example_isolates data set x <- example_isolates$mo %>% # keep only the unique ones @@ -299,28 +278,28 @@ print(run_it, unit = "ms", signif = 3) # Unit: milliseconds # expr min lq mean median uq max neval -# mo_name(x) 1920 1940 2010 1990 2060 2120 10 +# mo_name(x) 1810 1850 1980 1940 2130 2230 10So transforming 500,000 values (!!) of 50 unique values only takes 1.99 seconds. You only lose time on your unique input values.
+So transforming 500,000 values (!!) of 50 unique values only takes 1.94 seconds. You only lose time on your unique input values.
Precalculated results
What about precalculated results? If the input is an already precalculated result of a helper function like mo_name(), it almost doesn’t take any time at all (see ‘C’ below):
+-run_it <- microbenchmark(A = mo_name("B_STPHY_AURS"), B = mo_name("S. aureus"), C = mo_name("Staphylococcus aureus"), times = 10) print(run_it, unit = "ms", signif = 3) # Unit: milliseconds -# expr min lq mean median uq max neval -# A 5.90 6.16 6.93 7.24 7.43 7.89 10 -# B 11.40 12.00 16.20 13.20 14.20 45.20 10 -# C 1.05 1.07 1.18 1.13 1.28 1.40 10 +# 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 10So 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:@@ -348,7 +327,7 @@+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:run_it <- microbenchmark(A = mo_species("aureus"), B = mo_genus("Staphylococcus"), C = mo_name("Staphylococcus aureus"), @@ -332,15 +311,15 @@ times = 10) print(run_it, unit = "ms", signif = 3) # Unit: milliseconds -# expr min lq mean median uq max neval -# A 0.976 0.993 1.13 1.07 1.29 1.34 10 -# B 1.000 1.060 1.13 1.07 1.15 1.40 10 -# C 0.927 1.030 1.08 1.06 1.11 1.35 10 -# D 0.896 0.983 1.11 1.08 1.27 1.41 10 -# E 0.866 1.020 1.10 1.07 1.23 1.37 10 -# F 0.967 0.993 1.16 1.05 1.32 1.61 10 -# G 0.855 1.020 1.07 1.07 1.17 1.31 10 -# H 0.966 1.010 1.12 1.06 1.19 1.36 10 +# 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 10Of 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.Results in other languages
When the system language is non-English and supported by this
-AMRpackage, some functions will have a translated result. This almost does’t take extra time: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 d8d20b120..42aa53ed8 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 e402db03a..93142c345 100644 --- a/docs/articles/datasets.html +++ b/docs/articles/datasets.html @@ -5,7 +5,7 @@ -+mo_name("CoNS", language = "en") # or just mo_name("CoNS") on an English system # [1] "Coagulase-negative Staphylococcus (CoNS)" @@ -368,14 +347,14 @@ times = 100) print(run_it, unit = "ms", signif = 4) # Unit: milliseconds -# expr min lq mean median uq max neval -# en 13.09 14.02 18.36 14.59 16.22 61.92 100 -# de 14.00 14.83 18.06 15.48 16.85 56.01 100 -# nl 17.71 18.99 24.79 20.09 22.22 58.50 100 -# es 13.99 15.02 20.94 15.88 16.70 141.90 100 -# it 13.68 14.97 19.02 15.52 16.72 50.26 100 -# fr 13.96 15.05 20.63 15.72 17.14 52.71 100 -# pt 14.13 14.86 19.81 15.69 17.46 53.89 100 +# expr min lq mean median uq max neval +# en 10.72 11.54 15.34 12.76 13.82 44.53 100 +# de 11.47 12.77 17.24 13.60 14.78 55.48 100 +# nl 14.58 15.91 19.06 16.96 18.46 46.24 100 +# es 11.24 11.96 16.86 13.17 14.70 50.05 100 +# it 11.35 12.42 17.79 13.52 16.28 50.31 100 +# fr 11.41 12.29 17.08 13.43 15.44 53.91 100 +# pt 11.34 12.06 16.23 13.25 14.80 51.96 100Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
Data sets for download • AMR (for R) +Data sets for download / own use • AMR (for R) @@ -18,7 +18,7 @@ - +