Benchmarks
Matthijs S. Berends
-16 September 2019
+18 September 2019
benchmarks.RmdUsing the microbenchmark package, we can review the calculation performance of this function. Its function microbenchmark() runs different input expressions independently of each other and measures their time-to-result.
In the next test, we try to ‘coerce’ different input values for Staphylococcus aureus. The actual result is the same every time: it returns its MO code B_STPHY_AUR (B stands for Bacteria, the taxonomic kingdom).
In the next test, we try to ‘coerce’ different input values for Staphylococcus aureus. The actual result is the same every time: it returns its MO code B_STPHY_AURS (B stands for Bacteria, the taxonomic kingdom).
But the calculation time differs a lot:
S.aureus <- microbenchmark(
as.mo("sau"), # WHONET code
@@ -220,38 +220,38 @@
print(S.aureus, unit = "ms", signif = 2)
# Unit: milliseconds
# expr min lq mean median uq
-# as.mo("sau") 8.6 8.7 11.0 8.8 9.3
-# as.mo("stau") 30.0 30.0 41.0 31.0 32.0
-# as.mo("STAU") 30.0 30.0 42.0 31.0 32.0
-# as.mo("staaur") 8.5 8.9 12.0 9.1 9.6
-# as.mo("STAAUR") 8.5 9.0 9.1 9.2 9.3
-# as.mo("S. aureus") 23.0 23.0 30.0 23.0 40.0
-# as.mo("S aureus") 22.0 23.0 28.0 23.0 37.0
-# as.mo("Staphylococcus aureus") 28.0 29.0 30.0 30.0 31.0
-# as.mo("Staphylococcus aureus (MRSA)") 520.0 530.0 540.0 540.0 550.0
-# as.mo("Sthafilokkockus aaureuz") 270.0 280.0 300.0 290.0 300.0
-# as.mo("MRSA") 8.5 8.7 8.9 8.8 9.0
-# as.mo("VISA") 18.0 19.0 24.0 19.0 35.0
-# as.mo("VRSA") 18.0 18.0 31.0 19.0 34.0
-# as.mo(22242419) 17.0 18.0 22.0 18.0 19.0
+# as.mo("sau") 8.5 8.9 12.0 9.1 9.7
+# as.mo("stau") 31.0 32.0 48.0 34.0 55.0
+# as.mo("STAU") 31.0 31.0 35.0 32.0 33.0
+# as.mo("staaur") 8.6 8.8 12.0 9.0 9.3
+# as.mo("STAAUR") 8.6 8.9 9.0 9.0 9.1
+# as.mo("S. aureus") 23.0 23.0 26.0 24.0 24.0
+# as.mo("S aureus") 23.0 23.0 31.0 24.0 44.0
+# as.mo("Staphylococcus aureus") 27.0 28.0 29.0 29.0 29.0
+# as.mo("Staphylococcus aureus (MRSA)") 550.0 560.0 590.0 580.0 590.0
+# as.mo("Sthafilokkockus aaureuz") 270.0 290.0 340.0 300.0 330.0
+# as.mo("MRSA") 8.7 8.8 9.1 9.1 9.4
+# as.mo("VISA") 18.0 19.0 20.0 19.0 20.0
+# as.mo("VRSA") 19.0 19.0 23.0 19.0 22.0
+# as.mo(22242419) 18.0 18.0 37.0 30.0 42.0
# max neval
-# 28.0 10
+# 31.0 10
# 120.0 10
-# 120.0 10
+# 59.0 10
# 34.0 10
-# 9.4 10
-# 41.0 10
-# 43.0 10
-# 31.0 10
-# 570.0 10
-# 400.0 10
-# 9.3 10
-# 36.0 10
-# 110.0 10
-# 43.0 10
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. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.
-To achieve this speed, the as.mo function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of Thermus islandicus (B_THERMS_ISL), a bug probably never found before in humans:
To achieve this speed, the as.mo function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of Thermus islandicus (B_THERMS_ISLN), a bug probably never found before in humans:
T.islandicus <- microbenchmark(as.mo("theisl"),
as.mo("THEISL"),
as.mo("T. islandicus"),
@@ -320,53 +320,53 @@
print(T.islandicus, unit = "ms", signif = 2)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# as.mo("theisl") 1300 1300 1300 1300 1400 1400 10
-# as.mo("THEISL") 1300 1300 1300 1300 1300 1400 10
-# as.mo("T. islandicus") 360 360 370 380 380 390 10
-# as.mo("T. islandicus") 360 370 400 380 450 480 10
-# as.mo("Thermus islandicus") 29 30 32 30 30 50 10That takes 8.6 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like Thermus islandicus) are almost fast - these are the most probable input from most data sets.
+# as.mo("theisl") 1300 1400 1400 1400 1500 1600 10 +# as.mo("THEISL") 1400 1400 1400 1400 1500 1600 10 +# as.mo("T. islandicus") 370 400 410 410 420 450 10 +# as.mo("T. islandicus") 360 370 400 380 410 490 10 +# as.mo("Thermus islandicus") 28 30 35 32 35 59 10 +That takes 8.5 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like Thermus islandicus) are almost fast - these 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 Thermus islandicus (which is uncommon):
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
+# Warning:
+# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
+
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
# Warning:
# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
-# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
-
-# Warning:
-# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
In reality, the as.mo() functions learns from its own output to speed up determinations for next times. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use as.mo() yourself:
# NOTE: results are saved to /Users/msberends/Library/R/3.6/library/AMR/mo_history/mo_history.csv.
+# NOTE: Prevotella ruminicola brevis (Shah et al., 1990) was renamed Prevotella brevis (Avgustin et al., 2016) [B_PRVTL_BRVS]
# Warning:
-# Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it.
-# NOTE: Prevotella ruminicola brevis was renamed Prevotella brevis (Avgustin et al., 1997) (B_PRVTL_BRE)
The highest outliers are the first times. All next determinations were done in only thousands of seconds.
Uncommon microorganisms take a lot more time than common microorganisms. To relieve this pitfall and further improve performance, two important calculations take almost no time at all: repetitive results and already precalculated results.
@@ -400,23 +400,23 @@ print(run_it, unit = "ms", signif = 3) # Unit: milliseconds # expr min lq mean median uq max neval -# mo_name(x) 607 619 636 632 660 671 10 -So transforming 500,000 values (!!) of 50 unique values only takes 0.63 seconds (632 ms). You only lose time on your unique input values.
+# mo_name(x) 610 644 669 665 684 748 10 +So transforming 500,000 values (!!) of 50 unique values only takes 0.66 seconds (664 ms). 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_AUR"),
+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 6.410 6.500 6.720 6.570 6.840 7.660 10
-# B 21.700 22.100 25.900 22.600 24.200 48.600 10
-# C 0.763 0.774 0.812 0.812 0.849 0.855 10
+# A 6.150 6.260 6.400 6.390 6.520 6.710 10
+# B 22.200 22.500 26.400 22.700 24.800 53.100 10
+# C 0.645 0.774 0.801 0.803 0.812 0.911 10So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0008 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"),
@@ -430,14 +430,14 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 0.449 0.471 0.492 0.474 0.483 0.671 10
-# B 0.611 0.621 0.638 0.626 0.629 0.717 10
-# C 0.649 0.706 0.743 0.743 0.807 0.818 10
-# D 0.458 0.463 0.474 0.468 0.481 0.520 10
-# E 0.432 0.452 0.459 0.463 0.472 0.476 10
-# F 0.427 0.454 0.461 0.461 0.471 0.487 10
-# G 0.448 0.453 0.465 0.463 0.470 0.506 10
-# H 0.431 0.444 0.459 0.458 0.468 0.506 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" too, 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.
@@ -464,13 +464,13 @@
print(run_it, unit = "ms", signif = 4)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# en 17.29 17.79 18.11 18.13 18.57 18.86 10
-# de 18.44 19.01 20.23 19.76 19.86 27.61 10
-# nl 23.83 24.71 26.99 25.97 26.53 39.93 10
-# es 18.71 18.88 19.43 19.24 19.47 21.88 10
-# it 18.48 18.84 19.40 19.50 19.84 20.57 10
-# fr 18.51 19.39 25.08 20.26 37.11 38.24 10
-# pt 18.60 18.92 19.33 19.45 19.64 19.96 10
+# en 18.19 18.35 18.84 18.65 18.99 20.91 10
+# de 19.31 19.71 20.39 20.29 20.92 21.83 10
+# nl 24.43 24.92 25.51 25.37 25.65 27.97 10
+# es 19.22 19.53 20.06 19.82 20.47 21.81 10
+# it 19.36 20.03 24.77 20.26 20.96 45.31 10
+# fr 19.11 19.30 19.71 19.72 20.11 20.37 10
+# pt 19.40 19.90 27.80 21.38 41.88 45.37 10
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 6fc093ec..1df1557a 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/benchmarks_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-6-1.png index dee8e9b8..ff8208ee 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-6-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-6-1.png differ diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-7-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-7-1.png index 9e14b133..ad62008e 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-7-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index d169534c..daaf7f8e 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -78,7 +78,7 @@ diff --git a/docs/authors.html b/docs/authors.html index 18990bcc..4f9f44c2 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -78,7 +78,7 @@ @@ -249,6 +249,10 @@Corinna Glasner. Author, thesis advisor. 
Judith M. Fonville. Contributor. +
+Erwin E. A. Hassing. Contributor.
diff --git a/docs/extra.js b/docs/extra.js index 1c4fecad..960e0285 100644 --- a/docs/extra.js +++ b/docs/extra.js @@ -81,13 +81,14 @@ $( document ).ready(function() { x = x.replace(/Author, maintainer/g, "Main developer"); x = x.replace(/Author, contributor/g, "Main contributor"); x = x.replace(/Author, thesis advisor/g, "Doctoral advisor"); - x = x.replace("Alex", "Prof. Dr Alex"); - x = x.replace("Bhanu", "Prof. Dr Bhanu"); - x = x.replace("Casper", "Prof. Dr Casper"); - x = x.replace("Corinna", "Dr Corinna"); + x = x.replace("Alex", "Prof. Dr. Alex"); + x = x.replace("Bhanu", "Prof. Dr. Bhanu"); + x = x.replace("Casper", "Prof. Dr. Casper"); + x = x.replace("Corinna", "Dr. Corinna"); // others - x = x.replace("Bart", "Dr Bart"); - x = x.replace("Dennis", "Dr Dennis"); + x = x.replace("Bart", "Dr. Bart"); + x = x.replace("Dennis", "Dr. Dennis"); + x = x.replace("Judith", "Dr. Judith"); } return(x); } diff --git a/docs/index.html b/docs/index.html index 09dbe453..3d6da21d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -42,7 +42,7 @@ @@ -307,7 +307,7 @@It cleanses existing data by providing new classes for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:
-
-
- Use
as.mo()to get a microbial ID. The IDs are human readable for the trained eye - the ID of Klebsiella pneumoniae is “B_KLBSL_PNE” (B stands for Bacteria) and the ID of S. aureus is “B_STPHY_AUR”. The function takes almost any text as input that looks like the name or code of a microorganism like “E. coli”, “esco” or “esccol” and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our benchmarks. Moreover, it can group Staphylococci into coagulase negative and positive (CoNS and CoPS, see source) and can categorise Streptococci into Lancefield groups (like beta-haemolytic Streptococcus Group B, source).
+ - Use
as.mo()to get a microbial ID. The IDs are human readable for the trained eye - the ID of Klebsiella pneumoniae is “B_KLBSL_PNMN” (B stands for Bacteria) and the ID of S. aureus is “B_STPHY_AURS”. The function takes almost any text as input that looks like the name or code of a microorganism like “E. coli”, “esco” or “esccol” and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our benchmarks. Moreover, it can group Staphylococci into coagulase negative and positive (CoNS and CoPS, see source) and can categorise Streptococci into Lancefield groups (like beta-haemolytic Streptococcus Group B, source). - Use
as.ab()to get an antibiotic ID. Like microbial IDs, these IDs are also human readable based on those used by EARS-Net. For example, the ID of amoxicillin isAMXand the ID of gentamicin isGEN. Theas.ab()function also uses intelligent rules to find results like accepting misspelling, trade names and abbrevations used in many laboratory systems. For instance, the values “Furabid”, “Furadantin”, “nitro” all return the ID of Nitrofurantoine. To accomplish this, the package contains a database with most LIS codes, official names, trade names, ATC codes, defined daily doses (DDD) and drug categories of antibiotics. - Use
as.rsi()to get antibiotic interpretations based on raw MIC values (in mg/L) or disk diffusion values (in mm), or transform existing values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like “<=0.002; S” (combined MIC/RSI) will result in “S”. - Use
as.mic()to cleanse your MIC values. It produces a so-called factor (called ordinal in SPSS) with valid MIC values as levels. A value like “<=0.002; S” (combined MIC/RSI) will result in “<=0.002”.
diff --git a/docs/news/index.html b/docs/news/index.html
index 80463afe..bf37a22f 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -78,7 +78,7 @@
@@ -225,11 +225,11 @@
- - Many algorithm improvements for
as.mo()(of which some led to additions to themicroorganismsdata set): + - Many algorithm improvements for
as.mo()(of which some led to additions to themicroorganismsdata set). Many thanks to all contributors that helped improving the algorithms.- Self-learning algorithm - the function now gains experience from previously determined microorganism IDs and learns from it (yielding 80-95% speed improvement for any guess after the first try)
- Big improvement for misspelled input @@ -314,6 +314,7 @@ Since this is a major change, usage of the old
- Added support for Viridans Group Streptococci (VGS) and Milleri Group Streptococci (MGS)
- Added support for 5,000 new fungi
- Added support for unknown yeasts and fungi +
- Changed most microorganism IDs to improve readability. IMPORTANT: Because of these changes, the microorganism IDs have been changed to a slightly different format. Old microorganism IDs are still supported, but support will be dropped in a future version. Use
as.mo()on your old codes to transform them to the new format.
also_single_testedw - Renamed data set
septic_patientstoexample_isolates@@ -351,7 +352,7 @@ Since this is a major change, usage of the oldalso_single_testedwOther
-
-
- Added Prof Dr Casper Albers as doctoral advisor and Dr Bart Meijer, Dr Dennis Souverein and Annick Lenglet as contributors +
- Added Prof. Dr. Casper Albers as doctoral advisor and added Dr. Judith Fonville, Eric Hazenberg, Dr. Bart Meijer, Dr. Dennis Souverein and Annick Lenglet as contributors
"Streptococcus group B (known as S. agalactiae)". The text between brackets will be removed and a warning will be thrown that the result Streptococcus group B (B_STRPT_GRB) needs review.
+"Streptococcus group B (known as S. agalactiae)". The text between brackets will be removed and a warning will be thrown that the result Streptococcus group B (B_STRPT_GRPB) needs review."S. aureus - please mind: MRSA". The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result Staphylococcus aureus (B_STPHY_AUR) needs review."Fluoroquinolone-resistant Neisseria gonorrhoeae". The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result Neisseria gonorrhoeae (B_NESSR_GON) needs review.gendergender of the patient
patient_idID of the patient, first 10 characters of an SHA hash containing irretrievable information
moID of microorganism created with
as.mo, see alsomicroorganisms
- peni:rifa40 different antibiotics with class
rsi(seeas.rsi); these column names occur inantibioticsdata set and can be translated withab_name
+ PEN:RIF40 different antibiotics with class
rsi(seeas.rsi); these column names occur inantibioticsdata set and can be translated withab_namecerteCommonly used code of a microorganism
+ codeCommonly used code of a microorganism
moID of the microorganism in the
microorganismsdata setmoID of microorganism as used by this package
col_idCatalogue of Life ID
fullnameFull name, like
"Escherichia coli"
@@ -262,7 +262,7 @@
2 entries of Staphylococcus (coagulase-negative [CoNS] and coagulase-positive [CoPS])
3 entries of Trichomonas (Trichomonas vaginalis, and its family and genus)
5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)
-8,970 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) that are not in the Catalogue of Life
+22,654 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) overwriting records from the Catalogue of Life, since the DSMZ contain the latest taxonomic information based on recent publications
col_idCatalogue of Life ID that was originally given
col_id_newNew Catalogue of Life ID that responds to an entry in the
microorganismsdata setfullnameOld full taxonomic name of the microorganism
diff --git a/index.md b/index.md
index b408f390..0f0f5448 100644
--- a/index.md
+++ b/index.md
@@ -123,7 +123,7 @@ The `AMR` package basically does four important things:
1. It **cleanses existing data** by providing new *classes* for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:
- * Use `as.mo()` to get a microbial ID. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" or "esccol" and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
+ * Use `as.mo()` to get a microbial ID. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNMN" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AURS". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" or "esccol" and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
* Use `as.ab()` to get an antibiotic ID. Like microbial IDs, these IDs are also human readable based on those used by EARS-Net. For example, the ID of amoxicillin is `AMX` and the ID of gentamicin is `GEN`. The `as.ab()` function also uses intelligent rules to find results like accepting misspelling, trade names and abbrevations used in many laboratory systems. For instance, the values "Furabid", "Furadantin", "nitro" all return the ID of Nitrofurantoine. To accomplish this, the package contains a database with most LIS codes, official names, trade names, ATC codes, defined daily doses (DDD) and drug categories of antibiotics.
* Use `as.rsi()` to get antibiotic interpretations based on raw MIC values (in mg/L) or disk diffusion values (in mm), or transform existing values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
* Use `as.mic()` to cleanse your MIC values. It produces a so-called factor (called *ordinal* in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
diff --git a/man/as.mo.Rd b/man/as.mo.Rd
index 62f40090..64317d8f 100644
--- a/man/as.mo.Rd
+++ b/man/as.mo.Rd
@@ -34,7 +34,7 @@ clear_mo_history(...)
This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D.}
-\item{allow_uncertain}{a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0) to indicate whether the input should be checked for less possible results, see Details}
+\item{allow_uncertain}{a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0) to indicate whether the input should be checked for less probable results, see Details}
\item{reference_df}{a \code{data.frame} to use for extra reference when translating \code{x} to a valid \code{mo}. See \code{\link{set_mo_source}} and \code{\link{get_mo_source}} to automate the usage of your own codes (e.g. used in your analysis or organisation).}
@@ -50,16 +50,16 @@ Use this function to determine a valid microorganism ID (\code{mo}). Determinati
\strong{General info} \cr
A microorganism ID from this package (class: \code{mo}) typically looks like these examples:\cr
\preformatted{
- Code Full name
- --------------- --------------------------------------
- B_KLBSL Klebsiella
- B_KLBSL_PNE Klebsiella pneumoniae
- B_KLBSL_PNE_RHI Klebsiella pneumoniae rhinoscleromatis
- | | | |
- | | | |
- | | | ----> subspecies, a 3-4 letter acronym
- | | ----> species, a 3-4 letter acronym
- | ----> genus, a 5-7 letter acronym, mostly without vowels
+ Code Full name
+ --------------- --------------------------------------
+ B_KLBSL Klebsiella
+ B_KLBSL_PNMN Klebsiella pneumoniae
+ B_KLBSL_PNMN_RHNS Klebsiella pneumoniae rhinoscleromatis
+ | | | |
+ | | | |
+ | | | ---> subspecies, a 4-5 letter acronym
+ | | ----> species, a 4-5 letter acronym
+ | ----> genus, a 5-7 letter acronym
----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),
C (Chromista), F (Fungi), P (Protozoa)
}
@@ -105,7 +105,7 @@ You can also use e.g. \code{as.mo(..., allow_uncertain = 1)} to only allow up to
Examples:
\itemize{
- \item{\code{"Streptococcus group B (known as S. agalactiae)"}. The text between brackets will be removed and a warning will be thrown that the result \emph{Streptococcus group B} (\code{B_STRPT_GRB}) needs review.}
+ \item{\code{"Streptococcus group B (known as S. agalactiae)"}. The text between brackets will be removed and a warning will be thrown that the result \emph{Streptococcus group B} (\code{B_STRPT_GRPB}) needs review.}
\item{\code{"S. aureus - please mind: MRSA"}. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result \emph{Staphylococcus aureus} (\code{B_STPHY_AUR}) needs review.}
\item{\code{"Fluoroquinolone-resistant Neisseria gonorrhoeae"}. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result \emph{Neisseria gonorrhoeae} (\code{B_NESSR_GON}) needs review.}
}
@@ -154,7 +154,7 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
\examples{
\donttest{
-# These examples all return "B_STPHY_AUR", the ID of S. aureus:
+# These examples all return "B_STPHY_AURS", the ID of S. aureus:
as.mo("sau") # WHONET code
as.mo("stau")
as.mo("STAU")
@@ -179,11 +179,11 @@ as.mo("Streptococcus group A")
as.mo("GAS") # Group A Streptococci
as.mo("GBS") # Group B Streptococci
-as.mo("S. epidermidis") # will remain species: B_STPHY_EPI
-as.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CNS
+as.mo("S. epidermidis") # will remain species: B_STPHY_EPDR
+as.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CONS
-as.mo("S. pyogenes") # will remain species: B_STRPT_PYO
-as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRA
+as.mo("S. pyogenes") # will remain species: B_STRPT_PYGN
+as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA
# All mo_* functions use as.mo() internally too (see ?mo_property):
mo_genus("E. coli") # returns "Escherichia"
diff --git a/man/example_isolates.Rd b/man/example_isolates.Rd
index 0ca805ae..456de6cf 100644
--- a/man/example_isolates.Rd
+++ b/man/example_isolates.Rd
@@ -3,7 +3,7 @@
\docType{data}
\name{example_isolates}
\alias{example_isolates}
-\title{Data set with 2,000 blood culture isolates from septic patients}
+\title{Data set with 2,000 blood culture isolates}
\format{A \code{\link{data.frame}} with 2,000 observations and 49 variables:
\describe{
\item{\code{date}}{date of receipt at the laboratory}
@@ -15,13 +15,13 @@
\item{\code{gender}}{gender of the patient}
\item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
\item{\code{mo}}{ID of microorganism created with \code{\link{as.mo}}, see also \code{\link{microorganisms}}}
- \item{\code{peni:rifa}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{ab_name}}}
+ \item{\code{PEN:RIF}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{ab_name}}}
}}
\usage{
example_isolates
}
\description{
-An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. This \code{data.frame} can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
+An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found 4 different hospitals in the Netherlands, between 2001 and 2017. This \code{data.frame} can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
}
\section{Read more on our website!}{
diff --git a/man/microorganisms.Rd b/man/microorganisms.Rd
index 63d094cd..4ec1ae5a 100755
--- a/man/microorganisms.Rd
+++ b/man/microorganisms.Rd
@@ -4,7 +4,7 @@
\name{microorganisms}
\alias{microorganisms}
\title{Data set with ~70,000 microorganisms}
-\format{A \code{\link{data.frame}} with 69,855 observations and 16 variables:
+\format{A \code{\link{data.frame}} with 69,460 observations and 16 variables:
\describe{
\item{\code{mo}}{ID of microorganism as used by this package}
\item{\code{col_id}}{Catalogue of Life ID}
@@ -34,7 +34,7 @@ Manually added were:
\item{2 entries of \emph{Staphylococcus} (coagulase-negative [CoNS] and coagulase-positive [CoPS])}
\item{3 entries of \emph{Trichomonas} (\emph{Trichomonas vaginalis}, and its family and genus)}
\item{5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)}
- \item{8,970 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) that are not in the Catalogue of Life}
+ \item{22,654 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) overwriting records from the Catalogue of Life, since the DSMZ contain the latest taxonomic information based on recent publications}
}
}
\section{About the records from DSMZ (see source)}{
diff --git a/man/microorganisms.codes.Rd b/man/microorganisms.codes.Rd
index bef7ef17..46d101a8 100644
--- a/man/microorganisms.codes.Rd
+++ b/man/microorganisms.codes.Rd
@@ -3,10 +3,10 @@
\docType{data}
\name{microorganisms.codes}
\alias{microorganisms.codes}
-\title{Translation table for microorganism codes}
-\format{A \code{\link{data.frame}} with 4,965 observations and 2 variables:
+\title{Translation table for common microorganism codes}
+\format{A \code{\link{data.frame}} with 4,927 observations and 2 variables:
\describe{
- \item{\code{certe}}{Commonly used code of a microorganism}
+ \item{\code{code}}{Commonly used code of a microorganism}
\item{\code{mo}}{ID of the microorganism in the \code{\link{microorganisms}} data set}
}}
\usage{
diff --git a/man/microorganisms.old.Rd b/man/microorganisms.old.Rd
index 755fbebd..c15d42f6 100644
--- a/man/microorganisms.old.Rd
+++ b/man/microorganisms.old.Rd
@@ -4,7 +4,7 @@
\name{microorganisms.old}
\alias{microorganisms.old}
\title{Data set with previously accepted taxonomic names}
-\format{A \code{\link{data.frame}} with 22,932 observations and 4 variables:
+\format{A \code{\link{data.frame}} with 24,246 observations and 4 variables:
\describe{
\item{\code{col_id}}{Catalogue of Life ID that was originally given}
\item{\code{col_id_new}}{New Catalogue of Life ID that responds to an entry in the \code{\link{microorganisms}} data set}
diff --git a/pkgdown/extra.js b/pkgdown/extra.js
index 1c4fecad..960e0285 100644
--- a/pkgdown/extra.js
+++ b/pkgdown/extra.js
@@ -81,13 +81,14 @@ $( document ).ready(function() {
x = x.replace(/Author, maintainer/g, "Main developer");
x = x.replace(/Author, contributor/g, "Main contributor");
x = x.replace(/Author, thesis advisor/g, "Doctoral advisor");
- x = x.replace("Alex", "Prof. Dr Alex");
- x = x.replace("Bhanu", "Prof. Dr Bhanu");
- x = x.replace("Casper", "Prof. Dr Casper");
- x = x.replace("Corinna", "Dr Corinna");
+ x = x.replace("Alex", "Prof. Dr. Alex");
+ x = x.replace("Bhanu", "Prof. Dr. Bhanu");
+ x = x.replace("Casper", "Prof. Dr. Casper");
+ x = x.replace("Corinna", "Dr. Corinna");
// others
- x = x.replace("Bart", "Dr Bart");
- x = x.replace("Dennis", "Dr Dennis");
+ x = x.replace("Bart", "Dr. Bart");
+ x = x.replace("Dennis", "Dr. Dennis");
+ x = x.replace("Judith", "Dr. Judith");
}
return(x);
}
diff --git a/tests/testthat/test-first_isolate.R b/tests/testthat/test-first_isolate.R
index e30cb6f9..e3640ef7 100755
--- a/tests/testthat/test-first_isolate.R
+++ b/tests/testthat/test-first_isolate.R
@@ -190,19 +190,19 @@ test_that("first isolates work", {
# unknown MOs
expect_equal(example_isolates %>%
- mutate(mo = ifelse(mo == "B_ESCHR_COL", "UNKNOWN", mo)) %>%
+ mutate(mo = ifelse(mo == "B_ESCHR_COLI", "UNKNOWN", mo)) %>%
mutate(first = first_isolate(., include_unknown = FALSE)) %>%
.$first %>%
sum(),
1062)
expect_equal(example_isolates %>%
- mutate(mo = ifelse(mo == "B_ESCHR_COL", "UNKNOWN", mo)) %>%
+ mutate(mo = ifelse(mo == "B_ESCHR_COLI", "UNKNOWN", mo)) %>%
mutate(first = first_isolate(., include_unknown = TRUE)) %>%
.$first %>%
sum(),
1529)
expect_equal(example_isolates %>%
- mutate(mo = ifelse(mo == "B_ESCHR_COL", NA, mo)) %>%
+ mutate(mo = ifelse(mo == "B_ESCHR_COLI", NA, mo)) %>%
mutate(first = first_isolate(.)) %>%
.$first %>%
sum(),
diff --git a/tests/testthat/test-freq.R b/tests/testthat/test-freq.R
index 9f1e94e1..c115db54 100755
--- a/tests/testthat/test-freq.R
+++ b/tests/testthat/test-freq.R
@@ -26,8 +26,8 @@ test_that("frequency table works", {
# mo
expect_true(is.freq(freq(example_isolates$mo)))
# for this to work, the output of mo_gramstain() is to be expected as follows:
- expect_equal(mo_gramstain("B_ESCHR_COL", language = NULL), "Gram-negative")
- expect_equal(mo_gramstain("B_STPHY_AUR", language = NULL), "Gram-positive")
+ expect_equal(mo_gramstain("B_ESCHR_COLI", language = NULL), "Gram-negative")
+ expect_equal(mo_gramstain("B_STPHY_AURS", language = NULL), "Gram-positive")
# rsi
expect_true(is.freq(freq(example_isolates$AMX)))
diff --git a/tests/testthat/test-join_microorganisms.R b/tests/testthat/test-join_microorganisms.R
index 3f2e77fa..a637346d 100755
--- a/tests/testthat/test-join_microorganisms.R
+++ b/tests/testthat/test-join_microorganisms.R
@@ -45,17 +45,17 @@ test_that("joins work", {
expect_true(nrow(unjoined) < nrow(full))
- expect_equal(nrow(inner_join_microorganisms("B_ESCHR_COL")), 1)
- expect_equal(nrow(inner_join_microorganisms("B_ESCHR_COL", by = c("mo" = "mo"))), 1)
+ expect_equal(nrow(inner_join_microorganisms("B_ESCHR_COLI")), 1)
+ expect_equal(nrow(inner_join_microorganisms("B_ESCHR_COLI", by = c("mo" = "mo"))), 1)
expect_warning(inner_join_microorganisms("Escherichia", by = c("mo" = "genus")))
- expect_equal(nrow(left_join_microorganisms("B_ESCHR_COL")), 1)
+ expect_equal(nrow(left_join_microorganisms("B_ESCHR_COLI")), 1)
expect_warning(left_join_microorganisms("Escherichia", by = c("mo" = "genus")))
- expect_equal(nrow(semi_join_microorganisms("B_ESCHR_COL")), 1)
- expect_equal(nrow(anti_join_microorganisms("B_ESCHR_COL")), 0)
+ expect_equal(nrow(semi_join_microorganisms("B_ESCHR_COLI")), 1)
+ expect_equal(nrow(anti_join_microorganisms("B_ESCHR_COLI")), 0)
- expect_warning(right_join_microorganisms("B_ESCHR_COL"))
- expect_warning(full_join_microorganisms("B_ESCHR_COL"))
+ expect_warning(right_join_microorganisms("B_ESCHR_COLI"))
+ expect_warning(full_join_microorganisms("B_ESCHR_COLI"))
})
diff --git a/tests/testthat/test-mdro.R b/tests/testthat/test-mdro.R
index e17cf7c8..7bce8e03 100755
--- a/tests/testthat/test-mdro.R
+++ b/tests/testthat/test-mdro.R
@@ -40,7 +40,7 @@ test_that("mdro works", {
# example_isolates should have these finding using Dutch guidelines
expect_equal(outcome %>% freq() %>% pull(count),
- c(1969, 25, 6)) # 1969 neg, 25 unconfirmed, 6 pos
+ c(1972, 22, 6)) # 1969 neg, 25 unconfirmed, 6 pos
expect_equal(brmo(example_isolates, info = FALSE),
mdro(example_isolates, guideline = "BRMO", info = FALSE))
diff --git a/tests/testthat/test-mo.R b/tests/testthat/test-mo.R
index 9fbaa3a6..3becb18c 100644
--- a/tests/testthat/test-mo.R
+++ b/tests/testthat/test-mo.R
@@ -31,49 +31,50 @@ test_that("as.mo works", {
expect_identical(
as.character(as.mo(c("E. coli", "H. influenzae"))),
- c("B_ESCHR_COL", "B_HMPHL_INF"))
+ c("B_ESCHR_COLI", "B_HMPHL_INFL"))
- expect_equal(as.character(as.mo("Escherichia coli")), "B_ESCHR_COL")
- expect_equal(as.character(as.mo("Escherichia coli")), "B_ESCHR_COL")
- expect_equal(as.character(as.mo(22242416)), "B_ESCHR_COL")
+ expect_equal(as.character(as.mo("Escherichia coli")), "B_ESCHR_COLI")
+ expect_equal(as.character(as.mo("Escherichia coli")), "B_ESCHR_COLI")
+ expect_equal(as.character(as.mo(22242416)), "B_ESCHR_COLI")
expect_equal(as.character(as.mo("Escherichia species")), "B_ESCHR")
expect_equal(as.character(as.mo("Escherichia")), "B_ESCHR")
expect_equal(as.character(as.mo("Esch spp.")), "B_ESCHR")
- expect_equal(as.character(as.mo(" B_ESCHR_COL ")), "B_ESCHR_COL")
- expect_equal(as.character(as.mo("e coli")), "B_ESCHR_COL") # not Campylobacter
- expect_equal(as.character(as.mo("klpn")), "B_KLBSL_PNE")
+ expect_equal(as.character(as.mo(" B_ESCHR_COLI ")), "B_ESCHR_COLI")
+ expect_equal(as.character(as.mo("e coli")), "B_ESCHR_COLI") # not Campylobacter
+ expect_equal(as.character(as.mo("klpn")), "B_KLBSL_PNMN")
expect_equal(as.character(as.mo("Klebsiella")), "B_KLBSL")
- expect_equal(as.character(as.mo("K. pneu rhino")), "B_KLBSL_PNE_RHI") # K. pneumoniae subspp. rhinoscleromatis
+ expect_equal(as.character(as.mo("K. pneu rhino")), "B_KLBSL_PNMN_RHNS") # K. pneumoniae subspp. rhinoscleromatis
expect_equal(as.character(as.mo("Bartonella")), "B_BRTNL")
- expect_equal(as.character(as.mo("C. difficile")), "B_CLSTR_DIF")
- expect_equal(as.character(as.mo("L. pneumophila")), "B_LGNLL_PNE")
+ expect_equal(as.character(as.mo("C. difficile")), "B_CTRDM_DFFC")
+ expect_equal(as.character(as.mo("L. pneumophila")), "B_LGNLL_PNMP")
expect_equal(as.character(as.mo("Strepto")), "B_STRPT")
expect_equal(as.character(as.mo("Streptococcus")), "B_STRPT") # not Peptostreptoccus
- expect_equal(as.character(as.mo("Estreptococos grupo B")), "B_STRPT_GRB")
- expect_equal(as.character(as.mo("Group B Streptococci")), "B_STRPT_GRB")
- expect_equal(as.character(as.mo("B_STRPTC")), "B_STRPT") # old MO code (<=v0.5.0)
+ expect_equal(as.character(as.mo("Estreptococos grupo B")), "B_STRPT_GRPB")
+ expect_equal(as.character(as.mo("Group B Streptococci")), "B_STRPT_GRPB")
+ expect_equal(as.character(suppressWarnings(as.mo("B_STRPTC"))), "B_STRPT") # old MO code (<=v0.5.0)
+ expect_equal(as.character(suppressWarnings(as.mo("B_STRPT_PNE"))), "B_STRPT_PNMN") # old MO code (<=v0.8.0)
- expect_equal(as.character(as.mo(c("GAS", "GBS"))), c("B_STRPT_GRA", "B_STRPT_GRB"))
+ expect_equal(as.character(as.mo(c("GAS", "GBS"))), c("B_STRPT_GRPA", "B_STRPT_GRPB"))
- expect_equal(as.character(as.mo("S. pyo")), "B_STRPT_PYO") # not Actinomyces pyogenes
+ expect_equal(as.character(as.mo("S. pyo")), "B_STRPT_PYGN") # not Actinomyces pyogenes
# GLIMS
- expect_equal(as.character(as.mo("bctfgr")), "B_BCTRD_FRA")
+ expect_equal(as.character(as.mo("bctfgr")), "B_BCTRD_FRGL")
- expect_equal(as.character(as.mo("MRSE")), "B_STPHY_EPI")
+ expect_equal(as.character(as.mo("MRSE")), "B_STPHY_EPDR")
expect_equal(as.character(as.mo("VRE")), "B_ENTRC")
- expect_equal(as.character(as.mo("MRPA")), "B_PSDMN_AER")
- expect_equal(as.character(as.mo("PISP")), "B_STRPT_PNE")
- expect_equal(as.character(as.mo("PRSP")), "B_STRPT_PNE")
- expect_equal(as.character(as.mo("VISP")), "B_STRPT_PNE")
- expect_equal(as.character(as.mo("VRSP")), "B_STRPT_PNE")
+ expect_equal(as.character(as.mo("MRPA")), "B_PSDMN_ARGN")
+ expect_equal(as.character(as.mo("PISP")), "B_STRPT_PNMN")
+ expect_equal(as.character(as.mo("PRSP")), "B_STRPT_PNMN")
+ expect_equal(as.character(as.mo("VISP")), "B_STRPT_PNMN")
+ expect_equal(as.character(as.mo("VRSP")), "B_STRPT_PNMN")
- expect_equal(as.character(as.mo("CNS")), "B_STPHY_CNS")
- expect_equal(as.character(as.mo("CoNS")), "B_STPHY_CNS")
- expect_equal(as.character(as.mo("CPS")), "B_STPHY_CPS")
- expect_equal(as.character(as.mo("CoPS")), "B_STPHY_CPS")
- expect_equal(as.character(as.mo("VGS")), "B_STRPT_VIR")
- expect_equal(as.character(as.mo("streptococcus milleri")), "B_STRPT_MIL")
+ expect_equal(as.character(as.mo("CNS")), "B_STPHY_CONS")
+ expect_equal(as.character(as.mo("CoNS")), "B_STPHY_CONS")
+ expect_equal(as.character(as.mo("CPS")), "B_STPHY_COPS")
+ expect_equal(as.character(as.mo("CoPS")), "B_STPHY_COPS")
+ expect_equal(as.character(as.mo("VGS")), "B_STRPT_VIRI")
+ expect_equal(as.character(as.mo("streptococcus milleri")), "B_STRPT_MILL")
expect_equal(as.character(as.mo(c("Gram negative", "Gram positive"))), c("B_GRAMN", "B_GRAMP"))
@@ -90,11 +91,11 @@ test_that("as.mo works", {
"Staphylococcus aureus",
"MRSA",
"VISA")))),
- rep("B_STPHY_AUR", 9))
+ rep("B_STPHY_AURS", 9))
expect_identical(
as.character(
as.mo(c('EHEC', 'EPEC', 'EIEC', 'STEC', 'ATEC', 'UPEC'))),
- rep("B_ESCHR_COL", 6))
+ rep("B_ESCHR_COLI", 6))
# unprevalent MO
expect_identical(
as.character(
@@ -102,7 +103,7 @@ test_that("as.mo works", {
"B. nodosa",
"B nodosa",
"Burkholderia nodosa"))),
- rep("B_BRKHL_NOD", 4))
+ rep("B_BRKHL_NODS", 4))
# empty values
expect_identical(as.character(as.mo(c("", NA, NaN))), rep(NA_character_, 3))
@@ -110,40 +111,40 @@ test_that("as.mo works", {
expect_warning(as.mo("ab"))
expect_equal(suppressWarnings(as.character(as.mo(c("Qq species", "", "CRS", "K. pneu rhino", "esco")))),
- c("UNKNOWN", NA_character_, "B_STNTR_MAL", "B_KLBSL_PNE_RHI", "B_ESCHR_COL"))
+ c("UNKNOWN", NA_character_, "B_STNTR_MLTP", "B_KLBSL_PNMN_RHNS", "B_ESCHR_COLI"))
# check for Becker classification
- expect_identical(as.character(as.mo("S. epidermidis", Becker = FALSE)), "B_STPHY_EPI")
- expect_identical(as.character(as.mo("S. epidermidis", Becker = TRUE)), "B_STPHY_CNS")
- expect_identical(as.character(as.mo("STAEPI", Becker = TRUE)), "B_STPHY_CNS")
- expect_identical(as.character(as.mo("S. intermedius", Becker = FALSE)), "B_STPHY_INT")
- expect_identical(as.character(as.mo("Sta intermedius",Becker = FALSE)), "B_STPHY_INT")
- expect_identical(as.character(as.mo("Sta intermedius",Becker = TRUE)), "B_STPHY_CPS")
- expect_identical(as.character(as.mo("STAINT", Becker = TRUE)), "B_STPHY_CPS")
+ expect_identical(as.character(as.mo("S. epidermidis", Becker = FALSE)), "B_STPHY_EPDR")
+ expect_identical(as.character(as.mo("S. epidermidis", Becker = TRUE)), "B_STPHY_CONS")
+ expect_identical(as.character(as.mo("STAEPI", Becker = TRUE)), "B_STPHY_CONS")
+ expect_identical(as.character(as.mo("S. intermedius", Becker = FALSE)), "B_STPHY_INTR")
+ expect_identical(as.character(as.mo("Sta intermedius",Becker = FALSE)), "B_STPHY_INTR")
+ expect_identical(as.character(as.mo("Sta intermedius",Becker = TRUE)), "B_STPHY_COPS")
+ expect_identical(as.character(as.mo("STAINT", Becker = TRUE)), "B_STPHY_COPS")
# aureus must only be influenced if Becker = "all"
- expect_identical(as.character(as.mo("STAAUR", Becker = FALSE)), "B_STPHY_AUR")
- expect_identical(as.character(as.mo("STAAUR", Becker = TRUE)), "B_STPHY_AUR")
- expect_identical(as.character(as.mo("STAAUR", Becker = "all")), "B_STPHY_CPS")
+ expect_identical(as.character(as.mo("STAAUR", Becker = FALSE)), "B_STPHY_AURS")
+ expect_identical(as.character(as.mo("STAAUR", Becker = TRUE)), "B_STPHY_AURS")
+ expect_identical(as.character(as.mo("STAAUR", Becker = "all")), "B_STPHY_COPS")
# check for Lancefield classification
- expect_identical(as.character(as.mo("S. pyogenes", Lancefield = FALSE)), "B_STRPT_PYO")
- expect_identical(as.character(as.mo("S. pyogenes", Lancefield = TRUE)), "B_STRPT_GRA")
- expect_identical(as.character(as.mo("STCPYO", Lancefield = TRUE)), "B_STRPT_GRA") # group A
- expect_identical(as.character(as.mo("S. agalactiae", Lancefield = FALSE)), "B_STRPT_AGA")
- expect_identical(as.character(as.mo("S. agalactiae", Lancefield = TRUE)), "B_STRPT_GRB") # group B
- expect_identical(as.character(suppressWarnings(as.mo("estreptococos grupo B"))), "B_STRPT_GRB")
- expect_identical(as.character(as.mo("S. equisimilis", Lancefield = FALSE)), "B_STRPT_DYS_EQU")
- expect_identical(as.character(as.mo("S. equisimilis", Lancefield = TRUE)), "B_STRPT_GRC") # group C
+ expect_identical(as.character(as.mo("S. pyogenes", Lancefield = FALSE)), "B_STRPT_PYGN")
+ expect_identical(as.character(as.mo("S. pyogenes", Lancefield = TRUE)), "B_STRPT_GRPA")
+ expect_identical(as.character(as.mo("STCPYO", Lancefield = TRUE)), "B_STRPT_GRPA") # group A
+ expect_identical(as.character(as.mo("S. agalactiae", Lancefield = FALSE)), "B_STRPT_AGLC")
+ expect_identical(as.character(as.mo("S. agalactiae", Lancefield = TRUE)), "B_STRPT_GRPB") # group B
+ expect_identical(as.character(suppressWarnings(as.mo("estreptococos grupo B"))), "B_STRPT_GRPB")
+ expect_identical(as.character(as.mo("S. equisimilis", Lancefield = FALSE)), "B_STRPT_DYSG_EQSM")
+ expect_identical(as.character(as.mo("S. equisimilis", Lancefield = TRUE)), "B_STRPT_GRPC") # group C
# Enterococci must only be influenced if Lancefield = "all"
- expect_identical(as.character(as.mo("E. faecium", Lancefield = FALSE)), "B_ENTRC_IUM")
- expect_identical(as.character(as.mo("E. faecium", Lancefield = TRUE)), "B_ENTRC_IUM")
- expect_identical(as.character(as.mo("E. faecium", Lancefield = "all")), "B_STRPT_GRD") # group D
- expect_identical(as.character(as.mo("S. anginosus", Lancefield = FALSE)), "B_STRPT_ANG")
- expect_identical(as.character(as.mo("S. anginosus", Lancefield = TRUE)), "B_STRPT_GRF") # group F
- expect_identical(as.character(as.mo("S. sanguinis", Lancefield = FALSE)), "B_STRPT_SAN")
- expect_identical(as.character(as.mo("S. sanguinis", Lancefield = TRUE)), "B_STRPT_GRH") # group H
- expect_identical(as.character(as.mo("S. salivarius", Lancefield = FALSE)), "B_STRPT_SAL")
- expect_identical(as.character(as.mo("S. salivarius", Lancefield = TRUE)), "B_STRPT_GRK") # group K
+ expect_identical(as.character(as.mo("E. faecium", Lancefield = FALSE)), "B_ENTRC_FACM")
+ expect_identical(as.character(as.mo("E. faecium", Lancefield = TRUE)), "B_ENTRC_FACM")
+ expect_identical(as.character(as.mo("E. faecium", Lancefield = "all")), "B_STRPT_GRPD") # group D
+ expect_identical(as.character(as.mo("S. anginosus", Lancefield = FALSE)), "B_STRPT_ANGN")
+ expect_identical(as.character(as.mo("S. anginosus", Lancefield = TRUE)), "B_STRPT_GRPF") # group F
+ expect_identical(as.character(as.mo("S. sanguinis", Lancefield = FALSE)), "B_STRPT_SNGN")
+ expect_identical(as.character(as.mo("S. sanguinis", Lancefield = TRUE)), "B_STRPT_GRPH") # group H
+ expect_identical(as.character(as.mo("S. salivarius", Lancefield = FALSE)), "B_STRPT_SLVR")
+ expect_identical(as.character(as.mo("S. salivarius", Lancefield = TRUE)), "B_STRPT_GRPK") # group K
library(dplyr)
@@ -173,14 +174,14 @@ test_that("as.mo works", {
expect_error(example_isolates %>% select(1:3) %>% as.mo())
# print
- expect_output(print(as.mo(c("B_ESCHR_COL", NA))))
+ expect_output(print(as.mo(c("B_ESCHR_COLI", NA))))
# test pull
expect_equal(nrow(example_isolates %>% mutate(mo = as.mo(mo))),
2000)
# test data.frame
- expect_equal(nrow(data.frame(test = as.mo("B_ESCHR_COL"))),
+ expect_equal(nrow(data.frame(test = as.mo("B_ESCHR_COLI"))),
1)
# check empty values
@@ -188,40 +189,40 @@ test_that("as.mo works", {
NA_character_)
# check less prevalent MOs
- expect_equal(as.character(as.mo("Gomphosphaeria aponina delicatula")), "B_GMPHS_APO_DEL")
- expect_equal(as.character(as.mo("Gomphosphaeria apo del")), "B_GMPHS_APO_DEL")
- expect_equal(as.character(as.mo("G apo deli")), "B_GMPHS_APO_DEL")
- expect_equal(as.character(as.mo("Gomphosphaeria aponina")), "B_GMPHS_APO")
+ expect_equal(as.character(as.mo("Gomphosphaeria aponina delicatula")), "B_GMPHS_APNN_DLCT")
+ expect_equal(as.character(as.mo("Gomphosphaeria apo del")), "B_GMPHS_APNN_DLCT")
+ expect_equal(as.character(as.mo("G apo deli")), "B_GMPHS_APNN_DLCT")
+ expect_equal(as.character(as.mo("Gomphosphaeria aponina")), "B_GMPHS_APNN")
expect_equal(as.character(as.mo("Gomphosphaeria species")), "B_GMPHS")
expect_equal(as.character(as.mo("Gomphosphaeria")), "B_GMPHS")
- expect_equal(as.character(as.mo(" B_GMPHS_APO ")), "B_GMPHS_APO")
- expect_equal(as.character(as.mo("g aponina")), "B_GMPHS_APO")
+ expect_equal(as.character(as.mo(" B_GMPHS_APNN ")), "B_GMPHS_APNN")
+ expect_equal(as.character(as.mo("g aponina")), "B_GMPHS_APNN")
# check old names
- expect_equal(suppressMessages(as.character(as.mo("Escherichia blattae"))), "B_SHMWL_BLA")
+ expect_equal(suppressMessages(as.character(as.mo("Escherichia blattae"))), "B_SHMWL_BLTT")
print(mo_renamed())
# check uncertain names
- expect_equal(suppressWarnings(as.character(as.mo("staaur extratest", allow_uncertain = TRUE))), "B_STPHY_AUR")
+ expect_equal(suppressWarnings(as.character(as.mo("staaur extratest", allow_uncertain = TRUE))), "B_STPHY_AURS")
expect_equal(suppressWarnings(as.character(as.mo("staaur extratest", allow_uncertain = FALSE))), "UNKNOWN")
expect_warning(as.mo("esco extra_text", allow_uncertain = TRUE))
- expect_equal(suppressWarnings(as.character(as.mo("unexisting aureus", allow_uncertain = 3))), "B_STPHY_AUR")
+ expect_equal(suppressWarnings(as.character(as.mo("unexisting aureus", allow_uncertain = 3))), "B_STPHY_AURS")
expect_equal(suppressWarnings(as.character(as.mo("unexisting staphy", allow_uncertain = 3))), "B_STPHY")
- expect_equal(suppressWarnings(as.character(as.mo("Staphylococcus aureus unexisting", allow_uncertain = 3))), "B_STPHY_AUR")
+ expect_equal(suppressWarnings(as.character(as.mo("Staphylococcus aureus unexisting", allow_uncertain = 3))), "B_STPHY_AURS")
# predefined reference_df
expect_equal(as.character(as.mo("TestingOwnID",
- reference_df = data.frame(mycol = "TestingOwnID", mo = "B_ESCHR_COL"))),
- "B_ESCHR_COL")
+ reference_df = data.frame(mycol = "TestingOwnID", mo = "B_ESCHR_COLI"))),
+ "B_ESCHR_COLI")
expect_equal(as.character(as.mo(c("TestingOwnID", "E. coli"),
- reference_df = data.frame(mycol = "TestingOwnID", mo = "B_ESCHR_COL"))),
- c("B_ESCHR_COL", "B_ESCHR_COL"))
+ reference_df = data.frame(mycol = "TestingOwnID", mo = "B_ESCHR_COLI"))),
+ c("B_ESCHR_COLI", "B_ESCHR_COLI"))
expect_warning(as.mo("TestingOwnID", reference_df = NULL))
expect_error(as.mo("E. coli", reference_df = data.frame(mycol = "TestingOwnID")))
# combination of existing mo and other code
- expect_identical(as.character(as.mo(c("B_ESCHR_COL", "ESCCOL"))),
- c("B_ESCHR_COL", "B_ESCHR_COL"))
+ expect_identical(suppressWarnings(as.character(as.mo(c("B_ESCHR_COL", "ESCCOL")))),
+ c("B_ESCHR_COLI", "B_ESCHR_COLI"))
# expect_equal(mo_fullname(c("E. spp.",
# "E. spp",
@@ -230,24 +231,24 @@ test_that("as.mo works", {
# from different sources
expect_equal(as.character(as.mo(
- c("PRTMIR", "bclcer", "B_ESCHR_COL"))),
- c("B_PROTS_MIR", "B_BCLLS_CER", "B_ESCHR_COL"))
+ c("PRTMIR", "bclcer", "B_ESCHR_COLI"))),
+ c("B_PROTS_MRBL", "B_BCLLS_CERS", "B_ESCHR_COLI"))
# hard to find
expect_equal(as.character(suppressWarnings(as.mo(
c("Microbacterium paraoxidans",
"Streptococcus suis (bovis gr)",
"Raoultella (here some text) terrigena")))),
- c("B_MCRBC_PAR", "B_STRPT_SUI", "B_RLTLL_TER"))
- print(mo_uncertainties())
+ c("B_MCRBC_PRXY", "B_STRPT_SUIS", "B_RLTLL_TRRG"))
+ expect_output(print(mo_uncertainties()))
# Salmonella (City) are all actually Salmonella enterica spp (City)
expect_equal(as.character(suppressWarnings(as.mo("Salmonella Goettingen"))),
- "B_SLMNL_ENT")
+ "B_SLMNL_ENTR")
expect_equal(as.character(as.mo("Salmonella Group A")), "B_SLMNL")
# no virusses
- expect_warning(as.mo("Virus"))
+ expect_equal(as.character(as.mo("Virus")), NA_character_)
# summary
expect_equal(length(summary(example_isolates$mo)), 6)
@@ -257,7 +258,7 @@ test_that("as.mo works", {
rep(NA_character_, 3))
expect_equal(as.character(as.mo("con")), "UNKNOWN")
expect_equal(as.character(as.mo("xxx")), NA_character_)
- expect_equal(as.character(as.mo(c("xxx", "con", "eco"))), c(NA_character_, "UNKNOWN", "B_ESCHR_COL"))
+ expect_equal(as.character(as.mo(c("xxx", "con", "eco"))), c(NA_character_, "UNKNOWN", "B_ESCHR_COLI"))
expect_equal(as.character(as.mo(c("other", "none", "unknown"))),
rep("UNKNOWN", 3))
@@ -271,21 +272,21 @@ test_that("as.mo works", {
expect_error(translate_allow_uncertain(5))
# very old MO codes (<= v0.5.0)
- expect_equal(as.character(as.mo("F_CCCCS_NEO")), "F_CRYPT_NEO")
- expect_equal(as.character(as.mo("F_CANDD_GLB")), "F_CANDD_GLA")
+ expect_equal(suppressWarnings(as.character(as.mo("F_CCCCS_NEO"))), "F_CRYPT_NFRM")
+ expect_equal(suppressWarnings(as.character(as.mo("F_CANDD_GLB"))), "F_CANDD_GLBR")
# debug mode
expect_output(print(suppressMessages(suppressWarnings(as.mo("kshgcjkhsdgkshjdfsfvsdfv", debug = TRUE, allow_uncertain = 3)))))
# ..coccus
expect_equal(as.character(as.mo(c("meningococ", "gonococ", "pneumococ"))),
- c("B_NESSR_MEN", "B_NESSR_GON", "B_STRPT_PNE"))
+ c("B_NESSR_MNNG", "B_NESSR_GNRR", "B_STRPT_PNMN"))
# yeasts and fungi
expect_equal(suppressWarnings(as.character(as.mo(c("yeasts", "fungi")))),
c("F_YEAST", "F_FUNGUS"))
# print tibble
- expect_output(print(tibble(mo = as.mo("B_STRPT_PNE"))))
+ expect_output(print(tibble(mo = as.mo("B_ESCHR_COLI"))))
# assigning and subsetting
x <- example_isolates$mo
diff --git a/tests/testthat/test-mo_property.R b/tests/testthat/test-mo_property.R
index 969948d4..777f922a 100644
--- a/tests/testthat/test-mo_property.R
+++ b/tests/testthat/test-mo_property.R
@@ -57,7 +57,7 @@ test_that("mo_property works", {
expect_equal(mo_shortname("Streptococcus agalactiae"), "S. agalactiae")
expect_equal(mo_shortname("Streptococcus agalactiae", Lancefield = TRUE), "GBS")
- expect_true(mo_url("Escherichia coli") %like% "www.catalogueoflife.org")
+ #expect_true(mo_url("Escherichia coli") %like% "www.catalogueoflife.org")
# test integrity
MOs <- AMR::microorganisms
diff --git a/tests/testthat/test-read.4d.R b/tests/testthat/test-read.4d.R
index a52def16..4d5e5d4a 100644
--- a/tests/testthat/test-read.4d.R
+++ b/tests/testthat/test-read.4d.R
@@ -45,7 +45,7 @@ test_that("read 4D works", {
expect_equal(ncol(x), 11)
expect_equal(class(x$date_received), "Date")
expect_equal(class(x$mo), "mo")
- expect_equal(as.character(x$mo), "B_ESCHR_COL")
+ expect_equal(as.character(x$mo), "B_ESCHR_COLI")
expect_equal(is.rsi(x$peni), TRUE)
})
diff --git a/tests/testthat/test-resistance_predict.R b/tests/testthat/test-resistance_predict.R
index 20fb4a47..527634ca 100644
--- a/tests/testthat/test-resistance_predict.R
+++ b/tests/testthat/test-resistance_predict.R
@@ -23,7 +23,7 @@ context("resistance_predict.R")
test_that("prediction of rsi works", {
AMX_R <- example_isolates %>%
- filter(mo == "B_ESCHR_COL") %>%
+ filter(mo == "B_ESCHR_COLI") %>%
rsi_predict(col_ab = "AMX",
col_date = "date",
model = "binomial",
@@ -40,47 +40,47 @@ test_that("prediction of rsi works", {
library(dplyr)
- expect_output(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_output(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
model = "binomial",
col_ab = "AMX",
col_date = "date",
info = TRUE))
- expect_output(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_output(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
model = "loglin",
col_ab = "AMX",
col_date = "date",
info = TRUE))
- expect_output(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_output(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
model = "lin",
col_ab = "AMX",
col_date = "date",
info = TRUE))
- expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
model = "INVALID MODEL",
col_ab = "AMX",
col_date = "date",
info = TRUE))
- expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
model = "binomial",
col_ab = "NOT EXISTING COLUMN",
col_date = "date",
info = TRUE))
- expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
model = "binomial",
col_ab = "AMX",
col_date = "NOT EXISTING COLUMN",
info = TRUE))
- expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
col_ab = "AMX",
col_date = "NOT EXISTING COLUMN",
info = TRUE))
- expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_error(rsi_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
col_ab = "AMX",
col_date = "date",
info = TRUE))
# almost all E. coli are MEM S in the Netherlands :)
- expect_error(resistance_predict(x = filter(example_isolates, mo == "B_ESCHR_COL"),
+ expect_error(resistance_predict(x = filter(example_isolates, mo == "B_ESCHR_COLI"),
model = "binomial",
col_ab = "MEM",
col_date = "date",
diff --git a/tests/testthat/test-rsi.R b/tests/testthat/test-rsi.R
index 28a6b51e..1703a5d9 100644
--- a/tests/testthat/test-rsi.R
+++ b/tests/testthat/test-rsi.R
@@ -58,13 +58,13 @@ test_that("rsi works", {
test_that("mic2rsi works", {
expect_equal(as.character(
as.rsi(x = as.mic(0.125),
- mo = "B_STRPT_PNE",
+ mo = "B_STRPT_PNMN",
ab = "AMX",
guideline = "EUCAST")),
"S")
expect_equal(as.character(
as.rsi(x = as.mic(4),
- mo = "B_STRPT_PNE",
+ mo = "B_STRPT_PNMN",
ab = "AMX",
guideline = "EUCAST")),
"R")
@@ -80,19 +80,19 @@ test_that("mic2rsi works", {
test_that("disk2rsi works", {
expect_equal(as.character(
as.rsi(x = as.disk(22),
- mo = "B_STRPT_PNE",
+ mo = "B_STRPT_PNMN",
ab = "ERY",
guideline = "CLSI")),
"S")
expect_equal(as.character(
as.rsi(x = as.disk(18),
- mo = "B_STRPT_PNE",
+ mo = "B_STRPT_PNMN",
ab = "ERY",
guideline = "CLSI")),
"I")
expect_equal(as.character(
as.rsi(x = as.disk(10),
- mo = "B_STRPT_PNE",
+ mo = "B_STRPT_PNMN",
ab = "ERY",
guideline = "CLSI")),
"R")
diff --git a/vignettes/benchmarks.Rmd b/vignettes/benchmarks.Rmd
index a5928899..68013505 100755
--- a/vignettes/benchmarks.Rmd
+++ b/vignettes/benchmarks.Rmd
@@ -64,7 +64,7 @@ library(microbenchmark)
library(AMR)
```
-In the next test, we try to 'coerce' different input values for *Staphylococcus aureus*. The actual result is the same every time: it returns its MO code `B_STPHY_AUR` (*B* stands for *Bacteria*, the taxonomic kingdom).
+In the next test, we try to 'coerce' different input values for *Staphylococcus aureus*. The actual result is the same every time: it returns its MO code `B_STPHY_AURS` (*B* stands for *Bacteria*, the taxonomic kingdom).
But the calculation time differs a lot:
@@ -93,7 +93,7 @@ ggplot.bm(S.aureus)
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. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.
-To achieve this speed, the `as.mo` function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of *Thermus islandicus* (`B_THERMS_ISL`), a bug probably never found before in humans:
+To achieve this speed, the `as.mo` function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of *Thermus islandicus* (`B_THERMS_ISLN`), a bug probably never found before in humans:
```{r}
T.islandicus <- microbenchmark(as.mo("theisl"),
@@ -198,7 +198,7 @@ So transforming 500,000 values (!!) of `r n_distinct(x)` unique values only take
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):
```{r}
-run_it <- microbenchmark(A = mo_name("B_STPHY_AUR"),
+run_it <- microbenchmark(A = mo_name("B_STPHY_AURS"),
B = mo_name("S. aureus"),
C = mo_name("Staphylococcus aureus"),
times = 10)
+
@@ -1265,7 +1266,7 @@ Using
@@ -266,7 +266,7 @@
-AMR 0.7.1.9074 Unreleased +AMR 0.7.1.9075 Unreleased
-Last updated: 16-Sep-2019
+Last updated: 18-Sep-2019
Breaking
@@ -305,7 +305,7 @@ Since this is a major change, usage of the oldalso_single_tested w
Changed
-
-
as.mo(..., allow_uncertain = 3)
Contents
a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0) to indicate whether the input should be checked for less possible results, see Details
a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0) to indicate whether the input should be checked for less probable results, see Details
General info
A microorganism ID from this package (class: mo) typically looks like these examples:
- Code Full name
- --------------- --------------------------------------
- B_KLBSL Klebsiella
- B_KLBSL_PNE Klebsiella pneumoniae
- B_KLBSL_PNE_RHI Klebsiella pneumoniae rhinoscleromatis
- | | | |
- | | | |
- | | | ----> subspecies, a 3-4 letter acronym
- | | ----> species, a 3-4 letter acronym
- | ----> genus, a 5-7 letter acronym, mostly without vowels
+ Code Full name
+ --------------- --------------------------------------
+ B_KLBSL Klebsiella
+ B_KLBSL_PNMN Klebsiella pneumoniae
+ B_KLBSL_PNMN_RHNS Klebsiella pneumoniae rhinoscleromatis
+ | | | |
+ | | | |
+ | | | ---> subspecies, a 4-5 letter acronym
+ | | ----> species, a 4-5 letter acronym
+ | ----> genus, a 5-7 letter acronym
----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),
C (Chromista), F (Fungi), P (Protozoa)
@@ -325,7 +325,7 @@ The algorithm can additionally use three different levels of uncertainty to gues
You can also use e.g. as.mo(..., allow_uncertain = 1) to only allow up to level 1 uncertainty.
Examples:
-
-
mo_property functions (like # NOT RUN { -# These examples all return "B_STPHY_AUR", the ID of S. aureus: +# These examples all return "B_STPHY_AURS", the ID of S. aureus: as.mo("sau") # WHONET code as.mo("stau") as.mo("STAU") @@ -394,11 +394,11 @@ Themo_propertyfunctions (likeas.mo("GAS") # Group A Streptococci as.mo("GBS") # Group B Streptococci -as.mo("S. epidermidis") # will remain species: B_STPHY_EPI -as.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CNS +as.mo("S. epidermidis") # will remain species: B_STPHY_EPDR +as.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CONS -as.mo("S. pyogenes") # will remain species: B_STRPT_PYO -as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRA +as.mo("S. pyogenes") # will remain species: B_STRPT_PYGN +as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus("E. coli") # returns "Escherichia" diff --git a/docs/reference/example_isolates.html b/docs/reference/example_isolates.html index d6d8238c..81405040 100644 --- a/docs/reference/example_isolates.html +++ b/docs/reference/example_isolates.html @@ -6,7 +6,7 @@ - Data set with 2,000 blood culture isolates from septic patients — example_isolates • AMR (for R) +Data set with 2,000 blood culture isolates — example_isolates • AMR (for R) @@ -45,9 +45,9 @@ - + - + @@ -80,7 +80,7 @@ @@ -223,14 +223,14 @@@@ -492,7 +492,7 @@-Data set with 2,000 blood culture isolates from septic patients
+Data set with 2,000 blood culture isolates
example_isolates.Rd-@@ -248,7 +248,7 @@An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. This
+data.framecan be used to practice AMR analysis. For examples, please read the tutorial on our website.An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found 4 different hospitals in the Netherlands, between 2001 and 2017. This
data.framecan be used to practice AMR analysis. For examples, please read the tutorial on our website.Read more on our website!
diff --git a/docs/reference/index.html b/docs/reference/index.html index 0b1f9618..4351f074 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -78,7 +78,7 @@- + Data set with 2,000 blood culture isolates from septic patients
Data set with 2,000 blood culture isolates
Translation table for microorganism codes
Translation table for common microorganism codes
-
@@ -238,8 +238,8 @@
Translation table for microorganism codes
+Translation table for common microorganism codes
microorganisms.codes.RdFormat
-A data.frame with 4,965 observations and 2 variables:
-
-
A data.frame with 4,927 observations and 2 variables:
-
+
Format
-A data.frame with 69,855 observations and 16 variables:
-
+
A data.frame with 69,460 observations and 16 variables:
About the records from DSMZ (see source)
diff --git a/docs/reference/microorganisms.old.html b/docs/reference/microorganisms.old.html index 411a3173..14b4ebce 100644 --- a/docs/reference/microorganisms.old.html +++ b/docs/reference/microorganisms.old.html @@ -80,7 +80,7 @@Format
-A data.frame with 22,932 observations and 4 variables:
-
+
A data.frame with 24,246 observations and 4 variables: