So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values M and F. From a researcher perspective: there are slightly more men. Nothing we didn’t already know.
The data is already quite clean, but we still need to transform some variables. The bacteria column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The mutate() function of the dplyr package makes this really easy:
data <- data %>%
@@ -443,10 +443,10 @@
#> Kingella kingae (no changes)
#>
#> EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-#> Table 1: Intrinsic resistance in Enterobacteriaceae (1307 changes)
+#> Table 1: Intrinsic resistance in Enterobacteriaceae (1261 changes)
#> Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)
#> Table 3: Intrinsic resistance in other Gram-negative bacteria (no changes)
-#> Table 4: Intrinsic resistance in Gram-positive bacteria (2811 changes)
+#> Table 4: Intrinsic resistance in Gram-positive bacteria (2655 changes)
#> Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)
#> Table 9: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)
#> Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria (no changes)
@@ -462,9 +462,9 @@
#> Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)
#> Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)
#>
-#> => EUCAST rules affected 7,381 out of 20,000 rows
+#> => EUCAST rules affected 7,230 out of 20,000 rows
#> -> added 0 test results
-#> -> changed 4,118 test results (0 to S; 0 to I; 4,118 to R)So only 28.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 28.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:
| isolate | @@ -654,8 +654,8 @@||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-01-30 | -J7 | +2010-01-09 | +V4 | B_ESCHR_COL | S | S | @@ -666,8 +666,8 @@|||||||
| 2 | -2010-01-30 | -J7 | +2010-04-24 | +V4 | B_ESCHR_COL | S | S | @@ -678,11 +678,11 @@|||||||
| 3 | -2010-03-30 | -J7 | +2010-05-30 | +V4 | B_ESCHR_COL | -R | S | +R | S | S | FALSE | @@ -690,23 +690,23 @@|||
| 4 | -2010-04-11 | -J7 | +2010-06-10 | +V4 | B_ESCHR_COL | -S | -S | R | S | +S | +S | FALSE | TRUE | |
| 5 | -2010-08-02 | -J7 | +2010-06-17 | +V4 | B_ESCHR_COL | S | -R | +S | S | S | FALSE | @@ -714,46 +714,46 @@|||
| 6 | -2010-10-14 | -J7 | +2010-07-30 | +V4 | B_ESCHR_COL | S | S | -R | +S | S | FALSE | -TRUE | +FALSE | |
| 7 | -2010-11-02 | -J7 | +2010-09-20 | +V4 | B_ESCHR_COL | R | -I | -R | +S | +S | S | FALSE | TRUE | |
| 8 | -2011-02-10 | -J7 | +2010-10-21 | +V4 | B_ESCHR_COL | +R | S | S | S | -S | -TRUE | -TRUE | +FALSE | +FALSE |
| 9 | -2011-03-27 | -J7 | +2010-11-06 | +V4 | B_ESCHR_COL | -S | +R | S | S | S | @@ -762,23 +762,23 @@||||
| 10 | -2011-04-21 | -J7 | +2011-05-21 | +V4 | B_ESCHR_COL | R | S | -R | S | -FALSE | +S | +TRUE | TRUE |
Instead of 2, now 8 isolates are flagged. In total, 78.9% of all isolates are marked ‘first weighted’ - 50.5% 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 6 isolates are flagged. In total, 79.4% of all isolates are marked ‘first weighted’ - 50.9% 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:
So we end up with 15,784 isolates for analysis.
+So we end up with 15,871 isolates for analysis.
We can remove unneeded columns:
@@ -804,78 +804,78 @@Or can be used like the dplyr way, which is easier readable:
Frequency table of genus and species from a data.frame (15,784 x 13)
Frequency table of genus and species from a data.frame (15,871 x 13)
Columns: 2
-Length: 15,784 (of which NA: 0 = 0.00%)
+Length: 15,871 (of which NA: 0 = 0.00%)
Unique: 4
Shortest: 16
Longest: 24
The functions portion_R, portion_RI, portion_I, portion_IS and portion_S can be used to determine the portion of a specific antimicrobial outcome. They can be used on their own:
Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:
data_1st %>%
group_by(hospital) %>%
@@ -984,19 +984,19 @@ Longest: 24
Hospital A
-0.4792724
+0.4684758
Hospital B
-0.4777657
+0.4675514
Hospital C
-0.4674139
+0.4904459
Hospital D
-0.4796148
+0.4804110
@@ -1014,23 +1014,23 @@ Longest: 24
Hospital A
-0.4792724
-4728
+0.4684758
+4901
Hospital B
-0.4777657
-5532
+0.4675514
+5501
Hospital C
-0.4674139
-2409
+0.4904459
+2355
Hospital D
-0.4796148
-3115
+0.4804110
+3114
@@ -1050,27 +1050,27 @@ Longest: 24
Escherichia
-0.7321061
-0.9013903
-0.9751545
+0.7295964
+0.8977603
+0.9743136
Klebsiella
-0.7368078
-0.9016287
-0.9687296
+0.7299035
+0.8958199
+0.9774920
Staphylococcus
-0.7378543
-0.9164980
-0.9792510
+0.7281164
+0.9260095
+0.9806872
Streptococcus
-0.7406089
+0.7353669
0.0000000
-0.7406089
+0.7353669
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diff --git a/docs/articles/benchmarks.html b/docs/articles/benchmarks.html
index 4f640dc4..5878fed4 100644
--- a/docs/articles/benchmarks.html
+++ b/docs/articles/benchmarks.html
@@ -218,15 +218,15 @@
times = 10)
print(S.aureus, unit = "ms", signif = 3)
#> Unit: milliseconds
-#> expr min lq mean median uq max
-#> as.mo("sau") 42.500 42.800 44.200 43.100 43.900 53.700
-#> as.mo("stau") 76.300 76.800 82.800 77.000 78.800 116.000
-#> as.mo("staaur") 42.700 43.000 47.100 43.600 43.900 80.700
-#> as.mo("S. aureus") 18.400 18.500 18.800 18.800 19.200 19.300
-#> as.mo("S. aureus") 18.400 18.400 23.600 18.600 19.300 67.100
-#> as.mo("STAAUR") 42.700 42.800 43.200 43.000 43.600 44.100
-#> as.mo("Staphylococcus aureus") 11.400 11.500 11.700 11.600 11.800 12.500
-#> as.mo("B_STPHY_AUR") 0.267 0.297 0.403 0.431 0.478 0.509
+#> expr min lq mean median uq max
+#> as.mo("sau") 42.300 42.500 47.00 43.100 43.200 82.000
+#> as.mo("stau") 75.900 76.100 82.70 76.700 77.900 125.000
+#> as.mo("staaur") 42.400 43.300 53.60 44.600 49.000 98.200
+#> as.mo("S. aureus") 18.400 18.600 20.60 18.700 19.200 34.100
+#> as.mo("S. aureus") 18.400 18.500 18.80 18.600 19.200 19.600
+#> as.mo("STAAUR") 42.300 42.700 43.30 43.000 43.800 45.700
+#> as.mo("Staphylococcus aureus") 11.400 11.500 11.80 11.600 11.800 13.400
+#> as.mo("B_STPHY_AUR") 0.261 0.418 0.44 0.434 0.493 0.542
#> neval
#> 10
#> 10
@@ -236,7 +236,7 @@
#> 10
#> 10
#> 10In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 10 milliseconds means it can determine 100 input values per second. It case of 50 milliseconds, this is only 20 input values per second. The more an input value resembles a full name, the faster the result will be found. In case of as.mo("B_STPHY_AUR"), the input is already a valid MO code, so it only almost takes no time at all (267 millionths of seconds).
In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 10 milliseconds means it can determine 100 input values per second. It case of 50 milliseconds, this is only 20 input values per second. The more an input value resembles a full name, the faster the result will be found. In case of as.mo("B_STPHY_AUR"), the input is already a valid MO code, so it only almost takes no time at all (261 millionths of seconds).
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 Mycoplasma leonicaptivi (B_MYCPL_LEO), a bug probably never found before in humans:
M.leonicaptivi <- microbenchmark(as.mo("myle"),
as.mo("mycleo"),
@@ -249,22 +249,22 @@
print(M.leonicaptivi, unit = "ms", signif = 4)
#> Unit: milliseconds
#> expr min lq mean median
-#> as.mo("myle") 111.9000 112.0000 112.4000 112.2000
-#> as.mo("mycleo") 381.4000 381.8000 388.5000 382.1000
-#> as.mo("M. leonicaptivi") 203.0000 203.2000 212.5000 203.6000
-#> as.mo("M. leonicaptivi") 203.0000 203.1000 212.7000 203.6000
-#> as.mo("MYCLEO") 381.8000 382.4000 394.5000 382.9000
-#> as.mo("Mycoplasma leonicaptivi") 102.8000 103.0000 103.4000 103.2000
-#> as.mo("B_MYCPL_LEO") 0.3183 0.5657 0.5693 0.5727
+#> as.mo("myle") 111.9000 112.1000 121.9000 112.4000
+#> as.mo("mycleo") 381.6000 381.9000 397.9000 384.7000
+#> as.mo("M. leonicaptivi") 202.9000 203.8000 205.5000 204.1000
+#> as.mo("M. leonicaptivi") 203.1000 203.3000 208.7000 203.8000
+#> as.mo("MYCLEO") 381.5000 381.7000 388.1000 381.9000
+#> as.mo("Mycoplasma leonicaptivi") 103.0000 103.1000 103.6000 103.3000
+#> as.mo("B_MYCPL_LEO") 0.3021 0.5631 0.5459 0.5664
#> uq max neval
-#> 112.4000 113.5000 10
-#> 385.4000 439.9000 10
-#> 205.8000 253.9000 10
-#> 207.2000 252.3000 10
-#> 421.1000 422.1000 10
-#> 103.4000 105.7000 10
-#> 0.5994 0.7446 10That takes 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:
+#> 113.5000 169.7000 10 +#> 420.5000 420.7000 10 +#> 206.1000 215.4000 10 +#> 204.6000 249.4000 10 +#> 386.0000 433.7000 10 +#> 103.8000 105.4000 10 +#> 0.5712 0.6199 10 +That takes 5.9 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:
par(mar = c(5, 16, 4, 2)) # set more space for left margin text (16)
# highest value on y axis
@@ -301,8 +301,8 @@
print(run_it, unit = "ms", signif = 3)
#> Unit: milliseconds
#> expr min lq mean median uq max neval
-#> mo_fullname(x) 438 453 468 468 482 499 10So transforming 500,000 values (!) of 95 unique values only takes 0.47 seconds (468 ms). You only lose time on your unique input values.
+#> mo_fullname(x) 438 448 467 470 476 500 10 +So transforming 500,000 values (!) of 95 unique values only takes 0.47 seconds (469 ms). You only lose time on your unique input values.
So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0004 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"),
@@ -331,14 +331,14 @@
print(run_it, unit = "ms", signif = 3)
#> Unit: milliseconds
#> expr min lq mean median uq max neval
-#> A 0.282 0.311 0.372 0.359 0.437 0.513 10
-#> B 0.285 0.316 0.355 0.363 0.382 0.443 10
-#> C 0.258 0.408 0.439 0.430 0.504 0.565 10
-#> D 0.268 0.304 0.322 0.321 0.360 0.366 10
-#> E 0.259 0.273 0.312 0.295 0.357 0.391 10
-#> F 0.250 0.275 0.327 0.294 0.343 0.614 10
-#> G 0.254 0.281 0.312 0.320 0.338 0.369 10
-#> H 0.257 0.265 0.311 0.316 0.329 0.397 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.
Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
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