benchmarks.RmdIn 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:
T.islandicus <- microbenchmark(as.mo("theisl"),
@@ -229,12 +229,12 @@
print(T.islandicus, unit = "ms", signif = 2)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# as.mo("theisl") 270 270 280 290 290 300 10
-# as.mo("THEISL") 280 290 290 290 300 300 10
-# as.mo("T. islandicus") 130 130 150 150 160 160 10
-# as.mo("T. islandicus") 130 130 150 150 150 160 10
-# as.mo("Thermus islandicus") 46 48 54 50 63 71 10That takes 8.8 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") 270 280 290 290 310 320 10 +# as.mo("THEISL") 280 290 300 290 300 310 10 +# as.mo("T. islandicus") 140 140 150 140 160 170 10 +# as.mo("T. islandicus") 140 150 160 160 160 170 10 +# as.mo("Thermus islandicus") 48 49 60 60 68 77 10 +That takes 9.5 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 very uncommon):
par(mar = c(5, 16, 4, 2)) # set more space for left margin text (16)
@@ -280,8 +280,8 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# mo_name(x) 623 631 659 637 697 729 10So transforming 500,000 values (!!) of 50 unique values only takes 0.64 seconds (637 ms). You only lose time on your unique input values.
+# mo_name(x) 596 622 635 626 635 704 10 +So transforming 500,000 values (!!) of 50 unique values only takes 0.63 seconds (626 ms). You only lose time on your unique input values.
So 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:
So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0009 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"),
@@ -310,14 +310,14 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 0.455 0.458 0.471 0.465 0.482 0.504 10
-# B 0.480 0.482 0.497 0.491 0.497 0.554 10
-# C 0.662 0.687 0.754 0.750 0.788 0.964 10
-# D 0.484 0.484 0.496 0.488 0.501 0.544 10
-# E 0.442 0.450 0.459 0.456 0.462 0.492 10
-# F 0.440 0.447 0.456 0.452 0.463 0.486 10
-# G 0.450 0.452 0.462 0.459 0.463 0.485 10
-# H 0.455 0.461 0.467 0.467 0.471 0.492 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.
diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png index 938b62bf..e58bb316 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index b8b53703..4b572158 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 c4e81532..38ef6d49 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -78,7 +78,7 @@ diff --git a/docs/index.html b/docs/index.html index a5a1bb68..67083bd3 100644 --- a/docs/index.html +++ b/docs/index.html @@ -42,7 +42,7 @@ diff --git a/docs/news/index.html b/docs/news/index.html index 9575cea0..b1435014 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -78,7 +78,7 @@ @@ -225,9 +225,9 @@ -Added tibble printing support for classes rsi, mic, ab and mo. When using tibbles containing antibiotic columns, values S will print in green, values I will print in yellow and values R will print in red:
Added tibble printing support for classes rsi, mic, disk, ab mo. When using tibbles containing antibiotic columns, values S will print in green, values I will print in yellow and values R will print in red. Microbial IDs (class mo) will emphasise on the genus and species, not on the kingdom.
# (run this on your own console, as this page does not support colour printing)
-tibble(mo = sample(AMR::microorganisms$fullname, 10),
- drug1 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
- prob = c(0.6, 0.1, 0.3))),
- drug2 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
- prob = c(0.6, 0.1, 0.3))),
- drug3 = as.rsi(sample(c("S", "I", "R"), 10, replace = TRUE,
- prob = c(0.6, 0.1, 0.3))))atc - using as.atc() is now deprecated in favour of ab_atc() and this will return a character, not the atc class anymoreabname(), ab_official(), atc_name(), atc_official(), atc_property(), atc_tradenames(), atc_trivial_nl()
@@ -316,8 +313,10 @@
mo_* functions where the coercion uncertainties and failures would not be available through mo_uncertainties() and mo_failures() anymorecountry parameter of mdro() in favour of the already existing guideline parameter to support multiple guidelines within one countryname of RIF is now Rifampicin instead of Rifampinantibiotics data set is now sorted by name and all cephalosporines now have their generation between bracketsSpeed improvement for guess_ab_col() which is now 30 times faster for antibiotic abbreviations
antibiotics data set is now sorted by name and all cephalosporins now have their generation between bracketsguess_ab_col() which is now 30 times faster for antibiotic abbreviationsfilter_ab_class() to be more reliable and to support 5th generation cephalosporinsClasses ab and mo will now be preserved in any subsetting
Function rsi_df() to transform a data.frame to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combination of the existing functions count_df() and portion_df() to immediately show resistance percentages and number of available isolates:
septic_patients %>%
- select(AMX, CIP) %>%
+ select(AMX, CIP) %>%
rsi_df()
# antibiotic interpretation value isolates
# 1 Amoxicillin SI 0.4442636 546
@@ -472,7 +471,7 @@ Please boxplot()
# grouped boxplots:
septic_patients %>%
- group_by(hospital_id) %>%
+ group_by(hospital_id) %>%
freq(age) %>%
boxplot()as.atc()filter_first_isolate(septic_patients, ...)is equal to:
septic_patients %>%
- mutate(only_firsts = first_isolate(septic_patients, ...)) %>%
- filter(only_firsts == TRUE) %>%
- select(-only_firsts)availability() to check the number of available (non-empty) results in a data.frame
as.mo(..., allow_uncertain = 3)# Determine genus of microorganisms (mo) in `septic_patients` data set:
# OLD WAY
septic_patients %>%
- mutate(genus = mo_genus(mo)) %>%
+ mutate(genus = mo_genus(mo)) %>%
freq(genus)
# NEW WAY
septic_patients %>%
@@ -721,7 +720,7 @@ Using as.mo(..., allow_uncertain = 3)
# Even supports grouping variables:
septic_patients %>%
- group_by(gender) %>%
+ group_by(gender) %>%
freq(mo_genus(mo))
header functionas.mo(..., allow_uncertain = 3)
New parameter droplevels to exclude empty factor levels when input is a factor
Factor levels will be in header when present in input data (maximum of 5)
-Fix for using select() on frequency tables
+Fix for using select() on frequency tables
Function scale_y_percent() now contains the limits parameter
@@ -814,14 +813,14 @@ Using as.mo(..., allow_uncertain = 3)
Support for grouping variables, test with:
Support for (un)selecting columns:
septic_patients %>%
freq(hospital_id) %>%
- select(-count, -cum_count) # only get item, percent, cum_percent
+ select(-count, -cum_count) # only get item, percent, cum_percent
Check for hms::is.hms
@@ -978,7 +977,7 @@ Using as.mo(..., allow_uncertain = 3)
Support for quasiquotation in the functions series count_* and portions_*, and n_rsi. This allows to check for more than 2 vectors or columns.
-septic_patients %>% select(amox, cipr) %>% count_IR()
+
diff --git a/docs/reference/filter_ab_class.html b/docs/reference/filter_ab_class.html
index 3a558c84..bc5e04dc 100644
--- a/docs/reference/filter_ab_class.html
+++ b/docs/reference/filter_ab_class.html
@@ -80,7 +80,7 @@
@@ -234,40 +234,42 @@
- filter_ab_class(tbl, ab_class, result = NULL, scope = "any", ...)
+ filter_ab_class(x, ab_class, result = NULL, scope = "any", ...)
-filter_aminoglycosides(tbl, result = NULL, scope = "any", ...)
+filter_aminoglycosides(x, result = NULL, scope = "any", ...)
-filter_carbapenems(tbl, result = NULL, scope = "any", ...)
+filter_carbapenems(x, result = NULL, scope = "any", ...)
-filter_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_1st_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_1st_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_2nd_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_2nd_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_3rd_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_3rd_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_4th_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_4th_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_fluoroquinolones(tbl, result = NULL, scope = "any", ...)
+filter_5th_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_glycopeptides(tbl, result = NULL, scope = "any", ...)
+filter_fluoroquinolones(x, result = NULL, scope = "any", ...)
-filter_macrolides(tbl, result = NULL, scope = "any", ...)
+filter_glycopeptides(x, result = NULL, scope = "any", ...)
-filter_tetracyclines(tbl, result = NULL, scope = "any", ...)
+filter_macrolides(x, result = NULL, scope = "any", ...)
+
+filter_tetracyclines(x, result = NULL, scope = "any", ...)
Arguments
- tbl
+ x
a data set
ab_class
- an antimicrobial class, like "carbapenems". More specifically, this should be a text that can be found in a 4th level ATC group (chemical subgroup) or a 5th level ATC group (chemical substance), please see this explanation on the WHOCC website.
an antimicrobial class, like "carbapenems", as can be found in AMR::antibiotics$group
result
@@ -285,7 +287,7 @@
Details
- The antibiotics data set will be searched for ab_class in the columns atc_group1 and atc_group2 (case-insensitive). Next, tbl will be checked for column names with a value in any abbreviations, codes or official names found in the antibiotics data set.
+ The group column in antibiotics data set will be searched for ab_class (case-insensitive). If no results are found, the atc_group1 and atc_group2 columns will be searched. Next, x will be checked for column names with a value in any abbreviations, codes or official names found in the antibiotics data set.
Examples
diff --git a/docs/reference/index.html b/docs/reference/index.html
index de5910f1..18ece678 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -78,7 +78,7 @@
@@ -428,7 +428,7 @@
- filter_ab_class() filter_aminoglycosides() filter_carbapenems() filter_cephalosporins() filter_1st_cephalosporins() filter_2nd_cephalosporins() filter_3rd_cephalosporins() filter_4th_cephalosporins() filter_fluoroquinolones() filter_glycopeptides() filter_macrolides() filter_tetracyclines()
+ filter_ab_class() filter_aminoglycosides() filter_carbapenems() filter_cephalosporins() filter_1st_cephalosporins() filter_2nd_cephalosporins() filter_3rd_cephalosporins() filter_4th_cephalosporins() filter_5th_cephalosporins() filter_fluoroquinolones() filter_glycopeptides() filter_macrolides() filter_tetracyclines()
Filter isolates on result in antibiotic class
diff --git a/docs/reference/like.html b/docs/reference/like.html
index b5d0978f..74fb7a39 100644
--- a/docs/reference/like.html
+++ b/docs/reference/like.html
@@ -80,7 +80,7 @@
diff --git a/docs/reference/translate.html b/docs/reference/translate.html
index 083b5d6f..e97f7a94 100644
--- a/docs/reference/translate.html
+++ b/docs/reference/translate.html
@@ -80,7 +80,7 @@
diff --git a/man/filter_ab_class.Rd b/man/filter_ab_class.Rd
index 31859f4b..55101198 100644
--- a/man/filter_ab_class.Rd
+++ b/man/filter_ab_class.Rd
@@ -9,40 +9,43 @@
\alias{filter_2nd_cephalosporins}
\alias{filter_3rd_cephalosporins}
\alias{filter_4th_cephalosporins}
+\alias{filter_5th_cephalosporins}
\alias{filter_fluoroquinolones}
\alias{filter_glycopeptides}
\alias{filter_macrolides}
\alias{filter_tetracyclines}
\title{Filter isolates on result in antibiotic class}
\usage{
-filter_ab_class(tbl, ab_class, result = NULL, scope = "any", ...)
+filter_ab_class(x, ab_class, result = NULL, scope = "any", ...)
-filter_aminoglycosides(tbl, result = NULL, scope = "any", ...)
+filter_aminoglycosides(x, result = NULL, scope = "any", ...)
-filter_carbapenems(tbl, result = NULL, scope = "any", ...)
+filter_carbapenems(x, result = NULL, scope = "any", ...)
-filter_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_1st_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_1st_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_2nd_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_2nd_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_3rd_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_3rd_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_4th_cephalosporins(tbl, result = NULL, scope = "any", ...)
+filter_4th_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_fluoroquinolones(tbl, result = NULL, scope = "any", ...)
+filter_5th_cephalosporins(x, result = NULL, scope = "any", ...)
-filter_glycopeptides(tbl, result = NULL, scope = "any", ...)
+filter_fluoroquinolones(x, result = NULL, scope = "any", ...)
-filter_macrolides(tbl, result = NULL, scope = "any", ...)
+filter_glycopeptides(x, result = NULL, scope = "any", ...)
-filter_tetracyclines(tbl, result = NULL, scope = "any", ...)
+filter_macrolides(x, result = NULL, scope = "any", ...)
+
+filter_tetracyclines(x, result = NULL, scope = "any", ...)
}
\arguments{
-\item{tbl}{a data set}
+\item{x}{a data set}
-\item{ab_class}{an antimicrobial class, like \code{"carbapenems"}. More specifically, this should be a text that can be found in a 4th level ATC group (chemical subgroup) or a 5th level ATC group (chemical substance), please see \href{https://www.whocc.no/atc/structure_and_principles/}{this explanation on the WHOCC website}.}
+\item{ab_class}{an antimicrobial class, like \code{"carbapenems"}, as can be found in \code{AMR::antibiotics$group}}
\item{result}{an antibiotic result: S, I or R (or a combination of more of them)}
@@ -54,7 +57,7 @@ filter_tetracyclines(tbl, result = NULL, scope = "any", ...)
Filter isolates on results in specific antibiotic variables based on their class (ATC groups). This makes it easy to get a list of isolates that were tested for e.g. any aminoglycoside.
}
\details{
-The \code{\link{antibiotics}} data set will be searched for \code{ab_class} in the columns \code{atc_group1} and \code{atc_group2} (case-insensitive). Next, \code{tbl} will be checked for column names with a value in any abbreviations, codes or official names found in the \code{antibiotics} data set.
+The \code{group} column in \code{\link{antibiotics}} data set will be searched for \code{ab_class} (case-insensitive). If no results are found, the \code{atc_group1} and \code{atc_group2} columns will be searched. Next, \code{x} will be checked for column names with a value in any abbreviations, codes or official names found in the \code{antibiotics} data set.
}
\examples{
library(dplyr)
diff --git a/tests/testthat/test-mo.R b/tests/testthat/test-mo.R
index 85a07fe6..8340ed48 100644
--- a/tests/testthat/test-mo.R
+++ b/tests/testthat/test-mo.R
@@ -159,8 +159,7 @@ test_that("as.mo works", {
septic_patients[1:10,] %>%
left_join_microorganisms() %>%
select(genus, species) %>%
- as.mo() %>%
- as.character())
+ as.mo())
# unknown results
expect_warning(as.mo(c("INVALID", "Yeah, unknown")))
@@ -271,7 +270,7 @@ test_that("as.mo works", {
expect_equal(as.character(as.mo("F_CANDD_GLB")), "F_CANDD_GLA")
# debug mode
- expect_warning(as.mo("kshgcjkhsdgkshjdfsfvsdfv", debug = TRUE, allow_uncertain = 3))
+ expect_output(print(suppressWarnings(as.mo("kshgcjkhsdgkshjdfsfvsdfv", debug = TRUE, allow_uncertain = 3))))
# ..coccus
expect_equal(as.character(as.mo(c("meningococ", "gonococ", "pneumococ"))),