How to conduct AMR analysis
Matthijs S. Berends
-08 January 2019
+11 January 2019
AMR.RmdNote: values on this page will change with every website update since they are based on randomly created values and the page was written in RMarkdown. However, the methodology remains unchanged. This page was generated on 08 January 2019.
+Note: values on this page will change with every website update since they are based on randomly created values and the page was written in RMarkdown. However, the methodology remains unchanged. This page was generated on 11 January 2019.
Introduction
@@ -196,21 +203,21 @@Put everything together
-Using the sample() function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the prob parameter.
data <- data.frame(date = sample(dates, 5000, replace = TRUE),
- patient_id = sample(patients, 5000, replace = TRUE),
- hospital = sample(hospitals, 5000, replace = TRUE, prob = c(0.30, 0.35, 0.15, 0.20)),
- bacteria = sample(bacteria, 5000, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10)),
- amox = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.60, 0.05, 0.35)),
- amcl = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.75, 0.10, 0.15)),
- cipr = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.80, 0.00, 0.20)),
- gent = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.92, 0.00, 0.08))
+Using the sample() function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the prob parameter.
+data <- data.frame(date = sample(dates, 5000, replace = TRUE),
+ patient_id = sample(patients, 5000, replace = TRUE),
+ hospital = sample(hospitals, 5000, replace = TRUE, prob = c(0.30, 0.35, 0.15, 0.20)),
+ bacteria = sample(bacteria, 5000, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10)),
+ amox = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.60, 0.05, 0.35)),
+ amcl = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.75, 0.10, 0.15)),
+ cipr = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.80, 0.00, 0.20)),
+ gent = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.92, 0.00, 0.08))
)
-Using the left_join() function from the dplyr package, we can ‘map’ the gender to the patient ID using the patients_table object we created earlier:
-
+Using the left_join() function from the dplyr package, we can ‘map’ the gender to the patient ID using the patients_table object we created earlier:
+
The resulting data set contains 5,000 blood culture isolates. With the head() function we can preview the first 6 values of this data set:
@@ -296,70 +303,70 @@
-2011-02-27
-M6
-Hospital C
+2014-02-02
+P8
+Hospital D
Escherichia coli
S
-S
-R
-S
-M
-
-
-2012-07-12
-C2
-Hospital B
-Streptococcus pneumoniae
-S
-R
-S
-S
-M
-
-
-2016-09-13
-O7
-Hospital A
-Escherichia coli
-R
R
S
S
F
-2016-12-05
-E4
-Hospital A
+2013-10-26
+Q1
+Hospital B
Escherichia coli
I
+R
+R
S
+F
+
+
+2017-06-12
+E5
+Hospital D
+Streptococcus pneumoniae
+S
+R
+S
+S
+M
+
+
+2013-06-16
+K7
+Hospital B
+Escherichia coli
+S
+I
S
S
M
-2017-10-05
-M1
-Hospital A
-Escherichia coli
-R
+2013-01-11
+M4
+Hospital B
+Staphylococcus aureus
I
-R
+S
+S
S
M
-2012-02-22
-H9
-Hospital C
-Streptococcus pneumoniae
+2016-11-18
+W10
+Hospital A
+Staphylococcus aureus
S
S
-R
S
-M
+S
+F
@@ -379,15 +386,15 @@
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------ -------- ----------- -------------
-# 1 M 2,635 52.7% 2,635 52.7%
-# 2 F 2,365 47.3% 5,000 100.0%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:
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:
We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The as.rsi() function ensures reliability and reproducibility in these kind of variables. The mutate_at() will run the as.rsi() function on defined variables:
We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The as.rsi() function ensures reliability and reproducibility in these kind of variables. The mutate_at() will run the as.rsi() function on defined variables:
Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the eucast_rules() function can also apply additional rules, like forcing
Because the amoxicillin (column amox) and amoxicillin/clavulanic acid (column amcl) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The eucast_rules() fixes this:
data <- eucast_rules(data, col_mo = "bacteria")
@@ -411,10 +418,10 @@
# Kingella kingae (no changes)
#
# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-# Table 1: Intrinsic resistance in Enterobacteriaceae (349 changes)
+# Table 1: Intrinsic resistance in Enterobacteriaceae (333 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 (694 changes)
+# Table 4: Intrinsic resistance in Gram-positive bacteria (692 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)
@@ -430,14 +437,14 @@
# Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)
# Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)
#
-# => EUCAST rules affected 1,854 out of 5,000 rows -> changed 1,043 test results.Adding new variables
Now that we have the microbial ID, we can add some taxonomic properties:
data <- data %>%
- mutate(gramstain = mo_gramstain(bacteria),
+ mutate(gramstain = mo_gramstain(bacteria),
genus = mo_genus(bacteria),
species = mo_species(bacteria))
@@ -451,14 +458,14 @@
-
+This AMR package includes this methodology with the first_isolate() function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:
data <- data %>%
- mutate(first = first_isolate(.))
+ mutate(first = first_isolate(.))
# NOTE: Using column `bacteria` as input for `col_mo`.
# NOTE: Using column `date` as input for `col_date`.
# NOTE: Using column `patient_id` as input for `col_patient_id`.
-# => Found 2,929 first isolates (58.6% of total)So only 58.6% is suitable for resistance analysis! We can now filter on is with the filter() function, also from the dplyr package:
So only 59.2% is suitable for resistance analysis! We can now filter on is 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:
Only 3 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and show be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
If a column exists with a name like ‘key(…)ab’ the first_isolate() function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:
data <- data %>%
- mutate(keyab = key_antibiotics(.)) %>%
- mutate(first_weighted = first_isolate(.))
+ mutate(keyab = key_antibiotics(.)) %>%
+ mutate(first_weighted = first_isolate(.))
# NOTE: Using column `bacteria` as input for `col_mo`.
# amox amcl cipr gent
# "amox" "amcl" "cipr" "gent"
@@ -608,7 +615,7 @@
# NOTE: Using column `patient_id` as input for `col_patient_id`.
# NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.
# [Criterion] Inclusion based on key antibiotics, ignoring I.
-# => Found 4,435 first weighted isolates (88.7% of total)| isolate | @@ -625,80 +632,80 @@||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-01-31 | -L4 | +2010-05-23 | +E7 | B_ESCHR_COL | R | S | -S | +R | S | TRUE | TRUE | ||
| 2 | -2010-08-01 | -L4 | +2010-08-03 | +E7 | B_ESCHR_COL | -R | +S | S | S | S | FALSE | -FALSE | +TRUE | |
| 3 | -2010-12-29 | -L4 | +2011-01-20 | +E7 | B_ESCHR_COL | -I | -S | R | +R | +S | S | FALSE | TRUE | |
| 4 | -2011-01-21 | -L4 | +2011-02-21 | +E7 | B_ESCHR_COL | -R | -I | S | +R | +R | S | FALSE | TRUE | |
| 5 | -2011-02-06 | -L4 | +2011-08-04 | +E7 | B_ESCHR_COL | -I | S | -R | +S | +S | S | TRUE | TRUE | |
| 6 | -2011-05-30 | -L4 | +2011-11-15 | +E7 | B_ESCHR_COL | -S | -I | R | S | +S | +S | FALSE | -FALSE | +TRUE |
| 7 | -2011-08-16 | -L4 | +2012-01-13 | +E7 | B_ESCHR_COL | S | S | @@ -709,8 +716,8 @@|||||||
| 8 | -2012-01-15 | -L4 | +2012-03-10 | +E7 | B_ESCHR_COL | S | S | @@ -721,23 +728,23 @@|||||||
| 9 | -2014-07-16 | -L4 | +2012-11-09 | +E7 | B_ESCHR_COL | S | S | -S | +R | S | TRUE | TRUE | ||
| 10 | -2014-09-19 | -L4 | +2013-04-06 | +E7 | B_ESCHR_COL | S | -R | +S | S | S | FALSE | @@ -745,14 +752,14 @@
Instead of 3, now 8 isolates are flagged. In total, 88.7% of all isolates are marked ‘first weighted’ - 147.3% 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 3, now 10 isolates are flagged. In total, 88% of all isolates are marked ‘first weighted’ - 147.2% 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 4,435 isolates for analysis.
+So we end up with 4,399 isolates for analysis.
We can remove unneeded columns:
+ select(-c(first, keyab))Now our data looks like:
| 1 | -2011-02-27 | -M6 | -Hospital C | +2014-02-02 | +P8 | +Hospital D | B_ESCHR_COL | S | -S | -R | -S | -M | -Gram negative | -Escherichia | -coli | -TRUE | -|||
| 2 | -2012-07-12 | -C2 | -Hospital B | -B_STRPTC_PNE | -S | -R | -S | -R | -M | -Gram positive | -Streptococcus | -pneumoniae | -TRUE | -||||||
| 3 | -2016-09-13 | -O7 | -Hospital A | -B_ESCHR_COL | -R | R | S | S | @@ -822,30 +797,30 @@TRUE | ||||||||||
| 5 | -2017-10-05 | -M1 | -Hospital A | +2 | +2013-10-26 | +Q1 | +Hospital B | B_ESCHR_COL | -R | I | R | +R | S | -M | +F | Gram negative | Escherichia | coli | TRUE |
| 6 | -2012-02-22 | -H9 | -Hospital C | +3 | +2017-06-12 | +E5 | +Hospital D | B_STRPTC_PNE | S | -S | R | +S | R | M | Gram positive | @@ -854,15 +829,47 @@TRUE | |||
| 7 | -2011-01-13 | -S6 | -Hospital D | +4 | +2013-06-16 | +K7 | +Hospital B | +B_ESCHR_COL | +S | +I | +S | +S | +M | +Gram negative | +Escherichia | +coli | +TRUE | +||
| 5 | +2013-01-11 | +M4 | +Hospital B | B_STPHY_AUR | I | S | S | S | +M | +Gram positive | +Staphylococcus | +aureus | +TRUE | +||||||
| 7 | +2012-01-05 | +S10 | +Hospital D | +B_STPHY_AUR | +R | +I | +R | +S | F | Gram positive | Staphylococcus | @@ -884,7 +891,7 @@||||||||
| 1 | Escherichia coli | -2,203 | -49.7% | -2,203 | -49.7% | +2,138 | +48.6% | +2,138 | +48.6% | ||||||||||
| 2 | Staphylococcus aureus | -1,072 | -24.2% | -3,275 | -73.8% | +1,070 | +24.3% | +3,208 | +72.9% | ||||||||||
| 3 | Streptococcus pneumoniae | -689 | -15.5% | -3,964 | -89.4% | +697 | +15.8% | +3,905 | +88.8% | ||||||||||
| 4 | Klebsiella pneumoniae | -471 | -10.6% | -4,435 | +494 | +11.2% | +4,399 | 100.0% |
| hospital | @@ -950,26 +957,26 @@ Longest: 24||
|---|---|---|
| Hospital A | -0.4504164 | +0.4591382 |
| Hospital B | -0.4782034 | +0.5000000 |
| Hospital C | -0.4670571 | +0.4682171 |
| Hospital D | -0.4471101 | +0.4953380 |
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the n_rsi() can be used, which works exactly like n_distinct() from the dplyr package. It counts all isolates available for every group (i.e. values S, I or R):
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the n_rsi() can be used, which works exactly like n_distinct() from the dplyr package. It counts all isolates available for every group (i.e. values S, I or R):
data_1st %>%
- group_by(hospital) %>%
- summarise(amoxicillin = portion_IR(amox),
+ group_by(hospital) %>%
+ summarise(amoxicillin = portion_IR(amox),
available = n_rsi(amox))| Hospital A | -0.4504164 | -1321 | +0.4591382 | +1346 |
| Hospital B | -0.4782034 | -1514 | +0.5000000 | +1550 |
| Hospital C | -0.4670571 | -683 | +0.4682171 | +645 |
| Hospital D | -0.4471101 | -917 | +0.4953380 | +858 |
These functions can also be used to get the portion of multiple antibiotics, to calculate co-resistance very easily:
data_1st %>%
- group_by(genus) %>%
- summarise(amoxicillin = portion_S(amcl),
+ group_by(genus) %>%
+ summarise(amoxicillin = portion_S(amcl),
gentamicin = portion_S(gent),
"amox + gent" = portion_S(amcl, gent))| Escherichia | -0.7162960 | -0.9051294 | -0.9786655 | +0.7193639 | +0.9111319 | +0.9742750 |
| Klebsiella | -0.7494692 | -0.9129512 | -0.9766454 | +0.7226721 | +0.9028340 | +0.9777328 |
| Staphylococcus | -0.7341418 | -0.9244403 | -0.9776119 | +0.7392523 | +0.9084112 | +0.9831776 |
| Streptococcus | -0.7576197 | +0.7532281 | 0.0000000 | -0.7576197 | +0.7532281 |
To make a transition to the next part, let’s see how this difference could be plotted:
data_1st %>%
- group_by(genus) %>%
- summarise("1. Amoxicillin" = portion_S(amcl),
+ group_by(genus) %>%
+ summarise("1. Amoxicillin" = portion_S(amcl),
"2. Gentamicin" = portion_S(gent),
"3. Amox + gent" = portion_S(amcl, gent)) %>%
- tidyr::gather("Antibiotic", "S", -genus) %>%
+ tidyr::gather("Antibiotic", "S", -genus) %>%
ggplot(aes(x = genus,
y = S,
fill = Antibiotic)) +
@@ -1076,7 +1083,7 @@ Longest: 24
Omit the translate_ab = FALSE to have the antibiotic codes (amox, amcl, cipr, gent) translated to official WHO names (amoxicillin, amoxicillin and betalactamase inhibitor, ciprofloxacin, gentamicin).
If we group on e.g. the genus column and add some additional functions from our package, we can create this:
# group the data on `genus`
-ggplot(data_1st %>% group_by(genus)) +
+ggplot(data_1st %>% group_by(genus)) +
# create bars with genus on x axis
# it looks for variables with class `rsi`,
# of which we have 4 (earlier created with `as.rsi`)
@@ -1098,7 +1105,7 @@ Longest: 24
To simplify this, we also created the ggplot_rsi() function, which combines almost all above functions:
We can transform the data and apply the test in only a couple of lines:
septic_patients %>%
- filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
- select(hospital_id, fosf) %>% # select the hospitals and fosfomycin
- group_by(hospital_id) %>% # group on the hospitals
+ filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
+ select(hospital_id, fosf) %>% # select the hospitals and fosfomycin
+ group_by(hospital_id) %>% # group on the hospitals
count_df(combine_IR = TRUE) %>% # count all isolates per group (hospital_id)
- tidyr::spread(hospital_id, Value) %>% # transform output so A and D are columns
- select(A, D) %>% # and select these only
+ tidyr::spread(hospital_id, Value) %>% # transform output so A and D are columns
+ select(A, D) %>% # and select these only
as.matrix() %>% # transform to good old matrix for fisher.test()
fisher.test() # do Fisher's Exact Test
#
diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png
index f9b2d55e..c9ff2753 100644
Binary files a/docs/articles/AMR_files/figure-html/plot 1-1.png and b/docs/articles/AMR_files/figure-html/plot 1-1.png differ
diff --git a/docs/articles/AMR_files/figure-html/plot 3-1.png b/docs/articles/AMR_files/figure-html/plot 3-1.png
index 265ee49b..6bc000b5 100644
Binary files a/docs/articles/AMR_files/figure-html/plot 3-1.png and b/docs/articles/AMR_files/figure-html/plot 3-1.png differ
diff --git a/docs/articles/AMR_files/figure-html/plot 4-1.png b/docs/articles/AMR_files/figure-html/plot 4-1.png
index eecd78d2..53cfc234 100644
Binary files a/docs/articles/AMR_files/figure-html/plot 4-1.png and b/docs/articles/AMR_files/figure-html/plot 4-1.png differ
diff --git a/docs/articles/AMR_files/figure-html/plot 5-1.png b/docs/articles/AMR_files/figure-html/plot 5-1.png
index 882d3802..5cc23525 100644
Binary files a/docs/articles/AMR_files/figure-html/plot 5-1.png and b/docs/articles/AMR_files/figure-html/plot 5-1.png differ
diff --git a/docs/articles/EUCAST.html b/docs/articles/EUCAST.html
index 4f91ef91..48d7603c 100644
--- a/docs/articles/EUCAST.html
+++ b/docs/articles/EUCAST.html
@@ -111,6 +111,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -171,7 +178,7 @@
How to apply EUCAST rules
Matthijs S. Berends
- 08 January 2019
+ 11 January 2019
EUCAST.Rmd
diff --git a/docs/articles/G_test.html b/docs/articles/G_test.html
index 184ca5e6..fb6b4173 100644
--- a/docs/articles/G_test.html
+++ b/docs/articles/G_test.html
@@ -111,6 +111,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -171,7 +178,7 @@
How to use the G-test
Matthijs S. Berends
- 08 January 2019
+ 11 January 2019
G_test.Rmd
diff --git a/docs/articles/Predict.html b/docs/articles/Predict.html
index 26cd4754..7cbb6fbd 100644
--- a/docs/articles/Predict.html
+++ b/docs/articles/Predict.html
@@ -111,6 +111,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -171,7 +178,7 @@
How to predict antimicrobial resistance
Matthijs S. Berends
- 08 January 2019
+ 11 January 2019
Predict.Rmd
diff --git a/docs/articles/ab_property.html b/docs/articles/ab_property.html
index e7d88cbe..85478e42 100644
--- a/docs/articles/ab_property.html
+++ b/docs/articles/ab_property.html
@@ -111,6 +111,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -171,7 +178,7 @@
How to get properties of an antibiotic
Matthijs S. Berends
- 08 January 2019
+ 11 January 2019
ab_property.Rmd
diff --git a/docs/articles/benchmarks.html b/docs/articles/benchmarks.html
new file mode 100644
index 00000000..a3249e69
--- /dev/null
+++ b/docs/articles/benchmarks.html
@@ -0,0 +1,371 @@
+
+
+
+
+
+
+
+Benchmarks • AMR (for R)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Benchmarks
+ Matthijs S. Berends
+
+ 11 January 2019
+
+
+ benchmarks.Rmd
+
+
+
+
+
+One of the most important features of this package is the complete microbial taxonomic database, supplied by ITIS (https://www.itis.gov). We created a function as.mo() that transforms any user input value to a valid microbial ID by using AI (Artificial Intelligence) and based on the taxonomic tree of ITIS.
+Using the microbenchmark package, we can review the calculation performance of this function.
+
+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).
+But the calculation time differs a lot. Here, the AI effect can be reviewed best:
+microbenchmark(A = as.mo("stau"),
+ B = as.mo("staaur"),
+ C = as.mo("S. aureus"),
+ D = as.mo("S. aureus"),
+ E = as.mo("STAAUR"),
+ F = as.mo("Staphylococcus aureus"),
+ G = as.mo("B_STPHY_AUR"),
+ times = 10,
+ unit = "ms")
+# Unit: milliseconds
+# expr min lq mean median uq max neval
+# A 34.745551 34.798630 35.2596102 34.8994810 35.258325 38.067062 10
+# B 7.095386 7.125348 7.2219948 7.1613865 7.240377 7.495857 10
+# C 11.677114 11.733826 11.8304789 11.7715050 11.843756 12.317559 10
+# D 11.694435 11.730054 11.9859313 11.8775585 12.206371 12.750016 10
+# E 7.044402 7.117387 7.2271630 7.1923610 7.246104 7.742396 10
+# F 6.642326 6.778446 6.8988042 6.8753165 6.923577 7.513945 10
+# G 0.106788 0.131023 0.1351229 0.1357725 0.144014 0.146458 10
+In the table above, all measurements are shown in milliseconds (thousands of seconds), tested on a quite regular Linux server from 2007 (Core 2 Duo 2.7 GHz, 2 GB DDR2 RAM). A value of 6.9 milliseconds means it will roughly determine 144 input values per second. It case of 39.2 milliseconds, this is only 26 input values per second. The more an input value resembles a full name (like C, D and F), the faster the result will be found. In case of G, the input is already a valid MO code, so it only almost takes no time at all (0.0001 seconds on our server).
+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 far less faster. See this example for the ID of Burkholderia nodosa (B_BRKHL_NOD):
+microbenchmark(A = as.mo("buno"),
+ B = as.mo("burnod"),
+ C = as.mo("B. nodosa"),
+ D = as.mo("B. nodosa"),
+ E = as.mo("BURNOD"),
+ F = as.mo("Burkholderia nodosa"),
+ G = as.mo("B_BRKHL_NOD"),
+ times = 10,
+ unit = "ms")
+# Unit: milliseconds
+# expr min lq mean median uq max neval
+# A 124.175427 124.474837 125.8610536 125.3750560 126.160945 131.485994 10
+# B 154.249713 155.364729 160.9077032 156.8738940 157.136183 197.315105 10
+# C 66.066571 66.162393 66.5538611 66.4488130 66.698077 67.623404 10
+# D 86.747693 86.918665 90.7831016 87.8149725 89.440982 116.767991 10
+# E 154.863827 155.208563 162.6535954 158.4062465 168.593785 187.378088 10
+# F 32.427028 32.638648 32.9929454 32.7860475 32.992813 34.674241 10
+# G 0.213155 0.216578 0.2369226 0.2338985 0.253734 0.285581 10
+That takes up to 11 times as much time! A value of 158.4 milliseconds means it can only determine ~6 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.
+To relieve this pitfall and further improve performance, two important calculations take almost no time at all: repetitive results and already precalculated results.
+
+
+Repetitive results
+Repetitive results mean that unique values are present more than once. Unique values will only be calculated once by as.mo(). We will use mo_fullname() for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) and uses as.mo() internally.
+library(dplyr)
+# take 500,000 random MO codes from the septic_patients data set
+x = septic_patients %>%
+ sample_n(500000, replace = TRUE) %>%
+ pull(mo)
+
+# got the right length?
+length(x)
+# [1] 500000
+
+# and how many unique values do we have?
+n_distinct(x)
+# [1] 96
+
+# only 96, but distributed in 500,000 results. now let's see:
+microbenchmark(X = mo_fullname(x),
+ times = 10,
+ unit = "ms")
+# Unit: milliseconds
+# expr min lq mean median uq max neval
+# X 114.9342 117.1076 129.6448 120.2047 131.5005 168.6371 10
+So transforming 500,000 values (!) of 96 unique values only takes 0.12 seconds (120 ms). You only lose time on your unique input values.
+Results of a tenfold - 5,000,000 values:
+# Unit: milliseconds
+# expr min lq mean median uq max neval
+# X 882.9045 901.3011 1001.677 940.3421 1168.088 1226.846 10
+Even the full names of 5 Million values are calculated within a second.
+
+
+
+Precalculated results
+What about precalculated results? If the input is an already precalculated result of a helper function like mo_fullname(), it almost doesn’t take any time at all (see ‘C’ below):
+microbenchmark(A = mo_fullname("B_STPHY_AUR"),
+ B = mo_fullname("S. aureus"),
+ C = mo_fullname("Staphylococcus aureus"),
+ times = 10,
+ unit = "ms")
+# Unit: milliseconds
+# expr min lq mean median uq max neval
+# A 11.364086 11.460537 11.5104799 11.4795330 11.524860 11.818263 10
+# B 11.976454 12.012352 12.1704592 12.0853020 12.210004 12.881737 10
+# C 0.095823 0.102528 0.1167754 0.1153785 0.132629 0.140661 10
+So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0001 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:
+microbenchmark(A = mo_species("aureus"),
+ B = mo_genus("Staphylococcus"),
+ C = mo_fullname("Staphylococcus aureus"),
+ D = mo_family("Staphylococcaceae"),
+ E = mo_order("Bacillales"),
+ F = mo_class("Bacilli"),
+ G = mo_phylum("Firmicutes"),
+ H = mo_subkingdom("Posibacteria"),
+ I = mo_kingdom("Bacteria"),
+ times = 10,
+ unit = "ms")
+# Unit: milliseconds
+# expr min lq mean median uq max neval
+# A 0.105181 0.121314 0.1478538 0.1465265 0.166711 0.211409 10
+# B 0.132558 0.146388 0.1584278 0.1499835 0.164895 0.208477 10
+# C 0.135492 0.160355 0.2341847 0.1884665 0.348857 0.395931 10
+# D 0.109650 0.115727 0.1270481 0.1264130 0.128648 0.168317 10
+# E 0.081574 0.096940 0.0992582 0.0980915 0.101479 0.120477 10
+# F 0.081575 0.088489 0.0988463 0.0989650 0.103365 0.126482 10
+# G 0.091981 0.095333 0.1043568 0.1001530 0.111327 0.129625 10
+# H 0.092610 0.093169 0.1009135 0.0985455 0.101828 0.120406 10
+# I 0.087371 0.091213 0.1069758 0.0941815 0.109302 0.192831 10
+Of course, when running mo_phylum("Firmicutes") the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be "Firmicutes" too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known microorganisms (according to ITIS), it can just return the initial value immediately.
+
+
+
+Results in other languages
+When the system language is non-English and supported by this AMR package, some functions take a little while longer:
+mo_fullname("CoNS", language = "en") # or just mo_fullname("CoNS") on an English system
+# "Coagulase Negative Staphylococcus (CoNS)"
+
+mo_fullname("CoNS", language = "fr") # or just mo_fullname("CoNS") on a French system
+# "Staphylococcus à coagulase négative (CoNS)"
+
+microbenchmark(en = mo_fullname("CoNS", language = "en"),
+ de = mo_fullname("CoNS", language = "de"),
+ nl = mo_fullname("CoNS", language = "nl"),
+ es = mo_fullname("CoNS", language = "es"),
+ it = mo_fullname("CoNS", language = "it"),
+ fr = mo_fullname("CoNS", language = "fr"),
+ pt = mo_fullname("CoNS", language = "pt"),
+ times = 10,
+ unit = "ms")
+# Unit: milliseconds
+# expr min lq mean median uq max neval
+# en 6.093583 6.51724 6.555105 6.562986 6.630663 6.99698 100
+# de 13.934874 14.35137 16.891587 14.462210 14.764658 43.63956 100
+# nl 13.900092 14.34729 15.943268 14.424565 14.581535 43.76283 100
+# es 13.833813 14.34596 14.574783 14.439757 14.653994 17.49168 100
+# it 13.811883 14.36621 15.179060 14.453515 14.812359 43.64284 100
+# fr 13.798683 14.37019 16.344731 14.468775 14.697610 48.62923 100
+# pt 13.789674 14.36244 15.706321 14.443772 14.679905 44.76701 100
+Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/articles/freq.html b/docs/articles/freq.html
index eb3ca3d8..656b19d0 100644
--- a/docs/articles/freq.html
+++ b/docs/articles/freq.html
@@ -111,6 +111,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -171,7 +178,7 @@
How to create frequency tables
Matthijs S. Berends
- 08 January 2019
+ 11 January 2019
freq.Rmd
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 3742304a..329fd1bd 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -149,6 +149,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -220,6 +227,7 @@
How to use the *G*-test
How to predict antimicrobial resistance
How to get properties of an antibiotic
+ Benchmarks
How to create frequency tables
How to get properties of a microorganism
diff --git a/docs/articles/mo_property.html b/docs/articles/mo_property.html
index 4d794a18..8c22946d 100644
--- a/docs/articles/mo_property.html
+++ b/docs/articles/mo_property.html
@@ -111,6 +111,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -171,7 +178,7 @@
How to get properties of a microorganism
Matthijs S. Berends
- 08 January 2019
+ 11 January 2019
mo_property.Rmd
diff --git a/docs/authors.html b/docs/authors.html
index 58b38cd1..eeddf4b3 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -149,6 +149,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
diff --git a/docs/extra.css b/docs/extra.css
index df38f6b5..2c0d74ab 100644
--- a/docs/extra.css
+++ b/docs/extra.css
@@ -50,6 +50,7 @@ a, a:hover, a:focus {
}
a[href], a[href]:hover, a[href]:focus,
code a[href], code a[href]:hover, code a[href]:focus,
+a[href] code, a[href] code:hover, a[href] code:focus,
pre a[href], pre a[href]:hover, pre a[href]:focus,
a code[href], a code[href]:hover, a code[href]:focus,
a pre[href], a pre[href]:hover, a pre[href]:focus {
@@ -139,3 +140,9 @@ thead {
tbody {
border-bottom: 2px solid black;
}
+table a:not(.btn), .table a:not(.btn) {
+ text-decoration: inherit;
+}
+table a:not(.btn):hover, .table a:not(.btn):hover {
+ text-decoration: underline;
+}
diff --git a/docs/index.html b/docs/index.html
index b5501c45..66eca03e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -113,6 +113,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -228,7 +235,8 @@
Short introduction
This package contains the complete microbial taxonomic data (with all nine taxonomic ranks - from kingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, https://www.itis.gov).
-All (sub)species from the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package, as well as all previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria. ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists.
+All (sub)species from the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package, as well as all previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria.
+Read more about ITIS in our manual.
The AMR package basically does four important things:
-
diff --git a/docs/news/index.html b/docs/news/index.html
index 87fcccc4..49aaec0b 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -149,6 +149,13 @@
Use the G-test
+ -
+
+
+
+ Other: benchmarks
+
+
@@ -229,7 +236,7 @@
Support for dplyr version 0.8.0
-Function guess_ab to find an antibiotic column in a table
+Function guess_ab_col to find an antibiotic column in a table
Function mo_failures() to review values that could not be coerced to a valid MO code, using as.mo(). This latter function will now only show a maximum of 25 uncoerced values.
Function mo_renamed() to get a list of all returned values from as.mo() that have had taxonomic renaming
Function age() to calculate the (patients) age in years
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 1f121089..31227eab 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -7,6 +7,7 @@ articles:
G_test: G_test.html
Predict: Predict.html
ab_property: ab_property.html
+ benchmarks: benchmarks.html
freq: freq.html
mo_property: mo_property.html
urls:
diff --git a/docs/reference/AMR-deprecated.html b/docs/reference/AMR-deprecated.html
index 2797d1a9..a3ad0f9a 100644
--- a/docs/reference/AMR-deprecated.html
+++ b/docs/reference/AMR-deprecated.html
@@ -151,6 +151,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
diff --git a/docs/reference/AMR.html b/docs/reference/AMR.html
index f90f8804..6e5b904b 100644
--- a/docs/reference/AMR.html
+++ b/docs/reference/AMR.html
@@ -151,6 +151,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
diff --git a/docs/reference/ITIS.html b/docs/reference/ITIS.html
index 67f4bc3b..8463d5f7 100644
--- a/docs/reference/ITIS.html
+++ b/docs/reference/ITIS.html
@@ -151,6 +151,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
diff --git a/docs/reference/ab_property.html b/docs/reference/ab_property.html
index 881ae1ec..3b186ce0 100644
--- a/docs/reference/ab_property.html
+++ b/docs/reference/ab_property.html
@@ -151,6 +151,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
diff --git a/docs/reference/abname.html b/docs/reference/abname.html
index 07508cdd..2cb1232f 100644
--- a/docs/reference/abname.html
+++ b/docs/reference/abname.html
@@ -151,6 +151,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
diff --git a/docs/reference/age.html b/docs/reference/age.html
index b8f1053b..f3b014a1 100644
--- a/docs/reference/age.html
+++ b/docs/reference/age.html
@@ -151,6 +151,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
diff --git a/docs/reference/age_groups.html b/docs/reference/age_groups.html
index 2e31aa90..7ab69c23 100644
--- a/docs/reference/age_groups.html
+++ b/docs/reference/age_groups.html
@@ -151,6 +151,13 @@
Use the G-test
+
+
+
+
+ Other: benchmarks
+
+
@@ -289,11 +296,11 @@ On our website https://msberends.gitla
# resistance of ciprofloxacine per age group
library(dplyr)
septic_patients %>%
- mutate(first_isolate = first_isolate(.)) %>%
- filter(first_isolate == TRUE,
+ mutate(first_isolate = first_isolate(.)) %>%
+ filter(first_isolate == TRUE,
mo == as.mo("E. coli")) %>%
- group_by(age_group = age_groups(age)) %>%
- select(age_group,
+ group_by(age_group = age_groups(age)) %>%
+ select(age_group,
cipr) %>%
ggplot_rsi(x = "age_group")
# } mo_property functions (like Details
These functions are meant to count isolates. Use the portion_* functions to calculate microbial resistance.
n_rsi is an alias of count_all. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct. Their function is equal to count_S(...) + count_IR(...).
n_rsi is an alias of count_all. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct. Their function is equal to count_S(...) + count_IR(...).
count_df takes any variable from data that has an "rsi" class (created with as.rsi) and counts the amounts of R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
Read more on our website!
@@ -314,13 +321,13 @@ On our website https://msberends.gitla library(dplyr) septic_patients %>% - group_by(hospital_id) %>% - summarise(R = count_R(cipr), + group_by(hospital_id) %>% + summarise(R = count_R(cipr), I = count_I(cipr), S = count_S(cipr), n1 = count_all(cipr), # the actual total; sum of all three n2 = n_rsi(cipr), # same - analogous to n_distinct - total = n()) # NOT the amount of tested isolates! + total = n()) # NOT the amount of tested isolates! # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. @@ -338,13 +345,13 @@ On our website https://msberends.gitla # Get portions S/I/R immediately of all rsi columns septic_patients %>% - select(amox, cipr) %>% + select(amox, cipr) %>% count_df(translate = FALSE) # It also supports grouping variables septic_patients %>% - select(hospital_id, amox, cipr) %>% - group_by(hospital_id) %>% + select(hospital_id, amox, cipr) %>% + group_by(hospital_id) %>% count_df(translate = FALSE) # } diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html index 1d287876..062f79e9 100644 --- a/docs/reference/eucast_rules.html +++ b/docs/reference/eucast_rules.html @@ -151,6 +151,13 @@ Use the G-testeucast_rules(tbl, col_mo = NULL, info = TRUE, rules = c("breakpoints", "expert", "other", "all"), verbose = FALSE, - amcl = guess_ab(), amik = guess_ab(), amox = guess_ab(), - ampi = guess_ab(), azit = guess_ab(), azlo = guess_ab(), - aztr = guess_ab(), cefa = guess_ab(), cfep = guess_ab(), - cfot = guess_ab(), cfox = guess_ab(), cfra = guess_ab(), - cfta = guess_ab(), cftr = guess_ab(), cfur = guess_ab(), - chlo = guess_ab(), cipr = guess_ab(), clar = guess_ab(), - clin = guess_ab(), clox = guess_ab(), coli = guess_ab(), - czol = guess_ab(), dapt = guess_ab(), doxy = guess_ab(), - erta = guess_ab(), eryt = guess_ab(), fosf = guess_ab(), - fusi = guess_ab(), gent = guess_ab(), imip = guess_ab(), - kana = guess_ab(), levo = guess_ab(), linc = guess_ab(), - line = guess_ab(), mero = guess_ab(), mezl = guess_ab(), - mino = guess_ab(), moxi = guess_ab(), nali = guess_ab(), - neom = guess_ab(), neti = guess_ab(), nitr = guess_ab(), - norf = guess_ab(), novo = guess_ab(), oflo = guess_ab(), - oxac = guess_ab(), peni = guess_ab(), pipe = guess_ab(), - pita = guess_ab(), poly = guess_ab(), pris = guess_ab(), - qida = guess_ab(), rifa = guess_ab(), roxi = guess_ab(), - siso = guess_ab(), teic = guess_ab(), tetr = guess_ab(), - tica = guess_ab(), tige = guess_ab(), tobr = guess_ab(), - trim = guess_ab(), trsu = guess_ab(), vanc = guess_ab()) + amcl = guess_ab_col(), amik = guess_ab_col(), + amox = guess_ab_col(), ampi = guess_ab_col(), + azit = guess_ab_col(), azlo = guess_ab_col(), + aztr = guess_ab_col(), cefa = guess_ab_col(), + cfep = guess_ab_col(), cfot = guess_ab_col(), + cfox = guess_ab_col(), cfra = guess_ab_col(), + cfta = guess_ab_col(), cftr = guess_ab_col(), + cfur = guess_ab_col(), chlo = guess_ab_col(), + cipr = guess_ab_col(), clar = guess_ab_col(), + clin = guess_ab_col(), clox = guess_ab_col(), + coli = guess_ab_col(), czol = guess_ab_col(), + dapt = guess_ab_col(), doxy = guess_ab_col(), + erta = guess_ab_col(), eryt = guess_ab_col(), + fosf = guess_ab_col(), fusi = guess_ab_col(), + gent = guess_ab_col(), imip = guess_ab_col(), + kana = guess_ab_col(), levo = guess_ab_col(), + linc = guess_ab_col(), line = guess_ab_col(), + mero = guess_ab_col(), mezl = guess_ab_col(), + mino = guess_ab_col(), moxi = guess_ab_col(), + nali = guess_ab_col(), neom = guess_ab_col(), + neti = guess_ab_col(), nitr = guess_ab_col(), + norf = guess_ab_col(), novo = guess_ab_col(), + oflo = guess_ab_col(), oxac = guess_ab_col(), + peni = guess_ab_col(), pipe = guess_ab_col(), + pita = guess_ab_col(), poly = guess_ab_col(), + pris = guess_ab_col(), qida = guess_ab_col(), + rifa = guess_ab_col(), roxi = guess_ab_col(), + siso = guess_ab_col(), teic = guess_ab_col(), + tetr = guess_ab_col(), tica = guess_ab_col(), + tige = guess_ab_col(), tobr = guess_ab_col(), + trim = guess_ab_col(), trsu = guess_ab_col(), + vanc = guess_ab_col()) EUCAST_rules(...) @@ -300,7 +318,7 @@Antibiotics
-To define antibiotics column names, leave as it is to determine it automatically with
+guess_abor input a text (case-insensitive) or useNULLto skip a column (e.g.tica = NULL). Non-existing columns will anyway be skipped with a warning.To define antibiotics column names, leave as it is to determine it automatically with
guess_ab_color input a text (case-insensitive) or useNULLto skip a column (e.g.tica = NULL). Non-existing columns will anyway be skipped with a warning.Abbrevations of the column containing antibiotics in the form: abbreviation: generic name (ATC code)
amcl: amoxicillin+clavulanic acid (J01CR02), amik: amikacin (J01GB06), diff --git a/docs/reference/first_isolate.html b/docs/reference/first_isolate.html index 64174a59..679cb3a7 100644 --- a/docs/reference/first_isolate.html +++ b/docs/reference/first_isolate.html @@ -151,6 +151,13 @@ Use the G-test
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/index.html b/docs/reference/index.html
index c298ed40..a85507b4 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -149,6 +149,13 @@
Use the G-test
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Guess antibiotic column
+ +guess_ab_col.Rd
+
+
+
+ This tries to find a column name in a data set based on information from the antibiotics data set. You can look for an antibiotic (trade) of abbreviation and it will search the data for any column containing a name or ATC code of that antibiotic.
guess_ab_col(tbl = NULL, col = NULL, verbose = FALSE)+ +
Arguments
+| tbl | +a |
+
|---|---|
| col | +a character to look for |
+
| verbose | +a logical to indicate whether additional info should be printed |
+
Read more on our website!
+ + +
+On our website https://msberends.gitlab.io/AMR you can find a omprehensive tutorial about how to conduct AMR analysis and find the complete documentation of all functions, which reads a lot easier than in R.
Examples
+# NOT RUN { +df <- data.frame(amox = "S", + tetr = "R") + +guess_ab_col(df, "amoxicillin") +# [1] "amox" +guess_ab_col(df, "J01AA07") # ATC code of Tetracycline +# [1] "tetr" + +guess_ab_col(df, "J01AA07", verbose = TRUE) +# using column `tetr` for col "J01AA07" +# [1] "tetr" +# }+
Background information
-Some pages about our package and its external sources.
+Some pages about our package and its external sources. Be sure to read our How To’s for more information about how to work with functions in this package.
Guess antibiotic column
Other
- +These functions are mostly for internal use, but some of them may also be suitable for your analysis. Especially the ‘like’ function can be useful: if (x %like% y) {...}.
key_antibiotics(tbl, col_mo = NULL, universal_1 = guess_ab(tbl, - "amox"), universal_2 = guess_ab(tbl, "amcl"), - universal_3 = guess_ab(tbl, "cfur"), universal_4 = guess_ab(tbl, - "pita"), universal_5 = guess_ab(tbl, "cipr"), - universal_6 = guess_ab(tbl, "trsu"), GramPos_1 = guess_ab(tbl, - "vanc"), GramPos_2 = guess_ab(tbl, "teic"), GramPos_3 = guess_ab(tbl, - "tetr"), GramPos_4 = guess_ab(tbl, "eryt"), GramPos_5 = guess_ab(tbl, - "oxac"), GramPos_6 = guess_ab(tbl, "rifa"), GramNeg_1 = guess_ab(tbl, - "gent"), GramNeg_2 = guess_ab(tbl, "tobr"), GramNeg_3 = guess_ab(tbl, - "coli"), GramNeg_4 = guess_ab(tbl, "cfot"), GramNeg_5 = guess_ab(tbl, - "cfta"), GramNeg_6 = guess_ab(tbl, "mero"), warnings = TRUE, ...) +key_antibiotics(tbl, col_mo = NULL, universal_1 = guess_ab_col(tbl, + "amox"), universal_2 = guess_ab_col(tbl, "amcl"), + universal_3 = guess_ab_col(tbl, "cfur"), + universal_4 = guess_ab_col(tbl, "pita"), + universal_5 = guess_ab_col(tbl, "cipr"), + universal_6 = guess_ab_col(tbl, "trsu"), + GramPos_1 = guess_ab_col(tbl, "vanc"), GramPos_2 = guess_ab_col(tbl, + "teic"), GramPos_3 = guess_ab_col(tbl, "tetr"), + GramPos_4 = guess_ab_col(tbl, "eryt"), GramPos_5 = guess_ab_col(tbl, + "oxac"), GramPos_6 = guess_ab_col(tbl, "rifa"), + GramNeg_1 = guess_ab_col(tbl, "gent"), GramNeg_2 = guess_ab_col(tbl, + "tobr"), GramNeg_3 = guess_ab_col(tbl, "coli"), + GramNeg_4 = guess_ab_col(tbl, "cfot"), GramNeg_5 = guess_ab_col(tbl, + "cfta"), GramNeg_6 = guess_ab_col(tbl, "mero"), warnings = TRUE, ...) key_antibiotics_equal(x, y, type = c("keyantibiotics", "points"), ignore_I = TRUE, points_threshold = 2, info = FALSE)@@ -248,15 +258,15 @@
column names of broad-spectrum antibiotics, case-insensitive
column names of broad-spectrum antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col.
column names of antibiotics for Gram positives, case-insensitive
column names of antibiotics for Gram positives, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col.
column names of antibiotics for Gram negatives, case-insensitive
column names of antibiotics for Gram negatives, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col.
Details
-The function key_antibiotics returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using key_antibiotics_equal, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot ("."). The first_isolate function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible S. aureus (MSSA) found within the same episode (see episode parameter of first_isolate). Without key antibiotic comparison it wouldn't.
The function key_antibiotics returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using key_antibiotics_equal, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot ("."). The first_isolate function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible S. aureus (MSSA) found within the same episode (see episode parameter of first_isolate). Without key antibiotic comparison it would not.
At default, the antibiotics that are used for Gram positive bacteria are (colum names):
"amox", "amcl", "cfur", "pita", "cipr", "trsu" (until here is universal), "vanc", "teic", "tetr", "eryt", "oxac", "rifa".
At default, the antibiotics that are used for Gram negative bacteria are (colum names):
diff --git a/docs/reference/kurtosis.html b/docs/reference/kurtosis.html
index 17a3c09c..a946dc17 100644
--- a/docs/reference/kurtosis.html
+++ b/docs/reference/kurtosis.html
@@ -151,6 +151,13 @@
Use the G-test
+
mdro(tbl, country = NULL, col_mo = NULL, info = TRUE, - amcl = guess_ab(), amik = guess_ab(), amox = guess_ab(), - ampi = guess_ab(), azit = guess_ab(), aztr = guess_ab(), - cefa = guess_ab(), cfra = guess_ab(), cfep = guess_ab(), - cfot = guess_ab(), cfox = guess_ab(), cfta = guess_ab(), - cftr = guess_ab(), cfur = guess_ab(), chlo = guess_ab(), - cipr = guess_ab(), clar = guess_ab(), clin = guess_ab(), - clox = guess_ab(), coli = guess_ab(), czol = guess_ab(), - dapt = guess_ab(), doxy = guess_ab(), erta = guess_ab(), - eryt = guess_ab(), fosf = guess_ab(), fusi = guess_ab(), - gent = guess_ab(), imip = guess_ab(), kana = guess_ab(), - levo = guess_ab(), linc = guess_ab(), line = guess_ab(), - mero = guess_ab(), metr = guess_ab(), mino = guess_ab(), - moxi = guess_ab(), nali = guess_ab(), neom = guess_ab(), - neti = guess_ab(), nitr = guess_ab(), novo = guess_ab(), - norf = guess_ab(), oflo = guess_ab(), peni = guess_ab(), - pipe = guess_ab(), pita = guess_ab(), poly = guess_ab(), - qida = guess_ab(), rifa = guess_ab(), roxi = guess_ab(), - siso = guess_ab(), teic = guess_ab(), tetr = guess_ab(), - tica = guess_ab(), tige = guess_ab(), tobr = guess_ab(), - trim = guess_ab(), trsu = guess_ab(), vanc = guess_ab()) + amcl = guess_ab_col(), amik = guess_ab_col(), + amox = guess_ab_col(), ampi = guess_ab_col(), + azit = guess_ab_col(), aztr = guess_ab_col(), + cefa = guess_ab_col(), cfra = guess_ab_col(), + cfep = guess_ab_col(), cfot = guess_ab_col(), + cfox = guess_ab_col(), cfta = guess_ab_col(), + cftr = guess_ab_col(), cfur = guess_ab_col(), + chlo = guess_ab_col(), cipr = guess_ab_col(), + clar = guess_ab_col(), clin = guess_ab_col(), + clox = guess_ab_col(), coli = guess_ab_col(), + czol = guess_ab_col(), dapt = guess_ab_col(), + doxy = guess_ab_col(), erta = guess_ab_col(), + eryt = guess_ab_col(), fosf = guess_ab_col(), + fusi = guess_ab_col(), gent = guess_ab_col(), + imip = guess_ab_col(), kana = guess_ab_col(), + levo = guess_ab_col(), linc = guess_ab_col(), + line = guess_ab_col(), mero = guess_ab_col(), + metr = guess_ab_col(), mino = guess_ab_col(), + moxi = guess_ab_col(), nali = guess_ab_col(), + neom = guess_ab_col(), neti = guess_ab_col(), + nitr = guess_ab_col(), novo = guess_ab_col(), + norf = guess_ab_col(), oflo = guess_ab_col(), + peni = guess_ab_col(), pipe = guess_ab_col(), + pita = guess_ab_col(), poly = guess_ab_col(), + qida = guess_ab_col(), rifa = guess_ab_col(), + roxi = guess_ab_col(), siso = guess_ab_col(), + teic = guess_ab_col(), tetr = guess_ab_col(), + tica = guess_ab_col(), tige = guess_ab_col(), + tobr = guess_ab_col(), trim = guess_ab_col(), + trsu = guess_ab_col(), vanc = guess_ab_col()) brmo(..., country = "nl") @@ -524,7 +541,7 @@Antibiotics
-To define antibiotics column names, leave as it is to determine it automatically with
+guess_abor input a text (case-insensitive) or useNULLto skip a column (e.g.tica = NULL). Non-existing columns will anyway be skipped with a warning.To define antibiotics column names, leave as it is to determine it automatically with
guess_ab_color input a text (case-insensitive) or useNULLto skip a column (e.g.tica = NULL). Non-existing columns will anyway be skipped with a warning.Abbrevations of the column containing antibiotics in the form: abbreviation: generic name (ATC code)
amcl: amoxicillin+clavulanic acid (J01CR02), amik: amikacin (J01GB06), diff --git a/docs/reference/microorganisms.certe.html b/docs/reference/microorganisms.certe.html index ef184a02..198fc3e5 100644 --- a/docs/reference/microorganisms.certe.html +++ b/docs/reference/microorganisms.certe.html @@ -151,6 +151,13 @@ Use the G-test