Introduction
@@ -203,21 +203,21 @@As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the tidyverse packages dplyr and ggplot2 by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
Our AMR package depends on these packages and even extends their use and functions.
library(dplyr)
-library(ggplot2)
-library(AMR)
-
-# (if not yet installed, install with:)
-# install.packages(c("tidyverse", "AMR"))library(dplyr)
+library(ggplot2)
+library(AMR)
+
+# (if not yet installed, install with:)
+# install.packages(c("tidyverse", "AMR"))
@@ -247,51 +247,51 @@
Patients
To start with patients, we need a unique list of patients.
- +The LETTERS object is available in R - it’s a vector with 26 characters: A to Z. The patients object we just created is now a vector of length 260, with values (patient IDs) varying from A1 to Z10. Now we we also set the gender of our patients, by putting the ID and the gender in a table:
patients_table <- data.frame(patient_id = patients,
+ gender = c(rep("M", 135),
+ rep("F", 125)))The first 135 patient IDs are now male, the other 125 are female.
Dates
Let’s pretend that our data consists of blood cultures isolates from 1 January 2010 until 1 January 2018.
- +This dates object now contains all days in our date range.
Microorganisms
For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:
-bacteria <- c("Escherichia coli", "Staphylococcus aureus",
- "Streptococcus pneumoniae", "Klebsiella pneumoniae")bacteria <- c("Escherichia coli", "Staphylococcus aureus",
+ "Streptococcus pneumoniae", "Klebsiella pneumoniae")Other variables
For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:
- +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))
- )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:
data <- data %>% left_join(patients_table)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:
head(data)| date | @@ -306,69 +306,69 @@|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012-01-02 | -K9 | +2015-08-07 | +D6 | Hospital A | -Escherichia coli | -I | -S | +Staphylococcus aureus | +R | +R | S | S | M |
| 2011-05-22 | -Y3 | +2016-06-04 | +T3 | Hospital B | -Streptococcus pneumoniae | -S | +Staphylococcus aureus | +R | S | S | S | F | |
| 2015-11-10 | -F1 | +2012-09-25 | +R6 | Hospital C | Staphylococcus aureus | -S | -S | -S | -S | -M | -|||
| 2011-10-10 | -X7 | -Hospital D | -Staphylococcus aureus | R | S | S | S | F | |||||
| 2017-11-10 | -B3 | -Hospital D | +|||||||||||
| 2015-10-07 | +A4 | +Hospital B | Staphylococcus aureus | -R | S | R | +R | S | M | ||||
| 2014-11-09 | -O5 | -Hospital B | +|||||||||||
| 2016-04-12 | +S6 | +Hospital D | Klebsiella pneumoniae | -S | -S | R | S | +S | +S | +F | +|||
| 2010-11-24 | +Z3 | +Hospital C | +Escherichia coli | +R | +S | +S | +S | F | |||||
| 1 | -2010-08-27 | -Q10 | +2010-02-28 | +H3 | B_ESCHR_COL | -R | S | -R | +S | +S | S | TRUE | |
| 2 | -2010-10-24 | -Q10 | +2010-05-15 | +H3 | B_ESCHR_COL | -S | +R | S | S | S | @@ -514,87 +514,87 @@|||
| 3 | -2010-10-26 | -Q10 | +2010-07-09 | +H3 | B_ESCHR_COL | +R | S | S | -S | -S | +R | FALSE | |
| 4 | -2011-12-25 | -Q10 | +2011-01-05 | +H3 | B_ESCHR_COL | +S | +S | R | S | +FALSE | +|||
| 5 | +2011-08-01 | +H3 | +B_ESCHR_COL | +S | +I | S | S | TRUE | |||||
| 5 | -2012-05-04 | -Q10 | -B_ESCHR_COL | -I | -I | -S | -R | -FALSE | -|||||
| 6 | -2012-05-18 | -Q10 | +2011-08-28 | +H3 | B_ESCHR_COL | S | -I | +S | S | S | FALSE | ||
| 7 | -2012-05-23 | -Q10 | +2011-10-28 | +H3 | B_ESCHR_COL | S | -I | +S | S | S | FALSE | ||
| 8 | -2012-06-18 | -Q10 | -B_ESCHR_COL | -R | -R | -S | -R | -FALSE | -|||||
| 9 | -2013-10-02 | -Q10 | +2012-11-10 | +H3 | B_ESCHR_COL | S | S | -R | +S | S | TRUE | ||
| 10 | -2013-10-22 | -Q10 | +|||||||||||
| 9 | +2013-01-14 | +H3 | B_ESCHR_COL | S | +R | +R | +S | +FALSE | +|||||
| 10 | +2013-03-14 | +H3 | +B_ESCHR_COL | +R | S | S | S | @@ -604,16 +604,16 @@
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(.))
-# NOTE: Using column `bacteria` as input for `col_mo`.
-# 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`.
-# 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 4,414 first weighted isolates (88.3% of total)data <- data %>%
+ mutate(keyab = key_antibiotics(.)) %>%
+ mutate(first_weighted = first_isolate(.))
+# NOTE: Using column `bacteria` as input for `col_mo`.
+# 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`.
+# 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 4,388 first weighted isolates (87.8% of total)| isolate | @@ -630,22 +630,22 @@|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-08-27 | -Q10 | +2010-02-28 | +H3 | B_ESCHR_COL | -R | S | -R | +S | +S | S | TRUE | TRUE | ||
| 2 | -2010-10-24 | -Q10 | +2010-05-15 | +H3 | B_ESCHR_COL | -S | +R | S | S | S | @@ -654,59 +654,59 @@|||||
| 3 | -2010-10-26 | -Q10 | -B_ESCHR_COL | -S | -S | -S | -S | -FALSE | -FALSE | -||||||
| 4 | -2011-12-25 | -Q10 | +2010-07-09 | +H3 | B_ESCHR_COL | R | S | S | -S | +R | +FALSE | TRUE | +|||
| 4 | +2011-01-05 | +H3 | +B_ESCHR_COL | +S | +S | +R | +S | +FALSE | TRUE | ||||||
| 5 | -2012-05-04 | -Q10 | +2011-08-01 | +H3 | B_ESCHR_COL | -I | +S | I | S | -R | -FALSE | +S | +TRUE | TRUE | |
| 6 | -2012-05-18 | -Q10 | +2011-08-28 | +H3 | B_ESCHR_COL | S | -I | +S | S | S | FALSE | -TRUE | +FALSE | ||
| 7 | -2012-05-23 | -Q10 | +2011-10-28 | +H3 | B_ESCHR_COL | S | -I | +S | S | S | FALSE | @@ -714,34 +714,34 @@||||
| 8 | -2012-06-18 | -Q10 | +2012-11-10 | +H3 | B_ESCHR_COL | -R | -R | S | -R | -FALSE | +S | +S | +S | +TRUE | TRUE |
| 9 | -2013-10-02 | -Q10 | +2013-01-14 | +H3 | B_ESCHR_COL | S | -S | +R | R | S | -TRUE | +FALSE | TRUE | ||
| 10 | -2013-10-22 | -Q10 | +2013-03-14 | +H3 | B_ESCHR_COL | -S | +R | S | S | S | @@ -750,19 +750,18 @@
Instead of 3, now 8 isolates are flagged. In total, 88.3% of all isolates are marked ‘first weighted’ - 146.8% 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 8 isolates are flagged. In total, 87.8% of all isolates are marked ‘first weighted’ - 146.5% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
As with filter_first_isolate(), there’s a shortcut for this new algorithm too:
So we end up with 4,414 isolates for analysis.
+data_1st <- data %>%
+ filter_first_weighted_isolate()So we end up with 4,388 isolates for analysis.
We can remove unneeded columns:
- +Now our data looks like:
- +head(data_1st)| date | patient_id | hospital | @@ -779,43 +778,40 @@|||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | -2011-05-22 | -Y3 | -Hospital B | -B_STRPTC_PNE | -S | -S | -S | +2015-08-07 | +D6 | +Hospital A | +B_STPHY_AUR | R | -F | +R | +S | +S | +M | Gram positive | -Streptococcus | -pneumoniae | +Staphylococcus | +aureus | TRUE |
| 3 | -2015-11-10 | -F1 | +2016-06-04 | +T3 | +Hospital B | +B_STPHY_AUR | +R | +S | +S | +S | +F | +Gram positive | +Staphylococcus | +aureus | +TRUE | +||||||||
| 2012-09-25 | +R6 | Hospital C | B_STPHY_AUR | -S | -S | -S | -S | -M | -Gram positive | -Staphylococcus | -aureus | -TRUE | -|||||||||||
| 4 | -2011-10-10 | -X7 | -Hospital D | -B_STPHY_AUR | R | S | S | @@ -827,14 +823,13 @@TRUE | |||||||||||||||
| 5 | -2017-11-10 | -B3 | -Hospital D | +2015-10-07 | +A4 | +Hospital B | B_STPHY_AUR | -R | S | R | +R | S | M | Gram positive | @@ -843,14 +838,13 @@TRUE | ||||||||
| 6 | -2014-11-09 | -O5 | -Hospital B | +2016-04-12 | +S6 | +Hospital D | B_KLBSL_PNE | R | S | -R | +S | S | F | Gram negative | @@ -859,16 +853,15 @@TRUE | ||||||||
| 7 | -2013-01-26 | -F4 | -Hospital B | +2010-11-24 | +Z3 | +Hospital C | B_ESCHR_COL | +R | S | S | S | -S | -M | +F | Gram negative | Escherichia | coli | @@ -884,12 +877,12 @@ Analysing the data||||||
| 1 | Escherichia coli | -2,153 | -48.8% | -2,153 | -48.8% | +2,157 | +49.2% | +2,157 | +49.2% | ||||||||||||||
| 2 | Staphylococcus aureus | -1,107 | -25.1% | -3,260 | -73.9% | +1,089 | +24.8% | +3,246 | +74.0% | ||||||||||||||
| 3 | Streptococcus pneumoniae | -677 | -15.3% | -3,937 | -89.2% | +694 | +15.8% | +3,940 | +89.8% | ||||||||||||||
| 4 | Klebsiella pneumoniae | -477 | -10.8% | -4,414 | +448 | +10.2% | +4,388 | 100.0% |
| hospital | @@ -955,27 +948,27 @@ Longest: 24||
|---|---|---|
| Hospital A | -0.4615385 | +0.4517110 |
| Hospital B | -0.4961089 | +0.4460916 |
| Hospital C | -0.4975767 | +0.4834337 |
| Hospital D | -0.4942288 | +0.4854054 |
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),
- available = n_rsi(amox))data_1st %>%
+ group_by(hospital) %>%
+ summarise(amoxicillin = portion_IR(amox),
+ available = n_rsi(amox))| hospital | @@ -985,32 +978,32 @@ Longest: 24||||
|---|---|---|---|---|
| Hospital A | -0.4615385 | -1300 | +0.4517110 | +1315 |
| Hospital B | -0.4961089 | -1542 | +0.4460916 | +1484 |
| Hospital C | -0.4975767 | -619 | +0.4834337 | +664 |
| Hospital D | -0.4942288 | -953 | +0.4854054 | +925 |
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),
- gentamicin = portion_S(gent),
- "amox + gent" = portion_S(amcl, gent))data_1st %>%
+ group_by(genus) %>%
+ summarise(amoxicillin = portion_S(amcl),
+ gentamicin = portion_S(gent),
+ "amox + gent" = portion_S(amcl, gent))| genus | @@ -1021,94 +1014,94 @@ Longest: 24||||||
|---|---|---|---|---|---|---|
| Escherichia | -0.7445425 | -0.9173247 | -0.9804923 | +0.7301808 | +0.9151599 | +0.9772833 |
| Klebsiella | -0.7169811 | -0.9119497 | -0.9769392 | +0.7142857 | +0.9040179 | +0.9888393 |
| Staphylococcus | -0.7705510 | -0.9168925 | -0.9828365 | +0.7557392 | +0.9155188 | +0.9770432 |
| Streptococcus | -0.7474151 | +0.7305476 | 0.0000000 | -0.7474151 | +0.7305476 |
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),
- "2. Gentamicin" = portion_S(gent),
- "3. Amox + gent" = portion_S(amcl, gent)) %>%
- tidyr::gather("Antibiotic", "S", -genus) %>%
- ggplot(aes(x = genus,
- y = S,
- fill = Antibiotic)) +
- geom_col(position = "dodge2")data_1st %>%
+ 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) %>%
+ ggplot(aes(x = genus,
+ y = S,
+ fill = Antibiotic)) +
+ geom_col(position = "dodge2")
Plots
To show results in plots, most R users would nowadays use the ggplot2 package. This package lets you create plots in layers. You can read more about it on their website. A quick example would look like these syntaxes:
ggplot(data = a_data_set,
- mapping = aes(x = year,
- y = value)) +
- geom_col() +
- labs(title = "A title",
- subtitle = "A subtitle",
- x = "My X axis",
- y = "My Y axis")
-
-ggplot(a_data_set,
- aes(year, value) +
- geom_bar()ggplot(data = a_data_set,
+ mapping = aes(x = year,
+ y = value)) +
+ geom_col() +
+ labs(title = "A title",
+ subtitle = "A subtitle",
+ x = "My X axis",
+ y = "My Y axis")
+
+ggplot(a_data_set,
+ aes(year, value) +
+ geom_bar()The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or portion_df() and show results in stacked bars. Its simplest and shortest example:
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)) +
- # create bars with genus on x axis
- # it looks for variables with class `rsi`,
- # of which we have 4 (earlier created with `as.rsi`)
- geom_rsi(x = "genus") +
- # split plots on antibiotic
- facet_rsi(facet = "Antibiotic") +
- # make R red, I yellow and S green
- scale_rsi_colours() +
- # show percentages on y axis
- scale_y_percent(breaks = 0:4 * 25) +
- # turn 90 degrees, make it bars instead of columns
- coord_flip() +
- # add labels
- labs(title = "Resistance per genus and antibiotic",
- subtitle = "(this is fake data)") +
- # and print genus in italic to follow our convention
- # (is now y axis because we turned the plot)
- theme(axis.text.y = element_text(face = "italic"))# group the data on `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`)
+ geom_rsi(x = "genus") +
+ # split plots on antibiotic
+ facet_rsi(facet = "Antibiotic") +
+ # make R red, I yellow and S green
+ scale_rsi_colours() +
+ # show percentages on y axis
+ scale_y_percent(breaks = 0:4 * 25) +
+ # turn 90 degrees, make it bars instead of columns
+ coord_flip() +
+ # add labels
+ labs(title = "Resistance per genus and antibiotic",
+ subtitle = "(this is fake data)") +
+ # and print genus in italic to follow our convention
+ # (is now y axis because we turned the plot)
+ theme(axis.text.y = element_text(face = "italic"))
To simplify this, we also created the ggplot_rsi() function, which combines almost all above functions:
data_1st %>%
- group_by(genus) %>%
- ggplot_rsi(x = "genus",
- facet = "Antibiotic",
- breaks = 0:4 * 25,
- datalabels = FALSE) +
- coord_flip()data_1st %>%
+ group_by(genus) %>%
+ ggplot_rsi(x = "genus",
+ facet = "Antibiotic",
+ breaks = 0:4 * 25,
+ datalabels = FALSE) +
+ coord_flip()
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
- 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
- as.matrix() %>% # transform to good old matrix for fisher.test()
- fisher.test() # do Fisher's Exact Test
-#
-# Fisher's Exact Test for Count Data
-#
-# data: .
-# p-value = 0.03104
-# alternative hypothesis: true odds ratio is not equal to 1
-# 95 percent confidence interval:
-# 1.054283 4.735995
-# sample estimates:
-# odds ratio
-# 2.228006septic_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
+ 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
+ as.matrix() %>% # transform to good old matrix for fisher.test()
+ fisher.test() # do Fisher's Exact Test
+#
+# Fisher's Exact Test for Count Data
+#
+# data: .
+# p-value = 0.03104
+# alternative hypothesis: true odds ratio is not equal to 1
+# 95 percent confidence interval:
+# 1.054283 4.735995
+# sample estimates:
+# odds ratio
+# 2.228006As can be seen, the p value is 0.03, which means that the fosfomycin resistances found in hospital A and D are really different.
- Added 65 antibiotics to the
antibioticsdata set, from the Pharmaceuticals Community Register of the European Commission - Removed columns
atc_group1_nlandatc_group2_nlfrom theantibioticsdata set
- - Function
atc_dddhas been renamedatc_online_ddd()-
- - Function
atc_groupshas been renamedatc_online_groups()-
+ - Functions
atc_ddd()andatc_groups()have been renamedatc_online_ddd()andatc_online_groups(). The old function are deprecated and will be removed in a future version.
+ - Function
guess_mo()is now deprecated in favour ofas.mo()and will be removed in future versions
+ - Function
guess_atc()is now deprecated in favour ofas.atc()and will be removed in future versions - Function
eucast_rules(): - Updated EUCAST Clinical breakpoints to version 9.0 of 1 January 2019
- Fixed a critical bug where some rules that depend on previous applied rules would not be applied adequately
- Emphasised in manual that penicillin is meant as benzylpenicillin (ATC J01CE01) -
- Function
guess_mo()is now deprecated in favour ofas.mo()and will be removed in future versions - Improvements for
as.mo(): - Fix for vector containing only empty values
- Finds better results when input is in other languages diff --git a/docs/reference/AMR-deprecated.html b/docs/reference/AMR-deprecated.html index 52b83fd4..ed19a525 100644 --- a/docs/reference/AMR-deprecated.html +++ b/docs/reference/AMR-deprecated.html @@ -6,7 +6,7 @@ -
guess_atc(x) - -ratio(x, ratio) +ratio(x, ratio) guess_mo(...) +guess_atc(...) + ab_property(...) ab_atc(...) @@ -247,7 +247,11 @@ ab_umcg(...) -ab_tradenames(...)+ab_tradenames(...) + +atc_ddd(...) + +atc_groups(...)
Read more on our website!
diff --git a/docs/reference/index.html b/docs/reference/index.html index 8c6d9bbb..92d234d8 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -521,7 +521,7 @@guess_atc() ratio() guess_mo() ab_property() ab_atc() ab_official() ab_name() ab_trivial_nl() ab_certe() ab_umcg() ab_tradenames()
ratio() guess_mo() guess_atc() ab_property() ab_atc() ab_official() ab_name() ab_trivial_nl() ab_certe() ab_umcg() ab_tradenames() atc_ddd() atc_groups()
Deprecated functions