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 R Markdown. However, the methodology remains unchanged. This page was generated on 30 July 2020.
+
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 R Markdown. However, the methodology remains unchanged. This page was generated on 10 August 2020.
Introduction
@@ -226,21 +226,21 @@
-
2020-07-30
+
2020-08-10
abcd
Escherichia coli
S
S
-
2020-07-30
+
2020-08-10
abcd
Escherichia coli
S
R
-
2020-07-30
+
2020-08-10
efgh
Escherichia coli
R
@@ -253,13 +253,15 @@
Needed R packages
As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the tidyverse packagesdplyr and ggplot2 by RStudio. 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.
We will also use the cleaner package, that can be used for cleaning data and creating frequency tables.
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:
+dates<-seq(as.Date("2010-01-01"), as.Date("2018-01-01"), by = "day")
+
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:
For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:
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.
We also created a package dedicated to data cleaning and checking, called the cleaner package. It freq() function can be used to create frequency tables.
Class: character
Length: 20,000
@@ -432,16 +452,16 @@ Longest: 1
1
M
-
10,312
-
51.56%
-
10,312
-
51.56%
+
10,386
+
51.93%
+
10,386
+
51.93%
2
F
-
9,688
-
48.44%
+
9,614
+
48.07%
20,000
100.00%
@@ -449,23 +469,31 @@ Longest: 1
So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values M and F. From a researchers 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:
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 ampicillin = R when amoxicillin/clavulanic acid = R.
Because the amoxicillin (column AMX) and amoxicillin/clavulanic acid (column AMC) in our data were generated randomly, some rows will undoubtedly contain AMX = S and AMC = R, which is technically impossible. The eucast_rules() fixes this:
(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype). The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded. M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4
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(.))
# 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`.
-
So only 28.2% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient Q3, sorted on date:
+
We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient A7, sorted on date:
isolate
@@ -507,8 +541,8 @@ Longest: 1
1
-
2010-02-06
-
Q3
+
2010-02-03
+
A7
B_ESCHR_COLI
S
S
@@ -518,10 +552,10 @@ Longest: 1
2
-
2010-04-09
-
Q3
+
2010-02-04
+
A7
B_ESCHR_COLI
-
S
+
R
S
S
S
@@ -529,19 +563,30 @@ Longest: 1
3
-
2010-07-03
-
Q3
+
2010-04-05
+
A7
B_ESCHR_COLI
S
S
-
R
+
S
S
FALSE
4
-
2010-07-31
-
Q3
+
2010-06-15
+
A7
+
B_ESCHR_COLI
+
S
+
S
+
S
+
S
+
FALSE
+
+
+
5
+
2010-08-28
+
A7
B_ESCHR_COLI
S
S
@@ -549,10 +594,21 @@ Longest: 1
R
FALSE
+
+
6
+
2010-09-03
+
A7
+
B_ESCHR_COLI
+
S
+
S
+
R
+
S
+
FALSE
+
-
5
-
2010-10-26
-
Q3
+
7
+
2010-11-14
+
A7
B_ESCHR_COLI
S
S
@@ -561,53 +617,31 @@ Longest: 1
FALSE
-
6
-
2010-12-11
-
Q3
+
8
+
2011-02-14
+
A7
B_ESCHR_COLI
-
S
-
S
-
S
-
R
-
FALSE
-
-
-
7
-
2011-03-20
-
Q3
-
B_ESCHR_COLI
-
R
+
I
S
S
S
TRUE
-
-
8
-
2011-04-08
-
Q3
-
B_ESCHR_COLI
-
S
-
S
-
S
-
S
-
FALSE
-
9
-
2011-04-24
-
Q3
+
2011-03-06
+
A7
B_ESCHR_COLI
R
S
-
S
+
R
S
FALSE
10
-
2011-05-08
-
Q3
+
2011-03-09
+
A7
B_ESCHR_COLI
S
S
@@ -619,15 +653,17 @@ Longest: 1
Only 2 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 should 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: more than one result was found for item 1: amoxicillin/clavulanic acid, azidocillin# 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.
+# NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.
+
isolate
@@ -644,8 +680,8 @@ Longest: 1
1
-
2010-02-06
-
Q3
+
2010-02-03
+
A7
B_ESCHR_COLI
S
S
@@ -656,32 +692,44 @@ Longest: 1
2
-
2010-04-09
-
Q3
+
2010-02-04
+
A7
B_ESCHR_COLI
-
S
+
R
S
S
S
FALSE
-
FALSE
+
TRUE
3
-
2010-07-03
-
Q3
+
2010-04-05
+
A7
B_ESCHR_COLI
S
S
-
R
+
S
S
FALSE
TRUE
4
-
2010-07-31
-
Q3
+
2010-06-15
+
A7
+
B_ESCHR_COLI
+
S
+
S
+
S
+
S
+
FALSE
+
FALSE
+
+
+
5
+
2010-08-28
+
A7
B_ESCHR_COLI
S
S
@@ -690,46 +738,22 @@ Longest: 1
FALSE
TRUE
-
-
5
-
2010-10-26
-
Q3
-
B_ESCHR_COLI
-
S
-
S
-
S
-
S
-
FALSE
-
TRUE
-
6
-
2010-12-11
-
Q3
+
2010-09-03
+
A7
B_ESCHR_COLI
S
S
-
S
R
+
S
FALSE
TRUE
7
-
2011-03-20
-
Q3
-
B_ESCHR_COLI
-
R
-
S
-
S
-
S
-
TRUE
-
TRUE
-
-
-
8
-
2011-04-08
-
Q3
+
2010-11-14
+
A7
B_ESCHR_COLI
S
S
@@ -738,22 +762,34 @@ Longest: 1
FALSE
TRUE
+
+
8
+
2011-02-14
+
A7
+
B_ESCHR_COLI
+
I
+
S
+
S
+
S
+
TRUE
+
TRUE
+
9
-
2011-04-24
-
Q3
+
2011-03-06
+
A7
B_ESCHR_COLI
R
S
-
S
+
R
S
FALSE
TRUE
10
-
2011-05-08
-
Q3
+
2011-03-09
+
A7
B_ESCHR_COLI
S
S
@@ -764,16 +800,22 @@ Longest: 1
-
Instead of 2, now 9 isolates are flagged. In total, 78.8% of all isolates are marked ‘first weighted’ - 50.6% 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 9 isolates are flagged. In total, 79.0% 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.
@@ -918,13 +960,17 @@ Longest: 1
Dispersion of species
To just get an idea how the species are distributed, create a frequency table with our freq() function. We created the genus and species column earlier based on the microbial ID. With paste(), we can concatenate them together.
The freq() function can be used like the base R language was intended:
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
# `summarise()` ungrouping output (override with `.groups` argument)
@@ -1113,27 +1167,29 @@ Longest: 24
Hospital A
-
0.5322241
+
0.5349377
Hospital B
-
0.5324628
+
0.5382268
Hospital C
-
0.5388535
+
0.5455691
Hospital D
-
0.5552030
+
0.5325387
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):
# `summarise()` ungrouping output (override with `.groups` argument)
@@ -1144,32 +1200,34 @@ Longest: 24
Hospital A
-
0.5322241
-
4748
+
0.5349377
+
4737
Hospital B
-
0.5324628
-
5514
+
0.5382268
+
5572
Hospital C
-
0.5388535
-
2355
+
0.5455691
+
2381
Hospital D
-
0.5552030
-
3152
+
0.5325387
+
3104
These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
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")
# or as short as:
-ggplot(a_data_set) +
- geom_bar(aes(year))
The AMR package contains functions to extend this ggplot2 package, for example geom_rsi(). It automatically transforms data with count_df() or proportion_df() and show results in stacked bars. Its simplest and shortest example:
Omit the translate_ab = FALSE to have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin/clavulanic acid, 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)) +
+
+# 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") +
+ geom_rsi(x = "genus") +# split plots on antibiotic
- facet_rsi(facet="antibiotic") +
+ facet_rsi(facet = "antibiotic") +# set colours to the R/SI interpretations
- scale_rsi_colours() +
+ scale_rsi_colours() +# show percentages on y axis
- scale_y_percent(breaks=0:4 * 25) +
+ scale_y_percent(breaks = 0:4*25) +# turn 90 degrees, to make it bars instead of columns
- coord_flip() +
+ coord_flip() +# add labels
- labs(title="Resistance per genus and antibiotic",
- subtitle="(this is fake data)") +
+ 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"))
@@ -1278,26 +1346,29 @@ Longest: 24
Independence test
The next example uses the example_isolates data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis.
We will compare the resistance to fosfomycin (column FOS) in hospital A and D. The input for the fisher.test() can be retrieved with a transformation like this:
-
# use package 'tidyr' to pivot data:
-library(tidyr)
+
+# use package 'tidyr' to pivot data:
+library(tidyr)
-check_FOS<-example_isolates%>%
- filter(hospital_id%in%c("A", "D")) %>%# filter on only hospitals A and D
- select(hospital_id, FOS) %>%# select the hospitals and fosfomycin
- group_by(hospital_id) %>%# group on the hospitals
- count_df(combine_SI=TRUE) %>%# count all isolates per group (hospital_id)
- pivot_wider(names_from=hospital_id, # transform output so A and D are columns
- values_from=value) %>%
- select(A, D) %>%# and only select these columns
+check_FOS<-example_isolates%>%
+ filter(hospital_id%in%c("A", "D")) %>%# filter on only hospitals A and D
+ select(hospital_id, FOS) %>%# select the hospitals and fosfomycin
+ group_by(hospital_id) %>%# group on the hospitals
+ count_df(combine_SI = TRUE) %>%# count all isolates per group (hospital_id)
+ pivot_wider(names_from = hospital_id, # transform output so A and D are columns
+ values_from = value) %>%
+ select(A, D) %>%# and only select these columnsas.matrix() # transform to a good old matrix for fisher.test()
-check_FOS
+check_FOS# A D# [1,] 25 77
-# [2,] 24 33
+# [2,] 24 33
+
We can apply the test now with:
-
# do Fisher's Exact Test
-fisher.test(check_FOS)
+
+# do Fisher's Exact Test
+fisher.test(check_FOS)
# # Fisher's Exact Test for Count Data#
@@ -1308,7 +1379,8 @@ Longest: 24
# 0.2111489 0.9485124# sample estimates:# odds ratio
-# 0.4488318
+# 0.4488318
+
As can be seen, the p value is 0.031, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.
These rules can be used to discard impossible bug-drug combinations in your data. For example, Klebsiella produces beta-lactamase that prevents ampicillin (or amoxicillin) from working against it. In other words, practically every strain of Klebsiella is resistant to ampicillin.
Sometimes, laboratory data can still contain such strains with ampicillin being susceptible to ampicillin. This could be because an antibiogram is available before an identification is available, and the antibiogram is then not re-interpreted based on the identification (namely, Klebsiella). EUCAST expert rules solve this, that can be applied using eucast_rules():
+oops<-data.frame(mo = c("Klebsiella",
"Escherichia"),
- ampicillin="S")
-oops
+ ampicillin = "S")
+oops# mo ampicillin# 1 Klebsiella S# 2 Escherichia S
-eucast_rules(oops, info=FALSE)
+eucast_rules(oops, info = FALSE)
# mo ampicillin# 1 Klebsiella R
-# 2 Escherichia S
+# 2 Escherichia S
+
EUCAST rules can not only be used for correction, they can also be used for filling in known resistance and susceptibility based on results of other antimicrobials drugs. This process is called interpretive reading and is part of the eucast_rules() function as well:
# Warning: Not all columns with antimicrobial results are of class <rsi>.
# Transform eligible columns to class <rsi> on beforehand: your_data %>% mutate_if(is.rsi.eligible, as.rsi)
The mdro() function always returns an ordered factor. For example, the output of the default guideline by Magiorakos et al. returns a factor with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.
The next example uses the example_isolates data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:
-
library(dplyr) # to support pipes: %>%
-library(cleaner) # to create frequency tables
+example_isolates%>%
+ mdro() %>%freq() # show frequency table of the result# NOTE: Using column `mo` as input for `col_mo`.# NOTE: Auto-guessing columns suitable for analysis...OK.# NOTE: Reliability would be improved if these antimicrobial results would be available too: ceftaroline (CPT), fusidic acid (FUS), telavancin (TLV), daptomycin (DAP), quinupristin/dalfopristin (QDA), minocycline (MNO), gentamicin-high (GEH), streptomycin-high (STH), doripenem (DOR), levofloxacin (LVX), netilmicin (NET), ticarcillin/clavulanic acid (TCC), ertapenem (ETP), cefotetan (CTT), aztreonam (ATM), ampicillin/sulbactam (SAM), polymyxin B (PLB)# Warning in mdro(.): NA introduced for isolates where the available percentage of
-# antimicrobial classes was below 50% (set with `pct_required_classes`)
+# antimicrobial classes was below 50% (set with `pct_required_classes`)
+
+head(my_TB_data)
# rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-# 1 S S R R S I
-# 2 R S R R R S
-# 3 R S S S I S
-# 4 S S I S R S
-# 5 R I R S R S
-# 6 S S S S R R
+# 1 R R R R I I
+# 2 S S S R S R
+# 3 S I S S S S
+# 4 S I S R R R
+# 5 S S R S S R
+# 6 S R S R R R# kanamycin
-# 1 I
-# 2 I
+# 1 S
+# 2 R# 3 S
-# 4 R
-# 5 R
-# 6 S
+# 4 S
+# 5 S
+# 6 R
+
We can now add the interpretation of MDR-TB to our data set. You can use:
+my_TB_data$mdr<-mdr_tb(my_TB_data)
# NOTE: No column found as input for `col_mo`, assuming all records contain Mycobacterium tuberculosis.# NOTE: Auto-guessing columns suitable for analysis...OK.
-# NOTE: Reliability would be improved if these antimicrobial results would be available too: capreomycin (CAP), rifabutin (RIB), rifapentine (RFP)
+# NOTE: Reliability would be improved if these antimicrobial results would be available too: capreomycin (CAP), rifabutin (RIB), rifapentine (RFP)
+
Now to transform this to a data set with only resistance percentages per taxonomic order and genus:
-
resistance_data<-example_isolates%>%
- group_by(order=mo_order(mo), # group on anything, like order
- genus=mo_genus(mo)) %>%# and genus as we do here
- summarise_if(is.rsi, resistance) %>%# then get resistance of all drugs
- select(order, genus, AMC, CXM, CTX,
- CAZ, GEN, TOB, TMP, SXT) # and select only relevant columns
+
+resistance_data<-example_isolates%>%
+ group_by(order = mo_order(mo), # group on anything, like order
+ genus = mo_genus(mo)) %>%# and genus as we do here
+ summarise_if(is.rsi, resistance) %>%# then get resistance of all drugs
+ select(order, genus, AMC, CXM, CTX,
+ CAZ, GEN, TOB, TMP, SXT) # and select only relevant columns
-head(resistance_data)
+head(resistance_data)
# # A tibble: 6 x 10# # Groups: order [2]# order genus AMC CXM CTX CAZ GEN TOB TMP SXT
@@ -276,35 +279,46 @@
# 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA# 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA# 5 Actinomycetales Micrococcus NA NA NA NA NA NA NA NA
-# 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA
+# 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA
+
Perform principal component analysis
The new pca() function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:
Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the Proportion of Variance. We can create a so-called biplot with the base R biplot() function, to see which antimicrobial resistance per drug explain the difference per microorganism.
But we can’t see the explanation of the points. Perhaps this works better with our new ggplot_pca() function, that automatically adds the right labels and even groups:
+# not all values are valid MIC values:as.mic(0.125)
# Class <mic># [1] 0.125
@@ -253,13 +254,13 @@
# [1] "Gram-negative"# Klebsiella is intrinsic resistant to amoxicllin, according to EUCAST:
-klebsiella_test<-data.frame(mo="klebsiella",
- amox="S",
- stringsAsFactors=FALSE)
-klebsiella_test# (our original data)
+klebsiella_test<-data.frame(mo = "klebsiella",
+ amox = "S",
+ stringsAsFactors = FALSE)
+klebsiella_test# (our original data)# mo amox# 1 klebsiella S
-eucast_rules(klebsiella_test, info=FALSE) # (the edited data by EUCAST rules)
+eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules)# mo amox# 1 klebsiella R
@@ -271,7 +272,8 @@
# [4] "fluclox" "flucloxacilina" "flucloxacillin" # [7] "flucloxacilline" "flucloxacillinum" "fluorochloroxacillin"ab_atc("floxapen")
-# [1] "J01CF05"
+# [1] "J01CF05"
+
@@ -287,7 +289,8 @@
If you want named variables to be imported as factors so it resembles SPSS more, use as_factor().
The difference is this:
-
SPSS_data
+
+SPSS_data# # A tibble: 4,203 x 4# v001 sex status statusage# <dbl> <dbl+lbl> <dbl+lbl> <dbl>
@@ -303,7 +306,7 @@
# 10 10018 0 1 66.6# # … with 4,193 more rows
-as_factor(SPSS_data)
+as_factor(SPSS_data)
# # A tibble: 4,203 x 4# v001 sex status statusage# <dbl> <fct> <fct> <dbl>
@@ -317,67 +320,82 @@
# 8 10011 Male alive 73.1# 9 10017 Male alive 56.7# 10 10018 Female alive 66.6
-# # … with 4,193 more rows
+# # … with 4,193 more rows
+
Base R
To import data from SPSS, SAS or Stata, you can use the great haven package yourself:
-
# download and install the latest version:
+
+# download and install the latest version:install.packages("haven")
# load the package you just installed:
-library(haven)
Do not forget about as_factor(), as mentioned above.
To export your R objects to the SPSS file format:
-
# save as .sav file:
-write_sav(data=yourdata, path="path/to/file")
+
+# save as .sav file:
+write_sav(data = yourdata, path = "path/to/file")
# save as compressed .zsav file:
-write_sav(data=yourdata, path="path/to/file", compress=TRUE)
# save as regular SAS file:
-write_sas(data=yourdata, path="path/to/file")
+
+# save as regular SAS file:
+write_sas(data = yourdata, path = "path/to/file")
# the SAS transport format is an open format # (required for submission of the data to the FDA)
-write_xpt(data=yourdata, path="path/to/file", version=8)
+write_xpt(data = yourdata, path = "path/to/file", version = 8)
+
To export your R objects to the Stata file format:
-
# save as .dta file, Stata version 14:
+
+# save as .dta file, Stata version 14:# (supports Stata v8 until v15 at the time of writing)
-write_dta(data=yourdata, path="/path/to/file", version=14)
+write_dta(data = yourdata, path = "/path/to/file", version = 14)
+
This tutorial assumes you already imported the WHONET data with e.g. the readxl package. In RStudio, this can be done using the menu button ‘Import Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and select your exported file. Make sure date fields are imported correctly.
First, load the relevant packages if you did not yet did this. I use the tidyverse for all of my analyses. All of them. If you don’t know it yet, I suggest you read about it on their website: https://www.tidyverse.org/.
-
library(dplyr) # part of tidyverse
-library(ggplot2) # part of tidyverse
-library(AMR) # this package
-library(cleaner) # to create frequency tables
We will have to transform some variables to simplify and automate the analysis:
Microorganisms should be transformed to our own microorganism IDs (called an mo) using our Catalogue of Life reference data set, which contains all ~70,000 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa. We do the tranformation with as.mo(). This function also recognises almost all WHONET abbreviations of microorganisms.
Antimicrobial results or interpretations have to be clean and valid. In other words, they should only contain values "S", "I" or "R". That is exactly where the as.rsi() function is for.
-
# transform variables
-data<-WHONET%>%
+
+# transform variables
+data<-WHONET%>%# get microbial ID based on given organism
- mutate(mo=as.mo(Organism)) %>%
+ mutate(mo = as.mo(Organism)) %>%# transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class
- mutate_at(vars(AMP_ND10:CIP_EE), as.rsi)
No errors or warnings, so all values are transformed succesfully.
We also created a package dedicated to data cleaning and checking, called the cleaner package. Its freq() function can be used to create frequency tables.
So let’s check our data, with a couple of frequency tables:
-
# our newly created `mo` variable, put in the mo_name() function
-data%>%freq(mo_name(mo), nmax=10)
+
+# our newly created `mo` variable, put in the mo_name() function
+data%>%freq(mo_name(mo), nmax = 10)
+
Frequency table
Class: character
Length: 500
@@ -328,9 +336,11 @@ Longest: 40
(omitted 27 entries, n = 56 [11.20%])
-
# our transformed antibiotic columns
+
+# our transformed antibiotic columns# amoxicillin/clavulanic acid (J01CR02) as an example
-data%>%freq(AMC_ND2)
One of the most important features of this package is the complete microbial taxonomic database, supplied by the Catalogue of Life. We created a function as.mo() that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.
Using the microbenchmark package, we can review the calculation performance of this function. Its function microbenchmark() runs different input expressions independently of each other and measures their time-to-result.
In the next test, we try to ‘coerce’ different input values into the microbial code of Staphylococcus aureus. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.
The actual result is the same every time: it returns its microorganism code B_STPHY_AURS (B stands for Bacteria, the taxonomic kingdom).
In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second.
To achieve this speed, the as.mo function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of Methanosarcina semesiae (B_MTHNSR_SEMS), a bug probably never found before in humans:
Looking up arbitrary codes of less prevalent microorganisms costs the most time. Full names (like Methanosarcina semesiae) are always very fast and only take some thousands of seconds to coerce - they 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 Methanosarcina semesiae (which is uncommon):
@@ -267,102 +273,110 @@
Repetitive results
Repetitive results are unique values that are present more than once. Unique values will only be calculated once by as.mo(). We will use mo_name() for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses as.mo() internally.
-
# take all MO codes from the example_isolates data set
-x<-example_isolates$mo%>%
+
+# take all MO codes from the example_isolates data set
+x<-example_isolates$mo%>%# keep only the unique ones
- unique() %>%
+ unique() %>%# pick 50 of them at random
- sample(50) %>%
+ sample(50) %>%# paste that 10,000 times
- rep(10000) %>%
+ rep(10000) %>%# scramble itsample()
-
+
# got indeed 50 times 10,000 = half a million?
-length(x)
+length(x)
# [1] 500000# and how many unique values do we have?
-n_distinct(x)
+n_distinct(x)
# [1] 50# now let's see:
-run_it<-microbenchmark(mo_name(x),
- times=10)
-print(run_it, unit="ms", signif=3)
+run_it<-microbenchmark(mo_name(x),
+ times = 10)
+print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds# expr min lq mean median uq max neval
-# mo_name(x) 1720 1760 1820 1800 1830 1990 10
-
So transforming 500,000 values (!!) of 50 unique values only takes 1.8 seconds. You only lose time on your unique input values.
+# mo_name(x) 1840 1870 1950 1940 1980 2140 10
+
+
So transforming 500,000 values (!!) of 50 unique values only takes 1.94 seconds. You only lose time on your unique input values.
Precalculated results
What about precalculated results? If the input is an already precalculated result of a helper function like mo_name(), it almost doesn’t take any time at all (see ‘C’ below):
+run_it<-microbenchmark(A = mo_name("B_STPHY_AURS"),
+ B = mo_name("S. aureus"),
+ C = mo_name("Staphylococcus aureus"),
+ times = 10)
+print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds# expr min lq mean median uq max neval
-# A 8.16 8.35 9.06 8.97 9.75 10.20 10
-# B 10.50 10.60 15.50 12.20 12.80 49.90 10
-# C 1.04 1.15 1.21 1.19 1.27 1.53 10
-
So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0012 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 0.0013 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"),
+ D = mo_family("Staphylococcaceae"),
+ E = mo_order("Bacillales"),
+ F = mo_class("Bacilli"),
+ G = mo_phylum("Firmicutes"),
+ H = mo_kingdom("Bacteria"),
+ times = 10)
+print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds# expr min lq mean median uq max neval
-# A 0.948 0.971 1.14 1.020 1.39 1.52 10
-# B 0.968 1.040 1.22 1.190 1.41 1.56 10
-# C 0.979 1.020 1.31 1.260 1.58 1.66 10
-# D 0.964 1.010 1.24 1.190 1.45 1.83 10
-# E 0.977 0.995 1.15 1.030 1.40 1.45 10
-# F 0.878 0.982 1.11 1.010 1.37 1.43 10
-# G 0.929 0.961 1.18 1.000 1.43 1.58 10
-# H 0.901 0.967 1.09 0.998 1.35 1.40 10
+# A 1.020 1.030 1.11 1.060 1.22 1.33 10
+# B 0.982 1.010 1.10 1.040 1.21 1.38 10
+# C 0.992 1.020 1.13 1.040 1.24 1.58 10
+# D 0.987 1.000 1.07 1.030 1.08 1.29 10
+# E 0.978 0.982 1.02 0.999 1.03 1.15 10
+# F 0.975 0.992 1.05 1.000 1.03 1.26 10
+# G 0.976 0.983 1.02 0.994 1.03 1.22 10
+# H 0.977 1.010 1.11 1.090 1.21 1.28 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" anyway, 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.
Results in other languages
When the system language is non-English and supported by this AMR package, some functions will have a translated result. This almost does’t take extra time:
-
mo_name("CoNS", language="en") # or just mo_name("CoNS") on an English system
+
+mo_name("CoNS", language = "en") # or just mo_name("CoNS") on an English system# [1] "Coagulase-negative Staphylococcus (CoNS)"
-mo_name("CoNS", language="es") # or just mo_name("CoNS") on a Spanish system
+mo_name("CoNS", language = "es") # or just mo_name("CoNS") on a Spanish system# [1] "Staphylococcus coagulasa negativo (SCN)"
-mo_name("CoNS", language="nl") # or just mo_name("CoNS") on a Dutch system
+mo_name("CoNS", language = "nl") # or just mo_name("CoNS") on a Dutch system# [1] "Coagulase-negatieve Staphylococcus (CNS)"
-run_it<-microbenchmark(en=mo_name("CoNS", language="en"),
- de=mo_name("CoNS", language="de"),
- nl=mo_name("CoNS", language="nl"),
- es=mo_name("CoNS", language="es"),
- it=mo_name("CoNS", language="it"),
- fr=mo_name("CoNS", language="fr"),
- pt=mo_name("CoNS", language="pt"),
- times=100)
-print(run_it, unit="ms", signif=4)
+run_it<-microbenchmark(en = mo_name("CoNS", language = "en"),
+ de = mo_name("CoNS", language = "de"),
+ nl = mo_name("CoNS", language = "nl"),
+ es = mo_name("CoNS", language = "es"),
+ it = mo_name("CoNS", language = "it"),
+ fr = mo_name("CoNS", language = "fr"),
+ pt = mo_name("CoNS", language = "pt"),
+ times = 100)
+print(run_it, unit = "ms", signif = 4)
# Unit: milliseconds
-# expr min lq mean median uq max neval
-# en 12.09 12.46 15.90 13.86 14.55 57.62 100
-# de 12.92 13.26 19.73 14.63 16.01 61.55 100
-# nl 16.53 17.00 20.26 17.64 19.93 57.54 100
-# es 12.98 13.28 18.27 14.76 15.64 179.30 100
-# it 12.92 13.15 19.20 14.08 16.08 64.07 100
-# fr 12.99 13.21 17.81 13.59 15.71 67.97 100
-# pt 13.00 13.23 17.30 14.35 15.65 69.85 100
+# expr min lq mean median uq max neval
+# en 12.40 14.34 17.88 14.89 15.48 55.22 100
+# de 13.17 14.30 17.90 15.84 16.66 56.60 100
+# nl 17.14 19.86 24.99 20.78 21.70 64.66 100
+# es 13.43 15.29 17.65 15.93 16.59 54.38 100
+# it 13.33 14.83 18.35 15.68 16.36 57.61 100
+# fr 13.40 15.43 18.66 16.01 16.59 54.35 100
+# pt 13.47 15.33 18.93 16.15 16.84 57.28 100
+
Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the tidyverse packagesdplyr 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.
Our package contains a function resistance_predict(), which takes the same input as functions for other AMR analysis. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
It is basically as easy as:
-
# resistance prediction of piperacillin/tazobactam (TZP):
-resistance_predict(tbl = example_isolates, col_date = "date", col_ab = "TZP", model = "binomial")
-
-# or:
-example_isolates %>%
- resistance_predict(col_ab = "TZP",
- model "binomial")
-
-# to bind it to object 'predict_TZP' for example:
-predict_TZP <- example_isolates %>%
- resistance_predict(col_ab = "TZP",
- model = "binomial")
+
# resistance prediction of piperacillin/tazobactam (TZP):
+resistance_predict(tbl = example_isolates, col_date ="date", col_ab ="TZP", model ="binomial")
+
+# or:
+example_isolates %>%
+resistance_predict(col_ab ="TZP",
+ model "binomial")
+
+# to bind it to object 'predict_TZP' for example:
+predict_TZP <-example_isolates %>%
+resistance_predict(col_ab ="TZP",
+model ="binomial")
The function will look for a date column itself if col_date is not set.
# NOTE: Using column `date` as input for `col_date`.
This text is only a printed summary - the actual result (output) of the function is a data.frame containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:
-
predict_TZP
+
+predict_TZP# year value se_min se_max observations observed estimated# 1 2002 0.20000000 NA NA 15 0.20000000 0.05616378# 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839
@@ -258,27 +261,36 @@ predict_TZP <- example_isolates %>%
# 26 2027 0.41315710 0.3244399 0.5018743 NA NA 0.41315710# 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688# 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755
-# 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359
+# 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359
+
The function plot is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:
This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.
We also support the ggplot2 package with our custom function ggplot_rsi_predict() to create more appealing plots:
+example_isolates%>%
+ filter(mo_gramstain(mo, language = NULL) =="Gram-positive") %>%
+ resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "binomial") %>%ggplot_rsi_predict()
-# NOTE: Using column `date` as input for `col_date`.
+# NOTE: Using column `date` as input for `col_date`.
+
Vancomycin resistance could be 100% in ten years, but might also stay around 0%.
You can define the model with the model parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.
For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:
July 2020 PLEASE TAKE PART IN OUR SURVEY!
-Since you are one of our users, we would like to know how you use the package and what it brought you or your organisation. If you have a minute, please anonymously fill in this short questionnaire. Your valuable input will help to improve the package and its functionalities. You can answer the open questions in either English, Spanish, French, Dutch, or German. Thank you very much in advance! Take me to the 5-min survey!
+Since you are one of our users, we would like to know how you use the package and what it brought you or your organisation. If you have a minute, please anonymously fill in this short questionnaire. Your valuable input will help to improve the package and its functionalities. You can answer the open questions in either English, Spanish, French, Dutch, or German. Thank you very much in advance! Take me to the 5-min survey!
@@ -246,7 +246,9 @@ Since you are one of our users, we would like to know how you use the package an
Latest released version
This package is available here on the official R network (CRAN), which has a peer-reviewed submission process. Install this package in R from CRAN by using the command:
It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install.
Note: Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version.
@@ -254,8 +256,10 @@ Since you are one of our users, we would like to know how you use the package an
Latest development version
The latest and unpublished development version can be installed from GitHub using:
Support for using dplyr’s across() in as.rsi() to interpret MIC values or disk zone diameters, that now also automatically determines the column with microorganism names or codes.
-
# until dplyr 1.0.0
-your_data%>%mutate_if(is.mic, as.rsi)
-your_data%>%mutate_if(is.disk, as.rsi)
+
+# until dplyr 1.0.0
+your_data%>%mutate_if(is.mic, as.rsi)
+your_data%>%mutate_if(is.disk, as.rsi)
# since dplyr 1.0.0
- your_data%>%mutate(across(where(is.mic), as.rsi))
-your_data%>%mutate(across(where(is.disk), as.rsi))
Function ab_from_text() to retrieve antimicrobial drug names, doses and forms of administration from clinical texts in e.g. health care records, which also corrects for misspelling since it uses as.ab() internally
Tidyverse selection helpers for antibiotic classes, that help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations. They can be used in any function that allows selection helpers, like dplyr::select() and tidyr::pivot_longer():
+library(dplyr)
# Columns 'IPM' and 'MEM' are in the example_isolates data set
-example_isolates%>%
+example_isolates%>%select(carbapenems())
-#> Selecting carbapenems: `IPM` (imipenem), `MEM` (meropenem)
Added function filter_penicillins() to filter isolates on a specific result in any column with a name in the antimicrobial ‘penicillins’ class (more specific: ATC subgroup Beta-lactam antibacterials, penicillins)
@@ -294,7 +298,7 @@
95% speed improvement by using other base R functions for calculation
Using unexisting columns wil now return an error instead of dropping them silently
-
Using variables for column names (as well as selectors like dplyr::all_of()) now works again
+
Using variables for column names (as well as selectors like dplyr::all_of()) now works again
@@ -349,7 +353,7 @@
Making this package independent of especially the tidyverse (e.g. packages dplyr and tidyr) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Good for users, but hard for package maintainers. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. Another upside it that this package can now be used with all versions of R since R-3.0.0 (April 2013). Our package is being used in settings where the resources are very limited. Fewer dependencies on newer software is helpful for such settings.
Negative effects of this change are:
-
Function freq() that was borrowed from the cleaner package was removed. Use cleaner::freq(), or run library("cleaner") before you use freq().
Printing values of class mo or rsi in a tibble will no longer be in colour and printing rsi in a tibble will show the class <ord>, not <rsi> anymore. This is purely a visual effect.
All functions from the mo_* family (like mo_name() and mo_gramstain()) are noticeably slower when running on hundreds of thousands of rows.
For developers: classes mo and ab now both also inherit class character, to support any data transformation. This change invalidates code that checks for class length == 1.
@@ -453,11 +457,13 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Fixed important floating point error for some MIC comparisons in EUCAST 2020 guideline
Interpretation from MIC values (and disk zones) to R/SI can now be used with mutate_at() of the dplyr package:
+yourdata%>%
+ mutate_at(vars(antibiotic1:antibiotic25), as.rsi, mo = .$mybacteria)
+
Added antibiotic abbreviations for a laboratory manufacturer (GLIMS) for cefuroxime, cefotaxime, ceftazidime, cefepime, cefoxitin and trimethoprim/sulfamethoxazole
Added uti (as abbreviation of urinary tract infections) as parameter to as.rsi(), so interpretation of MIC values and disk zones can be made dependent on isolates specifically from UTIs
@@ -480,21 +486,25 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Support for LOINC codes in the antibiotics data set. Use ab_loinc() to retrieve LOINC codes, or use a LOINC code for input in any ab_* function:
Support for SNOMED CT codes in the microorganisms data set. Use mo_snomed() to retrieve SNOMED codes, or use a SNOMED code for input in any mo_* function:
Support for a new MDRO guideline: Magiorakos AP, Srinivasan A et al. “Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.” Clinical Microbiology and Infection (2012).
@@ -593,7 +609,8 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
More intelligent way of coping with some consonants like “l” and “r”
+as.mo(c("Stafylococcus aureus",
"staphylokok aureuz"))
#> Warning: #> Results of two values were guessed with uncertainty. Use mo_uncertainties() to review them.
@@ -602,7 +619,8 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
mo_uncertainties()
#> "Stafylococcus aureus" -> Staphylococcus aureus (B_STPHY_AURS, score: 95.2%)
-#> "staphylokok aureuz" -> Staphylococcus aureus (B_STPHY_AURS, score: 85.7%)
@@ -650,25 +668,29 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Determination of first isolates now excludes all ‘unknown’ microorganisms at default, i.e. microbial code "UNKNOWN". They can be included with the new parameter include_unknown:
For WHONET users, this means that all records/isolates with organism code "con" (contamination) will be excluded at default, since as.mo("con") = "UNKNOWN". The function always shows a note with the number of ‘unknown’ microorganisms that were included or excluded.
For code consistency, classes ab and mo will now be preserved in any subsetting or assignment. For the sake of data integrity, this means that invalid assignments will now result in NA:
-
# how it works in base R:
-x<-factor("A")
-x[1] <-"B"
+
+# how it works in base R:
+x<-factor("A")
+x[1] <-"B"#> Warning message:#> invalid factor level, NA generated# how it now works similarly for classes 'mo' and 'ab':
-x<-as.mo("E. coli")
-x[1] <-"testvalue"
+x<-as.mo("E. coli")
+x[1] <-"testvalue"#> Warning message:
-#> invalid microorganism code, NA generated
+#> invalid microorganism code, NA generated
+
This is important, because a value like "testvalue" could never be understood by e.g. mo_name(), although the class would suggest a valid microbial code.
-
Function freq() has moved to a new package, clean (CRAN link), since creating frequency tables actually does not fit the scope of this package. The freq() function still works, since it is re-exported from the clean package (which will be installed automatically upon updating this AMR package).
+
Function freq() has moved to a new package, clean (CRAN link), since creating frequency tables actually does not fit the scope of this package. The freq() function still works, since it is re-exported from the clean package (which will be installed automatically upon updating this AMR package).
Renamed data set septic_patients to example_isolates
@@ -678,9 +700,10 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Function bug_drug_combinations() to quickly get a data.frame with the results of all bug-drug combinations in a data set. The column containing microorganism codes is guessed automatically and its input is transformed with mo_shortname() at default:
+x<-bug_drug_combinations(example_isolates)
#> NOTE: Using column `mo` as input for `col_mo`.
-x[1:4, ]
+x[1:4, ]
#> mo ab S I R total#> 1 A. baumannii AMC 0 0 3 3#> 2 A. baumannii AMK 0 0 0 0
@@ -689,45 +712,52 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
#> NOTE: Use 'format()' on this result to get a publicable/printable format.# change the transformation with the FUN argument to anything you like:
-x<-bug_drug_combinations(example_isolates, FUN=mo_gramstain)
+x<-bug_drug_combinations(example_isolates, FUN = mo_gramstain)
#> NOTE: Using column `mo` as input for `col_mo`.
-x[1:4, ]
+x[1:4, ]
#> mo ab S I R total#> 1 Gram-negative AMC 469 89 174 732#> 2 Gram-negative AMK 251 0 2 253#> 3 Gram-negative AMP 227 0 405 632#> 4 Gram-negative AMX 227 0 405 632
-#> NOTE: Use 'format()' on this result to get a publicable/printable format.
+#> NOTE: Use 'format()' on this result to get a publicable/printable format.
+
You can format this to a printable format, ready for reporting or exporting to e.g. Excel with the base R format() function:
Additional way to calculate co-resistance, i.e. when using multiple antimicrobials as input for portion_* functions or count_* functions. This can be used to determine the empiric susceptibility of a combination therapy. A new parameter only_all_tested (which defaults to FALSE) replaces the old also_single_tested and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the portion and count help pages), where the %SI is being determined:
-
# --------------------------------------------------------------------
-# only_all_tested = FALSE only_all_tested = TRUE
-# ----------------------- -----------------------
-# Drug A Drug B include as include as include as include as
-# numerator denominator numerator denominator
-# -------- -------- ---------- ----------- ---------- -----------
-# S or I S or I X X X X
-# R S or I X X X X
-# <NA> S or I X X - -
-# S or I R X X X X
-# R R - X - X
-# <NA> R - - - -
-# S or I <NA> X X - -
-# R <NA> - - - -
-# <NA> <NA> - - - -
-# --------------------------------------------------------------------
+
+# --------------------------------------------------------------------
+# only_all_tested = FALSE only_all_tested = TRUE
+# ----------------------- -----------------------
+# Drug A Drug B include as include as include as include as
+# numerator denominator numerator denominator
+# -------- -------- ---------- ----------- ---------- -----------
+# S or I S or I X X X X
+# R S or I X X X X
+# <NA> S or I X X - -
+# S or I R X X X X
+# R R - X - X
+# <NA> R - - - -
+# S or I <NA> X X - -
+# R <NA> - - - -
+# <NA> <NA> - - - -
+# --------------------------------------------------------------------
+
Since this is a major change, usage of the old also_single_tested will throw an informative error that it has been replaced by only_all_tested.
tibble printing support for classes rsi, mic, disk, abmo. When using tibbles containing antimicrobial 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)
-library(dplyr)
-example_isolates%>%
- select(mo:AMC) %>%
- as_tibble()
+
+# (run this on your own console, as this page does not support colour printing)
+library(dplyr)
+example_isolates%>%
+ select(mo:AMC) %>%
+ as_tibble()
+
@@ -804,14 +834,16 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
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:
Function mo_info() as an analogy to ab_info(). The mo_info() prints a list with the full taxonomy, authors, and the URL to the online database of a microorganism
Function mo_synonyms() to get all previously accepted taxonomic names of a microorganism
@@ -862,7 +896,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Fixed bug where not all old taxonomic names would be printed, when using a vector as input for as.mo()
Manually added Trichomonas vaginalis from the kingdom of Protozoa, which is missing from the Catalogue of Life
-
Small improvements to plot() and barplot() for MIC and RSI classes
+
Small improvements to plot() and barplot() for MIC and RSI classes
Allow Catalogue of Life IDs to be coerced by as.mo()
@@ -925,21 +959,23 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
The age() function gained a new parameter exact to determine ages with decimals
The antibiotics data set will be searched, after which the input data will be checked for column names with a value in any abbreviations, codes or official names found in the antibiotics data set. For example:
+septic_patients%>%filter_glycopeptides(result = "R")
# Filtering on glycopeptide antibacterials: any of `vanc` or `teic` is R
-septic_patients%>%filter_glycopeptides(result="R", scope="all")
-# Filtering on glycopeptide antibacterials: all of `vanc` and `teic` is R
+septic_patients%>%filter_glycopeptides(result = "R", scope = "all")
+# Filtering on glycopeptide antibacterials: all of `vanc` and `teic` is R
+
All ab_* functions are deprecated and replaced by atc_* functions:
These functions use as.atc() internally. The old atc_property has been renamed atc_online_property(). This is done for two reasons: firstly, not all ATC codes are of antibiotics (ab) but can also be of antivirals or antifungals. Secondly, the input must have class atc or must be coerable to this class. Properties of these classes should start with the same class name, analogous to as.mo() and e.g. mo_genus.
These functions use as.atc() internally. The old atc_property has been renamed atc_online_property(). This is done for two reasons: firstly, not all ATC codes are of antibiotics (ab) but can also be of antivirals or antifungals. Secondly, the input must have class atc or must be coerable to this class. Properties of these classes should start with the same class name, analogous to as.mo() and e.g. mo_genus.
@@ -1062,21 +1104,27 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
New function age() to calculate the (patients) age in years
New function age_groups() to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
New function availability() to check the number of available (non-empty) results in a data.frame
New vignettes about how to conduct AMR analysis, predict antimicrobial resistance, use the G-test and more. These are also available (and even easier readable) on our website: https://msberends.gitlab.io/AMR.
@@ -1097,40 +1145,46 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Removed data sets microorganisms.oldDT, microorganisms.prevDT, microorganisms.unprevDT and microorganismsDT since they were no longer needed and only contained info already available in the microorganisms data set
Removed columns atc_group1_nl and atc_group2_nl from the antibiotics data set
-
Functions atc_ddd() and atc_groups() have been renamed atc_online_ddd() and atc_online_groups(). The old functions are deprecated and will be removed in a future version.
+
Functions atc_ddd() and atc_groups() have been renamed atc_online_ddd() and atc_online_groups(). The old functions are deprecated and will be removed in a future version.
Function guess_mo() is now deprecated in favour of as.mo() and will be removed in future versions
Function guess_atc() is now deprecated in favour of as.atc() and will be removed in future versions
Uncertainty of the algorithm is now divided into four levels, 0 to 3, where the default allow_uncertain = TRUE is equal to uncertainty level 2. Run ?as.mo for more info about these levels.
Implemented the latest publication of Becker et al. (2019), for categorising coagulase-negative Staphylococci
All microbial IDs that found are now saved to a local file ~/.Rhistory_mo. Use the new function clean_mo_history() to delete this file, which resets the algorithms.
Incoercible results will now be considered ‘unknown’, MO code UNKNOWN. On foreign systems, properties of these will be translated to all languages already previously supported: German, Dutch, French, Italian, Spanish and Portuguese:
+mo_genus("qwerty", language = "es")
# Warning: # one unique value (^= 100.0%) could not be coerced and is considered 'unknown': "qwerty". Use mo_failures() to review it.
-#> [1] "(género desconocido)"
+#> [1] "(género desconocido)"
+
Fix for vector containing only empty values
Finds better results when input is in other languages
@@ -1171,23 +1225,25 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Added parameter combine_IR (TRUE/FALSE) to functions portion_df and count_df, to indicate that all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
Fix for portion_*(..., as_percent = TRUE) when minimal number of isolates would not be met
@@ -1268,19 +1326,23 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Using portion_* functions now throws a warning when total available isolate is below parameter minimum
Functions as.mo, as.rsi, as.mic, as.atc and freq will not set package name as attribute anymore
+mo_gramstain("Esc blattae")
# Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)
-# [1] "Gram negative"
+# [1] "Gram negative"
+
Functions count_R, count_IR, count_I, count_SI and count_S to selectively count resistant or susceptible isolates
@@ -1378,18 +1444,22 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Function is.rsi.eligible to check for columns that have valid antimicrobial results, but do not have the rsi class yet. Transform the columns of your raw data with: data %>% mutate_if(is.rsi.eligible, as.rsi)
Functions as.mo and is.mo as replacements for as.bactid and is.bactid (since the microoganisms data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The as.mo function determines microbial IDs using intelligent rules:
+thousands_of_E_colis<-rep("E. coli", 25000)
+microbenchmark::microbenchmark(as.mo(thousands_of_E_colis), unit = "s")
# Unit: seconds# min median max neval
-# 0.01817717 0.01843957 0.03878077 100
+# 0.01817717 0.01843957 0.03878077 100
+
Added parameter reference_df for as.mo, so users can supply their own microbial IDs, name or codes as a reference table
@@ -1417,12 +1487,14 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Added three antimicrobial agents to the antibiotics data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)
Added 163 trade names to the antibiotics data set, it now contains 298 different trade names in total, e.g.:
+septic_patients%>%select(amox, cipr) %>%count_IR()
# which is the same as:
-septic_patients%>%count_IR(amox, cipr)
+septic_patients%>%count_IR(amox, cipr)
-septic_patients%>%portion_S(amcl)
-septic_patients%>%portion_S(amcl, gent)
-septic_patients%>%portion_S(amcl, gent, pita)
These functions are so-called 'Deprecated'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The data itself was based on our example_isolates data set.
Use these functions to return a specific property of an antibiotic from the antibiotics data set. All input values will be evaluated internally with as.ab().
+ab_property(x, property = "name", language = get_locale(), ...)
Arguments
@@ -348,15 +348,15 @@ The lifecycle of this function is stableab_url("AMX") # link to the official WHO page# smart lowercase tranformation
-ab_name(x=c("AMC", "PLB")) # "Amoxicillin/clavulanic acid" "Polymyxin B"
-ab_name(x=c("AMC", "PLB"),
- tolower=TRUE) # "amoxicillin/clavulanic acid" "polymyxin B"
+ab_name(x = c("AMC", "PLB")) # "Amoxicillin/clavulanic acid" "Polymyxin B"
+ab_name(x = c("AMC", "PLB"),
+ tolower = TRUE) # "amoxicillin/clavulanic acid" "polymyxin B"# defined daily doses (DDD)ab_ddd("AMX", "oral") # 1
-ab_ddd("AMX", "oral", units=TRUE) # "g"
+ab_ddd("AMX", "oral", units = TRUE) # "g"ab_ddd("AMX", "iv") # 1
-ab_ddd("AMX", "iv", units=TRUE) # "g"
+ab_ddd("AMX", "iv", units = TRUE) # "g"ab_info("AMX") # all properties as a list
@@ -388,7 +388,7 @@ The lifecycle of this function is stable
Use these selection helpers inside any function that allows Tidyverse selections, like dplyr::select() or tidyr::pivot_longer(). They help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations.
Two data sets containing all antibiotics/antimycotics and antivirals. Use as.ab() or one of the ab_property() functions to retrieve values from the antibiotics data set. Three identifiers are included in this data set: an antibiotic ID (ab, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (atc) as defined by the WHO, and a Compound ID (cid) as found in PubChem. Other properties in this data set are derived from one or more of these codes.
-
antibiotics
+
antibiotics
-antivirals
+antivirals
Format
@@ -334,7 +334,7 @@ This package contains all ~550 antibiotic, antimycotic and antiviral dru
Use this function to determine the antibiotic code of one or more antibiotics. The data set antibiotics will be searched for abbreviations, official names and synonyms (brand names).
This transforms a vector to a new class mic, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as NA with a warning.
-
as.mic(x, na.rm=FALSE)
+
as.mic(x, na.rm = FALSE)
-is.mic(x)
+is.mic(x)
Arguments
@@ -275,24 +275,24 @@ The lifecycle of this function is stable
as.mo(
- x,
- Becker=FALSE,
- Lancefield=FALSE,
- allow_uncertain=TRUE,
- reference_df=get_mo_source(),
- ...
+ x,
+ Becker = FALSE,
+ Lancefield = FALSE,
+ allow_uncertain = TRUE,
+ reference_df = get_mo_source(),
+ ...
)
-is.mo(x)
+is.mo(x)
mo_failures()
@@ -299,10 +299,10 @@
B_KLBSL_PNMN_RHNS Klebsiella pneumoniae rhinoscleromatis
| | | |
| | | |
- | | | ---> subspecies, a 4-5 letter acronym
- | | ----> species, a 4-5 letter acronym
- | ----> genus, a 5-7 letter acronym
- ----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),
+ | | | ---> subspecies, a 4-5 letter acronym
+ | | ----> species, a 4-5 letter acronym
+ | ----> genus, a 5-7 letter acronym
+ ----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),
C (Chromista), F (Fungi), P (Protozoa)
@@ -412,32 +412,32 @@ This package contains the complete taxonomic tree of almost all microorganisms (
as.mo("GBS") # Group B Streptococcias.mo("S. epidermidis") # will remain species: B_STPHY_EPDR
-as.mo("S. epidermidis", Becker=TRUE) # will not remain species: B_STPHY_CONS
+as.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CONSas.mo("S. pyogenes") # will remain species: B_STRPT_PYGN
-as.mo("S. pyogenes", Lancefield=TRUE) # will not remain species: B_STRPT_GRPA
+as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA# All mo_* functions use as.mo() internally too (see ?mo_property):mo_genus("E. coli") # returns "Escherichia"mo_gramstain("E. coli") # returns "Gram negative"# }
-if (FALSE) {
-df$mo<-as.mo(df$microorganism_name)
+if (FALSE) {
+df$mo<-as.mo(df$microorganism_name)
# the select function of the Tidyverse is also supported:
-library(dplyr)
-df$mo<-df%>%
- select(microorganism_name) %>%
+library(dplyr)
+df$mo<-df%>%
+ select(microorganism_name) %>%as.mo()
# and can even contain 2 columns, which is convenient for genus/species combinations:
-df$mo<-df%>%
- select(genus, species) %>%
+df$mo<-df%>%
+ select(genus, species) %>%as.mo()
# although this works easier and does the same:
-df<-df%>%
- mutate(mo=as.mo(paste(genus, species)))
+df<-df%>%
+ mutate(mo = as.mo(paste(genus, species)))
}
@@ -454,7 +454,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
Interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing R/SI values. This transforms the input to a new class rsi, which is an ordered factor with levels S < I < R. Values that cannot be interpreted will be returned as NA with a warning.
For cleaning raw / untransformed data. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with R/SI interpretations and MIC values such as "<0.25; S" will be coerced to "S". Combined interpretations for multiple test methods (as seen in laboratory records) such as "S; S" will be coerced to "S", but a value like "S; I" will return NA with a warning that the input is unclear.
For interpreting minimum inhibitory concentration (MIC) values according to EUCAST or CLSI. You must clean your MIC values first using as.mic(), that also gives your columns the new data class mic. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the mo parameter.
-
Using dplyr, R/SI interpretation can be done very easily with either:
your_data%>%mutate_if(is.mic, as.rsi) # until dplyr 1.0.0
-your_data%>%mutate(across(where(is.mic), as.rsi)) # since dplyr 1.0.0
+
Using dplyr, R/SI interpretation can be done very easily with either:
your_data%>%mutate_if(is.mic, as.rsi) # until dplyr 1.0.0
+your_data%>%mutate(across(where(is.mic), as.rsi)) # since dplyr 1.0.0
Operators like "<=" will be stripped before interpretation. When using conserve_capped_values = TRUE, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (conserve_capped_values = FALSE) considers ">2" to be lower than ">=4" and might in this case return "S" or "I".
For interpreting disk diffusion diameters according to EUCAST or CLSI. You must clean your disk zones first using as.disk(), that also gives your columns the new data class disk. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the mo parameter.
-
Using dplyr, R/SI interpretation can be done very easily with either:
your_data%>%mutate_if(is.disk, as.rsi) # until dplyr 1.0.0
-your_data%>%mutate(across(where(is.disk), as.rsi)) # since dplyr 1.0.0
+
Using dplyr, R/SI interpretation can be done very easily with either:
your_data%>%mutate_if(is.disk, as.rsi) # until dplyr 1.0.0
+your_data%>%mutate(across(where(is.disk), as.rsi)) # since dplyr 1.0.0
For interpreting a complete data set, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running as.rsi(data).
@@ -389,53 +389,54 @@ The lifecycle of this function is stable
diff --git a/docs/reference/atc_online.html b/docs/reference/atc_online.html
index 90c4476e4..50e0ee685 100644
--- a/docs/reference/atc_online.html
+++ b/docs/reference/atc_online.html
@@ -83,7 +83,7 @@ This function requires an internet connection." />
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -238,15 +238,15 @@ This function requires an internet connection." />
if (FALSE) {
# oral DDD (Defined Daily Dose) of amoxicillinatc_online_property("J01CA04", "DDD", "O")
# parenteral DDD (Defined Daily Dose) of amoxicillinatc_online_property("J01CA04", "DDD", "P")
-atc_online_property("J01CA04", property="groups") # search hierarchical groups of amoxicillin
+atc_online_property("J01CA04", property = "groups") # search hierarchical groups of amoxicillin# [1] "ANTIINFECTIVES FOR SYSTEMIC USE"# [2] "ANTIBACTERIALS FOR SYSTEMIC USE"# [3] "BETA-LACTAM ANTIBACTERIALS, PENICILLINS"
@@ -342,7 +342,7 @@ The lifecycle of this function is questioni
Easy check for data availability of all columns in a data set. This makes it easy to get an idea of which antimicrobial combinations can be used for calculation with e.g. susceptibility() and resistance().
Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use format() on the result to prettify it to a publicable/printable format, see Examples.
diff --git a/docs/reference/catalogue_of_life.html b/docs/reference/catalogue_of_life.html
index e9b31fd7e..9894975b1 100644
--- a/docs/reference/catalogue_of_life.html
+++ b/docs/reference/catalogue_of_life.html
@@ -82,7 +82,7 @@
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -313,7 +313,7 @@ Function as.mo() to use the data for intel
diff --git a/docs/reference/catalogue_of_life_version.html b/docs/reference/catalogue_of_life_version.html
index 4c9c8cd10..9dd4d2aa3 100644
--- a/docs/reference/catalogue_of_life_version.html
+++ b/docs/reference/catalogue_of_life_version.html
@@ -82,7 +82,7 @@
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -275,7 +275,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/count.html b/docs/reference/count.html
index d247b241e..fe2a01331 100644
--- a/docs/reference/count.html
+++ b/docs/reference/count.html
@@ -83,7 +83,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -233,34 +233,34 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
-
These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in summarise() from the dplyr package and also support grouped variables, please see Examples.
+
These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in summarise() from the dplyr package and also support grouped variables, please see Examples.
count_resistant() should be used to count resistant isolates, count_susceptible() should be used to count susceptible isolates.
@@ -338,22 +338,21 @@ A microorganism is categorised as Susceptible, Increased exposure when
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
- <NA> S or I X X - -
+ <NA> S or I X X - -
S or I R X X X X
R R - X - X
- <NA> R - - - -
- S or I <NA> X X - -
- R <NA> - - - -
- <NA> <NA> - - - -
+ <NA> R - - - -
+ S or I <NA> X X - -
+ R <NA> - - - -
+ <NA> <NA> - - - -
--------------------------------------------------------------------
-
Please note that, in combination therapies, for only_all_tested = TRUE applies that:
Using only_all_tested has no impact when only using one antibiotic as input.
Read more on our website!
@@ -367,63 +366,63 @@ A microorganism is categorised as Susceptible, Increased exposure when
Examples
# example_isolates is a data set available in the AMR package.
-?example_isolates
+?example_isolates
-count_resistant(example_isolates$AMX) # counts "R"
-count_susceptible(example_isolates$AMX) # counts "S" and "I"
-count_all(example_isolates$AMX) # counts "S", "I" and "R"
+count_resistant(example_isolates$AMX) # counts "R"
+count_susceptible(example_isolates$AMX) # counts "S" and "I"
+count_all(example_isolates$AMX) # counts "S", "I" and "R"# be more specific
-count_S(example_isolates$AMX)
-count_SI(example_isolates$AMX)
-count_I(example_isolates$AMX)
-count_IR(example_isolates$AMX)
-count_R(example_isolates$AMX)
+count_S(example_isolates$AMX)
+count_SI(example_isolates$AMX)
+count_I(example_isolates$AMX)
+count_IR(example_isolates$AMX)
+count_R(example_isolates$AMX)
# Count all available isolates
-count_all(example_isolates$AMX)
-n_rsi(example_isolates$AMX)
+count_all(example_isolates$AMX)
+n_rsi(example_isolates$AMX)
# n_rsi() is an alias of count_all().# Since it counts all available isolates, you can# calculate back to count e.g. susceptible isolates.# These results are the same:
-count_susceptible(example_isolates$AMX)
-susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX)
+count_susceptible(example_isolates$AMX)
+susceptibility(example_isolates$AMX) *n_rsi(example_isolates$AMX)
-if (require("dplyr")) {
- example_isolates%>%
- group_by(hospital_id) %>%
- summarise(R=count_R(CIP),
- I=count_I(CIP),
- S=count_S(CIP),
- n1=count_all(CIP), # the actual total; sum of all three
- n2=n_rsi(CIP), # same - analogous to n_distinct
- total=n()) # NOT the number of tested isolates!
-
+if (require("dplyr")) {
+ example_isolates%>%
+ group_by(hospital_id) %>%
+ summarise(R = count_R(CIP),
+ I = count_I(CIP),
+ S = count_S(CIP),
+ n1 = count_all(CIP), # the actual total; sum of all three
+ n2 = n_rsi(CIP), # same - analogous to n_distinct
+ total = n()) # NOT the number 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.# Please mind that `susceptibility()` calculates percentages right away instead.
- example_isolates%>%count_susceptible(AMC) # 1433
- example_isolates%>%count_all(AMC) # 1879
-
- example_isolates%>%count_susceptible(GEN) # 1399
- example_isolates%>%count_all(GEN) # 1855
-
- example_isolates%>%count_susceptible(AMC, GEN) # 1764
- example_isolates%>%count_all(AMC, GEN) # 1936
-
+ example_isolates%>%count_susceptible(AMC) # 1433
+ example_isolates%>%count_all(AMC) # 1879
+
+ example_isolates%>%count_susceptible(GEN) # 1399
+ example_isolates%>%count_all(GEN) # 1855
+
+ example_isolates%>%count_susceptible(AMC, GEN) # 1764
+ example_isolates%>%count_all(AMC, GEN) # 1936
+
# Get number of S+I vs. R immediately of selected columns
- example_isolates%>%
- select(AMX, CIP) %>%
- count_df(translate=FALSE)
-
+ example_isolates%>%
+ select(AMX, CIP) %>%
+ count_df(translate = FALSE)
+
# It also supports grouping variables
- example_isolates%>%
- select(hospital_id, AMX, CIP) %>%
- group_by(hospital_id) %>%
- count_df(translate=FALSE)
+ example_isolates%>%
+ select(hospital_id, AMX, CIP) %>%
+ group_by(hospital_id) %>%
+ count_df(translate = FALSE)
}
@@ -440,7 +439,7 @@ A microorganism is categorised as Susceptible, Increased exposure when
diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html
index 74e0a2f03..550cd3830 100644
--- a/docs/reference/eucast_rules.html
+++ b/docs/reference/eucast_rules.html
@@ -83,7 +83,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -238,12 +238,12 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
@@ -407,21 +407,21 @@ The lifecycle of this function is maturing<
Examples
# \donttest{
-a<-data.frame(mo=c("Staphylococcus aureus",
+a<-data.frame(mo = c("Staphylococcus aureus",
"Enterococcus faecalis",
"Escherichia coli",
"Klebsiella pneumoniae",
"Pseudomonas aeruginosa"),
- VAN="-", # Vancomycin
- AMX="-", # Amoxicillin
- COL="-", # Colistin
- CAZ="-", # Ceftazidime
- CXM="-", # Cefuroxime
- PEN="S", # Penicillin G
- FOX="S", # Cefoxitin
- stringsAsFactors=FALSE)
+ VAN = "-", # Vancomycin
+ AMX = "-", # Amoxicillin
+ COL = "-", # Colistin
+ CAZ = "-", # Ceftazidime
+ CXM = "-", # Cefuroxime
+ PEN = "S", # Penicillin G
+ FOX = "S", # Cefoxitin
+ stringsAsFactors = FALSE)
-a
+a# mo VAN AMX COL CAZ CXM PEN FOX# 1 Staphylococcus aureus - - - - - S S# 2 Enterococcus faecalis - - - - - S S
@@ -431,9 +431,9 @@ The lifecycle of this function is maturing<
# apply EUCAST rules: 18 results are forced as R or S
-b<-eucast_rules(a)
+b<-eucast_rules(a)
-b
+b# mo VAN AMX COL CAZ CXM PEN FOX# 1 Staphylococcus aureus - S R R S S S# 2 Enterococcus faecalis - - R R R S R
@@ -444,7 +444,7 @@ The lifecycle of this function is maturing<
# do not apply EUCAST rules, but rather get a data.frame# with 18 rows, containing all details about the transformations:
-c<-eucast_rules(a, verbose=TRUE)
+c<-eucast_rules(a, verbose = TRUE)
# }
@@ -461,7 +461,7 @@ The lifecycle of this function is maturing<
A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read the tutorial on our website.
A data set containing 3,000 microbial isolates that are not cleaned up and consequently not ready for AMR analysis. This data set can be used for practice.
Filter isolates on results in specific antimicrobial classes. This makes it easy to filter on isolates that were tested for e.g. any aminoglycoside, or to filter on carbapenem-resistant isolates without the need to specify the drugs.
if (FALSE) {
+library(dplyr)
# filter on isolates that have any result for any aminoglycoside
-example_isolates%>%filter_ab_class("aminoglycoside")
-example_isolates%>%filter_aminoglycosides()
+example_isolates%>%filter_ab_class("aminoglycoside")
+example_isolates%>%filter_aminoglycosides()
# this is essentially the same as (but without determination of column names):
-example_isolates%>%
- filter_at(.vars=vars(c("GEN", "TOB", "AMK", "KAN")),
- .vars_predicate=any_vars(.%in%c("S", "I", "R")))
+example_isolates%>%
+ filter_at(.vars = vars(c("GEN", "TOB", "AMK", "KAN")),
+ .vars_predicate = any_vars(.%in%c("S", "I", "R")))
# filter on isolates that show resistance to ANY aminoglycoside
-example_isolates%>%filter_aminoglycosides("R", "any")
+example_isolates%>%filter_aminoglycosides("R", "any")
# filter on isolates that show resistance to ALL aminoglycosides
-example_isolates%>%filter_aminoglycosides("R", "all")
+example_isolates%>%filter_aminoglycosides("R", "all")
# filter on isolates that show resistance to# any aminoglycoside and any fluoroquinolone
-example_isolates%>%
- filter_aminoglycosides("R") %>%
+example_isolates%>%
+ filter_aminoglycosides("R") %>%filter_fluoroquinolones("R")
# filter on isolates that show resistance to# all aminoglycosides and all fluoroquinolones
-example_isolates%>%
- filter_aminoglycosides("R", "all") %>%
+example_isolates%>%
+ filter_aminoglycosides("R", "all") %>%filter_fluoroquinolones("R", "all")
# with dplyr 1.0.0 and higher (that adds 'across()'), this is equal:
-example_isolates%>%filter_carbapenems("R", "all")
-example_isolates%>%filter(across(carbapenems(), ~.=="R"))
+example_isolates%>%filter_carbapenems("R", "all")
+example_isolates%>%filter(across(carbapenems(), ~.=="R"))
}
@@ -353,7 +353,7 @@ The lifecycle of this function is stable
WHY THIS IS SO IMPORTANT
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode (ref). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all S. aureus isolates would be overestimated, because you included this MRSA more than once. It would be selection bias.
All isolates with a microbial ID of NA will be excluded as first isolate.
-
The functions filter_first_isolate() and filter_first_weighted_isolate() are helper functions to quickly filter on first isolates. The function filter_first_isolate() is essentially equal to one of:
The functions filter_first_isolate() and filter_first_weighted_isolate() are helper functions to quickly filter on first isolates. The function filter_first_isolate() is essentially equal to one of:
@@ -402,24 +402,24 @@ The lifecycle of this function is stable# `example_isolates` is a dataset available in the AMR package.# See ?example_isolates.
-if (FALSE) {
-library(dplyr)
+if (FALSE) {
+library(dplyr)
# Filter on first isolates:
-example_isolates%>%
- mutate(first_isolate=first_isolate(.)) %>%
- filter(first_isolate==TRUE)
+example_isolates%>%
+ mutate(first_isolate = first_isolate(.)) %>%
+ filter(first_isolate==TRUE)
# Now let's see if first isolates matter:
-A<-example_isolates%>%
- group_by(hospital_id) %>%
- summarise(count=n_rsi(GEN), # gentamicin availability
- resistance=resistance(GEN)) # gentamicin resistance
+A<-example_isolates%>%
+ group_by(hospital_id) %>%
+ summarise(count = n_rsi(GEN), # gentamicin availability
+ resistance = resistance(GEN)) # gentamicin resistance
-B<-example_isolates%>%
- filter_first_weighted_isolate() %>%# the 1st isolate filter
- group_by(hospital_id) %>%
- summarise(count=n_rsi(GEN), # gentamicin availability
- resistance=resistance(GEN)) # gentamicin resistance
+B<-example_isolates%>%
+ filter_first_weighted_isolate() %>%# the 1st isolate filter
+ group_by(hospital_id) %>%
+ summarise(count = n_rsi(GEN), # gentamicin availability
+ resistance = resistance(GEN)) # gentamicin resistance# Have a look at A and B.# B is more reliable because every isolate is counted only once.
@@ -430,21 +430,21 @@ The lifecycle of this function is stable## OTHER EXAMPLES:
# Short-hand versions:
-example_isolates%>%
+example_isolates%>%filter_first_isolate()
-
-example_isolates%>%
+
+example_isolates%>%filter_first_weighted_isolate()
# set key antibiotics to a new variable
-x$keyab<-key_antibiotics(x)
+x$keyab<-key_antibiotics(x)
-x$first_isolate<-first_isolate(x)
+x$first_isolate<-first_isolate(x)
-x$first_isolate_weighed<-first_isolate(x, col_keyantibiotics='keyab')
+x$first_isolate_weighed<-first_isolate(x, col_keyantibiotics = 'keyab')
-x$first_blood_isolate<-first_isolate(x, specimen_group="Blood")
+x$first_blood_isolate<-first_isolate(x, specimen_group = "Blood")
}
@@ -461,7 +461,7 @@ The lifecycle of this function is stable
diff --git a/docs/reference/g.test.html b/docs/reference/g.test.html
index d0656816d..cf6331483 100644
--- a/docs/reference/g.test.html
+++ b/docs/reference/g.test.html
@@ -6,7 +6,7 @@
-<em>G</em>-test for Count Data — g.test • AMR (for R)
+G-test for Count Data — g.test • AMR (for R)
@@ -48,7 +48,7 @@
-
+
@@ -82,7 +82,7 @@
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -235,7 +235,7 @@
g.test() performs chi-squared contingency table tests and goodness-of-fit tests, just like chisq.test() but is more reliable (1). A G-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a G-test of goodness-of-fit), or to see whether the proportions of one variable are different for different values of the other variable (called a G-test of independence).
g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)
Arguments
@@ -319,7 +319,7 @@
Unlike the exact test of goodness-of-fit (fisher.test()), the G-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the G-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.
The G-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate a G-statistic is:
\(G = 2 * sum(x * log(x / E))\)
-
where E are the expected values. Since this is chi-square distributed, the p value can be calculated in R with:
If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use G-tests for each category, of course.
@@ -356,8 +356,8 @@ The lifecycle of this function is questioni
# by a single gene with two co-dominant alleles, you would expect a 1:2:1# ratio.
-x<-c(772, 1611, 737)
-G<-g.test(x, p=c(1, 2, 1) / 4)
+x<-c(772, 1611, 737)
+G<-g.test(x, p = c(1, 2, 1) /4)
# G$p.value = 0.12574.# There is no significant difference from a 1:2:1 ratio.
@@ -372,12 +372,13 @@ The lifecycle of this function is questioni
# keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992)# observed 1752 right-billed and 1895 left-billed crossbills.
-x<-c(1752, 1895)
-g.test(x)
+x<-c(1752, 1895)
+g.test(x)
# p = 0.01787343# There is a significant difference from a 1:1 ratio.
-# Meaning: there are significantly more left-billed birds.
+# Meaning: there are significantly more left-billed birds.
+
@@ -388,31 +388,31 @@ The lifecycle of this function is stable# `example_isolates` is a dataset available in the AMR package.# See ?example_isolates.
-if (FALSE) {
-library(dplyr)
+if (FALSE) {
+library(dplyr)
# set key antibiotics to a new variable
-my_patients<-example_isolates%>%
- mutate(keyab=key_antibiotics(.)) %>%
+my_patients<-example_isolates%>%
+ mutate(keyab = key_antibiotics(.)) %>%mutate(
# now calculate first isolates
- first_regular=first_isolate(., col_keyantibiotics=FALSE),
+ first_regular = first_isolate(., col_keyantibiotics = FALSE),
# and first WEIGHTED isolates
- first_weighted=first_isolate(., col_keyantibiotics="keyab")
+ first_weighted = first_isolate(., col_keyantibiotics = "keyab")
)
# Check the difference, in this data set it results in 7% more isolates:
-sum(my_patients$first_regular, na.rm=TRUE)
-sum(my_patients$first_weighted, na.rm=TRUE)
+sum(my_patients$first_regular, na.rm = TRUE)
+sum(my_patients$first_weighted, na.rm = TRUE)
}
# output of the `key_antibiotics` function could be like this:
-strainA<-"SSSRR.S.R..S"
-strainB<-"SSSIRSSSRSSS"
+strainA<-"SSSRR.S.R..S"
+strainB<-"SSSIRSSSRSSS"
-key_antibiotics_equal(strainA, strainB)
+key_antibiotics_equal(strainA, strainB)
# TRUE, because I is ignored (as well as missing values)
-key_antibiotics_equal(strainA, strainB, ignore_I=FALSE)
+key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
# FALSE, because I is not ignored and so the 4th value differs
@@ -429,7 +429,7 @@ The lifecycle of this function is stable
diff --git a/docs/reference/lifecycle.html b/docs/reference/lifecycle.html
index 9e48a75bf..cb673094f 100644
--- a/docs/reference/lifecycle.html
+++ b/docs/reference/lifecycle.html
@@ -6,7 +6,7 @@
-Lifecycles of functions in the <code>AMR</code> package — lifecycle • AMR (for R)
+Lifecycles of functions in the AMR package — lifecycle • AMR (for R)
@@ -48,7 +48,7 @@
-
+
@@ -84,7 +84,7 @@ This page contains a section for every lifecycle (with text borrowed from the af
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -288,7 +288,7 @@ The lifecycle of this function is questioning. This function mi
Convenient wrapper around grep() to match a pattern: x %like% pattern. It always returns a logical vector and is always case-insensitive (use x %like_case% pattern for case-sensitive matching). Also, pattern can be as long as x to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.
A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with set_mo_source(). They will all be searched when using as.mo() and consequently all the mo_* functions.
-
microorganisms.codes
+
microorganisms.codes
Format
@@ -276,7 +276,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by as.mo().
-
microorganisms.old
+
microorganisms.old
Format
@@ -282,7 +282,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
Use these functions to return a specific property of a microorganism. All input values will be evaluated internally with as.mo(), which makes it possible to use microbial abbreviations, codes and names as input. Please see Examples.
mo_name(x, language = get_locale(), ...)
-mo_fullname(x, language=get_locale(), ...)
+mo_fullname(x, language = get_locale(), ...)
-mo_shortname(x, language=get_locale(), ...)
+mo_shortname(x, language = get_locale(), ...)
-mo_subspecies(x, language=get_locale(), ...)
+mo_subspecies(x, language = get_locale(), ...)
-mo_species(x, language=get_locale(), ...)
+mo_species(x, language = get_locale(), ...)
-mo_genus(x, language=get_locale(), ...)
+mo_genus(x, language = get_locale(), ...)
-mo_family(x, language=get_locale(), ...)
+mo_family(x, language = get_locale(), ...)
-mo_order(x, language=get_locale(), ...)
+mo_order(x, language = get_locale(), ...)
-mo_class(x, language=get_locale(), ...)
+mo_class(x, language = get_locale(), ...)
-mo_phylum(x, language=get_locale(), ...)
+mo_phylum(x, language = get_locale(), ...)
-mo_kingdom(x, language=get_locale(), ...)
+mo_kingdom(x, language = get_locale(), ...)
-mo_domain(x, language=get_locale(), ...)
+mo_domain(x, language = get_locale(), ...)
-mo_type(x, language=get_locale(), ...)
+mo_type(x, language = get_locale(), ...)
-mo_gramstain(x, language=get_locale(), ...)
+mo_gramstain(x, language = get_locale(), ...)
-mo_snomed(x, ...)
+mo_snomed(x, ...)
-mo_ref(x, ...)
+mo_ref(x, ...)
-mo_authors(x, ...)
+mo_authors(x, ...)
-mo_year(x, ...)
+mo_year(x, ...)
-mo_rank(x, ...)
+mo_rank(x, ...)
-mo_taxonomy(x, language=get_locale(), ...)
+mo_taxonomy(x, language = get_locale(), ...)
-mo_synonyms(x, ...)
+mo_synonyms(x, ...)
-mo_info(x, language=get_locale(), ...)
+mo_info(x, language = get_locale(), ...)
-mo_url(x, open=FALSE, ...)
+mo_url(x, open = FALSE, ...)
-mo_property(x, property="fullname", language=get_locale(), ...)
+mo_property(x, property = "fullname", language = get_locale(), ...)
Arguments
@@ -416,21 +416,21 @@ This package contains the complete taxonomic tree of almost all microorganisms (
# \donttest{# Becker classification, see ?as.mo ----------------------------------------mo_fullname("S. epi") # "Staphylococcus epidermidis"
-mo_fullname("S. epi", Becker=TRUE) # "Coagulase-negative Staphylococcus (CoNS)"
+mo_fullname("S. epi", Becker = TRUE) # "Coagulase-negative Staphylococcus (CoNS)"mo_shortname("S. epi") # "S. epidermidis"
-mo_shortname("S. epi", Becker=TRUE) # "CoNS"
+mo_shortname("S. epi", Becker = TRUE) # "CoNS"# Lancefield classification, see ?as.mo ------------------------------------mo_fullname("S. pyo") # "Streptococcus pyogenes"
-mo_fullname("S. pyo", Lancefield=TRUE) # "Streptococcus group A"
+mo_fullname("S. pyo", Lancefield = TRUE) # "Streptococcus group A"mo_shortname("S. pyo") # "S. pyogenes"
-mo_shortname("S. pyo", Lancefield=TRUE) # "GAS" (='Group A Streptococci')
+mo_shortname("S. pyo", Lancefield = TRUE) # "GAS" (='Group A Streptococci')# language support for German, Dutch, Spanish, Portuguese, Italian and French
-mo_gramstain("E. coli", language="de") # "Gramnegativ"
-mo_gramstain("E. coli", language="nl") # "Gram-negatief"
-mo_gramstain("E. coli", language="es") # "Gram negativo"
+mo_gramstain("E. coli", language = "de") # "Gramnegativ"
+mo_gramstain("E. coli", language = "nl") # "Gram-negatief"
+mo_gramstain("E. coli", language = "es") # "Gram negativo"# mo_type is equal to mo_kingdom, but mo_kingdom will remain officialmo_kingdom("E. coli") # "Bacteria" on a German system
@@ -438,11 +438,11 @@ This package contains the complete taxonomic tree of almost all microorganisms (
mo_type("E. coli") # "Bacteria" on an English systemmo_fullname("S. pyogenes",
- Lancefield=TRUE,
- language="de") # "Streptococcus Gruppe A"
+ Lancefield = TRUE,
+ language = "de") # "Streptococcus Gruppe A"mo_fullname("S. pyogenes",
- Lancefield=TRUE,
- language="nl") # "Streptococcus groep A"
+ Lancefield = TRUE,
+ language = "nl") # "Streptococcus groep A"# get a list with the complete taxonomy (from kingdom to subspecies)
@@ -465,7 +465,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
diff --git a/docs/reference/mo_source.html b/docs/reference/mo_source.html
index 75d498109..902a406c1 100644
--- a/docs/reference/mo_source.html
+++ b/docs/reference/mo_source.html
@@ -83,7 +83,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -237,7 +237,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
This is the fastest way to have your organisation (or analysis) specific codes picked up and translated by this package.
-
set_mo_source(path)
+
set_mo_source(path)
get_mo_source()
@@ -270,19 +270,19 @@ This is the fastest way to have your organisation (or analysis) specific codes p
We save it as "home/me/ourcodes.xlsx". Now we have to set it as a source:
set_mo_source("home/me/ourcodes.xlsx")
-#> NOTE: Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'
-#> (columns "Organisation XYZ" and "mo")
+#> NOTE: Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'
+#> (columns "Organisation XYZ" and "mo")
It has now created a file "~/.mo_source.rds" with the contents of our Excel file. Only the first column with foreign values and the 'mo' column will be kept when creating the RDS file.
And now we can use it in our functions:
as.mo("lab_mo_ecoli")
-#> [1] B_ESCHR_COLI
+#> [1] B_ESCHR_COLImo_genus("lab_mo_kpneumoniae")
-#> [1] "Klebsiella"
+#> [1] "Klebsiella"# other input values still work tooas.mo(c("Escherichia coli", "E. coli", "lab_mo_ecoli"))
-#> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI
+#> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI
If we edit the Excel file by, let's say, adding row 4 like this:
| A | B |
--|--------------------|--------------|
@@ -294,12 +294,12 @@ This is the fastest way to have your organisation (or analysis) specific codes p
...any new usage of an MO function in this package will update your data file:
as.mo("lab_mo_ecoli")
-#> NOTE: Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'
-#> (columns "Organisation XYZ" and "mo")
-#> [1] B_ESCHR_COLI
+#> NOTE: Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'
+#> (columns "Organisation XYZ" and "mo")
+#> [1] B_ESCHR_COLImo_genus("lab_Staph_aureus")
-#> [1] "Staphylococcus"
+#> [1] "Staphylococcus"
To delete the reference data file, just use "", NULL or FALSE as input for set_mo_source():
@@ -315,22 +315,22 @@ The lifecycle of this function is maturing<
# `example_isolates` is a dataset available in the AMR package.# See ?example_isolates.
-if (FALSE) {
+if (FALSE) {
# calculate the resistance per group first
-library(dplyr)
-resistance_data<-example_isolates%>%
- group_by(order=mo_order(mo), # group on anything, like order
- genus=mo_genus(mo)) %>%# and genus as we do here
- summarise_if(is.rsi, resistance) # then get resistance of all drugs
-
+library(dplyr)
+resistance_data<-example_isolates%>%
+ group_by(order = mo_order(mo), # group on anything, like order
+ genus = mo_genus(mo)) %>%# and genus as we do here
+ summarise_if(is.rsi, resistance) # then get resistance of all drugs
+
# now conduct PCA for certain antimicrobial agents
-pca_result<-resistance_data%>%
- pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
-
-pca_result
-summary(pca_result)
-biplot(pca_result)
-ggplot_pca(pca_result) # a new and convenient plot function
+pca_result<-resistance_data%>%
+ pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
+
+pca_result
+summary(pca_result)
+biplot(pca_result)
+ggplot_pca(pca_result) # a new and convenient plot function
}
@@ -347,7 +347,7 @@ The lifecycle of this function is maturing<
diff --git a/docs/reference/proportion.html b/docs/reference/proportion.html
index 47cf409c9..34a1399f3 100644
--- a/docs/reference/proportion.html
+++ b/docs/reference/proportion.html
@@ -83,7 +83,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -233,42 +233,42 @@ resistance() should be used to calculate resistance, susceptibility() should be
-
These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in summarise() from the dplyr package and also support grouped variables, please see Examples.
+
These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in summarise() from the dplyr package and also support grouped variables, please see Examples.
resistance() should be used to calculate resistance, susceptibility() should be used to calculate susceptibility.
@@ -336,22 +336,21 @@ resistance() should be used to calculate resistance, susceptibility() should be
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
- <NA> S or I X X - -
+ <NA> S or I X X - -
S or I R X X X X
R R - X - X
- <NA> R - - - -
- S or I <NA> X X - -
- R <NA> - - - -
- <NA> <NA> - - - -
+ <NA> R - - - -
+ S or I <NA> X X - -
+ R <NA> - - - -
+ <NA> <NA> - - - -
--------------------------------------------------------------------
-
Please note that, in combination therapies, for only_all_tested = TRUE applies that:
Data set to interpret MIC and disk diffusion to R/SI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2020). Use as.rsi() to transform MICs or disks measurements to R/SI values.
diff --git a/docs/reference/skewness.html b/docs/reference/skewness.html
index 067d1097a..c8b4bdb2e 100644
--- a/docs/reference/skewness.html
+++ b/docs/reference/skewness.html
@@ -83,7 +83,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr
AMR (for R)
- 1.3.0
+ 1.3.0.9001
@@ -237,16 +237,16 @@ When negative: the left tail is longer; the mass of the distribution is concentr
When negative: the left tail is longer; the mass of the distribution is concentrated on the right of the figure. When positive: the right tail is longer; the mass of the distribution is concentrated on the left of the figure.
diff --git a/index.md b/index.md
index 6da490e12..8c0a1e0b0 100644
--- a/index.md
+++ b/index.md
@@ -4,7 +4,7 @@
> **PLEASE TAKE PART IN OUR SURVEY!**
> Since you are one of our users, we would like to know how you use the package and what it brought you or your organisation. **If you have a minute, please [anonymously fill in this short questionnaire](./survey.html)**. Your valuable input will help to improve the package and its functionalities. You can answer the open questions in either English, Spanish, French, Dutch, or German. Thank you very much in advance!
>
-> Take me to the 5-min survey!
+> Take me to the 5-min survey!
### What is `AMR` (for R)?
diff --git a/man/as.rsi.Rd b/man/as.rsi.Rd
index fa9827f9e..9be415bb6 100755
--- a/man/as.rsi.Rd
+++ b/man/as.rsi.Rd
@@ -162,6 +162,7 @@ as.rsi(df)
library(dplyr)
df \%>\% mutate_at(vars(AMP:TOB), as.rsi)
df \%>\% mutate(across(AMP:TOB), as.rsi)
+
df \%>\%
mutate_at(vars(AMP:TOB), as.rsi, mo = "E. coli")
diff --git a/vignettes/welcome_to_AMR.Rmd b/vignettes/welcome_to_AMR.Rmd
index 8287bfc8f..43a09455c 100644
--- a/vignettes/welcome_to_AMR.Rmd
+++ b/vignettes/welcome_to_AMR.Rmd
@@ -24,6 +24,8 @@ knitr::opts_chunk$set(
)
```
+**READ ALL VIGNETTES [ON OUR WEBSITE](https://msberends.github.io/AMR/articles/)**
+
# Welcome to the AMR package
`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
