So only 91% is suitable for resistance analysis! We can now filter on
it with the filter() function, also from the
dplyr package:
-
+
our_data_1st <- our_data %>%
filter(first == TRUE)
For future use, the above two syntaxes can be shortened:
-
+
So we end up with 2 724 isolates for analysis. Now our data looks
like:
-
+
our_data_1st
#> # A tibble: 2,724 × 9
#> patient_id hospital date bacteria AMX AMC CIP GEN first
@@ -437,7 +441,7 @@ like:
The base R summary() function gives a good first
impression, as it comes with support for the new mo and
sir classes that we now have in our data set:
-
+
summary(our_data_1st)
#> patient_id hospital date
#> Length:2724 Length:2724 Min. :2011-01-01
@@ -486,7 +490,7 @@ impression, as it comes with support for the new mo and
To just get an idea how the species are distributed, create a
frequency table with count() based on the name of the
microorganisms:
-
+
our_data %>%
count(mo_name(bacteria), sort = TRUE)
#> # A tibble: 4 × 2
@@ -513,7 +517,7 @@ microorganisms:
Using so-called antibiotic class selectors, you can select or filter
columns based on the antibiotic class that your antibiotic results are
in:
-
+
our_data_1st %>%
select(date, aminoglycosides())
#> ℹ For aminoglycosides() using column 'GEN' (gentamicin)
@@ -658,7 +662,7 @@ failure
function to create any of the above antibiogram types. For starters,
this is what the included example_isolates data set looks
like:
-
+
example_isolates
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
@@ -687,7 +691,7 @@ like:
should be used. The antibiotics argument in the
antibiogram() function supports any (combination) of the
previously mentioned antibiotic class selectors:
-
+
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()))
#> ℹ The function aminoglycosides() should be used inside a dplyr verb or
@@ -831,7 +835,7 @@ Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian,
Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish,
Swedish, Turkish, or Ukrainian. In this next example, we force the
language to be Spanish using the language argument:
-
+
antibiogram(example_isolates,
mo_transform = "gramstain",
antibiotics = aminoglycosides(),
@@ -885,7 +889,7 @@ language to be Spanish using the language argument:
To create a combined antibiogram, use antibiotic codes or names with
a plus + character like this:
-
+
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
@@ -965,7 +969,7 @@ a plus + character like this:
To create a syndromic antibiogram, the syndromic_group
argument must be used. This can be any column in the data, or e.g. an
ifelse() with calculations based on certain columns:
-
+
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward")
@@ -1165,7 +1169,7 @@ Antibiogram) in which cases are predefined based on clinical or
demographic characteristics (e.g., endocarditis in 75+ females). This
next example is a simplification without clinical characteristics, but
just gives an idea of how a WISCA can be created:
-
+
wisca <- antibiogram(example_isolates,
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
@@ -1233,7 +1237,7 @@ just gives an idea of how a WISCA can be created:
Antibiograms can be plotted using autoplot() from the
ggplot2 packages, since this AMR package
provides an extension to that function:
-
+
To calculate antimicrobial resistance in a more sensible way, also by
@@ -1262,12 +1266,12 @@ proportion of R (proportion_R()
I (proportion_SI(), equal to
susceptibility()). These functions can be used on their
own:
-
+
Or can be used in conjunction with group_by() and
summarise(), both from the dplyr package:
-
+
our_data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX))
diff --git a/articles/AMR_for_Python.html b/articles/AMR_for_Python.html
index e565012c..e053e1d1 100644
--- a/articles/AMR_for_Python.html
+++ b/articles/AMR_for_Python.html
@@ -29,7 +29,7 @@
AMR (for R)
- 2.1.1.9120
+ 2.1.1.9121
Now to transform this to a data set with only resistance percentages
per taxonomic order and genus:
diff --git a/articles/WHONET.html b/articles/WHONET.html
index a27c6beb..5ab2b35b 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -29,7 +29,7 @@
AMR (for R)
- 2.1.1.9120
+ 2.1.1.9121
@@ -98,6 +110,30 @@
column name of the names or codes of the microorganisms (see as.mo()) - the default is the first column of class mo. Values will be coerced using as.mo().
+
esbl
+
logical values, or a column name containing logical values, indicating the presence of an ESBL gene (or production of its proteins)
+
+
+
carbapenemase
+
logical values, or a column name containing logical values, indicating the presence of a carbapenemase gene (or production of its proteins)
+
+
+
mecA
+
logical values, or a column name containing logical values, indicating the presence of a mecA gene (or production of its proteins)
+
+
+
mecC
+
logical values, or a column name containing logical values, indicating the presence of a mecC gene (or production of its proteins)
+
+
+
vanA
+
logical values, or a column name containing logical values, indicating the presence of a vanA gene (or production of its proteins)
+
+
+
vanB
+
logical values, or a column name containing logical values, indicating the presence of a vanB gene (or production of its proteins)
+
+
info
a logical to indicate whether progress should be printed to the console - the default is only print while in interactive sessions
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 66ec8c06..3347d5bf 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -7,7 +7,7 @@
AMR (for R)
-
2.1.1.9120
+
2.1.1.9121