So only 90% 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 2 712 isolates for analysis. Now our data looks
like:
-
+
our_data_1st
#> # A tibble: 2,712 × 9
#> patient_id hospital date bacteria AMX AMC CIP GEN first
@@ -555,7 +564,7 @@ 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:2712 Length:2712 Min. :2011-01-01
@@ -577,8 +586,9 @@ impression, as it comes with support for the new mo and
#> %SI :58.0% (n=1574) %SI :62.7% (n=1701)
#> - %S :51.5% (n=1396) - %S :59.6% (n=1616)
#> - %I : 6.6% (n=178) - %I : 3.1% (n=85)
-#>
-
+#>
+
glimpse(our_data_1st)
#> Rows: 2,712
#> Columns: 9
@@ -590,8 +600,9 @@ impression, as it comes with support for the new mo and
#> $ AMC <sir> I, I, I, S, S, S, S, S, S, S, S, S, I, S, S, S, S, R, S, S,…
#> $ CIP <sir> S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…
#> $ GEN <sir> S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S, S,…
-#> $ first <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
-
+#> $ first <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
+
# number of unique values per column:
sapply(our_data_1st, n_distinct)
#> patient_id hospital date bacteria AMX AMC CIP
@@ -604,7 +615,7 @@ 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
@@ -613,8 +624,9 @@ microorganisms:
#> 1 Escherichia coli 1518
#> 2 Staphylococcus aureus 730
#> 3 Streptococcus pneumoniae 426
-#> 4 Klebsiella pneumoniae 326
-
+#> 4 Klebsiella pneumoniae 326
+
our_data_1st %>%
count(mo_name(bacteria), sort = TRUE)
#> # A tibble: 4 × 2
@@ -631,7 +643,7 @@ 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)
@@ -648,8 +660,9 @@ in:
#> 8 2015-04-27 S
#> 9 2011-06-21 S
#> 10 2014-09-05 S
-#> # ℹ 2,702 more rows
-
+#> # ℹ 2,702 more rows
+
our_data_1st %>%
select(bacteria, betalactams())
#> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'
@@ -667,8 +680,9 @@ in:
#> 8 B_STPHY_AURS S S
#> 9 B_ESCHR_COLI S S
#> 10 B_ESCHR_COLI S S
-#> # ℹ 2,702 more rows
-
+#> # ℹ 2,702 more rows
+
our_data_1st %>%
select(bacteria, where(is.sir))
#> # A tibble: 2,712 × 5
@@ -684,8 +698,9 @@ in:
#> 8 B_STPHY_AURS S S S S
#> 9 B_ESCHR_COLI S S S S
#> 10 B_ESCHR_COLI S S S S
-#> # ℹ 2,702 more rows
-
+#> # ℹ 2,702 more rows
+
# filtering using AB selectors is also possible:
our_data_1st %>%
filter(any(aminoglycosides() == "R"))
@@ -703,8 +718,9 @@ in:
#> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE
#> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE
#> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE
-#> # ℹ 1,001 more rows
-
+#> # ℹ 1,001 more rows
+
our_data_1st %>%
filter(all(betalactams() == "R"))
#> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'
@@ -722,8 +738,9 @@ in:
#> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE
#> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE
#> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE
-#> # ℹ 473 more rows
-
+#> # ℹ 473 more rows
+
# even works in base R (since R 3.0):
our_data_1st[all(betalactams() == "R"), ]
#> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'
@@ -776,7 +793,7 @@ 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
@@ -805,7 +822,7 @@ 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 For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
@@ -932,7 +949,7 @@ 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(),
@@ -978,7 +995,7 @@ 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"))
@@ -1060,7 +1077,7 @@ 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")
@@ -1254,7 +1271,7 @@ 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",
@@ -1322,7 +1339,7 @@ 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
@@ -1351,12 +1368,12 @@ 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:
-
+
+
+
eucast_rules(oops, info = FALSE)
#> mo ampicillin
#> 1 Klebsiella R
@@ -258,13 +259,14 @@ be applied using eucast_rules()mo_is_intrinsic_resistant() that uses the same guideline,
but allows to check for one or more specific microorganisms or
antibiotics:
-
+
+
+
mo_is_intrinsic_resistant(
"Klebsiella",
c("ampicillin", "kanamycin")
@@ -275,7 +277,7 @@ used for filling in known resistance and susceptibility based on results
of other antimicrobials drugs. This process is called interpretive
reading, is basically a form of imputation, and is part of the
eucast_rules() function as well:
-
+
data <- data.frame(
mo = c(
"Staphylococcus aureus",
@@ -293,7 +295,7 @@ reading, is basically a form of imputation, and is part of the
FOX = "S", # Cefoxitin
stringsAsFactors = FALSE
)