new, automated website

man/antibiotic_class_selectors.Rd

@@ -164,120 +164,109 @@ The \code{\link[=not_intrinsic_resistant]{not_intrinsic_resistant()}} function c
 }
 }
 
-\section{Stable Lifecycle}{
-
-\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:"5"} \cr}
-The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
-
-If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
-}
-
 \section{Reference Data Publicly Available}{
 
 All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this \code{AMR} package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find \href{https://msberends.github.io/AMR/articles/datasets.html}{all download links on our website}, which is automatically updated with every code change.
 }
 
-\section{Read more on Our Website!}{
-
-On our website \url{https://msberends.github.io/AMR/} you can find \href{https://msberends.github.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR data analysis, the \href{https://msberends.github.io/AMR/reference/}{complete documentation of all functions} and \href{https://msberends.github.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
-}
-
 \examples{
 # `example_isolates` is a data set available in the AMR package.
 # See ?example_isolates.
+df <- example_isolates[ , c("hospital_id", "mo",
+                            "AMP", "AMC", "TZP", "CXM", "CRO", "GEN",
+                            "TOB", "COL", "IPM", "MEM", "TEC", "VAN")]
 
 # base R ------------------------------------------------------------------
 
 # select columns 'IPM' (imipenem) and 'MEM' (meropenem)
-example_isolates[, carbapenems()]
+df[, carbapenems()]
 
 # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
-example_isolates[, c("mo", aminoglycosides())]
+df[, c("mo", aminoglycosides())]
 
 # select only antibiotic columns with DDDs for oral treatment
-example_isolates[, administrable_per_os()]
+df[, administrable_per_os()]
 
 # filter using any() or all()
-example_isolates[any(carbapenems() == "R"), ]
-subset(example_isolates, any(carbapenems() == "R"))
+df[any(carbapenems() == "R"), ]
+subset(df, any(carbapenems() == "R"))
 
 # filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
-example_isolates[any(carbapenems()), ]
-example_isolates[all(carbapenems()), ]
+df[any(carbapenems()), ]
+df[all(carbapenems()), ]
 
 # filter with multiple antibiotic selectors using c()
-example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
+df[all(c(carbapenems(), aminoglycosides()) == "R"), ]
 
 # filter + select in one go: get penicillins in carbapenems-resistant strains
-example_isolates[any(carbapenems() == "R"), penicillins()]
+df[any(carbapenems() == "R"), penicillins()]
 
 # You can combine selectors with '&' to be more specific. For example,
 # penicillins() would select benzylpenicillin ('peni G') and
 # administrable_per_os() would select erythromycin. Yet, when combined these
 # drugs are both omitted since benzylpenicillin is not administrable per os
 # and erythromycin is not a penicillin:
-example_isolates[, penicillins() & administrable_per_os()]
+df[, penicillins() & administrable_per_os()]
 
 # ab_selector() applies a filter in the `antibiotics` data set and is thus very
 # flexible. For instance, to select antibiotic columns with an oral DDD of at
 # least 1 gram:
-example_isolates[, ab_selector(oral_ddd > 1 & oral_units == "g")]
+df[, ab_selector(oral_ddd > 1 & oral_units == "g")]
 
 # dplyr -------------------------------------------------------------------
 \donttest{
 if (require("dplyr")) {
 
   # get AMR for all aminoglycosides e.g., per hospital:
-  example_isolates \%>\%
+  df \%>\%
     group_by(hospital_id) \%>\% 
     summarise(across(aminoglycosides(), resistance))
     
   # You can combine selectors with '&' to be more specific:
-  example_isolates \%>\%
+  df \%>\%
     select(penicillins() & administrable_per_os())
   
   # get AMR for only drugs that matter - no intrinsic resistance:
-  example_isolates \%>\%
+  df \%>\%
     filter(mo_genus() \%in\% c("Escherichia", "Klebsiella")) \%>\% 
     group_by(hospital_id) \%>\% 
     summarise(across(not_intrinsic_resistant(), resistance))
     
   # get susceptibility for antibiotics whose name contains "trim":
-  example_isolates \%>\%
+  df \%>\%
     filter(first_isolate()) \%>\% 
     group_by(hospital_id) \%>\% 
     summarise(across(ab_selector(name \%like\% "trim"), susceptibility))
 
   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
-  example_isolates \%>\% 
+  df \%>\% 
     select(carbapenems())
     
   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
-  example_isolates \%>\% 
+  df \%>\% 
     select(mo, aminoglycosides())
     
  # any() and all() work in dplyr's filter() too:
- example_isolates \%>\% 
+ df \%>\% 
     filter(any(aminoglycosides() == "R"),
            all(cephalosporins_2nd() == "R"))
     
  # also works with c():
- example_isolates \%>\% 
+ df \%>\% 
     filter(any(c(carbapenems(), aminoglycosides()) == "R"))
     
  # not setting any/all will automatically apply all():
- example_isolates \%>\% 
+ df \%>\% 
     filter(aminoglycosides() == "R")
- #> i Assuming a filter on all 4 aminoglycosides.
     
   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
-  example_isolates \%>\% 
+  df \%>\% 
     select(mo, ab_class("mycobact"))
     
-  # get bug/drug combinations for only macrolides in Gram-positives:
-  example_isolates \%>\% 
+  # get bug/drug combinations for only glycopeptides in Gram-positives:
+  df \%>\% 
     filter(mo_is_gram_positive()) \%>\% 
-    select(mo, macrolides()) \%>\% 
+    select(mo, glycopeptides()) \%>\% 
     bug_drug_combinations() \%>\%
     format()
     
@@ -286,10 +275,12 @@ if (require("dplyr")) {
     select(penicillins())         # only the 'J01CA01' column will be selected
     
     
-  # with dplyr 1.0.0 and higher (that adds 'across()'), this is all equal:
-  example_isolates[carbapenems() == "R", ]
-  example_isolates \%>\% filter(carbapenems() == "R")
-  example_isolates \%>\% filter(across(carbapenems(), ~.x == "R"))
+  # with recent versions of dplyr this is all equal:
+  x <- df[carbapenems() == "R", ]
+  y <- df \%>\% filter(carbapenems() == "R")
+  z <- df \%>\% filter(if_all(carbapenems(), ~.x == "R"))
+  identical(x, y)
+  identical(y, z)
 }
 }
 }