unit tests

man/antibiogram.Rd

@@ -153,39 +153,45 @@ example_isolates
 # Traditional antibiogram ----------------------------------------------
 
 antibiogram(example_isolates,
-            antibiotics = c(aminoglycosides(), carbapenems()))
-            
+  antibiotics = c(aminoglycosides(), carbapenems())
+)
+
 antibiogram(example_isolates,
-            antibiotics = aminoglycosides(),
-            ab_transform = "atc",
-            mo_transform = "gramstain")
-            
+  antibiotics = aminoglycosides(),
+  ab_transform = "atc",
+  mo_transform = "gramstain"
+)
+
 antibiogram(example_isolates,
-            antibiotics = carbapenems(),
-            ab_transform = "name",
-            mo_transform = "name")
+  antibiotics = carbapenems(),
+  ab_transform = "name",
+  mo_transform = "name"
+)
 
 
 # Combined antibiogram -------------------------------------------------
 
 # combined antibiotics yield higher empiric coverage
 antibiogram(example_isolates,
-            antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-            mo_transform = "gramstain")
-            
+  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
+  mo_transform = "gramstain"
+)
+
 antibiogram(example_isolates,
-            antibiotics = c("TZP", "TZP+TOB"),
-            mo_transform = "gramstain",
-            ab_transform = "name",
-            sep = " & ")
+  antibiotics = c("TZP", "TZP+TOB"),
+  mo_transform = "gramstain",
+  ab_transform = "name",
+  sep = " & "
+)
 
 
 # Syndromic antibiogram ------------------------------------------------
 
 # the data set could contain a filter for e.g. respiratory specimens
 antibiogram(example_isolates,
-            antibiotics = c(aminoglycosides(), carbapenems()),
-            syndromic_group = "ward")
+  antibiotics = c(aminoglycosides(), carbapenems()),
+  syndromic_group = "ward"
+)
 
 # now define a data set with only E. coli
 ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@@ -193,35 +199,41 @@ ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
 # with a custom language, though this will be determined automatically
 # (i.e., this table will be in Spanish on Spanish systems)
 antibiogram(ex1,
-            antibiotics = aminoglycosides(),
-            ab_transform = "name",
-            syndromic_group = ifelse(ex1$ward == "ICU",
-                                     "UCI", "No UCI"),
-            language = "es")
+  antibiotics = aminoglycosides(),
+  ab_transform = "name",
+  syndromic_group = ifelse(ex1$ward == "ICU",
+    "UCI", "No UCI"
+  ),
+  language = "es"
+)
 
 
 # Weighted-incidence syndromic combination antibiogram (WISCA) ---------
 
 # the data set could contain a filter for e.g. respiratory specimens
 antibiogram(example_isolates,
-            antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
-            mo_transform = "gramstain",
-            minimum = 10, # this should be >= 30, but now just as example
-            syndromic_group = ifelse(example_isolates$age >= 65 &
-                                       example_isolates$gender == "M",
-                                     "WISCA Group 1", "WISCA Group 2"))
+  antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
+  mo_transform = "gramstain",
+  minimum = 10, # this should be >= 30, but now just as example
+  syndromic_group = ifelse(example_isolates$age >= 65 &
+    example_isolates$gender == "M",
+  "WISCA Group 1", "WISCA Group 2"
+  )
+)
 
 
 # Generate plots with ggplot2 or base R --------------------------------
 
 ab1 <- antibiogram(example_isolates,
-                   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-                   mo_transform = "gramstain")
+  antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
+  mo_transform = "gramstain"
+)
 ab2 <- antibiogram(example_isolates,
-                   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-                   mo_transform = "gramstain",
-                   syndromic_group = "ward")
-                   
+  antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
+  mo_transform = "gramstain",
+  syndromic_group = "ward"
+)
+
 plot(ab1)
 
 if (requireNamespace("ggplot2")) {

man/get_episode.Rd

@@ -55,9 +55,13 @@ is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
 df[which(get_episode(df$date, 60) == 3), ]
 
 # the functions also work for less than a day, e.g. to include one per hour:
-get_episode(c(Sys.time(),
-              Sys.time() + 60 * 60),
-            episode_days = 1 / 24)
+get_episode(
+  c(
+    Sys.time(),
+    Sys.time() + 60 * 60
+  ),
+  episode_days = 1 / 24
+)
 
 \donttest{
 if (require("dplyr")) {
@@ -71,7 +75,7 @@ if (require("dplyr")) {
     )) \%>\%
     group_by(patient, condition) \%>\%
     mutate(new_episode = is_new_episode(date, 365)) \%>\%
-    select(patient, date, condition, new_episode) \%>\% 
+    select(patient, date, condition, new_episode) \%>\%
     arrange(patient, condition, date)
 }
 
@@ -82,7 +86,7 @@ if (require("dplyr")) {
       patient,
       new_index = get_episode(date, 60),
       new_logical = is_new_episode(date, 60)
-    ) \%>\% 
+    ) \%>\%
     arrange(patient, ward, date)
 }
 
@@ -117,7 +121,6 @@ if (require("dplyr")) {
 # but is_new_episode() has a lot more flexibility than first_isolate(),
 # since you can now group on anything that seems relevant:
 if (require("dplyr")) {
-  
   df \%>\%
     group_by(patient, mo, ward) \%>\%
     mutate(flag_episode = is_new_episode(date, 365)) \%>\%