First CRAN submission edits

man/EUCAST.Rd

@@ -44,11 +44,14 @@ interpretive_reading(...)
 
 \item{...}{parameters that are passed on to \code{EUCAST_rules}}
 }
+\value{
+table with edited variables of antibiotics.
+}
 \description{
 Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
 }
 \examples{
 \dontrun{
-tbl <- interpretive_reading(tbl)
+tbl <- EUCAST_rules(tbl)
 }
 }

man/abname.Rd

@@ -40,7 +40,6 @@ abname("AMCL", to = "atc")
 
 abname("J01CR02", from = "atc", to = "umcg")
 # "AMCL"
-
 }
 \keyword{ab}
 \keyword{antibiotics}

man/as.mic.Rd

@@ -20,3 +20,15 @@ New class \code{mic}
 \description{
 This transforms a vector to a new class\code{mic}, which is an ordered factor valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
 }
+\examples{
+mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
+is.mic(mic_data)
+plot(mic_data)
+
+\donttest{
+library(dplyr)
+tbl \%>\%
+  mutate_at(vars(ends_with("_mic")), as.mic)
+sapply(mic_data, is.mic)
+}
+}

man/as.rsi.Rd

@@ -20,6 +20,14 @@ This transforms a vector to a new class \code{rsi}, which is an ordered factor w
 }
 \examples{
 rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-
 rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
+is.rsi(rsi_data)
+plot(rsi_data)
+
+\donttest{
+library(dplyr)
+tbl \%>\%
+  mutate_at(vars(ends_with("_rsi")), as.rsi)
+sapply(mic_data, is.rsi)
+}
 }

man/atc_property.Rd

@@ -20,7 +20,7 @@ atc_property(atc_code, property, administration = "O",
 \item{url}{url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.}
 }
 \description{
-Gets data from the WHO to determine properties of an ATC of e.g. an antibiotic.
+Gets data from the WHO to determine properties of an ATC of e.g. an antibiotic. \strong{This function requires an internet connection.}
 }
 \details{
 Abbreviations for the property \code{"Adm.R"} (parameter \code{administration}):
@@ -49,3 +49,9 @@ Abbreviations for the property \code{"U"} (unit):
   \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
 }
 }
+\examples{
+\donttest{
+atc_property("J01CA04", "DDD", "O") # oral DDD of amoxicillin
+atc_property("J01CA04", "DDD", "P") # parenteral DDD of amoxicillin
+}
+}

man/first_isolate.Rd

@@ -57,6 +57,7 @@ To conduct an analysis of antimicrobial resistance, you should only include the
 \examples{
 \dontrun{
 
+# set key antibiotics to a new variable
 tbl$keyab <- key_antibiotics(tbl)
 
 tbl$first_isolate <-

man/join.Rd

@@ -9,7 +9,7 @@
 \alias{full_join_bactlist}
 \alias{semi_join_bactlist}
 \alias{anti_join_bactlist}
-\title{Join van tabel en \code{bactlist}}
+\title{Join a table with \code{bactlist}}
 \usage{
 inner_join_bactlist(x, by = "bactid", ...)
 
@@ -36,3 +36,15 @@ Join the list of microorganisms \code{\link{bactlist}} easily to an existing tab
 \details{
 As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
 }
+\examples{
+df <- data.frame(date = seq(from = as.Date("2018-01-01"),
+                            to = as.Date("2018-01-07"),
+                            by = 1),
+                 bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
+                                 "ESCCOL", "ESCCOL", "ESCCOL"),
+                 stringsAsFactors = FALSE)
+                 
+colnames(df)
+df2 <- left_join_bactlist(df, "bacteria_id")
+colnames(df2)
+}

man/key_antibiotics.Rd

@@ -20,9 +20,18 @@ key_antibiotics(tbl, col_bactcode = "bacteriecode", info = TRUE,
 
 \item{amcl, amox, cfot, cfta, cftr, cfur, cipr, clar, clin, clox, doxy, gent, line, mero, peni, pita, rifa, teic, trsu, vanc}{column names of antibiotics.}
 }
+\value{
+Character of length 1.
+}
 \description{
 Key antibiotics based on bacteria ID
 }
+\examples{
+\donttest{
+#' # set key antibiotics to a new variable
+tbl$keyab <- key_antibiotics(tbl)
+}
+}
 \seealso{
 \code{\link{mo_property}} \code{\link{ablist}}
 }

man/key_antibiotics_equal.Rd

@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/first_isolates.R
-\name{key_antibiotics_equal}
-\alias{key_antibiotics_equal}
-\title{Compare key antibiotics}
-\usage{
-key_antibiotics_equal(x, y, ignore_I = TRUE, info = FALSE)
-}
-\arguments{
-\item{x, y}{tekst (or multiple text vectors) with antimicrobial interpretations}
-
-\item{ignore_I}{ignore \code{"I"} as antimicrobial interpretation of key antibiotics (with \code{FALSE}, changes in antibiograms from S to I and I to R will be interpreted as difference)}
-
-\item{info}{print progress}
-}
-\value{
-logical
-}
-\description{
-Check whether two text values with key antibiotics match. Supports vectors.
-}
-\seealso{
-\code{\link{key_antibiotics}}
-}

man/rsi.Rd

@@ -24,28 +24,27 @@ rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30, percent = FALSE,
 Double or, when \code{percent = TRUE}, a character.
 }
 \description{
-This function can be used in \code{\link[dplyr]{summarise}}, see \emph{Examples}. CaBerekent het percentage S, SI, I, IR of R van een lijst isolaten.
+This function can be used in \code{dplyr}s \code{\link[dplyr]{summarise}}, see \emph{Examples}. Calculate the percentage S, SI, I, IR or R of a vector of isolates.
 }
 \details{
 This function uses the \code{\link{rsi_df}} function internally.
 }
 \examples{
 \dontrun{
+tbl \%>\%
+  group_by(hospital) \%>\%
+  summarise(cipr = rsi(cipr))
+  
 tbl \%>\%
   group_by(year, hospital) \%>\%
   summarise(
     isolates = n(),
-    cipro = rsi(cipr, percent = TRUE),
-    amoxi = rsi(amox, percent = TRUE)
-  )
+    cipro = rsi(cipr \%>\% as.rsi(), percent = TRUE),
+    amoxi = rsi(amox \%>\% as.rsi(), percent = TRUE))
+    
+rsi(as.rsi(isolates$amox))
 
-tbl \%>\%
-  group_by(hospital) \%>\%
-  summarise(cipr = rsi(cipr))
-
-rsi(isolates$amox)
-
-rsi(isolates$amcl, interpretation = "S")
+rsi(as.rsi(isolates$amcl), interpretation = "S")
 }
 }
 \keyword{antibiotics}

man/rsi_predict.Rd

@@ -40,6 +40,7 @@ Create a prediction model to predict antimicrobial resistance for the next years
 rsi_predict(tbl[which(first_isolate == TRUE & genus == "Haemophilus"),], "amcl")
   
 # or with dplyr so you can actually read it:
+library(dplyr)
 tbl \%>\%
   filter(first_isolate == TRUE,
          genus == "Haemophilus") \%>\%