First CRAN submission edits

R/EUCAST.R

@@ -28,6 +28,7 @@
 #' @rdname EUCAST
 #' @export
 #' @importFrom dplyr %>% left_join select
+#' @return table with edited variables of antibiotics.
 #' @source
 #'   EUCAST Expert Rules Version 2.0: \cr
 #'   Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
@@ -37,7 +38,7 @@
 #'   \url{http://www.eucast.org/expert_rules_and_intrinsic_resistance}
 #' @examples
 #' \dontrun{
-#' tbl <- interpretive_reading(tbl)
+#' tbl <- EUCAST_rules(tbl)
 #' }
 EUCAST_rules <- function(tbl,
                          col_bactcode = 'bacteriecode',

R/atc.R

@@ -18,7 +18,7 @@
 
 #' Properties of an ATC code
 #'
-#' 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.}
 #' @param atc_code a character or character vector with ATC code(s) of antibiotic(s)
 #' @param property property of an ATC code. Valid values are \code{"ATC code"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"} en \code{"Note"}.
 #' @param administration type of administration, see \emph{Details}
@@ -54,6 +54,11 @@
 #' @importFrom xml2 read_html
 #' @importFrom rvest html_nodes html_table
 #' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
+#' @examples 
+#' \donttest{
+#' atc_property("J01CA04", "DDD", "O") # oral DDD of amoxicillin
+#' atc_property("J01CA04", "DDD", "P") # parenteral DDD of amoxicillin
+#' }
 atc_property <- function(atc_code,
                          property,
                          administration = 'O',
@@ -128,6 +133,7 @@ atc_property <- function(atc_code,
 #' @param textbetween text to put between multiple returned texts
 #' @param tolower return output as lower case with function \code{\link{tolower}}.
 #' @keywords ab antibiotics
+#' @source \code{\link{ablist}}
 #' @export
 #' @importFrom dplyr %>% filter select slice 
 #' @examples
@@ -148,8 +154,6 @@ atc_property <- function(atc_code,
 #'
 #' abname("J01CR02", from = "atc", to = "umcg")
 #' # "AMCL"
-#'
-#' @source \code{\link{ablist}}
 abname <- function(abcode, from = 'umcg', to = 'official', textbetween = ' + ', tolower = FALSE) {
   
   ablist <- AMR::ablist

R/classes.R

@@ -26,8 +26,16 @@
 #' @importFrom dplyr %>%
 #' @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)
+#' }
 as.rsi <- function(x) {
   if (is.rsi(x)) {
     x
@@ -157,6 +165,17 @@ plot.rsi <- function(x, ...) {
 #' @return New class \code{mic}
 #' @export
 #' @importFrom dplyr %>%
+#' @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)
+#' }
 as.mic <- function(x, na.rm = FALSE) {
   if (is.mic(x)) {
     x

R/first_isolates.R

@@ -46,6 +46,7 @@
 #' @examples
 #' \dontrun{
 #'
+#' # set key antibiotics to a new variable
 #' tbl$keyab <- key_antibiotics(tbl)
 #'
 #' tbl$first_isolate <-
@@ -355,7 +356,13 @@ first_isolate <- function(tbl,
 #' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics.
 #' @export
 #' @importFrom dplyr %>% mutate if_else 
+#' @return Character of length 1.
 #' @seealso \code{\link{mo_property}} \code{\link{ablist}}
+#' @examples 
+#' \donttest{
+#' #' # set key antibiotics to a new variable
+#' tbl$keyab <- key_antibiotics(tbl)
+#' }
 key_antibiotics <- function(tbl,
                             col_bactcode = 'bacteriecode',
                             info = TRUE,
@@ -439,15 +446,17 @@ key_antibiotics <- function(tbl,
   
 }
 
-#' Compare key antibiotics
-#'
-#' Check whether two text values with key antibiotics match. Supports vectors.
-#' @param x,y tekst (or multiple text vectors) with antimicrobial interpretations
-#' @param 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)
-#' @param info print progress
-#' @return logical
-#' @export
-#' @seealso \code{\link{key_antibiotics}}
+# Compare key antibiotics
+#
+# Check whether two text values with key antibiotics match. Supports vectors.
+# @param x,y tekst (or multiple text vectors) with antimicrobial interpretations
+# @param 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)
+# @param info print progress
+# @return logical
+# @export
+# @seealso \code{\link{key_antibiotics}}
+
+# only internal use
 key_antibiotics_equal <- function(x, y, ignore_I = TRUE, info = FALSE) {
   if (length(x) != length(y)) {
     stop('Length of `x` and `y` must be equal.')

R/join.R

@@ -1,4 +1,4 @@
-#' Join van tabel en \code{bactlist}
+#' Join a table with \code{bactlist}
 #'
 #' Join the list of microorganisms \code{\link{bactlist}} easily to an existing table.
 #' @rdname join
@@ -9,6 +9,17 @@
 #' @param ... other parameters to pass trhough to \code{dplyr::\link[dplyr]{join}}.
 #' @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.
 #' @export
+#' @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)
 inner_join_bactlist <- function(x, by = 'bactid', ...) {
   # no name set to `by` parameter
   if (is.null(names(by))) {

R/rsi_analysis.R

@@ -167,7 +167,7 @@ rsi_df <- function(tbl,
 
 #' Resistance of isolates
 #'
-#' 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.
 #' @param ab1,ab2 list with interpretations of an antibiotic
 #' @inheritParams rsi_df
 #' @details This function uses the \code{\link{rsi_df}} function internally.
@@ -177,20 +177,19 @@ rsi_df <- function(tbl,
 #' @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")
 #' }
 rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FALSE, info = FALSE, warning = FALSE) {
   functietekst <- as.character(match.call())
@@ -258,6 +257,7 @@ rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FA
 #' 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") %>%