- Added new algorithm to determine weighted isolates, can now be points or keyantibiotics, see ?first_isolate`

- Function `first_isolate` supports tidyverse-like evaluation of parameters (no need to quote them anymore) - Functions `as.rsi` and `as.mic` now add the package name and version as attribute
2024-12-25 19:26:13 +01:00 · 2018-03-19 20:39:23 +01:00 · 2018-03-19 20:39:23 +01:00 · dd2517ecb7
commit dd2517ecb7
parent 2db25b3b38
9 changed files with 214 additions and 68 deletions
--- a/1
+++ b/1
@ -73,4 +73,5 @@ importFrom(graphics,text)
 importFrom(reshape2,dcast)
 importFrom(rvest,html_nodes)
 importFrom(rvest,html_table)
+importFrom(utils,packageDescription)
 importFrom(xml2,read_html)
--- a/3
+++ b/3
@ -3,6 +3,9 @@
 - Renamed `ablist` to `antibiotics`
 - Added support for character vector in join functions
 - Altered `%like%` to make it case insensitive
+- Added new algorithm to determine weighted isolates, can now be `points` or `keyantibiotics, see `?first_isolate`
+- Function `first_isolate` supports tidyverse-like evaluation of parameters (no need to quote them anymore)
+- Functions `as.rsi` and `as.mic` now add the package name and version as attribute

 ## 0.1.1
 - `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
--- a/R/classes.R
+++ b/R/classes.R
@ -24,6 +24,7 @@
 #' @return New class \code{rsi}
 #' @export
 #' @importFrom dplyr %>%
+#' @importFrom utils packageDescription
 #' @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"))
@ -54,13 +55,15 @@ as.rsi <- function(x) {
        sort()
      list_missing <- paste0('"', list_missing , '"', collapse = ", ")
      warning(na_after - na_before, ' results truncated (',
-              round(((na_after - na_before) / length(x)) / 100),
+              round(((na_after - na_before) / length(x)) * 100),
              '%) that were invalid antimicrobial interpretations: ',
              list_missing, call. = FALSE)
    }
    
    x <- x %>% toupper() %>% factor(levels = c("S", "I", "R"), ordered = TRUE)
    class(x) <- c('rsi', 'ordered', 'factor')
+    attr(x, 'package') <- 'AMR'
+    attr(x, 'package.version') <- packageDescription('AMR')$Version
    x
  }
 }
@ -192,6 +195,7 @@ barplot.rsi <- function(height, ...) {
 #' @return New class \code{mic}
 #' @export
 #' @importFrom dplyr %>%
+#' @importFrom utils packageDescription
 #' @examples
 #' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
 #' is.mic(mic_data)
@ -289,7 +293,7 @@ as.mic <- function(x, na.rm = FALSE) {
        sort()
      list_missing <- paste0('"', list_missing , '"', collapse = ", ")
      warning(na_after - na_before, ' results truncated (',
-              round(((na_after - na_before) / length(x)) / 100),
+              round(((na_after - na_before) / length(x)) * 100),
              '%) that were invalid MICs: ',
              list_missing, call. = FALSE)
    }
@ -298,6 +302,8 @@ as.mic <- function(x, na.rm = FALSE) {
                levels = lvls,
                ordered = TRUE)
    class(x) <- c('mic', 'ordered', 'factor')
+    attr(x, 'package') <- 'AMR'
+    attr(x, 'package.version') <- packageDescription('AMR')$Version
    x
  }
 }
--- a/R/first_isolates.R
+++ b/R/first_isolates.R
@ -20,31 +20,48 @@
 #'
 #' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. 
 #' @param tbl a \code{data.frame} containing isolates.
-#' @param col_date column name of the result date (or date that is was received on the lab)
-#' @param col_patient_id column name of the unique IDs of the patients
-#' @param col_genus column name of the genus of the microorganisms
-#' @param col_species column name of the species of the microorganisms
-#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.
-#' @param col_specimen column name of the specimen type or group
-#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
-#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}.
+#' @param col_date column name of the result date (or date that is was received on the lab), supports tidyverse-like quotation
+#' @param col_patient_id column name of the unique IDs of the patients, supports tidyverse-like quotation
+#' @param col_genus column name of the genus of the microorganisms, supports tidyverse-like quotation
+#' @param col_species column name of the species of the microorganisms, supports tidyverse-like quotation
+#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.
+#' @param col_specimen column name of the specimen type or group, supports tidyverse-like quotation
+#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU), supports tidyverse-like quotation
+#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.
 #' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
-#' @param testcodes_exclude character vector with test codes that should be excluded (caseINsensitive)
+#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
 #' @param icu_exclude logical whether ICU isolates should be excluded
 #' @param filter_specimen specimen group or type that should be excluded
-#' @param output_logical return output as \code{logical} (will else the values \code{0} or \code{1})
-#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate, see Details
+#' @param output_logical return output as \code{logical} (will else be the values \code{0} or \code{1})
+#' @param type type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details
+#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details
+#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details
 #' @param info print progress
-#' @details \strong{Why this is so important} \cr
+#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
 #'     To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
 #'
-#'     \strong{Using parameter \code{points_threshold}} \cr
-#'     To compare key antibiotics, the difference between antimicrobial interpretations will be measured. A difference from I to S|R (or vice versa) means 0.5 points. A difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
+#'     \strong{DETERMINING WEIGHTED ISOLATES} \cr
+#'     \strong{1. Using \code{type = "keyantibiotics"} and parameter \code{ignore_I}} \cr
+#'     To determine weighted isolates, the difference between key antibiotics will be checked. Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I == FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable and fast method. \cr
+#'     \strong{2. Using \code{type = "points"} and parameter \code{points_threshold}} \cr
+#'     To determine weighted isolates, difference between antimicrobial interpretations will be measured with points. A difference from I to S|R (or vice versa) means 0.5 points. A difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. This method is being used by the Infection Prevention department (Dr M. Lokate) of the University Medical Center Groningen (UMCG).
 #' @keywords isolate isolates first
 #' @export
 #' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
 #' @return A vector to add to table, see Examples.
 #' @examples
+#' # septic_patients is a dataset available in the AMR package
+#' ?septic_patients
+#' my_patients <- septic_patients
+#' 
+#' library(dplyr)
+#' my_patients$first_isolate <- my_patients %>%
+#'   left_join_bactlist() %>%
+#'   first_isolate(col_date = date,
+#'                 col_patient_id = patient_id,
+#'                 col_genus = genus,
+#'                 col_species = species)
+#'                 
 #' \dontrun{
 #'
 #' # set key antibiotics to a new variable
@ -90,18 +107,30 @@ first_isolate <- function(tbl,
                          col_genus,
                          col_species,
                          col_testcode = NA,
-                          col_specimen,
-                          col_icu,
+                          col_specimen = NA,
+                          col_icu = NA,
                          col_keyantibiotics = NA,
                          episode_days = 365,
                          testcodes_exclude = '',
                          icu_exclude = FALSE,
                          filter_specimen = NA,
                          output_logical = TRUE,
+                          type = "keyantibiotics",
+                          ignore_I = TRUE,
                          points_threshold = 2,
                          info = TRUE) {
  
-  # controleren of kolommen wel bestaan
+  # support tidyverse-like quotation
+  col_date <- quasiquotate(deparse(substitute(col_date)), col_date)
+  col_patient_id <- quasiquotate(deparse(substitute(col_patient_id)), col_patient_id)
+  col_genus <- quasiquotate(deparse(substitute(col_genus)), col_genus)
+  col_species <- quasiquotate(deparse(substitute(col_species)), col_species)
+  col_testcode <- quasiquotate(deparse(substitute(col_testcode)), col_testcode)
+  col_specimen <- quasiquotate(deparse(substitute(col_specimen)), col_specimen)
+  col_icu <- quasiquotate(deparse(substitute(col_icu)), col_icu)
+  col_keyantibiotics <- quasiquotate(deparse(substitute(col_keyantibiotics)), col_keyantibiotics)
+  
+  # check if columns exist
  check_columns_existance <- function(column, tblname = tbl) {
    if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
      stop('Please check tbl for existance.')
@ -125,7 +154,7 @@ first_isolate <- function(tbl,
  if (is.na(col_testcode)) {
    testcodes_exclude <- NA
  }
-  # testcodes verwijderen die ingevuld zijn
+  # remove testcodes
  if (!is.na(testcodes_exclude[1]) & testcodes_exclude[1] != '' & info == TRUE) {
    cat('Isolates from these test codes will be ignored:\n', toString(testcodes_exclude), '\n')
  }
@ -137,9 +166,13 @@ first_isolate <- function(tbl,
      mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
  }
  
+  if (is.na(col_specimen)) {
+    filter_specimen <- ''
+  }
+  
  specgroup.notice <- ''
  weighted.notice <- ''
-  # filteren op materiaalgroep en sleutelantibiotica gebruiken wanneer deze ingevuld zijn
+  # filter on specimen group and keyantibiotics when they are filled in
  if (!is.na(filter_specimen) & filter_specimen != '') {
    check_columns_existance(col_specimen, tbl)
    if (info == TRUE) {
@ -158,8 +191,7 @@ first_isolate <- function(tbl,
    testcodes_exclude <- ''
  }
  
-  # nieuwe dataframe maken met de oorspronkelijke rij-index, 0-bepaling en juiste sortering
-  #cat('Sorting table...')
+  # create new dataframe with original row index and right sorting
  tbl <- tbl %>%
    mutate(first_isolate_row_index = 1:nrow(tbl),
           eersteisolaatbepaling = 0,
@ -203,7 +235,7 @@ first_isolate <- function(tbl,
    }
    
  } else {
-    # sorteren op materiaal en alleen die rijen analyseren om tijd te besparen
+    # sort on specimen and only analyse these row to save time
    if (icu_exclude == FALSE) {
      if (info == TRUE) {
        cat('Isolates from ICU will *NOT* be ignored.\n')
@ -247,7 +279,7 @@ first_isolate <- function(tbl,
    if (info == TRUE) {
      cat('No isolates found.\n')
    }
-    # NA's maken waar genus niet beschikbaar is
+    # NA's where genus is unavailable
    tbl <- tbl %>%
      mutate(real_first_isolate = if_else(genus == '', NA, FALSE))
    if (output_logical == FALSE) {
@ -263,7 +295,7 @@ first_isolate <- function(tbl,
           genus != '') %>%
    nrow()
  
-  # Analyse van eerste isolaat ----
+  # Analysis of first isolate ----
  all_first <- tbl %>%
    mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
                                     & genus == lag(genus)
@ -277,13 +309,24 @@ first_isolate <- function(tbl,
  
  if (col_keyantibiotics != '') {
    if (info == TRUE) {
-      cat(paste0('Comparing key antibiotics for first weighted isolates (using points threshold of '
-                 , points_threshold, ')...\n'))
+      if (type == 'keyantibiotics') {
+        cat('Comparing key antibiotics for first weighted isolates (')
+        if (ignore_I == FALSE) {
+          cat('NOT ')
+        }
+        cat('ignoring I)...\n')
+      }
+      if (type == 'points') {
+        cat(paste0('Comparing antibiotics for first weighted isolates (using points threshold of '
+                   , points_threshold, ')...\n'))
+      }
    }
    all_first <- all_first %>%
      mutate(key_ab_lag = lag(key_ab)) %>%
      mutate(key_ab_other = !key_antibiotics_equal(x = key_ab,
                                                   y = key_ab_lag,
+                                                   type = type,
+                                                   ignore_I = ignore_I,
                                                   points_threshold = points_threshold,
                                                   info = info)) %>%
      mutate(
@ -312,9 +355,9 @@ first_isolate <- function(tbl,
            FALSE))
  }
  
-  # allereerst isolaat als TRUE
+  # first one as TRUE
  all_first[row.start, 'real_first_isolate'] <- TRUE
-  # geen testen die uitgesloten moeten worden, of ICU
+  # no tests that should be included, or ICU
  if (!is.na(col_testcode)) {
    all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE
  }
@ -322,7 +365,7 @@ first_isolate <- function(tbl,
    all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
  }
  
-  # NA's maken waar genus niet beschikbaar is
+  # NA's where genus is unavailable
  all_first <- all_first %>%
    mutate(real_first_isolate = if_else(genus == '', NA, real_first_isolate))
  
@ -353,7 +396,7 @@ first_isolate <- function(tbl,
 #' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
 #' @param col_bactcode column of bacteria IDs in \code{tbl}; these should occur in \code{bactlist$bactid}, see \code{\link{bactlist}}
 #' @param info print warnings
-#' @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.
+#' @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, case-insensitive
 #' @export
 #' @importFrom dplyr %>% mutate if_else 
 #' @return Character of length 1.
@ -394,6 +437,13 @@ key_antibiotics <- function(tbl,
                clin, clox, doxy, gent, line, mero, peni,
                pita, rifa, teic, trsu, vanc)
  col.list <- col.list[!is.na(col.list)]
+  for (i in 1:length(col.list)) {
+    if (toupper(col.list[i]) %in% colnames(tbl)) {
+      col.list[i] <- toupper(col.list[i])
+    } else if (tolower(col.list[i]) %in% colnames(tbl)) {
+      col.list[i] <- tolower(col.list[i])
+    }
+  }
  if (!all(col.list %in% colnames(tbl))) {
    if (info == TRUE) {
      warning('These columns do not exist and will be ignored:\n',
@ -402,6 +452,25 @@ key_antibiotics <- function(tbl,
              call. = FALSE)
    }
  }
+  amox <- col.list[1]
+  cfot <- col.list[2]
+  cfta <- col.list[3]
+  cftr <- col.list[4]
+  cfur <- col.list[5]
+  cipr <- col.list[6]
+  clar <- col.list[7]
+  clin <- col.list[8]
+  clox <- col.list[9]
+  doxy <- col.list[10]
+  gent <- col.list[11]
+  line <- col.list[12]
+  mero <- col.list[13]
+  peni <- col.list[14]
+  pita <- col.list[15]
+  rifa <- col.list[16]
+  teic <- col.list[17]
+  trsu <- col.list[18]
+  vanc <- col.list[19]
  
  # join bactlist
  tbl <- tbl %>% left_join_bactlist(col_bactcode)
@ -448,9 +517,16 @@ key_antibiotics <- function(tbl,

 #' @importFrom dplyr progress_estimated %>%
 #' @noRd
-key_antibiotics_equal <- function(x, y, points_threshold = 2, info = FALSE) {
+key_antibiotics_equal <- function(x,
+                                  y, 
+                                  type = c("keyantibiotics", "points"), 
+                                  ignore_I = TRUE,
+                                  points_threshold = 2, 
+                                  info = FALSE) {
  # x is active row, y is lag
  
+  type <- type[1]
+
  if (length(x) != length(y)) {
    stop('Length of `x` and `y` must be equal.')
  }
@ -484,17 +560,42 @@ key_antibiotics_equal <- function(x, y, points_threshold = 2, info = FALSE) {
      
    } else {
      
-      # count points for every single character:
-      # - no change is 0 points
-      # - I <-> S|R is 0.5 point
-      # - S|R <-> R|S is 1 point
-      # use the levels of as.rsi (S = 1, I = 2, R = 3)
+      x2 <- strsplit(x[i], "")[[1]]
+      y2 <- strsplit(y[i], "")[[1]]
      
-      x2 <- strsplit(x[i], "")[[1]] %>% as.rsi() %>% as.double()
-      y2 <- strsplit(y[i], "")[[1]] %>% as.rsi() %>% as.double()
+      if (type == 'points') {
+        # count points for every single character:
+        # - no change is 0 points
+        # - I <-> S|R is 0.5 point
+        # - S|R <-> R|S is 1 point
+        # use the levels of as.rsi (S = 1, I = 2, R = 3)
        
-      points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
-      result[i] <- ((points / 2) >= points_threshold)
+        suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
+        suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
+        
+        points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
+        result[i] <- ((points / 2) >= points_threshold)
+        
+      } else if (type == 'keyantibiotics') {
+        # check if key antibiotics are exactly the same
+        # also possible to ignore I, so only S <-> R and S <-> R are counted
+        if (ignore_I == TRUE) {
+          valid_chars <- c('S', 's', 'R', 'r')
+        } else {
+          valid_chars <- c('S', 's', 'I', 'i', 'R', 'r')
+        }
+        
+        # remove invalid values (like "-", NA) on both locations
+        x2[which(!x2 %in% valid_chars)] <- '?'
+        x2[which(!y2 %in% valid_chars)] <- '?'
+        y2[which(!x2 %in% valid_chars)] <- '?'
+        y2[which(!y2 %in% valid_chars)] <- '?'
+        
+        result[i] <- all(x2 == y2)
+        
+      } else {
+        stop('No valid value for type, must be `points` or `keyantibiotics`. See ?first_isolate.')
+      }
    }
  }
  if (info == TRUE) {
--- a/R/join.R
+++ b/R/join.R
@ -13,6 +13,9 @@
 #' @examples 
 #' left_join_bactlist("STAAUR")
 #' 
+#' library(dplyr)
+#' septic_patients %>% left_join_bactlist()
+#' 
 #' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
 #'                             to = as.Date("2018-01-07"),
 #'                             by = 1),
--- a/README.md
+++ b/README.md
@ -18,18 +18,27 @@ This package is available on CRAN and also here on GitHub.
 ### From CRAN (recommended, latest stable version)
 [![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
 [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
+[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/AMR)](http://cran.r-project.org/package=AMR)

- RStudio:
-  - Click on `Tools` and then `Install Packages...`
-  -  Type in `AMR` and press <kbd>Install</kbd>
-
- R console:
+- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px">In R:
  - `install.packages("AMR")`
  
+- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> In RStudio:
+  - Click on `Tools` and then `Install Packages...`
+  - Type in `AMR` and press <kbd>Install</kbd>
+  
+- <img src="https://exploratory.io/favicon.ico" alt="Exploratory favicon" height="20px"> In Exploratory.io:
+  - Click on your username at the right hand side top
+  - Click on `R Packages`
+  - Click on the `Install` tab
+  - Type in `AMR` and press <kbd>Install</kbd>
+  - Once it’s installed it will show up in the `User Packages` section under the `Packages` tab.
+
 ### From GitHub (latest development version)
 [![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
 [![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)
-[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)
+[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg)](https://github.com/msberends/AMR/commits/master)
+[![Code_Coverage](https://codecov.io/gh/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gh/msberends/AMR)

 ```r
 install.packages("devtools")
--- a/man/first_isolate.Rd
+++ b/man/first_isolate.Rd
@ -5,41 +5,46 @@
 \title{Determine first (weighted) isolates}
 \usage{
 first_isolate(tbl, col_date, col_patient_id, col_genus, col_species,
-  col_testcode = NA, col_specimen, col_icu, col_keyantibiotics = NA,
-  episode_days = 365, testcodes_exclude = "", icu_exclude = FALSE,
-  filter_specimen = NA, output_logical = TRUE, points_threshold = 2,
+  col_testcode = NA, col_specimen = NA, col_icu = NA,
+  col_keyantibiotics = NA, episode_days = 365, testcodes_exclude = "",
+  icu_exclude = FALSE, filter_specimen = NA, output_logical = TRUE,
+  type = "keyantibiotics", ignore_I = TRUE, points_threshold = 2,
  info = TRUE)
 }
 \arguments{
 \item{tbl}{a \code{data.frame} containing isolates.}

-\item{col_date}{column name of the result date (or date that is was received on the lab)}
+\item{col_date}{column name of the result date (or date that is was received on the lab), supports tidyverse-like quotation}

-\item{col_patient_id}{column name of the unique IDs of the patients}
+\item{col_patient_id}{column name of the unique IDs of the patients, supports tidyverse-like quotation}

-\item{col_genus}{column name of the genus of the microorganisms}
+\item{col_genus}{column name of the genus of the microorganisms, supports tidyverse-like quotation}

-\item{col_species}{column name of the species of the microorganisms}
+\item{col_species}{column name of the species of the microorganisms, supports tidyverse-like quotation}

-\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.}
+\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.}

-\item{col_specimen}{column name of the specimen type or group}
+\item{col_specimen}{column name of the specimen type or group, supports tidyverse-like quotation}

-\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
+\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU), supports tidyverse-like quotation}

-\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}.}
+\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.}

 \item{episode_days}{episode in days after which a genus/species combination will be determined as 'first isolate' again}

-\item{testcodes_exclude}{character vector with test codes that should be excluded (caseINsensitive)}
+\item{testcodes_exclude}{character vector with test codes that should be excluded (case-insensitive)}

 \item{icu_exclude}{logical whether ICU isolates should be excluded}

 \item{filter_specimen}{specimen group or type that should be excluded}

-\item{output_logical}{return output as \code{logical} (will else the values \code{0} or \code{1})}
+\item{output_logical}{return output as \code{logical} (will else be the values \code{0} or \code{1})}

-\item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate, see Details}
+\item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details}
+
+\item{ignore_I}{logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details}
+
+\item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details}

 \item{info}{print progress}
 }
@ -50,13 +55,28 @@ A vector to add to table, see Examples.
 Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
 }
 \details{
-\strong{Why this is so important} \cr
+\strong{WHY THIS IS SO IMPORTANT} \cr
    To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.

-    \strong{Using parameter \code{points_threshold}} \cr
-    To compare key antibiotics, the difference between antimicrobial interpretations will be measured. A difference from I to S|R (or vice versa) means 0.5 points. A difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
+    \strong{DETERMINING WEIGHTED ISOLATES} \cr
+    \strong{1. Using \code{type = "keyantibiotics"} and parameter \code{ignore_I}} \cr
+    To determine weighted isolates, the difference between key antibiotics will be checked. Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I == FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable and fast method. \cr
+    \strong{2. Using \code{type = "points"} and parameter \code{points_threshold}} \cr
+    To determine weighted isolates, difference between antimicrobial interpretations will be measured with points. A difference from I to S|R (or vice versa) means 0.5 points. A difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. This method is being used by the Infection Prevention department (Dr M. Lokate) of the University Medical Center Groningen (UMCG).
 }
 \examples{
+# septic_patients is a dataset available in the AMR package
+?septic_patients
+my_patients <- septic_patients
+
+library(dplyr)
+my_patients$first_isolate <- my_patients \%>\%
+  left_join_bactlist() \%>\%
+  first_isolate(col_date = date,
+                col_patient_id = patient_id,
+                col_genus = genus,
+                col_species = species)
+                
 \dontrun{

 # set key antibiotics to a new variable
--- a/man/join.Rd
+++ b/man/join.Rd
@ -41,6 +41,9 @@ As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, character
 \examples{
 left_join_bactlist("STAAUR")

+library(dplyr)
+septic_patients \%>\% left_join_bactlist()
+
 df <- data.frame(date = seq(from = as.Date("2018-01-01"),
                            to = as.Date("2018-01-07"),
                            by = 1),
--- a/man/key_antibiotics.Rd
+++ b/man/key_antibiotics.Rd
@ -18,7 +18,7 @@ key_antibiotics(tbl, col_bactcode = "bactid", info = TRUE, amcl = "amcl",

 \item{info}{print warnings}

-\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.}
+\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, case-insensitive}
 }
 \value{
 Character of length 1.