diff --git a/DESCRIPTION b/DESCRIPTION
index d76ea4a0..e4009bc4 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: AMR
-Version: 0.5.0.9005
-Date: 2018-12-15
+Version: 0.5.0.9007
+Date: 2018-12-22
 Title: Antimicrobial Resistance Analysis
 Authors@R: c(
     person(
diff --git a/NAMESPACE b/NAMESPACE
index 1f82d17b..11acfd14 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,7 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(as.data.frame,atc)
-S3method(as.data.frame,bactid)
 S3method(as.data.frame,frequency_tbl)
 S3method(as.data.frame,mo)
 S3method(as.double,mic)
@@ -21,13 +20,11 @@ S3method(plot,frequency_tbl)
 S3method(plot,mic)
 S3method(plot,rsi)
 S3method(print,atc)
-S3method(print,bactid)
 S3method(print,frequency_tbl)
 S3method(print,mic)
 S3method(print,mo)
 S3method(print,rsi)
 S3method(pull,atc)
-S3method(pull,bactid)
 S3method(pull,mo)
 S3method(skewness,data.frame)
 S3method(skewness,default)
@@ -50,7 +47,6 @@ export(age)
 export(age_groups)
 export(anti_join_microorganisms)
 export(as.atc)
-export(as.bactid)
 export(as.mic)
 export(as.mo)
 export(as.rsi)
@@ -68,6 +64,8 @@ export(count_df)
 export(eucast_exceptional_phenotypes)
 export(eucast_rules)
 export(facet_rsi)
+export(filter_first_isolate)
+export(filter_first_weighted_isolate)
 export(first_isolate)
 export(freq)
 export(frequency_tbl)
@@ -77,12 +75,10 @@ export(geom_rsi)
 export(get_locale)
 export(ggplot_rsi)
 export(guess_atc)
-export(guess_bactid)
 export(guess_mo)
 export(inner_join_microorganisms)
 export(interpretive_reading)
 export(is.atc)
-export(is.bactid)
 export(is.mic)
 export(is.mo)
 export(is.rsi)
@@ -137,7 +133,6 @@ export(skewness)
 export(theme_rsi)
 export(top_freq)
 exportMethods(as.data.frame.atc)
-exportMethods(as.data.frame.bactid)
 exportMethods(as.data.frame.frequency_tbl)
 exportMethods(as.data.frame.mo)
 exportMethods(as.double.mic)
@@ -158,13 +153,11 @@ exportMethods(plot.frequency_tbl)
 exportMethods(plot.mic)
 exportMethods(plot.rsi)
 exportMethods(print.atc)
-exportMethods(print.bactid)
 exportMethods(print.frequency_tbl)
 exportMethods(print.mic)
 exportMethods(print.mo)
 exportMethods(print.rsi)
 exportMethods(pull.atc)
-exportMethods(pull.bactid)
 exportMethods(pull.mo)
 exportMethods(skewness)
 exportMethods(skewness.data.frame)
@@ -214,6 +207,7 @@ importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,mutate_all)
 importFrom(dplyr,mutate_at)
+importFrom(dplyr,n)
 importFrom(dplyr,n_distinct)
 importFrom(dplyr,progress_estimated)
 importFrom(dplyr,pull)
diff --git a/NEWS.md b/NEWS.md
index 6f70e83f..96b766ed 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,10 +1,25 @@
 # 0.5.0.90xx (latest development version)
 
 #### New
+* **BREAKING**: removed deprecated functions, parameters and references to 'bactid'. Use `as.mo` to identify an MO code.
+* Support for `dplyr` version 0.8.0
 * Function `mo_failures` to review values that could not be coerced to a valid MO code, using `as.mo`. This latter function will now only show a maximum of 25 uncoerced values.
 * Function `mo_renamed` to get a list of all returned values from `as.mo` that have had taxonomic renaming
 * Function `age` to calculate the (patients) age in years
-* Function `age_groups` to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis (per age group).
+* Function `age_groups` to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
+* Functions `filter_first_isolate` and `filter_first_weighted_isolate()` to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
+  ```r
+  septic_patients %>% filter_first_isolate()
+  # or
+  filter_first_isolate(septic_patients)
+  ```
+  is the same as:
+  ```r
+  septic_patients %>%
+    mutate(only_firsts = first_isolate(septic_patients, ...)) %>%
+    filter(only_firsts == TRUE) %>%
+    select(-only_firsts)
+  ```
 
 #### Changed
 * Improvements for `as.mo`:
@@ -18,6 +33,8 @@
 * Function `first_isolate`:
   * Will now use a column named like "patid" for the patient ID (parameter `col_patientid`), when this parameter was left blank
   * Will now use a column named like "key(...)ab" or "key(...)antibiotics" for the key antibiotics (parameter `col_keyantibiotics`), when this parameter was left blank
+  * Removed parameter `output_logical`, the function will now always return a logical value
+  * Renamed parameter `filter_specimen` to `specimen_group`, although using `filter_specimen` will still work
 * A note to the manual pages of the `portion` functions, that low counts can influence the outcome and that the `portion` functions may camouflage this, since they only return the portion (albeit being dependent on the `minimum` parameter)
 * Function `mo_taxonomy` now contains the kingdom too
 * Function `first_isolate` will now use a column named like "patid" for the patient ID, when this parameter was left blank
@@ -29,7 +46,11 @@
   * Now honours the `decimal.mark` setting, which just like `format` defaults to `getOption("OutDec")`
   * The new `big.mark` parameter will at default be `","` when `decimal.mark = "."` and `"."` otherwise
   * Fix for header text where all observations are `NA`
+  * New parameter `droplevels` to exclude empty factor levels when input is a factor
+  * Factor levels will be in header when present
 * Function `scale_y_percent` now has the `limits` parameter
+* Automatic parameter filling for `mdro`, `key_antibiotics` and `eucast_rules`
+* Updated examples for resistance prediction (`resistance_predict` function)
 
 #### Other
 * Updated licence text to emphasise GPL 2.0 and that this is an R package.
diff --git a/R/age.R b/R/age.R
index b1268344..3ab523cc 100644
--- a/R/age.R
+++ b/R/age.R
@@ -19,28 +19,28 @@
 #' Age in years of individuals
 #'
 #' Calculates age in years based on a reference date, which is the sytem time at default.
-#' @param x date(s) - will be coerced with \code{\link{as.POSIXlt}}
-#' @param y reference date(s) - defaults to \code{\link{Sys.Date}} - will be coerced with \code{\link{as.POSIXlt}}
+#' @param x date(s), will be coerced with \code{\link{as.POSIXlt}}
+#' @param reference reference date(s) (defaults to today), will be coerced with \code{\link{as.POSIXlt}}
 #' @return Integer (no decimals)
-#' @seealso age_groups
+#' @seealso \code{\link{age_groups}} to splits age into groups
 #' @importFrom dplyr if_else
 #' @export
-age <- function(x, y = Sys.Date()) {
-  if (length(x) != length(y)) {
-    if (length(y) == 1) {
-      y <- rep(y, length(x))
+age <- function(x, reference = Sys.Date()) {
+  if (length(x) != length(reference)) {
+    if (length(reference) == 1) {
+      reference <- rep(reference, length(x))
     } else {
-      stop("`x` and `y` must be of same length, or `y` must be of length 1.")
+      stop("`x` and `reference` must be of same length, or `reference` must be of length 1.")
     }
   }
   x <- base::as.POSIXlt(x)
-  y <- base::as.POSIXlt(y)
-  if (any(y < x)) {
-    stop("`y` cannot be lower (older) than `x`.")
+  reference <- base::as.POSIXlt(reference)
+  if (any(reference < x)) {
+    stop("`reference` cannot be lower (older) than `x`.")
   }
-  years_gap <- y$year - x$year
+  years_gap <- reference$year - x$year
   # from https://stackoverflow.com/a/25450756/4575331
-  ages <- if_else(y$mon < x$mon | (y$mon == x$mon & y$mday < x$mday),
+  ages <- if_else(reference$mon < x$mon | (reference$mon == x$mon & reference$mday < x$mday),
          as.integer(years_gap - 1),
          as.integer(years_gap))
   if (any(ages > 120)) {
@@ -51,9 +51,9 @@ age <- function(x, y = Sys.Date()) {
 
 #' Split ages into age groups
 #'
-#' Splits ages into groups defined by the \code{split} parameter.
+#' Split ages into age groups defined by the \code{split} parameter. This allows for easier demographic (antimicrobial resistance) analysis.
 #' @param x age, e.g. calculated with \code{\link{age}}
-#' @param split_at values to split \code{x}, defaults to 0-11, 12-24, 26-54, 55-74 and 75+. See Details.
+#' @param split_at values to split \code{x} at, defaults to age groups 0-11, 12-24, 26-54, 55-74 and 75+. See Details.
 #' @details To split ages, the input can be:
 #' \itemize{
 #'   \item{A numeric vector. A vector of \code{c(10, 20)} will split on 0-9, 10-19 and 20+. A value of only \code{50} will split on 0-49 and 50+.
@@ -68,7 +68,7 @@ age <- function(x, y = Sys.Date()) {
 #' }
 #' @keywords age_group age
 #' @return Ordered \code{\link{factor}}
-#' @seealso age
+#' @seealso \code{\link{age}} to determine ages based on one or more reference dates
 #' @export
 #' @examples
 #' ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)
diff --git a/R/deprecated.R b/R/deprecated.R
index 88e0b91f..f26f6119 100644
--- a/R/deprecated.R
+++ b/R/deprecated.R
@@ -23,57 +23,6 @@
 #' @keywords internal
 #' @name AMR-deprecated
 #' @rdname AMR-deprecated
-as.bactid <- function(...) {
-  .Deprecated("as.mo", package = "AMR")
-  as.mo(...)
-}
-
-#' @rdname AMR-deprecated
-#' @export
-is.bactid <- function(...) {
-  .Deprecated(new = "is.mo", package = "AMR")
-  is.mo(...)
-}
-
-#' @rdname AMR-deprecated
-#' @export
-guess_bactid <- function(...) {
-  .Deprecated(new = "guess_mo", package = "AMR")
-  guess_mo(...)
-}
-
-#' @exportMethod print.bactid
-#' @export
-#' @noRd
-print.bactid <- function(x, ...) {
-  cat("Class 'bactid'\n")
-  print.default(as.character(x), quote = FALSE)
-}
-
-#' @exportMethod as.data.frame.bactid
-#' @export
-#' @noRd
-as.data.frame.bactid <- function (x, ...) {
-  # same as as.data.frame.character but with removed stringsAsFactors
-  nm <- paste(deparse(substitute(x), width.cutoff = 500L),
-              collapse = " ")
-  if (!"nm" %in% names(list(...))) {
-    as.data.frame.vector(x, ..., nm = nm)
-  } else {
-    as.data.frame.vector(x, ...)
-  }
-}
-
-#' @exportMethod pull.bactid
-#' @export
-#' @importFrom dplyr pull
-#' @noRd
-pull.bactid <- function(.data, ...) {
-  pull(as.data.frame(.data), ...)
-}
-
-#' @rdname AMR-deprecated
-#' @export
 ratio <- function(x, ratio) {
   .Deprecated(package = "AMR")
 
diff --git a/R/eucast_rules.R b/R/eucast_rules.R
index 8040bd2f..38167f36 100755
--- a/R/eucast_rules.R
+++ b/R/eucast_rules.R
@@ -24,7 +24,6 @@
 #' @param rules a character vector that specifies which rules should be applied - one or more of \code{c("breakpoints", "expert", "other", "all")}
 #' @param verbose a logical to indicate whether extensive info should be returned as a \code{data.frame} with info about which rows and columns are effected
 #' @param amcl,amik,amox,ampi,azit,azlo,aztr,cefa,cfep,cfot,cfox,cfra,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,mezl,mino,moxi,nali,neom,neti,nitr,norf,novo,oflo,oxac,peni,pipe,pita,poly,pris,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column name of an antibiotic, see Antibiotics
-#' @param col_bactid deprecated, use \code{col_mo} instead.
 #' @param ... parameters that are passed on to \code{eucast_rules}
 #' @inheritParams first_isolate
 #' @section Antibiotics:
@@ -217,8 +216,7 @@ eucast_rules <- function(tbl,
                          tobr = 'tobr',
                          trim = 'trim',
                          trsu = 'trsu',
-                         vanc = 'vanc',
-                         col_bactid = NULL) {
+                         vanc = 'vanc') {
 
   EUCAST_VERSION_BREAKPOINTS <- "8.1, 2018"
   EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016"
@@ -229,12 +227,12 @@ eucast_rules <- function(tbl,
 
   # try to find columns based on type
   # -- mo
-  if (!is.null(col_bactid)) {
-    col_mo <- col_bactid
-    warning("Use of `col_bactid` is deprecated. Use `col_mo` instead.")
-  } else if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
-    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"]
-    message("NOTE: Using column `", col_mo, "` as input for `col_mo`.")
+  if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
+    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
+    message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
+  }
+  if (is.null(col_mo)) {
+    stop("`col_mo` must be set.", call. = FALSE)
   }
 
   if (!all(rules %in% c("breakpoints", "expert", "other", "all"))) {
@@ -1731,12 +1729,14 @@ eucast_rules <- function(tbl,
     } else {
       colour <- blue
     }
+    decimal.mark <- getOption("OutDec")
+    big.mark <- ifelse(decimal.mark != ",", ",", ".")
     cat(bold(paste('\n=> EUCAST rules', paste0(wouldve, 'affected'),
-             amount_affected_rows %>% length() %>% format(big.mark = ","),
-             'out of', nrow(tbl_original) %>% format(big.mark = ","),
+             amount_affected_rows %>% length() %>% format(big.mark = big.mark, decimal.mark = decimal.mark),
+             'out of', nrow(tbl_original) %>% format(big.mark = big.mark, decimal.mark = decimal.mark),
              'rows ->',
              colour(paste0(wouldve, 'changed'),
-                    amount_changed %>% format(big.mark = ","), 'test results.\n\n'))))
+                    amount_changed %>% format(big.mark = big.mark, decimal.mark = decimal.mark), 'test results.\n\n'))))
   }
 
   if (verbose == TRUE) {
diff --git a/R/first_isolate.R b/R/first_isolate.R
index 9866b733..1b78ddc3 100755
--- a/R/first_isolate.R
+++ b/R/first_isolate.R
@@ -29,18 +29,32 @@
 #' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use \code{col_keyantibiotics = FALSE} to prevent this.
 #' @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 (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 be the values \code{0} or \code{1})
+#' @param icu_exclude logical whether ICU isolates should be excluded (rows with value \code{TRUE} in column \code{col_icu})
+#' @param specimen_group value in column \code{col_specimen} to filter on
 #' @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
-#' @param col_bactid (deprecated, use \code{col_mo} instead)
-#' @param col_genus (deprecated, use \code{col_mo} instead) column name of the genus of the microorganisms
-#' @param col_species (deprecated, use \code{col_mo} instead) column name of the species of the microorganisms
+#' @param ... parameters passed on to the \code{first_isolate} function
 #' @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}.
+#' 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}.
+#'
+#' The function \code{filter_first_isolate} is essentially equal to:
+#' \preformatted{
+#'  tbl \%>\%
+#'    mutate(only_firsts = first_isolate(tbl, ...)) \%>\%
+#'    filter(only_firsts == TRUE) \%>\%
+#'    select(-only_firsts)
+#' }
+#' The function \code{filter_first_weighted_isolate} is essentially equal to:
+#' \preformatted{
+#'  tbl \%>\%
+#'    mutate(keyab = key_antibiotics(.)) \%>\%
+#'    mutate(only_weighted_firsts = first_isolate(tbl,
+#'                                                col_keyantibiotics = "keyab", ...)) \%>\%
+#'    filter(only_weighted_firsts == TRUE) \%>\%
+#'    select(-only_weighted_firsts)
+#' }
 #' @section Key antibiotics:
 #'     There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
 #'
@@ -49,31 +63,42 @@
 #'
 #'     \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
 #'     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.
+#' @rdname first_isolate
 #' @keywords isolate isolates first
 #' @seealso \code{\link{key_antibiotics}}
 #' @export
 #' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
-#' @return A vector to add to table, see Examples.
+#' @importFrom crayon blue bold silver
+#' @return Logical vector
 #' @source Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
 #' @examples
 #' # septic_patients is a dataset available in the AMR package. It is true, genuine data.
 #' ?septic_patients
 #'
 #' library(dplyr)
-#' my_patients <- septic_patients %>%
+#' # Filter on first isolates:
+#' septic_patients %>%
 #'   mutate(first_isolate = first_isolate(.,
 #'                                        col_date = "date",
 #'                                        col_patient_id = "patient_id",
-#'                                        col_mo = "mo"))
+#'                                        col_mo = "mo")) %>%
+#'   filter(first_isolate == TRUE)
+#'
+#' # Which can be shortened to:
+#' septic_patients %>%
+#'   filter_first_isolate()
+#' # or for first weighted isolates:
+#' septic_patients %>%
+#'   filter_first_weighted_isolate()
 #'
 #' # Now let's see if first isolates matter:
-#' A <- my_patients %>%
+#' A <- septic_patients %>%
 #'   group_by(hospital_id) %>%
 #'   summarise(count = n_rsi(gent),            # gentamicin availability
 #'             resistance = portion_IR(gent))  # gentamicin resistance
 #'
-#' B <- my_patients %>%
-#'   filter(first_isolate == TRUE) %>%         # the 1st isolate filter
+#' B <- septic_patients %>%
+#'   filter_first_weighted_isolate() %>%       # the 1st isolate filter
 #'   group_by(hospital_id) %>%
 #'   summarise(count = n_rsi(gent),            # gentamicin availability
 #'             resistance = portion_IR(gent))  # gentamicin resistance
@@ -83,6 +108,7 @@
 #' # Gentamicin resitance in hospital D appears to be 5.4% higher than
 #' # when you (erroneously) would have used all isolates!
 #'
+#'
 #' ## OTHER EXAMPLES:
 #'
 #' \dontrun{
@@ -99,29 +125,29 @@
 #'
 #' tbl$first_blood_isolate <-
 #'   first_isolate(tbl,
-#'                 filter_specimen = 'Blood')
+#'                 specimen_group = 'Blood')
 #'
 #' tbl$first_blood_isolate_weighed <-
 #'   first_isolate(tbl,
-#'                 filter_specimen = 'Blood',
+#'                 specimen_group = 'Blood',
 #'                 col_keyantibiotics = 'keyab')
 #'
 #' tbl$first_urine_isolate <-
 #'   first_isolate(tbl,
-#'                 filter_specimen = 'Urine')
+#'                 specimen_group = 'Urine')
 #'
 #' tbl$first_urine_isolate_weighed <-
 #'   first_isolate(tbl,
-#'                 filter_specimen = 'Urine',
+#'                 specimen_group = 'Urine',
 #'                 col_keyantibiotics = 'keyab')
 #'
 #' tbl$first_resp_isolate <-
 #'   first_isolate(tbl,
-#'                 filter_specimen = 'Respiratory')
+#'                 specimen_group = 'Respiratory')
 #'
 #' tbl$first_resp_isolate_weighed <-
 #'   first_isolate(tbl,
-#'                 filter_specimen = 'Respiratory',
+#'                 specimen_group = 'Respiratory',
 #'                 col_keyantibiotics = 'keyab')
 #' }
 first_isolate <- function(tbl,
@@ -135,28 +161,34 @@ first_isolate <- function(tbl,
                           episode_days = 365,
                           testcodes_exclude = NULL,
                           icu_exclude = FALSE,
-                          filter_specimen = NULL,
-                          output_logical = TRUE,
+                          specimen_group = NULL,
                           type = "keyantibiotics",
                           ignore_I = TRUE,
                           points_threshold = 2,
                           info = TRUE,
-                          col_bactid = NULL,
-                          col_genus = NULL,
-                          col_species = NULL) {
+                          ...) {
 
   if (!is.data.frame(tbl)) {
-    stop("`tbl` must be a data frame.", call. = FALSE)
+    stop("`tbl` must be a data.frame.", call. = FALSE)
+  }
+
+  dots <- unlist(list(...))
+  if (length(dots) != 0) {
+    # backwards compatibility with old parameters
+    dots.names <- dots %>% names()
+    if ('filter_specimen' %in% dots.names) {
+      specimen_group <- dots[which(dots.names == 'filter_specimen')]
+    }
   }
 
   # try to find columns based on type
   # -- mo
-  if (!is.null(col_bactid)) {
-    col_mo <- col_bactid
-    warning("Use of `col_bactid` is deprecated. Use `col_mo` instead.")
-  } else if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
+  if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
     col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
-    message("NOTE: Using column `", col_mo, "` as input for `col_mo`.")
+    message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
+  }
+  if (is.null(col_mo)) {
+    stop("`col_mo` must be set.", call. = FALSE)
   }
 
   # -- date
@@ -164,7 +196,7 @@ first_isolate <- function(tbl,
     for (i in 1:ncol(tbl)) {
       if ("Date" %in% class(tbl %>% pull(i)) | "POSIXct" %in% class(tbl %>% pull(i))) {
         col_date <- colnames(tbl)[i]
-        message("NOTE: Using column `", col_date, "` as input for `col_date`.")
+        message(blue(paste0("NOTE: Using column `", bold(col_date), "` as input for `col_date`.")))
         break
       }
     }
@@ -178,7 +210,7 @@ first_isolate <- function(tbl,
   # -- patient id
   if (is.null(col_patient_id) & any(colnames(tbl) %like% "^(patient|patid)")) {
     col_patient_id <- colnames(tbl)[colnames(tbl) %like% "^(patient|patid)"][1]
-    message("NOTE: Using column `", col_patient_id, "` as input for `col_patient_id`.")
+    message(blue(paste0("NOTE: Using column `", bold(col_patient_id), "` as input for `col_patient_id`.")))
   }
   if (is.null(col_patient_id)) {
     stop("`col_patient_id` must be set.", call. = FALSE)
@@ -187,18 +219,12 @@ first_isolate <- function(tbl,
   # -- key antibiotics
   if (is.null(col_keyantibiotics) & any(colnames(tbl) %like% "^key.*(ab|antibiotics)")) {
     col_keyantibiotics <- colnames(tbl)[colnames(tbl) %like% "^key.*(ab|antibiotics)"][1]
-    message("NOTE: Using column `", col_keyantibiotics, "` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.")
+    message(blue(paste0("NOTE: Using column `", bold(col_keyantibiotics), "` as input for `col_keyantibiotics`. Use ", bold("col_keyantibiotics = FALSE"), " to prevent this.")))
   }
   if (isFALSE(col_keyantibiotics)) {
     col_keyantibiotics <- NULL
   }
 
-  # col_mo OR col_genus+col_species must be available
-  if (is.null(col_mo) & (is.null(col_genus) | is.null(col_species))) {
-    stop("`col_mo` or both `col_genus` and `col_species` must be set.", call. = FALSE)
-  }
-
-
   # check if columns exist
   check_columns_existance <- function(column, tblname = tbl) {
     if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
@@ -215,27 +241,23 @@ first_isolate <- function(tbl,
   check_columns_existance(col_date)
   check_columns_existance(col_patient_id)
   check_columns_existance(col_mo)
-  check_columns_existance(col_genus)
-  check_columns_existance(col_species)
   check_columns_existance(col_testcode)
   check_columns_existance(col_icu)
   check_columns_existance(col_keyantibiotics)
 
-  if (!is.null(col_mo)) {
-    # join to microorganisms data set
-    tbl <- tbl %>%
-      mutate_at(vars(col_mo), as.mo) %>%
-      left_join_microorganisms(by = col_mo)
-    col_genus <- "genus"
-    col_species <- "species"
-  }
+  # join to microorganisms data set
+  tbl <- tbl %>%
+    mutate_at(vars(col_mo), as.mo) %>%
+    left_join_microorganisms(by = col_mo)
+  col_genus <- "genus"
+  col_species <- "species"
 
   if (is.null(col_testcode)) {
     testcodes_exclude <- NULL
   }
   # remove testcodes
   if (!is.null(testcodes_exclude) & info == TRUE) {
-    cat('[Criteria] Excluded test codes:\n', toString(testcodes_exclude), '\n')
+    cat('[Criterion] Excluded test codes:\n', toString(testcodes_exclude), '\n')
   }
 
   if (is.null(col_icu)) {
@@ -246,14 +268,14 @@ first_isolate <- function(tbl,
   }
 
   if (is.null(col_specimen)) {
-    filter_specimen <- NULL
+    specimen_group <- NULL
   }
 
   # filter on specimen group and keyantibiotics when they are filled in
-  if (!is.null(filter_specimen)) {
+  if (!is.null(specimen_group)) {
     check_columns_existance(col_specimen, tbl)
     if (info == TRUE) {
-      cat('[Criteria] Excluded other than specimen group \'', filter_specimen, '\'\n', sep = '')
+      cat('[Criterion] Excluded other than specimen group \'', specimen_group, '\'\n', sep = '')
     }
   }
   if (!is.null(col_keyantibiotics)) {
@@ -274,11 +296,11 @@ first_isolate <- function(tbl,
     mutate(species = if_else(is.na(species) | species == "(no MO)", "", species),
            genus = if_else(is.na(genus) | genus == "(no MO)", "", genus))
 
-  if (is.null(filter_specimen)) {
+  if (is.null(specimen_group)) {
     # not filtering on specimen
     if (icu_exclude == FALSE) {
       if (info == TRUE & !is.null(col_icu)) {
-        cat('[Criteria] Included isolates from ICU.\n')
+        cat('[Criterion] Included isolates from ICU.\n')
       }
       tbl <- tbl %>%
         arrange_at(c(col_patient_id,
@@ -289,7 +311,7 @@ first_isolate <- function(tbl,
       row.end <- nrow(tbl)
     } else {
       if (info == TRUE) {
-        cat('[Criteria] Excluded isolates from ICU.\n')
+        cat('[Criterion] Excluded isolates from ICU.\n')
       }
       tbl <- tbl %>%
         arrange_at(c(col_icu,
@@ -310,7 +332,7 @@ first_isolate <- function(tbl,
     # filtering on specimen and only analyse these row to save time
     if (icu_exclude == FALSE) {
       if (info == TRUE & !is.null(col_icu)) {
-        cat('[Criteria] Included isolates from ICU.\n')
+        cat('[Criterion] Included isolates from ICU.\n')
       }
       tbl <- tbl %>%
         arrange_at(c(col_specimen,
@@ -319,14 +341,14 @@ first_isolate <- function(tbl,
                      col_species,
                      col_date))
       suppressWarnings(
-        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% min(na.rm = TRUE)
+        row.start <- which(tbl %>% pull(col_specimen) == specimen_group) %>% min(na.rm = TRUE)
       )
       suppressWarnings(
-        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% max(na.rm = TRUE)
+        row.end <- which(tbl %>% pull(col_specimen) == specimen_group) %>% max(na.rm = TRUE)
       )
     } else {
       if (info == TRUE) {
-        cat('[Criteria] Excluded isolates from ICU.\n')
+        cat('[Criterion] Excluded isolates from ICU.\n')
       }
       tbl <- tbl %>%
         arrange_at(c(col_icu,
@@ -336,11 +358,11 @@ first_isolate <- function(tbl,
                      col_species,
                      col_date))
       suppressWarnings(
-        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen
+        row.start <- which(tbl %>% pull(col_specimen) == specimen_group
                            & tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
       )
       suppressWarnings(
-        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen
+        row.end <- which(tbl %>% pull(col_specimen) == specimen_group
                          & tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
       )
     }
@@ -352,12 +374,10 @@ first_isolate <- function(tbl,
       message('No isolates found.')
     }
     # NAs where genus is unavailable
-    tbl <- tbl %>%
-      mutate(real_first_isolate = if_else(genus == '', NA, FALSE))
-    if (output_logical == FALSE) {
-      tbl$real_first_isolate <- tbl %>% pull(real_first_isolate) %>% as.integer()
-    }
-    return(tbl %>% pull(real_first_isolate))
+    return(tbl %>%
+             mutate(real_first_isolate = if_else(genus == '', NA, FALSE)) %>%
+             pull(real_first_isolate)
+    )
   }
 
   # suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
@@ -388,14 +408,14 @@ first_isolate <- function(tbl,
     weighted.notice <- 'weighted '
     if (info == TRUE) {
       if (type == 'keyantibiotics') {
-        cat('[Criteria] Inclusion based on key antibiotics, ')
+        cat('[Criterion] Inclusion based on key antibiotics, ')
         if (ignore_I == FALSE) {
           cat('not ')
         }
         cat('ignoring I.\n')
       }
       if (type == 'points') {
-        cat(paste0('[Criteria] Inclusion based on key antibiotics, using points threshold of '
+        cat(paste0('[Criterion] Inclusion based on key antibiotics, using points threshold of '
                    , points_threshold, '.\n'))
       }
     }
@@ -458,19 +478,62 @@ first_isolate <- function(tbl,
     pull(real_first_isolate)
 
   if (info == TRUE) {
-    message(paste0('Found ',
-               all_first %>% sum(na.rm = TRUE),
-               ' first ', weighted.notice, 'isolates (',
-               (all_first %>% sum(na.rm = TRUE) / scope.size) %>% percent(),
-               ' of isolates in scope [where genus was not empty] and ',
-               (all_first %>% sum(na.rm = TRUE) / tbl %>% nrow()) %>% percent(),
-               ' of total)'))
-  }
-
-  if (output_logical == FALSE) {
-    all_first <- all_first %>% as.integer()
+    decimal.mark <- getOption("OutDec")
+    big.mark <- ifelse(decimal.mark != ",", ",", ".")
+    n_found <- base::sum(all_first, na.rm = TRUE)
+    p_found_total <- percent(n_found / nrow(tbl), force_zero = TRUE)
+    p_found_scope <- percent(n_found / scope.size, force_zero = TRUE)
+    # mark up number of found
+    n_found <- base::format(n_found, big.mark = big.mark, decimal.mark = decimal.mark)
+    if (p_found_total != p_found_scope) {
+      msg_txt <- paste0("=> Found ",
+                        bold(paste0(n_found, " first ", weighted.notice, "isolates")),
+                        " (", p_found_scope, " within scope and ", p_found_total, " of total)")
+    } else {
+      msg_txt <- paste0("=> Found ",
+                        bold(paste0(n_found, " first ", weighted.notice, "isolates")),
+                        " (", p_found_total, " of total)")
+    }
+    base::message(msg_txt)
   }
 
   all_first
 
 }
+
+#' @rdname first_isolate
+#' @importFrom dplyr filter
+#' @export
+filter_first_isolate <- function(tbl,
+                                 col_date = NULL,
+                                 col_patient_id = NULL,
+                                 col_mo = NULL,
+                                 ...) {
+  filter(tbl, first_isolate(tbl = tbl,
+                            col_date = col_date,
+                            col_patient_id = col_patient_id,
+                            col_mo = col_mo,
+                            ...))
+}
+
+#' @rdname first_isolate
+#' @importFrom dplyr %>% mutate filter
+#' @export
+filter_first_weighted_isolate <- function(tbl,
+                                          col_date = NULL,
+                                          col_patient_id = NULL,
+                                          col_mo = NULL,
+                                          col_keyantibiotics = NULL,
+                                          ...) {
+  tbl_keyab <- tbl %>%
+    mutate(keyab = suppressMessages(key_antibiotics(.,
+                                                    col_mo = col_mo,
+                                                    ...))) %>%
+    mutate(firsts = first_isolate(.,
+                                  col_date = col_date,
+                                  col_patient_id = col_patient_id,
+                                  col_mo = col_mo,
+                                  col_keyantibiotics = "keyab",
+                                  ...))
+  tbl[which(tbl_keyab$firsts == TRUE),]
+}
diff --git a/R/freq.R b/R/freq.R
index 9d8e2819..cbafcaf0 100755
--- a/R/freq.R
+++ b/R/freq.R
@@ -31,6 +31,7 @@
 #' @param header a logical value indicating whether an informative header should be printed
 #' @param title text to show above frequency table, at default to tries to coerce from the variables passed to \code{x}
 #' @param na a character string to should be used to show empty (\code{NA}) values (only useful when \code{na.rm = FALSE})
+#' @param droplevels a logical value indicating whether in factors empty levels should be dropped
 #' @param sep a character string to separate the terms when selecting multiple columns
 #' @inheritParams base::format
 #' @param f a frequency table
@@ -56,11 +57,12 @@
 #'   \item{Median, using \code{\link[stats]{median}}, with percentage since oldest}
 #' }
 #'
+#' In factors, all factor levels that are not existing in the input data will be dropped.
 #'
 #' The function \code{top_freq} uses \code{\link[dplyr]{top_n}} internally and will include more than \code{n} rows if there are ties.
 #' @importFrom stats fivenum sd mad
 #' @importFrom grDevices boxplot.stats
-#' @importFrom dplyr %>% arrange arrange_at desc filter_at funs group_by mutate mutate_at n_distinct pull select summarise tibble ungroup vars all_vars
+#' @importFrom dplyr %>% arrange arrange_at desc filter_at funs group_by mutate mutate_at n n_distinct pull select summarise tibble ungroup vars all_vars
 #' @importFrom utils browseVignettes
 #' @importFrom hms is.hms
 #' @importFrom crayon red green silver
@@ -183,6 +185,7 @@ frequency_tbl <- function(x,
                           header = !markdown,
                           title = NULL,
                           na = "<NA>",
+                          droplevels = TRUE,
                           sep = " ",
                           decimal.mark = getOption("OutDec"),
                           big.mark = ifelse(decimal.mark != ",", ",", ".")) {
@@ -190,23 +193,23 @@ frequency_tbl <- function(x,
   mult.columns <- 0
   x.group = character(0)
   df <- NULL
-
+  # x_haslevels <- !is.null(levels(x))
   x.name <- NULL
   cols <- NULL
-  if (any(class(x) == 'list')) {
+  if (any(class(x) == "list")) {
     cols <- names(x)
     x <- as.data.frame(x, stringsAsFactors = FALSE)
     x.name <- "a list"
-  } else if (any(class(x) == 'matrix')) {
+  } else if (any(class(x) == "matrix")) {
     x <- as.data.frame(x, stringsAsFactors = FALSE)
     x.name <- "a matrix"
     cols <- colnames(x)
-    if (all(cols %like% 'V[0-9]')) {
+    if (all(cols %like% "V[0-9]")) {
       cols <- NULL
     }
   }
 
-  if (any(class(x) == 'data.frame')) {
+  if (any(class(x) == "data.frame")) {
     x.group <- group_vars(x)
     if (length(x.group) > 1) {
       x.group <- x.group[1L]
@@ -225,13 +228,18 @@ frequency_tbl <- function(x,
     if (ndots < 10) {
       cols <- as.character(dots)
       if (!all(cols %in% colnames(x))) {
-        stop("one or more columns not found: `", paste(cols, collapse = "`, `"), '`', call. = FALSE)
+        stop("one or more columns not found: `", paste(cols, collapse = "`, `"), "`", call. = FALSE)
       }
       if (length(x.group) > 0) {
         x.group_cols <- c(x.group, cols)
-        df <- x %>%
-          group_by_at(vars(x.group_cols)) %>%
-          summarise(count = n())
+        # if (droplevels == TRUE) {
+        #   x <- x %>% mutate_at(vars(x.group_cols), droplevels)
+        # }
+        suppressWarnings(
+          df <- x %>%
+            group_by_at(vars(x.group_cols)) %>%
+            summarise(count = n())
+        )
         if (na.rm == TRUE) {
           df <- df %>% filter_at(vars(cols), all_vars(!is.na(.)))
         }
@@ -250,16 +258,21 @@ frequency_tbl <- function(x,
           mutate_at(vars(x.group), funs(ifelse(lag(.) == ., "", .)))
         df[1, 1] <- df.topleft
         colnames(df)[1:2] <- c("group", "item")
+
+        if (!is.null(levels(df$item)) & droplevels == TRUE) {
+          # is factor
+          df <- df %>% filter(count != 0)
+        }
       }
       if (length(cols) > 0) {
         x <- x[, cols]
       }
     } else if (ndots >= 10) {
-      stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
+      stop("A maximum of 9 columns can be analysed at the same time.", call. = FALSE)
     } else {
       cols <- NULL
     }
-  } else if (any(class(x) == 'table')) {
+  } else if (any(class(x) == "table")) {
     x <- as.data.frame(x, stringsAsFactors = FALSE)
     # now this DF contains 3 columns: the 2 vars and a Freq column
     # paste the first 2 cols and repeat them Freq times:
@@ -274,18 +287,18 @@ frequency_tbl <- function(x,
   }
 
   if (!is.null(ncol(x))) {
-    if (ncol(x) == 1 & any(class(x) == 'data.frame')) {
+    if (ncol(x) == 1 & any(class(x) == "data.frame")) {
       x <- x %>% pull(1)
     } else if (ncol(x) < 10) {
       mult.columns <- ncol(x)
       x <- do.call(paste, c(x[colnames(x)], sep = sep))
     } else {
-      stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
+      stop("A maximum of 9 columns can be analysed at the same time.", call. = FALSE)
     }
   }
 
   if (mult.columns > 1) {
-    NAs <- x[is.na(x) | x == trimws(strrep('NA ', mult.columns))]
+    NAs <- x[is.na(x) | x == trimws(strrep("NA ", mult.columns))]
   } else {
     NAs <- x[is.na(x)]
   }
@@ -296,91 +309,109 @@ frequency_tbl <- function(x,
     class(x) <- x_class
   }
 
-  # if (sort.count == FALSE & 'factor' %in% class(x)) {
-  #   warning("Sorting a factor sorts on factor level, not necessarily alphabetically.", call. = FALSE)
-  # }
   header_txt <- character(0)
 
-  markdown_line <- ''
+  markdown_line <- ""
   if (markdown == TRUE) {
-    markdown_line <- '\n'
+    markdown_line <- "\n"
   }
-  x_align <- 'l'
+  x_align <- "l"
 
   if (mult.columns > 0) {
-    header_txt <- header_txt %>% paste0(markdown_line, 'Columns:   ', mult.columns)
+    header_txt <- header_txt %>% paste0(markdown_line, "Columns:   ", mult.columns)
   } else {
-    header_txt <- header_txt %>% paste0(markdown_line, 'Class:     ', class(x) %>% rev() %>% paste(collapse = " > "))
+    header_txt <- header_txt %>% paste0(markdown_line, "Class:     ", class(x) %>% rev() %>% paste(collapse = " > "))
     if (!mode(x) %in% class(x)) {
       header_txt <- header_txt %>% paste0(silver(paste0(" (", mode(x), ")")))
     }
   }
 
   if ((length(NAs) + length(x) > 0) > 0) {
-    na_txt <- paste0(NAs %>% length() %>% format(decimal.mark = decimal.mark, big.mark = big.mark), ' = ',
+    na_txt <- paste0(NAs %>% length() %>% format(decimal.mark = decimal.mark, big.mark = big.mark), " = ",
                      (NAs %>% length() / (NAs %>% length() + x %>% length())) %>% percent(force_zero = TRUE, round = digits, decimal.mark = decimal.mark) %>%
-                       sub('NaN', '0', ., fixed = TRUE))
+                       sub("NaN", "0", ., fixed = TRUE))
     if (!na_txt %like% "^0 =") {
       na_txt <- red(na_txt)
     } else {
       na_txt <- green(na_txt)
     }
-    na_txt <- paste0('(of which NA: ', na_txt, ')')
+    na_txt <- paste0("(of which NA: ", na_txt, ")")
   } else {
     na_txt <- ""
   }
 
-  header_txt <- header_txt %>% paste0(markdown_line, '\nLength:    ', (NAs %>% length() + x %>% length()) %>% format(decimal.mark = decimal.mark, big.mark = big.mark),
-                                      ' ', na_txt)
-  header_txt <- header_txt %>% paste0(markdown_line, '\nUnique:    ', x %>% n_distinct() %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
+  if (!is.null(levels(x))) {
+    n_levels <- x %>% levels() %>% length()
+    n_levels_empty <- n_levels - x %>% droplevels() %>% levels() %>% length()
+    n_levels_list <- levels(x)
+    if (n_levels > 5) {
+      n_levels_list <- c(n_levels_list[1:5], "...")
+    }
+    if (is.ordered(x)) {
+      n_levels_list <- paste0(levels(x), collapse = " < ")
+    } else {
+      n_levels_list <- paste0(levels(x), collapse = ", ")
+    }
+
+    header_txt <- header_txt %>% paste0(markdown_line, "\nLevels:    ", n_levels_list)
+    # drop levels of non-existing factor values,
+    # since dplyr >= 0.8.0 does not do this anymore in group_by
+    if (droplevels == TRUE) {
+      x <- droplevels(x)
+    }
+  }
+
+  header_txt <- header_txt %>% paste0(markdown_line, "\nLength:    ", (NAs %>% length() + x %>% length()) %>% format(decimal.mark = decimal.mark, big.mark = big.mark),
+                                      " ", na_txt)
+  header_txt <- header_txt %>% paste0(markdown_line, "\nUnique:    ", x %>% n_distinct() %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
 
   if (NROW(x) > 0 & any(class(x) == "character")) {
-    header_txt <- header_txt %>% paste0('\n')
-    header_txt <- header_txt %>% paste0(markdown_line, '\nShortest:  ', x %>% base::nchar() %>% base::min(na.rm = TRUE) %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
-    header_txt <- header_txt %>% paste0(markdown_line, '\nLongest:   ', x %>% base::nchar() %>% base::max(na.rm = TRUE) %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
+    header_txt <- header_txt %>% paste0("\n")
+    header_txt <- header_txt %>% paste0(markdown_line, "\nShortest:  ", x %>% base::nchar() %>% base::min(na.rm = TRUE) %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
+    header_txt <- header_txt %>% paste0(markdown_line, "\nLongest:   ", x %>% base::nchar() %>% base::max(na.rm = TRUE) %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
   }
 
   if (NROW(x) > 0 & any(class(x) == "mo")) {
-    header_txt <- header_txt %>% paste0('\n')
-    header_txt <- header_txt %>% paste0(markdown_line, '\nFamilies:  ', x %>% mo_family() %>% n_distinct() %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
-    header_txt <- header_txt %>% paste0(markdown_line, '\nGenera:    ', x %>% mo_genus() %>% n_distinct() %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
-    header_txt <- header_txt %>% paste0(markdown_line, '\nSpecies:   ', x %>% mo_species() %>% n_distinct() %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
+    header_txt <- header_txt %>% paste0("\n")
+    header_txt <- header_txt %>% paste0(markdown_line, "\nFamilies:  ", x %>% mo_family() %>% n_distinct() %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
+    header_txt <- header_txt %>% paste0(markdown_line, "\nGenera:    ", x %>% mo_genus() %>% n_distinct() %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
+    header_txt <- header_txt %>% paste0(markdown_line, "\nSpecies:   ", x %>% mo_species() %>% n_distinct() %>% format(decimal.mark = decimal.mark, big.mark = big.mark))
   }
 
   if (NROW(x) > 0 & any(class(x) == "difftime") & !is.hms(x)) {
-    header_txt <- header_txt %>% paste0('\n')
-    header_txt <- header_txt %>% paste(markdown_line, '\nUnits:    ', attributes(x)$units)
+    header_txt <- header_txt %>% paste0("\n")
+    header_txt <- header_txt %>% paste(markdown_line, "\nUnits:    ", attributes(x)$units)
     x <- as.double(x)
     # after this, the numeric header_txt continues
   }
 
-  if (NROW(x) > 0 & any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
+  if (NROW(x) > 0 & any(class(x) %in% c("double", "integer", "numeric", "raw", "single"))) {
     # right align number
     Tukey_five <- stats::fivenum(x, na.rm = TRUE)
-    x_align <- 'r'
-    header_txt <- header_txt %>% paste0('\n')
-    header_txt <- header_txt %>% paste(markdown_line, '\nMean:     ', x %>% base::mean(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark))
-    header_txt <- header_txt %>% paste0(markdown_line, '\nStd. dev.: ', x %>% stats::sd(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark),
-                                        ' (CV: ', x %>% cv(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark),
-                                        ', MAD: ', x %>% stats::mad(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark), ')')
-    header_txt <- header_txt %>% paste0(markdown_line, '\nFive-Num:  ', Tukey_five %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark) %>% trimws() %>% paste(collapse = ' | '),
-                                        ' (IQR: ', (Tukey_five[4] - Tukey_five[2]) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark),
-                                        ', CQV: ', x %>% cqv(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark), ')')
+    x_align <- "r"
+    header_txt <- header_txt %>% paste0("\n")
+    header_txt <- header_txt %>% paste(markdown_line, "\nMean:     ", x %>% base::mean(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark))
+    header_txt <- header_txt %>% paste0(markdown_line, "\nStd. dev.: ", x %>% stats::sd(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark),
+                                        " (CV: ", x %>% cv(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark),
+                                        ", MAD: ", x %>% stats::mad(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark), ")")
+    header_txt <- header_txt %>% paste0(markdown_line, "\nFive-Num:  ", Tukey_five %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark) %>% trimws() %>% paste(collapse = " | "),
+                                        " (IQR: ", (Tukey_five[4] - Tukey_five[2]) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark),
+                                        ", CQV: ", x %>% cqv(na.rm = TRUE) %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark), ")")
     outlier_length <- length(boxplot.stats(x)$out)
-    header_txt <- header_txt %>% paste0(markdown_line, '\nOutliers:  ', outlier_length)
+    header_txt <- header_txt %>% paste0(markdown_line, "\nOutliers:  ", outlier_length)
     if (outlier_length > 0) {
-      header_txt <- header_txt %>% paste0(' (unique count: ', boxplot.stats(x)$out %>% n_distinct(), ')')
+      header_txt <- header_txt %>% paste0(" (unique count: ", boxplot.stats(x)$out %>% n_distinct(), ")")
     }
   }
   if (NROW(x) > 0 & any(class(x) == "rsi")) {
-    header_txt <- header_txt %>% paste0('\n')
+    header_txt <- header_txt %>% paste0("\n")
     cnt_S <- sum(x == "S", na.rm = TRUE)
     cnt_IR <- sum(x %in% c("I", "R"), na.rm = TRUE)
-    header_txt <- header_txt %>% paste(markdown_line, '\n%IR:      ',
+    header_txt <- header_txt %>% paste(markdown_line, "\n%IR:      ",
                                        (cnt_IR / sum(!is.na(x), na.rm = TRUE)) %>% percent(force_zero = TRUE, round = digits, decimal.mark = decimal.mark),
-                                       paste0('(ratio S : IR = 1.0 : ', (cnt_IR / cnt_S) %>% format(digits = 1, nsmall = 1, decimal.mark = decimal.mark, big.mark = big.mark), ")"))
+                                       paste0("(ratio S : IR = 1.0 : ", (cnt_IR / cnt_S) %>% format(digits = 1, nsmall = 1, decimal.mark = decimal.mark, big.mark = big.mark), ")"))
     if (NROW(x) < 30) {
-      header_txt <- header_txt %>% paste(markdown_line, red('\nToo few isolates for reliable resistance interpretation.'))
+      header_txt <- header_txt %>% paste(markdown_line, red("\nToo few isolates for reliable resistance interpretation."))
     }
   }
 
@@ -389,29 +420,29 @@ frequency_tbl <- function(x,
     x <- x %>% as.POSIXlt()
     formatdates <- "%H:%M:%S"
   }
-  if (NROW(x) > 0 & any(class(x) %in% c('Date', 'POSIXct', 'POSIXlt'))) {
-    header_txt <- header_txt %>% paste0('\n')
+  if (NROW(x) > 0 & any(class(x) %in% c("Date", "POSIXct", "POSIXlt"))) {
+    header_txt <- header_txt %>% paste0("\n")
     mindate <- x %>% min(na.rm = TRUE)
     maxdate <- x %>% max(na.rm = TRUE)
-    maxdate_days <- difftime(maxdate, mindate, units = 'auto') %>% as.double()
+    maxdate_days <- difftime(maxdate, mindate, units = "auto") %>% as.double()
     mediandate <- x %>% median(na.rm = TRUE)
-    median_days <- difftime(mediandate, mindate, units = 'auto') %>% as.double()
+    median_days <- difftime(mediandate, mindate, units = "auto") %>% as.double()
 
     if (formatdates == "%H:%M:%S") {
       # hms
-      header_txt <- header_txt %>% paste0(markdown_line, '\nEarliest:  ', mindate %>% format(formatdates) %>% trimws())
-      header_txt <- header_txt %>% paste0(markdown_line, '\nLatest:    ', maxdate %>% format(formatdates) %>% trimws(),
-                                          ' (+', difftime(maxdate, mindate, units = 'mins') %>% as.double() %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark), ' min.)')
+      header_txt <- header_txt %>% paste0(markdown_line, "\nEarliest:  ", mindate %>% format(formatdates) %>% trimws())
+      header_txt <- header_txt %>% paste0(markdown_line, "\nLatest:    ", maxdate %>% format(formatdates) %>% trimws(),
+                                          " (+", difftime(maxdate, mindate, units = "mins") %>% as.double() %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark), " min.)")
     } else {
       # other date formats
-      header_txt <- header_txt %>% paste0(markdown_line, '\nOldest:    ', mindate %>% format(formatdates) %>% trimws())
-      header_txt <- header_txt %>% paste0(markdown_line, '\nNewest:    ', maxdate %>% format(formatdates) %>% trimws(),
-                                          ' (+', difftime(maxdate, mindate, units = 'auto') %>% as.double() %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark), ')')
+      header_txt <- header_txt %>% paste0(markdown_line, "\nOldest:    ", mindate %>% format(formatdates) %>% trimws())
+      header_txt <- header_txt %>% paste0(markdown_line, "\nNewest:    ", maxdate %>% format(formatdates) %>% trimws(),
+                                          " (+", difftime(maxdate, mindate, units = "auto") %>% as.double() %>% format(digits = digits, decimal.mark = decimal.mark, big.mark = big.mark), ")")
     }
-    header_txt <- header_txt %>% paste0(markdown_line, '\nMedian:    ', mediandate %>% format(formatdates) %>% trimws(),
-                                        ' (~', percent(median_days / maxdate_days, round = 0, decimal.mark = decimal.mark), ')')
+    header_txt <- header_txt %>% paste0(markdown_line, "\nMedian:    ", mediandate %>% format(formatdates) %>% trimws(),
+                                        " (~", percent(median_days / maxdate_days, round = 0, decimal.mark = decimal.mark), ")")
   }
-  if (any(class(x) == 'POSIXlt')) {
+  if (any(class(x) == "POSIXlt")) {
     x <- x %>% format(formatdates)
   }
 
@@ -427,9 +458,9 @@ frequency_tbl <- function(x,
     nmax <- length(x)
   }
 
-  column_names <- c('Item', 'Count', 'Percent', 'Cum. Count', 'Cum. Percent')
-  column_names_df <- c('item', 'count', 'percent', 'cum_count', 'cum_percent')
-  column_align <- c(x_align, 'r', 'r', 'r', 'r')
+  column_names <- c("Item", "Count", "Percent", "Cum. Count", "Cum. Percent")
+  column_names_df <- c("item", "count", "percent", "cum_count", "cum_percent")
+  column_align <- c(x_align, "r", "r", "r", "r")
 
   if (is.null(df)) {
     # create table with counts and percentages
@@ -449,10 +480,10 @@ frequency_tbl <- function(x,
     column_align <- c("l", column_align)
   }
 
-  if (df$item %>% paste(collapse = ',') %like% '\033') {
+  if (df$item %>% paste(collapse = ",") %like% "\033") {
     # remove escape char
     # see https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
-    df <- df %>% mutate(item = item %>% gsub('\033', ' ', ., fixed = TRUE))
+    df <- df %>% mutate(item = item %>% gsub("\033", " ", ., fixed = TRUE))
   }
 
   if (quote == TRUE) {
@@ -475,9 +506,9 @@ frequency_tbl <- function(x,
   }
 
   if (markdown == TRUE) {
-    tbl_format <- 'markdown'
+    tbl_format <- "markdown"
   } else {
-    tbl_format <- 'pandoc'
+    tbl_format <- "pandoc"
   }
 
   if (!is.null(title)) {
@@ -485,7 +516,7 @@ frequency_tbl <- function(x,
   }
 
   structure(.Data = df,
-            class = c('frequency_tbl', class(df)),
+            class = c("frequency_tbl", class(df)),
             opt = list(title = title,
                        data = x.name,
                        vars = cols,
@@ -511,11 +542,11 @@ freq <- frequency_tbl
 #' @export
 #' @importFrom dplyr top_n pull
 top_freq <- function(f, n) {
-  if (!'frequency_tbl' %in% class(f)) {
-    stop('top_freq can only be applied to frequency tables', call. = FALSE)
+  if (!"frequency_tbl" %in% class(f)) {
+    stop("top_freq can only be applied to frequency tables", call. = FALSE)
   }
   if (!is.numeric(n) | length(n) != 1L) {
-    stop('For top_freq, `nmax` must be a number of length 1', call. = FALSE)
+    stop("For top_freq, `nmax` must be a number of length 1", call. = FALSE)
   }
   top <- f %>% top_n(n, count)
   vect <- top %>% pull(item)
@@ -562,10 +593,10 @@ diff.frequency_tbl <- function(x, y, ...) {
       diff.percent = percent(
         diff / count.x,
         force_zero = TRUE)) %>%
-    mutate(diff = ifelse(diff %like% '^-',
+    mutate(diff = ifelse(diff %like% "^-",
                          diff,
                          paste0("+", diff)),
-           diff.percent = ifelse(diff.percent %like% '^-',
+           diff.percent = ifelse(diff.percent %like% "^-",
                                  diff.percent,
                                  paste0("+", diff.percent)))
 
@@ -590,7 +621,7 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
                                 big.mark = ifelse(decimal.mark != ",", ",", "."),
                                 ...) {
 
-  opt <- attr(x, 'opt')
+  opt <- attr(x, "opt")
 
   if (length(opt$vars) == 0) {
     opt$vars <- NULL
@@ -666,7 +697,7 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
   }
 
   if (NROW(x) == 0) {
-    cat('\n\nNo observations.\n')
+    cat("\n\nNo observations.\n")
     return(invisible())
   }
 
@@ -680,7 +711,7 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
   if (nrow(x) > opt$nmax & opt$tbl_format != "markdown") {
 
     x.rows <- nrow(x)
-    x.unprinted <- base::sum(x[(opt$nmax + 1):nrow(x), 'count'], na.rm = TRUE)
+    x.unprinted <- base::sum(x[(opt$nmax + 1):nrow(x), "count"], na.rm = TRUE)
     x.printed <- base::sum(x$count) - x.unprinted
 
     if (opt$nmax.set == TRUE) {
@@ -692,18 +723,18 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
     x <- x[1:nmax,]
 
     if (opt$nmax.set == TRUE) {
-      footer <- paste('[ reached `nmax = ', opt$nmax, '`', sep = '')
+      footer <- paste("[ reached `nmax = ", opt$nmax, "`", sep = "")
     } else {
       footer <- '[ reached getOption("max.print.freq")'
     }
     footer <- paste(footer,
-                    ' -- omitted ',
+                    " -- omitted ",
                     format(x.rows - opt$nmax, big.mark = opt$big.mark),
-                    ' entries, n = ',
+                    " entries, n = ",
                     format(x.unprinted, big.mark = opt$big.mark),
-                    ' (',
+                    " (",
                     (x.unprinted / (x.unprinted + x.printed)) %>% percent(force_zero = TRUE, decimal.mark = opt$decimal.mark),
-                    ') ]\n', sep = '')
+                    ") ]\n", sep = "")
     if (opt$tbl_format == "pandoc") {
       footer <- silver(footer) # only silver in regular printing
     }
@@ -712,7 +743,7 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
   }
 
   if ("item" %in% colnames(x)) {
-    if (any(class(x$item) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
+    if (any(class(x$item) %in% c("double", "integer", "numeric", "raw", "single"))) {
       x$item <- format(x$item, decimal.mark = opt$decimal.mark, big.mark = opt$big.mark)
     }
   } else {
@@ -720,7 +751,7 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
   }
   if ("count" %in% colnames(x)) {
     if (all(x$count == 1)) {
-      warning('All observations are unique.', call. = FALSE)
+      warning("All observations are unique.", call. = FALSE)
     }
     x$count <- format(x$count, decimal.mark = opt$decimal.mark, big.mark = opt$big.mark)
   } else {
@@ -762,7 +793,7 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
   if (opt$tbl_format == "markdown") {
     cat("\n\n")
   } else {
-    cat('\n')
+    cat("\n")
   }
 
   # reset old kable setting
@@ -775,8 +806,8 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
 #' @exportMethod as.data.frame.frequency_tbl
 #' @export
 as.data.frame.frequency_tbl <- function(x, ...) {
-  attr(x, 'package') <- NULL
-  attr(x, 'opt') <- NULL
+  attr(x, "package") <- NULL
+  attr(x, "opt") <- NULL
   as.data.frame.data.frame(x, ...)
 }
 
@@ -785,8 +816,8 @@ as.data.frame.frequency_tbl <- function(x, ...) {
 #' @export
 #' @importFrom dplyr as_tibble
 as_tibble.frequency_tbl <- function(x, validate = TRUE, ..., rownames = NA) {
-  attr(x, 'package') <- NULL
-  attr(x, 'opt') <- NULL
+  attr(x, "package") <- NULL
+  attr(x, "opt") <- NULL
   as_tibble(x = as.data.frame(x), validate = validate, ..., rownames = rownames)
 }
 
@@ -794,10 +825,10 @@ as_tibble.frequency_tbl <- function(x, validate = TRUE, ..., rownames = NA) {
 #' @exportMethod hist.frequency_tbl
 #' @export
 #' @importFrom graphics hist
-hist.frequency_tbl <- function(x, breaks = "Sturges", main = NULL, ...) {
-  opt <- attr(x, 'opt')
+hist.frequency_tbl <- function(x, breaks = "Sturges", main = NULL, xlab = NULL, ...) {
+  opt <- attr(x, "opt")
   if (!class(x$item) %in% c("numeric", "double", "integer", "Date")) {
-    stop("'x' must be numeric or Date.", call. = FALSE)
+    stop("`x` must be numeric or Date.", call. = FALSE)
   }
   if (!is.null(opt$vars)) {
     title <- opt$vars
@@ -814,14 +845,17 @@ hist.frequency_tbl <- function(x, breaks = "Sturges", main = NULL, ...) {
   if (is.null(main)) {
     main <- paste("Histogram of", title)
   }
-  hist(x, main = main, xlab = title, ...)
+  if (is.null(xlab)) {
+    xlab <- title
+  }
+  hist(x, main = main, xlab = xlab, breaks = breaks, ...)
 }
 
 #' @noRd
 #' @exportMethod plot.frequency_tbl
 #' @export
 plot.frequency_tbl <- function(x, y, ...) {
-  opt <- attr(x, 'opt')
+  opt <- attr(x, "opt")
   if (!is.null(opt$vars)) {
     title <- opt$vars
   } else {
@@ -841,7 +875,7 @@ as.vector.frequency_tbl <- function(x, mode = "any") {
 #' @exportMethod format.frequency_tbl
 #' @export
 format.frequency_tbl <- function(x, digits = 1, ...) {
-  opt <- attr(x, 'opt')
+  opt <- attr(x, "opt")
   if (opt$nmax.set == TRUE) {
     nmax <- opt$nmax
   } else {
diff --git a/R/key_antibiotics.R b/R/key_antibiotics.R
index b23cfe6d..ab524357 100644
--- a/R/key_antibiotics.R
+++ b/R/key_antibiotics.R
@@ -26,6 +26,7 @@
 #' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram positives}, case-insensitive
 #' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram negatives}, case-insensitive
 #' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
+#' @param ... other parameters passed on to function
 #' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it wouldn't.
 #'
 #'   At default, the antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
@@ -40,22 +41,21 @@
 #' @rdname key_antibiotics
 #' @export
 #' @importFrom dplyr %>% mutate if_else
+#' @importFrom crayon blue bold
 #' @seealso \code{\link{first_isolate}}
 #' @examples
 #' # septic_patients is a dataset available in the AMR package
 #' ?septic_patients
-#' my_patients <- septic_patients
-#'
+
 #' library(dplyr)
 #' # set key antibiotics to a new variable
-#' my_patients <- my_patients %>%
+#' my_patients <- septic_patients %>%
 #'   mutate(keyab = key_antibiotics(.)) %>%
 #'   mutate(
 #'     # now calculate first isolates
-#'     first_regular = first_isolate(., "date", "patient_id", "mo"),
+#'     first_regular = first_isolate(., col_keyantibiotics = FALSE),
 #'     # and first WEIGHTED isolates
-#'     first_weighted = first_isolate(., "date", "patient_id", "mo",
-#'                                    col_keyantibiotics = "keyab")
+#'     first_weighted = first_isolate(., col_keyantibiotics = "keyab")
 #'   )
 #'
 #' # Check the difference, in this data set it results in 7% more isolates:
@@ -68,12 +68,12 @@
 #' strainB <- "SSSIRSSSRSSS"
 #'
 #' key_antibiotics_equal(strainA, strainB)
-#' # TRUE, because I is ignored (as are missing values)
+#' # TRUE, because I is ignored (as well as missing values)
 #'
 #' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
 #' # FALSE, because I is not ignored and so the 4th value differs
 key_antibiotics <- function(tbl,
-                            col_mo = "mo",
+                            col_mo = NULL,
                             universal_1 = "amox",
                             universal_2 = "amcl",
                             universal_3 = "cfur",
@@ -93,14 +93,16 @@ key_antibiotics <- function(tbl,
                             GramNeg_5 = "cfta",
                             GramNeg_6 = "mero",
                             warnings = TRUE,
-                            col_bactid = "bactid") {
+                            ...) {
 
-  if (col_bactid %in% colnames(tbl)) {
-    col_mo <- col_bactid
-    warning("Use of `col_bactid` is deprecated. Use `col_mo` instead.")
+  # try to find columns based on type
+  # -- mo
+  if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
+    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
+    message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
   }
-  if (!col_mo %in% colnames(tbl)) {
-    stop('Column ', col_mo, ' not found.', call. = FALSE)
+  if (is.null(col_mo)) {
+    stop("`col_mo` must be set.", call. = FALSE)
   }
 
   # check columns
@@ -140,13 +142,11 @@ key_antibiotics <- function(tbl,
                     GramNeg_4, GramNeg_5, GramNeg_6)
   gram_negative <- gram_negative[!is.na(gram_negative)]
 
-  if (!tbl %>% pull(col_mo) %>% is.mo()) {
-    tbl[, col_mo] <- as.mo(tbl[, col_mo])
-  }
-  # join microorganisms
-  tbl <- tbl %>% left_join_microorganisms(col_mo)
-
-  tbl$key_ab <- NA_character_
+  # join to microorganisms data set
+  tbl <- tbl %>%
+    mutate_at(vars(col_mo), as.mo) %>%
+    left_join_microorganisms(by = col_mo) %>%
+    mutate(key_ab = NA_character_)
 
   # Gram +
   tbl <- tbl %>% mutate(key_ab =
diff --git a/R/mdro.R b/R/mdro.R
index d922a370..7bf1e526 100755
--- a/R/mdro.R
+++ b/R/mdro.R
@@ -30,7 +30,7 @@
 #' @return Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}.
 #' @rdname mdro
 #' @importFrom dplyr %>%
-#' @importFrom crayon red blue
+#' @importFrom crayon red blue bold
 #' @export
 #' @examples
 #' library(dplyr)
@@ -101,8 +101,7 @@ mdro <- function(tbl,
                  tobr = 'tobr',
                  trim = 'trim',
                  trsu = 'trsu',
-                 vanc = 'vanc',
-                 col_bactid = NULL) {
+                 vanc = 'vanc') {
 
   if (!is.data.frame(tbl)) {
     stop("`tbl` must be a data frame.", call. = FALSE)
@@ -110,14 +109,12 @@ mdro <- function(tbl,
 
   # try to find columns based on type
   # -- mo
-  if (!is.null(col_bactid)) {
-    col_mo <- col_bactid
-    warning("Use of `col_bactid` is deprecated. Use `col_mo` instead.")
-  } else if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
+  if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
     col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
-    message("NOTE: Using column `", col_mo, "` as input for `col_mo`.")
-  } else if (!col_mo %in% colnames(tbl)) {
-    stop('Column ', col_mo, ' not found.', call. = FALSE)
+    message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
+  }
+  if (is.null(col_mo)) {
+    stop("`col_mo` must be set.", call. = FALSE)
   }
 
   # strip whitespaces
@@ -259,11 +256,8 @@ mdro <- function(tbl,
     }
   }
 
-  if (!tbl %>% pull(col_mo) %>% is.mo()) {
-    tbl[, col_mo] <- as.mo(tbl[, col_mo])
-  }
-
   tbl <- tbl %>%
+    mutate_at(vars(col_mo), as.mo) %>%
     # join to microorganisms data set
     left_join_microorganisms(by = col_mo) %>%
     # add unconfirmed to where genus is available
diff --git a/R/misc.R b/R/misc.R
index d10c0fd1..65925d0c 100755
--- a/R/misc.R
+++ b/R/misc.R
@@ -26,15 +26,19 @@ addin_insert_like <- function() {
   rstudioapi::insertText(" %like% ")
 }
 
+# No export, no Rd
+# works exactly like round(), but rounds `round(0.55, 1)` as 0.6
+round2 <- function(x, digits = 0) {
+  # https://stackoverflow.com/a/12688836/4575331
+  (trunc((abs(x) * 10 ^ digits) + 0.5) / 10 ^ digits) * sign(x)
+}
+
 # No export, no Rd
 percent <- function(x, round = 1, force_zero = FALSE, decimal.mark = getOption("OutDec"), ...) {
 
   decimal.mark.options <- getOption("OutDec")
   options(OutDec = ".")
 
-  # https://stackoverflow.com/a/12688836/4575331
-  round2 <- function(x, n) (trunc((abs(x) * 10 ^ n) + 0.5) / 10 ^ n) * sign(x)
-
   val <- round2(x, round + 2) # round up 0.5
   val <- round(x = val * 100, digits = round) # remove floating point error
 
diff --git a/R/mo.R b/R/mo.R
index 8c6a096c..d9669550 100644
--- a/R/mo.R
+++ b/R/mo.R
@@ -154,9 +154,7 @@ as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = TRUE,
 #' @rdname as.mo
 #' @export
 is.mo <- function(x) {
-  # bactid for older releases
-  # remove when is.bactid will be removed
-  identical(class(x), "mo") | identical(class(x), "bactid")
+  identical(class(x), "mo")
 }
 
 #' @rdname as.mo
diff --git a/R/resistance_predict.R b/R/resistance_predict.R
index 7d8933ad..b88a0b3b 100644
--- a/R/resistance_predict.R
+++ b/R/resistance_predict.R
@@ -44,7 +44,7 @@
 #' @rdname resistance_predict
 #' @export
 #' @importFrom stats predict glm lm
-#' @importFrom dplyr %>% pull mutate group_by_at summarise filter n_distinct arrange case_when
+#' @importFrom dplyr %>% pull mutate mutate_at n group_by_at summarise filter filter_at all_vars n_distinct arrange case_when
 # @importFrom tidyr spread
 #' @examples
 #' \dontrun{
@@ -83,11 +83,11 @@
 #' if (!require(ggplot2)) {
 #'
 #'   data <- septic_patients %>%
-#'     filter(mo == "ESCCOL") %>%
+#'     filter(mo == as.mo("E. coli")) %>%
 #'     resistance_predict(col_ab = "amox",
-#'                       col_date = "date",
-#'                       info = FALSE,
-#'                       minimum = 15)
+#'                        col_date = "date",
+#'                        info = FALSE,
+#'                         minimum = 15)
 #'
 #'   ggplot(data,
 #'          aes(x = year)) +
@@ -137,9 +137,10 @@ resistance_predict <- function(tbl,
     tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
   }
 
-  if (!tbl %>% pull(col_ab) %>% is.rsi()) {
-    tbl[, col_ab] <- tbl %>% pull(col_ab) %>% as.rsi()
-  }
+  tbl <- tbl %>% 
+    mutate_at(col_ab, as.rsi) %>%
+    filter_at(col_ab, all_vars(!is.na(.)))
+  tbl[, col_ab] <- droplevels(tbl[, col_ab])
 
   year <- function(x) {
     if (all(grepl('^[0-9]{4}$', x))) {
diff --git a/R/rsi_calc.R b/R/rsi_calc.R
index 24799870..c9b17a7c 100644
--- a/R/rsi_calc.R
+++ b/R/rsi_calc.R
@@ -73,7 +73,7 @@ rsi_calc <- function(...,
   print_warning <- FALSE
 
   type_trans <- as.integer(as.rsi(type))
-  type_others <- setdiff(1:3, type_trans)
+  type_others <- base::setdiff(1:3, type_trans)
 
   if (is.data.frame(x)) {
     rsi_integrity_check <- character(0)
diff --git a/man/AMR-deprecated.Rd b/man/AMR-deprecated.Rd
index 85a24ded..fee1cb82 100644
--- a/man/AMR-deprecated.Rd
+++ b/man/AMR-deprecated.Rd
@@ -2,18 +2,9 @@
 % Please edit documentation in R/deprecated.R
 \name{AMR-deprecated}
 \alias{AMR-deprecated}
-\alias{as.bactid}
-\alias{is.bactid}
-\alias{guess_bactid}
 \alias{ratio}
 \title{Deprecated functions}
 \usage{
-as.bactid(...)
-
-is.bactid(...)
-
-guess_bactid(...)
-
 ratio(x, ratio)
 }
 \description{
diff --git a/man/age.Rd b/man/age.Rd
index d0ef60a1..4371b883 100644
--- a/man/age.Rd
+++ b/man/age.Rd
@@ -4,12 +4,12 @@
 \alias{age}
 \title{Age in years of individuals}
 \usage{
-age(x, y = Sys.Date())
+age(x, reference = Sys.Date())
 }
 \arguments{
-\item{x}{date(s) - will be coerced with \code{\link{as.POSIXlt}}}
+\item{x}{date(s), will be coerced with \code{\link{as.POSIXlt}}}
 
-\item{y}{reference date(s) - defaults to \code{\link{Sys.Date}} - will be coerced with \code{\link{as.POSIXlt}}}
+\item{reference}{reference date(s) (defaults to today), will be coerced with \code{\link{as.POSIXlt}}}
 }
 \value{
 Integer (no decimals)
@@ -18,5 +18,5 @@ Integer (no decimals)
 Calculates age in years based on a reference date, which is the sytem time at default.
 }
 \seealso{
-age_groups
+\code{\link{age_groups}} to splits age into groups
 }
diff --git a/man/age_groups.Rd b/man/age_groups.Rd
index e00e8c16..8c837ae8 100644
--- a/man/age_groups.Rd
+++ b/man/age_groups.Rd
@@ -9,13 +9,13 @@ age_groups(x, split_at = c(12, 25, 55, 75))
 \arguments{
 \item{x}{age, e.g. calculated with \code{\link{age}}}
 
-\item{split_at}{values to split \code{x}, defaults to 0-11, 12-24, 26-54, 55-74 and 75+. See Details.}
+\item{split_at}{values to split \code{x} at, defaults to age groups 0-11, 12-24, 26-54, 55-74 and 75+. See Details.}
 }
 \value{
 Ordered \code{\link{factor}}
 }
 \description{
-Splits ages into groups defined by the \code{split} parameter.
+Split ages into age groups defined by the \code{split} parameter. This allows for easier demographic (antimicrobial resistance) analysis.
 }
 \details{
 To split ages, the input can be:
@@ -65,7 +65,7 @@ septic_patients \%>\%
   ggplot_rsi(x = "age_group")
 }
 \seealso{
-age
+\code{\link{age}} to determine ages based on one or more reference dates
 }
 \keyword{age}
 \keyword{age_group}
diff --git a/man/eucast_rules.Rd b/man/eucast_rules.Rd
index 9199f001..adddf147 100644
--- a/man/eucast_rules.Rd
+++ b/man/eucast_rules.Rd
@@ -40,7 +40,7 @@ eucast_rules(tbl, col_mo = NULL, info = TRUE,
   pita = "pita", poly = "poly", pris = "pris", qida = "qida",
   rifa = "rifa", roxi = "roxi", siso = "siso", teic = "teic",
   tetr = "tetr", tica = "tica", tige = "tige", tobr = "tobr",
-  trim = "trim", trsu = "trsu", vanc = "vanc", col_bactid = NULL)
+  trim = "trim", trsu = "trsu", vanc = "vanc")
 
 EUCAST_rules(...)
 
@@ -59,8 +59,6 @@ interpretive_reading(...)
 
 \item{amcl, amik, amox, ampi, azit, azlo, aztr, cefa, cfep, cfot, cfox, cfra, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli, czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana, levo, linc, line, mero, mezl, mino, moxi, nali, neom, neti, nitr, norf, novo, oflo, oxac, peni, pipe, pita, poly, pris, qida, rifa, roxi, siso, teic, tetr, tica, tige, tobr, trim, trsu, vanc}{column name of an antibiotic, see Antibiotics}
 
-\item{col_bactid}{deprecated, use \code{col_mo} instead.}
-
 \item{...}{parameters that are passed on to \code{eucast_rules}}
 }
 \value{
diff --git a/man/first_isolate.Rd b/man/first_isolate.Rd
index 68239b91..fb0698ce 100755
--- a/man/first_isolate.Rd
+++ b/man/first_isolate.Rd
@@ -2,6 +2,8 @@
 % Please edit documentation in R/first_isolate.R
 \name{first_isolate}
 \alias{first_isolate}
+\alias{filter_first_isolate}
+\alias{filter_first_weighted_isolate}
 \title{Determine first (weighted) isolates}
 \source{
 Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
@@ -11,10 +13,15 @@ first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
   col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
   col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365,
   testcodes_exclude = NULL, icu_exclude = FALSE,
-  filter_specimen = NULL, output_logical = TRUE,
-  type = "keyantibiotics", ignore_I = TRUE, points_threshold = 2,
-  info = TRUE, col_bactid = NULL, col_genus = NULL,
-  col_species = NULL)
+  specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE,
+  points_threshold = 2, info = TRUE, ...)
+
+filter_first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
+  col_mo = NULL, ...)
+
+filter_first_weighted_isolate(tbl, col_date = NULL,
+  col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL,
+  ...)
 }
 \arguments{
 \item{tbl}{a \code{data.frame} containing isolates.}
@@ -37,11 +44,9 @@ first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
 
 \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{icu_exclude}{logical whether ICU isolates should be excluded (rows with value \code{TRUE} in column \code{col_icu})}
 
-\item{filter_specimen}{specimen group or type that should be excluded}
-
-\item{output_logical}{return output as \code{logical} (will else be the values \code{0} or \code{1})}
+\item{specimen_group}{value in column \code{col_specimen} to filter on}
 
 \item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details}
 
@@ -51,21 +56,34 @@ first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
 
 \item{info}{print progress}
 
-\item{col_bactid}{(deprecated, use \code{col_mo} instead)}
-
-\item{col_genus}{(deprecated, use \code{col_mo} instead) column name of the genus of the microorganisms}
-
-\item{col_species}{(deprecated, use \code{col_mo} instead) column name of the species of the microorganisms}
+\item{...}{parameters passed on to the \code{first_isolate} function}
 }
 \value{
-A vector to add to table, see Examples.
+Logical vector
 }
 \description{
 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
-    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}.
+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}.
+
+The function \code{filter_first_isolate} is essentially equal to:
+\preformatted{
+ tbl \%>\%
+   mutate(only_firsts = first_isolate(tbl, ...)) \%>\%
+   filter(only_firsts == TRUE) \%>\%
+   select(-only_firsts)
+}
+The function \code{filter_first_weighted_isolate} is essentially equal to:
+\preformatted{
+ tbl \%>\%
+   mutate(keyab = key_antibiotics(.)) \%>\%
+   mutate(only_weighted_firsts = first_isolate(tbl,
+                                               col_keyantibiotics = "keyab", ...)) \%>\%
+   filter(only_weighted_firsts == TRUE) \%>\%
+   select(-only_weighted_firsts)
+}
 }
 \section{Key antibiotics}{
 
@@ -83,20 +101,29 @@ Determine first (weighted) isolates of all microorganisms of every patient per e
 ?septic_patients
 
 library(dplyr)
-my_patients <- septic_patients \%>\%
+# Filter on first isolates:
+septic_patients \%>\%
   mutate(first_isolate = first_isolate(.,
                                        col_date = "date",
                                        col_patient_id = "patient_id",
-                                       col_mo = "mo"))
+                                       col_mo = "mo")) \%>\%
+  filter(first_isolate == TRUE)
+
+# Which can be shortened to:
+septic_patients \%>\%
+  filter_first_isolate()
+# or for first weighted isolates:
+septic_patients \%>\%
+  filter_first_weighted_isolate()
 
 # Now let's see if first isolates matter:
-A <- my_patients \%>\%
+A <- septic_patients \%>\%
   group_by(hospital_id) \%>\%
   summarise(count = n_rsi(gent),            # gentamicin availability
             resistance = portion_IR(gent))  # gentamicin resistance
 
-B <- my_patients \%>\%
-  filter(first_isolate == TRUE) \%>\%         # the 1st isolate filter
+B <- septic_patients \%>\%
+  filter_first_weighted_isolate() \%>\%       # the 1st isolate filter
   group_by(hospital_id) \%>\%
   summarise(count = n_rsi(gent),            # gentamicin availability
             resistance = portion_IR(gent))  # gentamicin resistance
@@ -106,6 +133,7 @@ B <- my_patients \%>\%
 # Gentamicin resitance in hospital D appears to be 5.4\% higher than
 # when you (erroneously) would have used all isolates!
 
+
 ## OTHER EXAMPLES:
 
 \dontrun{
@@ -122,29 +150,29 @@ tbl$first_isolate_weighed <-
 
 tbl$first_blood_isolate <-
   first_isolate(tbl,
-                filter_specimen = 'Blood')
+                specimen_group = 'Blood')
 
 tbl$first_blood_isolate_weighed <-
   first_isolate(tbl,
-                filter_specimen = 'Blood',
+                specimen_group = 'Blood',
                 col_keyantibiotics = 'keyab')
 
 tbl$first_urine_isolate <-
   first_isolate(tbl,
-                filter_specimen = 'Urine')
+                specimen_group = 'Urine')
 
 tbl$first_urine_isolate_weighed <-
   first_isolate(tbl,
-                filter_specimen = 'Urine',
+                specimen_group = 'Urine',
                 col_keyantibiotics = 'keyab')
 
 tbl$first_resp_isolate <-
   first_isolate(tbl,
-                filter_specimen = 'Respiratory')
+                specimen_group = 'Respiratory')
 
 tbl$first_resp_isolate_weighed <-
   first_isolate(tbl,
-                filter_specimen = 'Respiratory',
+                specimen_group = 'Respiratory',
                 col_keyantibiotics = 'keyab')
 }
 }
diff --git a/man/freq.Rd b/man/freq.Rd
index cd478b27..e2b792eb 100755
--- a/man/freq.Rd
+++ b/man/freq.Rd
@@ -10,15 +10,16 @@
 frequency_tbl(x, ..., sort.count = TRUE,
   nmax = getOption("max.print.freq"), na.rm = TRUE, row.names = TRUE,
   markdown = !interactive(), digits = 2, quote = FALSE,
-  header = !markdown, title = NULL, na = "<NA>", sep = " ",
-  decimal.mark = getOption("OutDec"), big.mark = ifelse(decimal.mark !=
-  ",", ",", "."))
+  header = !markdown, title = NULL, na = "<NA>", droplevels = TRUE,
+  sep = " ", decimal.mark = getOption("OutDec"),
+  big.mark = ifelse(decimal.mark != ",", ",", "."))
 
 freq(x, ..., sort.count = TRUE, nmax = getOption("max.print.freq"),
   na.rm = TRUE, row.names = TRUE, markdown = !interactive(),
   digits = 2, quote = FALSE, header = !markdown, title = NULL,
-  na = "<NA>", sep = " ", decimal.mark = getOption("OutDec"),
-  big.mark = ifelse(decimal.mark != ",", ",", "."))
+  na = "<NA>", droplevels = TRUE, sep = " ",
+  decimal.mark = getOption("OutDec"), big.mark = ifelse(decimal.mark !=
+  ",", ",", "."))
 
 top_freq(f, n)
 
@@ -52,6 +53,8 @@ top_freq(f, n)
 
 \item{na}{a character string to should be used to show empty (\code{NA}) values (only useful when \code{na.rm = FALSE})}
 
+\item{droplevels}{a logical value indicating whether in factors empty levels should be dropped}
+
 \item{sep}{a character string to separate the terms when selecting multiple columns}
 
 \item{decimal.mark}{%
@@ -94,6 +97,7 @@ For dates and times of any class, these additional values will be calculated wit
   \item{Median, using \code{\link[stats]{median}}, with percentage since oldest}
 }
 
+In factors, all factor levels that are not existing in the input data will be dropped.
 
 The function \code{top_freq} uses \code{\link[dplyr]{top_n}} internally and will include more than \code{n} rows if there are ties.
 }
diff --git a/man/key_antibiotics.Rd b/man/key_antibiotics.Rd
index 70891a0d..a8382380 100755
--- a/man/key_antibiotics.Rd
+++ b/man/key_antibiotics.Rd
@@ -5,14 +5,13 @@
 \alias{key_antibiotics_equal}
 \title{Key antibiotics for first \emph{weighted} isolates}
 \usage{
-key_antibiotics(tbl, col_mo = "mo", universal_1 = "amox",
+key_antibiotics(tbl, col_mo = NULL, universal_1 = "amox",
   universal_2 = "amcl", universal_3 = "cfur", universal_4 = "pita",
   universal_5 = "cipr", universal_6 = "trsu", GramPos_1 = "vanc",
   GramPos_2 = "teic", GramPos_3 = "tetr", GramPos_4 = "eryt",
   GramPos_5 = "oxac", GramPos_6 = "rifa", GramNeg_1 = "gent",
   GramNeg_2 = "tobr", GramNeg_3 = "coli", GramNeg_4 = "cfot",
-  GramNeg_5 = "cfta", GramNeg_6 = "mero", warnings = TRUE,
-  col_bactid = "bactid")
+  GramNeg_5 = "cfta", GramNeg_6 = "mero", warnings = TRUE, ...)
 
 key_antibiotics_equal(x, y, type = c("keyantibiotics", "points"),
   ignore_I = TRUE, points_threshold = 2, info = FALSE)
@@ -30,7 +29,7 @@ key_antibiotics_equal(x, y, type = c("keyantibiotics", "points"),
 
 \item{warnings}{give warning about missing antibiotic columns, they will anyway be ignored}
 
-\item{col_bactid}{(deprecated, use \code{col_mo} instead)}
+\item{...}{other parameters passed on to function}
 
 \item{x, y}{characters to compare}
 
@@ -71,18 +70,15 @@ The function \code{key_antibiotics} returns a character vector with 12 antibioti
 \examples{
 # septic_patients is a dataset available in the AMR package
 ?septic_patients
-my_patients <- septic_patients
-
 library(dplyr)
 # set key antibiotics to a new variable
-my_patients <- my_patients \%>\%
+my_patients <- septic_patients \%>\%
   mutate(keyab = key_antibiotics(.)) \%>\%
   mutate(
     # now calculate first isolates
-    first_regular = first_isolate(., "date", "patient_id", "mo"),
+    first_regular = first_isolate(., col_keyantibiotics = FALSE),
     # and first WEIGHTED isolates
-    first_weighted = first_isolate(., "date", "patient_id", "mo",
-                                   col_keyantibiotics = "keyab")
+    first_weighted = first_isolate(., col_keyantibiotics = "keyab")
   )
 
 # Check the difference, in this data set it results in 7\% more isolates:
@@ -95,7 +91,7 @@ strainA <- "SSSRR.S.R..S"
 strainB <- "SSSIRSSSRSSS"
 
 key_antibiotics_equal(strainA, strainB)
-# TRUE, because I is ignored (as are missing values)
+# TRUE, because I is ignored (as well as missing values)
 
 key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
 # FALSE, because I is not ignored and so the 4th value differs
diff --git a/man/mdro.Rd b/man/mdro.Rd
index 2e374a10..32291ab6 100644
--- a/man/mdro.Rd
+++ b/man/mdro.Rd
@@ -22,8 +22,7 @@ mdro(tbl, country = NULL, col_mo = NULL, info = TRUE,
   peni = "peni", pipe = "pipe", pita = "pita", poly = "poly",
   qida = "qida", rifa = "rifa", roxi = "roxi", siso = "siso",
   teic = "teic", tetr = "tetr", tica = "tica", tige = "tige",
-  tobr = "tobr", trim = "trim", trsu = "trsu", vanc = "vanc",
-  col_bactid = NULL)
+  tobr = "tobr", trim = "trim", trsu = "trsu", vanc = "vanc")
 
 brmo(..., country = "nl")
 
@@ -160,8 +159,6 @@ eucast_exceptional_phenotypes(tbl, country = "EUCAST", ...)
 
 \item{vanc}{column name of an antibiotic, see Antibiotics}
 
-\item{col_bactid}{deprecated, use \code{col_mo} instead.}
-
 \item{...}{parameters that are passed on to methods}
 }
 \value{
diff --git a/man/resistance_predict.Rd b/man/resistance_predict.Rd
index 7f9e872e..65849223 100644
--- a/man/resistance_predict.Rd
+++ b/man/resistance_predict.Rd
@@ -89,11 +89,11 @@ septic_patients \%>\%
 if (!require(ggplot2)) {
 
   data <- septic_patients \%>\%
-    filter(mo == "ESCCOL") \%>\%
+    filter(mo == as.mo("E. coli")) \%>\%
     resistance_predict(col_ab = "amox",
-                      col_date = "date",
-                      info = FALSE,
-                      minimum = 15)
+                       col_date = "date",
+                       info = FALSE,
+                        minimum = 15)
 
   ggplot(data,
          aes(x = year)) +
diff --git a/tests/testthat/test-age.R b/tests/testthat/test-age.R
index e42311ad..04264963 100644
--- a/tests/testthat/test-age.R
+++ b/tests/testthat/test-age.R
@@ -20,17 +20,17 @@ context("age.R")
 
 test_that("age works", {
   expect_equal(age(x = c("1980-01-01", "1985-01-01", "1990-01-01"),
-                   y = "2019-01-01"),
+                   reference = "2019-01-01"),
                c(39, 34, 29))
 
   expect_error(age(x = c("1980-01-01", "1985-01-01", "1990-01-01"),
-                   y = c("2019-01-01", "2019-01-01")))
+                   reference = c("2019-01-01", "2019-01-01")))
 
   expect_error(age(x = c("1980-01-01", "1985-01-01", "1990-01-01"),
-                   y = "1975-01-01"))
+                   reference = "1975-01-01"))
 
   expect_warning(age(x = c("1800-01-01", "1805-01-01", "1810-01-01"),
-                     y = "2019-01-01"))
+                     reference = "2019-01-01"))
 })
 
 test_that("age_groups works", {
diff --git a/tests/testthat/test-deprecated.R b/tests/testthat/test-deprecated.R
index ef08be81..49b4c2f0 100644
--- a/tests/testthat/test-deprecated.R
+++ b/tests/testthat/test-deprecated.R
@@ -20,11 +20,6 @@ context("deprecated.R")
 
 test_that("deprecated functions work", {
 
-  expect_identical(is.mo(as.mo("esco")), suppressWarnings(is.bactid(as.bactid("esco"))))
-  expect_warning(identical(is.mo(as.mo("esco")), is.bactid(as.bactid("esco"))))
-
-  expect_identical(as.mo("esco"), suppressWarnings(guess_bactid("esco")))
-
   expect_error(suppressWarnings(ratio("A")))
   expect_error(suppressWarnings(ratio(1, ratio = "abc")))
   expect_error(suppressWarnings(ratio(c(1, 2), ratio = c(1, 2, 3))))
@@ -32,13 +27,4 @@ test_that("deprecated functions work", {
   expect_identical(suppressWarnings(ratio(c(772, 1611, 737), ratio = "1:2:1")), c(780, 1560,  780))
   expect_identical(suppressWarnings(ratio(c(1752, 1895), ratio = c(1, 1))), c(1823.5, 1823.5))
 
-  old_mo <- "ESCCOL"
-  class(old_mo) <- "bactid"
-  df_oldmo <- data.frame(test = old_mo)
-  # print
-  expect_output(print(old_mo))
-  # test pull
-  library(dplyr)
-  expect_identical(df_oldmo %>% pull(test), old_mo)
-
 })
diff --git a/tests/testthat/test-first_isolate.R b/tests/testthat/test-first_isolate.R
index f580b61b..80196198 100755
--- a/tests/testthat/test-first_isolate.R
+++ b/tests/testthat/test-first_isolate.R
@@ -19,7 +19,7 @@
 context("first_isolate.R")
 
 test_that("first isolates work", {
-  # septic_patients contains 1331 out of 2000 first isolates
+  # septic_patients contains 1315 out of 2000 first isolates
   expect_equal(
     sum(
       first_isolate(tbl = septic_patients,
@@ -139,16 +139,15 @@ test_that("first isolates work", {
                    mutate(first = first_isolate(., "date", "patient_id",
                                                 col_mo = "mo",
                                                 col_specimen = "specimen",
-                                                filter_specimen = "something_unexisting",
-                                                output_logical = FALSE)))
+                                                filter_specimen = "something_unexisting")))
 
   # printing of exclusion message
   expect_output(septic_patients %>%
-                            first_isolate(col_date = "date",
-                                          col_mo = "mo",
-                                          col_patient_id = "patient_id",
-                                          col_testcode = "gender",
-                                          testcodes_exclude = "M"))
+                  first_isolate(col_date = "date",
+                                col_mo = "mo",
+                                col_patient_id = "patient_id",
+                                col_testcode = "gender",
+                                testcodes_exclude = "M"))
 
   # errors
   expect_error(first_isolate("date", "patient_id", col_mo = "mo"))
@@ -158,18 +157,16 @@ test_that("first isolates work", {
 
   # look for columns itself
   expect_message(first_isolate(septic_patients))
-  expect_message(first_isolate(septic_patients %>%
-                                 mutate(mo = as.character(mo)) %>%
-                                 left_join_microorganisms(),
-                               col_genus = "genus",
-                               col_species = "species"))
+  expect_error(first_isolate(septic_patients %>%
+                               mutate(mo = as.character(mo)) %>%
+                               left_join_microorganisms()))
 
   # if mo is not an mo class, result should be the same
   expect_identical(septic_patients %>%
-                   mutate(mo = as.character(mo)) %>%
-                   first_isolate(col_date = "date",
-                                 col_mo = "mo",
-                                 col_patient_id = "patient_id"),
+                     mutate(mo = as.character(mo)) %>%
+                     first_isolate(col_date = "date",
+                                   col_mo = "mo",
+                                   col_patient_id = "patient_id"),
                    septic_patients %>%
                      first_isolate(col_date = "date",
                                    col_mo = "mo",
diff --git a/tests/testthat/test-mdro.R b/tests/testthat/test-mdro.R
index d53bd301..ef4e334e 100755
--- a/tests/testthat/test-mdro.R
+++ b/tests/testthat/test-mdro.R
@@ -21,8 +21,8 @@ context("mdro.R")
 test_that("mdro works", {
   library(dplyr)
 
-  expect_error(suppressWarnings(mdro(septic_patients, "invalid", col_bactid = "mo", info = TRUE)))
-  expect_error(suppressWarnings(mdro(septic_patients, "fr", col_bactid = "mo", info = TRUE)))
+  expect_error(suppressWarnings(mdro(septic_patients, "invalid", col_mo = "mo", info = TRUE)))
+  expect_error(suppressWarnings(mdro(septic_patients, "fr", info = TRUE)))
   expect_error(suppressWarnings(mdro(septic_patients, country = c("de", "nl"), info = TRUE)))
   expect_error(suppressWarnings(mdro(septic_patients, col_mo = "invalid", info = TRUE)))
 
diff --git a/tests/testthat/test-portion.R b/tests/testthat/test-portion.R
index 9dfd85a6..a62a79e2 100755
--- a/tests/testthat/test-portion.R
+++ b/tests/testthat/test-portion.R
@@ -147,53 +147,3 @@ test_that("old rsi works", {
 
 
 })
-
-test_that("prediction of rsi works", {
-  amox_R <- septic_patients %>%
-    filter(mo == "B_ESCHR_COL") %>%
-    rsi_predict(col_ab = "amox",
-                col_date = "date",
-                minimum = 10,
-                info = TRUE) %>%
-    pull("value")
-  # amox resistance will increase according to data set `septic_patients`
-  expect_true(amox_R[3] < amox_R[20])
-
-  expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
-                            model = "binomial",
-                            col_ab = "amox",
-                            col_date = "date",
-                            info = TRUE))
-  expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
-                            model = "loglin",
-                            col_ab = "amox",
-                            col_date = "date",
-                            info = TRUE))
-  expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
-                            model = "lin",
-                            col_ab = "amox",
-                            col_date = "date",
-                            info = TRUE))
-
-  expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
-                           model = "INVALID MODEL",
-                           col_ab = "amox",
-                           col_date = "date",
-                           info = TRUE))
-  expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
-                           col_ab = "NOT EXISTING COLUMN",
-                           col_date = "date",
-                           info = TRUE))
-  expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
-                           col_ab = "amox",
-                           col_date = "NOT EXISTING COLUMN",
-                           info = TRUE))
-  # almost all E. coli are mero S in the Netherlands :)
-  expect_error(resistance_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
-                                  col_ab = "mero",
-                                  col_date = "date",
-                                  info = TRUE))
-
-  expect_error(portion_df(c("A", "B", "C")))
-  expect_error(portion_df(septic_patients[,"date"]))
-})
diff --git a/tests/testthat/test-resistance_predict.R b/tests/testthat/test-resistance_predict.R
new file mode 100644
index 00000000..dbdc1a6f
--- /dev/null
+++ b/tests/testthat/test-resistance_predict.R
@@ -0,0 +1,71 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Analysis                              #
+#                                                                      #
+# AUTHORS                                                              #
+# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
+#                                                                      #
+# LICENCE                                                              #
+# This package is free software; you can redistribute it and/or modify #
+# it under the terms of the GNU General Public License version 2.0,    #
+# as published by the Free Software Foundation.                        #
+#                                                                      #
+# This R package is distributed in the hope that it will be useful,    #
+# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
+# GNU General Public License version 2.0 for more details.             #
+# ==================================================================== #
+
+context("portion.R")
+
+test_that("prediction of rsi works", {
+  amox_R <- septic_patients %>%
+    filter(mo == "B_ESCHR_COL") %>%
+    rsi_predict(col_ab = "amox",
+                col_date = "date",
+                minimum = 10,
+                info = TRUE) %>%
+    pull("value")
+  # amox resistance will increase according to data set `septic_patients`
+  expect_true(amox_R[3] < amox_R[20])
+
+  library(dplyr)
+
+  expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
+                            model = "binomial",
+                            col_ab = "amox",
+                            col_date = "date",
+                            info = TRUE))
+  expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
+                            model = "loglin",
+                            col_ab = "amox",
+                            col_date = "date",
+                            info = TRUE))
+  expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
+                            model = "lin",
+                            col_ab = "amox",
+                            col_date = "date",
+                            info = TRUE))
+
+  expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
+                           model = "INVALID MODEL",
+                           col_ab = "amox",
+                           col_date = "date",
+                           info = TRUE))
+  expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
+                           col_ab = "NOT EXISTING COLUMN",
+                           col_date = "date",
+                           info = TRUE))
+  expect_error(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
+                           col_ab = "amox",
+                           col_date = "NOT EXISTING COLUMN",
+                           info = TRUE))
+  # almost all E. coli are mero S in the Netherlands :)
+  expect_error(resistance_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
+                                  col_ab = "mero",
+                                  col_date = "date",
+                                  info = TRUE))
+
+  expect_error(portion_df(c("A", "B", "C")))
+  expect_error(portion_df(septic_patients[,"date"]))
+})