diff --git a/NEWS.md b/NEWS.md
index 60a74294..45a4364d 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -18,6 +18,18 @@
     
     - When `statistic == "min"` and `annotation %in% c("ws", "wd")`, the ws/wd returned will correspond to the minimum daily pollutant, rather than the minimum daily ws/wd.
 
+- `timeProp()` refinements:
+
+    - `proportion` is now treated more like `type` internally. For a user, this means it can now be passed `"default"` to avoid any conditioning and create a regular period average barchart.
+
+    - `sub` can now be defined via `...`; set `sub = NA` to remove the text annotation which appears by default at the bottom of a `timeProp()` plot.
+
+    - Gained the `key` argument to remove a legend.
+    
+    - `"season"` is now a permitted `avg.time` option in `timeProp()`, better aligning it with the options in `timeAverage()`.
+    
+    - `...` is now correctly passed to `cutData()` when using `type`/`proportion`.
+
 - `trajPlot()` and `trajLevel()` have gained the `grid.nx` and `grid.ny` arguments which can be used to control the number of ticks on the coordinate grid, or remove it altogether.
 
 ## Bug Fixes
diff --git a/R/timeAverage.R b/R/timeAverage.R
index fc896564..ed41de04 100644
--- a/R/timeAverage.R
+++ b/R/timeAverage.R
@@ -45,8 +45,8 @@
 #' @param avg.time This defines the time period to average to. Can be `"sec"`,
 #'   `"min"`, `"hour"`, `"day"`, `"DSTday"`, `"week"`, `"month"`, `"quarter"` or
 #'   `"year"`. For much increased flexibility a number can precede these options
-#'   followed by a space. For example, a timeAverage of 2 months would be
-#'   `period = "2 month"`. In addition, `avg.time` can equal `"season"`, in
+#'   followed by a space. For example, an average of 2 months would be
+#'   `avg.time = "2 month"`. In addition, `avg.time` can equal `"season"`, in
 #'   which case 3-month seasonal values are calculated with spring defined as
 #'   March, April, May and so on.
 #'
diff --git a/R/timePlot.R b/R/timePlot.R
index a4b1dd80..9371ceae 100644
--- a/R/timePlot.R
+++ b/R/timePlot.R
@@ -76,7 +76,7 @@
 #'   directly. This offers great flexibility for understanding the variation of
 #'   different variables and how they depend on one another.
 #'
-#'   Only one `type` is currently allowed in `timePlot`.
+#'   `type` must be of length one.
 #' @param cols Colours to be used for plotting; see [openColours()] for details.
 #' @param plot.type The `lattice` plot type, which is a line (`plot.type = "l"`)
 #'   by default. Another useful option is `plot.type = "h"`, which draws
diff --git a/R/timeProp.R b/R/timeProp.R
index 22392b54..7ecf77a1 100644
--- a/R/timeProp.R
+++ b/R/timeProp.R
@@ -3,92 +3,49 @@
 #' This function shows time series plots as stacked bar charts. The different
 #' categories in the bar chart are made up from a character or factor variable
 #' in a data frame. The function is primarily developed to support the plotting
-#' of cluster analysis output from [polarCluster()] and
-#' [trajCluster()] that consider local and regional (back trajectory)
-#' cluster analysis respectively. However, the function has more general use for
-#' understanding time series data.
+#' of cluster analysis output from [polarCluster()] and [trajCluster()] that
+#' consider local and regional (back trajectory) cluster analysis respectively.
+#' However, the function has more general use for understanding time series
+#' data.
 #'
 #' In order to plot time series in this way, some sort of time aggregation is
 #' needed, which is controlled by the option `avg.time`.
 #'
-#' The plot shows the value of `pollutant` on the y-axis (averaged
-#' according to `avg.time`). The time intervals are made up of bars split
-#' according to `proportion`. The bars therefore show how the total value
-#' of `pollutant` is made up for any time interval.
+#' The plot shows the value of `pollutant` on the y-axis (averaged according to
+#' `avg.time`). The time intervals are made up of bars split according to
+#' `proportion`. The bars therefore show how the total value of `pollutant` is
+#' made up for any time interval.
 #'
-#' @param mydata A data frame containing the fields `date`,
-#'   `pollutant` and a splitting variable `proportion`
+#' @inheritParams timePlot
+#'
+#' @param mydata A data frame containing the fields `date`, `pollutant` and a
+#'   splitting variable `proportion`
 #' @param pollutant Name of the pollutant to plot contained in `mydata`.
 #' @param proportion The splitting variable that makes up the bars in the bar
-#'   chart e.g. `proportion = "cluster"` if the output from
-#'   `polarCluster` is being analysed. If `proportion` is a numeric
-#'   variable it is split into 4 quantiles (by default) by `cutData`. If
-#'   `proportion` is a factor or character variable then the categories are
-#'   used directly.
-#' @param avg.time This defines the time period to average to. Can be
-#'   \dQuote{sec}, \dQuote{min}, \dQuote{hour}, \dQuote{day}, \dQuote{DSTday},
-#'   \dQuote{week}, \dQuote{month}, \dQuote{quarter} or \dQuote{year}. For much
-#'   increased flexibility a number can precede these options followed by a
-#'   space. For example, a timeAverage of 2 months would be `period = "2
-#'   month"`.
-#'
-#'   Note that `avg.time` when used in `timeProp` should be greater
-#'   than the time gap in the original data. For example, `avg.time =
-#'   "day"` for hourly data is OK, but `avg.time = "hour"` for daily data
-#'   is not.
-#' @param type `type` determines how the data are split i.e. conditioned,
-#'   and then plotted. The default is will produce a single plot using the
-#'   entire data. Type can be one of the built-in types as detailed in
-#'   `cutData` e.g. "season", "year", "weekday" and so on. For example,
-#'   `type = "season"` will produce four plots --- one for each season.
-#'
-#'   It is also possible to choose `type` as another variable in the data
-#'   frame. If that variable is numeric, then the data will be split into four
-#'   quantiles (if possible) and labelled accordingly. If type is an existing
-#'   character or factor variable, then those categories/levels will be used
-#'   directly. This offers great flexibility for understanding the variation of
-#'   different variables and how they depend on one another.
+#'   chart e.g. `proportion = "cluster"` if the output from `polarCluster` is
+#'   being analysed. If `proportion` is a numeric variable it is split into 4
+#'   quantiles (by default) by `cutData`. If `proportion` is a factor or
+#'   character variable then the categories are used directly.
+#' @param avg.time This defines the time period to average to. Can be `"sec"`,
+#'   `"min"`, `"hour"`, `"day"`, `"DSTday"`, `"week"`, `"month"`, `"quarter"` or
+#'   `"year"`. For much increased flexibility a number can precede these options
+#'   followed by a space. For example, an average of 2 months would be
+#'   `avg.time = "2 month"`. In addition, `avg.time` can equal `"season"`, in
+#'   which case 3-month seasonal values are calculated with spring defined as
+#'   March, April, May and so on.
 #'
-#'   `type` must be of length one.
-#' @param normalise If `normalise = TRUE` then each time interval is scaled
-#'   to 100. This is helpful to show the relative (percentage) contribution of
-#'   the proportions.
-#' @param cols Colours to be used for plotting. Options include
-#'   \dQuote{default}, \dQuote{increment}, \dQuote{heat}, \dQuote{jet} and
-#'   `RColorBrewer` colours --- see the `openair` `openColours`
-#'   function for more details. For user defined the user can supply a list of
-#'   colour names recognised by R (type `colours()` to see the full list).
-#'   An example would be `cols = c("yellow", "green", "blue")`
-#' @param date.breaks Number of major x-axis intervals to use. The function will
-#'   try and choose a sensible number of dates/times as well as formatting the
-#'   date/time appropriately to the range being considered.  This does not
-#'   always work as desired automatically. The user can therefore increase or
-#'   decrease the number of intervals by adjusting the value of
-#'   `date.breaks` up or down.
-#' @param date.format This option controls the date format on the x-axis. While
-#'   `timePlot` generally sets the date format sensibly there can be some
-#'   situations where the user wishes to have more control. For format types see
-#'   `strptime`. For example, to format the date like \dQuote{Jan-2012} set
-#'   `date.format = "\%b-\%Y"`.
-#' @param key.columns Number of columns to be used in the key. With many
-#'   pollutants a single column can make to key too wide. The user can thus
-#'   choose to use several columns by setting `columns` to be less than the
-#'   number of pollutants.
-#' @param key.position Location where the scale key is to plotted. Allowed
-#'   arguments currently include \dQuote{top}, \dQuote{right}, \dQuote{bottom}
-#'   and \dQuote{left}.
+#'   Note that `avg.time` when used in `timeProp` should be greater than the
+#'   time gap in the original data. For example, `avg.time = "day"` for hourly
+#'   data is OK, but `avg.time = "hour"` for daily data is not.
+#' @param normalise If `normalise = TRUE` then each time interval is scaled to
+#'   100. This is helpful to show the relative (percentage) contribution of the
+#'   proportions.
 #' @param key.title The title of the key.
-#' @param auto.text Either `TRUE` (default) or `FALSE`. If `TRUE`
-#'   titles and axis labels etc. will automatically try and format pollutant
-#'   names and units properly e.g.  by subscripting the `2' in NO2.
-#' @param plot Should a plot be produced? `FALSE` can be useful when
-#'   analysing data to extract plot components and plotting them in other ways.
-#' @param ... Other graphical parameters passed onto `timeProp` and
-#'   `cutData`. For example, `timeProp` passes the option
-#'   `hemisphere = "southern"` on to `cutData` to provide southern
-#'   (rather than default northern) hemisphere handling of `type =
-#'   "season"`. Similarly, common axis and title labelling options (such as
-#'   `xlab`, `ylab`, `main`) are passed to `xyplot` via
+#' @param ... Other graphical parameters passed onto `timeProp` and `cutData`.
+#'   For example, `timeProp` passes the option `hemisphere = "southern"` on to
+#'   `cutData` to provide southern (rather than default northern) hemisphere
+#'   handling of `type = "season"`. Similarly, common axis and title labelling
+#'   options (such as `xlab`, `ylab`, `main`) are passed to `xyplot` via
 #'   `quickText` to handle routine formatting.
 #' @export
 #' @return an [openair][openair-package] object
@@ -96,7 +53,7 @@
 #' @family time series and trend functions
 #' @family cluster analysis functions
 #' @examples
-#' ## monthly plot of SO2 showing the contribution by wind sector
+#' # monthly plot of SO2 showing the contribution by wind sector
 #' timeProp(mydata, pollutant = "so2", avg.time = "month", proportion = "wd")
 timeProp <- function(
   mydata,
@@ -104,149 +61,137 @@ timeProp <- function(
   proportion = "cluster",
   avg.time = "day",
   type = "default",
-  normalise = FALSE,
   cols = "Set1",
-  date.breaks = 7,
-  date.format = NULL,
+  normalise = FALSE,
+  key = TRUE,
   key.columns = 1,
   key.position = "right",
   key.title = proportion,
+  date.breaks = 7,
+  date.format = NULL,
   auto.text = TRUE,
   plot = TRUE,
   ...
 ) {
-  ## keep check happy
-  sums <- NULL
-  freq <- NULL
-  Var1 <- NULL
-  means <- NULL
-  date2 <- NULL
-  mean_value <- weighted_mean <- xleft <- NULL
-
-  ## greyscale handling
-  if (length(cols) == 1 && cols == "greyscale") {
-    trellis.par.set(list(strip.background = list(col = "white")))
-  }
-
+  # can only have one type
   if (length(type) > 1) {
-    stop("'type' can only be of length 1.")
-  }
-
-  ## if proportion is not categorical then make it so
-  if (!class(mydata[[proportion]]) %in% c("factor")) {
-    mydata <- cutData(mydata, proportion, ...)
+    cli::cli_abort("{.arg type} can only be of length {1L}.")
   }
 
-  ## extra.args setup
+  # extra.args setup
   extra.args <- list(...)
 
-  ## set graphaics
-  current.strip <- trellis.par.get("strip.background")
+  # reset graphic parameters
   current.font <- trellis.par.get("fontsize")
-
-  ## reset graphic parameters
   on.exit(trellis.par.set(
     fontsize = current.font
   ))
 
-  ## label controls
-
-  main <- if ("main" %in% names(extra.args)) {
-    quickText(extra.args$main, auto.text)
-  } else {
-    quickText("", auto.text)
-  }
-
-  xlab <- if ("xlab" %in% names(extra.args)) {
-    quickText(extra.args$xlab, auto.text)
-  } else {
-    "date"
-  }
-
-  ylab <- if ("ylab" %in% names(extra.args)) {
-    quickText(extra.args$ylab, auto.text)
-  } else {
-    quickText(pollutant, auto.text)
-  }
-
-  xlim <- if ("xlim" %in% names(extra.args)) {
-    extra.args$xlim
-  } else {
-    NULL
-  }
-
-  ylim <- if ("ylim" %in% names(extra.args)) {
-    extra.args$ylim
-  } else {
-    NULL
-  }
+  # label controls
+  main <- quickText(extra.args$main %||% "", auto.text)
+  xlab <- quickText(extra.args$xlab %||% "date", auto.text)
+  ylab <- quickText(extra.args$ylab %||% pollutant, auto.text)
+  sub <- extra.args$sub %||% "contribution weighted by mean"
 
+  # fontsize handling
   if ("fontsize" %in% names(extra.args)) {
     trellis.par.set(fontsize = list(text = extra.args$fontsize))
   }
 
-  ## variables needed
-  vars <- c("date", pollutant, proportion)
-
-  if (any(type %in% dateTypes)) {
-    vars <- unique(c("date", vars))
+  # greyscale handling
+  if (length(cols) == 1 && cols == "greyscale") {
+    trellis.par.set(list(strip.background = list(col = "white")))
   }
 
-  ## check the data
-  mydata <- checkPrep(mydata, vars, type, remove.calm = FALSE)
+  # variables needed
+  vars <- c("date", pollutant)
 
-  # time zone of input data
-  tzone <- attr(mydata$date, "tzone")
+  # check the data
+  mydata <- checkPrep(mydata, vars, c(type, proportion), remove.calm = FALSE)
 
   # cut data
-  mydata <- cutData(mydata, c(type, proportion))
+  mydata <- cutData(mydata, c(type, proportion), ...)
+
+  # time zone of input data
+  tzone <- attr(mydata$date, "tzone")
 
+  # groups for dplyr
   group_1 <- c("xleft", "xright", type)
   group_2 <- c(type, "xleft", "xright", proportion)
 
-  # summarise by proportion, type etc
-  # add the most common non-zero time interval
+  # calculate left and right extremes of each bar, add the most common
+  # non-zero time interval to left to get right
+  if (avg.time == "season") {
+    if (any(c("season", "seasonyear") %in% type)) {
+      cli::cli_abort(
+        "In {.fun openair::timeProp}, {.arg type} and {.arg avg.time} cannot both be 'season'."
+      )
+    }
+    results <- mydata |>
+      cutData(type = "seasonyear") |>
+      dplyr::mutate(xleft = min(.data$date), .by = c("seasonyear", type)) |>
+      dplyr::mutate(
+        xright = .data$xleft + median(diff(.data$xleft)[diff(.data$xleft) != 0])
+      ) |>
+      dplyr::select(-dplyr::any_of("seasonyear"))
+  } else {
+    results <-
+      dplyr::mutate(
+        mydata,
+        xleft = as.POSIXct(cut(.data$date, avg.time), tz = tzone),
+        xright = .data$xleft + median(diff(.data$xleft)[diff(.data$xleft) != 0])
+      )
+  }
 
-  results <- mydata |>
-    mutate(
-      xleft = as.POSIXct(cut(date, avg.time), tz = tzone),
-      xright = xleft + median(diff(xleft)[diff(xleft) != 0])
+  # summarise by proportion, type etc
+  results <-
+    results |>
+    # calculate group mean
+    dplyr::mutate(
+      mean_value = mean(.data[[pollutant]], na.rm = TRUE),
+      .by = dplyr::all_of(group_1)
     ) |>
-    group_by(across(group_1)) |> # group by type and date interval to get overall average
-    mutate(mean_value = mean(.data[[pollutant]], na.rm = TRUE)) |>
-    group_by(across(group_2)) |>
-    summarise(
+    # calculate mean & count per type & pollutant, retain type mean
+    dplyr::summarise(
       {{ pollutant }} := mean(.data[[pollutant]], na.rm = TRUE),
-      mean_value = mean(mean_value, na.rm = TRUE),
-      n = length(date)
+      mean_value = mean(.data$mean_value, na.rm = TRUE),
+      n = dplyr::n(),
+      .by = dplyr::all_of(group_2)
+    ) |>
+    # needs specific arrangement for lattice
+    dplyr::arrange(
+      .data[[type]],
+      .data$xleft,
+      .data$xright,
+      .data[[proportion]]
     ) |>
-    group_by(across(group_1)) |>
-    mutate(
+    # weighted mean, with cumulative sum for bar heights
+    dplyr::mutate(
       weighted_mean = .data[[pollutant]] * n / sum(n),
-      Var1 = replace_na(weighted_mean, 0),
-      var2 = cumsum(Var1),
-      date = xleft
+      Var1 = tidyr::replace_na(.data$weighted_mean, 0),
+      var2 = cumsum(.data$Var1),
+      date = .data$xleft,
+      .by = dplyr::all_of(group_1)
     )
 
-  ## normlaise to 100 if needed
-  vars <- c(type, "date")
+  # normalise to 100 if needed
   if (normalise) {
-    results <- results |>
-      group_by(across(vars)) |>
-      mutate(
-        Var1 = Var1 * (100 / sum(Var1, na.rm = TRUE)),
-        var2 = cumsum(Var1)
+    results <-
+      dplyr::mutate(
+        results,
+        Var1 = .data$Var1 * (100 / sum(.data$Var1, na.rm = TRUE)),
+        var2 = cumsum(.data$Var1),
+        .by = dplyr::all_of(c(type, "date"))
       )
   }
 
-  ## proper names of labelling ###################################################
+  # make sure we know order of data frame for adding other dates
+  results <- dplyr::arrange(results, .data[[type]], "date")
+
+  # proper names of labelling #
   strip.dat <- strip.fun(results, type, auto.text)
   strip <- strip.dat[[1]]
   strip.left <- strip.dat[[2]]
-  pol.name <- strip.dat[[3]]
-
-  ## work out width of each bar
-  nProp <- length(levels(results[[proportion]]))
 
   # labelling on plot
   labs <- sapply(
@@ -254,76 +199,53 @@ timeProp <- function(
     function(x) quickText(x, auto.text)
   )
 
-  # make sure we know order of data frame for adding other dates
-  results <- arrange(results, type, "date")
-
-  # xleft, xright used by plot function
-  # results$xleft <- results$date
-  # results$xright <- results$date2
-  # ## don't need date2
-  # results <- select(results, -date2)
-
   # the few colours used for scaling
+  nProp <- length(levels(results[[proportion]]))
   scaleCol <- openColours(cols, nProp)
 
-  # levels of proportion
-  thelevels <- levels(results[[proportion]])
-
-  # add colour directly to data frame for easy reference
-  cols <- data.frame(cols = scaleCol, stringsAsFactors = FALSE)
-  cols[[proportion]] <- as.character(levels(results[[proportion]]))
-
-  # need to merge based on character, not factor
-  results[[proportion]] <- as.character(results[[proportion]])
-
-  results <- full_join(results, cols, by = proportion)
-
-  results[[proportion]] <- factor(results[[proportion]], levels = thelevels)
-
-  # remove missing so we can do a cumsum
-  #  results <- na.omit(results)
-
-  # y values for plotting rectangles
-  # results <- results |>
-  #   group_by(across(vars)) |>
-  #   mutate(var2 = cumsum(Var1))
+  # add colours to the dataframe
+  results <-
+    dplyr::mutate(
+      results,
+      cols = scaleCol[as.integer(.data[[proportion]])]
+    )
 
+  # formula for lattice
   myform <- formula(paste("Var1 ~ date | ", type, sep = ""))
 
-  dates <- dateBreaks(results$date, date.breaks)$major ## for date scale
-
-  ## date axis formating
-  if (is.null(date.format)) {
-    formats <- dateBreaks(results$date, date.breaks)$format
-  } else {
-    formats <- date.format
-  }
-
+  # date axis formatting
+  breaks <- dateBreaks(results$date, date.breaks)
+  dates <- breaks$major
+  formats <- date.format %||% breaks$format
   scales <- list(x = list(at = dates, format = formats))
 
-  y.max <- max(results$var2, na.rm = TRUE)
-
-  if (is.null(xlim)) {
-    xlim <- range(c(results$xleft, results$xright))
-  }
-
+  # change in style if normalising data
   if (normalise) {
+    ylab <- quickText(paste("% contribution to", pollutant), auto.text)
     pad <- 1
   } else {
     pad <- 1.04
   }
-  if (is.null(ylim)) {
-    ylim <- c(0, pad * y.max)
-  }
-
-  if (normalise) {
-    ylab <- quickText(paste("% contribution to", pollutant), auto.text)
-  }
 
-  ## sub heading
+  # set limits, if not set by user
+  xlim <- extra.args$xlim %||% range(c(results$xleft, results$xright))
+  ylim <- extra.args$ylim %||% c(0, pad * max(results$var2, na.rm = TRUE))
 
-  sub <- "contribution weighted by mean"
+  # set up key, if required
+  if (key) {
+    key <- list(
+      rectangles = list(col = rev(scaleCol), border = NA),
+      text = list(labs),
+      space = key.position,
+      title = quickText(key.title, auto.text),
+      cex.title = 1,
+      columns = key.columns
+    )
+  } else {
+    key <- NULL
+  }
 
+  # construct plot
   plt <- xyplot(
     myform,
     data = results,
@@ -332,18 +254,10 @@ timeProp <- function(
     strip.left = strip.left,
     groups = get(proportion),
     stack = TRUE,
-    sub = sub,
     scales = scales,
     col = scaleCol,
     border = NA,
-    key = list(
-      rectangles = list(col = rev(scaleCol), border = NA),
-      text = list(labs),
-      space = key.position,
-      title = quickText(key.title, auto.text),
-      cex.title = 1,
-      columns = key.columns
-    ),
+    key = key,
     par.strip.text = list(cex = 0.8),
     ...,
     panel = function(..., col, subscripts) {
@@ -353,7 +267,7 @@ timeProp <- function(
     }
   )
 
-  ## update extra args; usual method does not seem to work...
+  # update extra args; usual method does not seem to work
   plt <- modifyList(
     plt,
     list(
@@ -361,7 +275,8 @@ timeProp <- function(
       xlab = xlab,
       x.limits = xlim,
       y.limits = ylim,
-      main = main
+      main = main,
+      sub = sub
     )
   )
 
@@ -378,8 +293,8 @@ timeProp <- function(
   invisible(output)
 }
 
-# plot individual rectangles as lattice panel.barchar is *very* slow
-
+#' plot individual rectangles as lattice panel.barchar is *very* slow
+#' @noRd
 panelBar <- function(dat) {
   xleft <- unclass(dat$xleft)
   ybottom <- lag(dat$var2, default = 0)
diff --git a/man/calcPercentile.Rd b/man/calcPercentile.Rd
index 50556f6f..6b426e0e 100644
--- a/man/calcPercentile.Rd
+++ b/man/calcPercentile.Rd
@@ -26,8 +26,8 @@ pollutant (e.g., \code{"o3"}).}
 \item{avg.time}{This defines the time period to average to. Can be \code{"sec"},
 \code{"min"}, \code{"hour"}, \code{"day"}, \code{"DSTday"}, \code{"week"}, \code{"month"}, \code{"quarter"} or
 \code{"year"}. For much increased flexibility a number can precede these options
-followed by a space. For example, a timeAverage of 2 months would be
-\code{period = "2 month"}. In addition, \code{avg.time} can equal \code{"season"}, in
+followed by a space. For example, an average of 2 months would be
+\code{avg.time = "2 month"}. In addition, \code{avg.time} can equal \code{"season"}, in
 which case 3-month seasonal values are calculated with spring defined as
 March, April, May and so on.
 
diff --git a/man/timeAverage.Rd b/man/timeAverage.Rd
index bec19b52..c5557036 100644
--- a/man/timeAverage.Rd
+++ b/man/timeAverage.Rd
@@ -27,8 +27,8 @@ or \code{Date}.}
 \item{avg.time}{This defines the time period to average to. Can be \code{"sec"},
 \code{"min"}, \code{"hour"}, \code{"day"}, \code{"DSTday"}, \code{"week"}, \code{"month"}, \code{"quarter"} or
 \code{"year"}. For much increased flexibility a number can precede these options
-followed by a space. For example, a timeAverage of 2 months would be
-\code{period = "2 month"}. In addition, \code{avg.time} can equal \code{"season"}, in
+followed by a space. For example, an average of 2 months would be
+\code{avg.time = "2 month"}. In addition, \code{avg.time} can equal \code{"season"}, in
 which case 3-month seasonal values are calculated with spring defined as
 March, April, May and so on.
 
diff --git a/man/timePlot.Rd b/man/timePlot.Rd
index 3e762a98..4aa07047 100644
--- a/man/timePlot.Rd
+++ b/man/timePlot.Rd
@@ -68,8 +68,8 @@ time series together in one panel.}
 \item{avg.time}{This defines the time period to average to. Can be \code{"sec"},
 \code{"min"}, \code{"hour"}, \code{"day"}, \code{"DSTday"}, \code{"week"}, \code{"month"}, \code{"quarter"} or
 \code{"year"}. For much increased flexibility a number can precede these options
-followed by a space. For example, a timeAverage of 2 months would be
-\code{period = "2 month"}. In addition, \code{avg.time} can equal \code{"season"}, in
+followed by a space. For example, an average of 2 months would be
+\code{avg.time = "2 month"}. In addition, \code{avg.time} can equal \code{"season"}, in
 which case 3-month seasonal values are calculated with spring defined as
 March, April, May and so on.
 
@@ -121,7 +121,7 @@ character or factor variable, then those categories/levels will be used
 directly. This offers great flexibility for understanding the variation of
 different variables and how they depend on one another.
 
-Only one \code{type} is currently allowed in \code{timePlot}.}
+\code{type} must be of length one.}
 
 \item{cols}{Colours to be used for plotting; see \code{\link[=openColours]{openColours()}} for details.}
 
diff --git a/man/timeProp.Rd b/man/timeProp.Rd
index 9a0c7d13..e99c9533 100644
--- a/man/timeProp.Rd
+++ b/man/timeProp.Rd
@@ -10,49 +10,50 @@ timeProp(
   proportion = "cluster",
   avg.time = "day",
   type = "default",
-  normalise = FALSE,
   cols = "Set1",
-  date.breaks = 7,
-  date.format = NULL,
+  normalise = FALSE,
+  key = TRUE,
   key.columns = 1,
   key.position = "right",
   key.title = proportion,
+  date.breaks = 7,
+  date.format = NULL,
   auto.text = TRUE,
   plot = TRUE,
   ...
 )
 }
 \arguments{
-\item{mydata}{A data frame containing the fields \code{date},
-\code{pollutant} and a splitting variable \code{proportion}}
+\item{mydata}{A data frame containing the fields \code{date}, \code{pollutant} and a
+splitting variable \code{proportion}}
 
 \item{pollutant}{Name of the pollutant to plot contained in \code{mydata}.}
 
 \item{proportion}{The splitting variable that makes up the bars in the bar
-chart e.g. \code{proportion = "cluster"} if the output from
-\code{polarCluster} is being analysed. If \code{proportion} is a numeric
-variable it is split into 4 quantiles (by default) by \code{cutData}. If
-\code{proportion} is a factor or character variable then the categories are
-used directly.}
-
-\item{avg.time}{This defines the time period to average to. Can be
-\dQuote{sec}, \dQuote{min}, \dQuote{hour}, \dQuote{day}, \dQuote{DSTday},
-\dQuote{week}, \dQuote{month}, \dQuote{quarter} or \dQuote{year}. For much
-increased flexibility a number can precede these options followed by a
-space. For example, a timeAverage of 2 months would be \code{period = "2 month"}.
-
-Note that \code{avg.time} when used in \code{timeProp} should be greater
-than the time gap in the original data. For example, \code{avg.time = "day"} for hourly data is OK, but \code{avg.time = "hour"} for daily data
-is not.}
-
-\item{type}{\code{type} determines how the data are split i.e. conditioned,
-and then plotted. The default is will produce a single plot using the
-entire data. Type can be one of the built-in types as detailed in
-\code{cutData} e.g. "season", "year", "weekday" and so on. For example,
-\code{type = "season"} will produce four plots --- one for each season.
-
-It is also possible to choose \code{type} as another variable in the data
-frame. If that variable is numeric, then the data will be split into four
+chart e.g. \code{proportion = "cluster"} if the output from \code{polarCluster} is
+being analysed. If \code{proportion} is a numeric variable it is split into 4
+quantiles (by default) by \code{cutData}. If \code{proportion} is a factor or
+character variable then the categories are used directly.}
+
+\item{avg.time}{This defines the time period to average to. Can be \code{"sec"},
+\code{"min"}, \code{"hour"}, \code{"day"}, \code{"DSTday"}, \code{"week"}, \code{"month"}, \code{"quarter"} or
+\code{"year"}. For much increased flexibility a number can precede these options
+followed by a space. For example, an average of 2 months would be
+\code{avg.time = "2 month"}. In addition, \code{avg.time} can equal \code{"season"}, in
+which case 3-month seasonal values are calculated with spring defined as
+March, April, May and so on.
+
+Note that \code{avg.time} when used in \code{timeProp} should be greater than the
+time gap in the original data. For example, \code{avg.time = "day"} for hourly
+data is OK, but \code{avg.time = "hour"} for daily data is not.}
+
+\item{type}{\code{type} determines how the data are split i.e. conditioned, and
+then plotted. The default is will produce a single plot using the entire
+data. Type can be one of the built-in types as detailed in \code{\link[=cutData]{cutData()}},
+e.g., \code{"season"}, \code{"year"}, \code{"weekday"} and so on. For example, \code{type = "season"} will produce four plots --- one for each season.
+
+It is also possible to choose \code{type} as another variable in the data frame.
+If that variable is numeric, then the data will be split into four
 quantiles (if possible) and labelled accordingly. If type is an existing
 character or factor variable, then those categories/levels will be used
 directly. This offers great flexibility for understanding the variation of
@@ -60,53 +61,48 @@ different variables and how they depend on one another.
 
 \code{type} must be of length one.}
 
-\item{normalise}{If \code{normalise = TRUE} then each time interval is scaled
-to 100. This is helpful to show the relative (percentage) contribution of
-the proportions.}
+\item{cols}{Colours to be used for plotting; see \code{\link[=openColours]{openColours()}} for details.}
 
-\item{cols}{Colours to be used for plotting. Options include
-\dQuote{default}, \dQuote{increment}, \dQuote{heat}, \dQuote{jet} and
-\code{RColorBrewer} colours --- see the \code{openair} \code{openColours}
-function for more details. For user defined the user can supply a list of
-colour names recognised by R (type \code{colours()} to see the full list).
-An example would be \code{cols = c("yellow", "green", "blue")}}
+\item{normalise}{If \code{normalise = TRUE} then each time interval is scaled to
+100. This is helpful to show the relative (percentage) contribution of the
+proportions.}
 
-\item{date.breaks}{Number of major x-axis intervals to use. The function will
-try and choose a sensible number of dates/times as well as formatting the
-date/time appropriately to the range being considered.  This does not
-always work as desired automatically. The user can therefore increase or
-decrease the number of intervals by adjusting the value of
-\code{date.breaks} up or down.}
-
-\item{date.format}{This option controls the date format on the x-axis. While
-\code{timePlot} generally sets the date format sensibly there can be some
-situations where the user wishes to have more control. For format types see
-\code{strptime}. For example, to format the date like \dQuote{Jan-2012} set
-\code{date.format = "\\\%b-\\\%Y"}.}
+\item{key}{Should a key be drawn? The default is \code{TRUE}.}
 
 \item{key.columns}{Number of columns to be used in the key. With many
 pollutants a single column can make to key too wide. The user can thus
 choose to use several columns by setting \code{columns} to be less than the
 number of pollutants.}
 
-\item{key.position}{Location where the scale key is to plotted. Allowed
-arguments currently include \dQuote{top}, \dQuote{right}, \dQuote{bottom}
-and \dQuote{left}.}
+\item{key.position}{Location where the scale key is to plotted. Can include
+\code{"top"}, \code{"bottom"}, \code{"right"} and \code{"left"}.}
 
 \item{key.title}{The title of the key.}
 
-\item{auto.text}{Either \code{TRUE} (default) or \code{FALSE}. If \code{TRUE}
-titles and axis labels etc. will automatically try and format pollutant
-names and units properly e.g.  by subscripting the `2' in NO2.}
+\item{date.breaks}{Number of major x-axis intervals to use. The function will
+try and choose a sensible number of dates/times as well as formatting the
+date/time appropriately to the range being considered. This does not always
+work as desired automatically. The user can therefore increase or decrease
+the number of intervals by adjusting the value of \code{date.breaks} up or down.}
+
+\item{date.format}{This option controls the date format on the x-axis. While
+\code{\link[=timePlot]{timePlot()}} generally sets the date format sensibly there can be some
+situations where the user wishes to have more control. For format types see
+\code{\link[=strptime]{strptime()}}. For example, to format the date like "Jan-2012" set
+\code{date.format = "\\\%b-\\\%Y"}.}
+
+\item{auto.text}{Either \code{TRUE} (default) or \code{FALSE}. If \code{TRUE} titles and
+axis labels will automatically try and format pollutant names and units
+properly, e.g., by subscripting the '2' in NO2.}
 
-\item{plot}{Should a plot be produced? \code{FALSE} can be useful when
-analysing data to extract plot components and plotting them in other ways.}
+\item{plot}{Should a plot be produced? \code{FALSE} can be useful when analysing
+data to extract plot components and plotting them in other ways.}
 
-\item{...}{Other graphical parameters passed onto \code{timeProp} and
-\code{cutData}. For example, \code{timeProp} passes the option
-\code{hemisphere = "southern"} on to \code{cutData} to provide southern
-(rather than default northern) hemisphere handling of \code{type = "season"}. Similarly, common axis and title labelling options (such as
-\code{xlab}, \code{ylab}, \code{main}) are passed to \code{xyplot} via
+\item{...}{Other graphical parameters passed onto \code{timeProp} and \code{cutData}.
+For example, \code{timeProp} passes the option \code{hemisphere = "southern"} on to
+\code{cutData} to provide southern (rather than default northern) hemisphere
+handling of \code{type = "season"}. Similarly, common axis and title labelling
+options (such as \code{xlab}, \code{ylab}, \code{main}) are passed to \code{xyplot} via
 \code{quickText} to handle routine formatting.}
 }
 \value{
@@ -116,22 +112,22 @@ an \link[=openair-package]{openair} object
 This function shows time series plots as stacked bar charts. The different
 categories in the bar chart are made up from a character or factor variable
 in a data frame. The function is primarily developed to support the plotting
-of cluster analysis output from \code{\link[=polarCluster]{polarCluster()}} and
-\code{\link[=trajCluster]{trajCluster()}} that consider local and regional (back trajectory)
-cluster analysis respectively. However, the function has more general use for
-understanding time series data.
+of cluster analysis output from \code{\link[=polarCluster]{polarCluster()}} and \code{\link[=trajCluster]{trajCluster()}} that
+consider local and regional (back trajectory) cluster analysis respectively.
+However, the function has more general use for understanding time series
+data.
 }
 \details{
 In order to plot time series in this way, some sort of time aggregation is
 needed, which is controlled by the option \code{avg.time}.
 
-The plot shows the value of \code{pollutant} on the y-axis (averaged
-according to \code{avg.time}). The time intervals are made up of bars split
-according to \code{proportion}. The bars therefore show how the total value
-of \code{pollutant} is made up for any time interval.
+The plot shows the value of \code{pollutant} on the y-axis (averaged according to
+\code{avg.time}). The time intervals are made up of bars split according to
+\code{proportion}. The bars therefore show how the total value of \code{pollutant} is
+made up for any time interval.
 }
 \examples{
-## monthly plot of SO2 showing the contribution by wind sector
+# monthly plot of SO2 showing the contribution by wind sector
 timeProp(mydata, pollutant = "so2", avg.time = "month", proportion = "wd")
 }
 \seealso{