Skip to contents

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.


  pollutant = "nox",
  proportion = "cluster",
  avg.time = "day",
  type = "default",
  normalise = FALSE,
  cols = "Set1",
  date.breaks = 7,
  date.format = NULL,
  key.columns = 1,
  key.position = "right",
  key.title = proportion,
  auto.text = TRUE,
  plot = TRUE,



A data frame containing the fields date, pollutant and a splitting variable proportion


Name of the pollutant to plot contained in mydata.


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.


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".

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.


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.

type must be of length one.


If normalise = TRUE then each time interval is scaled to 100. This is helpful to show the relative (percentage) contribution of the proportions.


Colours to be used for plotting. Options include “default”, “increment”, “heat”, “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")


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.


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 “Jan-2012” set date.format = "%b-%Y".


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.


Location where the scale key is to plotted. Allowed arguments currently include “top”, “right”, “bottom” and “left”.


The title of the key.


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.


Should a plot be produced? FALSE can be useful when analysing data to extract plot components and plotting them in other ways.


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.


an openair object


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.

See also

Other time series and trend functions: TheilSen(), calendarPlot(), runRegression(), smoothTrend(), timePlot(), timeVariation(), trendLevel()

Other cluster analysis functions: polarCluster(), trajCluster()


David Carslaw


## monthly plot of SO2 showing the contribution by wind sector
timeProp(mydata, pollutant = "so2", avg.time = "month", proportion = "wd")
#> Warning: 219 missing wind direction line(s) removed