Plot time series values in convential calendar format

This function will plot data by month laid out in a conventional calendar format. The main purpose is to help rapidly visualise potentially complex data in a familiar way. Users can also choose to show daily mean wind vectors if wind speed and direction are available.

Usage

calendarPlot(
  mydata,
  pollutant = "nox",
  year = 2003,
  month = 1:12,
  type = "default",
  annotate = "date",
  statistic = "mean",
  cols = "heat",
  limits = c(0, 100),
  lim = NULL,
  col.lim = c("grey30", "black"),
  col.arrow = "black",
  font.lim = c(1, 2),
  cex.lim = c(0.6, 1),
  digits = 0,
  data.thresh = 0,
  labels = NA,
  breaks = NA,
  w.shift = 0,
  remove.empty = TRUE,
  main = NULL,
  key.header = "",
  key.footer = "",
  key.position = "right",
  key = TRUE,
  auto.text = TRUE,
  ...
)

Arguments

mydata: A data frame minimally containing date and at least one other numeric variable. The date should be in either Date format or class POSIXct.
pollutant: Mandatory. A pollutant name corresponding to a variable in a data frame should be supplied e.g. pollutant = "nox".
year: Year to plot e.g. year = 2003. If not supplied all data potentially spanning several years will be plotted.
month: If only certain month are required. By default the function will plot an entire year even if months are missing. To only plot certain months use the month option where month is a numeric 1:12 e.g. month = c(1, 12) to only plot January and December.
type: Not yet implemented.
annotate: This option controls what appears on each day of the calendar. Can be: “date” --- shows day of the month; “wd” --- shows vector-averaged wind direction, or “ws” --- shows vector-averaged wind direction scaled by wind speed. Finally it can be “value” which shows the daily mean value.
statistic: Statistic passed to timeAverage.
cols: 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")
limits: Use this option to manually set the colour scale limits. This is useful in the case when there is a need for two or more plots and a consistent scale is needed on each. Set the limits to cover the maximimum range of the data for all plots of interest. For example, if one plot had data covering 0--60 and another 0--100, then set limits = c(0, 100). Note that data will be ignored if outside the limits range.
lim: A threshold value to help differentiate values above and below lim. It is used when annotate = "value". See next few options for control over the labels used.
col.lim: For the annotation of concentration labels on each day. The first sets the colour of the text below lim and the second sets the colour of the text above lim.
col.arrow: The colour of the annotated wind direction / wind speed arrows.
font.lim: For the annotation of concentration labels on each day. The first sets the font of the text below lim and the second sets the font of the text above lim. Note that font = 1 is normal text and font = 2 is bold text.
cex.lim: For the annotation of concentration labels on each day. The first sets the size of the text below lim and the second sets the size of the text above lim.
digits: The number of digits used to display concentration values when annotate = "value".
data.thresh: Data capture threshold passed to timeAverage. For example, data.thresh = 75 means that at least 75% of the data must be available in a day for the value to be calculate, else the data is removed.
labels: If a categorical scale is required then these labels will be used. Note there is one less label than break. For example, labels = c("good", "bad", "very bad"). breaks must also be supplied if labels are given.
breaks: If a categorical scale is required then these breaks will be used. For example, breaks = c(0, 50, 100, 1000). In this case “good” corresponds to values berween 0 and 50 and so on. Users should set the maximum value of breaks to exceed the maximum data value to ensure it is within the maximum final range e.g. 100--1000 in this case.
w.shift: Controls the order of the days of the week. By default the plot shows Saturday first (w.shift = 0). To change this so that it starts on a Monday for example, set w.shift = 2, and so on.
remove.empty: Should months with no data present be removed? Default is TRUE.
main: The plot title; default is pollutant and year.
key.header: Adds additional text/labels to the scale key. For example, passing calendarPlot(mydata, key.header = "header", key.footer = "footer") adds addition text above and below the scale key. These arguments are passed to drawOpenKey via quickText, applying the auto.text argument, to handle formatting.
key.footer: see key.header.
key.position: Location where the scale key is to plotted. Allowed arguments currently include "top", "right", "bottom" and "left".
key: Fine control of the scale key via drawOpenKey. See drawOpenKey for further details.
auto.text: Either TRUE (default) or FALSE. If TRUE titles and axis labels will automatically try and format pollutant names and units properly e.g. by subscripting the `2' in NO2.
...: Other graphical parameters are passed onto the lattice function lattice:levelplot, with common axis and title labelling options (such as xlab, ylab, main) being passed to via quickText to handle routine formatting.

Value

As well as generating the plot itself, calendarPlot also returns an object of class ``openair''. The object includes three main components: call, the command used to generate the plot;

data, the data frame of summarised information used to make the plot; and plot, the plot itself. If retained, e.g. using

output <- calendarPlot(mydata, "nox"), this output can be used to recover the data, reproduce or rework the original plot or undertake further analysis.

An openair output can be manipulated using a number of generic operations, including print, plot and summary.

Details

calendarPlot will plot data in a conventional calendar format i.e. by month and day of the week. Daily statistics are calculated using timeAverage, which by default will calculate the daily mean concentration.

If wind direction is available it is then possible to plot the wind direction vector on each day. This is very useful for getting a feel for the meteorological conditions that affect pollutant concentrations. Note that if hourly or higher time resolution are supplied, then calendarPlot will calculate daily averages using timeAverage, which ensures that wind directions are vector-averaged.

If wind speed is also available, then setting the option annotate = "ws" will plot the wind vectors whose length is scaled to the wind speed. Thus information on the daily mean wind speed and direction are available.

It is also possible to plot categorical scales. This is useful where, for example, an air quality index defines concentrations as bands e.g. “good”, “poor”. In these cases users must supply labels and corresponding breaks.

Note that is is possible to pre-calculate concentrations in some way before passing the data to calendarPlot. For example rollingMean could be used to calculate rolling 8-hour mean concentrations. The data can then be passed to calendarPlot and statistic = "max" chosen, which will plot maximum daily 8-hour mean concentrations.

Author

David Carslaw

Examples



# load example data from package
data(mydata)

# basic plot
calendarPlot(mydata, pollutant = "o3", year = 2003)


# show wind vectors
calendarPlot(mydata, pollutant = "o3", year = 2003, annotate = "wd")

if (FALSE) {
# show wind vectors scaled by wind speed and different colours
calendarPlot(mydata, pollutant = "o3", year = 2003, annotate = "ws",
cols = "heat")

# show only specific months with selectByDate
calendarPlot(selectByDate(mydata, month = c(3,6,10), year = 2003),
pollutant = "o3", year = 2003, annotate = "ws", cols = "heat")

# categorical scale example
calendarPlot(mydata, pollutant = "no2", breaks = c(0, 50, 100, 150, 1000),
labels = c("Very low", "Low", "High", "Very High"),
cols = c("lightblue", "green", "yellow",  "red"), statistic = "max")

}