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.
timeProp(
mydata,
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,
...
)an openair object
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.
David Carslaw
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.
Other time series and trend functions:
TheilSen(),
calendarPlot(),
runRegression(),
smoothTrend(),
timePlot(),
timeVariation(),
trendLevel()
Other cluster analysis functions:
polarCluster(),
trajCluster()
## monthly plot of SO2 showing the contribution by wind sector
timeProp(mydata, pollutant = "so2", avg.time = "month", proportion = "wd")
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